Chapter.2_FINAL.indd

130

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Table of Contents

Executive Summary

.................................................... 131

2.1 Introduction and Scope

................................... 133

2.2 Concept of Radiative Forcing

....................... 133

2.3 Chemically and Radiatively

Important

Gases

................................................ 137

2.3.1 Atmospheric

Carbon

Dioxide

.............................. 137

2.3.2 Atmospheric

Methane

......................................... 140

2.3.3 Other Kyoto Protocol Gases................................ 143

2.3.4 Montreal Protocol Gases ..................................... 145

2.3.5 Trends in the Hydroxyl Free Radical .................... 147

2.3.6 Ozone

.................................................................. 149

2.3.7 Stratospheric

Water

Vapour

................................ 152

2.3.8 Observations of Long-Lived Greenhouse

Gas Radiative Effects .......................................... 153

2.4 Aerosols

.................................................................. 153

2.4.1 Introduction and Summary of the Third
Assessment

Report

............................................. 153

2.4.2 Developments Related to Aerosol
Observations

....................................................... 154

2.4.3 Advances in Modelling the Aerosol
Direct

Effect

......................................................... 159

2.4.4 Estimates of Aerosol Direct Radiative Forcing .... 160

2.4.5 Aerosol

Infl uence on Clouds

(Cloud Albedo Effect) ........................................... 171

2.5 Anthropogenic Changes in Surface Albedo

and the Surface Energy Budget

.................... 180

2.5.1 Introduction

......................................................... 180

2.5.2 Changes in Land Cover Since 1750 .................... 182

2.5.3 Radiative Forcing by Anthropogenic Surface

Albedo Change: Land Use .................................. 182

2.5.4 Radiative Forcing by Anthropogenic Surface

Albedo Change: Black Carbon in Snow

and

Ice

................................................................. 184

2.5.5 Other Effects of Anthropogenic Changes

in Land Cover ...................................................... 185

2.5.6 Tropospheric Water Vapour from
Anthropogenic

Sources

....................................... 185

2.5.7 Anthropogenic Heat Release ............................... 185

2.5.8 Effects of Carbon Dioxide Changes on Climate

via Plant Physiology: ‘Physiological Forcing’ ...... 185

2.6 Contrails

and

Aircraft-Induced

Cloudiness

............................................................. 186

2.6.1 Introduction

......................................................... 186

2.6.2 Radiative Forcing Estimates for Persistent
Line-Shaped

Contrails

......................................... 186

2.6.3 Radiative Forcing Estimates for
Aviation-Induced

Cloudiness............................... 187

2.6.4 Aviation

Aerosols

................................................. 188

2.7 Natural

Forcings

................................................. 188

2.7.1 Solar

Variability

.................................................... 188

2.7.2 Explosive

Volcanic

Activity

.................................. 193

2.8 Utility of Radiative Forcing

............................ 195

2.8.1 Vertical Forcing Patterns and Surface

Energy Balance Changes .................................... 196

2.8.2 Spatial Patterns of Radiative Forcing .................. 196

2.8.3 Alternative Methods of Calculating
Radiative

Forcing

................................................. 196

2.8.4 Linearity of the Forcing-Response
Relationship

......................................................... 197

2.8.5 Effi cacy and Effective Radiative Forcing ............. 197

2.8.6 Effi cacy and the Forcing-Response
Relationship

......................................................... 199

2.9 Synthesis

................................................................ 199

2.9.1 Uncertainties in Radiative Forcing ....................... 199

2.9.2 Global Mean Radiative Forcing ........................... 200

2.9.3 Global Mean Radiative Forcing by
Emission

Precursor

.............................................. 205

2.9.4 Future Climate Impact of Current Emissions ....... 206

2.9.5 Time Evolution of Radiative Forcing and
Surface

Forcing

................................................... 208

2.9.6 Spatial Patterns of Radiative Forcing and
Surface

Forcing

................................................... 209

2.10 Global Warming Potentials and Other

Metrics for Comparing Different

Emissions

............................................................... 210

2.10.1 Defi nition of an Emission Metric and the

Global Warming Potential .................................. 210

2.10.2 Direct Global Warming Potentials ...................... 211

2.10.3 Indirect

GWPs

.................................................... 214

2.10.4 New Alternative Metrics for Assessing
Emissions

........................................................... 215

Frequently Asked Question

FAQ 2.1:

How Do Human Activities Contribute to Climate

Change and How Do They Compare With

Natural

uences?

.............................................. 135

References

........................................................................ 217

131

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

Executive Summary

Radiative forcing (RF)

is a concept used for quantitative

comparisons of the strength of different human and natural
agents in causing climate change. Climate model studies since
the Working Group I Third Assessment Report (TAR; IPCC,
2001) give

medium

con

dence that the equilibrium global mean

temperature response to a given RF is approximately the same
(to within 25%) for most drivers of climate change.

For the

rst time, the combined RF for all anthropogenic

agents is derived. Estimates are also made for the

rst time of

the separate RF components associated with the emissions of
each agent.

The combined anthropogenic RF is estimated to be +1.6

[–1.0, +0.8]

W m

–2

, indicating that, since 1750, it is

extremely

likely

that humans have exerted a substantial warming

uence on climate. This RF estimate is

likely

to be at least

ve times greater than that due to solar irradiance changes. For

the period 1950 to 2005, it is

exceptionally unlikely

that the

combined natural RF (solar irradiance plus volcanic aerosol)
has had a warming in

uence comparable to that of the combined

anthropogenic RF.

Increasing concentrations of the long-lived greenhouse

gases (carbon dioxide (CO

), methane (CH

), nitrous oxide

O), halocarbons and sulphur hexa

uoride (SF

); hereinafter

LLGHGs) have led to a combined RF of +2.63 [±0.26] W m

–2

Their RF has a

high

level of scienti

c understanding.

The 9%

increase in this RF since the TAR is the result of concentration
changes since 1998.

—

The global mean concentration of CO

in 2005 was 379

ppm, leading to an RF of +1.66 [±0.17] W m

–2

. Past emissions

of fossil fuels and cement production have

likely

contributed

about three-quarters of the current RF, with the remainder
caused by land use changes. For the 1995 to 2005 decade, the
growth rate of CO

in the atmosphere was 1.9 ppm yr

–1

and the

RF increased by 20%: this is the largest change observed

or inferred for any decade in at least the last 200 years. From
1999 to 2005, global emissions from fossil fuel and cement
production increased at a rate of roughly 3% yr

–1

—

The global mean concentration of CH

in 2005 was 1,774

ppb, contributing an RF of +0.48 [±0.05] W m

–2

. Over the past

two decades, CH

growth rates in the atmosphere have generally

decreased. The cause of this is not well understood. However,

this decrease and the negligible long-term change in its main
sink (the hydroxyl radical OH) imply that total CH

emissions

are not increasing.

—

The Montreal Protocol gases (chloro

uorocarbons (CFCs),

hydrochloro

uorocarbons (HCFCs), and chlorocarbons) as a

group contributed +0.32 [±0.03] W m

–2

to the RF in 2005. Their

RF peaked in 2003 and is now beginning to decline.

—

Nitrous oxide continues to rise approximately linearly

(0.26% yr

–1

) and reached a concentration of 319 ppb in 2005,

contributing an RF of +0.16 [±0.02] W m

–2

. Recent studies

reinforce the large role of emissions from tropical regions in
in

uencing the observed spatial concentration gradients.

—

Concentrations of many of the

uorine-containing Kyoto

Protocol gases (hydro

uorocarbons (HFCs), per

uorocarbons,

) have increased by large factors (between 4.3 and 1.3)

between 1998 and 2005. Their total RF in 2005 was +0.017
[±0.002] W m

–2

and is rapidly increasing by roughly 10% yr

–1

—

The reactive gas, OH, is a key chemical species that

uences the lifetimes and thus RF values of CH

, HFCs,

HCFCs and ozone; it also plays an important role in the
formation of sulphate, nitrate and some organic aerosol species.
Estimates of the global average OH concentration have shown
no detectable net change between 1979 and 2004.

Based on newer and better chemical transport models

than were available for the TAR, the RF from increases in
tropospheric ozone is estimated to be +0.35 [–0.1, +0.3]
W m

–2

, with a

medium

level of scienti

c understanding. There

are indications of signi

cant upward trends at low latitudes.

The trend of greater and greater depletion of global

stratospheric ozone observed during the 1980s and 1990s
is no longer occurring; however, it is not yet clear whether
these recent changes are indicative of ozone recovery. The
RF is largely due to the destruction of stratospheric ozone
by the Montreal Protocol gases and it is re-evaluated to
be –0.05 [±0.10] W m

–2

, with a

medium

level of scientific

understanding.

Based on chemical transport model studies, the RF from

the increase in stratospheric water vapour due to oxidation of
CH

is estimated to be +0.07 [± 0.05] W m

–2

, with a

low

level

of scienti

c understanding. Other potential human causes of

water vapour increase that could contribute an RF are poorly
understood.

The total direct aerosol RF as derived from models and

observations is estimated to be –0.5 [±0.4] W m

–2

, with a

The RF represents the stratospherically adjusted radiative fl ux change evaluated at the tropopause, as defi ned in the TAR. Positive RFs lead to a global mean surface warming

and negative RFs to a global mean surface cooling. Radiative forcing, however, is not designed as an indicator of the detailed aspects of climate response. Unless otherwise men-
tioned, RF here refers to global mean RF. Radiative forcings are calculated in various ways depending on the agent: from changes in emissions and/or changes in concentrations,
and from observations and other knowledge of climate change drivers. In this report, the RF value for each agent is reported as the difference in RF, unless otherwise mentioned,
between the present day (approximately 2005) and the beginning of the industrial era (approximately 1750), and is given in units of W m

–2

90% confi dence ranges are given in square brackets. Where the 90% confi dence range is asymmetric about a best estimate, it is given in the form A [–X, +Y] where the lower limit

of the range is (A – X) and the upper limit is (A + Y).

The use of ‘

extremely likely

’ is an example of the calibrated language used in this document, it represents a 95% confi dence level or higher; ‘

likely

’ (66%) is another example (See

Box TS.1).

Estimates of RF are accompanied by both an uncertainty range (value uncertainty) and a level of scientifi c understanding (structural uncertainty). The value uncertainties represent

the 5 to 95% (90%) confi dence range, and are based on available published studies; the level of scientifi c understanding is a subjective measure of structural uncertainty and
represents how well understood the underlying processes are. Climate change agents with a

high

level of scientifi c understanding are expected to have an RF that falls within

their respective uncertainty ranges (See Section 2.9.1 and Box TS.1 for more information).

132

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

medium-low

level of scienti

c understanding. The RF due to the

cloud albedo effect (also referred to as

rst indirect or Twomey

effect), in the context of liquid water clouds, is estimated
to be –0.7 [–1.1, +0.4] W m

–2

, with a

low

level of scienti

understanding.

—

Atmospheric models have improved and many now

represent all aerosol components of signi

cance. Improved

situ

, satellite and surface-based measurements have enabled

veri

cation of global aerosol models. The best estimate and

uncertainty range of the total direct aerosol RF are based on a
combination of modelling studies and observations.

—

The direct RF of the individual aerosol species is less

certain than the total direct aerosol RF. The estimates are:
sulphate, –0.4 [±0.2] W m

–2

; fossil fuel organic carbon, –0.05

[±0.05] W m

–2

; fossil fuel black carbon, +0.2 [±0.15] W m

–2

;

biomass burning, +0.03 [±0.12] W m

–2

; nitrate, –0.1 [±0.1]

W m

–2

; and mineral dust, –0.1 [±0.2] W m

–2

. For biomass

burning, the estimate is strongly in

uenced by aerosol overlying

clouds. For the

rst time best estimates are given for nitrate and

mineral dust aerosols.

—

Incorporation of more aerosol species and

improved treatment of aerosol-cloud interactions allow
a best estimate of the cloud albedo effect. However, the
uncertainty remains large. Model studies including more
aerosol species or constrained by satellite observations
tend to yield a relatively weaker RF. Other aspects
of aerosol-cloud interactions (e.g., cloud lifetime, semi-direct
effect) are not considered to be an RF (see Chapter 7).

Land cover changes, largely due to net deforestation, have

increased the surface albedo giving an RF of –0.2 [±0.2]
W m

–2

, with a

medium-low

level of scienti

c understanding.

Black carbon aerosol deposited on snow has reduced the surface
albedo, producing an associated RF of +0.1 [±0.1] W m

–2

, with

low

level of scienti

c understanding. Other surface property

changes can affect climate through processes that cannot be
quanti

ed by RF; these have a

very low

level of scienti

understanding.

Persistent linear contrails from aviation contribute an RF

of +0.01 [–0.007, +0.02] W m

–2

, with a

low

level of scienti

understanding; the best estimate is smaller than in the TAR. No
best estimates are available for the net forcing from spreading
contrails and their effects on cirrus cloudiness.

The direct RF due to increases in solar irradiance since 1750

is estimated to be +0.12 [–0.06, +0.18] W m

–2

, with a

low

level

of scienti

c understanding. This RF is less than half of the TAR

estimate.

—

The smaller RF is due to a re-evaluation of the long-term

change in solar irradiance, namely a smaller increase from the
Maunder Minimum to the present. However, uncertainties in

the RF remain large. The total solar irradiance, monitored from
space for the last three decades, reveals a well-established cycle
of 0.08% (cycle minimum to maximum) with no signi

cant

trend at cycle minima.

—

Changes (order of a few percent) in globally averaged

column ozone forced by the solar ultraviolet irradiance 11-year
cycle are now better understood, but ozone pro

le changes are

less certain. Empirical associations between solar-modulated
cosmic ray ionization of the atmosphere and globally averaged
low-level cloud cover remain ambiguous.

The global stratospheric aerosol concentrations in 2005 were

at their lowest values since satellite measurements began in
about 1980. This can be attributed to the absence of signi

cant

explosive volcanic eruptions since Mt. Pinatubo in 1991.
Aerosols from such episodic volcanic events exert a transitory
negative RF; however, there is limited knowledge of the RF
associated with eruptions prior to Mt. Pinatubo.

The spatial patterns of RFs for non-LLGHGs (ozone, aerosol

direct and cloud albedo effects, and land use changes) have
considerable uncertainties, in contrast to the relatively high
con

dence in that of the LLGHGs. The Southern Hemisphere

net positive RF

very likely

exceeds that in Northern Hemisphere

because of smaller aerosol contributions in the Southern
Hemisphere. The RF spatial pattern is not indicative of the
pattern of climate response.

The total global mean surface forcing

very likely

negative.

By reducing the shortwave radiative

ux at the surface, increases

in stratospheric and tropospheric aerosols are principally
responsible for the negative surface forcing. This is in contrast
to LLGHG increases, which are the principal contributors to the
total positive anthropogenic RF.

Surface forcing is the instantaneous radiative fl ux change at the surface; it is a useful diagnostic tool for understanding changes in the heat and moisture surface budgets.

However, unlike RF, it cannot be used for quantitative comparisons of the effects of different agents on the equilibrium global mean surface temperature change.

133

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

2.1 Introduction and Scope

This chapter updates information taken from Chapters 3

to 6 of the IPCC Working Group I Third Assessment Report
(TAR; IPCC, 2001). It concerns itself with trends in forcing
agents and their precursors since 1750, and estimates their
contribution to the radiative forcing (RF) of the climate system.
Discussion of the understanding of atmospheric composition
changes is limited to explaining the trends in forcing agents and
their precursors. Areas where signi

cant developments have

occurred since the TAR are highlighted. The chapter draws
on various assessments since the TAR, in particular the 2002
World Meteorological Organization (WMO)-United Nations
Environment Programme (UNEP) Scienti

c Assessment of

Ozone Depletion (WMO, 2003) and the IPCC-Technology
and Economic Assessment Panel (TEAP) special report on
Safeguarding the Ozone Layer and the Global Climate System
(IPCC/TEAP, 2005).

The chapter assesses anthropogenic greenhouse gas changes,

aerosol changes and their impact on clouds, aviation-induced
contrails and cirrus changes, surface albedo changes and
natural solar and volcanic mechanisms. The chapter reassesses
the ‘radiative forcing’ concept (Sections 2.2 and 2.8), presents
spatial and temporal patterns of RF, and examines the radiative
energy budget changes at the surface.

For the long-lived greenhouse gases (carbon dioxide

(CO

), methane (CH

), nitrous oxide (N

O), chloro

uoro-

carbons (CFCs), hydrochloro

uorocarbons

(HCFCs),

hydro

uorocarbons (HFCs), per

uorocarbons (PFCs) and

sulphur hexa

uoride (SF

), hereinafter collectively referred

to as the LLGHGs; Section 2.3), the chapter makes use of
new global measurement capabilities and combines long-
term measurements from various networks to update trends
through 2005. Compared to other RF agents, these trends are
considerably better quanti

ed; because of this, the chapter does

not devote as much space to them as previous assessments
(although the processes involved and the related budgets
are further discussed in Sections 7.3 and 7.4). Nevertheless,
LLGHGs remain the largest and most important driver of
climate change, and evaluation of their trends is one of the
fundamental tasks of both this chapter and this assessment.

The chapter considers only ‘forward calculation’ methods

of estimating RF. These rely on observations and/or modelling
of the relevant forcing agent. Since the TAR, several studies
have attempted to constrain aspects of RF using ‘inverse
calculation’ methods. In particular, attempts have been made
to constrain the aerosol RF using knowledge of the temporal
and/or spatial evolution of several aspects of climate. These
include temperatures over the last 100 years, other RFs, climate
response and ocean heat uptake. These methods depend on an
understanding of – and suf

ciently small uncertainties in – other

aspects of climate change and are consequently discussed in the
detection and attribution chapter (see Section 9.2).

Other discussions of atmospheric composition changes and

their associated feedbacks are presented in Chapter 7. Radiative

forcing and atmospheric composition changes before 1750 are
discussed in Chapter 6. Future RF scenarios that were presented
in Ramaswamy et al. (2001) are not updated in this report;
however, they are brie

y discussed in Chapter 10.

2.2 Concept of Radiative Forcing

The de

nition of RF from the TAR and earlier IPCC

assessment reports is retained. Ramaswamy et al. (2001) de

it as ‘the change in net (down minus up) irradiance (solar
plus longwave; in W m

–2

) at the tropopause after allowing for

stratospheric temperatures to readjust to radiative equilibrium,
but with surface and tropospheric temperatures and state held

xed at the unperturbed values’. Radiative forcing is used to

assess and compare the anthropogenic and natural drivers of
climate change. The concept arose from early studies of the
climate response to changes in solar insolation and CO

, using

simple radiative-convective models. However, it has proven
to be particularly applicable for the assessment of the climate
impact of LLGHGs (Ramaswamy et al., 2001). Radiative
forcing can be related through a linear relationship to the
global mean equilibrium temperature change at the surface
(

RF, where

is the climate sensitivity parameter

(e.g., Ramaswamy et al., 2001). This equation, developed from
these early climate studies, represents a linear view of global
mean climate change between two equilibrium climate states.
Radiative forcing is a simple measure for both quantifying
and ranking the many different in

uences on climate change;

it provides a limited measure of climate change as it does not
attempt to represent the overall climate response. However, as
climate sensitivity and other aspects of the climate response
to external forcings remain inadequately quanti

ed, it has the

advantage of being more readily calculable and comparable
than estimates of the climate response. Figure 2.1 shows how
the RF concept

ts within a general understanding of climate

change comprised of ‘forcing’ and ‘response’. This chapter
also uses the term ‘surface forcing’ to refer to the instantaneous
perturbation of the surface radiative balance by a forcing
agent. Surface forcing has quite different properties than RF
and should not be used to compare forcing agents (see Section
2.8.1). Nevertheless, it is a useful diagnostic, particularly for
aerosols (see Sections 2.4 and 2.9).

Since the TAR a number of studies have investigated the

relationship between RF and climate response, assessing the
limitations of the RF concept; related to this there has been
considerable debate whether some climate change drivers are
better considered as a ‘forcing’ or a ‘response’ (Hansen

et al.,

2005; Jacob

et al., 2005; Section 2.8). Emissions of forcing

agents, such as LLGHGs, aerosols and aerosol precursors,
ozone precursors and ozone-depleting substances, are the more
fundamental drivers of climate change and these emissions can
be used in state-of-the-art climate models to interactively evolve
forcing agent

elds along with their associated climate change.

In such models, some ‘response’ is necessary to evaluate the

137

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

change effects. They need to be accounted for when evaluating
the overall effect of humans on climate and their radiative
effects as discussed in Sections 7.2 and 7.5. However, in both
this chapter and the Fourth Assessment Report they are not
considered to be RFs, although the RF de

nition could be

altered to accommodate them. Reasons for this are twofold
and concern the need to be simple and pragmatic. Firstly, many
GCMs have some representation of these effects inherent in
their climate response and evaluation of variation in climate
sensitivity between mechanisms already accounts for them (see
‘ef

cacy’, Section 2.8.5). Secondly, the evaluation of these

tropospheric state changes rely on some of the most uncertain
aspects of a climate model’s response (e.g., the hydrologic
cycle); their radiative effects are very climate-model dependent
and such a dependence is what the RF concept was designed to
avoid. In practice these effects can also be excluded on practical
grounds – they are simply too uncertain to be adequately
quanti

ed (see Sections 7.5, 2.4.5 and 2.5.6).

The RF relationship to transient climate change is not

straightforward. To evaluate the overall climate response
associated with a forcing agent, its temporal evolution and its
spatial and vertical structure need to be taken into account.
Further, RF alone cannot be used to assess the potential climate
change associated with emissions, as it does not take into
account the different atmospheric lifetimes of the forcing agents.
Global Warming Potentials (GWPs) are one way to assess these
emissions. They compare the integrated RF over a speci

period (e.g., 100 years) from a unit mass pulse emission relative
to CO

(see Section 2.10).

croplands, pastures and forests. They have also modifi ed the refl ec-
tive properties of ice and snow. Overall, it is likely that more solar
radiation is now being refl ected from Earth’s surface as a result of
human activities. This change results in a negative forcing.

Aircraft produce persistent linear trails of condensation (‘con-

trails’) in regions that have suitably low temperatures and high
humidity. Contrails are a form of cirrus cloud that refl ect solar ra-
diation and absorb infrared radiation. Linear contrails from global
aircraft operations have increased Earth’s cloudiness and are esti-
mated to cause a small positive radiative forcing.

Radiative Forcing from Natural Changes

Natural forcings arise due to solar changes and explosive

volcanic eruptions. Solar output has increased gradually in the
industrial era, causing a small positive radiative forcing (see Figure
2). This is in addition to the cyclic changes in solar radiation that

follow an 11-year cycle. Solar energy directly heats the climate
system and can also affect the atmospheric abundance of some
greenhouse gases, such as stratospheric ozone. Explosive volcanic
eruptions can create a short-lived (2 to 3 years) negative forcing
through the temporary increases that occur in sulphate aerosol
in the stratosphere. The stratosphere is currently free of volcanic
aerosol, since the last major eruption was in 1991 (Mt. Pinatubo).

The differences in radiative forcing estimates between the

present day and the start of the industrial era for solar irradiance
changes and volcanoes are both very small compared to the differ-
ences in radiative forcing estimated to have resulted from human
activities. As a result, in today’s atmosphere, the radiative forcing
from human activities is much more important for current and
future climate change than the estimated radiative forcing from
changes in natural processes.

2.3 Chemically and Radiatively

Important

Gases

2.3.1 Atmospheric

Carbon

Dioxide

This section discusses the instrumental measurements of CO

documenting recent changes in atmospheric mixing ratios needed
for the RF calculations presented later in the section. In addition,
it provides data for the pre-industrial levels of CO

required as

the accepted reference level for the RF calculations. For dates
before about 1950 indirect measurements are relied upon. For
these periods, levels of atmospheric CO

are usually determined

from analyses of air bubbles trapped in polar ice cores. These
time periods are primarily considered in Chapter 6.

A wide range of direct and indirect measurements con

that the atmospheric mixing ratio of CO

has increased globally

by about 100 ppm (36%) over the last 250 years, from a range
of 275 to 285 ppm in the pre-industrial era (AD 1000–1750) to
379 ppm in 2005 (see FAQ 2.1, Figure 1). During this period,
the absolute growth rate of CO

in the atmosphere increased

substantially: the

rst 50 ppm increase above the pre-industrial

value was reached in the 1970s after more than 200 years,
whereas the second 50 ppm was achieved in about 30 years. In
the 10 years from 1995 to 2005 atmospheric CO

increased by

about 19 ppm; the highest average growth rate recorded for any
decade since direct atmospheric CO

measurements began in

the 1950s. The average rate of increase in CO

determined by

these direct instrumental measurements over the period 1960 to
2005 is 1.4 ppm yr

-1

138

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Figure 2.3.

Recent CO

concentrations and emissions. (a) CO

concentrations

(monthly averages) measured by continuous analysers over the period 1970 to
2005 from Mauna Loa, Hawaii (19°N, black; Keeling and Whorf, 2005) and Baring
Head, New Zealand (41°S, blue; following techniques by Manning et al., 1997). Due
to the larger amount of terrestrial biosphere in the NH, seasonal cycles in CO

are

larger there than in the SH. In the lower right of the panel, atmospheric oxygen (O

)

measurements from fl ask samples are shown from Alert, Canada (82°N, pink) and
Cape Grim, Australia (41°S, cyan) (Manning and Keeling, 2006). The O

concentration

is measured as ‘per meg’ deviations in the O

ratio from an arbitrary reference,

analogous to the ‘per mil’ unit typically used in stable isotope work, but where the ra-
tio is multiplied by 10

instead of 10

because much smaller changes are measured.

(b) Annual global CO

emissions from fossil fuel burning and cement manufacture

in GtC yr

–1

(black) through 2005, using data from the CDIAC website (Marland et al,

2006) to 2003. Emissions data for 2004 and 2005 are extrapolated from CDIAC using
data from the BP Statistical Review of World Energy (BP, 2006). Land use emissions
are not shown; these are estimated to be between 0.5 and 2.7 GtC yr

–1

for the 1990s

(Table 7.2). Annual averages of the

C ratio measured in atmospheric CO

Mauna Loa from 1981 to 2002 (red) are also shown (Keeling et al, 2005). The isotope
data are expressed as

C(CO

) ‰ (per mil) deviation from a calibration standard.

Note that this scale is inverted to improve clarity.

High-precision measurements of atmospheric CO

are

essential to the understanding of the carbon cycle budgets
discussed in Section 7.3. The

rst

in situ

continuous

measurements of atmospheric CO

made by a high-precision

non-dispersive infrared gas analyser were implemented by
C.D. Keeling from the Scripps Institution of Oceanography
(SIO) (see Section 1.3). These began in 1958 at Mauna Loa,
Hawaii, located at 19°N (Keeling

et al., 1995). The data

documented for the

rst time that not only was CO

increasing

in the atmosphere, but also that it was modulated by cycles
caused by seasonal changes in photosynthesis in the terrestrial
biosphere. These measurements were followed by continuous

in situ

analysis programmes at other sites in both hemispheres

(Conway et al., 1994; Nakazawa et al., 1997; Langenfelds et
al., 2002). In Figure 2.3, atmospheric CO

mixing ratio data at

Mauna Loa in the Northern Hemisphere (NH) are shown with
contemporaneous measurements at Baring Head, New Zealand
in the Southern Hemisphere (SH; Manning et al., 1997; Keeling
and Whorf, 2005). These two stations provide the longest
continuous records of atmospheric CO

in the NH and SH,

respectively. Remote sites such as Mauna Loa, Baring Head,
Cape Grim (Tasmania) and the South Pole were chosen because
air sampled at such locations shows little short-term variation
caused by local sources and sinks of CO

and provided the

rst

data from which the global increase of atmospheric CO

was

documented. Because CO

is a LLGHG and well mixed in

the atmosphere, measurements made at such sites provide an
integrated picture of large parts of the Earth including continents
and city point sources. Note that this also applies to the other
LLGHGs reported in Section 2.3.

In the 1980s and 1990s, it was recognised that greater

coverage of CO

measurements over continental areas was

required to provide the basis for estimating sources and sinks of
atmospheric CO

over land as well as ocean regions. Because

continuous CO

analysers are relatively expensive to maintain

and require meticulous on-site calibration, these records are
now widely supplemented by air sample

ask programmes,

where air is collected in glass and metal containers at a large
number of continental and marine sites. After collection, the

lled

asks are sent to central well-calibrated laboratories

for analysis. The most extensive network of international
air sampling sites is operated by the National Oceanic and
Atmospheric Administration’s Global Monitoring Division
(NOAA/GMD; formerly NOAA/Climate Monitoring and
Diagnostics Laboratory (CMDL)) in the USA. This organisation
collates measurements of atmospheric CO

from six continuous

analyser locations as well as weekly

ask air samples from a

global network of almost 50 surface sites. Many international
laboratories make atmospheric CO

observations and worldwide

databases of their measurements are maintained by the Carbon
Dioxide Information Analysis Center (CDIAC) and by the
World Data Centre for Greenhouse Gases (WDCGG) in the
WMO Global Atmosphere Watch (GAW) programme.

The increases in global atmospheric CO

since the industrial

revolution are mainly due to CO

emissions from the combustion

of fossil fuels, gas

aring and cement production. Other sources

include emissions due to land use changes such as deforestation
(Houghton, 2003) and biomass burning (Andreae and Merlet,
2001; van der Werf, 2004). After entering the atmosphere,
CO

exchanges rapidly with the short-lived components of the

terrestrial biosphere and surface ocean, and is then redistributed
on time scales of hundreds of years among all active carbon
reservoirs including the long-lived terrestrial biosphere and

CDIAC, http://cdiac.esd.ornl.gov/; WDCGG, http://gaw.kishou.go.jp/wdcgg.html.

139

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

deep ocean. The processes governing the movement of carbon
between the active carbon reservoirs, climate carbon cycle
feedbacks and their importance in determining the levels of
CO

remaining in the atmosphere, are presented in Section 7.3,

where carbon cycle budgets are discussed.

The increase in CO

mixing ratios continues to yield the

largest sustained RF of any forcing agent. The RF of CO

is a

function of the change in CO

in the atmosphere over the time

period under consideration. Hence, a key question is ‘How is the
CO

released from fossil fuel combustion, cement production

and land cover change distributed amongst the atmosphere,
oceans and terrestrial biosphere?’. This partitioning has been
investigated using a variety of techniques. Among the most
powerful of these are measurements of the carbon isotopes in
CO

as well as high-precision measurements of atmospheric

oxygen (O

) content. The carbon contained in CO

has two

naturally occurring stable isotopes denoted

C and

C. The

rst of these,

C, is the most abundant isotope at about 99%,

followed by

C at about 1%. Emissions of CO

from coal, gas

and oil combustion and land clearing have

C isotopic

ratios that are less than those in atmospheric CO

, and each

carries a signature related to its source. Thus, as shown in
Prentice et al. (2001), when CO

from fossil fuel combustion

enters the atmosphere, the

C isotopic ratio in atmospheric

decreases at a predictable rate consistent with emissions

of CO

from fossil origin. Note that changes in the

ratio of atmospheric CO

are also caused by other sources and

sinks, but the changing isotopic signal due to CO

from fossil

fuel combustion can be resolved from the other components
(Francey et al

, 1995). These changes can easily be measured

using modern isotope ratio mass spectrometry, which has the
capability of measuring

C in atmospheric CO

to better

than 1 part in 10

(Ferretti et al., 2000). Data presented in Figure

2.3 for the

C ratio of atmospheric CO

at Mauna Loa show

a decreasing ratio, consistent with trends in both fossil fuel CO

emissions and atmospheric CO

mixing ratios (Andres et al.,

2000; Keeling et al., 2005).

Atmospheric O

measurements provide a powerful and

independent method of determining the partitioning of CO

between the oceans and land (Keeling et al., 1996). Atmospheric
O

and CO

changes are inversely coupled during plant

respiration and photosynthesis. In addition, during the process
of combustion O

is removed from the atmosphere, producing

a signal that decreases as atmospheric CO

increases on a

molar basis (Figure 2.3). Measuring changes in atmospheric
O

is technically challenging because of the dif

culty of

resolving changes at the part-per-million level in a background
mixing ratio of roughly 209,000 ppm. These dif

culties were

rst overcome by Keeling and Shertz (1992), who used an

interferometric technique to show that it is possible to track both
seasonal cycles and the decline of O

in the atmosphere at the

part-per-million level (Figure 2.3). Recent work by Manning
and Keeling (2006) indicates that atmospheric O

is decreasing

at a faster rate than CO

is increasing, which demonstrates

the importance of the oceanic carbon sink. Measurements of

both the

C ratio in atmospheric CO

and atmospheric O

levels are valuable tools used to determine the distribution of
fossil-fuel derived CO

among the active carbon reservoirs, as

discussed in Section 7.3. In Figure 2.3, recent measurements in
both hemispheres are shown to emphasize the strong linkages
between atmospheric CO

increases, O

decreases, fossil fuel

consumption and the

C ratio of atmospheric CO

From 1990 to 1999, a period reported in Prentice et al.

(2001), the emission rate due to fossil fuel burning and cement
production increased irregularly from 6.1 to 6.5 GtC yr

–1

about 0.7% yr

–1

. From 1999 to 2005 however, the emission

rate rose systematically from 6.5 to 7.8 GtC yr

–1

(BP, 2006;

Marland

et al., 2006) or about 3.0% yr

–1

, representing a

period of higher emissions and growth in emissions than
those considered in the TAR (see Figure 2.3). Carbon dioxide
emissions due to global annual fossil fuel combustion and
cement manufacture combined have increased by 70% over the
last 30 years (Marland

et al., 2006). The relationship between

increases in atmospheric CO

mixing ratios and emissions

has been tracked using a scaling factor known as the apparent
‘airborne fraction’, de

ned as the ratio of the annual increase

in atmospheric CO

to the CO

emissions from annual fossil

fuel and cement manufacture combined (Keeling

et al., 1995).

On decadal scales, this fraction has averaged about 60% since
the 1950s. Assuming emissions of 7 GtC yr

–1

and an airborne

fraction remaining at about 60%, Hansen and Sato (2004)
predicted that the underlying long-term global atmospheric
CO

growth rate will be about 1.9 ppm yr

–1

, a value consistent

with observations over the 1995 to 2005 decade.

Carbon dioxide emissions due to land use changes during

the 1990s are estimated as 0.5 to 2.7 GtC yr

–1

(Section 7.3,

Table 7.2), contributing 6% to 39% of the CO

growth rate

(Brovkin et al., 2004). Prentice et al. (2001) cited an inventory-
based estimate that land use change resulted in net emissions of
121 GtC between 1850 and 1990, after Houghton (1999, 2000).
The estimate for this period was revised upwards to 134 GtC
by Houghton (2003), mostly due to an increase in estimated
emissions prior to 1960. Houghton (2003) also extended the
inventory emissions estimate to 2000, giving cumulative
emissions of 156 GtC since 1850. In carbon cycle simulations
by Brovkin et al. (2004) and Matthews et al. (2004), land use
change emissions contributed 12 to 35 ppm of the total CO

rise from 1850 to 2000 (Section 2.5.3, Table 2.8). Historical
changes in land cover are discussed in Section 2.5.2, and the
CO

budget over the 1980s and 1990s is discussed further in

Section 7.3.

In 2005, the global mean average CO

mixing ratio for the SIO

network of 9 sites was 378.75 ± 0.13 ppm and for the NOAA/
GMD network of 40 sites was 378.76 ± 0.05 ppm, yielding a
global average of almost 379 ppm. For both networks, only
sites in the remote marine boundary layer are used and high-
altitude locations are not included. For example, the Mauna Loa
site is excluded due to an ‘altitude effect’ of about 0.5 ppm. In
addition, the 2005 values are still pending

nal reference gas

calibrations used to measure the samples.

140

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

New measurements of CO

from Antarctic ice and

(MacFarling Meure et al., 2006) update and extend those from
Etheridge et al. (1996) to AD 0. The CO

mixing ratio in 1750

was 277 ± 1.2 ppm.

This record shows variations between

272 and 284 ppm before 1800 and also that CO

mixing ratios

dropped by 5 to 10 ppm between 1600 and 1800 (see Section
6.3). The RF calculations usually take 1750 as the pre-industrial
index (e.g., the TAR and this report). Therefore, using 1750
may slightly overestimate the RF, as the changes in the mixing
ratios of CO

, CH

and N

O after the end of this naturally

cooler period may not be solely attributable to anthropogenic
emissions. Using 1860 as an alternative start date for the RF
calculations would reduce the LLGHG RF by roughly 10%.
For the RF calculation, the data from Law Dome ice cap in the
Antarctic are used because they show the highest age resolution
(approximately 10 years) of any ice core record in existence. In
addition, the high-precision data from the cores are connected
to direct observational records of atmospheric CO

from Cape

Grim, Tasmania.

The simple formulae for RF of the LLGHG quoted in

Ramaswamy et al. (2001) are still valid. These formulae are
based on global RF calculations where clouds, stratospheric
adjustment and solar absorption are included, and give an RF of
+3.7 W m

–2

for a doubling in the CO

mixing ratio. (The formula

used for the CO

RF calculation in this chapter is the IPCC

(1990) expression as revised in the TAR. Note that for CO

, RF

increases logarithmically with mixing ratio.) Collins et al. (2006)
performed a comparison of

ve detailed line-by-line models and

20 GCM radiation schemes. The spread of line-by-line model
results were consistent with the ±10% uncertainty estimate for
the LLGHG RFs adopted in Ramaswamy

et al. (2001) and a

similar ±10% for the 90% con

dence interval is adopted here.

However, it is also important to note that these relatively small
uncertainties are not always achievable when incorporating the
LLGHG forcings into GCMs. For example, both Collins et al.
(2006) and Forster and Taylor (2006) found that GCM radiation
schemes could have inaccuracies of around 20% in their total
LLGHG RF (see also Sections 2.3.2 and 10.2).

Using the global average value of 379 ppm for atmospheric

in 2005 gives an RF of 1.66 ± 0.17 W m

–2

; a contribution

that dominates that of all other forcing agents considered in this
chapter. This is an increase of 13 to 14% over the value reported
for 1998 in Ramaswamy et al.

(2001). This change is solely

due to increases in atmospheric CO

and is also much larger

than the RF changes due to other agents. In the decade 1995 to
2005, the RF due to CO

increased by about 0.28 W m

–2

(20%),

an increase greater than that calculated for any decade since at
least 1800 (see Section 6.6 and FAQ 2.1, Figure 1).

Table 2.1 summarises the present-day mixing ratios and RF

for the LLGHGs, and indicates changes since 1998. The RF
from CO

and that from the other LLGHGs have a high level

of scienti

c understanding (Section 2.9, Table 2.11). Note that

the uncertainty in RF is almost entirely due to radiative transfer
assumptions and not mixing ratio estimates, therefore trends in
RF can be more accurately determined than the absolute RF.
From Section 2.5.3, Table 2.8, the contribution from land use
change to the present CO

RF is likely to be about 0.4 W m

–2

(since 1850). This implies that fossil fuel and cement production
have likely contributed about three-quarters of the current RF.

2.3.2 Atmospheric

Methane

This section describes the current global measurement

programmes for atmospheric CH

, which provide the data

required for the understanding of its budget and for the
calculation of its RF. In addition, this section provides data
for the pre-industrial levels of CH

required as the accepted

reference level for these calculations. Detailed analyses of CH

budgets and its biogeochemistry are presented in Section 7.4.

Methane has the second-largest RF of the LLGHGs after

(Ramaswamy et al., 2001). Over the last 650 kyr, ice

core records indicate that the abundance of CH

in the Earth’s

atmosphere has varied from lows of about 400 ppb during glacial
periods to highs of about 700 ppb during interglacials (Spahni
et al., 2005) with a single measurement from the Vostok core
reaching about 770 ppb (see Figure 6.3).

In 2005, the global average abundance of CH

measured at

the network of 40 surface air

ask sampling sites operated by

NOAA/GMD in both hemispheres was 1,774.62 ± 1.22 ppb.

This is the most geographically extensive network of sites
operated by any laboratory and it is important to note that the
calibration scale it uses has changed since the TAR (Dlugokencky

et al., 2005). The new scale (known as NOAA04) increases all
previously reported CH

mixing ratios from NOAA/GMD by

about 1%, bringing them into much closer agreement with the
Advanced Global Atmospheric Gases Experiment (AGAGE)
network. This scale will be used by laboratories participating
in the WMO’s GAW programme as a ‘common reference’.
Atmospheric CH

is also monitored at

ve sites in the NH

and SH by the AGAGE network. This group uses automated
systems to make 36 CH

measurements per day at each site, and

the mean for 2005 was 1,774.03 ± 1.68 ppb with calibration and
methods described by Cunnold et al.

(2002). For the NOAA/

GMD network, the 90% con

dence interval is calculated

with a Monte Carlo technique, which only accounts for the
uncertainty due to the distribution of sampling sites. For both
networks, only sites in the remote marine boundary layer are
used and continental sites are not included. Global databases
of atmospheric CH

measurements for these and other CH

measurement programmes (e.g., Japanese, European and
Australian) are maintained by the CDIAC and by the WDCGG
in the GAW programme.

Present atmospheric levels of CH

are unprecedented in at

least the last 650 kyr (Spahni et al., 2005). Direct atmospheric

The 90% confi dence range quoted is from the normal standard deviation error for trace gas measurements assuming a normal distribution (i.e., multiplying by a factor of 1.645).

For consistency with the TAR, the pre-industrial value of 278 ppm is retained in the CO

RF calculation.

141

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

Notes:

See Table 2.14 for common names of gases and the radiative effi ciencies used to calculate RF.

Mixing ratio errors are 90% confi dence ranges of combined 2005 data, including intra-annual standard deviation, measurement and global averaging uncertainty.

Standard deviations were multiplied by 1.645 to obtain estimates of the 90% confi dence range; this assumes normal distributions. Data for CO

are combined

measurements from the NOAA Earth System Research Laboratory (ESRL) and SIO networks (see Section 2.3.1); CH

measurements are combined data from the

ESRL and Advanced Global Atmospheric Gases Experiment (AGAGE) networks (see Section 2.3.2); halocarbon measurements are the average of ESRL and AGAGE

networks. University of East Anglia (UEA) and Pennsylvania State University (PSU) measurements were also used (see Section 2.3.3).

Pre-industrial values are zero except for CO

(278 ppm), CH

(715 ppb; 700 ppb was used in the TAR), N

O (270 ppb) and CF

(40 ppt).

90% confi dence ranges for RF are not shown but are approximately 10%. This confi dence range is almost entirely due to radiative transfer assumptions, therefore

trends remain valid when quoted to higher accuracies. Higher precision data are used for totals and affect rounding of the values. Percent changes are calculated

relative to 1998.

Data available from AGAGE network only.

Data for 1998 not available; values from 1999 are used.

Data from UEA only.

Data from 2003 are used due to lack of available data for 2004 and 2005.

Data from ESRL only.

1997 data from PSU (Khalil et al., 2003, not updated) are used.

CFC total includes a small 0.009 W m

–2

RF from CFC-13, CFC-114, CFC-115 and the halons, as measurements of these were not updated.

Table 2.1.

Present-day concentrations and RF for the measured LLGHGs. The changes since 1998 (the time of the TAR estimates) are also shown.

ppt

Concentrations

and their changes

Radiative

Forcing

Change since

Species

2005

1998

2005 (W m

–2

) 1998

(%)

379 ± 0.65 ppm

+13 ppm

1.66

+13

1,774 ± 1.8 ppb

+11 ppb

0.48

319 ± 0.12 ppb

+5 ppb

0.16

+11

CFC-11

251 ± 0.36

–13

0.063

–5

CFC-12

538 ± 0.18

0.17

CFC-113

79 ± 0.064

–4

0.024

–5

HCFC-22

169 ± 1.0

+38

0.033

+29

HCFC-141b

18 ± 0.068

0.0025

+93

HCFC-142b

15 ± 0.13

0.0031

+57

CCl

19 ± 0.47

–47

0.0011

–72

CCl

93 ± 0.17

–7

0.012

–7

HFC-125

3.7 ± 0.10

+2.6

0.0009

+234

HFC-134a

35 ± 0.73

+27

0.0055

+349

HFC-152a

3.9 ± 0.11

+2.4

0.0004

+151

HFC-23

18 ± 0.12

g,h

+4 0.0033

+29

5.6 ± 0.038

+1.5 0.0029

+36

(PFC-14)

74 ± 1.6

0.0034

(PFC-116)

2.9 ± 0.025

g,h

+0.5 0.0008

+22

CFCs Total

0.268 –1

HCFCs Total

0.039 +33

Montreal Gases

0.320 –1

Other Kyoto Gases

(HFCs + PFCs + SF

)

0.017 +69

Halocarbons

0.337 +1

Total LLGHGs

2.63 +9

142

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

measurements of the gas made at a wide variety of sites in
both hemispheres over the last 25 years show that, although
the abundance of CH

has increased by about 30% during that

time, its growth rate has decreased substantially from highs of
greater than 1% yr

–1

in the late 1 70s and early 1980s (Blake

and Rowland,

1988) to lows of close to zero towards the end of

the 1990s (Dlugokencky

et al., 1998; Simpson

et al., 2002). The

slowdown in the growth rate began in the 1980s, decreasing
from 14 ppb yr

–1

(about 1% yr

–1

) in 1984 to close to zero during

1999 to 2005, for the network of surface sites maintained by
NOAA/GMD (Dlugokencky

et al., 2003). Measurements by

Lowe et al.

(2004) for sites in the SH and Cunnold et al.

(2002)

for the network of GAGE/AGAGE sites show similar features.
A key feature of the global growth rate of CH

is its current

interannual variability, with growth rates ranging from a high
of 14 ppb yr

–1

in 1998 to less than zero in 2001, 2004 and 2005.

(Figure 2.4)

The reasons for the decrease in the atmospheric CH

growth rate and the implications for future changes in its
atmospheric burden are not understood (Prather

et al., 2001)

but are clearly related to changes in the imbalance between CH

sources and sinks. Most CH

is removed from the atmosphere

by reaction with the hydroxyl free radical (OH), which is
produced photochemically in the atmosphere. The role of
OH in controlling atmospheric CH

levels is discussed in

Section 2.3.5. Other minor sinks include reaction with free
chlorine (Platt

et al., 2004; Allan

et al., 2005), destruction in the

stratosphere and soil sinks (Born

et al., 1990).

The total global CH

source is relatively well known but

the strength of each source component and their trends are
not. As detailed in Section 7.4, the sources are mostly biogenic
and include wetlands, rice agriculture, biomass burning and
ruminant animals. Methane is also emitted by various industrial
sources including fossil fuel mining and distribution. Prather et
al. (2001) documented a large range in ‘bottom-up’ estimates
for the global source of CH

. New source estimates published

since then are documented in Table 7.6. However, as reported by
Bergamaschi et al.

(2005), national inventories based on ‘bottom-

up’ studies can grossly underestimate emissions and ‘top-
down’ measurement-based assessments of reported emissions
will be required for veri

cation. Keppler et al. (2006) reported

the discovery of emissions of CH

from living vegetation and

estimated that this contributed 10 to 30% of the global CH

source. This work extrapolates limited measurements to a global
source and has not yet been con

rmed by other laboratories,

but lends some support to space-borne observations of CH

plumes above tropical rainforests reported by Frankenberg et
al. (2005). That such a potentially large source of CH

could

have been missed highlights the large uncertainties involved
in current ‘bottom-up’ estimates of components of the global
source (see Section 7.4).

Several wide-ranging hypotheses have been put forward

to explain the reduction in the CH

growth rate and its

variability. For example, Hansen et al.

(2000) considered that

economic incentives have led to a reduction in anthropogenic
CH

emissions. The negligible long-term change in its main

sink (OH; see Section 2.3.5 and Figure 2.8) implies that CH

emissions are not increasing. Similarly, Dlugokencky et al.
(1998) and Francey et al.

(1999) suggested that the slowdown

in the growth rate re

ects a stabilisation of CH

emissions,

given that the observations are consistent with stable emissions
and lifetime since 1982.

Relatively large anomalies occurred in the growth rate

during 1991 and 1998, with peak values reaching 15 and 14
ppb yr

–1

, respectively (about 1% yr

–1

). The anomaly in 1991

was followed by a dramatic drop in the growth rate in 1992
and has been linked with the Mt. Pinatubo volcanic eruption in
June 1991, which injected large amounts of ash and (sulphur
dioxide) SO

into the lower stratosphere of the tropics with

subsequent impacts on tropical photochemistry and the removal
of CH

by atmospheric OH (Bekki

et al., 1994; Dlugokencky

et al., 1996). Lelieveld et al. (1998) and Walter et al.

(2001a,b)

proposed that lower temperatures and lower precipitation in the
aftermath of the Mt. Pinatubo eruption could have suppressed
CH

emissions from wetlands. At this time, and in parallel with

the growth rate anomaly in the CH

mixing ratio, an anomaly

was observed in the

C ratio of CH

at surface sites in the

SH. This was attributed to a decrease in emissions from an
isotopically heavy source such as biomass burning (Lowe

et al.,

1997; Mak et al., 2000), although these data were not con

rmed

by lower frequency measurements from the same period made
by Francey et al. (1999).

Figure 2.4.

Recent CH

concentrations and trends. (a) Time series of global CH

abundance mole fraction (in ppb) derived from surface sites operated by NOAA/GMD
(blue lines) and AGAGE (red lines). The thinner lines show the CH

global averages

and the thicker lines are the de-seasonalized global average trends from both
networks. (b) Annual growth rate (ppb yr

–1

) in global atmospheric CH

abundance

from 1984 through the end of 2005 (NOAA/GMD, blue), and from 1988 to the end
of 2005 (AGAGE, red). To derive the growth rates and their uncertainties for each
month, a linear least squares method that takes account of the autocorrelation of
residuals is used. This follows the methods of Wang et al. (2002) and is applied
to the de-seasonalized global mean mole fractions from (a) for values six months
before and after the current month. The vertical lines indicate ±2 standard deviation
uncertainties (95% confi dence interval), and 1 standard deviation uncertainties are
between 0.1 and 1.4 ppb yr

–1

for both AGAGE and NOAA/GMD. Note that the differ-

ences between AGAGE and NOAA/GMD calibration scales are determined through
occasional intercomparisons.

143

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

For the relatively large increase in the CH

growth rate

reported for 1998, Dlugokencky et al.

(2001) suggested that

wetland and boreal biomass burning sources might have
contributed to the anomaly, noting that 1998 was the warmest
year globally since surface instrumental temperature records
began. Using an inverse method, Chen and Prinn (2006)
attributed the same event primarily to increased wetland and
rice region emissions and secondarily to biomass burning.
The same conclusion was reached by Morimoto et al. (2006),
who used carbon isotopic measurements of CH

to constrain

the relative contributions of biomass burning (one-third) and
wetlands (two-thirds) to the increase.

Based on ice core measurements of CH

(Etheridge

et al.,

1998), the pre-industrial global value for CH

from 1700 to

1800 was 715 ± 4 ppb (it was also 715 ± 4 ppb in 1750), thus
providing the reference level for the RF calculation. This takes
into account the inter-polar difference in CH

as measured from

Greenland and Antarctic ice cores.

The RF due to changes in CH

mixing ratio is calculated

with the simpli

ed yet still valid expression for CH

given in

Ramaswamy et al. (2001). The change in the CH

mixing ratio

from 715 ppb in 1750 to 1,774 ppb (the average mixing ratio
from the AGAGE and GMD networks) in 2005 gives an RF
of +0.48 ± 0.05 W m

–2

, ranking CH

as the second highest RF

of the LLGHGs after CO

(Table 2.1). The uncertainty range

in mixing ratios for the present day represents intra-annual
variability, which is not included in the pre-industrial uncertainty
estimate derived solely from ice core sampling precision. The
estimate for the RF due to CH

is the same as in Ramaswamy et

al. (2001) despite the small increase in its
mixing ratio. The spectral absorption by
CH

is overlapped to some extent by N

lines (taken into account in the simpli

expression). Taking the overlapping lines
into account using current N

O mixing

ratios instead of pre-industrial mixing
ratios (as in Ramaswamy et al., 2001)
reduces the current RF due to CH

1%.

Collins et al. (2006) con

rmed that

line-by-line models agree extremely
well for the calculation of clear-sky
instantaneous RF from CH

and N

when the same atmospheric background
pro

le is used. However, GCM radiation

schemes were found to be in poor
agreement with the line-by-line models,
and errors of over 50% were possible for
CH

, N

O and the CFCs. In addition, a

small effect from the absorption of solar
radiation was found with the line-by-line
models, which the GCMs did not include
(Section 10.2).

2.3.3

Other Kyoto Protocol Gases

At the time of the TAR, N

O had the fourth largest RF among

the LLGHGs behind CO

, CH

and CFC-12. The TAR quoted

an atmospheric N

O abundance of 314 ppb in 1998, an increase

of 44 ppb from its pre-industrial level of around 270 ± 7 ppb,
which gave an RF of +0.15 ± 0.02 W m

–2

. This RF is affected by

atmospheric CH

levels due to overlapping absorptions. As N

is also the major source of ozone-depleting nitric oxide (NO)
and nitrogen dioxide (NO

) in the stratosphere, it is routinely

reviewed in the ozone assessments; the most recent assessment
(Montzka

et al., 2003) recommended an atmospheric lifetime

of 114 years for N

O. The TAR pointed out large uncertainties

in the major soil, agricultural, combustion and oceanic sources
of N

O. Given these emission uncertainties, its observed rate of

increase of 0.2 to 0.3% yr

–1

was not inconsistent with its better-

quanti

ed major sinks (principally stratospheric destruction).

The primary driver for the industrial era increase of N

O was

concluded to be enhanced microbial production in expanding
and fertilized agricultural lands.

Ice core data for N

O have been reported extending back

2,000 years and more before present (MacFarling Meure et
al., 2006; Section 6.6). These data, as for CO

and CH

, show

relatively little changes in mixing ratios over the

rst 1,800

years of this record, and then exhibit a relatively rapid rise (see
FAQ 2.1, Figure 1). Since 1998, atmospheric N

O levels have

steadily risen to 319 ± 0.12 ppb in 2005, and levels have been
increasing approximately linearly (at around 0.26% yr

–1

) for the

past few decades (Figure 2.5). A change in the N

O mixing ratio

Figure 2.5.

Hemispheric monthly mean N

O mole fractions (ppb) (crosses for the NH and triangles for the

SH). Observations (

in situ

) of N

O from the Atmospheric Lifetime Experiment (ALE) and GAGE (through the

mid-1990s) and AGAGE (since the mid-1990s) networks (Prinn et al., 2000, 2005b) are shown with monthly
standard deviations. Data from NOAA/GMD are shown without these standard deviations (Thompson et al.,
2004). The general decrease in the variability of the measurements over time is due mainly to improved
instrumental precision. The real signal emerges only in the last decade.

144

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

from 270 ppb in 1750 to 319 ppb in 2005 results in an RF of
+0.16 ± 0.02 W m

–2

, calculated using the simpli

ed expression

given in Ramaswamy et al. (2001). The RF has increased by
11% since the time of the TAR (Table 2.1). As CFC-12 levels
slowly decline (see Section 2.3.4), N

O should, with its current

trend, take over third place in the LLGHG RF ranking.

Since the TAR, understanding of regional N

uxes has

improved. The results of various studies that quanti

ed the

global N

O emissions from coastal upwelling areas, continental

shelves, estuaries and rivers suggest that these coastal areas
contribute 0.3 to 6.6 TgN yr

–1

of N

O or 7 to 61% of the total

oceanic emissions (Bange et al., 1996; Nevison et al., 2004b;
Kroeze et al., 2005; see also Section 7.4). Using inverse
methods and AGAGE Ireland measurements, Manning et al.
(2003) estimated EU N

O emissions of 0.9 ± 0.1 TgN yr

–1

that

agree well with the United Nations Framework Convention
on Climate Change (UNFCCC) N

O inventory (0.8 ± 0.1

TgN yr

–1

). Melillo et al. (2001) provided evidence from

Brazilian land use sequences that the conversion of tropical
forest to pasture leads to an initial increase but a later decline
in emissions of N

O relative to the original forest. They also

deduced that Brazilian forest soils alone contribute about 10%
of total global N

O production. Estimates of N

O sources

and sinks using observations and inverse methods had earlier
implied that a large fraction of global N

O emissions in 1978

to 1988 were tropical: speci

cally 20 to 29% from 0° to 30°S

and 32 to 39% from 0° to 30°N compared to 11 to 15% from
30°S to 90°S and 22 to 34% from 30°N to 90°N (Prinn

et al.,

1990). These estimates were uncertain due to their signi

cant

sensitivity to assumed troposphere-stratosphere exchange rates
that strongly in

uence inter-hemispheric gradients. Hirsch et al.

(2006) used inverse modelling to estimate signi

cantly lower

emissions from 30°S to 90°S (0 to 4%) and higher emissions
from 0° to 30°N (50 to 64%) than Prinn et al.

(1990) during

1998 to 2001, with 26 to 36% from the oceans. The stratosphere
is also proposed to play an important role in the seasonal cycles
of N

O (Nevison

et al., 2004a). For example, its well-de

ned

seasonal cycle in the SH has been interpreted as resulting from
the net effect of seasonal oceanic outgassing of microbially
produced N

O, stratospheric intrusion of low-N

O air and other

processes (Nevison

et al., 2005). Nevison et al. also estimated

a Southern Ocean (30°S–90°S) N

O source of 0.9 TgN yr

–1

or about 5% of the global total. The complex seasonal cycle in
the NH is more dif

cult to reconcile with seasonal variations in

the northern latitude soil sources and stratospheric intrusions
(Prinn

et al., 2000; T. Liao

et al., 2004). The destruction of N

in the stratosphere causes enrichment of its heavier isotopomers
and isotopologues, providing a potential method to differentiate
stratospheric and surface

ux in

uences on tropospheric N

(Morgan

et al., 2004).

Human-made PFCs, HFCs and SF

are very effective

absorbers of infrared radiation, so that even small amounts of
these gases contribute signi

cantly to the RF of the climate

system. The observations and global cycles of the major HFCs,
PFCs and SF

were reviewed in Velders

et al. (2005), and this

section only provides a brief review and an update for these

species. Table 2.1 shows the present mixing ratio and recent
trends in the halocarbons and their RFs. Absorption spectra of
most halocarbons reviewed here and in the following section
are characterised by strongly overlapping spectral lines that
are not resolved at tropospheric pressures and temperatures,
and there is some uncertainty in cross section measurements.
Apart from the uncertainties stemming from the cross sections
themselves, differences in the radiative

ux calculations can

arise from the spectral resolution used, tropopause heights,
vertical, spatial and seasonal distributions of the gases, cloud
cover and how stratospheric temperature adjustments are
performed. IPCC/TEAP (2005) concluded that the discrepancy
in the RF calculation for different halocarbons, associated with
uncertainties in the radiative transfer calculation and the cross
sections, can reach 40%. Studies reviewed in IPCC/TEAP
(2005) for the more abundant HFCs show that an agreement
better than 12% can be reached for these when the calculation
conditions are better constrained (see Section 2.10.2).

The HFCs of industrial importance have lifetimes in the

range 1.4 to 270 years. The HFCs with the largest observed
mole fractions in 1998 (as reported in the TAR) were, in
descending order, HFC-23, HFC-134a and HFC-152a. In
2005, the observed mixing ratios of the major HFCs in the
atmosphere were 35 ppt for HFC-134a, 17.5 ppt for HFC-
23 (2003 value), 3.7 ppt for HFC-125 and 3.9 ppt for HFC-
152a (Table 2.1). Within the uncertainties in calibration and
emissions estimates, the observed mixing ratios of the HFCs
in the atmosphere can be explained by the anthropogenic
emissions. Measurements are available from GMD (Thompson

et al., 2004) and AGAGE (Prinn

et al., 2000; O’Doherty

et al.,

2004; Prinn

et al., 2005b) networks as well as from University

of East Anglia (UEA) studies in Tasmania (updated from Oram

et al., 1998; Oram, 1999). These data, summarised in Figure
2.6, show a continuation of positive HFC trends and increasing
latitudinal gradients (larger trends in the NH) due to their
predominantly NH sources. The air conditioning refrigerant
HFC-134a is increasing at a rapid rate in response to growing
emissions arising from its role as a replacement for some CFC
refrigerants. With a lifetime of about 14 years, its current trends
are determined primarily by its emissions and secondarily by
its atmospheric destruction. Emissions of HFC-134a estimated
from atmospheric measurements are in approximate agreement
with industry estimates (Huang and Prinn, 2002; O’Doherty

et al., 2004). IPCC/TEAP (2005) reported that global HFC-
134a emissions started rapidly increasing in the early 1990s,
and that in Europe, sharp increases in emissions are noted for
HFC-134a from 1995 to 1998 and for HFC-152a from 1996 to
2000, with some levelling off through 2003. The concentration
of the foam blower HFC-152a, with a lifetime of only about 1.5
years, is rising approximately exponentially, with the effects of
increasing emissions only partly offset by its rapid atmospheric
destruction. Hydro

uorocarbon-23 has a very long atmospheric

lifetime (approximately 270 years) and is mainly produced as
a by-product of HCFC-22 production. Its concentrations are
rising approximately linearly, driven by these emissions, with
its destruction being only a minor factor in its budget. There are

145

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

Figure 2.6.

Temporal evolution of the global average dry-air mole fractions (ppt)

of the major halogen-containing LLGHGs. These are derived

mainly using monthly

mean measurements from the AGAGE and NOAA/GMD networks. For clarity, the two
network values are averaged with equal weight when both are available. While differ-
ences exist, these network measurements agree reasonably well with each other
(except for CCl

(differences of 2 – 4% between networks) and HCFC-142b (differ-

ences of 3 – 6% between networks)), and with other measurements where available
(see text for references for each gas).

also smaller but rising concentrations of HFC-125 and HFC-
143a, which are both refrigerants.

The PFCs, mainly CF

(PFC-14) and C

(PFC-116), and

have very large radiative ef

ciencies and lifetimes in the

range 1,000 to 50,000 years (see Section 2.10, Table 2.14), and
make an essentially permanent contribution to RF. The SF

and

concentrations and RFs have increased by over 20% since

the TAR (Table 2.1 and Figure 2.6), but CF

concentrations

have not been updated since 1997. Both anthropogenic and
natural sources of CF

are important to explain its observed

atmospheric abundance. These PFCs are produced as by-
products of traditional aluminium production, among other
activities. The CF

concentrations have been increasing linearly

since about 1960 and CF

has a natural source that accounts for

about one-half of its current atmospheric content (Harnisch

al., 1996). Sulphur hexa

uoride (SF

) is produced for use as an

electrical insulating

uid in power distribution equipment and

also deliberately released as an essentially inert tracer to study
atmospheric and oceanic transport processes. Its concentration
was 4.2 ppt in 1998 (TAR) and has continued to increase
linearly over the past decade, implying that emissions are
approximately constant. Its very long lifetime ensures that its
emissions accumulate essentially unabated in the atmosphere.

2.3.4

Montreal Protocol Gases

The Montreal Protocol on Substances that Deplete the Ozone

Layer regulates many radiatively powerful greenhouse gases
for the primary purpose of lowering stratospheric chlorine and
bromine concentrations. These gases include the CFCs, HCFCs,
chlorocarbons, bromocarbons and halons. Observations and
global cycles of these gases were reviewed in detail in Chapter
1 of the 2002 Scienti

c Assessment of Ozone Depletion (WMO,

2003) and in IPCC/TEAP (2005). The discussion here focuses
on developments since these reviews and on those gases that
contribute most to RF rather than to halogen loading. Using
observed 2005 concentrations, the Montreal Protocol gases have
contributed 12% (0.320 W m

–2

) to the direct RF of all LLGHGs

and 95% to the halocarbon RF (Table 2.1). This contribution is
dominated by the CFCs. The effect of the Montreal Protocol on
these gases has been substantial. IPCC/TEAP (2005) concluded
that the combined CO

-equivalent emissions of CFCs, HCFCs

and HFCs decreased from a peak of about 7.5 GtCO

-eq yr

–1

in the late 1980s to about 2.5 GtCO

-eq yr

–1

by the year 2000,

corresponding to about 10% of that year’s CO

emissions due

to global fossil fuel burning.

Measurements of the CFCs and HCFCs, summarised in

Figure 2.6, are available from the AGAGE network (Prinn

al., 2000, 2005b) and the GMD network (Montzka et al., 1999
updated; Thompson et al., 2004). Certain

ask measurements

are also available from the University of California at Irvine
(UCI; Blake et al., 2001 updated) and UEA (Oram

et al., 1998;

Oram, 1999 updated). Two of the major CFCs (CFC-11 and
CFC-113) have both been decreasing in the atmosphere since
the mid-1990s. While their emissions have decreased very
substantially in response to the Montreal Protocol, their long
lifetimes of around 45 and 85 years, respectively, mean that their
sinks can reduce their levels by only about 2% and 1% yr

–1

respectively. Nevertheless, the effect of the Montreal Protocol
has been to substantially reduce the growth of the halocarbon
RF, which increased rapidly from 1950 until about 1990. The
other major CFC (CFC-12), which is the third most important
LLGHG, is

nally reaching a plateau in its atmospheric levels

(emissions equal loss) and may have peaked in 2003. Its 100-
year lifetime means that it can decrease by only about 1% yr

–1

even when emissions are zero. The levelling off for CFC-12 and
approximately linear downward trends for CFC-11 and CFC-
113 continue. Latitudinal gradients of all three are very small
and decreasing as expected. The combined CFC and HCFC
RF has been slowly declining since 2003. Note that the 1998
concentrations of CFC-11 and CFC-12 were overestimated in
Table 6.1 of the TAR. This means that the total halocarbon RF
quoted for 2005 in Table 2.1 (0.337 W m

–2

) is slightly smaller

than the 0.34 W m

–2

quoted in the TAR, even though the

measurements indicate a small 1% rise in the total halocarbon
RF since the time of the TAR (Table 2.1).

The major solvent, methyl chloroform (CH

CCl

), is of

special importance regarding RFs, not because of its small RF
(see Table 2.1 and Figure 2.6), but because this gas is widely

146

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

used to estimate concentrations of OH, which
is the major sink species for CH

, HFCs, and

HCFCs and a major production mechanism
for sulphate, nitrate and some organic aerosols
as discussed in Section 2.3.5. The global
atmospheric methyl chloroform concentration
rose steadily from 1978 to reach a maximum in
1992 (Prinn

et al., 2001; Montzka

et al., 2003).

Since then, concentrations have decreased
rapidly, driven by a relatively short lifetime
of 4.9 years and phase-out under the Montreal
Protocol, to levels in 2003 less than 20% of the
levels when AGAGE measurements peaked
in 1992 (Prinn

et al., 2005a). Emissions of

methyl chloroform determined from industry
data (McCulloch and Midgley, 2001) may
be too small in recent years. The 2000 to
2003 emissions from Europe estimated using
surface observations (Reimann

et al., 2005)

show that 1.2 to 2.3 Gg yr

–1

need to be added

over this 4-year period to the above industry
estimates for Europe. Estimates of European
emissions in 2000 exceeding 20 Gg (Krol

al., 2003) are not supported by analyses of the
above extensive surface data (Reimann

et al.,

2005). From multi-year measurements, Li et
al. (2005) estimated 2001 to 2002 emissions
from the USA of 2.2 Gg yr

–1

(or about half of

those estimated from more temporally but less
geographically limited measurements by Millet
and Goldstein, 2004), and suggested that 1996
to 1998 US emissions may be underestimated
by an average of about 9.0 Gg yr

–1

over this 3-

year period. East Asian emissions deduced from
aircraft data in 2001 are about 1.7 Gg above
industry data (Palmer

et al., 2003; see also

Yokouchi et al.,

2005) while recent Australian

and Russian emissions are negligible (Prinn

al., 2001; Hurst

et al., 2004).

Carbon tetrachloride (CCl

) is the

second most rapidly decreasing atmospheric
chlorocarbon after methyl chloroform.
Levels peaked in early 1990 and decreased
approximately linearly since then (Figure 2.7).
Its major use was as a feedstock for CFC manufacturing. Unlike
methyl chloroform, a signi

cant inter-hemispheric CCl

gradient

still exists in 2005 in spite of its moderately long lifetime of
20 to 30 years, resulting from a persistence of signi

cant NH

emissions.

HCFCs of industrial importance have lifetimes in the range

of 1.3 to 20 years. Global and regional emissions of the CFCs
and HCFCs have been derived from observed concentrations
and can be used to check emission inventory estimates.
Montzka et al. (2003) and IPCC/TEAP (2005) concluded that
global emissions of HCFC-22 rose steadily over the period
1975 to 2000, while those of HCFC-141b and HCFC-142b

started increasing quickly in the early 1990s and then began to
decrease after 2000.

To provide a direct comparison of the effects on global

warming due to the annual changes in each of the non-CO

greenhouse gases (discussed in Sections 2.3.2, 2.3.3 and 2.3.4)
relative to CO

, Figure 2.7 shows these annual changes in

atmospheric mass multiplied by the GWP (100-year horizon)
for each gas (e.g., Prinn, 2004). By expressing them in this way,
the observed changes in all non-CO

gases in GtC equivalents

and the signi

cant roles of CH

, N

O and many halocarbons are

very evident. This highlights the importance of considering the
full suite of greenhouse gases for RF calculations.

Figure 2.7.

Annual rates of change in the global atmospheric masses of each of the major LLGHGs ex-

pressed in

common units of GtC yr

–1

. These rates are computed

from their actual annual mass changes

in Gt yr

–1

(as derived from their observed global and annual average mole fractions presented in Figures

2.3 to 2.6 and discussed in Sections 2.3.1 to 2.3.4) by multiplying them by their GWPs for 100-year time
horizons and then dividing by the ratio of the CO

to carbon (C) masses (44/12). These rates

are positive

or negative whenever the mole fractions are increasing or decreasing, respectively.

Use of these com-

mon units provides an approximate way to intercompare the fl uxes of LLGHGs, using the same approach
employed to intercompare the values of LLGHG emissions under the Kyoto Protocol (see, e.g., Prinn,
2004). Note that the negative indirect RF of CFCs and HCFCs due to stratospheric ozone depletion is not
included. The oscillations in the CF

curve may result partly from truncation in reported mole fractions.

147

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

2.3.5

Trends in the Hydroxyl Free Radical

The hydroxyl free radical (OH) is the major oxidizing

chemical in the atmosphere, destroying about 3.7 Gt of trace
gases, including CH

and all HFCs and HCFCs, each year

(Ehhalt, 1999). It therefore has a very signi

cant role in

limiting the LLGHG RF. IPCC/TEAP (2005) concluded that
the OH concentration might change in the 21st century by –18
to +5% depending on the emission scenario. The large-scale
concentrations and long-term trends in OH can be inferred
indirectly using global measurements of trace gases for which
emissions are well known and the primary sink is OH. The best
trace gas used to date for this purpose is methyl chloroform;
long-term measurements of this gas are reviewed in Section
2.3.4. Other gases that are useful OH indicators include

CO,

which is produced primarily by cosmic rays (Lowe and Allan,

2002). While the accuracy of the

CO cosmic ray and other

CO source estimates and the frequency and spatial coverage

of its measurements do not match those for methyl chloroform,
the

CO lifetime (2 months) is much shorter than that of methyl

chloroform (4.9 years). As a result,

CO provides estimates of

average concentrations of OH that are more regional, and is
capable of resolving shorter time scales than those estimated
from methyl chloroform. The

CO source variability is better

ned than its absolute magnitude so it is better for inferring

relative rather than absolute trends. Another useful gas is the
industrial chemical HCFC-22. It yields OH concentrations
similar to those derived from methyl chloroform, but with less
accuracy due to greater uncertainties in emissions and less
extensive measurements (Miller

et al., 1998). The industrial

gases HFC-134a, HCFC-141b and HCFC-142b are potentially
useful OH estimators, but the accuracy of their emission
estimates needs improvement (Huang and Prinn, 2002;
O’Doherty

et al., 2004).

Indirect measurements of OH using methyl chloroform

have established that the globally weighted average OH
concentration in the troposphere is roughly 10

radicals per

cubic centimetre (Prinn

et al., 2001; Krol and Lelieveld, 2003).

A similar average concentration is derived using

CO (Quay

et al., 2000), although the spatial weighting here is different.
Note that methods to infer global or hemispheric average OH
concentrations may be insensitive to compensating regional OH
changes such as OH increases over continents and decreases over
oceans (Lelieveld et al., 2002). In addition, the quoted absolute
OH concentrations (but not their relative trends) depend on the
choice of weighting (e.g., Lawrence et al., 2001). While the
global average OH concentration appears fairly well de

ned

by these indirect methods, the temporal trends in OH are more
dif

cult to discern since they require long-term measurements,

optimal inverse methods and very accurate calibrations, model
transports and methyl chloroform emissions data. From AGAGE
methyl chloroform measurements, Prinn et al. (2001) inferred
that global OH levels grew between 1979 and 1989, but then
declined between 1989 and 2000, and also exhibited signi

cant

interannual variations. They concluded that these decadal
global variations were driven principally by NH OH, with

SH OH decreasing from 1979 to 1989 and staying essentially
constant after that. Using the same AGAGE data and identical
methyl chloroform emissions, a three-dimensional model
analysis (Krol and Lelieveld, 2003) supported qualitatively
(but not quantitatively) the earlier result (Prinn

et al., 2001)

that OH concentrations increased in the 1980s and declined
in the 1990s. Prinn et al. (2001) also estimated the emissions
required to provide a zero trend in OH. These required methyl
chloroform emissions differed substantially from industry
estimates by McCulloch and Midgley (2001) particularly for
1996 to 2000. However, Krol and Lelieveld (2003) argued that
the combination of possible underestimated recent emissions,
especially the >20 Gg European emissions deduced by Krol
et al. (2003), and the recent decreasing effectiveness of the
stratosphere as a sink for tropospheric methyl chloroform, may
be suf

cient to yield a zero deduced OH trend. As discussed

in Section 2.3.4, estimates of European emissions by Reimann
et al. (2005) are an order of magnitude less than those of Krol
et al. (2003). In addition, Prinn et al. (2005a) extended the
OH estimates through 2004 and showed that the Prinn et al.
(2001) decadal and interannual OH estimates remain valid even
after accounting for the additional recent methyl chloroform
emissions discussed in Section 2.3.4. They also recon

rmed

the OH maximum around 1989 and a larger OH minimum
around 1998, with OH concentrations then recovering so that
in 2003 they were comparable to those in 1979. They noted that
the 1997 to 1999 OH minimum coincides with, and is likely
caused by, major global wild

res and an intense El Niño at that

time. The 1997 Indonesian

res alone have been estimated to

have lowered global late-1997 OH levels by 6% due to carbon
monoxide (CO) enhancements (Duncan

et al., 2003).

Methyl chloroform is also destroyed in the stratosphere.

Because its stratospheric loss frequency is less than that in the
troposphere, the stratosphere becomes a less effective sink for
tropospheric methyl chloroform over time (Krol and Lelieveld,
2003), and even becomes a small source to the troposphere
beginning in 1999 in the reference case in the Prinn et al. (2001,
2005a) model. Loss to the ocean has usually been considered
irreversible, and its rates and uncertainties have been obtained
from observations (Yvon-Lewis and Butler, 2002). However,
Wennberg et al. (2004) recently proposed that the polar oceans
may have effectively stored methyl chloroform during the pre-
1992 years when its atmospheric levels were rising, but began
re-emitting it in subsequent years, thus reducing the overall
oceanic sink. Prinn et al. (2005a) tried both approaches and
found that their inferred interannual and decadal OH variations
were present using either formulation, but inferred OH was
lower in the pre-1992 years and higher after that using the
Wennberg et al. (2004) formulation.

More recently, Bousquet et al. (2005) used an inverse

method with a three-dimensional model and methyl chloroform
measurements and concluded that substantial year-to-year
variations occurred in global average OH concentrations
between 1980 and 2000. This conclusion was previously
reached by Prinn et al. (2001), but subsequently challenged
by Krol and Lelieveld (2003) who argued that these variations

149

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

of many of these results to those from methyl chloroform
discussed above is very important, given the independence of
the two approaches.

RF calculations of the LLGHGs are calculated from

observed trends in the LLGHG concentrations and therefore
OH concentrations do not directly affect them. Nevertheless
OH trends are needed to quantify LLGHG budgets (Section
7.4) and for understanding future trends in the LLGHGs and
tropospheric ozone.

2.3.6 Ozone

In the TAR, separate estimates for RF due to changes in

tropospheric and stratospheric ozone were given. Stratospheric
ozone RF was derived from observations of ozone change
from roughly 1979 to 1998. Tropospheric ozone RF was based
on chemical model results employing changes in precursor
hydrocarbons, CO and nitrogen oxides (NO

). Over the satellite

era (since approximately 1980), stratospheric ozone trends
have been primarily caused by the Montreal Protocol gases,
and in Ramaswamy et al. (2001) the stratospheric ozone RF
was implicitly attributed to these gases. Studies since then have
investigated a number of possible causes of ozone change in the
stratosphere and troposphere and the attribution of ozone trends
to a given precursor is less clear. Nevertheless, stratospheric
ozone and tropospheric ozone RFs are still treated separately
in this report. However, the RFs are more associated with the
vertical location of the ozone change than they are with the
agent(s) responsible for the change.

2.3.6.1 Stratospheric

Ozone

The TAR reported that ozone depletion in the stratosphere

had caused a negative RF of –0.15 W m

–2

as a best estimate over

the period since 1750. A number of recent reports have assessed
changes in stratospheric ozone and the research into its causes,
including Chapters 3 and 4 of the 2002 Scienti

c Assessment of

Ozone Depletion (WMO, 2003) and Chapter 1 of IPCC/TEAP
(2005). This section summarises the material from these reports
and updates the key results using more recent research.

Global ozone amounts decreased between the late 1970s

and early 1990s, with the lowest values occurring during
1992 to 1993 (roughly 6% below the 1964 to 1980 average),
and slightly increasing values thereafter. Global ozone for the
period 2000 to 2003 was approximately 4% below the 1964
to 1980 average values. Whether or not recently observed
changes in ozone trends (Newchurch

et al., 2003; Weatherhead

and Andersen, 2006) are already indicative of recovery of the
global ozone layer is not yet clear and requires more detailed
attribution of the drivers of the changes (Steinbrecht

et al.,

2004a (see also comment and reply: Cunnold et al., 2004 and
Steinbrecht

et al.,

2004b); Hadjinicolaou

et al., 2005; Krizan

and Lastovicka, 2005; Weatherhead and Andersen, 2006). The
largest ozone changes since 1980 have occurred during the late
winter and spring over Antarctica where average total column
ozone in September and October is about 40 to 50% below pre-

1980 values (WMO, 2003). Ozone decreases over the Arctic
have been less severe than have those over the Antarctic, due
to higher temperature in the lower stratosphere and thus fewer
polar stratospheric clouds to cause the chemical destruction.
Arctic stratospheric ozone levels are more variable due to
interannual variability in chemical loss and transport.

The temporally and seasonally non-uniform nature of

stratospheric ozone trends has important implications for the
resulting RF. Global ozone decreases result primarily from
changes in the lower stratospheric extratropics. Total column
ozone changes over the mid-latitudes of the SH are signi

cantly

larger than over the mid-latitudes of the NH. Averaged over
the period 2000 to 2003, SH values are 6% below pre-1980
values, while NH values are 3% lower. There is also signi

cant

seasonality in the NH ozone changes, with 4% decreases in
winter to spring and 2% decreases in summer, while long-term
SH changes are roughly 6% year round (WMO, 2003). Southern
Hemisphere mid-latitude ozone shows signi

cant decreases

during the mid-1980s and essentially no response to the effects
of the Mt. Pinatubo volcanic eruption in June 1991; both of these
features remain unexplained. Pyle et al. (2005) and Chipper

eld

et al.

(2003) assessed several studies that show that a substantial

fraction (roughly 30%) of NH mid-latitude ozone trends are not
directly attributable to anthropogenic chemistry, but are related
to dynamical effects, such as tropopause height changes. These
dynamical effects are likely to have contributed a larger fraction
of the ozone RF in the NH mid-latitudes. The only study to
assess this found that 50% of the RF related to stratospheric
ozone changes between 20°N to 60°N over the period 1970 to
1997 is attributable to dynamics (Forster and Tourpali, 2001).
These dynamical changes may well have an anthropogenic
origin and could even be partly caused by stratospheric ozone
changes themselves through lower stratospheric temperature
changes (Chipper

eld

et al., 2003; Santer

et al., 2004), but are

not directly related to chemical ozone loss.

At the time of writing, no study has utilised ozone trend

observations after 1998 to update the RF values presented
in Ramaswamy et al. (2001). However, Hansen et al. (2005)
repeated the RF calculation based on the same trend data set
employed by studies assessed in Ramaswamy et al. (2001)
and found an RF of roughly –0.06 W m

–2

. A considerably

stronger RF of –0.2 ± 0.1 W m

–2

previously estimated by the

same group affected the Ramaswamy et al. (2001) assessment.
The two other studies assessed in Ramaswamy et al. (2001),
using similar trend data sets, found RFs of –0.01 W m

–2

and

–0.10 W m

–2

. Using the three estimates gives a revision of the

observationally based RF for 1979 to 1998 to about –0.05 ±
0.05 W m

–2

Gauss et al.

(2006) compared results from six chemical

transport models that included changes in ozone precursors to
simulate both the increase in the ozone in the troposphere and
the ozone reduction in the stratosphere over the industrial era.
The 1850 to 2000 annually averaged global mean stratospheric
ozone column reduction for these models ranged between 14 and
29 Dobson units (DU). The overall pattern of the ozone changes
from the models were similar but the magnitude of the ozone

150

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

changes differed. The models showed a reduction in the ozone
at high latitudes, ranging from around 20 to 40% in the SH and
smaller changes in the NH. All models have a maximum ozone
reduction around 15 km at high latitudes in the SH. Differences
between the models were also found in the tropics, with some
models simulating about a 10% increase in the lower stratosphere
and other models simulating decreases. These differences
were especially related to the altitude where the ozone trend
switched from an increase in the troposphere to a decrease in
the stratosphere, which ranged from close to the tropopause to
around 27 km. Several studies have shown that ozone changes
in the tropical lower stratosphere are very important for the
magnitude and sign of the ozone RF (Ramaswamy et al., 2001).
The resulting stratospheric ozone RF ranged between –0.12 and
+0.07 W m

–2

. Note that the models with either a small negative

or a positive RF also had a small increase in tropical lower
stratospheric ozone, resulting from increases in tropospheric
ozone precursors; most of this increase would have occurred
before the time of stratospheric ozone destruction by the Montreal
Protocol gases. These RF calculations also did not include any
negative RF that may have resulted from stratospheric water
vapour increases. It has been suggested (Shindell and Faluvegi,
2002) that stratospheric ozone during 1957 to 1975 was lower by
about 7 DU relative to the

rst half of the 20th century as a result

of possible stratospheric water vapour increases; however, these
long-term increases in stratospheric water vapour are uncertain
(see Sections 2.3.7 and 3.4).

The stratospheric ozone RF is assessed to be –0.05 ± 0.10

W m

–2

between pre-industrial times and 2005. The best estimate

is from the observationally based 1979 to 1998 RF of –0.05 ±
0.05 W m

–2

, with the uncertainty range increased to take into

account ozone change prior to 1979, using the model results
of Gauss et al. (2006) as a guide. Note that this estimate takes
into account causes of stratospheric ozone change in addition to
those due to the Montreal Protocol gases. The level of scienti

understanding is medium, unchanged from the TAR (see Section
2.9, Table 2.11).

2.3.6.2 Tropospheric

Ozone

The TAR identi

ed large regional differences in observed

trends in tropospheric ozone from ozonesondes and surface
observations. The TAR estimate of RF from tropospheric ozone
was +0.35 ± 0.15 W m

–2

. Due to limited spatial and temporal

coverage of observations of tropospheric ozone, the RF
estimate is based on model simulations. In the TAR, the models
considered only changes in the tropospheric photochemical
system, driven by estimated emission changes (NO

, CO, non-

methane volatile organic compounds (NMVOCs), and CH

)

since pre-industrial times. Since the TAR, there have been
major improvements in models. The new generation models
include several Chemical Transport Models (CTMs) that couple
stratospheric and tropospheric chemistry, as well as GCMs
with on-line chemistry (both tropospheric and stratospheric).
While the TAR simulations did not consider changes in ozone
within the troposphere caused by reduced in

ux of ozone from

the stratosphere (due to ozone depletion in the stratosphere),
the new models include this process (Gauss

et al., 2006). This

advancement in modelling capabilities and the need to be
consistent with how the RF due to changes in stratospheric
ozone is derived (based on observed ozone changes) have led
to a change in the de

nition of RF due to tropospheric ozone

compared with that in the TAR. Changes in tropospheric ozone
due to changes in transport of ozone across the tropopause,
which are in turn caused by changes in stratospheric ozone, are
now included.

Trends in anthropogenic emissions of ozone precursors for

the period 1990 to 2000 have been compiled by the Emission
Database for Global Atmospheric Research (EDGAR)
consortium (Olivier and Berdowski, 2001 updated). For
speci

c regions, there is signi

cant variability over the period

due to variations in the emissions from open biomass burning
sources. For all components (NO

, CO and volatile organic

compounds (VOCs)) industrialised regions like the USA and
Organisation for Economic Co-operation and Development
(OECD) Europe show reductions in emissions, while regions
dominated by developing countries show signi

cant growth

in emissions. Recently, the tropospheric burdens of CO and
NO

were estimated from satellite observations (Edwards et

al., 2004; Richter et al., 2005), providing much needed data for
model evaluation and very valuable constraints for emission
estimates.

Assessment of long-term trends in tropospheric ozone is

dif

cult due to the scarcity of representative observing sites with

long records. The long-term tropospheric ozone trends vary both
in terms of sign and magnitude and in the possible causes for the
change (Oltmans et al., 2006). Trends in tropospheric ozone at
northern middle and high latitudes have been estimated based on
ozonesonde data by WMO (2003), Naja et al. (2003), Naja and
Akimoto (2004), Tarasick et al. (2005) and Oltmans et al. (2006).
Over Europe, ozone in the free troposphere increased from the
early 20th century until the late 1980s; since then the trend has
levelled off or been slightly negative. Naja and Akimoto (2004)
analysed 33 years of ozonesonde data from Japanese stations,
and showed an increase in ozone in the lower troposphere (750–
550 hPa) between the periods 1970 to 1985 and 1986 to 2002 of
12 to 15% at Sapporo and Tsukuba (43°N and 36°N) and 35% at
Kagoshima (32°N). Trajectory analysis indicates that the more
southerly station, Kagoshima, is signi

cantly more in

uenced

by air originating over China, while Sapporo and Tsukuba are
more in

uenced by air from Eurasia. At Naha (26°N) a positive

trend (5% per decade) is found between 700 and 300 hPa
(1990–2004), while between the surface and 700 hPa a slightly
negative trend is observed (Oltmans et al., 2006). Ozonesondes
from Canadian stations show negative trends in tropospheric
ozone between 1980 and 1990, and a rebound with positive
trends during 1991 to 2001 (Tarasick et al., 2005). Analysis
of stratosphere-troposphere exchange processes indicates that
the rebound during the 1990s may be partly a result of small
changes in atmospheric circulation.

Trends are also derived from surface observations. Jaffe et

al.

(2003) derived a positive trend of 1.4% yr

–1

between 1988

151

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

and 2003 using measurements from Lassen Volcanic Park in
California (1,750 m above sea level), consistent with the trend
derived by comparing two aircraft campaigns (Parrish et al.,
2004). However, a number of other sites show insigni

cant

changes over the USA over the last 15 years (Oltmans et
al., 2006). Over Europe and North America, observations
from Whiteface Mountain, Wallops Island, Hohenpeisenberg,
Zugspitze and Mace Head (

ow from the European sector)

show small trends or reductions during summer, while there
is an increase during winter (Oltmans et al., 2006). These
observations are consistent with reduced NO

emissions

and Brasseur, 2001; Mickley et al., 2001; Shindell et al., 2003a;
Mickley et al., 2004; Wong et al., 2004; Liao and Seinfeld,
2005; Shindell et al., 2005). In addition, a multi-model
experiment including 10 global models was organised through
the Atmospheric Composition Change: an European Network
(ACCENT; Gauss et al., 2006). Four of the ten ACCENT
models have detailed stratospheric chemistry. The adjusted RF
for all models was calculated by the same radiative transfer
model. The normalised adjusted RF for the ACCENT models
was +0.032 ± 0.006 W m

–2

–1

, which is signi

cantly lower

than the TAR estimate of +0.042 W m

–2

–1

Figure 2.9.

Calculated RF due to tropospheric ozone change since pre-industrial time based on

CTM and GCM model simulations published since the TAR. Estimates with GCMs including the effect
of climate change since 1750 are given by orange bars (Adjusted RF, CC). Studies denoted with an
(*) give only the instantaneous RF in the original publications. Stratospheric-adjusted RFs for these
are estimated by reducing the instantaneous RF (indicated by the dashed bars) by 20%. The instan-
taneous RF from Mickley et al. (2001) is reported as an adjusted RF in Gauss et al. (2006). ACCENT
models include ULAQ: University of L’Aquila; DLR_E39C: Deutsches Zentrum für Luft- und Raumfahrt
European Centre Hamburg Model; NCAR_MACCM: National Center for Atmospheric Research Middle
Atmosphere Community Climate Model; CHASER: Chemical Atmospheric GCM for Study of Atmo-
spheric Environment and Radiative Forcing; STOCHEM_HadGEM1: United Kingdom Meteorological
Offi ce global atmospheric chemistry model /Hadley Centre Global Environmental Model 1; UM_CAM:
United Kingdom Meteorological Offi ce Unifi ed Model GCM with Cambridge University chemistry;
STOCHEM_HadAM3: United Kingdom Meteorological Offi ce global atmospheric chemistry model/
Hadley Centre Atmospheric Model; LMDzT-INCA: Laboratoire de Météorologie Dynamique GCM-
INteraction with Chemistry and Aerosols; UIO_CTM2: University of Oslo CTM; FRSGC_UCI: Frontier
Research System for Global Change/University of California at Irvine CTM.

(Jonson et al., 2005). North Atlantic stations
(Mace Head, Izana and Bermuda) indicate
increased ozone (Oltmans et al., 2006). Over the
North Atlantic (40°N–60°N) measurements from
ships (Lelieveld et al., 2004) show insigni

cant

trends in ozone, however, at Mace Head a positive
trend of 0.49 ± 0.19 ppb yr

–1

for the period 1987 to

2003 is found, with the largest contribution from
air coming from the Atlantic sector (Simmonds
et al., 2004).

In the tropics, very few long-term ozonesonde

measurements are available. At Irene in South
Africa (26°S), Diab et al. (2004) found an
increase between the 1990 to 1994 and 1998
to 2002 periods of about 10 ppb close to the
surface (except in summer) and in the upper
troposphere during winter. Thompson et al.
(2001) found no signi

cant trend during

1979 to 1992, based on Total Ozone Mapping
Spectrometer (TOMS) satellite data. More recent
observations (1994 to 2003,

in situ

data from

the Measurement of Ozone by Airbus In-service
Aircraft (MOZAIC) program) show signi

cant

trends in free-tropospheric ozone (7.7 to 11.3 km
altitude) in the tropics: 1.12 ± 0.05 ppb yr

–1

and

1.03 ± 0.08 ppb yr

–1

in the NH tropics and SH

tropics, respectively (Bortz and Prather, 2006).
Ozonesonde measurements over the southwest
Paci

c indicate an increased frequency of near-

zero ozone in the upper troposphere, suggesting a
link to an increased frequency of deep convection
there since the 1980s (Solomon et al., 2005).

At southern mid-latitudes, surface observations

from Cape Point, Cape Grim, the Atlantic Ocean
(from ship) and from sondes at Lauder (850–700
hPa) show positive trends in ozone concentrations,
in particular during the biomass burning season in
the SH (Oltmans et al., 2006). However, the trend
is not accompanied by a similar trend in CO, as
expected if biomass burning had increased. The
increase is largest at Cape Point, reaching 20% per
decade (in September). At Lauder, the increase is
con

ned to the lower troposphere.

Changes in tropospheric ozone and the

corresponding RF have been estimated in a
number of recent model studies (Hauglustaine

152

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

2000, since then water vapour concentrations in the lower
stratosphere have been decreasing (see Section 3.4 for details
and references). As well as CH

increases, several other indirect

forcing mechanisms have been proposed, including: a) volcanic
eruptions (Considi ne

et al., 2001; Joshi and Shine, 2003); b)

biomass burning aerosol (Sherwood, 2002); c) tropospheric (SO

;

Notholt

et al., 2005) and d) changes in CH

oxidation rates from

changes in stratospheric chlorine, ozone and OH (Rockmann

al., 2004). These are mechanisms that can be linked to an external
forcing agent. Other proposed mechanisms are more associated
with climate feedbacks and are related to changes in tropopause
temperatures or circulation (Stuber

et al., 2001a; Fueglistaler

et al., 2004). From these studies, there is little quanti

cation of

the stratospheric water vapour change attributable to different
causes. It is also likely that different mechanisms are affecting
water vapour trends at different altitudes.

Since the TAR, several further calculations of the radiative

balance change due to changes in stratospheric water vapour
have been performed (Forster and Shine, 1999; Oinas

et al.,

2001; Shindell, 2001; Smith

et al., 2001; Forster and Shine,

2002). Smith et al.

(2001) estimated a +0.12 to +0.2 W m

–2

per

decade range for the RF from the change in stratospheric water
vapour, using HALOE satellite data. Shindell (2001) estimated
an RF of about +0.2 W m

–2

in a period of two decades, using a

GCM to estimate the increase in water vapour in the stratosphere
from oxidation of CH

and including climate feedback changes

associated with an increase in greenhouse gases. Forster and
Shine (2002) used a constant 0.05 ppm yr

–1

trend in water

vapour at pressures of 100 to 10 hPa and estimated the RF
to be +0.29 W m

–2

for 1980 to 2000. GCM radiation codes

can have a factor of two uncertainty in their modelling of
this RF (Oinas et al., 2001). For the purposes of this chapter,
the above RF estimates are not readily attributable to forcing
agent(s) and uncertainty as to the causes of the observed change
precludes all but the component due to CH

increases being

considered a forcing. Two related CTM studies have calculated
the RF associated with increases in CH

since pre-industrial

times (Hansen and Sato, 2001; Hansen et al., 2005), but no
dynamical feedbacks were included in those estimates. Hansen
et al. (2005) estimated an RF of +0.07 ± 0.01 W m

–2

for the

stratospheric water vapour changes over 1750 to 2000, which is
at least a factor of three larger than the TAR value. The RF from
direct injection of water vapour by aircraft is believed to be an
order of magnitude smaller than this, at about +0.002 W m

–2

(IPCC, 1999). There has been little trend in CH

concentration

since 2000 (see Section 2.3.2); therefore the best estimate of
the stratospheric water vapour RF from CH

oxidation (+0.07

W m

–2

) is based on the Hansen et al. (2005) calculation. The

90% con

dence range is estimated as ±0.05 W m

–2

, from

the range of the RF studies that included other effects. There
is a low level of scienti

c understanding in this estimate, as

there is only a partial understanding of the vertical pro

le of

-induced stratospheric water vapour change (Section 2.9,

Table 2.11). Other human causes of stratospheric water vapour
change are unquanti

ed and have a very low level of scienti

understanding.

The simulated RFs for tropospheric ozone increases since

1750 are shown in Figure 2.9. Most of the calculations used
the same set of assumptions about pre-industrial emissions
(zero anthropogenic emissions and biomass burning sources
reduced by 90%). Emissions of NO

from soils and biogenic

hydrocarbons were generally assumed to be natural and
were thus not changed (see, e.g., Section 7.4). In one study
(Hauglustaine and Brasseur, 2001), pre-industrial NO

emissions from soils were reduced based on changes in the use
of fertilizers. Six of the ACCENT models also made coupled
climate-chemistry simulations including climate change since
pre-industrial times. The difference between the RFs in the
coupled climate-chemistry and the chemistry-only simulations,
which indicate the possible climate feedback to tropospheric
ozone, was positive in all models but generally small (Figure
2.9).

A general feature of the models is their inability to reproduce

the low ozone concentrations indicated by the very uncertain
semi-quantitative observations (e.g., Pavelin et al., 1999)
during the late 19th century. Mickley et al. (2001) tuned their
model by reducing pre-industrial lightning and soil sources of
NO

and increasing natural NMVOC emissions to obtain close

agreement with the observations. The ozone RF then increased
by 50 to 80% compared to their standard calculations. However,
there are still several aspects of the early observations that the
tuned model did not capture.

The best estimate for the RF of tropospheric ozone increases

is +0.35 W m

–2

, taken as the median of the RF values in

Figure 2.9 (adjusted and non-climate change values only, i.e.,
the red bars). The best estimate is unchanged from the TAR.
The uncertainties in the estimated RF by tropospheric ozone
originate from two factors: the models used (CTM/GCM
model formulation, radiative transfer models), and the potential
overestimation of pre-industrial ozone levels in the models.
The 5 to 95% con

dence interval, assumed to be represented

by the range of the results in Figure 2.9, is +0.25 to +0.65
W m

–2

. A medium level of scienti

c understanding is adopted,

also unchanged from the TAR (see Section 2.9, Table 2.11).

2.3.7 Stratospheric

Water

Vapour

The TAR noted that several studies had indicated long-term

increases in stratospheric water vapour and acknowledged that
these trends would contribute a signi

cant radiative impact.

However, it only considered the stratospheric water vapour
increase expected from CH

increases as an RF, and this was

estimated to contribute 2 to 5% of the total CH

RF (about

+0.02 W m

–2

Section 3.4 discusses the evidence for stratospheric

water vapour trends and presents the current understanding
of their possible causes. There are now 14 years of global
stratospheric water vapour measurements from Halogen
Occultation Experiment (HALOE) and continued balloon-
based measurements (since 1980) at Boulder, Colorado. There is
some evidence of a sustained long-term increase in stratospheric
water vapour of around 0.05 ppm yr

–1

from 1980 until roughly

153

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

2.3.8

Observations of Long-Lived Greenhouse

Gas Radiative Effects

Observations of the clear-sky radiation emerging at the top

of the atmosphere and at the surface have been conducted. Such
observations, by their nature, do not measure RF as de

ned here.

Instead, they yield a perspective on the in

uence of various

species on the transfer of radiation in the atmosphere. Most
importantly, the conditions involved with these observations
involve varying thermal and moisture pro

les in the atmosphere

such that they do not conform to the conditions underlying the
RF de

nition (see Section 2.2). There is a more comprehensive

discussion of observations of the Earth’s radiative balance in
Section 3.4.

Harries et al. (2001) analysed spectra of the outgoing

longwave radiation as measured by two satellites in 1970 and
1997 over the tropical Paci

c Ocean. The reduced brightness

temperature observed in the spectral regions of many of the
greenhouse gases is experimental evidence for an increase
in the Earth’s greenhouse effect. In particular, the spectral
signatures were large for CO

and CH

. The halocarbons, with

their large change between 1970 and 1997, also had an impact
on the brightness temperature. Philipona et al. (2004) found
an increase in the measured longwave downward radiation at
the surface over the period from 1995 to 2002 at eight stations
over the central Alps. A signi

cant increase in the clear-sky

longwave downward

ux was found to be due to an enhanced

greenhouse effect after combining the measurements with
model calculations to estimate the contribution from increases
in temperature and humidity. While both types of observations
attest to the radiative in

uences of the gases, they should not

be interpreted as having a direct linkage to the value of RFs in
Section 2.3.

2.4 Aerosols

2.4.1

Introduction and Summary of the Third
Assessment Report

The TAR categorised aerosol RFs into direct and indirect

effects. The direct effect is the mechanism by which aerosols
scatter and absorb shortwave and longwave radiation, thereby
altering the radiative balance of the Earth-atmosphere system.
Sulphate, fossil fuel organic carbon, fossil fuel black carbon,
biomass burning and mineral dust aerosols were all identi

as having a signi

cant anthropogenic component and exerting

a signi

cant direct RF. Key parameters for determining

the direct RF are the aerosol optical properties (the single
scattering albedo,

, speci

c extinction coef

cient, k

and

the scattering phase function), which vary as a function of
wavelength and relative humidity, and the atmospheric loading
and geographic distribution of the aerosols in the horizontal
and vertical, which vary as a function of time (e.g., Haywood

and Boucher, 2000; Penner

et al., 2001; Ramaswamy

et al.,

2001). Scattering aerosols exert a net negative direct RF, while
partially absorbing aerosols may exert a negative top-of-the-
atmosphere (TOA) direct RF over dark surfaces such as oceans
or dark forest surfaces, and a positive TOA RF over bright
surfaces such as desert, snow and ice, or if the aerosol is above
cloud (e.g., Chylek and Wong, 1995; Haywood and Shine,
1995). Both positive and negative TOA direct RF mechanisms
reduce the shortwave irradiance at the surface. The longwave
direct RF is only substantial if the aerosol particles are large
and occur in considerable concentrations at higher altitudes
(e.g., Tegen

et al., 1996). The direct RF due to tropospheric

aerosols is most frequently derived at TOA rather than at the
tropopause because shortwave radiative transfer calculations
have shown a negligible difference between the two (e.g.,
Haywood and Shine, 1997; Section 2.2). The surface forcing
will be approximately the same as the direct RF at the TOA
for scattering aerosols, but for partially absorbing aerosols the
surface forcing may be many times stronger than the TOA direct
RF (e.g., Ramanathan et al., 2001b and references therein).

The indirect effect is the mechanism by which aerosols

modify the microphysical and hence the radiative properties,
amount and lifetime of clouds (Figure 2.10). Key parameters
for determining the indirect effect are the effectiveness of an
aerosol particle to act as a cloud condensation nucleus, which
is a function of the size, chemical composition, mixing state
and ambient environment (e.g., Penner

et al., 2001). The

microphysically induced effect on the cloud droplet number
concentration and hence the cloud droplet size, with the liquid
water content held

xed has been called the ‘

rst indirect

effect’ (e.g., Ramaswamy et al., 2001), the ‘cloud albedo effect’
(e.g., Lohmann and Feichter, 2005), or the ‘Twomey effect’
(e.g., Twomey, 1977). The microphysically induced effect on
the liquid water content, cloud height, and lifetime of clouds
has been called the ‘second indirect effect’

(e.g., Ramaswamy

et al., 2001), the ‘cloud lifetime effect’ (e.g., Lohmann and
Feichter, 2005) or the ‘Albrecht effect’ (e.g., Albrecht, 1989).
The TAR split the indirect effect into the

rst indirect effect,

and the second indirect effect. Throughout this report, these
effects are denoted as ‘cloud albedo effect’ and ‘cloud lifetime
effect’, respectively, as these terms are more descriptive of the
microphysical processes that occur. The cloud albedo effect
was considered in the TAR to be an RF because global model
calculations could be performed to describe the in

uence of

increased aerosol concentration on the cloud optical properties
while holding the liquid water content of the cloud

xed (i.e.,

in an entirely diagnostic manner where feedback mechanisms
do not occur). The TAR considered the cloud albedo effect to
be a key uncertainty in the RF of climate but did not assign a
best estimate of the RF, and showed a range of RF between 0
and –2 W m

–2

in the context of liquid water clouds. The other

indirect effects were not considered to be RFs because, in
suppressing drizzle, increasing the cloud height or the cloud
lifetime in atmospheric models (Figure 2.10), the hydrological
cycle is invariably altered (i.e., feedbacks occur; see Section

156

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Satellite Instrument

Period of Operation

Spectral Bands

oducts

Comment and Refer

ence

VHRR (Advanced

1979 to pr

esent

5 bands (0.63,

aer

1-channel r

etrieval gives

=0.63

over ocean (Husar et al., 1997; Ignatov and

ery High Resolution

0.87, 3.7, 10.5

Stowe, 2002)

Radiometer)

and 11.5 µm)

2-channel using 0.63 µm and 0.86 µm gives

=0.55

and

over ocean assuming

mono-modal aer

osol size distribution (Mishchenko et al., 1999)

2-channel using 0.63 µm and 0.86 µm gives

=0.55

and

over dark for

ests and lake

surfaces

(Souffl

et et al., 1997)

2-channel using 0.64 µm and 0.83 µm gives

=0.55

and

over ocean assuming a

bimodal aer

osol size distribution (Higurashi and Nakajima, 1999; Higurashi et al., 2000)

TOMS

otal Ozone

1979 to pr

esent

0. 33 µm, 0.36 µm

Aer

osol Index,

Aer

osol index to

aer

conversion sensitive to the altitude of the 8 mono-modal

Mapping Spectr

ometer)

aer

osol models used in the r

etrieval (T

orr

es et al., 2002).

POLDER (Polarization

Nov 1996 to

8 bands

aer

, DRE

Multiple view angles and polarization capabilities.

and Dir

ectionality of the

June 1997; Apr 2003

(0.44 to 0.91 µm)

0.67 µm and 0.86 µm radiances used with 12 mono-modal aer

osol models over

Earth’

s Refl

ectances)

to Oct 2003;

ocean (Goloub et al., 1999; Deuzé et al., 2000).

Jan 2005 to pr

esent

Polarization allows fi

ne particle r

etrieval over land (Herman et al., 1997;

Goloub and Arino, 2000).

DRE determined over ocean (Boucher and T

anré, 2000; Bellouin et al., 2003).

OCTS (Ocean Colour

Nov 1996 to

9 bands

aer

0.67 µm and 0.86 µm r

etrieval gives

=0.50

and

over ocean. Bi-modal aer

osol

and T

emperatur

e Scanner)

Jun 1997; Apr 2003

(0.41 to 0.86 µm)

size distribution assumed (Nakajima and Higurashi, 1998; Higurashi et al., 2000).

to Oct 2003

and 3.9 µm

MODIS (Moderate

2000 to pr

esent

12 bands

aer

, DRE

Retrievals developed over ocean surfaces using bi-modal size distributions

Resolution Imaging

(0.41 to 2.1 µm)

anré et al., 1997; Remer et al., 2002).

Spectr

ometer)

Retrievals developed over land except bright surfaces (Kaufman et al., 1997;

Chu et al., 2002).

Optical depth speciation and DRE determined over ocean and land (e.g.,

Bellouin et al., 2005; Kaufman et al., 2005a).

MISR (Multi-angle Imaging

2000 to pr

esent

4 bands

aer

9 dif

fer

ent viewing angles. Five climatological mixing gr

oups composed of four

Spectr

o-Radiometer)

(0.47 to 0.86 µm)

component particles ar

e used in the r

etrieval algorithm (Kahn et al., 2001; Kahn

et al., 2005). Retrievals over bright surfaces ar

e possible (Martonchik et al., 2004).

CERES (Clouds and the

1998 to pr

esent

DRE

DRE determined by a r

egr

ession of, for example, V

isible Infrar

ed Scanner (VIRS;

Earth’

s Radiant Ener

gy System)

VHRR-like)

aer

against upwelling irradiance (Loeb and Kato; 2002).

GLAS (Geoscience

2003 to pr

esent

Active lidar

Aer

osol vertical

Lidar footprint r

oughly 70 m at 170 m intervals. 8-day r

epeat orbiting cycle (Sp

inhir

Laser Altimeter System)

(0.53, 1.06 µm)

ofi

et al., 2005).

SR-2/AA

TSR

1996 to pr

esent

4 bands

aer

Nadir and 52° forwar

d viewing geometry

. 40 aer

osol climatological mixtur

(Along T

rack Scanning

(0.56 to 1.65 µm)

containing up to six aer

osol species ar

e used in the r

etrievals (V

eefkind et al., 1998;

Radiometer/Advanced A

TSR)

Holzer

-Popp et al., 2002).

SeaWiFS (Sea-V

iewing Wide

1997 to pr

esent

0.765 and 0.865 µm

aer

2-channel using 0.765 µm and 0.856 µm gives

=0.856

and

over ocean. Bi-modal

Field-of-V

iew Sensor)

(ocean)

aer

osol size distribution assumed (M. W

ang et al., 2005). Retrievals over land and

0.41 to 0.67 µm

ocean using six visible channels fr

om 0.41 to 0.67µm (von Hoyningen-Huene, 2003;

(land)

Lee et al., 2004) also developed.

Notes:

DRE is the dir

ect radiative ef

fect and includes both natural and anthr

opogenic sour

ces (see T

able 2.3). The Angstr

om exponent,

, is the wavelength dependence of

aer

and is defi

ned

= –ln(

aer

) / ln(

) wher

= wavelength 1 and

= wavelength 2.

TOMS followed up by the Ozone Monitoring Instrument (OMI) on the Earth Observing System (EOS) Aura satellite, launched July 20

04.

able 2.2.

eriods of opera

tion,

spectral bands and products a

vailable from various different sa

tellite sensors tha

t ha

ve been used to re

trieve aerosol properties.

157

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

nitrates and industrial dust, is evident over many continental
regions of the NH. Sea salt aerosol is visible in oceanic regions
where the wind speed is high (e.g., south of 45°S). The MODIS
aerosol algorithm is currently unable to make routine retrievals
over highly re

ective surfaces such as deserts, snow cover, ice

and areas affected by ocean glint, or over high-latitude regions
when the solar insolation is insuf

cient.

Early retrievals for estimating

aer

include the Advanced

Very High Resolution Radiometer (AVHRR) single channel
retrieval (e.g., Husar et al., 1997; Ignatov and Stowe, 2002),
and the ultraviolet-based retrieval from the TOMS (e.g., Torres

et al., 2002). A dual channel AVHRR retrieval has also been
developed (e.g., Mishchenko

et al., 1999; Geogdzhayev

et al.,

2002). Retrievals by the AVHRR are generally only performed
over ocean surfaces where the surface re

ectance characteristics

are relatively well known, although retrievals are also possible
over dark land surfaces such as boreal forests and lakes (Souf

et al., 1997). The TOMS retrieval is essentially independent of
surface re

ectance thereby allowing retrievals over both land

and ocean (Torres

et al., 2002), but is sensitive to the altitude

of the aerosol, and has a relatively low spatial resolution. While
these retrievals only use a limited number of spectral bands and
lack sophistication compared to those from dedicated satellite
instruments, they have the advantage of offering continuous
long-term data sets (e.g., Geogdzhayev

et al., 2002).

Early retrievals have been superseded by those from

dedicated aerosol instruments (e.g., Kaufman

et al., 2002).

Polarization and Directionality of the Earth’s Re

ectance

(POLDER) uses a combination of spectral channels (0.44–0.91

m) with several viewing angles, and measures polarization

of radiation. Aerosol optical depth and Ångstrom exponent (

)

over ocean (Deuzé

et al., 2000),

aer

over land (Deuzé

et al.,

2001) and the direct radiative effect of aerosols (Boucher and
Tanré, 2000; Bellouin

et al., 2003) have all been developed.

Algorithms for aerosol retrievals using MODIS have been
developed and validated over both ocean (Tanré

et al., 1997)

and land surfaces (Kaufman

et al., 1997). The uncertainty in

these retrievals of

aer

is necessarily higher over land (Chu

et al., 2002) than over oceans (Remer et al., 2002) owing to
uncertainties in land surface re

ectance characteristics, but

can be minimised by careful selection of the viewing geometry
(Chylek et al., 2003). In addition, new algorithms have been
developed for discriminating between sea salt, dust or biomass
burning and industrial pollution over oceans (Bellouin

et al.,

2003, 2005; Kaufman

et al., 2005a) that allow for a more

comprehensive comparison against aerosol models. Multi-
angle Imaging Spectro-Radiometer (MISR) retrievals have
been developed using multiple viewing capability to determine
aerosol parameters over ocean (Kahn

et al., 2001) and land

surfaces, including highly re

ective surfaces such as deserts

(Martonchik

et al., 2004). Five typical aerosol climatologies,

each containing four aerosol components, are used in the
retrievals, and the optimum radiance signature is determined
for nine viewing geometries and two different radiances. The
results have been validated against those from the Aerosol

RObotic NETwork (AERONET; see Section 2.4.3). Along Track
Scanning Radiometer (ATSR) and ATSR-2 retrievals (Veefkind
et al., 1998; Holzer-Popp

et al., 2002) use a relatively wide

spectral range (0.56–1.65

m), and two viewing directions and

aerosol climatologies from the Optical Parameters of Aerosols
and Clouds (OPAC) database (Hess

et al., 1998) to make

aer

retrievals over both ocean and land (Robles-Gonzalez et al.,
2000). The Ocean Colour and Temperature Scanner (OCTS)
retrieval has a basis similar to the dual wavelength retrieval
from AVHRR and uses wavelengths over the range 0.41 to 0.86

m to derive

aer

and

over oceans (e.g., Higurashi

et al., 2000)

using a bi-modal aerosol size distribution. The Sea-Viewing
Wide Field-of-View Sensor (SeaWiFs) uses 0.765

m and 0.856

m radiances to provide

=0.856

and

over ocean using a bi-

modal aerosol size distribution (M. Wang et al., 2005). Further
SeaWiFs aerosol products have been developed over both land
and ocean using six and eight visible channels, respectively
(e.g., von Hoyningen-Heune et al., 2003; Lee et al., 2004).

Despite the increased sophistication and realism of the aerosol

retrieval algorithms, discrepancies exist between retrievals of

aer

even over ocean regions (e.g., Penner et al., 2002; Myhre et

al., 2004a, 2005b; Jeong et al., 2005; Kinne et al., 2006). These
discrepancies are due to different assumptions in the cloud
clearing algorithms, aerosol models, different wavelengths
and viewing geometries used in the retrievals, different
parametrizations of ocean surface re

ectance, etc. Comparisons

of these satellite aerosol retrievals with the surface AERONET
observations provide an opportunity to objectively evaluate
as well as improve the accuracy of these satellite retrievals.
Myhre et al. (2005b) showed that dedicated instruments using
multi-channel and multi-view algorithms perform better when
compared against AERONET than the simple algorithms that
they have replaced, and Zhao et al. (2005) showed that retrievals
based on dynamic aerosol models perform better than those
based on globally

xed aerosol models. While some systematic

biases in speci

c satellite products exist (e.g., Jeong et al., 2005;

Remer et al., 2005), these can be corrected for (e.g., Bellouin
et al., 2005; Kaufman et al., 2005b), which then enables an
assessment of the direct radiative effect and the direct RF from
an observational perspective, as detailed below.

2.4.2.1.2

Satellite retrievals of direct radiative effect

The solar direct radiative effect (DRE) is the sum of the direct

effects due to anthropogenic and natural aerosol species while
the direct RF only considers the anthropogenic components.
Satellite estimates of the global clear-sky DRE over oceans
have advanced since the TAR, owing to the development of
dedicated aerosol instruments and algorithms, as summarised
by Yu et al. (2006) (see Table 2.3). Table 2.3 suggests a
reasonable agreement of the global mean, diurnally averaged
clear-sky DRE from various studies, with a mean of –5.4 W m

–2

and a standard deviation of 0.9 W m

–2

. The clear-sky DRE is

converted to an all-sky DRE by Loeb and Manalo-Smith (2005)
who estimated an all-sky DRE over oceans of –1.6 to –2.0
W m

–2

but assumed no aerosol contribution to the DRE from

158

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

cloudy regions; such an assumption is not valid for optically
thin clouds or if partially absorbing aerosols exist above the
clouds (see Section 2.4.4.4).

Furthermore, use of a combination of sensors on the same

satellite offers the possibility of concurrently deriving

aer

and

the DRE (e.g., Zhang and Christopher, 2003; Zhang

et al., 2005),

which enables estimation of the DRE ef

ciency, that is, the

DRE divided by

aer

(W m

–2

aer

–1

). Because the DRE ef

ciency

removes the dependence on the geographic distribution of

aer

it is a useful parameter for comparison of models against

observations (e.g., Anderson

et al., 2005b); however, the DRE

ciency thus derived is not a linear function of

aer

at high

aer

such as those associated with intense mineral dust, biomass

burning or pollution events.

2.4.2.1.3. Satellite retrievals of direct radiative forcing

Kaufman et al. (2005a) estimated the anthropogenic-only

component of the aerosol

ne-mode fraction from the MODIS

product to deduce a clear sky RF over ocean of –1.4 W m

–2

Christopher et al. (2006) used a combination of the MODIS

ne-mode fraction and Clouds and the Earth’s Radiant Energy

System (CERES) broadband TOA

uxes and estimated an

identical value of –1.4 ± 0.9 W m

–2

. Bellouin et al. (2005) used

a combination of MODIS

aer

and

ne-mode fraction together

with data from AeroCom (see Section 2.4.3) to determine an
all-sky RF of aerosols over both land and ocean of –0.8 ± 0.2
W m

–2

, but this does not include the contribution to the RF and

associated uncertainty from cloudy skies. Chung et al. (2005)
performed a similar satellite/AERONET/model analysis, but
included the contribution from cloudy areas to deduce an RF
of –0.35 W m

–2

or –0.50 W m

–2

depending upon whether the

anthropogenic fraction is determined from a model or from the
MODIS

ne-mode fraction and suggest an overall uncertainty

range of –0.1 to –0.6 W m

–2

. Yu et al. (2006) used several

measurements to estimate a direct RF of –0.5 ± 0.33 W m

–2

These estimates of the RF are compared to those obtained from
modelling studies in Section 2.4.4.7.

2.4.2.2 Surface-Based Retrievals

A signi

cant advancement since the TAR is the continued

deployment and development of surface based remote sensing
sun-photometer sites such as AERONET (Holben et al., 1998),
and the establishment of networks of aerosol lidar systems such

Clear Sky DRE

Reference Instrument

Data Analysed

Brief Description

–2

) ocean

Bellouin et al. (2005)

MODIS;

TOMS;

2002

MODIS

fi ne and total

aer

with

–6.8

SSM/I

TOMS Aerosol Index and SSM/I to

discriminate dust from sea salt.

Loeb and

CERES; MODIS

Mar 2000

CERES radiances/irradiances and

–3.8 (NESDIS)

Manalo-Smith (2005)

to Dec 2003

angular distribution models and aerosol

to –5.5 (MODIS)

properties from either MODIS or from

NOAA-NESDIS

algorithm used to

estimate the direct radiative effect.

Remer and

MODIS

Aug 2001

Best-prescribed aerosol model fi tted to

–5.7 ± 0.4

Kaufman (2006)

to Dec 2003

MODIS data.

aer

from fi ne-mode fraction.

Zhang et al. (2005);

CERES; MODIS

Nov 2000

MODIS aerosol properties, CERES

–5.3 ± 1.7

Christopher and

to Aug 2001

radiances/irradiances and angular

Zhang (2004)

distribution models used to estimate the

direct radiative effect.

Bellouin et al. (2003)

POLDER

Nov 1996

Best-prescribed aerosol model fi tted to

–5.2

to Jun 1997

POLDER data

Loeb and Kato (2002)

CERES; VIRS

Jan 1998 to

aer

from VIRS regressed against the

–4.6 ± 1.0

Aug 1998;

TOA CERES irradiance (35°N to 35°S)

Mar

2000.

Chou et al. (2002)

SeaWiFs

1998

Radiative transfer calculations with

–5.4

SeaWiFS

aer

and prescribed optical

properties

Boucher and Tanré (2000)

POLDER

Nov 1996 to

Best-prescribed aerosol model fi tted to

–5 to –6

Jun 1997

POLDER data

Haywood et al. (1999)

ERBE

Jul 1987 to

DRE diagnosed from GCM-ERBE

–6.7

Dec 1988

TOA irradiances

Mean (standard deviation)

–5.4 (0.9)

Table 2.3.

The direct aerosol radiative effect (DRE) estimated from satellite remote sensing studies (adapted and updated from Yu et al., 2006).

Notes:

SSM/I: Special Sensor Microwave/Imager; VIRS: Visible Infrared Scanner; ERBE: Earth Radiation Budget Experiment.

NESDIS: National Environmental Satellite, Data and Information Service.

159

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

as the European Aerosol Research Lidar Network (EARLINET,
Matthias et al., 2004), the Asian Dust Network (ADNET,
Murayama et al., 2001), and the Micro-Pulse Lidar Network
(MPLNET, Welton et al., 2001).

The distribution of AERONET sites is also shown in Figure

2.11 (top panel). Currently there are approximately 150 sites
operating at any one time, many of which are permanent
to enable determination of climatological and interannual
column-averaged monthly and seasonal means. In addition
to measurements of

aer as a function of wavelength, new

algorithms have been developed that measure sky radiance as a
function of scattering angle (Nakajima et al., 1996; Dubovik and
King, 2000). From these measurements, the column-averaged
size distribution and, if the

aer is high enough (

aer

> 0.5),

the aerosol single scattering albedo,

, and refractive indices

may be determined at particular wavelengths (Dubovik et al.,
2000), allowing partitioning between scattering and absorption.
While these inversion products have not been comprehensively
validated, a number of studies show encouraging agreement for
both the derived size distribution and

when compared against

in situ

measurements by instrumented aircraft for different

aerosol species (e.g., Dubovik et al., 2002; Haywood et al.,
2003a; Reid et al., 2003; Osborne et al., 2004). A climatology
of the aerosol DRE based on the AERONET aerosols has also
been derived (Zhou et al., 2005).

The MPLNET Lidar network currently consists of 11 lidars

worldwide; 9 are co-located with AERONET sites and provide
complementary vertical distributions of aerosol backscatter and
extinction. Additional temporary MPLNET sites have supported
major aerosol

eld campaigns (e.g., Campbell et al., 2003).

The European-wide lidar network EARLINET currently has 15
aerosol lidars making routine retrievals of vertical pro

les of

aerosol extinction (Mathias et al., 2004), and ADNET is a network
of 12 lidars making routine measurements in Asia that have been
used to assess the vertical pro

les of Asian dust and pollution

events (e.g., Husar

et al., 2001; Murayama

et al., 2001).

2.4.3

Advances in Modelling the Aerosol Direct
Effect

Since the TAR, more complete aerosol modules in a larger

number of global atmospheric models now provide estimates
of the direct RF. Several models have resolutions better than 2°
by 2° in the horizontal and more than 20 to 30 vertical levels;
this represents a considerable enhancement over the models
used in the TAR. Such models now include the most important
anthropogenic and natural species. Tables 2.4, 2.5 and 2.6
summarise studies published since the TAR. Some of the more
complex models now account explicitly for the dynamics of the
aerosol size distribution throughout the aerosol atmospheric
lifetime and also parametrize the internal/external mixing of
the various aerosol components in a more physically realistic
way than in the TAR (e.g., Adams and Seinfeld, 2002; Easter et
al., 2004; Stier et al., 2005). Because the most important aerosol
species are now included, a comparison of key model output
parameters, such as the total

aer

, against satellite retrievals

and surface-based sun photometer and lidar observations
is possible (see Sections 2.4.2 and 2.4.4). Progress with
respect to modelling the indirect effects due to aerosol-cloud
interactions is detailed in Section 2.4.5 and Section 7.5. Several
studies have explored the sensitivity of aerosol direct RF to
current parametrization uncertainties. These are assessed in the
following sections.

Major progress since the TAR has been made in the

documentation of the diversity of current aerosol model
simulations. Sixteen groups have participated in the Global
Aerosol Model Intercomparison (AeroCom) initiative (Kinne et
al., 2006). Extensive model outputs are available via a dedicated
website (Schulz et al., 2004). Three model experiments
(named A, B, and PRE) were analysed. Experiment A models
simulate the years 1996, 1997, 2000 and 2001, or a

ve-year

mean encompassing these years. The model emissions and
parametrizations are those determined by each research group,
but the models are driven by observed meteorological

elds

to allow detailed comparisons with observations, including
those from MODIS, MISR and the AERONET sun photometer
network. Experiment B

models use prescribed AeroCom aerosol

emissions for the year 2000, and experiment PRE models use
prescribed aerosol emissions for the year 1750 (Dentener et
al., 2006; Schulz et al., 2006). The model diagnostics included
information on emission and deposition

uxes, vertical

distribution and sizes, thus enabling a better understanding of
the differences in lifetimes of the various aerosol components
in the models.

This paragraph discusses AeroCom results from Textor

et al. (2006). The model comparison study found a wide
range in several of the diagnostic parameters; these, in turn,
indicate which aerosol parametrizations are poorly constrained
and/or understood. For example, coarse aerosol fractions are
responsible for a large range in the natural aerosol emission

uxes (dust: ±49% and sea salt: ±200%, where uncertainty is

1 standard deviation of inter-model range), and consequently
in the dry deposition

uxes. The complex dependence of the

source strength on wind speed adds to the problem of computing
natural aerosol emissions. Dust emissions for the same time
period can vary by a factor of two or more depending on
details of the dust parametrization (Luo et al., 2003; Timmreck
and Schulz, 2004; Balkanski et al., 2004; Zender, 2004), and
even depend on the reanalysis meteorological data set used
(Luo et al., 2003). With respect to anthropogenic and natural
emissions of other aerosol components, modelling groups
tended to make use of similar best guess information, for
example, recently revised emissions information available via
the Global Emissions Inventory Activity (GEIA). The vertical
aerosol distribution was shown to vary considerably, which is
a consequence of important differences in removal and vertical
mixing parametrizations. The inter-model range for the fraction
of sulphate mass below 2.5 km to that of total sulphate is 45 ±
23%. Since humidi

cation takes place mainly in the boundary

layer, this source of inter-model variability increases the
range of modelled direct RF. Additionally, differences in the
parametrization of the wet deposition/vertical mixing process

160

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

become more pronounced above 5 km altitude. Some models
have a tendency to accumulate insoluble aerosol mass (dust
and carbonaceous aerosols) at higher altitudes, while others
have much more ef

cient wet removal schemes. Tropospheric

residence times, de

ned here as the ratio of burden over sinks

established for an equilibrated one-year simulation, vary by 20
to 30% for the

ne-mode aerosol species. These variations are of

interest, since they express the linearity of modelled emissions
to aerosol burden and eventually to RF.

Considerable progress has been made in the systematic

evaluation of global model results (see references in Tables
2.4 to 2.6).

The simulated global

aer

at a wavelength of 0.55

m in models ranges from 0.11 to 0.14. The values compare

favourably to those obtained by remote sensing from the ground
(AERONET, about 0.135) and space (satellite composite, about
0.15) (Kinne et al., 2003, 2006), but signi

cant differences exist

in regional and temporal distributions. Modelled absorption
optical thickness has been suggested to be underestimated by
a factor of two to four when compared to observations (Sato et
al., 2003) and DRE ef

ciencies have been shown to be lower

in models both for the global average and regionally (Yu et
al., 2006) (see Section 2.4.4.7). A merging of modelled and
observed

elds of aerosol parameters through assimilation

methods of different degrees of complexity has also been
performed since the TAR (e.g., Yu et al., 2003; Chung et
al., 2005). Model results are constrained to obtain present-
day aerosol

elds consistent with observations. Collins et al.

(2001) showed that assimilation of satellite-derived

elds of

aer

can reduce the model bias down to 10% with respect to

daily mean

aer

measured with a sun photometer at the Indian

Ocean Experiment (INDOEX) station Kaashidhoo. Liu et al.
(2005) demonstrated similar ef

cient reduction of errors in

aer

The magnitude of the global dust cycle has been suggested to
range between 1,500 and 2,600 Tg yr

–1

by minimising the bias

between model and multiple dust observations (Cakmur et al.,
2006). Bates et al. (2006) focused on three regions downwind
of major urban/population centres and performed radiative
transfer calculations constrained by intensive and extensive
observational parameters to derive 24-hour average clear-sky
DRE of –3.3 ± 0.47, –14 ± 2.6 and –6.4 ± 2.1 W m

–2

for the

north Indian Ocean, the northwest Paci

c and the northwest

Atlantic, respectively. By constraining aerosol models with
these observations, the uncertainty associated with the DRE
was reduced by approximately a factor of two.

2.4.4

Estimates of Aerosol Direct Radiative Forcing

Unless otherwise stated, this section discusses the TOA

direct RF of different aerosol types as a global annual mean
quantity inclusive of the effects of clouds. Where possible,
statistics from model results are used to assess the uncertainty
in the RF. Recently published results and those grouped within
AeroCom are assessed. Because the AeroCom results assessed
here are based on prescribed emissions, the uncertainty in these
results is lowered by having estimates of the uncertainties in
the emissions. The quoted uncertainties therefore include the

structural uncertainty (i.e., differences associated with the
model formulation and structure) associated with the RF, but
do not include the full range of parametric uncertainty (i.e.,
differences associated with the choice of key model parameters),
as the model results are essentially best estimates constrained
by observations of emissions, wet and dry deposition, size
distributions, optical parameters, hygroscopicity, etc. (Pan

et al., 1997). The uncertainties are reported as the 5 to 95%
con

dence interval to allow the uncertainty in the RF of each

species of aerosol to be quantitatively intercompared.

2.4.4.1 Sulphate

Aerosol

Atmospheric sulphate aerosol may be considered as

consisting of sulphuric acid particles that are partly or totally
neutralized by ammonia and that are present as liquid droplets
or partly crystallized. Sulphate is formed by aqueous phase
reactions within cloud droplets, oxidation of SO

via gaseous

phase reactions with OH, and by condensational growth onto
pre-existing particles (e.g., Penner

et al., 2001). Emission

estimates are summarised by Haywood and Boucher (2000).
The main source of sulphate aerosol is via SO

emissions from

fossil fuel burning (about 72%), with a small contribution
from biomass burning (about 2%), while natural sources are
from dimethyl sulphide emissions by marine phytoplankton
(about 19%) and by SO

emissions from volcanoes (about

7%). Estimates of global SO

emissions range from 66.8 to

92.4 TgS yr

–1

for anthropogenic emissions in the 1990s and from

91.7 to 125.5 TgS yr

–1

for total emissions. Emissions of SO

from 25 countries in Europe were reduced from approximately
18 TgS yr

–1

in 1980 to 4 TgS yr

–1

in 2002 (Vestreng et al.,

2004). In the USA, the emissions were reduced from about 12
to 8 TgS yr

–1

in the period 1980 to 2000 (EPA, 2003). However,

over the same period SO

emissions have been increasing

signi

cantly from Asia, which is estimated to currently emit 17

TgS yr

–1

(Streets et al., 2003), and from developing countries

in other regions (e.g., Lefohn

et al., 1999; Van Aardenne

et al.,

2001; Boucher and Pham, 2002). The most recent study (Stern,
2005) suggests a decrease in global anthropogenic emissions
from approximately 73 to 54 TgS yr

–1

over the period 1980

to 2000, with NH emission falling from 64 to 43 TgS yr

–1

and

SH emissions increasing from 9 to 11 TgS yr

–1

. Smith et al.

(2004) suggested a more modest decrease in global emissions,
by some 10 TgS yr

–1

over the same period. The regional shift in

the emissions of SO

from the USA, Europe, Russia, Northern

Atlantic Ocean and parts of Africa to Southeast Asia and the
Indian and Paci

c Ocean areas will lead to subsequent shifts

in the pattern of the RF (e.g., Boucher and Pham, 2002; Smith
et al., 2004; Pham et al., 2005). The recently used emission
scenarios take into account effective injection heights and their
regional and seasonal variability (e.g., Dentener et al., 2006).

The optical parameters of sulphate aerosol have been well

documented (see Penner

et al., 2001 and references therein).

Sulphate is essentially an entirely scattering aerosol across the
solar spectrum (

= 1) but with a small degree of absorption

in the near-infrared spectrum. Theoretical and experimental

161

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

data are available on the relative humidity dependence of the
speci

c extinction coef

cient, f

(e.g., Tang

et al., 1995).

Measurement campaigns concentrating on industrial pollution,
such as the Tropospheric Aerosol Radiative Forcing Experiment
(TARFOX; Russell

et al., 1999), the Aerosol Characterization

Experiment (ACE-2; Raes

et al., 2000), INDOEX (Ramanathan

et al., 2001b), the Mediterranean Intensive Oxidants Study
(MINOS, 2001 campaign), ACE-Asia (2001), Atmospheric
Particulate Environment Change Studies (APEX, from 2000 to
2003), the New England Air Quality Study (NEAQS, in 2003)
and the Chesapeake Lighthouse and Aircraft Measurements for
Satellites (CLAMS; Smith et al., 2005), continue to show that
sulphate contributes a signi

cant fraction of the sub-micron

aerosol mass, anthropogenic

aer

and RF (e.g., Hegg

et al., 1997;

Russell and Heintzenberg, 2000; Ramanathan

et al., 2001b;

Magi et al., 2005; Quinn and Bates, 2005). However, sulphate
is invariably internally and externally mixed to varying degrees
with other compounds such as biomass burning aerosol (e.g.,
Formenti

et al., 2003), fossil fuel black carbon (e.g., Russell

and Heintzenberg, 2000), organic carbon (Novakov

et al., 1997;

Brock

et al., 2004), mineral dust (e.g., Huebert

et al., 2003)

and nitrate aerosol (e.g., Schaap

et al., 2004). This results in a

composite aerosol state in terms of effective refractive indices,
size distributions, physical state, morphology, hygroscopicity
and optical properties.

The TAR reported an RF due to sulphate aerosol of –0.40

W m

–2

with an uncertainty of a factor of two, based on global

modelling studies that were available at that time. Results from
model studies since the TAR are summarised in Table 2.4.
For models A to L, the RF ranges from approximately –0.21
W m

–2

(Takemura

et al., 2005) to –0.96 W m

–2

(Adams

et al.,

2001) with a mean of –0.46 W m

–2

and a standard deviation

of 0.20 W m

–2

. The range in the RF per unit

aer

is substantial

due to differing representations of aerosol mixing state, optical
properties, cloud, surface re

ectance, hygroscopic growth, sub-

grid scale effects, radiative transfer codes, etc. (Ramaswamy

al., 2001). Myhre et al. (2004b) performed several sensitivity
studies and found that the uncertainty was particularly linked
to the hygroscopic growth and that differences in the model
relative humidity

elds could cause differences of up to 60%

in the RF. The RFs from the models M to U participating in the
AeroCom project are slightly weaker than those obtained from
the other studies, with a mean of approximately –0.35 W m

–2

and a standard deviation of 0.15 W m

–2

; the standard deviation

is reduced for the AeroCom models owing to constraints on
aerosol emissions, based on updated emission inventories (see
Table 2.4). Including the uncertainty in the emissions reported in
Haywood and Boucher (2000) increases the standard deviation
to 0.2 W m

–2

. As sulphate aerosol is almost entirely scattering,

the surface forcing will be similar or marginally stronger than
the RF diagnosed at the TOA. The uncertainty in the RF estimate
relative to the mean value remains relatively large compared to
the situation for LLGHGs.

The mean and median of the sulphate direct RF from

grouping all these studies together are identical at –0.41 W m

–2

Disregarding the strongest and weakest direct RF estimates to

approximate the 90% con

dence interval leads to an estimate

of –0.4 ± 0.2 W m

–2

2.4.4.2 Organic Carbon Aerosol from Fossil Fuels

Organic aerosols are a complex mixture of chemical

compounds containing carbon-carbon bonds produced from
fossil fuel and biofuel burning and natural biogenic emissions.
Organic aerosols are emitted as primary aerosol particles or
formed as secondary aerosol particles from condensation of
organic gases considered semi-volatile or having low volatility.
Hundreds of different atmospheric organic compounds have
been detected in the atmosphere (e.g., Hamilton

et al., 2004;

Murphy, 2005), which makes de

nitive modelling of the direct

and indirect effects extremely challenging (McFiggans

et al.,

2006). Emissions of primary organic carbon from fossil fuel
burning have been estimated to be 10 to 30 TgC yr

–1

(Liousse

et al., 1996; Cooke

et al., 1999; Scholes and Andreae, 2000).

More recently, Bond et al. (2004) provided a detailed analysis
of primary organic carbon emissions from fossil fuels, biofuels
and open burning, and suggested that contained burning
(approximately the sum of fossil fuel and biofuel) emissions
are in the range of 5 to 17 TgC yr

–1

, with fossil fuel contributing

only 2.4 TgC yr

–1

. Ito and Penner (2005) estimated global fossil

fuel particulate organic matter (POM, which is the sum of the
organic carbon and the other associated chemical elements)
emissions of around 2.2 Tg(POM) yr

–1

, and global biofuel

emissions of around 7.5 Tg(POM) yr

–1

. Ito and Penner (2005)

estimated that emissions of fossil and biofuel organic carbon
increased by a factor of three over the period 1870 to 2000.
Subsequent to emission, the hygroscopic, chemical and optical
properties of organic carbon particles continue to change
because of chemical processing by gas-phase oxidants such
as ozone, OH, and the nitrate radical (NO

) (e.g., Kanakidou

et al., 2005). Atmospheric concentrations of organic aerosol
are frequently similar to those of industrial sulphate aerosol.
Novakov et al. (1997) and Hegg et al. (1997) measured organic
carbon in pollution off the East Coast of the USA during the
TARFOX campaign, and found organic carbon primarily from
fossil fuel burning contributed up to 40% of the total submicron
aerosol mass and was frequently the most signi

cant contributor

aer

. During INDOEX, which studied the industrial plume

over the Indian Ocean, Ramanathan et al. (2001b) found that
organic carbon was the second largest contributor to

aer

after

sulphate aerosol.

Observational evidence suggests that some organic aerosol

compounds from fossil fuels are relatively weakly absorbing
but do absorb solar radiation at some ultraviolet and visible
wavelengths (e.g., Bond

et al., 1999; Jacobson, 1999; Bond,

2001) although organic aerosol from high-temperature
combustion such as fossil fuel burning (Dubovik

et al., 1998;

Kirchstetter

et al., 2004) appears less absorbing than from

low-temperature combustion such as open biomass burning.
Observations suggest that a considerable fraction of organic
carbon is soluble to some degree, while at low relative humidity
more water is often associated with the organic fraction than

162

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

No Model

LOAD

aer

aer ant

RF NRFM

NRF

Reference

(mg(SO

) m

–2

)

(0.55 µm)

(%)

(W m

–2

) (W

–1

)

(W m

–2

aer

–1

)

Published since IPCC, 2001

A CCM3

2.23

–0.56

–251

(Kiehl et al., 2000)

B GEOSCHEM

1.53

0.018

–0.33

–216

–18

(Martin et al., 2004)

C GISS

3.30

0.022

–0.65

–206

–32

(Koch,

2001)

D GISS

3.27

–0.96

–293

(Adams et al., 2001)

E GISS

2.12

–0.57

–269

(Liao and Seinfeld, 2005)

F SPRINTARS

1.55

0.015

–0.21

–135

–8

(Takemura et al., 2005)

G LMD

2.76

–0.42

–152

(Boucher and Pham., 2002)

H LOA

3.03

0.030

–0.41

–135

–14

(Reddy et al., 2005b)

I GATORG

3.06

–0.32

–105

(Jacobson,

2001a)

J PNNL

5.50

0.042

–0.44

–80

–10

(Ghan et al., 2001)

K UIO_CTM

1.79

0.019

–0.37

–207

–19

(Myhre et al., 2004b)

L UIO_GCM

2.28

–0.29

–127

(Kirkevag and Iversen, 2002)

AeroCom: identical emissions used for year 1750 and 2000

M UMI

2.64

0.020

–0.58

–220

–28

(Liu and Penner, 2002)

N UIO_CTM

1.70

0.019

–0.35

–208

–19

(Myhre et al., 2003)

O LOA

3.64

0.035

–0.49

–136

–14

(Reddy and Boucher, 2004)

P LSCE

3.01

0.023

–0.42

–138

–18

(Schulz et al., 2006)

Q ECHAM5-HAM

2.47

0.016

–0.46

–186

–29

(Stier et al., 2005)

R GISS

1.34

0.006

–0.19

–139

–31

(Koch,

2001)

S UIO_GCM

1.72

0.012

–0.25

–145

–21

(Iversen and Seland, 2002;

Kirkevag and Iversen, 2002)

T SPRINTARS

1.19

0.013

–0.16

–137

–13

(Takemura et al., 2005)

U ULAQ

1.62

0.020

–0.22

–136

–11

(Pitari et al., 2002)

Average A to L

2.80 0.024

–0.46

–176

–17

Average M to U

2.15

0.018

–0.35

–161

–20

Minimum A to U

1.19

0.006

–0.96

–293

–32

Maximum A to U

5.50

0.042 72 –0.16

–72 –8

Std. dev. A to L

1.18

0.010

0.20

Std. dev. M to U

0.83

0.008

8 0.15

34 7

Notes:

CCM3: Community Climate Model; GEOSCHEM: Goddard Earth Observing System-Chemistry; GISS: Goddard Institute for Space Studies; SPRINTARS: Spectral

Radiation-Transport Model for Aerosol Species; LMD: Laboratoire de Météorologie Dynamique; LOA: Laboratoire d’Optique Atmospherique; GATORG: Gas, Aerosol,
Transport, Radiation, and General circulation model; PNNL: Pacifi c Northwest National Laboratory; UIO_CTM: University of Oslo CTM; UIO_GCM: University of Oslo
GCM; UMI: University of Michigan; LSCE: Laboratoire des Sciences du Climat et de l’Environnement; ECHAM5-HAM: European Centre Hamburg with Hamburg
Aerosol Module; ULAQ: University of L’Aquila.

Table 2.4.

The direct radiative forcing for sulphate aerosol derived from models published since the TAR and from the AeroCom simulations where different models used

identical emissions. Load and aerosol optical depth (

aer

) refer to the anthropogenic sulphate;

aer ant

is the fraction of anthropogenic sulphate to total sulphate

aer

for present

day, NRFM is the normalised RF by mass, and NRF is the normalised RF per unit

aer

with inorganic material. At higher relative humidities, the
hygroscopicity of organic carbon is considerably less than
that of sulphate aerosol (Kotchenruther and Hobbs, 1998;
Kotchenruther

et al., 1999).

Based on observations and fundamental chemical kinetic

principles, attempts have been made to formulate organic
carbon composition by functional group analysis in some main
classes of organic chemical species (e.g., Decesari et al., 2000,
2001; Maria et al., 2002; Ming and Russell, 2002), capturing

some general characteristics in terms of refractive indices,
hygroscopicity and cloud activation properties. This facilitates
improved parametrizations in global models (e.g., Fuzzi et al.,
2001; Kanakidou et al., 2005; Ming et al., 2005a).

Organic carbon aerosol from fossil fuel sources is invariably

internally and externally mixed to some degree with other
combustion products such as sulphate and black carbon (e.g.,
Novakov

et al., 1997; Ramanathan

et al., 2001b). Theoretically,

coatings of essentially non-absorbing components such

163

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

1.645 is the factor relating the standard deviation to the 90% confi dence interval for a normal distribution.

as organic carbon or sulphate on strongly absorbing core
components such as black carbon can increase the absorption
of the composite aerosol (e.g., Fuller

et al., 1999; Jacobson,

2001a; Stier et al., 2006a), with results backed up by laboratory
studies (e.g., Schnaiter et al., 2003). However, coatings of
organic carbon aerosol on hygroscopic aerosol such as sulphate
may lead to suppression of the rate of water uptake during cloud
activation (Xiong

et al., 1998; Chuang, 2003).

Current global models generally treat organic carbon using

one or two tracers (e.g., water-insoluble tracer, water-soluble
tracer) and highly parametrized schemes have been developed
to represent the direct RF. Secondary organic carbon is highly
simpli

ed in the global models and in many cases treated

as an additional source similar to primary organic carbon.
Considerable uncertainties still exist in representing the
refractive indices and the water of hydration associated with the
particles because the aerosol properties will invariably differ
depending on the combustion process, chemical processing in
the atmosphere, mixing with the ambient aerosol, etc. (e.g.,
McFiggans

et al., 2006).

The TAR reported an RF of organic carbon aerosols from

fossil fuel burning of –0.10 W m

–2

with a factor of three

uncertainty. Many of the modelling studies performed since the
TAR have investigated the RF of organic carbon aerosols from
both fossil fuel and biomass burning aerosols, and the combined
RF of both components. These studies are summarised in
Table 2.5. The RF from total organic carbon (POM) from
both biomass burning and fossil fuel emissions from recently
published models A to K and AeroCom models (L to T) is
–0.24 W m

–2

with a standard deviation of 0.08 W m

–2

and –0.16

W m

–2

with a standard deviation of 0.10 W m

–2

, respectively.

Where the RF due to organic carbon from fossil fuels is not
explicitly accounted for in the studies, an approximate scaling
based on the source apportionment of 0.25:0.75 is applied for
fossil fuel organic carbon:biomass burning organic carbon
(Bond et al., 2004). The mean RF of the fossil fuel component
of organic carbon from those studies other than in AeroCom
is –0.06 W m

–2

, while those from AeroCom produce an RF of

–0.03 W m

–2

with a range of –0.01 W m

–2

to –0.06 W m

–2

and

a standard deviation of around 0.02 W m

–2

. Note that these RF

estimates, to a large degree, only take into account primary
emitted organic carbon. These studies all use optical properties
for organic carbon that are either entirely scattering or only
weakly absorbing and hence the surface forcing is only slightly
stronger than that at the TOA.

The mean and median for the direct RF of fossil fuel organic

carbon from grouping all these studies together are identical
at –0.05 W m

–2

with a standard deviation of 0.03 W m

–2

. The

standard deviation is multiplied by 1.645 to approximate the
90% con

dence interval.

This leads to a direct RF estimate of

–0.05 ± 0.05 W m

–2

2.4.4.3

Black Carbon Aerosol from Fossil Fuels

Black carbon (BC) is a primary aerosol emitted directly at

the source from incomplete combustion processes such as fossil
fuel and biomass burning and therefore much atmospheric
BC is of anthropogenic origin. Global, present-day fossil fuel
emission estimates range from 5.8 to 8.0 TgC yr

–1

(Haywood

and Boucher, 2000 and references therein). Bond et al. (2004)
estimated the total current global emission of BC to be
approximately 8 TgC yr

–1

, with contributions of 4.6 TgC yr

–1

from fossil fuel and biofuel combustion and 3.3 TgC yr

–1

from

open biomass burning, and estimated an uncertainty of about
a factor of two. Ito and Penner (2005) suggested fossil fuel
BC emissions for 2000 of around 2.8 TgC yr

–1

. The trends in

emission of fossil fuel BC have been investigated in industrial
areas by Novakov et al. (2003) and Ito and Penner (2005).
Novakov et al. (2003) reported that signi

cant decreases were

recorded in the UK, Germany, the former Soviet Union and the
USA over the period 1950 to 2000, while signi

cant increases

were reported in India and China. Globally, Novakov et al.
(2003) suggested that emissions of fossil fuel BC increased by
a factor of three between 1950 and 1990 (2.2 to 6.7 TgC yr

–1

)

owing to the rapid expansion of the USA, European and Asian
economies (e.g., Streets et al., 2001, 2003), and have since fallen
to around 5.6 TgC yr

–1

owing to further emission controls. Ito

and Penner (2005) determined a similar trend in emissions over
the period 1950 to 2000 of approximately a factor of three, but
the absolute emissions are smaller than in Novakov et al. (2003)
by approximately a factor of 1.7.

Black carbon aerosol strongly absorbs solar radiation.

Electron microscope images of BC particles show that they are
emitted as complex chain structures (e.g., Posfai et al., 2003),
which tend to collapse as the particles age, thereby modifying
the optical properties (e.g., Abel et al., 2003). The Indian Ocean
Experiment (Ramanathan et al., 2001b and references therein)
focussed on emissions of aerosol from the Indian sub-continent,
and showed the importance of absorption by aerosol in the
atmospheric column. These observations showed that the local
surface forcing (–23 W m

–2

) was signi

cantly stronger than the

local RF at the TOA (–7 W m

–2

). Additionally, the presence

of BC in the atmosphere above highly re

ective surfaces such

as snow and ice, or clouds, may cause a signi

cant positive

RF (Ramaswamy et al., 2001). The vertical pro

le is therefore

important, as BC aerosols or mixtures of aerosols containing
a relatively large fraction of BC will exert a positive RF when
located above clouds. Both microphysical (e.g., hydrophilic-to-
hydrophobic nature of emissions into the atmosphere, aging of
the aerosols, wet deposition) and meteorological aspects govern
the horizontal and vertical distribution patterns of BC aerosols,
and the residence time of these aerosols is thus sensitive to these
factors (Cooke et al., 2002; Stier et al., 2006b).

The TAR assessed the RF due to fossil fuel BC as being +0.2

W m

–2

with an uncertainty of a factor of two. Those models

since the TAR that explicitly model and separate out the RF

164

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

able 2.5.

Estima

tes of anthropogenic carbonaceous aerosol forcing derived from models published since the

R and from the

AeroCom simula

tions where different models used identical emissions.

POM:

particula

te organic ma

tter;

BC:

black carbon; BCPOM:

BC and POM; FFBC:

fossil fuel black carbon; FFPOM:

fossil fuel particula

te organic ma

tter; BB:

biomass

burning sources inc

luded.

Notes:

MOZGN: MOZAR

T (Model for OZone and Related chemical T

racers-GFDL(Geophysical Fluid Dynamics Laboratory)-NCAR (National Center

for Atmospheric Resear

ch); for other models see Note (a) in T

able 2.4.

Models A to C ar

e used to pr

ovide a split in sour

ces derived fr

om total POM and total BC: FFPOM = POM × 0.25; FFBC = BC × 0.5;

BB = (BCPOM) – (FFPOM + FFBC); BC = 2 × FFBC; POM = 4 × FFPOM.

Models L, O and Q to T ar

e used to pr

ovide a split in components: POM = BCPOM × (–1.16); BC = BCPOM × 2.25.

Model

LOAD

POM

(mgPOM m

–2

)

aer

POM

aer

POM

ant

(%)

LOAD BC

(mg m

–2

)

RF BCPOM

(W m

–2

)

POM

(W m

–2

)

(W m

–2

)

RF FFPOM

(W m

–2

)

FFBC

(W m

–2

)

(W m

–2

)

Refer

ence

Published since IPCC, 2001

SPRINT

0.12

–0.24

0.36

–0.05

0.15

–0.01

akemura et al., 2001)

LOA

2.33

0.016

0.37

0.30

–0.25

0.55

–0.02

0.19

0.14

(Reddy et al., 2005b)

GISS

1.86

0.017

0.29

0.35

–0.26

0.61

–0.13

0.49

0.065

(Hansen et al., 2005)

GISS

1.86

0.015

0.29

0.05

–0.30

0.35

–0.08

0.18

–0.05

(Koch, 2001)

GISS

2.39

0.39

0.32

–0.18

0.50

–0.05

0.25

0.12

(Chung and Seinfeld., 2002)

GISS

2.49

0.43

0.30

–0.23

0.53

–0.06

0.27

0.09

(Liao and Seinfeld, 2005)

SPRINT

ARS

2.67

0.029

0.53

0.15

–0.27

0.42

–0.07

0.21

0.01

akemura et al., 2005)

TORG

2.55

0.39

0.47

–0.06

0.55

–0.01

0.27

0.22

(Jacobson, 2001b)

MOZGN

3.03

0.018

–0.34

(Ming et al., 2005a)

CCM

0.33

0.34

ang, 2004)

UIO-GCM

0.30

0.19

(Kirkevag and Iversen, 2002)

AeroCom: identical emissions used for year 1750 and 2000 (Schulz et al., 2006)

UMI

1.16

0.0060

0.19

0.02

–0.23

0.25

–0.06

0.12

–0.01

(Liu and Penner

, 2002)

UIO_CTM

1.12

0.0058

0.19

0.02

–0.16

0.22

–0.04

0.11

–0.05

(Myhr

e et al., 2003)

LOA

1.41

0.0085

0.25

0.14

–0.16

0.32

–0.04

0.16

0.02

(Reddy and Boucher

, 2004)

LSCE

1.50

0.0079

0.25

0.13

–0.17

0.30

–0.04

0.15

0.02

(Schulz et al., 2006)

ECHAM5-HAM

1.00

0.0077

0.16

0.09

–0.10

0.20

–0.03

0.10

0.01

(Stier et al., 2005)

GISS

1.22

0.0060

0.24

0.08

–0.14

0.22

–0.03

0.11

0.01

(Koch, 2001)

UIO_GCM

0.88

0.0046

0.19

0.24

–0.06

0.36

–0.02

0.18

0.08

(Iversen and Seland, 2002)

SPRINT

ARS

1.84

0.0200

0.37

0.22

–0.10

0.32

–0.01

0.13

0.06

akemura et al., 2005)

ULAQ

1.71

0.0075

0.38

–0.01

–0.09

0.08

–0.02

0.04

–0.03

(Pitari et al., 2002)

verage A–K

2.38

0.019

0.38

0.26

–0.24

0.44

–0.06

0.25

0.07

verage L–T

1.32

0.008

0.25

0.10

–0.13

0.25

–0.03

0.12

0.01

Stddev A–K

0.42

0.006

0.08

0.14

0.08

0.13

0.04

0.11

0.09

Stddev L–T

0.32

0.005

0.08

0.09

0.05

0.08

0.01

0.04

165

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

due to BC from fossil fuels include those from Takemura et
al. (2000), Reddy et al. (2005a) and Hansen et al. (2005) as
summarised in Table 2.5. The results from a number of studies
that continue to group the RF from fossil fuel with that from
biomass burning are also shown. Recently published results (A
to K) and AeroCom studies (L to T) suggest a combined RF
from both sources of +0.44 ± 0.13 W m

–2

and +0.29 ± 0.15

W m

–2

respectively. The stronger RF estimates from the models

A to K appear to be primarily due to stronger sources and
column loadings as the direct RF/column loading is similar at
approximately 1.2 to 1.3 W mg

–1

(Table 2.5). Carbonaceous

aerosol emission inventories suggest that approximately 34
to 38% of emissions come from biomass burning sources and
the remainder from fossil fuel burning sources. Models that
separate fossil fuel from biomass burning suggest an equal split
in RF. This is applied to those estimates where the BC emissions
are not explicitly separated into emission sources to provide
an estimate of the RF due to fossil fuel BC. For the AeroCom
results, the fossil fuel BC RF ranges from +0.08 to +0.18 W m

–2

with a mean of +0.13 W m

–2

and a standard deviation of 0.03

W m

–2

. For model results A to K, the RFs range from +0.15

W m

–2

to approximately +0.27 W m

–2

, with a mean of +0.25

W m

–2

and a standard deviation of 0.11 W m

–2

The mean and median of the direct RF for fossil fuel BC

from grouping all these studies together are +0.19 and +0.16
W m

–2

, respectively, with a standard deviation of nearly 0.10

W m

–2

. The standard deviation is multiplied by 1.645 to

approximate the 90% con

dence interval and the best estimate

is rounded upwards slightly for simplicity, leading to a direct
RF estimate of +0.20 ± 0.15 W m

–2

. This estimate does not

include the semi-direct effect or the BC impact on snow and ice
surface albedo (see Sections 2.5.4 and 2.8.5.6)

2.4.4.4 Biomass

Burning

Aerosol

The TAR reported a contribution to the RF of roughly

–0.4 W m

–2

from the scattering components (mainly organic

carbon and inorganic compounds) and +0.2 W m

–2

from the

absorbing components (BC) leading to an estimate of the RF of
biomass burning aerosols of –0.20 W m

–2

with a factor of three

uncertainty. Note that the estimates of the BC RF from Hansen
and Sato (2001), Hansen et al. (2002), Hansen and Nazarenko
(2004) and Jacobson (2001a) include the RF component of
BC from biomass burning aerosol. Radiative forcing due to
biomass burning (primarily organic carbon, BC and inorganic
compounds such as nitrate and sulphate) is grouped into a
single RF, because biomass burning emissions are essentially
uncontrolled. Emission inventories show more signi

cant

differences for biomass burning aerosols than for aerosols of
fossil fuel origin (Kasischke and Penner, 2004). Furthermore,
the pre-industrial levels of biomass burning aerosols are also
dif

cult to quantify (Ito and Penner, 2005; Mouillot et al.,

2006).

The Southern African Regional Science Initiative (SAFARI

2000: see Swap

et al., 2002, 2003) took place in 2000 and 2001.

The main objectives of the aerosol research were to investigate

pyrogenic and biogenic emissions of aerosol in southern
Africa (Eatough

et al., 2003; Formenti et al., 2003; Hély

al., 2003), validate the remote sensing retrievals (Haywood

et al., 2003b; Ichoku

et al., 2003) and to study the in

uence

of aerosols on the radiation budget via the direct and indirect
effects (e.g., Bergstrom

et al., 2003; Keil and Haywood, 2003;

Myhre

et al., 2003; Ross

et al., 2003). The physical and optical

properties of fresh and aged biomass burning aerosol were
characterised by making intensive observations of aerosol
size distributions, optical properties, and DRE through

situ

aircraft measurements (e.g., Abel

et al., 2003; Formenti

et al., 2003; Haywood

et al., 2003b; Magi and Hobbs, 2003;

Kirchstetter

et al., 2004) and radiometric measurements (e.g.,

Bergstrom

et al., 2003; Eck

et al., 2003). The

at 0.55

derived from near-source AERONET sites ranged from 0.85 to
0.89 (Eck

et al., 2003), while

at 0.55

m for aged aerosol

was less absorbing at approximately 0.91 (Haywood

et al.,

2003b). Abel et al. (2003) showed evidence that

at 0.55

m increased from approximately 0.85 to 0.90 over a time

period of approximately two hours subsequent to emission,
and attributed the result to the condensation of essentially non-
absorbing organic gases onto existing aerosol particles. Fresh
biomass burning aerosols produced by boreal forest

res appear

to have weaker absorption than those from tropical

res, with

at 0.55

m greater than 0.9 (Wong and Li 2002). Boreal

res

may not exert a signi

cant direct RF because a large proportion

of the

res are of natural origin and no signi

cant change over

the industrial era is expected. However, Westerling et al. (2006)
showed that earlier spring and higher temperatures in USA have
increased wild

re activity and duration. The partially absorbing

nature of biomass burning aerosol means it exerts an RF that is
larger at the surface and in the atmospheric column than at the
TOA (see Figure 2.12).

Modelling efforts have used data from measurement

campaigns to improve the representation of the physical and
optical properties as well as the vertical pro

le of biomass

burning aerosol (Myhre

et al., 2003; Penner

et al., 2003; Section

2.4.5). These modi

cations have had important consequences

for estimates of the RF due to biomass burning aerosols
because the RF is signi

cantly more positive when biomass

burning aerosol overlies cloud than previously estimated (Keil
and Haywood, 2003; Myhre

et al., 2003; Abel

et al., 2005).

While the RF due to biomass burning aerosol in clear skies is
certainly negative, the overall RF of biomass burning aerosol
may be positive. In addition to modelling studies, observations
of this effect have been made with satellite instruments. Hsu
et al. (2003) used SeaWiFs, TOMS and CERES data to show
that biomass burning aerosol emitted from Southeast Asia is
frequently lifted above the clouds, leading to a reduction in
re

ected solar radiation over cloudy areas by up to 100 W m

–2

and pointed out that this effect could be due to a combination
of direct and indirect effects. Similarly, Haywood et al. (2003a)
showed that remotely sensed cloud liquid water and effective
radius underlying biomass burning aerosol off the coast
of Africa are subject to potentially large systematic biases.
This may have important consequences for studies that use

167

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

correlations of

aer

and cloud effective radius in estimating the

indirect radiative effect of aerosols.

Since the biomass burning aerosols can exert a signi

cant

positive RF when above clouds, the aerosol vertical pro

le is

critical in assessing the magnitude and even the sign of the
direct RF in cloudy areas. Textor et al. (2006) showed that
there are signi

cant differences in aerosol vertical pro

les

between global aerosol models. These differences are evident
in the results from the recently published studies and AeroCom
models in Table 2.5. The most negative RF of –0.05 W m

–2

is from the model of Koch (2001) and from the Myhre et al.
(2003) AeroCom submission, while several models have RFs
that are slightly positive. Hence, even the sign of the RF due to
biomass burning aerosols is in question.

The mean and median of the direct RF for biomass burning

aerosol from grouping all these studies together are similar at
+0.04 and +0.02 W m

–2

, respectively, with a standard deviation

of 0.07 W m

–2

. The standard deviation is multiplied by 1.645 to

approximate the 90% con

dence interval, leading to a direct RF

estimate of +0.03 ± 0.12 W m

–2

. This estimate of the direct RF

is more positive than that of the TAR owing to improvements
in the models in representing the absorption properties of the
aerosol and the effects of biomass burning aerosol overlying
clouds.

2.4.4.5 Nitrate

Aerosol

Atmospheric ammonium nitrate aerosol forms if sulphate

aerosol is fully neutralised and there is excess ammonia.
The direct RF due to nitrate aerosol is therefore sensitive
to atmospheric concentrations of ammonia as well as NO

emissions. In addition, the weakening of the RF of sulphate
aerosol in many regions due to reduced emissions (Section
2.4.4.1) will be partially balanced by increases in the RF of
nitrate aerosol (e.g., Liao and Seinfeld, 2005). The TAR did
not quantify the RF due to nitrate aerosol owing to the large
discrepancies in the studies available at that time. Van Dorland
(1997) and Jacobson (2001a) suggested relatively minor global
mean RFs of –0.03 and –0.05 W m

–2

, respectively, while

Adams et al. (2001) suggested a global mean RF as strong as
–0.22 W m

–2

. Subsequent studies include those of Schaap et

al. (2004), who estimated that the RF of nitrate over Europe is
about 25% of that due to sulphate aerosol, and of Martin et al.
(2004), who reported –0.04 to –0.08 W m

–2

for global mean

RF due to nitrate. Further, Liao and Seinfeld (2005) estimated
a global mean RF due to nitrate of –0.16 W m

–2

. In this study,

heterogeneous chemistry reactions on particles were included;
this strengthens the RF due to nitrate and accounts for 25% of
its RF. Feng and Penner (2007) estimated a large, global,

ne-

mode nitrate burden of 0.58 mg NO

–2

, which would imply an

equivalent of 20% of the mean anthropogenic sulphate burden.
Surface observations of

ne-mode nitrate particles show that

high concentrations are mainly found in highly industrialised
regions, while low concentrations are found in rural areas
(Malm et al., 2004; Putaud et al., 2004). Atmospheric nitrate is

essentially non-absorbing in the visible spectrum, and laboratory
studies have been performed to determine the hygroscopicity of
the aerosols (e.g., Tang 1997; Martin et al., 2004 and references
therein). In the AeroCom exercise, nitrate aerosols were not
included so fewer estimates of this compound exist compared
to the other aerosol species considered.

The mean direct RF for nitrate is estimated to be –0.10

W m

–2

at the TOA, and the conservative scattering nature means

a similar

ux change at the surface. However, the uncertainty in

this estimate is necessarily large owing to the relatively small
number of studies that have been performed and the considerable
uncertainty in estimates, for example, of the nitrate

aer

. Thus,

a direct RF of –0.10 ± 0.10 W m

–2

is tentatively adopted, but

it is acknowledged that the number of studies performed is
insuf

cient for accurate characterisation of the magnitude and

uncertainty of the RF.

2.4.4.6 Mineral

Dust

Aerosol

Mineral dust from anthropogenic sources originates

mainly from agricultural practices (harvesting, ploughing,
overgrazing), changes in surface water (e.g., Caspian and
Aral Sea, Owens Lake) and industrial practices (e.g., cement
production, transport) (Prospero et al., 2002). The TAR reported
that the RF due to anthropogenic mineral dust lies in the range of
+0.4 to –0.6 W m

–2

, and did not assign a best estimate because

of the dif

culties in determining the anthropogenic contribution

to the total dust loading and the uncertainties in the optical
properties of dust and in evaluating the competing shortwave
and longwave radiative effects. For the sign and magnitude of
the mineral dust RF, the most important factor for the shortwave
RF is the single scattering albedo whereas the longwave RF is
dependent on the vertical pro

le of the dust.

Tegen and Fung (1995) estimated the anthropogenic

contribution to mineral dust to be 30 to 50% of the total dust
burden in the atmosphere. Tegen et al. (2004) provided an
updated, alternative estimate by comparing observations of
visibility, as a proxy for dust events, from over 2,000 surface
stations with model results, and suggested that only 5 to 7%
of mineral dust comes from anthropogenic agricultural sources.
Yoshioka et al. (2005) suggested that a model simulation best
reproduces the North African TOMS aerosol index observations
when the cultivation source in the Sahel region contributes 0
to 15% to the total dust emissions in North Africa. A 35-year
dust record established from Barbados surface dust and satellite
observations from TOMS and the European geostationary
meteorological satellite (Meteosat) show the importance
of climate control and Sahel drought for interannual and
decadal dust variability, with no overall trend yet documented
(Chiapello et al., 2005). As further detailed in Section 7.3,
climate change and CO

variations on various time scales can

change vegetation cover in semi-arid regions. Such processes
dominate over land use changes as de

ned above, which would

give rise to anthropogenic dust emissions (Mahowald and Luo,
2003; Moulin and Chiapello, 2004; Tegen et al., 2004). A best

168

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

guess of 0 to 20% anthropogenic dust burden from these works
is used here, but it is acknowledged that a very large uncertainty
remains because the methods used cannot exclude either a
reduction of 24% in present-day dust nor a large anthropogenic
contribution of up to 50% (Mahowald and Luo, 2003;
Mahowald et al., 2004; Tegen et al., 2005). The RF ef

ciency of

anthropogenic dust has not been well differentiated from that of
natural dust and it is assumed that they are equal. The RF due to
dust emission changes induced by circulation changes between
1750 and the present are dif

cult to quantify and not included

here (see also Section 7.5).

In situ

measurements of the optical properties of local

Saharan dust (e.g., Haywood

et al., 2003c; Tanré

et al., 2003),

transported Saharan mineral dust (e.g., Kaufman et al., 2001;
Moulin et al., 2001; Coen et al., 2004) and Asian mineral dust
(Huebert

et al., 2003; Clarke

et al., 2004; Shi et al., 2005;

Mikami et al., 2006) reveal that dust is considerably less
absorbing in the solar spectrum than suggested by previous
dust models such as that of WMO (1986). These new, spectral,
simultaneous remote and

in situ

observations suggest that

the single scattering albedo (

) of pure dust at a wavelength

of 0.67

m is predominantly in the range 0.90 to 0.99, with

a central global estimate of 0.96. This is in accordance with
the bottom-up modelling of

based on the haematite content

in desert dust sources (Claquin et al., 1999; Shi et al., 2005).
Analyses of

from long-term AERONET sites in

uenced by

Saharan dust suggest an average

of 0.95 at 0.67

m (Dubovik

et al., 2002), while unpolluted Asian dust during the Aeolian
Dust Experiment on Climate (ADEC) had an average

0.93 at 0.67

m (Mikami et al., 2006 and references therein).

These high

values suggest that a positive RF by dust in the

solar region of the spectrum is unlikely. However, absorption
by particles from source regions with variable mineralogical
distributions is generally not represented by global models.

Measurements of the DRE of mineral dust over ocean

regions, where natural and anthropogenic contributions are
indistinguishably mixed, suggest that the local DRE may be
extremely strong: Haywood et al. (2003b) made aircraft-based
measurements of the local instantaneous shortwave DRE of as
strong as –130 W m

–2

off the coast of West Africa. Hsu et al.

(2000) used Earth Radiation Budget Experiment (ERBE) and
TOMS data to determine a peak monthly mean shortwave DRE
of around –45 W m

–2

for July 1985. Interferometer measurements

from aircraft and the surface have now measured the spectral
signature of mineral dust for a number of cases (e.g., Highwood

et al., 2003) indicating an absorption peak in the centre of the
8 to 13

m atmospheric window. Hsu et al. (2000) determined

a longwave DRE over land areas of North Africa of up to +25
W m

–2

for July 1985; similar results were presented by Haywood

et al. (2005) who determined a peak longwave DRE of up to
+50 W m

–2

at the top of the atmosphere for July 2003.

Recent model simulations report the total anthropogenic

and natural dust DRE, its components and the net effect as
follows (shortwave / longwave = net TOA, in W m

–2

): H.

Liao et al. (2004): –0.21 / +0.31 = +0.1; Reddy et al. (2005a):
–0.28 / +0.14 = –0.14; Jacobson (2001a): –0.20 / +0.07 =

–0.13; reference case and [range] of sensitivity experiments in
Myhre and Stordal (2001a, except case 6 and 7): –0.53 [–1.4
to +0.2] / +0.13 [+0.0 to +0.8] = –0.4 [–1.4 to +1.0]; and from
AeroCom database models, GISS: –0.75 / (+0.19) = (–0.56);
UIO-CTM*: –0.56 / (+0.19) = (–0.37); LSCE*: –0.6 / +0.3 =
–0.3; UMI*: –0.54 / (+0.19) = (–0.35). (See Table 2.4, Note
(a) for model descriptions.) The (*) star marked models use a
single scattering albedo (approximately 0.96 at 0.67

m) that

is more representative of recent measurements and show more
negative shortwave effects. A mean longwave DRE of 0.19
W m

–2

is assumed for GISS, UMI and UIO-CTM. The scatter

of dust DRE estimates re

ects the fact that dust burden and

aer

vary by ±40 and ±44%, respectively, computed as standard

deviation from 16 AeroCom A model simulations (Textor et
al., 2006; Kinne et al., 2006). Dust emissions from different
studies range between 1,000 and 2,150 Tg yr

–1

(Zender, 2004).

Finally, a major effect of dust may be in reducing the burden of
anthropogenic species at sub-micron sizes and reducing their
residence time (Bauer and Koch, 2005; see Section 2.4.5.7).

The range of the reported dust net DRE (–0.56 to +0.1

W m

–2

), the revised anthropogenic contribution to dust DRE of

0 to 20% and the revised absorption properties of dust support a
small negative value for the anthropogenic direct RF for dust of
–0.1 W m

–2

. The 90% con

dence level is estimated to be ±0.2

W m

–2

, re

ecting the uncertainty in total dust emissions and

burdens and the range of possible anthropogenic dust fractions.
At the limits of this uncertainty range, anthropogenic dust RF
is as negative as –0.3 W m

–2

and as positive as +0.1 W m

–2

This range includes all dust DREs reported above, assuming
a maximum 20% anthropogenic dust fraction, except the most
positive DRE from Myhre and Stordal (2001a).

2.4.4.7

Direct RF for Combined Total Aerosol

The TAR reported RF values associated with several aerosol

components but did not provide an estimate of the overall
aerosol RF. Improved and intensi

in situ

observations and

remote sensing of aerosols suggest that the range of combined
aerosol RF is now better constrained. For model results,
extensive validation now exists for combined aerosol properties,
representing the whole vertical column of the atmosphere,
such as

aer

. Using a combined estimate implicitly provides an

alternative procedure to estimating the RF uncertainty. This
approach may be more robust than propagating uncertainties
from all individual aerosol components. Furthermore, a
combined RF estimate accounts for nonlinear processes due to
aerosol dynamics and interactions between radiation

eld and

aerosols. The role of nonlinear processes of aerosol dynamics
in RF has been recently studied in global aerosol models that
account for the internally mixed nature of aerosol particles
(Jacobson, 2001a; Kirkevåg and Iversen, 2002; Liao and
Seinfeld, 2005; Takemura

et al., 2005; Stier

et al., 2006b).

Mixing of aerosol particle populations in

uences the radiative

properties of the combined aerosol, because mixing changes
size, chemical composition, state and shape, and this feed backs
to the aerosol removal and formation processes itself. Chung

169

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

and Seinfeld (2002), in reviewing studies where BC is mixed
either externally or internally with various other components,
showed that BC exerts a stronger positive direct RF when
mixed internally. Although the source-related processes for
anthropogenic aerosols favour their submicron nature, natural
aerosols enter the picture by providing a condensation surface
for aerosol precursor gases. Heterogeneous reactions on sea
salt and dust can reduce the sub-micron sulphate load by 28%
(H. Liao

et al., 2004) thereby reducing the direct and indirect

RFs. Bauer and Koch (2005) estimated the sulphate RF to
weaken from –0.25 to –0.18 W m

–2

when dust is allowed to

interact with the sulphur cycle. It would be useful to identify
the RF contribution attributable to different source categories
(Section 2.9.3 investigates this). However, few models have
separated out the RF from speci

c emission source categories.

Estimating the combined aerosol RF is a

rst step to quantify

the anthropogenic perturbation to the aerosol and climate
system caused by individual source categories.

A central model-derived estimate for the aerosol direct RF

is based here on a compilation of recent simulation results
using multi-component global aerosol models (see Table 2.6).
This is a robust method for several reasons. The complexity
of multi-component aerosol simulations captures nonlinear
effects. Combining model results removes part of the errors
in individual model formulations. As shown
by Textor et al. (2006), the model-speci

treatment of transport and removal processes
is partly responsible for the correlated
dispersion of the different aerosol components.
A less dispersive model with smaller burdens
necessarily has fewer scattering and absorbing
aerosols interacting with the radiation

eld. An

error in accounting for cloud cover would affect
the all-sky RF from all aerosol components.
Such errors result in correlated RF ef

ciencies

for major aerosol components within a given
model. Directly combining aerosol RF results
gives a more realistic aerosol RF uncertainty
estimate. The AeroCom compilation suggests
signi

cant differences in the modelled local and

regional composition of the aerosol (see also
Figure 2.12), but an overall reproduction of the
total

aer

variability can be performed (Kinne

et al., 2006). The scatter in model performance
suggests that currently no preference or
weighting of individual model results can be
used (Kinne

et al., 2006). The aerosol RF taken

together from several models is more robust
than an analysis per component or by just one
model. The mean estimate from Table 2.6 of
the total aerosol direct RF is –0.2 W m

–2

, with

a standard deviation of ±0.2 W m

–2

. This is a

low-end estimate for both the aerosol RF and
uncertainty because nitrate (estimated as –0.1
W m

–2

, see Section 2.4.4.5) and anthropogenic

mineral dust (estimated as –0.1 W m

–2

, see

Section 2.4.4.6) are missing in most of the model simulations.
Adding their contribution yields an overall model-derived
aerosol direct RF of –0.4 W m

–2

(90% con

dence interval: 0

to –0.8 W m

–2

Three satellite-based measurement estimates of the aerosol

direct RF have become available, which all suggest a more
negative aerosol RF than the model studies (see Section
2.4.2.1.3). Bellouin et al. (2005) computed a TOA aerosol RF
of –0.8 ± 0.1 W m

–2

. Chung et al. (2005), based upon similarly

extensive calculations, estimated the value to be –0.35 ± 0.25
W m

–2

and Yu et al. (2006) estimated it to be –0.5 ± 0.33

W m

–2

. A central measurement-based estimate would suggest

an aerosol direct RF of –0.55 W m

–2

. Figure 2.13 shows the

observationally based aerosol direct RF estimates together with
the model estimates published since the TAR.

The discrepancy between measurements and models

is also apparent in oceanic clear-sky conditions where the
measurement-based estimate of the combined aerosol DRE
including natural aerosols is considered unbiased. In these
areas, models underestimate the negative aerosol DRE by 20
to 40% (Yu

et al., 2006). The anthropogenic fraction of

aer

is similar between model and measurement based studies.
Kaufman et al.

(2005a) used satellite-observed

ne-mode

aer

to estimate the anthropogenic

aer

. Correcting for

ne-mode

aer

Figure 2.13.

Estimates of the direct aerosol RF from observationally based studies, independent model-

ling studies, and AeroCom results with identical aerosol and aerosol precursor emissions. GISS_1 refers
to a study employing an internal mixture of aerosol, and GISS_2 to a study employing an external mixture.
See Table 2.4, Note (a) for descriptions of models.

170

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

able 2.6.

Quantities rela

ted to estima

tes of the aerosol direct RF

. Recent estima

tes of anthropogenic aerosol load (LOAD),

anthropogenic

aerosol optical depth (

aer

), its fraction of the present-day total aerosol optical depth (

aer

ant

cloud cover in aerosol model,

total aerosol direct radia

tive forcing (RF) for c

lear sky and all sky conditions,

surface forcing

and a

tmospheric all-sky forcing.

Model

LOAD

aer

(0.55 µm)

aer

ant

(0.55 µm)

Cloud Cover

RF top

clear sky

RF top

all sky

Surface For

cing

all sky

Atmospheric

For

cing all sky

Refer

ence

(mg m

–2

)

(%)

(W m

–2

)

(W m

–2

)

(W m

–2

)

(W m

–2

)

Published since IPCC, 2001

GISS

5.0

79%

–0.39

+0.01

–1.98

–2.42

1.59

2.43

(Liao and Seinfeld, 2005)

LOA

6.0

0.049

34%

70%

–0.53

–0.09

(Reddy and Boucher

, 2004)

SPRINT

ARS

4.8

0.044

50%

63%

–0.77

–0.06

–1.92

1.86

akemura et al., 2005)

UIO-GCM

2.7

57%

–0.11

(Kirkevag and Iversen, 2002)

TORG

6.4

62%

–0.89

–0.12

–2.5

2.38

(Jacobson, 2001a)

GISS

6.7

0.049

–0.23

(Hansen et al., 2005)

GISS

5.6

0.040

–0.63

(Koch, 2001)

AeroCom: identical emissions used for year 1750 and 2000 (Schulz et al., 2006)

UMI

4.0

0.028

25%

63%

–0.80

–0.41

–1.24

0.84

(Liu and Penner

, 2002)

UIO_CTM

3.0

0.026

19%

70%

–0.85

–0.34

–0.95

0.61

(Myhr

e et al., 2003)

LOA

5.3

0.046

28%

70%

–0.80

–0.35

–1.49

1.14

(Reddy and Boucher

, 2004)

LSCE

4.8

0.033

40%

62%

–0.94

–0.28

–0.93

0.66

(Schulz et al., 2006)

ECHAM5

4.3

0.032

30%

62%

–0.64

–0.27

–0.98

0.71

(Stier et al., 2005)

GISS

2.8

0.014

11%

57%

–0.29

–0.11

–0.81

0.79

(Koch, 2001)

UIO_GCM

2.8

0.017

11%

57%

–0.01

–0.84

0.84

(Kirkevag and Iversen, 2002)

SPRINT

ARS

3.2

0.036

44%

62%

–0.35

+0.04

–0.91

0.96

akemura et al., 2005)

ULAQ

3.7

0.030

23%

–0.79

–0.24

(Pitari et al., 2002)

verage A–G

5.1

0.046

42%

67%

–0.73

–0.23

–2.21

2.07

verage H–P

3.8

0.029

26%

63%

–0.68

–0.22

–1.02

0.82

Stddev A–G

1.4

0.004

0.18

0.21

Stddev H–P

0.9

0.010

11%

0.24

0.16

0.23

0.17

verage A–P

4.3

0.035

29%

64%

–0.70

–0.22

–1.21

1.24

Stddev A–P

1.3

0.012

13%

0.26

0.18

0.44

0.65

Minimum A–P

2.7

0.014

11%

57%

–0.94

–0.63

–1.98

0.61

Maximum A–P

6.7

0.049

50%

79%

–0.29

0.04

–0.81

2.43

Notes:

See Note (a) in T

able 2.4 for model information.

Exter

nal

mixtur

Inter

nal

mixtur

The load excludes that of mineral dust, some of which was consider

ed anthr

opogenic in Jacobson (2001a).

171

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

contributions from dust and sea salt, they found 21% of the total

aer

to be anthropogenic, while Table 2.6 suggests that 29% of

aer

is anthropogenic. Finally, cloud contamination of satellite

products, aerosol absorption above clouds, not accounted for
in some of the measurement-based estimates, and the complex
assumptions about aerosol properties in both methods can
contribute to the present discrepancy and increase uncertainty
in aerosol RF.

A large source of uncertainty in the aerosol RF estimates is

associated with aerosol absorption. Sato et al. (2003) determined
the absorption

aer

from AERONET measurements and suggested

that aerosol absorption simulated by global aerosol models is
underestimated by a factor of two to four. Schuster et al. (2005)
estimated the BC loading over continental-scale regions. The
results suggest that the model concentrations and absorption

aer

of BC from models are lower than those derived from

AERONET. Some of this difference in concentrations could
be explained by the assumption that all aerosol absorption is
due to BC (Schuster et al., 2005), while a signi

cant fraction

may be due to absorption by organic aerosol and mineral dust
(see Sections 2.4.4.2, and 2.4.4.6). Furthermore, Reddy et al.
(2005a) show that comparison of the aerosol absorption

aer

from models against those from AERONET must be performed
very carefully, reducing the discrepancy between their model
and AERONET derived aerosol absorption

aer

from a factor

of 4 to a factor of 1.2 by careful co-sampling of AERONET
and model data. As mentioned above, uncertainty in the vertical
position of absorbing aerosol relative to clouds can lead to large
uncertainty in the TOA aerosol RF.

The partly absorbing nature of the aerosol is responsible for

a heating of the lower-tropospheric column and also results
in the surface forcing being considerably more negative than
TOA RF, results that have been con

rmed through several

experimental and observational studies as discussed in earlier
sections. Table 2.6 summarises the surface forcing obtained
in the different models. Figure 2.12 depicts the regional
distribution of several important parameters for assessing
the regional impact of aerosol RF. The results are based on a
mean model constructed from AeroCom simulation results B
and PRE. Anthropogenic

aer

(Figure 2.12a) is shown to have

local maxima in industrialised regions and in areas dominated
by biomass burning. The difference between simulated and
observed

aer

shows that regionally

aer

can be up to 0.1 (Figure

2.12b). Figure 2.12c suggests that there are regions off Southern
Africa where the biomass burning aerosol above clouds leads
to a local positive RF. Figure 2.12d shows the local variability
as the standard deviation from nine models of the overall RF.
The largest uncertainties of ±3 W m

–2

are found in East Asia

and in the African biomass burning regions. Figure 2.12e
reveals that an average of 0.9 W m

–2

heating can be expected

in the atmospheric column as a consequence of absorption by
anthropogenic aerosols. Regionally, this can reach annually
averaged values exceeding 5 W m

–2

. These regional effects and

the negative surface forcing in the shortwave (Figure 2.12f)
is expected to exert an important effect on climate through
alteration of the hydrological cycle.

An uncertainty estimate for the model-derived aerosol direct

RF can be based upon two alternative error analyses:

1) An error propagation analysis using the errors given in the

sections on sulphate, fossil fuel BC and organic carbon,
biomass burning aerosol, nitrate and anthropogenic
mineral dust. Assuming linear additivity of the errors, this
results in an overall 90% con

dence level uncertainty of

0.4 W m

–2

2) The standard deviation of the aerosol direct RF results in

Table 2.6, multiplied by 1.645, suggests a 90% con

dence

level uncertainty of 0.3 W m

–2

, or 0.4 W m

–2

when mineral

dust and nitrate aerosol are accounted for.

Therefore, the results summarised in Table 2.6 and Figure

2.13, together with the estimates of nitrate and mineral dust RF
combined with the measurement-based estimates, provide an
estimate for the combined aerosol direct RF of –0.50 ± 0.40
W m

–2

. The progress in both global modelling and measurements

of the direct RF of aerosol leads to a medium-low level of
scienti

c understanding (see Section 2.9, Table 2.11).

2.4.5 Aerosol

Infl uence on Clouds (Cloud Albedo

Effect)

As pointed out in Section 2.4.1, aerosol particles affect

the formation and properties of clouds. Only a subset of the
aerosol population acts as cloud condensation nuclei (CCN)
and/or ice nuclei (IN). Increases in ambient concentrations of
CCN and IN due to anthropogenic activities can modify the
microphysical properties of clouds, thereby affecting the climate
system (Penner

et al., 2001; Ramanathan

et al., 2001a, Jacob

et al., 2005). Several mechanisms are involved, as presented
schematically in Figure 2.10. As noted in Ramaswamy et al.
(2001), enhanced aerosol concentrations can lead to an increase
in the albedo of clouds under the assumption of

xed liquid

water content (Junge, 1975; Twomey, 1977); this mechanism
is referred to in this report as the ‘cloud albedo effect’. The
aerosol enhancements have also been hypothesised to lead
to an increase in the lifetime of clouds (Albrecht, 1989); this
mechanism is referred to in this report as the ‘cloud lifetime
effect’ and discussed in Section 7.5.

The interactions between aerosol particles (natural and

anthropogenic in origin) and clouds are complex and can be
nonlinear (Ramaswamy

et al., 2001). The size and chemical

composition of the initial nuclei (e.g., anthropogenic sulphates,
nitrates, dust, organic carbon and BC) are important in the
activation and early growth of the cloud droplets, particularly
the water-soluble fraction and presence of compounds that affect
surface tension (McFiggans et al., 2006 and references therein).
Cloud optical properties are a function of wavelength. They
depend on the characteristics of the droplet size distributions
and ice crystal concentrations, and on the morphology of the
various cloud types.

172

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

The interactions of increased concentrations of

anthropogenic particles with shallow (stratocumulus and
shallow cumulus) and deep convective clouds (with mixed
phase) are discussed in this subsection. This section presents
new observations and model estimates of the albedo effect.
The associated RF in the context of liquid water clouds is
assessed. In-depth discussion of the induced changes that
are not considered as RFs (e.g., semi-direct and cloud cover
and lifetime effects, thermodynamic response and changes in
precipitation development) are presented in Section 7.5. The
impacts of contrails and aviation-induced cirrus are discussed in
Section 2.6 and the indirect impacts of aerosol on snow albedo
are discussed in Section 2.5.4.

2.4.5.1

Link Between Aerosol Particles and Cloud
Microphysics

The local impact of anthropogenic aerosols has been known

for a long time. For example, smoke from sugarcane and
forest

res was shown to reduce cloud droplet sizes in early

case studies utilising

in situ

aircraft observations (Warner

and Twomey, 1967; Eagan

et al., 1974). On a regional scale,

studies have shown that heavy smoke from forest

res in the

Amazon Basin have led to increased cloud droplet number
concentrations and to reduced cloud droplet sizes (Reid

et al.,

1999; Andreae

et al., 2004; Mircea et al., 2005). The evidence

concerning aerosol modi

cation of clouds provided by the

ship track observations reported in the TAR has been further
con

rmed, to a large extent qualitatively, by results from a

number of studies using

in situ

aircraft and satellite data,

covering continental cases and regional studies. Twohy et al.
(2005) explored the relationship between aerosols and clouds
in nine stratocumulus cases, indicating an inverse relationship
between particle number and droplet size, but no correlation
was found between albedo and particle concentration in the
entire data set. Feingold et al. (2003), Kim et al. (2003) and
Penner et al. (2004) presented evidence of an increase in the
re

ectance in continental stratocumulus cases, utilising remote

sensing techniques at speci

eld sites. The estimates in

Feingold et al. (2003) con

rm that the relationship between

aerosol and cloud droplet number concentrations is nonlinear,
that is

≈

)

, where

is the cloud drop number density

and

is the aerosol number concentration. The parameter

this relationship can vary widely, with values ranging from 0.06
to 0.48 (low values of

correspond to low hygroscopicity).

This range highlights the sensitivity to aerosol characteristics
(primarily size distribution), updraft velocity and the usage
of aerosol extinction as a proxy for CCN (Feingold, 2003).
Disparity in the estimates of

(or equivalent) based on satellite

studies (Nakajima et al., 2001; Breon et al., 2002) suggests that
a quantitative estimate of the albedo effect from remote sensors
is problematic (Rosenfeld and Feingold 2003), particularly
since measurements are not considered for similar liquid water
paths.

Many recent studies highlight the importance of aerosol

particle composition in the activation process and droplet

spectral evolution (indicated in the early laboratory work of
Gunn and Philips, 1957), but the picture that emerges is not
complete. Airborne aerosol mass spectrometers provide

evidence that ambient aerosols consist mostly of internal
mixtures, for example, biomass burning components, organics
and soot are mixed with other aerosol components (McFiggans

et al., 2006). Mircea et al. (2005) showed the importance of the
organic aerosol fraction in the activation of biomass burning
aerosol particles. The presence of internal mixtures (e.g., sea
salt and organic compounds) can affect the uptake of water
and the resulting optical properties compared to a pure sea salt
particle (Randles

et al., 2004). Furthermore, the varying contents

of water-soluble and insoluble substances in internally mixed
particles, the vast diversity of organics, and the resultant effects
on cloud droplet sizes, makes the situation even more complex.
Earlier observations of fog water (Facchini et al., 1999, 2000)
suggested that the presence of organic aerosols would reduce
surface tension and lead to a signi

cant increase in the cloud

droplet number concentration (Nenes et al., 2002; Rissler et al.,
2004; Lohmann and Leck, 2005; Ming et al., 2005a; McFiggans
et al., 2006). On the other hand, Feingold and Chuang (2002)
and Shantz et al. (2003) indicated that organic coating on CCN
delayed activation, leading to a reduction in drop number and
a broadening of the cloud droplet spectrum, which had not
been previously considered. Ervens et al. (2005) addressed
numerous composition effects in unison to show that the effect
of composition on droplet number concentration is much less
than suggested by studies that address individual composition
effects, such as surface tension. The different relationships
observed between cloud optical depth and liquid water path in
clean and polluted stratocumulus clouds (Penner et al., 2004)
have been explained by differences in sub-cloud aerosol particle
distributions, while some contribution can be attributed to CCN
composition (e.g., internally mixed insoluble dust; Asano et al.,
2002). Nevertheless, the review by McFiggans et al. (2006)
points to the remaining dif

culties in quantitatively explaining

the relationship between aerosol size and composition and the
resulting droplet size distribution. Dusek et al. (2006) concluded
that the ability of a particle to act as a CCN is largely controlled
by size rather than composition.

The complexity of the aerosol-cloud interactions and local

atmospheric conditions where the clouds are developing are
factors in the large variation evidenced for this phenomenon.
Advances have been made in the understanding of the regional
and/or global impact based on observational studies, particularly
for low-level stratiform clouds that constitute a simpler cloud
system to study than many of the other cloud types. Column
aerosol number concentration and column cloud droplet
concentration over the oceans from the AVHRR (Nakajima et
al., 2001) indicated a positive correlation, and an increase in
shortwave re

ectance of low-level, warm clouds with increasing

cloud optical thickness, while liquid water path (LWP) remained
unmodi

ed. While these results are only applicable over the

oceans and are based on data for only four months, the positive
correlation between an increase in cloud re

ectance and an

enhanced ambient aerosol concentration has been con

rmed by

173

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

other studies (Brenguier

et al., 2000a,b; Rosenfeld

et al., 2002).

However, other studies highlight the sensitivity to LWP, linking
high pollution entrained into clouds to a decrease in LWP and
a reduction in the observed cloud re

ectance (Jiang

et al.,

2002; Brenguier

et al., 2003; Twohy et al., 2005). Still others

(Han et al., 2002, using AVHRR observations) have reported
an absence of LWP changes in response to increases in the
column-averaged droplet number concentration, this occurred
for one-third of the cloud cases studied for which optical depths
ranged between 1 and 15. Results of large-eddy simulations of
stratocumulus (Jiang et al., 2002; Ackerman et al., 2004; Lu
and Seinfeld, 2005) and cumulus clouds (Jiang and Feingold,
2006; Xue and Feingold, 2006) seem to con

rm the lack of

increase in LWP due to increases in aerosols; they point to a
dependence on precipitation rate and relative humidity above
the clouds (Ackerman et al., 2004). The studies above highlight
the dif

culty of devising observational studies that can isolate

the albedo effect from other effects (e.g., meteorological
variability, cloud dynamics) that in

uence LWP and therefore

cloud RF.

Results from the POLDER satellite instrument, which

retrieves both submicron aerosol loading and cloud droplet
size, suggest much larger cloud effective radii in remote oceanic
regions than in the highly polluted continental source areas and
downwind adjacent oceanic areas, namely from a maximum of
14

m down to 6

m (Bréon

et al., 2002). This con

rms earlier

studies of hemispheric differences using AVHRR. Further, the
POLDER- and AVHRR-derived correlations between aerosol
and cloud parameters are consistent with an aerosol indirect
effect (Sekiguchi et al., 2003). These results suggest that the
impact of aerosols on cloud microphysics is global. Note that
the satellite measurements of aerosol loading and cloud droplet
size are not coincident, and an aerosol index is not determined in
the presence of clouds. Further, there is a lack of simultaneous
measurements of LWP, which makes assessment of the cloud
albedo RF dif

cult.

The albedo effect is also estimated from studies that

combined satellite retrievals with a CTM, for example, in the
case of two pollution episodes over the mid-latitude Atlantic
Ocean. Results indicated a brightening of clouds over a time
scale of a few days in instances when LWP did not undergo
any signi

cant changes (Hashvardhan

et al., 2002; Schwartz

et al., 2002; Krüger and Gra

l, 2002). There have been fewer

studies on aerosol-cloud relationships under more complex
meteorological conditions (e.g., simultaneous presence of
different cloud types).

The presence of insoluble particles within ice crystals

constituting clouds formed at cold temperatures has a signi

cant

uence on the radiation transfer. The inclusions of scattering

and absorbing particles within large ice crystals (Macke

et al.,

1996) suggest a signi

cant effect. Hence, when soot particles are

embedded, there is an increase in the asymmetry parameter and
thus forward scattering. In contrast, inclusions of ammonium
sulphate or air bubbles lead to a decrease in the asymmetry
parameter of ice clouds. Given the recent observations of
partially insoluble nuclei in ice crystals (Cziczo et al., 2004)

and the presence of small crystal populations, there is a need to
further develop the solution for radiative transfer through such
systems.

2.4.5.2

Estimates of the Radiative Forcing from Models

General Circulation Models constitute an important and

useful tool to estimate the global mean RF associated with
the cloud albedo effect of anthropogenic aerosols. The model
estimates of the changes in cloud re

ectance are based on

forward calculations, considering emissions of anthropogenic
primary particles and secondary particle production from
anthropogenic gases. Since the TAR, the cloud albedo effect
has been estimated in a more systematic and rigorous way
(allowing, for example, for the relaxation of the

xed LWC

criterion), and more modelling results are now available. Most
climate models use parametrizations to relate the cloud droplet
number concentration to aerosol concentration; these vary
in complexity from simple empirical

ts to more physically

based relationships. Some models are run under an increasing
greenhouse gas concentration scenario and include estimates of
present-day aerosol loadings (including primary and secondary
aerosol production from anthropogenic sources). These global
modelling studies (Table 2.7) have a limitation arising from the
underlying uncertainties in aerosol emissions (e.g., emission
rates of primary particles and of secondary particle precursors).
Another limitation is the inability to perform a meaningful
comparison between the various model results owing to
differing formulations of relationships between aerosol particle
concentrations and cloud droplet or ice crystal populations;
this, in turn, yields differences in the impact of microphysical
changes on the optical properties of clouds. Further, even when
the relationships used in different models are similar, there
are noticeable differences in the spatial distributions of the
simulated low-level clouds. Individual models’ physics have
undergone considerable evolution, and it is dif

cult to clearly

identify all the changes in the models as they have evolved.
While GCMs have other well-known limitations, such as coarse
spatial resolution, inaccurate representation of convection and
hence updraft velocities leading to aerosol activation and cloud
formation processes, and microphysical parametrizations, they
nevertheless remain an essential tool for quantifying the global
cloud albedo effect. In Table 2.7, differences in the treatment of
the aerosol mixtures (internal or external, with the latter being
the more frequently employed method) are noted. Case studies
of droplet activation indicate a clear sensitivity to the aerosol
composition (McFiggans et al., 2006); additionally, radiative
transfer is sensitive to the aerosol composition and the insoluble
fraction present in the cloud droplets.

All models estimate a negative global mean RF associated

with the cloud albedo effect, with the range of model results
varying widely, from –0.22 to –1.85 W m

–2

. There are

considerable differences in the treatment of aerosol, cloud
processes and aerosol-cloud interaction processes in these
models. Several models include an interactive sulphur cycle
and anthropogenic aerosol particles composed of sulphate, as

174

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

able 2.7.

Published model studies of the RF due to c

loud albedo effect,

in the context of liquid wa

ter c

louds,

with a listing of the rele

vant modelling details.

Model

Aer

osol

Aer

osol

Cloud types

Micr

ophysics

Radiative For

cing

type

species

mixtur

included

–2

)

Lohmann et al. (2000)

AGCM +

S, OC, BC,

warm and

oplet number concentration and L

WC, Beheng (1994);

–1.1 (total)

sulphur cycle

SS, D

mixed phase

Sundqvist et al. (1989). Also, mass and number fr

–0.45 (albedo)

(ECHAM4)

fi eld

observations

–1.5

(total)

Jones et al. (2001)

AGCM +

S, SS, D

stratiform and

oplet number concentration and L

WC, Wilson and

–1.89 (total)

sulphur cycle,

(a crude attempt

shallow

Ballar

d (1999); Smith (1990); T

ripoli and Cotton (1980);

–1.34 (albedo)

xed SST

for D over land,

cumulus

Bower et al. (1994). W

arm and mixed phase, radiative

(Hadley)

no radiation)

eatment of anvil cirrus, non-spherical ice particles

Williams et al. (2001b)

GCM with slab

S, SS

stratiform and

Jones et al. (2001)

–1.69 (total)

ocean + sulphur

shallow cumulus

–1.37 (albedo)

cycle

(Hadley)

AGCM,

xed SST

–1.62 (total)

–1.43(albedo)

Rotstayn and Penner

AGCM (CSIRO),

n.a.

warm and

Rotstayn (1997); Rotstayn et al. (2000)

–1.39 (albedo)

(2001)

xed SST and

mixed phase

sulphur

Rotstayn and Liu (2003)

Interactive sulphur

Inclusion of dispersion

12 to 35% decr

ease

cycle

–1.12 (albedo, mid

value

decr

eased)

Ghan et al. (2001)

AGCM (PNNL) +

S, OC, BC,

E (for dif

fer

ent

warm and

oplet number concentration and L

WC, crystal

–1.7 (total)

chemistry (MIRAGE),

SS, N, D

modes); I

mixed phase

concentration and ice water content. Dif

fer

ent pr

ocesses

–0.85 (albedo)

xed SST

(within modes)

fecting the various modes

Chuang et al. (2002)

CCM1 (NCAR) +

S, OC, BC,

E (for emitted

warm and

Modifi

ed fr

om Chuang and Penner (1995),

–1.85 (albedo)

chemistry

SS, D

particles); I:

mixed phase

no collision/coalescence

(GRANTOUR),

when

owing

xed SST

by condensation

Menon et al. (2002a)

GCM (GISS) +

S,OC, SS

warm

oplet number concentration and L

WC, Del Genio et al.

–2.41 (total)

sulphur cycle,

(1996), Sundqvist et al. (1989). W

arm and mixed phase,

–1.55 (albedo)

xed SST

impr

oved vertical distribution of clouds (but only nine

layers).Global aer

osol bur

dens poorly constrained

Kristjansson (2002)

CCM3 (NCAR)

S, OC, BC,

E (for nucleation

warm and

Rasch and Kristjánsson (1998). Stratiform and detraining

–1.82 (total)

xed SST

SS, D

mode and fossil

mixed phase

convective clouds

–1.35 (albedo)

fuel BC); I (for

accumulation

mode)

Suzuki et al. (2004)

AGCM (Japan),

S, OC, BC,

stratiform

Berry(1967), Sundqvist(1978)

0.54 (albedo)

xed

SST

Quaas et al. (2004)

AGCM (LMDZ) +

n.a.

warm and

Aer

osol mass and cloud dr

oplet number concentration,

–1.3 (albedo)

interactive sulphur

mixed phase

Boucher and Lohmann (1995); Boucher et al. (1995)

cycle,

fi xed

SST

175

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

Model

Aer

osol

Aer

osol

Cloud types

Micr

ophysics

Radiative For

cing

type

species

mixtur

included

–2

)

Hansen et al. (2005)

GCM (GISS) + 3

S, OC, BC,

warm and

Schmidt et al. (2005), 20 vertical layers. Dr

oplet number

–0.77 (albedo)

dif

fer

ent ocean

SS, N, D

shallow (below

concentration (Menon and Del Genio, 2007)

parametrizations

(D not included

720hPa)

clouds)

Kristjansson et al.

CCM3 (NCAR) +

S, OC, BC,

E (for nucleation

warm and

Kristjansson (2002). Stratiform and detraining

–1.15 (total,

(2005)

sulphur and carbon

SS, D

mode and fossil

mixed phase

convective clouds

at the surface)

cycles slab ocean

fuel BC); I (for

accumulation

mode)

Quaas and Boucher

AGCM (LMDZ) +

S, OC, BC,

warm and

Aer

osol mass and cloud dr

oplet number concentration,

–0.9 (albedo)

(2005)

interactive sulphur

SS, D

mixed phase

Boucher and Lohmann (1995); Boucher et al. (1995)

cycle,

xed SST

contr

ol run

t to POLDER data

–0.5 (albedo)

t to MODIS data

–0.3 (albedo)

Quaas et al. (2005)

AGCM (LMDZ

S, OC, BC,

warm and

Aer

osol mass and cloud dr

oplet number concentration,

–0.84 (total

and ECHAM4)

SS, D

mixed phase

Boucher and Lohmann, (1995), contr

ol runs (ctl)

LMDZ-ctl)

–1.54

(total

(ECHAM4-ctl)

Aer

osol mass and cloud dr

oplet number concentration

–0.53 (total

tted to MODIS data

LMDZ)

–0.29

(total

(ECHAM4)

Dufr

esne et al. (2005)

AGCM (LMDZ) +

n.a.

warm

Aer

osol mass and cloud dr

oplet number concentration,

–0.22 (albedo)

interactive sulphur

Boucher and Lohmann, (1995), fi

tted to POLDER data

cycle,

fi xed

SST

akemura et al. (2005)

AGCM (SPRINT

ARS)

S, OC, BC,

E (50% BC

warm

Activation based on Kohler theory and updraft velocity

–0.94 (total)

+ slab ocean

SS, D

om fossil fuel);

–0.52 (albedo)

I (for OC and BC)

Chen and Penner

AGCM (UM) +

S, SS, D,

warm and

Aer

osol mass and cloud dr

oplet number concentration

(2005)

xed SST

OC, BC

mixed phase

(lognormal)

Contr

ol (Abdul-Razzak and Ghan, 2002)

–1.30 (albedo,

UM_ctrl)

Relationship between dr

oplet concentration and

–0.75 (albedo,

dispersion

coeffi

cient:

High

UM_1)

Relationship between dr

oplet concentration and

–0.86 (albedo,

dispersion

coeffi

cient: Medium

UM_2)

Updraft

velocity

–1.07 (albedo,

UM_3)

Relationship between dr

oplet concentration and

–1.10 (albedo,

dispersion

coeffi

cient:

Low

UM_4)

Chuang et al. (1997)

–1.29 (albedo,

UM_5)

Nenes and Seinfeld (2003)

–1.79 (albedo,

UM_6)

ble 2.7 (continued)

176

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Notes:

AGCM: Atmospheric GCM; SST

: sea surface temperatur

e; CSIRO: Commonwealth Scientifi

c and Industrial Resear

ch Or

ganisation; MIRAGE: Model for Integrated Resear

ch on Atmospheric Global Exchanges;

GRANTOUR: Global Aer

osol T

ransport and Removal model; GFDL: Geophysical Fluid Dynamics Laboratory; CCSR: Centr

e for Climate Sys

tem Resear

ch; see T

able 2.4, Note (a) for listing of other models and

modelling centr

es listed in this column.

S: sulphate; SS: sea salt; D: mineral dust; BC: black carbon; OC: or

ganic carbon; N: nitrate.

E: exter

nal mixtur

s; I: inter

nal mixtur

es.

Only the bold numbers wer

e used to construct Figur

e 2.16.

These simulations have been constrained by satellite observations, using the same empirical fi

t to r

elate aer

osol mass and cloud dr

oplet number concentration.

The notation UM corr

esponds to University of Michigan, as listed in Figur

e 2.14.

Model

Aer

osol

Aer

osol

Cloud types

Micr

ophysics

Radiative For

cing

type

species

mixtur

included

–2

)

Ming et al. (2005b)

AGCM (GFDL),

n.a.

warm

Rotstayn et al. (2000), Khainr

outdinov and Kogan (2000).

–2.3 (total)

xed SST and

Aer

osols of

f-line

–1.4 (albedo)

sulphur loading

Penner et al. (2006)

LMDZ, Oslo and

S, SS, D,

warm and

Aer

osol mass and cloud dr

oplet number concentration;

–0.65 (albedo Oslo)

results fr

CCSR

OC, BC

mixed phase

Boucher and Lohmann, (1995); Chen and Penner (2005);

–0.68 (albedo LMDZ)

experiment 1

Sundqvist

(1978)

–0.74 (albedo CCSR)

ble 2.7 (continued)

178

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

type of aerosol species included in the simulations. In the top
panel of Figure 2.14, which shows estimates from models that
mainly include anthropogenic sulphate, there is an indication
that the results are converging, even though the range of models
comes from studies published between 2001 and 2006. These
studies show much less scatter than in the TAR, with a mean
and standard deviation of –1.37 ± 0.14 W m

–2

. In contrast,

in the bottom panel of Figure 2.14, which shows the studies
that include more species, a much larger variability is found.
These latter models (see Table 2.7) include ‘state of the art’
parametrizations of droplet activation for a variety of aerosols,
and include both internal and external mixtures.

Some studies have commented on inconsistencies between

some of the earlier estimates of the cloud albedo RF from
forward and inverse calculations (Anderson et al., 2003).
Notwithstanding the fact that these two streams of calculations
rely on very different formulations, the results here appear to be
within range of the estimates from inverse calculations.

2.4.5.3 Estimates of the Radiative Forcing from

Observations and Constrained Models

It is dif

cult to obtain a best estimate of the cloud albedo

RF from pre-industrial times to the present day based solely on
observations. The satellite record is not long enough, and other
long-term records do not provide the pre-industrial aerosol and
cloud microphysical properties needed for such an assessment.
Some studies have attempted to estimate the RF by incorporating
empirical relationships derived from satellite observations. This
approach is valid as long as the observations are robust, but
problems still remain, particularly with the use of the aerosol
optical depth as proxy for CCN (Feingold et al., 2003), droplet
size and cloud optical depth from broken clouds (Marshak et
al., 2006), and relative humidity effects (Kapustin et al., 2006)
to discriminate between hydrated aerosols and cloud. Radiative
forcing estimates constrained by satellite observations need to
be considered with these caveats in mind.

By assuming a bimodal lognormal size distribution,

Nakajima et al. (2001) determined the Ångstrom exponent from
AVHRR data over the oceans (for a period of four months),
together with cloud properties, optical thickness and effective
radii. The nonlinear relationship between aerosol number
concentration and cloud droplet concentration (

≈

)

obtained is consistent with Twomey’s hypothesis; however, the
parameter

is smaller than previous estimates (0.5 compared

with 0.7 to 0.8; Kaufman et al., 1991), but larger than the 0.26
value obtained by Martin et al. (1994). Using this relationship,
Nakajima et al. (2001) provided an estimate of the cloud albedo
RF in the range between –0.7 and –1.7 W m

–2

, with a global

average of –1.3 W m

–2

. Lohmann and Lesins (2002) used

POLDER data to estimate aerosol index and cloud droplet
radius; they then scaled the results of the simulations with the
European Centre Hamburg (ECHAM4) model. The results
show that changes in

lead to larger changes in

in the

model than in observations, particularly over land, leading to an
overestimate of the cloud albedo effect. The scaled values using

the constraint from POLDER yield a global cloud albedo RF
of –0.85 W m

–2

, an almost 40% reduction from their previous

estimate. Sekiguchi et al. (2003) presented results from the
analysis of AVHRR data over the oceans, and of POLDER
data over land and ocean. Assuming that the aerosol column
number concentration increased by 30% from the pre-industrial
era, they estimated the effect due to the aerosol in

uence on

clouds as the difference between the forcing under present and
pre-industrial conditions. They estimated a global effect due to
the total aerosol in

uence on clouds (sum of cloud albedo and

lifetime effects) to be between –0.6 and –1.2 W m

–2

, somewhat

lower than the Nakajima et al. (2001) ocean estimate. When the
assumption is made that the liquid water content is constant, the
cloud albedo RF estimated from AVHRR data is –0.64

0.16

W m

–2

and the estimate using POLDER data is –0.37 ± 0.09

W m

–2

. The results from these two studies are very sensitive

to the magnitude of the increase in the aerosol concentration
from pre-industrial to current conditions, and the spatial
distributions.

Quaas and Boucher (2005) used the POLDER and MODIS

data to evaluate the relationship between cloud properties and
aerosol concentrations on a global scale in order to incorporate
it in a GCM. They derived relationships corresponding to
marine stratiform clouds and convective clouds over land that
show a decreasing effective radius as the aerosol optical depth
increases. These retrievals involve a variety of assumptions that
introduce uncertainties in the relationships, in particular the
fact that the retrievals for aerosol and cloud properties are not
coincident and the assumption that the aerosol optical depth can
be linked to the sub-cloud aerosol concentration. When these
empirical parametrizations are included in a climate model,
the simulated RF due to the cloud albedo effect is reduced by
50% from their baseline simulation. Quaas et al. (2005) also
utilised satellite data to establish a relationship between cloud
droplet number concentration and

ne-mode aerosol optical

depth, minimising the dependence on cloud liquid water
content but including an adiabatic assumption that may not
be realistic in many cases. This relationship is implemented
in the ECHAM4 and Laboratoire de Météorologie Dynamique
Zoom (LMDZ) climate models and the results indicate that the
original parametrizations used in both models overestimated the
magnitude of the cloud albedo effect. Even though both models
show a consistent weakening of the RF, it should be noted that
the original estimates of their respective RFs are very different
(by almost a factor of two); the amount of the reduction was 37%
in LMDZ and 81% in ECHAM4. Note that the two models have
highly different spatial distributions of low clouds, simulated
aerosol concentrations and anthropogenic fractions.

When only sulphate aerosols were considered, Dufresne et

al. (2005) obtained a weaker cloud albedo RF. Their model used
a relationship between aerosol mass concentration and cloud
droplet number concentration, modi

ed from that originally

proposed by Boucher and Lohmann (1995) and adjusted to
POLDER data. Their simulations give a factor of two weaker
RF compared to the previous parametrization, but it is noted
that the results are highly sensitive to the distribution of clouds
over land.

179

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

nitrate, BC and organic compounds, which in turn affect
activation. Models also have weaknesses in representing
convection processes and aerosol distributions, and simulating
updraft velocities and convection-cloud interactions. Even
without considering the existing biases in the model-generated
clouds, differences in the aerosol chemical composition and the
subsequent treatment of activation lead to uncertainties that are
dif

cult to quantify and assess. The presence of organic carbon,

owing to its distinct hygroscopic and absorption properties,
can be particularly important for the cloud albedo effect in the
tropics (Ming

et al., 2007).

Modelling the cloud albedo effect from

rst principles has

proven dif

cult because the representation of aerosol-cloud and

convection-cloud interactions in climate models are still crude
(Lohmann and Feichter, 2005). Clouds often do not cover a
complete grid box and are inhomogeneous in terms of droplet
concentration, effective radii and LWP, which introduces added
complications in the microphysical and radiative transfer
calculations. Model intercomparisons (e.g., Lohmann

et al.,

2001; Menon

et al., 2003) suggest that the predicted cloud

distributions vary signi

cantly between models, particularly

their horizontal and vertical extents; also, the vertical resolution
and parametrization of convective and stratiform clouds are
quite different between models (Chen and Penner, 2005). Even
high-resolution models have dif

culty in accurately estimating

the amount of cloud liquid and ice water content in a grid box.

It has proven dif

cult to compare directly the results from

the different models, as uncertainties are not well identi

and quanti

ed. All models could be suffering from similar

biases, and modelling studies do not often quote the statistical
signi

cance of the RF estimates that are presented. Ming et

al. (2005b) demonstrated that it is only in the mid-latitude
NH that their model yields a RF result at the 95% con

dence

level when compared to the unforced model variability. There
are also large differences in the way that the different models
treat the appearance and evolution of aerosol particles and
the subsequent cloud droplet formation. Differences in the
horizontal and vertical resolution introduce uncertainties in
their ability to accurately represent the shallow warm cloud
layers over the oceans that are most susceptible to the changes
due to anthropogenic aerosol particles. A more fundamental
problem is that GCMs do not resolve the small scales (order
of hundreds of metres) at which aerosol-cloud interactions
occur. Chemical composition and size distribution spectrum
are also likely insuf

ciently understood at a microphysical

level, although some modelling studies suggest that the albedo
effect is more sensitive to the size than to aerosol composition
(Feingold, 2003; Ervens et al., 2005; Dusek et al., 2006).
Observations indicate that aerosol particles in nature tend to
be composed of several compounds and can be internally or
externally mixed. The actual conditions are dif

cult to simulate

and possibly lead to differences among climate models. The
calculation of the cloud albedo effect is sensitive to the details
of particle chemical composition (activation) and state of
the mixture (external or internal). The relationship between
ambient aerosol particle concentrations and resulting cloud

2.4.5.4

Uncertainties in Satellite Estimates

The improvements in the retrievals and satellite

instrumentation have provided valuable data to begin
observation-motivated assessments of the effect of aerosols
on cloud properties, even though satellite measurements
cannot unambiguously distinguish natural from anthropogenic
aerosols. Nevertheless, an obvious advantage of the satellite
data is their global coverage, and such extensive coverage can
be analysed to determine the relationships between aerosol and
cloud properties at a number of locations around the globe.
Using these data some studies (Sekiguchi et al., 2003; Quaas
et al., 2004) indicate that the magnitude of the RF is resolution
dependent, since the representation of convection and clouds
in the GCMs and the simulation of updraft velocity that affects
activation themselves are resolution dependent. The rather low
spatial and temporal resolution of some of the satellite data sets
can introduce biases by failing to distinguish aerosol species
with different properties. This, together with the absence of
coincident LWP measurements in several instances, handicaps
the inferences from such studies, and hinders an accurate
analysis and estimate of the RF. Furthermore, the ability to
separate meteorological from chemical in

uences in satellite

observations depends on the understanding of how clouds
respond to meteorological conditions.

Retrievals involve a variety of assumptions that introduce

uncertainties in the relationships. As mentioned above, the
retrievals for aerosol and cloud properties are not coincident
and the assumption is made that the aerosol optical depth can
be linked to the aerosol concentration below the cloud. The
POLDER instrument may underestimate the mean cloud-top
droplet radius due to uncertainties in the sampling of clouds
(Rosenfeld and Feingold, 2003). The retrieval of the aerosol
index over land may be less reliable and lead to an underestimate
of the cloud albedo effect over land. There is an indication
of a systematic bias between MODIS-derived cloud droplet
radius and that derived from POLDER (Breon and Doutriaux-
Boucher, 2005), as well as differences in the aerosol optical
depth retrieved from those instruments (Myhre et al., 2004a)
that need to be resolved.

2.4.5.5

Uncertainties Due to Model Biases

One of the large sources of uncertainties is the poor

knowledge of the amount and distribution of anthropogenic
aerosols used in the model simulations, particularly for pre-
industrial conditions. Some studies show a large sensitivity
in the RF to the ratio of pre-industrial to present-day aerosol
number concentrations.

All climate models discussed above include sulphate

particles; some models produce them from gaseous precursors
over oceans, where ambient concentrations are low, while some
models only condense mass onto pre-existing particles over
the continents. Some other climate models also include sea salt
and dust particles produced naturally, typically relating particle
production to wind speed. Some models include anthropogenic

180

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

droplet size distribution is important during the activation
process; this is a critical parametrization element in the climate
models. It is treated in different ways in different models,
ranging from simple empirical functions (Menon

et al., 2002a)

to more complex physical parametrizations that also tend to
be more computationally costly (Abdul-Razzak and Ghan,
2002; Nenes and Seinfeld, 2003; Ming

et al., 2006). Finally,

comparisons with observations have not yet risen to the same
degree of veri

cation as, for example, those for the direct RF

estimates; this is not merely due to model limitations, since the
observational basis also has not yet reached a sound footing.

Further uncertainties may be due to changes in the droplet

spectral shape, typically considered invariant in climate
models under clean and polluted conditions, but which can be
substantially different in typical atmospheric conditions (e.g.,
Feingold et al., 1997; Ackerman et al., 2000b; Erlick

et al.,

2001; Liu and Daum, 2002). Liu and Daum (2002) estimated
that a 15% increase in the width of the size distribution can
lead to a reduction of between 10 and 80% in the estimated RF
of the cloud albedo indirect effect. Peng and Lohmann (2003),
Rotstayn and Liu (2003) and Chen and Penner (2005) studied
the sensitivity of their estimates to this dispersion effect. These
studies con

rm that their estimates of the cloud albedo RF,

without taking the droplet spectra change into account, are
overestimated by about 15 to 35%.

The effects of aerosol particles on heterogeneous ice

formation are currently insuf

ciently understood and present

another level of challenge for both observations and modelling.
Ice crystal concentrations cannot be easily measured with present

in situ

instrumentation because of the dif

culty of detecting

small particles (Hirst et al., 2001) and frequent shattering of ice
particles on impact with the probes (Korolev and Isaac, 2005).
Current GCMs do not have suf

ciently rigorous microphysics

or sub-grid scale processes to accurately predict cirrus clouds
or super-cooled clouds explicitly. Ice particles in clouds are
often represented by simple shapes (e.g., spheres), even though
it is well known that few ice crystals are like that in reality.
The radiative properties of ice particles in GCMs often do
not effectively simulate the irregular shapes that are normally
found, nor do they simulate the inclusions of crustal material or
soot in the crystals.

2.4.5.6

Assessment of the Cloud Albedo Radiative Forcing

As in the TAR, only the aerosol interaction in the context

of liquid water clouds is assessed, with knowledge of the
interaction with ice clouds deemed insuf

cient. Since the TAR,

the cloud albedo effect has been estimated in a more systematic
way, and more modelling results are now available. Models now
are more advanced in capturing the complexity of the aerosol-
cloud interactions through forward computations. Even though
major uncertainties remain, clear progress has been made,
leading to a convergence of the estimates from the different
modelling efforts. Based on the results from all the modelling
studies shown in Figure 2.14, compared to the TAR it is now
possible to present a best estimate for the cloud albedo RF of

–0.7 W m

–2

as the median, with a 5 to 95% range of –0.3 to

–1.8 W m

–2

. The increase in the knowledge of the aerosol-cloud

interactions and the reduction in the spread of the cloud albedo
RF since the TAR result in an elevation of the level of scienti

understanding to low (Section 2.9, Table 2.11).

2.5 Anthropogenic Changes in

Surface Albedo and the Surface

Energy

Budget

2.5.1 Introduction

Anthropogenic changes to the physical properties of the

land surface can perturb the climate, both by exerting an RF
and by modifying other processes such as the

uxes of latent

and sensible heat and the transfer of momentum from the
atmosphere. In addition to contributing to changes in greenhouse
gas concentrations and aerosol loading, anthropogenic changes
in the large-scale character of the vegetation covering the
landscape (‘land cover’) can affect physical properties such
as surface albedo. The albedo of agricultural land can be very
different from that of a natural landscape, especially if the latter
is forest. The albedo of forested land is generally lower than that
of open land because the greater leaf area of a forest canopy and
multiple re

ections within the canopy result in a higher fraction

of incident radiation being absorbed. Changes in surface albedo
induce an RF by perturbing the shortwave radiation budget
(Ramaswamy

et al., 2001). The effect is particularly accentuated

when snow is present, because open land can become entirely
snow-covered and hence highly re

ective, while trees can

remain exposed above the snow (Betts, 2000). Even a snow-
covered canopy exhibits a relatively low albedo as a result of
multiple re

ections within the canopy (Harding and Pomeroy,

1996). Surface albedo change may therefore provide the
dominant in

uence of mid- and high-latitude land cover change

on climate (Betts, 2001; Bounoua

et al., 2002).

The TAR cited

two estimates of RF due to the change in albedo resulting from
anthropogenic land cover change relative to potential natural
vegetation (PNV), –0.4 W m

–2

and –0.2 W m

–2

, and assumed

that the RF relative to 1750 was half of that relative to PNV, so
gave a central estimate of the RF due to surface albedo change
of –0.2 W m

–2

± 0.2 W m

–2

Surface albedo can also be modi

ed by t

he settling of

anthropogenic aerosols on the ground, especially in the case of
BC on snow (Hansen and Nazarenko, 2004). This mechanism
may be considered an RF mechanism because diagnostic
calculations may be performed under the strict de

nition of RF

(see Sections 2.2 and 2.8). This mechanism was not discussed
in the TAR.

Land cover change can also affect other physical properties

such as surface emissivity, the

uxes of moisture through

evaporation and transpiration, the ratio of latent to sensible
heat

uxes (the Bowen ratio) and the aerodynamic roughness,

which exerts frictional drag on the atmosphere and also affects

182

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

2.5.2

Changes in Land Cover Since 1750

In 1750, 7.9 to 9.2 million km

(6 to 7% of the global land

surface) were under cultivation or pasture (Figure 2.15), mainly
in Europe, the Indo-Gangetic Plain and China (Ramankutty and
Foley, 1999; Klein Goldewijk, 2001). Over the next hundred
years, croplands and pasture expanded and intensi

ed in these

areas, and new agricultural areas emerged in North America.
The period 1850 to 1950 saw a more rapid rate of increase
in cropland and pasture areas. In the last 50 years, several
regions of the world have seen cropland areas stabilise, and
even decrease. In the USA, as cultivation shifted from the east
to the Midwest, croplands were abandoned along the eastern
seaboard around the turn of the century and the eastern forests
have regenerated over the last century. Similarly, cropland
areas have decreased in China and Europe. Overall, global
cropland and pasture expansion was slower after 1950 than
before. However, deforestation is occurring more rapidly in the
tropics. Latin America, Africa and South and Southeast Asia
experienced slow cropland expansion until the 20th century, but
have had exponential increases in the last 50 years. By 1990,
croplands and pasture covered 45.7 to 51.3 million km

(35% to

39% of global land), and forest cover had decreased by roughly
11 million km

(Ramankutty and Foley, 1999; Klein Goldewijk,

2001; Table 2.8).

Overall, until the mid-20th century most deforestation

occurred in the temperate regions (Figure 2.15). In more
recent decades, however, land abandonment in Western Europe
and North America has been leading to reforestation while
deforestation is now progressing rapidly in the tropics. In the
1990s compared to the 1980s, net removal of tropical forest
cover had slowed in the Americas but increased in Africa and
Asia.

2.5.3

Radiative Forcing by Anthropogenic Surface
Albedo Change: Land Use

Since the TAR, a number of estimates of the RF from land

use changes over the industrial era have been made (Table
2.8). Unlike the main TAR estimate, most of the more recent
studies are ‘pure’ RF calculations with the only change being
land cover; feedbacks such as changes in snow cover are
excluded. Brovkin et al. (2006) estimated the global mean RF
relative to 1700 to be –0.15 W m

–2

, considering only cropland

changes (Ramankutty and Foley, 1999) and not pastures.
Hansen et al. (2005) also considered only cropland changes
(Ramankutty and Foley, 1999) and simulated the RF relative
to 1750 to be –0.15 W m

–2

. Using historical reconstructions of

both croplands (Ramankutty and Foley, 1999) and pasturelands
(Klein Goldewijk, 2001), Betts et al. (2007) simulated an RF
of –0.18 W m

–2

since 1750. This study also estimated the RF

relative to PNV to be –0.24 W m

–2

. Other studies since the TAR

have also estimated the RF at the present day relative to PNV
(Table 2.8). Govindasamy et al. (2001a) estimated this RF as
–0.08 W m

–2

. Myhre et al. (2005a) used land cover and albedo

data from MODIS (Friedl

et al., 2002; Schaaf et al., 2002) and

estimated this RF as –0.09 W m

–2

. The results of Betts et al.

(2007) and Brovkin et al. (2006) suggest that the RF relative to
1750 is approximately 75% of that relative to PNV. Therefore,
by employing this factor published RFs relative to PNV can be
used to estimate the RF relative to 1750 (Table 2.8).

In all the published studies, the RF showed a very high

degree of spatial variability, with some areas showing no RF
in 1990 relative to 1750 while values more negative than –5
W m

–2

were typically found in the major agricultural areas

of North America and Eurasia.

The local RF depends on

local albedo changes, which depend on the nature of the PNV
replaced by agriculture (see top panel of Figure 2.15). In
historical simulations, the spatial patterns of RF relative to the
PNV remain generally similar over time, with the regional RFs
in 1750 intensifying and expanding in the area covered. The
major new areas of land cover change since 1750 are North
America and central and eastern Russia.

Changes in the underlying surface albedo could affect the

RF due to aerosols if such changes took place in the same
regions. Similarly, surface albedo RF may depend on aerosol
concentrations. Estimates of the temporal evolution of aerosol
RF and surface albedo RF may need to consider changes in
each other (Betts et al., 2007).

2.5.3.1 Uncertainties

Uncertainties in estimates of RF due to anthropogenic

surface albedo change arise from several factors.

2.5.3.1.1

Uncertainties in the mapping and characterisation
of present-day vegetation

The RF estimates reported in the TAR used atlas-based

data sets for present-day vegetation (Matthews, 1983; Wilson
and Henderson-Sellers, 1985). More recent data sets of land
cover have been obtained from satellite remote sensing. Data
from the AVHRR in 1992 to 1993 were used to generate two
global land cover data sets at 1 km resolution using different
methodologies (Hansen and Reed, 2000; Loveland

et al.,

2000) The International Geosphere-Biosphere Programme
Data and Information System (IGBP-DIS) data set is used as
the basis for global cropland maps (Ramankutty and Foley,
1999) and historical reconstructions of croplands, pasture and
other vegetation types (Ramankutty and Foley, 1999; Klein
Goldewijk, 2001) (Table 2.8). The MODIS (Friedl

et al., 2002)

and Global Land Cover 2000 (Bartholome and Belward, 2005)
provide other products. The two interpretations of the AVHRR
data agree on the classi

cation of vegetation as either tall (forest

and woody savannah) or short (all other land cover) over 84%
of the land surface (Hansen and Reed, 2000). However, some of
the key disagreements are in regions subject to anthropogenic
land cover change

so may be important for the estimation of

anthropogenic RF. Using the Hadley Centre Atmospheric
Model (HadAM3) GCM, Betts

et al. (2007) found that the

estimate of RF relative to PNV varied from –0.2 W m

–2

with the

Wilson and Henderson-Sellers (1985) atlas-based land use data
set to –0.24 W m

–2

with a version of the Wilson and Henderson-

183

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

able 2.8.

Estima

tes of forest area,

contribution to CO

increase from anthropogenic land cover change,

and RF due to the land use change-induced CO

increase and surface albedo change,

rela

tive to pre-industrial vegeta

tion

and PNV

. The CO

RFs are for 2000 rela

tive to 1850,

calcula

ted from the land use change contribution to the total increase in CO

from 1850 to 2000 simula

ted with both land use and fossil fuel emissions by the carbon c

le models.

Carbon emissions from land cover change for the 1980s and 1990s are discussed in Section 7.3 and

Table 7.2.

Notes:

The available literatur

e simulates CO

rises with and without land use r

elative to 1850.

1750 for

est ar

ea r

eported as 51.85

1992 for

est ar

ea.

Land use contribution CO

rise fr

om Br

ovkin et al. (2004).

Albedo RF fr

om Betts et al. (2007). Land cover data combined fr

om Ramankutty and Foley (1999), Klein

Goldelwijk (2001) and Wilson and Henderson-Sellers (1985).

Albedo RF fr

om Myhr

e and Myhr

e (2003). Range of estimate for each land cover data set arises fr

om use

of dif

fer

ent albedo values.

Albedo RF fr

om model of Goosse et al. (2005) in Br

ovkin et al. (2006).

RF r

elative to 1750 estimated her

e as 0.75 of RF r

elative to PNV following Betts et al. (2007) and Br

ovkin

et al. (2006).

Estimate r

elative to 1700.

Albedo RF fr

om Matthews et al. (2003).

Main Sour

ce of Land

Cover Data

For

est Ar

PNV

For

est Ar

cir

ca 1700

For

est Ar

cir

ca 1990

Contribution to CO

Incr

ease 1850–2000

(ppm)

(W m

–2

)

Albedo RF vs. PNV

(W m

–2

)

Albedo RF vs. 1750

(W m

–2

)

Ramankutty and Foley (1999)

55.27

52.77

43.97

0.27

–0.24

–0.29 to +0.02

–0.2

–0.18

–0.22 to +0.02

–0.14

g,i

–0.15 to –0.28

i,j

–0.15

–0.075 to –0.325

i,l

Klein Goldewijk (2001)

58.6

54.4

41.5

0.20

–0.66 to +0.1

–0.50 to +0.08

–0.275

i,l

Houghton (1983

, 2003)

62.15

50.53

0.57

0.44

MODIS (Schaaf et al., 2002)

–0.09

–0.07

Wilson and Henderson-Sellers

(1985)

–0.2

–0.29

–0.15

–0.22

SARB

–0.11 to –0.55

–0.08 to –0.41

Matthews (1983)

–0.12

–0.4

–0.08

–0.09

–0.3

–0.06

Albedo RF fr

om Hansen et al. (2005).

Albedo RF fr

om Matthews et al. (2004).

For

est ar

eas aggr

egated by Richar

ds (1990).

1980 for

est ar

ea.

Land use contribution to CO

rise fr

om Matthews et al. (2004). Estimate only available r

elative to 1850

not 1750.

Albedo RF fr

om Myhr

e et al. (2005a).

Albedo RF fr

om Betts (2001).

Surface and Atmospher

e Radiation Budget; http://www-surf.lar

c.nasa.gov/surf/.

Albedo RF fr

om Hansen et al. (1997).

Albedo RF fr

om Govindasamy et al. (2001a).

184

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Sellers (1985) data set adjusted to agree with the cropland data
of Ramankutty and Foley (1999). Myhre and Myhre (2003)
found the RF relative to PNV to vary from –0.66 W m

–2

to 0.29

W m

–2

according to whether the present-day land cover was

from Wilson and Henderson-Sellers (1985), Ramankutty and
Foley (1999) or other sources.

2.5.3.1.2

Uncertainties in the mapping and characterisation
of the reference historical state

Reconstructions of historical land use states require

information or assumptions regarding the nature and extent of
land under human use and the nature of the PNV. Ramankutty
and Foley (1999) reconstructed the fraction of land under crops
at 0.5° resolution from 1700 to 1990 (Figure 2.15, Table 2.8) by
combining the IGBP Global Land Cover Dataset with historical
inventory data, assuming that all areas of past vegetation occur
within areas of current vegetation. Klein Goldewijk (2001)
reconstructed all land cover types from 1700 to 1990 (Figure
2.15, Table 2.8), combining cropland and pasture inventory
data with historical population density maps and PNV. Klein
Goldewijk used a Boolean approach, which meant that crops,
for example, covered either 100% or 0% of a 0.5° grid box.
The total global cropland of Klein Goldewijk is generally 25%
less than that reconstructed by Ramankutty and Foley (1999)
throughout 1700 to 1990. At local scales, the disagreement is
greater due to the high spatial heterogeneity in both data sets.
Large-scale PNV (Figure 2.15) is reconstructed either with
models or by assuming that small-scale examples of currently
undisturbed vegetation are representative of the PNV at the
large scale. Matthews et al. (2004) simulated RF relative to
1700 as –0.20 W m

–2

and –0.28 W m

–2

with the above land use

reconstructions.

2.5.3.1.3

Uncertainties in the parametrizations of the surface
radiation processes

The albedo for a given land surface or vegetation type

may either be prescribed or simulated on the basis of more
fundamental characteristics such as vegetation leaf area.
But either way, model parameters are set on the basis of
observational data that may come from a number of con

icting

sources. Both the AVHRR and MODIS (Schaaf et al., 2002;
Gao et al., 2005) instruments have been used to quantify surface
albedo for the IGBP vegetation classes in different regions and
different seasons, and in some cases the albedo for a given
vegetation type derived from one source can be twice that
derived from the other (e.g., Strugnell

et al., 2001; Myhre

et al.,

2005a). Myhre and Myhre (2003) examined the implications of
varying the albedo of different vegetation types either together
or separately, and found the RF relative to PNV to vary from –
0.65 W m

–2

to +0.47 W m

–2

; however, the positive RFs occurred

in only a few cases and resulted from large reductions in surface
albedo in semi-arid regions on conversion to pasture, so were
considered unrealistic by the study’s authors. The single most
important factor for the uncertainty in the study by Myhre and
Myhre (2003) was found to be the surface albedo for cropland.
In simulations where only the cropland surface albedo was

varied between 0.15, 0.18 and 0.20, the resulting RFs relative to
PNV were –0.06, –0.20 and –0.29 W m

–2

, respectively. Similar

results were found by Matthews

et al.

(2003) considering only

cropland changes and not pasture; with cropland surface albedos
of 0.17 and 0.20, RFs relative to 1700 were –0.15 and –0.28
W m

–2

, respectively.

2.5.3.1.3

Uncertainties in other parts of the model

When climate models are used to estimate the RF,

uncertainties in other parts of the model also affect the
estimates. In particular, the simulation of snow cover affects
the extent to which land cover changes affect surface albedo.
Betts (2000) estimated that the systematic biases in snow cover
in HadAM3 introduced errors of up to approximately 10% in
the simulation of local RF due to conversion between forest
and open land. Such uncertainties could be reduced by the use
of an observational snow climatology in a model that just treats
the radiative transfer (Myhre and Myhre, 2003). The simulation
of cloud cover affects the extent to which the simulated surface
albedo changes affect planetary albedo – too much cloud cover
could diminish the contribution of surface albedo changes to
the planetary albedo change.

On the basis of the studies assessed here, including a number

of new estimates since the TAR, the assessment is that the best
estimate of RF relative to 1750 due to land-use related surface
albedo change should remain at –0.2 ± 0.2 W m

–2

. In the light

of the additional modelling studies, the exclusion of feedbacks,
the improved incorporation of large-scale observations and the
explicit consideration of land use reconstructions for 1750,
the level of scienti

c understanding is raised to medium-low,

compared to low in the TAR (Section 2.9, Table 2.11).

2.5.4

Radiative Forcing by Anthropogenic

Surface Albedo Change: Black Carbon in

Snow and Ice

The presence of soot particles in snow could cause a decrease

in the albedo of snow and affect snowmelt. Initial estimates by
Hansen et al. (2000) suggested that BC could thereby exert a
positive RF of +0.2 W m

–2

. This estimate was re

ned by Hansen

and Nazarenko (2004), who used measured BC concentrations
within snow and ice at a wide range of geographic locations
to deduce the perturbation to the surface and planetary albedo,
deriving an RF of +0.15 W m

–2

. The uncertainty in this estimate

is substantial due to uncertainties in whether BC and snow
particles are internally or externally mixed, in BC and snow
particle shapes and sizes, in voids within BC particles, and in
the BC imaginary refractive index. Jacobson (2004) developed
a global model that allows the BC aerosol to enter snow via
precipitation and dry deposition, thereby modifying the snow
albedo and emissivity. They found modelled concentrations
of BC within snow that were in reasonable agreement with
those from many observations. The model study found that BC
on snow and sea ice caused a decrease in the surface albedo
of 0.4% globally and 1% in the NH, although RFs were not
reported. Hansen et al. (2005) allowed the albedo change to be

185

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

proportional to local BC deposition according to Koch (2001)
and presented a further revised estimate of 0.08 W m

–2

. They

also suggested that this RF mechanism produces a greater
temperature response by a factor of 1.7 than an equivalent CO

RF, that is, the ‘ef

cacy’ may be higher for this RF mechanism

(see Section 2.8.5.7). This report adopts a best estimate for the
BC on snow RF of +0.10 ± 0.10 W m

–2

, with a low level of

scienti

c understanding (Section 2.9, Table 2.11).

2.5.5

Other Effects of Anthropogenic Changes

in Land Cover

Anthropogenic land use and land cover change can also

modify climate through other mechanisms, some directly
perturbing the Earth radiation budget and some perturbing other
processes. Impacts of land cover change on emissions of CO

, biomass burning aerosols and dust aerosols are discussed

in Sections 2.3 and 2.4. Land cover change itself can also modify
the surface energy and moisture budgets through changes in
evaporation and the

uxes of latent and sensible heat, directly

affecting precipitation and atmospheric circulation as well as
temperature. Model results suggest that the combined effects of
past tropical deforestation may have exerted regional warmings
of approximately 0.2°C relative to PNV, and may have perturbed
the global atmospheric circulation affecting regional climates
remote from the land cover change (Chase

et al., 2000; Zhao

al., 2001; Pielke et al., 2002; Chapters 7, 9 and 11).

Since the dominant aspect of land cover change since 1750

has been deforestation in temperate regions, the overall effect
of anthropogenic land cover change on global temperature will
depend largely on the relative importance of increased surface
albedo in winter and spring (exerting a cooling) and reduced
evaporation in summer and in the tropics (exerting a warming)
(Bounoua

et al., 2002). Estimates of global temperature

responses from past deforestation vary from 0.01°C (Zhao

al., 2001) to –0.25°C (Govindasamy

et al., 2001a; Brovkin

al., 2006). If cooling by increased surface albedo dominates,
then the historical effect of land cover change may still be
adequately represented by RF. With tropical deforestation
becoming more signi

cant in recent decades, warming due to

reduced evaporation may become more signi

cant globally

than increased surface albedo. Radiative forcing would then be
less useful as a metric of climate change induced by land cover
change recently and in the future.

2.5.6

Tropospheric Water Vapour from

Anthropogenic Sources

Anthropogenic use of water is less than 1% of natural sources

of water vapour and about 70% of the use of water for human
activity is from irrigation (Döll, 2002). Several regional studies
have indicated an impact of irrigation on temperature, humidity
and precipitation (Barnston and Schickedanz, 1984; Lohar and
Pal, 1995; de Ridder and Gallée, 1998; Moore and Rojstaczer,
2001; Zhang

et al., 2002). Boucher et al. (2004) used a GCM

to show that irrigation has a global impact on temperature and

humidity. Over Asia where most of the irrigation takes place,
the simulations showed a change in the water vapour content in
the lower troposphere of up to 1%, resulting in an RF of +0.03
W m

–2

. However, the effect of irrigation on surface temperature

was dominated by evaporative cooling rather than by the excess
greenhouse effect and thus a decrease in surface temperature was
found. Irrigation affects the temperature, humidity, clouds and
precipitation as well as the natural evaporation through changes
in the surface temperature, raising questions about the strict use
of RF in this case. Uncertainties in the water vapour

ow to

the atmosphere from irrigation are signi

cant and Gordon et al.

(2005) gave a substantially higher estimate compared to that of
Boucher et al.

(2004). Most of this uncertainty is likely to be

linked to differences between the total withdrawal for irrigation
and the amount actually used (Boucher et al., 2004). Furthermore,
Gordon et al. (2005) also estimated a reduced water vapour

ow to the atmosphere from deforestation, most importantly

in tropical areas. This reduced water vapour

ow is a factor of

three larger than the water vapour increase due to irrigation in
Boucher et al.

(2004), but so far there are no estimates of the

effect of this on the water vapour content of the atmosphere
and its RF. Water vapour changes from deforestation will,
like irrigation, affect the surface evaporation and temperature
and the water cycle in the atmosphere. Radiative forcing from
anthropogenic sources of tropospheric water vapour is not
evaluated here, since these sources affect surface temperature
more signi

cantly through these non-radiative processes, and

a strict use of the RF is problematic. The emission of water
vapour from fossil fuel combustion is signi

cantly lower than

the emission from changes in land use (Boucher et al., 2004).

2.5.7

Anthropogenic Heat Release

Urban heat islands result partly from the physical properties

of the urban landscape and partly from the release of heat into
the environment by the use of energy for human activities such
as heating buildings and powering appliances and vehicles
(‘human energy production’). The global total heat

ux from

this is estimated as 0.03 W m

–2

(Nakicenovic, 1998). If this

energy release were concentrated in cities, which are estimated
to cover 0.046% of the Earth’s surface (Loveland

et al., 2000)

the mean local heat

ux in a city would be 65 W m

–2

. Daytime

values in central Tokyo typically exceed 400 W m

–2

with a

maximum of 1,590 W m

–2

in winter (Ichinose

et al., 1999).

Although human energy production is a small in

uence at the

global scale, it may be very important for climate changes in
cities (Betts and Best, 2004; Crutzen, 2004).

2.5.8

Effects of Carbon Dioxide Changes on
Climate via Plant Physiology: ‘Physiological
Forcing’

As well as exerting an RF on the climate system, increasing

concentrations of atmospheric CO

can perturb the climate

system through direct effects on plant physiology. Plant stomatal
apertures open less under higher CO

concentrations (Field

186

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

al., 1995), which directly reduces the

ux of moisture from the

surface to the atmosphere through transpiration (Sellers

et al.,

1996). A decrease in moisture

ux modi

es the surface energy

balance, increasing the ratio of sensible heat

ux to latent heat

ux and therefore warming the air near the surface (Sellers

al., 1996; Betts

et al., 1997; Cox et al., 1999). Betts et al. (2004)

proposed the term ‘physiological forcing’ for this mechanism.
Although no studies have yet explicitly quanti

ed the present-

day temperature response to physiological forcing, the presence
of this forcing has been detected in global hydrological budgets
(Gedney et al., 2006; Section 9.5). This process can be considered
a non-initial radiative effect, as distinct from a feedback, since the
mechanism involves a direct response to increasing atmospheric
CO

and not a response to climate change. It is not possible

to quantify this with RF. Reduced global transpiration would
also be expected to reduce atmospheric water vapour causing a
negative forcing, but no estimates of this have been made.

Increased CO

concentrations can also ‘fertilize’ plants

by stimulating photosynthesis, which models suggest has
contributed to increased vegetation cover and leaf area over the
20th century (Cramer et al., 2001). Increases in the Normalized
Difference Vegetation Index, a remote sensing product
indicative of leaf area, biomass and potential photosynthesis,
have been observed (Zhou et al., 2001), although other
causes including climate change itself are also likely to have
contributed. Increased vegetation cover and leaf area would
decrease surface albedo, which would act to oppose the increase
in albedo due to deforestation. The RF due to this process has not
been evaluated and there is a

very low scienti

c understanding

of these effects.

2.6 Contrails and Aircraft-Induced

Cloudiness

2.6.1 Introduction

The IPCC separately evaluated the RF from subsonic and

supersonic aircraft operations in the Special Report on Aviation
and the Global Atmosphere (IPCC, 1999), hereinafter designated
as IPCC-1999. Like many other sectors, subsonic aircraft
operations around the globe contribute directly and indirectly to
the RF of climate change. This section only assesses the aspects
that are unique to the aviation sector, namely the formation of
persistent condensation trails (contrails), their impact on cirrus
cloudiness, and the effects of aviation aerosols. Persistent
contrail formation and induced cloudiness are indirect effects
from aircraft operations because they depend on variable
humidity and temperature conditions along aircraft

ight tracks.

Thus, future changes in atmospheric humidity and temperature
distributions in the upper troposphere will have consequences
for aviation-induced cloudiness. Also noted here is the potential
role of aviation aerosols in altering the properties of clouds that
form later in air containing aircraft emissions.

Table 2.9.

Radiative forcing terms for contrail and cirrus effects caused by global

subsonic aircraft operations.

Notes:

Values for contrails are best estimates. Values in parentheses give the

uncertainty range.

Values from IPCC-1999 (IPCC, 1999).

Values interpolated from 1992 and 2015 estimates in IPCC-1999 (Sausen et

al., 2005).

Sausen et al. (2005). Values are considered valid (within 10%) for 2005

because of slow growth in aviation fuel use between 2000 and 2005.

Radiative forcing (W m

–2

)

1992 IPCC

2000 IPCC

2000

0.018

0.025

Persistent linear
contrails

0.020

0.034

0.010

(0.006 to 0.015)

Aviation-induced
cloudiness without
persistent contrails

0 to 0.040

n.a.

Aviation-induced
cloudiness with
persistent contrails

0.030

(0.010 to 0.080)

2.6.2

Radiative Forcing Estimates for Persistent
Line-Shaped Contrails

Aircraft produce persistent contrails in the upper troposphere

in ice-supersaturated air masses (IPCC, 1999). Contrails
are thin cirrus clouds, which re

ect solar radiation and trap

outgoing longwave radiation. The latter effect is expected to
dominate for thin cirrus (Hartmann

et al., 1992; Meerkötter

et al., 1999), thereby resulting in a net positive RF value for
contrails. Persistent contrail cover has been calculated globally
from meteorological data (e.g., Sausen

et al., 1998) or by using

a modi

ed cirrus cloud parametrization in a GCM (Ponater

al., 2002). Contrail cover calculations are uncertain because the
extent of supersaturated regions in the atmosphere is poorly
known. The associated contrail RF follows from determining
an optical depth for the computed contrail cover. The global RF
values for contrail and induced cloudiness are assumed to vary
linearly with distances

own by the global

eet if

ight ambient

conditions remain unchanged. The current best estimate for the
RF of persistent linear contrails for aircraft operations in 2000
is +0.010 W m

–2

(Table 2.9; Sausen et al.,

2005). The value

is based on independent estimates derived from Myhre and
Stordal (2001b) and Marquart

et al. (2003) that were updated

for increased aircraft traf

c in Sausen et al. (2005) to give RF

estimates of +0.015 W m

–2

and +0.006 W m

–2

, respectively.

The uncertainty range is conservatively estimated to be a factor
of three. The +0.010 W m

–2

value is also considered to be the

best estimate for 2005 because of the slow overall growth in
aviation fuel use in the 2000 to 2005 period. The decrease in
the best estimate from the TAR by a factor of two results from
reassessments of persistent contrail cover and lower optical
depth estimates (Marquart and Mayer, 2002; Meyer

et al., 2002;

Ponater

et al., 2002; Marquart

et al., 2003). The new estimates

187

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

W m

–2

derived from surface and satellite cloudiness observations

(Minnis

et al., 2004). A value of +0.03 W m

–2

is close to the

upper-limit estimate of +0.04 W m

–2

derived for non-contrail

cloudiness in IPCC-1999. Without an AIC best estimate, the best
estimate of the total RF value for aviation-induced cloudiness
(Section 2.9.2, Table 2.12 and Figure 2.20) includes only that
due to persistent linear contrails. Radiative forcing estimates
for AIC made using cirrus trend data necessarily cannot
distinguish between the components of aviation cloudiness,
namely persistent linear contrails, spreading contrails and other
aviation aerosol effects. Some aviation effects might be more
appropriately considered feedback processes rather than an RF
(see Sections 2.2 and 2.4.5). However, the low understanding of
the processes involved and the lack of quantitative approaches
preclude reliably making the forcing/feedback distinction for
all aviation effects in this assessment.

Two issues related to the climate response of aviation

cloudiness are worth noting here. First, Minnis et al. (2004,
2005) used their RF estimate for total AIC over the USA in an
empirical model, and concluded that the surface temperature
response for the period 1973 to 1994 could be as large as the
observed surface warming over the USA (around 0.3°C per
decade). In response to the Minnis et al.

conclusion, contrail RF

was examined in two global climate modelling studies (Hansen

et al., 2005; Ponater

et al., 2005). Both studies concluded that

the surface temperature response calculated by Minnis et al.
(2004) is too large by one to two orders of magnitude. For the
Minnis et al. result to be correct, the climate ef

cacy or climate

sensitivity of contrail RF would need to be much greater than
that of other larger RF terms, (e.g., CO

). Instead, contrail RF

is found to have a smaller ef

cacy than an equivalent CO

(Hansen et al., 2005; Ponater et al., 2005) (see Section 2.8.5.7),
which is consistent with the general ineffectiveness of high
clouds in in

uencing diurnal surface temperatures (Hansen

et al., 1995, 2005). Several substantive explanations for the
incorrectness of the enhanced response found in the Minnis et
al. study have been presented (Hansen et al., 2005; Ponater et
al., 2005; Shine, 2005).

The second issue is that the absence of AIC has been

proposed as the cause of the increased diurnal temperature
range (DTR) found in surface observations made during the
short period when all USA air traf

c was grounded starting on

11 September 2001 (Travis

et al., 2002, 2004). The Travis et

al. studies show that during this period: (i) DTR was enhanced
across the conterminous USA, with increases in the maximum
temperatures that were not matched by increases of similar
magnitude in the minimum temperatures, and (ii) the largest
DTR changes corresponded to regions with the greatest contrail
cover. The Travis et al. conclusions are weak because they are
based on a correlation rather than a quantitative model and rely
(necessarily) on very limited data (Schumann, 2005). Unusually
clear weather across the USA during the shutdown period also
has been proposed to account for the observed DTR changes
(Kalkstein and Balling, 2004). Thus, more evidence and a

include diurnal changes in the solar RF, which decreases the net
RF for a given contrail cover by about 20% (Myhre and Stordal,
2001b). The level of scienti

c understanding of contrail RF is

considered low, since important uncertainties remain in the
determination of global values (Section 2.9, Table 2.11). For
example, unexplained regional differences are found in contrail
optical depths between Europe and the USA that have not been
fully accounted for in model calculations (Meyer et al., 2002;
Ponater et al., 2002; Palikonda et al., 2005).

2.6.3

Radiative Forcing Estimates for Aviation-

Induced

Cloudiness

Individual persistent contrails are routinely observed to

shear and spread, covering large additional areas with cirrus
cloud (Minnis

et al., 1998). Aviation aerosol could also lead to

changes in cirrus cloud (see Section 2.6.4). Aviation-induced
cloudiness (AIC) is de

ned to be the sum of all changes in

cloudiness associated with aviation operations. Thus, an AIC
estimate includes persistent contrail cover. Because spreading
contrails lose their characteristic linear shape, a component of
AIC is indistinguishable from background cirrus. This basic
ambiguity, which prevented the formulation of a best estimate
of AIC amounts and the associated RF in IPCC-1999, still
exists for this assessment. Estimates of the ratio of induced
cloudiness cover to that of persistent linear contrails range
from 1.8 to 10 (Minnis

et al., 2004; Mannstein and Schumann,

2005

), indicating the uncertainty in estimating AIC amounts.

Initial attempts to quantify AIC used trend differences in cirrus
cloudiness between regions of high and low aviation fuel
consumption (Boucher, 1999). Since IPCC-1999, two studies
have also found signi

cant positive trends in cirrus cloudiness

in some regions of high air traf

c and found lower to negative

trends outside air traf

c regions (Zerefos

et al., 2003; Stordal

al., 2005). Using the International Satellite Cloud Climatology
Project (ISCCP) database, these studies derived cirrus cover
trends for Europe of 1 to 2% per decade over the last one to
two decades. A study with the Television Infrared Observation
Satellite (TIROS) Operational Vertical Sounder (TOVS)
provides further support for these trends (Stubenrauch and
Schumann, 2005). However, cirrus trends that occurred due
to natural variability, climate change or other anthropogenic
effects could not be accounted for in these studies. Cirrus trends
over the USA (but not over Europe) were found to be consistent
with changes in contrail cover and frequency (Minnis et al.,
2004). Thus, signi

cant uncertainty remains in attributing

observed cirrus trends to aviation.

Regional cirrus trends were used as a basis to compute a

global mean RF value for AIC in 2000 of +0.030 W m

–2

with a

range of +0.01 to +0.08 W m

–2

(Stordal

et al., 2005). This value

is not considered a best estimate because of the uncertainty in
the optical properties of AIC and in the assumptions used to
derive AIC cover. However, this value is in good agreement
with the upper limit estimate for AIC RF in 1992 of +0.026

A corrigendum to this paper has been submitted for publication by these authors but has not been assessed here.

188

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

quantitative physical model are needed before the validity of
the proposed relationship between regional contrail cover and
DTR can be considered further.

2.6.4 Aviation

Aerosols

Global aviation operations emit aerosols and aerosol

precursors into the upper troposphere and lower stratosphere
(IPCC, 1999; Hendricks

et al., 2004). As a result, aerosol

number and/or mass are enhanced above background values in
these regions. Aviation-induced cloudiness includes the possible
in

uence of aviation aerosol on cirrus cloudiness amounts. The

most important aerosols are those composed of sulphate and
BC (soot). Sulphate aerosols arise from the emissions of fuel
sulphur and BC aerosol results from incomplete combustion
of aviation fuel. Aviation operations cause enhancements of
sulphate and BC in the background atmosphere (IPCC, 1999;
Hendricks

et al., 2004). An important concern is that aviation

aerosol can act as nuclei in ice cloud formation, thereby altering
the microphysical properties of clouds (Jensen and Toon, 1997;
Kärcher, 1999; Lohmann

et al., 2004) and perhaps cloud cover.

A modelling study by Hendricks et al. (2005) showed the
potential for signi

cant cirrus modi

cations by aviation caused

by increased numbers of BC particles. The modi

cations would

occur in

ight corridors as well as in regions far away from

ight

corridors because of aerosol transport. In the study, aviation
aerosols either increase or decrease ice nuclei in background
cirrus clouds, depending on assumptions about the cloud
formation process. Results from a cloud chamber experiment
showed that a sulphate coating on soot particles reduced their
effectiveness as ice nuclei (Möhler et al., 2005). Changes in
ice nuclei number or nucleation properties of aerosols can
alter the radiative properties of cirrus clouds and, hence, their
radiative impact on the climate system, similar to the aerosol-
cloud interactions discussed in Sections 2.4.1, 2.4.5 and 7.5.
No estimates are yet available for the global or regional RF
changes caused by the effect of aviation aerosol on background
cloudiness, although some of the RF from AIC, determined by
correlation studies (see Section 2.6.3), may be associated with
these aerosol effects.

2.7 Natural

Forcings

2.7.1 Solar

Variability

The estimates of long-term solar irradiance changes used

in the TAR (e.g., Hoyt and Schatten, 1993; Lean et al., 1995)
have been revised downwards, based on new studies indicating
that bright solar faculae likely contributed a smaller irradiance
increase since the Maunder Minimum than was originally
suggested by the range of brightness in Sun-like stars (Hall and
Lockwood, 2004; M. Wang et al., 2005). However, empirical
results since the TAR have strengthened the evidence for solar
forcing of climate change by identifying detectable tropospheric

changes associated with solar variability, including during the
solar cycle (Section 9.2; van Loon and Shea, 2000; Douglass and
Clader, 2002; Gleisner and Thejll, 2003; Haigh, 2003; Stott et
al., 2003; White

et al., 2003; Coughlin and Tung, 2004; Labitzke,

2004; Crooks and Gray, 2005). The most likely mechanism is
considered to be some combination of direct forcing by changes
in total solar irradiance, and indirect effects of ultraviolet (UV)
radiation on the stratosphere. Least certain, and under ongoing
debate as discussed in the TAR, are indirect effects induced
by galactic cosmic rays (e.g., Marsh and Svensmark, 2000a,b;
Kristjánsson et al., 2002; Sun and Bradley, 2002).

2.7.1.1

Direct Observations of Solar Irradiance

2.7.1.1.1

Satellite measurements of total solar irradiance

Four independent space-based instruments directly measure

total solar irradiance at present, contributing to a database
extant since November 1978 (Fröhlich and Lean, 2004). The
Variability of Irradiance and Gravity Oscillations (VIRGO)
experiment on the Solar Heliospheric Observatory (SOHO)
has been operating since 1996, the ACRIM III on the Active
Cavity Radiometer Irradiance Monitor Satellite (ACRIMSAT)
since 1999 and the Earth Radiation Budget Satellite (ERBS)
(intermittently) since 1984. Most recent are the measurements
made by the Total Solar Irradiance Monitor (TIM) on the
Solar Radiation and Climate Experiment (SORCE) since 2003
(Rottman, 2005).

2.7.1.1.2

Observed decadal trends and variability

Different composite records of total solar irradiance have

been constructed from different combinations of the direct
radiometric measurements. The Physikalisch-Meteorologisches
Observatorium Davos (PMOD) composite (Fröhlich and Lean,
2004), shown in Figure 2.16, combines the observations by
the ACRIM I on the Solar Maximum Mission (SMM), the
Hickey-Friedan radiometer on Nimbus 7, ACRIM II on the
Upper Atmosphere Research Satellite (UARS) and VIRGO on
SOHO by analysing the sensitivity drifts in each radiometer
prior to determining radiometric offsets. In contrast, the
ACRIM composite (Willson and Mordvinov, 2003), also
shown in Figure 2.16, utilises ACRIMSAT rather than VIRGO
observations in recent times and cross calibrates the reported
data assuming that radiometric sensitivity drifts have already
been fully accounted for. A third composite, the Space Absolute
Radiometric Reference (SARR) composite, uses individual
absolute irradiance measurements from the shuttle to cross
calibrate satellite records (Dewitte

et al., 2005). The gross

temporal features of the composite irradiance records are very
similar, each showing day-to-week variations associated with
the Sun’s rotation on its axis, and decadal

uctuations arising

from the 11-year solar activity cycle. But the linear slopes differ
among the three different composite records, as do levels at
solar activity minima (1986 and 1996). These differences are
the result of different cross calibrations and drift adjustments
applied to individual radiometric sensitivities when constructing
the composites (Fröhlich and Lean, 2004).

189

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

Figure 2.16.

Percentage change in monthly values of the total solar irradiance

composites of Willson and Mordvinov (2003; WM2003, violet symbols and line) and
Fröhlich and Lean (2004; FL2004, green solid line).

Solar irradiance levels are comparable in the two most recent

cycle minima when absolute uncertainties and sensitivity drifts
in the measurements are assessed (Fröhlich and Lean, 2004 and
references therein). The increase in excess of 0.04% over the
27-year period of the ACRIM irradiance composite (Willson
and Mordvinov, 2003), although incompletely understood,
is thought to be more of instrumental rather than solar origin
(Fröhlich and Lean, 2004). The irradiance increase in the
ACRIM composite is indicative of an episodic increase between
1989 and 1992 that is present in the Nimbus 7 data (Lee et al.,
1995; Chapman et al., 1996). Independent, overlapping ERBS
observations do not show this increase; nor do they suggest
a signi

cant secular trend (Lee et al., 1995). Such a trend is

not present in the PMOD composite, in which total irradiance
between successive solar minima is nearly constant, to better
than 0.01% (Fröhlich and Lean, 2004). Although a long-term
trend of order 0.01% is present in the SARR composite between
successive solar activity minima (in 1986 and 1996), it is not
statistically signi

cant because the estimated uncertainty is

±0.026% (Dewitte et al., 2005).

Current understanding of solar activity and the known sources

of irradiance variability suggests comparable irradiance levels
during the past two solar minima. The primary known cause
of contemporary irradiance variability is the presence on the
Sun’s disk of sunspots (compact, dark features where radiation
is locally depleted) and faculae (extended bright features where
radiation is locally enhanced). Models that combine records of
the global sunspot darkening calculated directly from white light
images and the magnesium (Mg) irradiance index as a proxy
for the facular signal do not exhibit a signi

cant secular trend

during activity minima (Fröhlich and Lean, 2004; Preminger and
Walton, 2005). Nor do the modern instrumental measurements
of galactic cosmic rays, 10.7 cm

ux and the

geomagnetic

index since the 1950s (Benestad, 2005) indicate this feature.
While changes in surface emissivity by magnetic sunspot and
facular regions are, from a theoretical view, the most effective
in altering irradiance (Spruit, 2000), other mechanisms have
also been proposed that may cause additional, possibly secular,
irradiance changes. Of these, changes in solar diameter have
been considered a likely candidate (e.g., So

a and Li, 2001). But

recent analysis of solar imagery, primarily from the Michelson

Doppler Imager (MDI) instrument on SOHO, indicates that
solar diameter changes are no more than a few kilometres per
year during the solar cycle (Dziembowski

et al., 2001), for

which associated irradiance changes are 0.001%, two orders of
magnitude less than the measured solar irradiance cycle.

2.7.1.1.3

Measurements of solar spectral irradiance

The solar UV spectrum from 120 to 400 nm continues to

be monitored from space, with SORCE observations extending
those made since 1991 by two instruments on the UARS (Woods

et al., 1996). SORCE also monitors, for the

rst time from

space, solar spectral irradiance in the visible and near-infrared
spectrum, providing unprecedented spectral coverage that
affords a detailed characterisation of solar spectral irradiance
variability. Initial results (Harder

et al., 2005; Lean

et al., 2005)

indicate that, as expected, variations occur at all wavelengths,
primarily in response to changes in sunspots and faculae.
Ultraviolet spectral irradiance variability in the extended
database is consistent with that seen in the UARS observations
since 1991, as described in the TAR.

Radiation in the visible and infrared spectrum has a notably

different temporal character than the spectrum below 300 nm.
Maximum energy changes occur at wavelengths from 400 to
500 nm. Fractional changes are greatest at UV wavelengths
but the actual energy change is considerably smaller than in
the visible spectrum. Over the time scale of the 11-year solar
cycle, bolometric facular brightness exceeds sunspot blocking
by about a factor of two, and there is an increase in spectral
irradiance at most, if not all, wavelengths from the minimum
to the maximum of the solar cycle. Estimated solar cycle
changes are 0.08% in the total solar irradiance. Broken down by
wavelength range these irradiance changes are 1.3% at 200 to
300 nm, 0.2% at 315 to 400 nm, 0.08% at 400 to 700 nm, 0.04%
at 700 to 1,000 nm and 0.025% at 1,000 to 1,600 nm.

However, during episodes of strong solar activity, sunspot

blocking can dominate facular brightening, causing decreased
irradiance at most wavelengths. Spectral irradiance changes
on these shorter time scales now being measured by SORCE
provide tests of the wavelength-dependent sunspot and facular
parametrizations in solar irradiance variability models. The
modelled spectral irradiance changes are in good overall
agreement with initial SORCE observations but as yet the
SORCE observations are too short to provide de

nitive

information about the amplitude of solar spectral irradiance
changes during the solar cycle.

2.7.1.2

Estimating Past Solar Radiative Forcing

2.7.1.2.1

Reconstructions of past variations in solar
irradiance

Long-term solar irradiance changes over the past 400 years

may be less by a factor of two to four than in the reconstructions
employed by the TAR for climate change simulations. Irradiance
reconstructions such as those of Hoyt and Schatten (1993),
Lean et al. (1995), Lean (2000), Lockwood and Stamper (1999)
and Solanki and Fligge (1999), used in the TAR, assumed the

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Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

which variations arise, in part, from heliospheric modulation.
This gives con

dence that the approach is plausible. A small

accumulation of total

ux (and possibly ephemeral regions)

produces a net increase in facular brightness, which, in
combination with sunspot blocking, permits the reconstruction
of total solar irradiance shown in Figure 2.17. There is a 0.04%
increase from the Maunder Minimum to present-day cycle
minima.

Prior to direct telescopic measurements of sunspots, which

commenced around 1610, knowledge of solar activity is inferred
indirectly from the

C and

Be cosmogenic isotope records

in tree rings and ice cores, respectively, which exhibit solar-
related cycles near 90, 200 and 2,300 years. Some studies of
cosmogenic isotopes (Jirikowic and Damon, 1994) and spectral
analysis of the sunspot record (Rigozo

et al., 2001) suggest that

solar activity during the 12th-century Medieval Solar Maximum
was comparable to the present Modern Solar Maximum. Recent
work attempts to account for the chain of physical processes in
which solar magnetic

elds modulate the heliosphere, in turn

altering the penetration of the galactic cosmic rays, the

ux of

which produces the cosmogenic isotopes that are subsequently
deposited in the terrestrial system following additional transport
and chemical processes. An initial effort reported exceptionally
high levels of solar activity in the past 70 years, relative to the
preceding 8,000 years (Solanki

et al., 2004). In contrast, when

differences among isotopes records are taken into account
and the

C record corrected for fossil fuel burning, current

levels of solar activity are found to be historically high, but not
exceptionally so (Muscheler

et al., 2007).

existence of a long-term variability component in addition to
the known 11-year cycle, in which the 17th-century Maunder
Minimum total irradiance was reduced in the range of 0.15% to
0.3% below contemporary solar minima. The temporal structure
of this long-term component, typically associated with facular
evolution, was assumed to track either the smoothed amplitude
of the solar activity cycle or the cycle length. The motivation
for adopting a long-term irradiance component was three-fold.
Firstly, the range of variability in Sun-like stars (Baliunas and
Jastrow, 1990), secondly, the long-term trend in geomagnetic
activity, and thirdly, solar modulation of cosmogenic isotopes,
all suggested that the Sun is capable of a broader range of
activity than witnessed during recent solar cycles (i.e., the
observational record in Figure 2.16). Various estimates of the
increase in total solar irradiance from the 17th-century Maunder
Minimum to the current activity minima from these irradiance
reconstructions are compared with recent results in Table 2.10.

Each of the above three assumptions for the existence of a

signi

cant long-term irradiance component is now questionable.

A reassessment of the stellar data was unable to recover the
original bimodal separation of lower calcium (Ca) emission in
non-cycling stars (assumed to be in Maunder-Minimum type
states) compared with higher emission in cycling stars (Hall and
Lockwood, 2004), which underpins the Lean et al. (1995) and
Lean (2000) irradiance reconstructions. Rather, the current Sun
is thought to have ‘typical’ (rather than high) activity relative
to other stars. Plausible lowest brightness levels inferred from
stellar observations are higher than the peak of the lower mode
of the initial distribution of Baliunas and Jastrow (1990

). O

ther

studies raise the possibility of long-term instrumental drifts
in historical indices of geomagnetic activity (Svalgaard

et al.,

2004), which would reduce somewhat the long-term trend in
the Lockwood and Stamper (1999) irradiance reconstruction.
Furthermore, the relationship between solar irradiance and
geomagnetic and cosmogenic indices is complex, and not
necessarily linear. Simulations of the transport of magnetic

on the Sun and propagation of open

ux into the heliosphere

indicate that ‘open’ magnetic

ux (which modulates geomagnetic

activity and cosmogenic isotopes) can accumulate on inter-
cycle time scales even when closed

ux (such as in sunspots

and faculae) does not (Lean

et al., 2002; Y. Wang

et al., 2005).

A new reconstruction of solar irradiance based on a model

of solar magnetic

ux variations (Y. Wang et al., 2005), which

does not invoke geomagnetic, cosmogenic or stellar proxies,
suggests that the amplitude of the background component
is signi

cantly less than previously assumed, speci

cally

0.27 times that of Lean (2000). This estimate results from
simulations of the eruption, transport and accumulation of
magnetic

ux during the past 300 years using a

ux transport

model with variable meridional

ow. Variations in both the total

ux and in just the

ux that extends into the heliosphere (the

open

ux) are estimated, arising from the deposition of bipolar

magnetic regions (active regions) and smaller-scale bright
features (ephemeral regions) on the Sun’s surface in strengths
and numbers proportional to the sunspot number. The open

compares reasonably well with the cosmogenic isotopes for

Figure 2.17.

Reconstructions of the total solar irradiance time series starting as

early as 1600. The upper envelope of the shaded regions shows irradiance variations
arising from the 11-year activity cycle. The lower envelope is the total irradiance
reconstructed by Lean (2000), in which the long-term trend was inferred from bright-
ness changes in Sun-like stars. In comparison, the recent reconstruction of Y. Wang
et al. (2005) is based on solar considerations alone, using a fl ux transport model to
simulate the long-term evolution of the closed fl ux that generates bright faculae.

192

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

2.7.1.2.2

Implications for solar radiative forcing

In terms of plausible physical understanding, the most

likely secular increase in total irradiance from the Maunder
Minimum to current cycle minima is 0.04% (an irradiance
increase of roughly 0.5 W m

–2

in 1,365 W m

–2

), corresponding

to an RF

of +0.1 W m

–2

. The larger RF estimates in Table

2.10, in the range of +0.38 to +0.68 W m

–2

, correspond to

assumed changes in solar irradiance at cycle minima derived
from brightness

uctuations in Sun-like stars that are no longer

valid. Since the 11-year cycle amplitude has increased from the
Maunder Minimum to the present, the total irradiance increase
to the present-day cycle mean is 0.08%. From 1750 to the
present there was a net 0.05% increase in total solar irradiance,
according to the 11-year smoothed total solar irradiance time
series of Y. Wang et al. (2005), shown in Figure 2.17. This
corresponds to an RF of +0.12 W m

–2

, which is more than a

factor of two less than the solar RF estimate in the TAR, also
from 1750 to the present. Using the Lean (2000) reconstruction
(the lower envelope in Figure 2.17) as an upper limit, there is
a 0.12% irradiance increase since 1750, for which the RF is
+0.3 W m

–2

. The lower limit of the irradiance increase from

1750 to the present is 0.026% due to the increase in the 11-
year cycle only. The corresponding lower limit of the RF is
+0.06 W m

–2

. As with solar cycle changes, long-term irradiance

variations are expected to have signi

cant spectral dependence.

For example, the Y. Wang et al. (2005)

ux transport estimates

imply decreases during the Maunder Minimum relative to
contemporary activity cycle minima of 0.43% at 200 to 300
nm, 0.1% at 315 to 400 nm, 0.05% at 400 to 700 nm, 0.03% at
700 to 1,000 nm and 0.02% at 1,000 to 1,600 nm (Lean

et al.,

2005), compared with 1.4%, 0.32%, 0.17%, 0.1% and 0.06%,
respectively, in the earlier model of Lean (2000).

2.7.1.3 Indirect Effects of Solar Variability

Approximately 1% of the Sun’s radiant energy is in the UV

portion of the spectrum at wavelengths below about 300 nm,
which the Earth’s atmosphere absorbs. Although of considerably
smaller absolute energy than the total irradiance, solar UV
radiation is fractionally more variable by at least an order of
magnitude. It contributes signi

cantly to changes in total solar

irradiance (15% of the total irradiance cycle; Lean

et al., 1997)

and creates and modi

es the ozone layer, but is not considered

as a direct RF because it does not reach the troposphere.
Since the TAR, new studies have con

rmed and advanced the

plausibility of indirect effects involving the modi

cation of the

stratosphere by solar UV irradiance variations (and possibly by
solar-induced variations in the overlying mesosphere and lower
thermosphere), with subsequent dynamical and radiative coupling
to the troposphere (Section 9.2). Whether solar wind

uctuations

(Boberg and Lundstedt, 2002) or solar-induced heliospheric
modulation of galactic cosmic rays (Marsh and Svensmark,
2000b) also contribute indirect forcings remains ambiguous.

As in the troposphere, anthropogenic effects, internal cycles

(e.g., the Quasi-Biennial Oscillation) and natural in

uences

all affect the stratosphere. It is now well established from
both empirical and model studies that solar cycle changes in
UV radiation alter middle atmospheric ozone concentrations
(Fioletov

et al., 2002; Geller and Smyshlyaev, 2002; Hood,

2003), temperatures and winds (Ramaswamy

et al., 2001;

Labitzke

et al., 2002; Haigh, 2003; Labitzke, 2004; Crooks

and Gray, 2005), including the Quasi-Biennial Oscillation
(McCormack, 2003; Salby and Callaghan, 2004). In their recent
survey of solar in

uences on climate, Gray et al. (2005) noted

that updated observational analyses have con

rmed earlier 11-

year cycle signals in zonally averaged stratospheric temperature,
ozone and circulation with increased statistical con

dence.

There is a solar-cycle induced increase in global total ozone of
2 to 3% at solar cycle maximum, accompanied by temperature
responses that increase with altitude, exceeding 1°C around 50
km. However, the amplitudes and geographical and altitudinal
patterns of these variations are only approximately known, and
are not linked in an easily discernible manner to the forcing.
For example, solar forcing appears to induce a signi

cant

lower stratospheric response (Hood, 2003), which may have a
dynamical origin caused by changes in temperature affecting
planetary wave propagation, but it is not currently reproduced
by models.

When solar activity is high, the more complex magnetic

con

guration of the heliosphere reduces the

ux of galactic

cosmic rays in the Earth’s atmosphere. Various scenarios have
been proposed whereby solar-induced galactic cosmic ray

uctuations might in

uence climate (as surveyed by Gray

al., 2005). Carslaw

et al. (2002) suggested that since the plasma

produced by cosmic ray ionization in the troposphere is part of
an electric circuit that extends from the Earth’s surface to the
ionosphere, cosmic rays may affect thunderstorm electri

cation.

By altering the population of CCN and hence microphysical
cloud properties (droplet number and concentration), cosmic
rays may also induce processes analogous to the indirect
effect of tropospheric aerosols. The presence of ions, such as
produced by cosmic rays, is recognised as in

uencing several

microphysical mechanisms (Harrison and Carslaw, 2003).
Aerosols may nucleate preferentially on atmospheric cluster
ions. In the case of low gas-phase sulphuric acid concentrations,
ion-induced nucleation may dominate over binary sulphuric
acid-water nucleation. In addition, increased ion nucleation
and increased scavenging rates of aerosols in turbulent regions
around clouds seem likely. Because of the dif

culty in tracking

the in

uence of one particular modi

cation brought about by

To estimate RF, the change in total solar irradiance is multiplied by 0.25 to account for Earth-Sun geometry and then multiplied by 0.7 to account for the planetary albedo (e.g.,

Ramaswamy et al., 2001). Ideally this resulting RF should also be reduced by 15% to account for solar variations in the UV below 300 nm (see Section 2.7.1.3) and further
reduced by about 4% to account for stratospheric absorption of solar radiation above 300 nm and the resulting stratospheric adjustment (Hansen et al., 1997). However, these
corrections are not made to the RF estimates in this report because they: 1) represent small adjustments to the RF; 2) may in part be compensated by indirect effects of solar-
ozone interaction in the stratosphere (see Section 2.7.1.3); and 3) are not routinely reported in the literature.

193

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

ions through the long chain of complex interacting processes,
quantitative estimates of galactic cosmic-ray induced changes
in aerosol and cloud formation have not been reached.

Many empirical associations have been reported between

globally averaged low-level cloud cover and cosmic ray

uxes (e.g., Marsh and Svensmark, 2000a,b). Hypothesised

to result from changing ionization of the atmosphere from
solar-modulated cosmic ray

uxes, an empirical association

of cloud cover variations during 1984 to 1990 and the solar
cycle remains controversial because of uncertainties about the
reality of the decadal signal itself, the phasing or anti-phasing
with solar activity, and its separate dependence for low, middle
and high clouds. In particular, the cosmic ray time series
does not correspond to global total cloud cover after 1991 or
to global low-level cloud cover after 1994 (Kristjánsson and
Kristiansen, 2000; Sun and Bradley, 2002) without unproven
de-trending (Usoskin et al., 2004). Furthermore, the correlation
is signi

cant with low-level cloud cover based only on infrared

(not visible) detection. Nor do multi-decadal (1952 to 1997)
time series of cloud cover from ship synoptic reports exhibit a
relationship to cosmic ray

ux. However, there appears to be a

small but statistically signi

cant positive correlation between

cloud over the UK and galactic cosmic ray

ux during 1951 to

2000 (Harrison and Stephenson, 2006). Contrarily, cloud cover
anomalies from 1900 to 1987 over the USA do have a signal
at 11 years that is anti-phased with the galactic cosmic ray

ux (Udelhofen and Cess, 2001). Because the mechanisms are

uncertain, the apparent relationship between solar variability
and cloud cover has been interpreted to result not only from
changing cosmic ray

uxes modulated by solar activity in the

heliosphere (Usoskin et al., 2004) and solar-induced changes in
ozone (Udelhofen and Cess, 2001), but also from sea surface
temperatures altered directly by changing total solar irradiance
(Kristjánsson et al., 2002) and by internal variability due to
the El Niño-Southern Oscillation (Kernthaler et al., 1999). In
reality, different direct and indirect physical processes (such as
those described in Section 9.2) may operate simultaneously.

The direct RF due to increase in solar irradiance is reduced

from the TAR. The best estimate is +0.12 W m

–2

(90%

con

dence interval: +0.06 to +0.30 W m

–2

). While there have

been advances in the direct solar irradiance variation, there
remain large uncertainties. The level of scienti

c understanding

is elevated to

low relative to TAR for solar forcing due to direct

irradiance change, while declared as very low for cosmic ray
in

uences (Section 2.9, Table 2.11).

2.7.2 Explosive

Volcanic

Activity

2.7.2.1

Radiative Effects of Volcanic Aerosols

Volcanic sulphate aerosols are formed as a result of oxidation

of the sulphur gases emitted by explosive volcanic eruptions into
the stratosphere. The process of gas-to-particle conversion has
an e-folding time of roughly 35 days (Bluth et al., 1992; Read
et al., 1993). The e-folding time (by mass) for sedimentation of

sulphate aerosols is typically about 12 to 14 months (Lambert
et al., 1993; Baran and Foot, 1994; Barnes and Hoffman, 1997;
Bluth et al., 1997). Also emitted directly during an eruption
are volcanic ash particulates (siliceous material). These are
particles usually larger than 2

m that sediment out of the

stratosphere fairly rapidly due to gravity (within three months
or so), but could also play a role in the radiative perturbations
in the immediate aftermath of an eruption. Stratospheric aerosol
data incorporated for climate change simulations tends to be
mostly that of the sulphates (Sato

et al., 1993; Stenchikov

al., 1998; Ramachandran

et al., 2000; Hansen

et al., 2002; Tett

et al., 2002; Ammann

et al., 2003). As noted in the Second

Assessment Report (SAR) and the TAR, explosive volcanic
events are episodic, but the stratospheric aerosols resulting
from them yield substantial transitory perturbations to the
radiative energy balance of the planet, with both shortwave and
longwave effects sensitive to the microphysical characteristics
of the aerosols (e.g., size distribution).

Long-term ground-based and balloon-borne instrumental

observations have resulted in an understanding of the optical
effects and microphysical evolution of volcanic aerosols
(Deshler

et al., 2003; Hofmann et al., 2003). Important ground-

based observations of aerosol characteristics from pre-satellite
era spectral extinction measurements have been analysed by
Stothers (2001a,b), but they do not provide global coverage.
Global observations of stratospheric aerosol over the last 25 years
have been possible owing to a number of satellite platforms, for
example, TOMS and TOVS have been used to estimate SO

loadings from volcanic eruptions (Krueger

et al., 2000; Prata

et al., 2003). The Stratospheric Aerosol and Gas Experiment
(SAGE) and Stratospheric Aerosol Measurement (SAM) projects
(e.g., McCormick, 1987) have provided vertically resolved
stratospheric aerosol spectral extinction data for over 20 years,
the longest such record. This data set has signi

cant gaps in

coverage at the time of the El Chichón eruption in 1982 (the
second most important in the 20th century after Mt. Pinatubo in
1991) and when the aerosol cloud is dense; these gaps have been
partially

lled by lidar measurements and

eld campaigns (e.g.,

Antuña et al., 2003; Thomason and Peter, 2006).

Volcanic aerosols transported in the atmosphere to polar

regions are preserved in the ice sheets, thus recording the history
of the Earth’s volcanism for thousands of years (Bigler et al.,
2002; Palmer et al., 2002; Mosley-Thompson et al., 2003).
However, the atmospheric loadings obtained from ice records
suffer from uncertainties due to imprecise knowledge of the
latitudinal distribution of the aerosols, depositional noise that
can affect the signal for an individual eruption in a single ice
core, and poor constraints on aerosol microphysical properties.

The best-documented explosive volcanic event to date, by

way of reliable and accurate observations, is the 1991 eruption
of Mt. Pinatubo. The growth and decay of aerosols resulting
from this eruption have provided a basis for modelling the RF
due to explosive volcanoes. There have been no explosive and
climatically signi

cant volcanic events since Mt. Pinatubo.

As pointed out in Ramaswamy et al. (2001), stratospheric

194

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

better constrained for the Mt. Pinatubo eruption, and to some
extent for the El Chichón and Agung eruptions, the reliability
degrades for aerosols from explosive volcanic events further
back in time as there are few, if any, observational constraints
on their optical depth and size evolution.

The radiative effects due to volcanic aerosols from major

eruptions are manifest in the global mean anomaly of re

ected

solar radiation; this variable affords a good estimate of radiative
effects that can actually be tested against observations. However,
unlike RF, this variable contains effects due to feedbacks (e.g.,
changes in cloud distributions) so that it is actually more
a signature of the climate response. In the case of the Mt.
Pinatubo eruption, with a peak global visible optical depth of
about 0.15, simulations yield a large negative perturbation as
noted above of about –3 W m

–2

(Ramachandran

et al., 2000;

Hansen

et al., 2002) (see also Section 9.2). This modelled

estimate of re

ected solar radiation compares reasonably

with ERBS observations (Minnis et al., 1993). However, the
ERBS observations were for a relatively short duration, and the
model-observation comparisons are likely affected by differing
cloud effects in simulations and measurements. It is interesting
to note (Stenchikov et al., 2006) that, in the Mt. Pinatubo case,
the Goddard Institute for Space Studies (GISS) models that use
the Sato et al. (1993) data yield an even greater solar re

ection

than the National Center for Atmospheric Research (NCAR)
model that uses the larger (Ammann et al., 2003) optical depth
estimate.

aerosol concentrations are now at the lowest
concentrations since the satellite era and
global coverage began in about 1980. Altitude-
dependent stratospheric optical observations at a
few wavelengths, together with columnar optical
and physical measurements, have been used to
construct the time-dependent global

eld of

stratospheric aerosol size distribution formed in
the aftermath of volcanic events. The wavelength-
dependent stratospheric aerosol single-scattering
characteristics calculated for the solar and
longwave spectrum are deployed in climate
models to account for the resulting radiative
(shortwave plus longwave) perturbations.

Using available satellite- and ground-based

observations, Hansen et al. (2002) constructed
a volcanic aerosols data set for the 1850 to
1999 period (Sato

et al., 1993). This has yielded

zonal mean vertically resolved aerosol optical
depths for visible wavelengths and column
average effective radii. Stenchikov et al. (2006)
introduced a slight variation to this data set,
employing UARS observations to modify the
effective radii relative to Hansen et al. (2002), thus
accounting for variations with altitude. Ammann
et al. (2003) developed a data set of total aerosol
optical depth for the period since 1890 that does
not include the Krakatau eruption. The data set
is based on empirical estimates of atmospheric loadings, which
are then globally distributed using a simpli

ed parametrization

of atmospheric transport, and employs a

xed aerosol effective

radius (0.42

m) for calculating optical properties. The above

data sets have essentially provided the bases for the volcanic
aerosols implemented in virtually all of the models that have
performed the 20th-century climate integrations (Stenchikov et
al., 2006). Relative to Sato et al. (1993), the Ammann et al.
(2003) estimate yields a larger value of the optical depth, by 20
to 30% in the second part of the 20th century, and by 50% for
eruptions at the end of 19th and beginning of 20th century, for
example, the 1902 Santa Maria eruption (Figure 2.18).

The global mean RF calculated using the Sato et al. (1993)

data yields a peak in radiative perturbation of about –3 W m

–2

for the strong (rated in terms of emitted SO

) 1860 and 1991

eruptions of Krakatau and Mt. Pinatubo, respectively. The value
is reduced to about –2 W m

–2

for the relatively less intense El

Chichón and Agung eruptions (Hansen et al., 2002). As expected
from the arguments above, Ammann’s RF is roughly 20 to 30%
larger than Sato’s RF.

Not all features of the aerosols are well quanti

ed, and

extending and improving the data sets remains an important
area of research. This includes improved estimates of the
aerosol size parameters (Bingen et al., 2004), a new approach
for calculating aerosol optical characteristics using SAGE and
UARS data (Bauman et al., 2003), and intercomparison of
data from different satellites and combining them to

ll gaps

(Randall et al., 2001). While the aerosol characteristics are

Figure 2.18.

Visible (wavelength 0.55

m) optical depth estimates of stratospheric sulphate

aerosols formed in the aftermath of explosive volcanic eruptions that occurred between 1860 and
2000. Results are shown from two different data sets that have been used in recent climate model
integrations. Note that the Ammann et al. (2003) data begins in 1890.

195

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

2004, 2006; Shindell

et al., 2003b, 2004; Perlwitz and Harnik,

2003; Rind et al., 2005; Miller

et al., 2006).

Stratospheric aerosols affect the chemistry and transport

processes in the stratosphere, resulting in the depletion of
ozone (Brasseur and Granier, 1992; Tie et al., 1994; Solomon
et al., 1996; Chipper

eld

et al., 2003). Stenchikov et al. (2002)

demonstrated a link between ozone depletion and Arctic
Oscillation response; this is essentially a secondary radiative
mechanism induced by volcanic aerosols through stratospheric
chemistry. Stratospheric cooling in the polar region associated
with a stronger polar vortex initiated by volcanic effects can
increase the probability of formation of polar stratospheric
clouds and therefore enhance the rate of heterogeneous chemical
destruction of stratospheric ozone, especially in the NH
(Tabazadeh et al., 2002). The above studies indicate effects on
the stratospheric ozone layer in the wake of a volcanic eruption
and under conditions of enhanced anthropogenic halogen
loading. Interactive microphysics-chemistry-climate models
(Rozanov et al., 2002, 2004; Shindell et al., 2003b; Timmreck
et al., 2003; Dameris et al., 2005) indicate that aerosol-induced
stratospheric heating affects the dispersion of the volcanic
aerosol cloud, thus affecting the spatial RF. However the models’
simpli

ed treatment of aerosol microphysics introduces biases;

further, they usually overestimate the mixing at the tropopause
level and intensity of meridional transport in the stratosphere
(Douglass

et al., 2003; Schoeberl et al., 2003). For present

climate studies, it is practical to utilise simpler approaches that
are reliably constrained by aerosol observations.

Because of its episodic and transitory nature, it is dif

cult to

give a best estimate for the volcanic RF, unlike the other agents.
Neither a best estimate nor a level of scienti

c understanding

was given in the TAR. For the well-documented case of the
explosive 1991 Mt. Pinatubo eruption, there is a good scienti

understanding. However, the limited knowledge of the RF
associated with prior episodic, explosive events indicates a

low

level of scienti

c understanding (Section 2.9, Table 2.11).

2.8 Utility of Radiative Forcing

The TAR and other assessments have concluded that RF is

a useful tool for estimating, to a

rst order, the relative global

climate impacts of differing climate change mechanisms
(Ramaswamy

et al., 2001; Jacob

et al., 2005). In particular,

RF can be used to estimate the relative equilibrium globally
averaged surface temperature change due to different forcing
agents. However, RF is not a measure of other aspects of
climate change or the role of emissions (see Sections 2.2 and
2.10). Previous GCM studies have indicated that the climate
sensitivity parameter was more or less constant (varying by
less than 25%) between mechanisms (Ramaswamy

et al., 2001;

Chipper

eld

et al., 2003). However, this level of agreement

was found not to hold for certain mechanisms such as ozone
changes at some altitudes and changes in absorbing aerosol.

2.7.2.2 Thermal

Dynamical and Chemistry Perturbations

Forced by Volcanic Aerosols

Four distinct mechanisms have been invoked with regards

to the climate response to volcanic aerosol RF. First, these
forcings can directly affect the Earth’s radiative balance and
thus alter surface temperature. Second, they introduce horizontal
and vertical heating gradients; these can alter the stratospheric
circulation, in turn affecting the troposphere. Third, the forcings
can interact with internal climate system variability (e.g., El
Niño-Southern Oscillation, North Atlantic Oscillation, Quasi-
Biennial Oscillation) and dynamical noise, thereby triggering,
amplifying or shifting these modes (see Section 9.2; Yang and
Schlesinger, 2001; Stenchikov

et al., 2004). Fourth, volcanic

aerosols provide surfaces for heterogeneous chemistry affecting
global stratospheric ozone distributions (Chipper

eld

et al.,

2003) and perturbing other trace gases for a considerable
period following an eruption. Each of the above mechanisms
has its own spatial and temporal response pattern. In addition,
the mechanisms could depend on the background state of the
climate system, and thus on other forcings (e.g., due to well-
mixed gases, Meehl

et al., 2004), or interact with each other.

The complexity of radiative-dynamical response forced by

volcanic impacts suggests that it is important to calculate aerosol
radiative effects interactively within the model rather than
prescribe them (Andronova et al., 1999; Broccoli et al., 2003).
Despite differences in volcanic aerosol parameters employed,
models computing the aerosol radiative effects interactively
yield tropical and global mean lower-stratospheric warmings
that are fairly consistent with each other and with observations
(Ramachandran et al., 2000; Hansen et al., 2002; Yang and
Schlesinger, 2002; Stenchikov et al., 2004; Ramaswamy et al.,
2006b); however, there is a considerable range in the responses
in the polar stratosphere and troposphere. The global mean
warming of the lower stratosphere is due mainly to aerosol
effects in the longwave spectrum, in contrast to the

ux changes

at the TOA that are essentially due to aerosol effects in the solar
spectrum. The net radiative effects of volcanic aerosols on
the thermal and hydrologic balance (e.g., surface temperature
and moisture) have been highlighted by recent studies (Free
and Angell, 2002; Jones

et al., 2003; see Chapter 6; and see

Chapter 9 for signi

cance of the simulated responses and

model-observation comparisons for 20th-century eruptions).
A mechanism closely linked to the optical depth perturbation
and ensuing warming of the tropical lower stratosphere is the
potential change in the cross-tropopause water vapour

(Joshi and Shine, 2003; see Section 2.3.7).

Anomalies in the volcanic-aerosol induced global radiative

heating distribution can force signi

cant changes in atmospheric

circulation, for example, perturbing the equator-to-pole heating
gradient (Stenchikov et al., 2002; Ramaswamy

et al., 2006a;

see Section 9.2) and forcing a positive phase of the Arctic
Oscillation that in turn causes a counterintuitive boreal winter
warming at middle and high latitudes over Eurasia and North
America (Perlwitz and Graf, 2001; Stenchikov et al., 2002,

196

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

and only this aspect of the forcing-response relationship is
discussed. However, patterns of RF are presented as a diagnostic
in Section 2.9.5.

2.8.3

Alternative Methods of Calculating Radiative
Forcing

RFs are increasingly being diagnosed from GCM

integrations where the calculations are complex (Stuber

et al.,

2001b; Tett

et al., 2002; Gregory

et al., 2004). This chapter also

discusses several mechanisms that include some response in the
troposphere, such as cloud changes. These mechanisms are not
initially radiative in nature, but will eventually lead to a radiative
perturbation of the surface-troposphere system that could
conceivably be measured at the TOA. Jacob

et al. (2005) refer

to these mechanisms as non-radiative forcings (see also Section
2.2). Alternatives to the standard stratospherically adjusted
RF de

nition have been proposed that may help account for

these processes. Since the TAR, several studies have employed
GCMs to diagnose the zero-surface-temperature-change RF (see
Figure 2.2 and Section 2.2). These studies have used a number
of different methodologies. Shine et al. (2003)

xed both land

and sea surface temperatures globally and calculated a radiative
energy imbalance: this technique is only feasible in GCMs with
relatively simple land surface parametrizations. Hansen et al.
(2005)

xed sea surface temperatures and calculated an RF by

adding an extra term to the radiative imbalance that took into
account how much the land surface temperatures had responded.
Sokolov (2006) diagnosed the zero-surface-temperature-
change RF by computing surface-only and atmospheric-only
components of climate feedback separately in a slab model
and then modifying the stratospherically adjusted RF by the
atmospheric-only feedback component. Gregory et al. (2004;
see also Hansen et al., 2005; Forster and Taylor, 2006) used a
regression method with a globally averaged temperature change
ordinate to diagnose the zero-surface-temperature-change RF:
this method had the largest uncertainties. Shine et al. (2003),
Hansen et al. (2005) and Sokolov (2006) all found that that
the

xed-surface-temperature RF was a better predictor of the

equilibrium global mean surface temperature response than
the stratospherically adjusted RF. Further, it was a particularly
useful diagnostic for changes in absorbing aerosol where the
stratospherically adjusted RF could fail as a predictor of the
surface temperature response (see Section 2.8.5.5). Differences
between the zero-surface-temperature-change RF and the
stratospherically adjusted RF can be caused by semi-direct and
cloud-aerosol interaction effects beyond the cloud albedo RF.
For most mechanisms, aside from the case of certain aerosol
changes, the difference is likely to be small (Shine

et al., 2003;

Hansen

et al., 2005; Sokolov, 2006). These calculations also

remove problems associated with de

ning the tropopause in

the stratospherically adjusted RF de

nition (Shine

et al., 2003;

Hansen

et al., 2005). However, stratospherically adjusted

RF has the advantage that it does not depend on relatively
uncertain components of a GCM’s response, such as cloud

Because the climate responses, and in particular the equilibrium
climate sensitivities, exhibited by GCMs vary by much more
than 25% (see Section 9.6), Ramaswamy

et al. (2001) and

Jacob

et al. (2005) concluded that RF is the most simple and

straightforward measure for the quantitative assessment of
climate change mechanisms, especially for the LLGHGs. This
section discusses the several studies since the TAR that have
examined the relationship between RF and climate response.
Note that this assessment is entirely based on climate model
simulations.

2.8.1

Vertical Forcing Patterns and Surface Energy
Balance Changes

The vertical structure of a forcing agent is important

both for ef

cacy (see Section 2.8.5) and for other aspects of

climate response, particularly for evaluating regional and
vertical patterns of temperature change and also changes in
the hydrological cycle. For example, for absorbing aerosol,
the surface forcings are arguably a more useful measure of
the climate response (particularly for the hydrological cycle)
than the RF (Ramanathan

et al., 2001a; Menon

et al., 2002b).

It should be noted that a perturbation to the surface energy
budget involves sensible and latent heat

uxes besides solar and

longwave irradiance; therefore, it can quantitatively be very
different from the RF, which is calculated at the tropopause,
and thus is

not representative of the energy balance perturbation

to the surface-troposphere (climate) system. While the surface
forcing adds to the overall description of the total perturbation
brought about by an agent, the RF and surface forcing should
not be directly compared nor should the surface forcing be
considered in isolation for evaluating the climate response (see,
e.g., the caveats expressed in Manabe and Wetherald, 1967;
Ramanathan, 1981). Therefore, surface forcings are presented as
an important and useful diagnostic tool that aids understanding
of the climate response (see Sections 2.9.4 and 2.9.5).

2.8.2

Spatial Patterns of Radiative Forcing

Each RF agent has a unique spatial pattern (see, e.g., Figure

6.7 in Ramaswamy et al., 2001). When combining RF agents it
is not just the global mean RF that needs to be considered. For
example, even with a net global mean RF of zero, signi

cant

regional RFs can be present and these can affect the global mean
temperature response (see Section 2.8.5). Spatial patterns of RF
also affect the pattern of climate response. However, note that,
to

rst order, very different RF patterns can have similar patterns

of surface temperature response and the location of maximum
RF is rarely coincident with the location of maximum response
(Boer and Yu, 2003b). Identi

cation of different patterns of

response is particularly important for attributing past climate
change to particular mechanisms, and is also important for the
prediction of regional patterns of future climate change. This
chapter employs RF as the method for ranking the effect of a
forcing agent on the equilibrium global temperature change,

197

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

changes. For the LLGHGs, the stratospherically adjusted RF
also has the advantage that it is readily calculated in detailed
off-line radiation codes. For these reasons, the stratospherically
adjusted RF is retained as the measure of comparison used
in this chapter (see Section 2.2). However, to

rst order, all

methods are comparable and all prove useful for understanding
climate response.

2.8.4

Linearity of the Forcing-Response
Relationship

Reporting

ndings from several studies, the TAR concluded

that responses to individual RFs could be linearly added to
gauge the global mean response, but not necessarily the regional
response (Ramaswamy

et al., 2001). Since then, studies with

several equilibrium and/or transient integrations of several
different GCMs have found no evidence of any nonlinearity for
changes in greenhouse gases and sulphate aerosol (Boer and
Yu, 2003b; Gillett

et al., 2004; Matthews

et al., 2004; Meehl et

al., 2004). Two of these studies also examined realistic changes
in many other forcing agents without

nding evidence of a

nonlinear response (Meehl

et al., 2004; Matthews

et al., 2004).

In all four studies, even the regional changes typically added
linearly. However, Meehl et al. (2004) observed that neither
precipitation changes nor all regional temperature changes were
linearly additive. This linear relationship also breaks down for
global mean temperatures when aerosol-cloud interactions
beyond the cloud albedo RF are included in GCMs (Feichter

et al., 2004; see also Rotstayn and Penner, 2001; Lohmann
and Feichter, 2005). Studies that include these
effects modify clouds in their models, producing an
additional radiative imbalance. Rotstayn and Penner
(2001) found that if these aerosol-cloud effects
are accounted for as additional forcing terms, the
inference of linearity can be restored (see Sections
2.8.3 and 2.8.5). Studies also

nd nonlinearities for

large negative RFs, where static stability changes in
the upper troposphere affect the climate feedback
(e.g., Hansen

et al., 2005). For the magnitude and

range of realistic RFs discussed in this chapter,
and excluding cloud-aerosol interaction effects,
there is high con

dence in a linear relationship

between global mean RF and global mean surface
temperature response.

2.8.5 Effi cacy and Effective Radiative

Forcing

cacy (E) is de

ned as the ratio of the climate

sensitivity parameter for a given forcing agent
(

) to the climate sensitivity parameter for CO

changes, that is, E

CO2

(Joshi

et al., 2003;

Hansen and Nazarenko, 2004). Ef

cacy can then

be used to de

ne an effective RF (= E

) (Joshi

et al., 2003; Hansen et al., 2005). For the effective
RF, the climate sensitivity parameter is independent

Figure 2.19.

Effi cacies as calculated by several GCM models for realistic changes in RF agents.

Letters are centred on effi cacy value and refer to the literature study that the value is taken from (see
text of Section 2.8.5 for details and further discussion). In each RF category, only one result is taken
per model or model formulation. Cloud-albedo effi cacies are evaluated in two ways: the standard
letters include cloud lifetime effects in the effi cacy term and the letters with asterisks exclude these
effects. Studies assessed in the fi gure are: a) Hansen et al. (2005); b) Wang et al. (1991); c) Wang et
al. (1992); d) Govindasamy et al. (2001b); e) Lohmann and Feichter (2005); f) Forster et al. (2000); g)
Joshi et al. (2003; see also Stuber et al., 2001a); h) Gregory et al. (2004); j) Sokolov (2006); k) Cook
and Highwood (2004); m) Mickley et al. (2004); n) Rotstayn and Penner (2001); o) Roberts and Jones
(2004) and p) Williams et al. (2001a).

of the mechanism, so comparing this forcing is equivalent to
comparing the equilibrium global mean surface temperature
change. That is,

CO2

Preliminary studies have

found that ef

cacy values for a number of forcing agents show

less model dependency than the climate sensitivity values (Joshi

et al., 2003). Effective RFs have been used get one step closer to
an estimator of the likely surface temperature response than can
be achieved by using RF alone (Sausen and Schumann, 2000;
Hansen

et al., 2005; Lohmann and Feichter, 2005). Adopting

the zero-surface-temperature-change RF, which has ef

cacies

closer to unity, may be another way of achieving similar goals
(see Section 2.8.3). This section assesses the ef

cacy associated

with stratospherically adjusted RF, as this is the de

nition of

RF adopted in this chapter (see Section 2.2). Therefore, cloud-
aerosol interaction effects beyond the cloud albedo RF are
included in the ef

cacy term. The

ndings presented in this

section are from an assessment of all the studies referenced in
the caption of Figure 2.19, which presents a synthesis of ef

cacy

results. As space is limited not all these studies are explicitly
discussed in the main text.

2.8.5.1 Generic

Understanding

Since the TAR, several GCM studies have calculated

cacies and a general understanding is beginning to emerge

as to how and why ef

cacies vary between mechanisms. The

initial climate state, and the sign and magnitude of the RF have
less importance but can still affect ef

cacy (Boer and Yu, 2003a;

Joshi

et al., 2003; Hansen

et al., 2005). These studies have also

198

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

irradiance change; any indirect solar effects (see Section
2.7.1.3) are not included in this ef

cacy estimate. Overall, there

is medium con

dence that the direct solar ef

cacy is within the

0.7 to 1.0 range.

2.8.5.4 Ozone

Stratospheric ozone ef

cacies have normally been calculated

from idealised ozone increases. Experiments with three models
(Stuber

et al., 2001a; Joshi

et al., 2003; Stuber

et al., 2005)

found higher ef

cacies for such changes; these were due to

larger than otherwise tropical tropopause temperature changes
which led to a positive stratospheric water vapour feedback.
However, this mechanism may not operate in the two versions
of the GISS model, which found smaller ef

cacies. Only one

study has used realistic stratospheric ozone changes (see Figure
2.19); thus, knowledge is still incomplete. Conclusions are only
drawn from the idealised studies where there is (1) medium
con

dence that the ef

cacy is within a 0.5 to 2.0 range and

(2) established but incomplete physical understanding of how
and why the ef

cacy could be larger than 1.0. There is medium

con

dence that for realistic tropospheric ozone perturbations

the ef

cacy is within the 0.6 to 1.1 range.

2.8.5.5 Scattering

Aerosol

For idealised global perturbations, the ef

cacy for the direct

effect of scattering aerosol is very similar to that for changes in
the solar constant (Cook and Highwood, 2004). As for ozone,
realistic perturbations of scattering aerosol exhibit larger
changes at higher latitudes and thus have a higher ef

cacy than

solar changes (Hansen

et al., 2005). Although the number of

modelling results is limited, it is expected that ef

cacies would

be similar to other solar effects; thus there is medium con

dence

that ef

cacies for scattering aerosol would be in the 0.7 to 1.1

range. Ef

cacies are likely to be similar for scattering aerosol in

the troposphere and stratosphere.

With the formulation of RF employed in this chapter, the

cacy of the cloud albedo RF accounts for cloud lifetime

effects (Section 2.8.3). Only two studies contained enough
information to calculate ef

cacy in this way and both found

cacies higher than 1.0. However, the uncertainties in

quantifying the cloud lifetime effect make this ef

cacy very

uncertain. If cloud lifetime effects were excluded from the
ef

cacy term, the cloud albedo ef

cacy would

very likely be

similar to that of the direct effect (see Figure 2.19).

2.8.5.6 Absorbing

Aerosol

For absorbing aerosols, the simple ideas of a linear forcing-

response relationship and ef

cacy can break down (Hansen

et al., 1997; Cook and Highwood, 2004; Feichter

et al., 2004;

Roberts and Jones, 2004; Hansen

et al., 2005; Penner et al.,

2007). Aerosols within a particular range of single scattering
albedos have negative RFs but induce a global mean warming,
that is, the ef

cacy can be negative. The surface albedo and

developed useful conceptual models to help explain variations
in ef

cacy with forcing mechanism. The ef

cacy primarily

depends on the spatial structure of the forcings and the way
they project onto the various different feedback mechanisms
(Boer and Yu, 2003b). Therefore, different patterns of RF and
any nonlinearities in the forcing response relationship affects
the ef

cacy (Boer and Yu, 2003b; Joshi

et al., 2003; Hansen

et al., 2005; Stuber

et al., 2005; Sokolov, 2006). Many of the

studies presented in Figure 2.19

nd that both the geographical

and vertical distribution of the forcing can have the most
signi

cant effect on ef

cacy (in particular see Boer and Yu,

2003b; Joshi

et al., 2003; Stuber

et al., 2005; Sokolov, 2006).

Nearly all studies that examine it

nd that high-latitude forcings

have higher ef

cacies than tropical forcings. Ef

cacy has also

been shown to vary with the vertical distribution of an applied
forcing (Hansen

et al., 1997; Christiansen, 1999; Joshi

et al.,

2003; Cook and Highwood, 2004; Roberts and Jones, 2004;
Forster and Joshi, 2005; Stuber

et al., 2005; Sokolov, 2006).

Forcings that predominately affect the upper troposphere are
often found to have smaller ef

cacies compared to those that

affect the surface. However, this is not ubiquitous as climate
feedbacks (such as cloud and water vapour) will depend on
the static stability of the troposphere and hence the sign of the
temperature change in the upper troposphere (Govindasamy

al., 2001b; Joshi

et al., 2003; Sokolov, 2006).

2.8.5.2

Long-Lived Greenhouse Gases

The few models that have examined ef

cacy for combined

LLGHG changes generally

nd ef

cacies slightly higher than

1.0 (Figure 2.19). Further, the most recent result from the NCAR
Community Climate Model (CCM3) GCM (Govindasamy

et al., 2001b) indicates an ef

cacy of over 1.2 with no clear

reason of why this changed from earlier versions of the same
model. Individual LLGHG ef

cacies have only been analysed

in two or three models. Two GCMs suggest higher ef

cacies

from individual components (over 30% for CFCs in Hansen

al., 2005). In contrast another GCM gives ef

cacies for CFCs

(Forster and Joshi, 2005) and CH

(Berntsen

et al., 2005) that are

slightly less than one. Overall there is medium con

dence that

the observed changes in the combined LLGHG changes have
an ef

cacy close to 1.0 (within 10%), but there are not enough

studies to constrain the ef

cacies for individual species.

2.8.5.3 Solar

Solar changes, compared to CO

, have less high-latitude

RF and more of the RF realised at the surface. Established
but incomplete knowledge suggests that there is partial
compensation between these effects, at least in some models,
which leads to solar ef

cacies close to 1.0. All models with a

positive solar RF

nd ef

cacies of 1.0 or smaller. One study

nds a smaller ef

cacy than other models (0.63: Gregory

al., 2004). However, their unique methodology for calculating
climate sensitivity has large uncertainties (see Section 2.8.4).
These studies have only examined solar RF from total solar

199

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

0.5 to 2.0 range. Further, zero-surface-temperature-change RFs
are very likely to have ef

cacies signi

cantly closer to 1.0 for

all mechanisms. It should be noted that ef

cacies have only

been evaluated in GCMs and actual climate ef

cacies could be

different from those quoted in Section 2.8.5.

2.9 Synthesis

This section begins by synthesizing the discussion of the RF

concept. It presents summaries of the global mean RFs assessed
in earlier sections and discusses time evolution and spatial
patterns of RF. It also presents a brief synthesis of surface
forcing diagnostics. It breaks down the analysis of RF in several
ways to aid and advance the understanding of the drivers of
climate change.

RFs are calculated in various ways depending on the agent:

from changes in emissions and/or changes in concentrations;
and from observations and other knowledge of climate change
drivers. Current RF depends on present-day concentrations of
a forcing agent, which in turn depend on the past history of
emissions. Some climate response to these RFs is expected to
have already occurred. Additionally, as RF is a comparative
measure of equilibrium climate change and the Earth’s climate
is not in an equilibrium state, additional climate change in the
future is also expected from present-day RFs (see Sections 2.2
and 10.7). As previously stated in Section 2.2, RF alone is not
a suitable metric for weighting emissions; for this purpose, the
lifetime of the forcing agent also needs to be considered (see
Sections 2.9.4 and 2.10).

RFs are considered external to the climate system (see

Section 2.2). Aside from the natural RFs (solar, volcanoes),
the other RFs are considered to be anthropogenic (i.e., directly
attributable to human activities). For the LLGHGs it is assumed
that all changes in their concentrations since pre-industrial
times are human-induced (either directly through emissions or
from land use changes); these concentration changes are used
to calculate the RF. Likewise, stratospheric ozone changes are
also taken from satellite observations and changes are primarily
attributed to Montreal-Protocol controlled gases, although there
may also be a climate feedback contribution to these trends (see
Section 2.3.4). For the other RFs, anthropogenic emissions and/
or human-induced land use changes are used in conjunction
with CTMs and/or GCMs to estimate the anthropogenic RF.

2.9.1

Uncertainties in Radiative Forcing

The TAR assessed uncertainties in global mean RF by

attaching an error bar to each RF term that was ‘guided by the
range of published values and physical understanding’. It also
quoted a level of scienti

c understanding (LOSU) for each RF,

which was a subjective judgment of the estimate’s reliability.

The concept of LOSU has been slightly modi

ed based

on the IPCC Fourth Assessment Report (AR4) uncertainty
guidelines. Error bars now represent the 5 to 95% (90%)

height of the aerosol layer relative to the cloud also affects
this relationship (Section 7.5; Penner

et al., 2003; Cook and

Highwood, 2004; Feichter

et al., 2004; Johnson

et al., 2004;

Roberts and Jones, 2004; Hansen

et al., 2005). Studies that

increase BC in the planetary boundary layer

nd ef

cacies

much larger than 1.0 (Cook and Highwood, 2004; Roberts and
Jones, 2004; Hansen

et al., 2005). These studies also

nd that

cacies are considerably smaller than 1.0 when BC aerosol is

changed above the boundary layer. These changes in ef

cacy

are at least partly attributable to a semi-direct effect whereby
absorbing aerosol modi

es the background temperature pro

and tropospheric cloud (see Section 7.5). Another possible
feedback mechanism is the modi

cation of snow albedo by BC

aerosol (Hansen and Nazarenko, 2004; Hansen

et al., 2005);

however, this report does not classify this as part of the response,
but rather as a separate RF (see Section 2.5.4 and 2.8.5.7).
Most GCMs likely have some representation of the semi-direct
effect (Cook and Highwood, 2004) but its magnitude is very
uncertain (see Section 7.5) and dependent on aspects of cloud
parametrizations within GCMs (Johnson, 2005). Two studies
using realistic vertical and horizontal distributions of BC

that overall the ef

cacy is around 0.7 (Hansen

et al., 2005;

Lohmann and Feichter, 2005). However, Hansen et al. (2005)
acknowledge that they may have underestimated BC within
the boundary layer and another study with realistic vertical
distribution of BC changes

nds an ef

cacy of 1.3 (Sokolov,

2006). Further, Penner et al.

(2007) also modelled BC changes

and found ef

cacies very much larger and very much smaller

than 1.0 for biomass and fossil fuel carbon, respectively (Hansen
et al. (2005) found similar ef

cacies for biomass and fossil fuel

carbon). In summary there is no consensus as to BC ef

cacy

and this may represent problems with the stratospherically
adjusted de

nition of RF (see Section 2.8.3).

2.8.5.7 Other

Forcing

Agents

cacies for some other effects have been evaluated by one

or two modelling groups. Hansen et al. (2005) found that land
use albedo RF had an ef

cacy of roughly 1.0, while the BC-

snow albedo RF had an ef

cacy of 1.7. Ponater

et al. (2005)

found an ef

cacy of 0.6 for contrail RF and this agrees with a

suggestion from Hansen et al. (2005) that high-cloud changes
should have smaller ef

cacies. The results of Hansen et al.

(2005) and Forster and Shine (1999) suggest that stratospheric
water vapour ef

cacies are roughly one.

2.8.6 Effi cacy and the Forcing-Response

Relationship

cacy is a new concept introduced since the TAR and its

physical understanding is becoming established (see Section
2.8.5). When employing the stratospherically adjusted RF, there
is medium con

dence that ef

cacies are within the 0.75 to 1.25

range for most realistic RF mechanisms aside from aerosol and
stratospheric ozone changes. There is medium con

dence that

realistic aerosol and ozone changes have ef

cacies within the

200

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

con

dence range (see Box TS.1). Only ‘well-established’

RFs are quanti

ed. ‘Well established’ implies that there is

qualitatively both suf

cient evidence and suf

cient consensus

from published results to estimate a central RF estimate and
a range. ‘Evidence’ is assessed by an A to C grade, with an
A grade implying strong evidence and C insuf

cient evidence.

Strong evidence implies that observations have veri

ed aspects

of the RF mechanism and that there is a sound physical model to
explain the RF. ‘Consensus’ is assessed by assigning a number
between 1 and 3, where 1 implies a good deal of consensus
and 3 insuf

cient consensus. This ranks the number of studies,

how well studies agree on quantifying the RF and especially
how well observation-based studies agree with models. The
product of ‘Evidence’ and ‘Consensus’ factors give the LOSU
rank. These ranks are high, medium, medium-low, low or very
low. Ranks of very low are not evaluated. The quoted 90%
con

dence range of RF quanti

es the value uncertainty, as

derived from the expert assessment of published values and their
ranges. For most RFs, many studies have now been published,
which generally makes the sampling of parameter space more
complete and the value uncertainty more realistic, compared to
the TAR. This is particularly true for both the direct and cloud
albedo aerosol RF (see Section 2.4). Table 2.11 summarises the
key certainties and uncertainties and indicates the basis for the
90% con

dence range estimate. Note that the aerosol terms will

have added uncertainties due to the uncertain semi-direct and
cloud lifetime effects. These uncertainties in the response to the
RF (ef

cacy) are discussed in Section 2.8.5.

Table 2.11 indicates that there is now stronger evidence for

most of the RFs discussed in this chapter. Some effects are not
quanti

ed, either because they do not have enough evidence

or because their quanti

cation lacks consensus. These include

certain mechanisms associated with land use, stratospheric
water vapour and cosmic rays. Cloud lifetime and the semi-
direct effects are also excluded from this analysis as they are
deemed to be part of the climate response (see Section 7.5). The
RFs from the LLGHGs have both a high degree of consensus
and a very large amount of evidence and, thereby, place
understanding of these effects at a considerably higher level
than any other effect.

2.9.2

Global Mean Radiative Forcing

The RFs discussed in this chapter, their uncertainty ranges

and their ef

cacies are summarised in Figure 2.20 and Table

2.12. Radiative forcings from forcing agents have been combined
into their main groupings. This is particularly useful for aerosol
as its total direct RF is considerably better constrained than the
RF from individual aerosol types (see Section 2.4.4). Table 2.1
gives a further component breakdown of RF for the LLGHGs.
Radiative forcings are the stratospherically adjusted RF and
they have not been multiplied by ef

cacies (see Sections 2.2

and 2.8).

In the TAR, no estimate of the total combined RF from all

anthropogenic forcing agents was given because: a) some of
the forcing agents did not have central or best estimates; b) a

degree of subjectivity was included in the error estimates; and
c) uncertainties associated with the linear additivity assumption
and ef

cacy had not been evaluated. Some of these limitations

still apply. However, methods for objectively adding the RF
of individual species have been developed (e.g., Schwartz and
Andreae, 1996; Boucher and Haywood, 2001). In addition, as
ef

cacies are now better understood and quanti

ed (see Section

2.8.5), and as the linear additivity assumption has been more
thoroughly tested (see Section 2.8.4), it becomes scienti

cally

justi

able for RFs from different mechanisms to be combined,

with certain exceptions as noted below. Adding together the
anthropogenic RF values shown in panel (A) of Figure 2.20 and
combining their individual uncertainties gives the probability
density functions (PDFs) of RF that are shown in panel (B).
Three PDFs are shown: the combined RF from greenhouse gas
changes (LLGHGs and ozone); the combined direct aerosol
and cloud albedo RFs and the combination of all anthropogenic
RFs. The solar RF is not included in any of these distributions.
The PDFs are generated by combining the 90% con

dence

estimates for the RFs, assuming independence and employing
a one-million point Monte Carlo simulation to derive the PDFs
(see Boucher and Haywood, 2001; and Figure 2.20 caption for
details).

The PDFs show that LLGHGs and ozone contribute a

positive RF of +2.9 ± 0.3 W m

–2

. The combined aerosol direct

and cloud albedo effect exert an RF that is virtually certain

to be negative, with a median RF of –1.3 W m

–2

and a –2.2

to –0.5 W m

–2

90% con

dence range. The asymmetry in the

combined aerosol PDF is caused by the estimates in Tables 2.6
and 2.7 being non-Gaussian. The combined net RF estimate
for all anthropogenic drivers has a value of +1.6 W m

–2

with

a 0.6 to 2.4 W m

–2

90% con

dence range. Note that the RFs

from surface albedo change, stratospheric water vapour change
and persistent contrails are only included in the combined
anthropogenic PDF and not the other two.

Statistically, the PDF shown in Figure 2.20 indicates

just a 0.2% probability that the total RF from anthropogenic
agents is negative, which would suggest that it is virtually
certain that the combined RF from anthropogenic agents is
positive. Additionally, the PDF presented here suggests that
it is extremely likely that the total anthropogenic RF is larger
than +0.6 W m

–2

. This combined anthropogenic PDF is better

constrained than that shown in Boucher and Haywood (2001)
because each of the individual RFs have been quanti

ed to

90% con

dence levels, enabling a more de

nite assessment,

and because the uncertainty in some of the RF estimates is
considerably reduced. For example, modelling of the total
direct RF due to aerosols is better constrained by satellite and
surface-based observations (Section 2.4.2), and the current
estimate of the cloud albedo indirect effect has a best estimate
and uncertainty associated with it, rather than just a range. The
LLGHG RF has also increased by 0.20 W m

–2

since 1998,

making a positive RF more likely than in Boucher and Haywood
(2001).

Nevertheless, there are some structural uncertainties

associated with the assumptions used in the construction of

201

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

able 2.11.

Uncertainty assessment of forcing a

gents discussed in this cha

pter

. Evidence for the forcing is given a grade (A to C),

with

implying strong evidence and C insuffi

cient evidence.

The degree of consensus among forcing

estima

tes is given a 1,

2 or 3 grade,

where grade 1 implies a good deal of consensus and grade 3 implies an insuffi

cient consen

sus.

rom these two factors,

a level of scientifi

c understanding is determined (LOSU).

Uncertainties are in

pproxima

te order of importance with fi

rst-order uncertainties listed fi

rst.

Evidence

Consensus

LOSU

Certainties

Uncertainties

Basis of RF range

LLGHGs

High

Past and pr

esent concentrations;

spectr

oscopy

e-industrial concentrations of some

species; vertical pr

ofi

le in stratospher

spectr

oscopic str

ength of minor gases

Uncertainty assessment of measur

ends fr

om dif

fer

ent observed data

sets and dif

fer

ences between radiative

transfer models

Stratospheric

ozone

Medium

Measur

ed tr

ends and its vertical pr

ofi

since 1980; cooling of stratospher

spectr

oscopy

Changes prior to 1970; tr

ends near

opopause; ef

fect of r

ecent tr

ends

Range of model r

esults weighted to

calculations employing trustworthy

observed ozone tr

end data

ropospheric

ozone

Medium

esent-day concentration at surface

and some knowledge of vertical and

spatial structur

e of concentrations and

emissions; spectr

oscopy

e-industrial values and r

ole of changes

in lightning; vertical structur

e of tr

ends

near tr

opopause; aspects of emissions

and chemistry

Range of published model r

esults,

upper bound incr

eased to account

for anthr

opogenic tr

end in lightning

Stratospheric

water vapour

om CH

Low

Global tr

ends since 1990; CH

contribution to tr

end; spectr

oscopy

Global tr

ends prior to 1990; radiative

transfer in climate models; CTM models

of CH

oxidation

Range based on uncertainties in CH

contribution to tr

end and published RF

estimates

Dir

ect aer

osol

to 3

Medium

to Low

ound-based and satellite observations;

some sour

ce r

egions and modelling

Emission sour

ces and their history vertical

structur

e of aer

osol, optical pr

operties,

mixing and separation fr

om natural

backgr

ound aer

osol

Range of published model r

esults with

allowances made for comparisons with

satellite data

Cloud albedo

fect (all

aer

osols)

Low

Observed in case studies – e.g., ship

tracks; GCMs model an ef

fect

Lack of dir

ect observational evidence of

a global for

cing

Range of published model r

esults and

published r

esults wher

e models have

been constrained by satellite data

Surface albedo

(land use)

to 3

Medium

to Low

Some quantifi

cation of defor

estation

and desertifi

cation

Separation of anthr

opogenic changes

om natural

Based on range of published estimates

and published uncertainty analyses

Surface albedo

(BC aer

osol on

snow)

Low

Estimates of BC aer

osol on snow; some

model studies suggest link

Separation of anthr

opogenic changes fr

natural; mixing of snow and BC aer

osol;

quantifi

cation of RF

Estimates based on a few published

model studies

Persistent linear

Contrails

Low

Cirrus radiative and micr

ophysical

operties; aviation emissions; contrail

coverage in certain r

egions

Global contrail coverage and optical

operties

Best estimate based on r

ecent work

and range fr

om published model

sults

202

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Solar irradiance

Low

Measur

ements over last 25 years; pr

oxy

indicators of solar activity

Relationship between pr

oxy data and total

solar irradiance; indir

ect ozone ef

fects

Range fr

om available r

econstructions

of solar irradiance and their qualitative

assessment

lcanic aer

osol

Low

Observed aer

osol changes fr

om Mt.

Pinatubo and El Chichón; pr

oxy data

for past eruptions; radiative ef

fect of

volcanic aer

osol

Stratospheric aer

osol concentrations

om pr

e-1980 eruptions; atmospheric

feedbacks

Past r

econstructions/estimates of

explosive volcanoes and observations

of Mt. Pinatubo aer

osol

Stratospheric

water vapour fr

causes other than

oxidation

ry Low

Empirical and simple model studies

suggest link; spectr

oscopy

Other causes of water vapour tr

ends

poorly understood

Not given

ropospheric

water vapour fr

irrigation

ry Low

ocess understood; spectr

oscopy;

some r

egional information

Global injection poorly quantifi

Not

given

iation-induced

cirrus

ry Low

Cirrus radiative and micr

ophysical

operties; aviation emissions; contrail

coverage in certain r

egions

ransformation of contrails to cirrus;

aviation’

s ef

fect on cirrus clouds

Not given

Cosmic rays

ry Low

Some empirical evidence and some

observations as well as micr

ophysical

models suggest link to clouds

General lack/doubt r

egar

ding physical

mechanism; dependence on corr

elation

studies

Not given

Other surface

fects

ry Low

Some model studies suggest link and

some evidence of r

elevant pr

ocesses

Quantifi

cation of RF and interpr

etation of

sults in for

cing feedback context diffi

cult

Not given

Evidence

Consensus

LOSU

Certainties

Uncertainties

Basis of RF range

ble 2.11 (continued)

204

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Table 2.12.

Global mean radiative forcings since 1750 and comparison with earlier assessments. Bold rows appear on Figure 2.20. The fi rst row shows the combined

anthropogenic RF from the probability density function in panel B of Figure 2.20. The sum of the individual RFs and their estimated errors are not quite the same as the numbers
presented in this row due to the statistical construction of the probability density function.

Notes:

For the AR4 column, 90% value uncertainties appear in brackets: when adding these numbers to the best estimate the 5 to 95% confi dence range is

obtained. When two numbers are quoted for the value uncertainty, the distribution is non-normal. Uncertainties in the SAR and the TAR had a similar

basis, but their evaluation was more subjective. [15%] indicates 15% relative uncertainty, [2x], etc. refer to a factor of two, etc. uncertainty and a lognormal

distribution of RF estimates.

The TAR RF for halocarbons and hence the total LLGHG RF was incorrectly evaluated some 0.01 W m

–2

too high. The actual trends in these RFs are

therefore more positive than suggested by numbers in this table (Table 2.1 shows updated trends).

Global mean radiative forcing (W m

–2

)

Summary comments on changes

SAR (1750–1993)

TAR (1750–1998)

AR4 (1750–2005)

since the TAR

Combined

Not evaluated

1.6 [–1.0, +0.8]

Newly evaluated. Probability

Anthropogenic RF

density function estimate

Long-lived

+2.45 [15%]

+2.43 [10%]

+2.63 [±0.26]

Total increase in RF, due to

Greenhouse gases

(CO

1.56; CH

(CO

1.46; CH

0.48;

(CO

1.66 [±0.17];

upward trends, particularly in

(Comprising CO

0.47;

O 0.14;

O 0.15;

0.48 [±0.05];

. Halocarbon RF trend

, N

O, and

Halocarbons 0.28)

Halocarbons 0.34

) N

O 0.16 [±0.02];

is positive

halocarbons)

Halocarbons

0.34

[±0.03])

Stratospheric ozone

–0.1 [2x]

–0.15 [67%]

–0.05 [±0.10]

Re-evaluated to be weaker

Tropospheric ozone

+0.40 [50%]

+0.35 [43%]

+0.35 [–0.1, +0.3]

Best estimate unchanged.

However, a larger RF could

possible

Stratospheric

Not evaluated

+0.01 to +0.03

+0.07 [±0.05]

Re-evaluated to be higher

water vapour
from CH

Total direct aerosol

Not evaluated

–0.50 [±0.40]

Newly evaluated

Direct sulphate aerosol

–0.40 [2x]

–0.40 [±0.20]

Better constrained

Direct fossil fuel aerosol

Not evaluated

–0.10 [3x]

–0.05 [±0.05]

Re-evaluated to be weaker

(organic carbon)

Direct fossil fuel

+0.10 [3x]

+0.20 [2x]

+0.20 [±0.15]

Similar best estimate to the TAR.

aerosol (BC)

Response affected by semi-direct

effects

Direct biomass

–0.20 [3x]

+0.03 [±0.12]

Re-evaluated and sign changed.

burning aerosol

Response affected by semi-direct

effects

Direct nitrate aerosol

Not evaluated

–0.10 [±0.10]

Newly evaluated

Direct mineral

Not evaluated

–0.60 to +0.40

–0.10 [±0.20]

Re-evaluated to have a smaller

dust aerosol

anthropogenic

fraction

Cloud albedo effect

0 to –1.5

0.0 to –2.0

–0.70 [–1.1, +0.4]

Best estimate now given

(sulphate only)

(all aerosols)

Surface albedo

Not evaluated

–0.20 [100%]

–0.20 [±0.20]

Additional studies

(land use

)

Surface albedo

Not evaluated

+0.10 [±0.10]

Newly evaluated

(BC aerosol on snow)

Persistent linear

Not evaluated

0.02 [3.5x]

0.01 [–0.007, +0.02]

Re-evaluated to be smaller

contrails

Solar irradiance

+0.30 [67%]

+0.12 [–0.06, +0.18]

Re-evaluated to be less than half

207

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

able. 2.13.

Emission-based RFs for emitted components with radia

tive effects other than through changes in their a

tmospheric abundance.

Min

or effects where the estima

ted RF is less than 0.01

W m

–2

are not inc

luded.

Effects on

sulpha

te aerosols are not inc

luded since SO

emission is the only signifi

cant factor affecting sulpha

te aerosols.

Method of calcula

tion and uncertainty ranges are given in

the footnotes.

alues represent RF in 2005 due to emissions and

changes since 1750.

See F

igure 2.21 for gra

phical presenta

tion of these values.

Notes:

opospheric ozone.

stratospheric ozone.

stratospheric water vapour

Derived fr

om the total RF of the observed CO

change (T

able 2.12), with the contributions fr

om CH

, CO and VOC emissions fr

om fossil sour

ces subtracted. Historical emissions of CH

, CO and VOCs fr

om Emission

Database for Global Atmospheric Resear

ch (EDGAR)-HistorY Database of the Envir

onment (HYDE) (V

an Aar

denne et al., 2001), CO

contribution fr

om these sour

ces calculated with CO

model described by Joos et al.

(1996).

Derived fr

om the total RF of the observed CH

change (T

able 2.12). Subtracted fr

om this wer

e the contributions thr

ough lifetime changes caused by emissions of NO

, CO and VOC that change OH concentrations. The

fects of NO

, CO and VOCs ar

e fr

om Shindell et al. (2005). Ther

e ar

e signifi

cant uncertainties r

elated to these r

elations. Following Shindell et al. (2005) the uncertainty estimate is taken to be ±20% f

or CH

emissions,

and ±50% for CO, VOC and NO

emissions.

All the radiative for

cing fr

om changes in stratospheric water vapour is attributed to CH

emissions (Section 2.3.7 and T

able 2.12).

RF calculated based on observed concentration change, see T

able 2.12 and Section 2.3

80% of RF fr

om observed ozone depletion in the stratospher

e (T

able 2.12) is attributed to CFCs/HFCs, r

emaining 20% to N

O (Based on Nevison et al., 1999 and WMO, 2003).

RF fr

om T

able 2.12, uncertainty ±0.10 W m

–2

Uncertainty too lar

ge to apportion the indir

ect cloud albedo ef

fect to each aer

osol type (Hansen et al., 2005).

Mean of all studies in T

able 2.5, includes fossil fuel, biofuel and biomass bur

ning. Uncertainty (90% confi

dence ranges) ±0.25 W m

–2

(BC) and ±0.20 W m

–2

(or

ganic carbon) based on range of r

eported values in T

able 2.5.

RF fr

om T

able 2.12, uncertainty ±0.10 W m

–2

CFC/

HCFC

HFC/

PFC/SF

BC-

dir

ect

BC snow

albedo

Organic

carbon

(T)

(S)

O(S)

Nitrate

aer

osols

Indir

ect

cloud albedo

fect

Component emitted

Atmospheric or surface change dir

ectly causing radiative for

cing

1.56

0.016

0.57

0.2

0.07

CFC/HCFC/halons

0.32

–0.04

0.15

–0.01

HFC/PFC/SF

0.017

CO/VOC

0.06

0.08

0.13

NOx

–0.17

0.06

–0.10

0.34

0.1

–0.19

210

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

an aerosol cloud albedo effect while the Geophysical Fluid
Dynamics Laboratory Coupled Climate Model (GFDL CM2.1)
(Delworth et al., 2005; Knutson et al., 2006) does not. Radiative
forcing over most of the globe is positive and is dominated by
the LLGHGs. This is more so for the SH than for the NH, owing
to the pronounced aerosol presence in the mid-latitude NH (see
also Figure 2.12), with the regions of substantial aerosol RF
clearly manifest over the source-rich continental areas. There
are quantitative differences between the two GCMs in the
global mean RF, which are indicative of the uncertainties in
the RF from the non-LLGHG agents, particularly aerosols
(see Section 2.4 and Figure 2.12d). The direct effect of
aerosols is seen in the total RF of the GFDL model over NH
land regions, whereas the cloud albedo effect dominates the
MIROC+SPRINTARS model in the stratocumulus low-latitude
ocean regions. Note that the spatial pattern of the forcing is not
indicative of the climate response pattern.

Wherever aerosol presence is considerable (namely the

NH), the surface forcing is negative, relative to pre-industrial
times (Figure 2.24). Because of the aerosol in

uence on the

reduction of the shortwave radiation reaching the surface
(see also Figure 2.12f), there is a net (sum of shortwave and
longwave) negative surface forcing over a large part of the
globe (see also Figure 2.23). In the absence of aerosols,
LLGHGs increase the atmospheric longwave emission, with an
accompanying increase in the longwave radiative

ux reaching

the surface. At high latitudes and in parts of the SH, there are
fewer anthropogenic aerosols and thus the surface forcing has a
positive value, owing to the LLGHGs.

These spatial patterns of RF and surface forcing imply

different changes in the NH equator-to-pole gradients for the
surface and tropopause. These, in turn, imply different changes
in the amount of energy absorbed by the troposphere at low
and high latitudes. The aerosol in

uences are also manifest in

the difference between the NH and SH in both RF and surface
forcing.

2.10 Global Warming Potentials and

Other Metrics for Comparing

Different

Emissions

2.10.1 Defi nition of an Emission Metric and the

Global Warming Potential

Multi-component abatement strategies to limit anthropogenic

climate change need a framework and numerical values for
the trade-off between emissions of different forcing agents.
Global Warming Potentials or other emission metrics provide
a tool that can be used to implement comprehensive and cost-
effective policies (Article 3 of the UNFCCC) in a decentralised
manner so that multi-gas emitters (nations, industries) can
compose mitigation measures, according to a speci

ed emission

constraint, by allowing for substitution between different
climate agents. The metric formulation will differ depending on

whether a long-term climate change constraint has been set (e.g.,
Manne and Richels, 2001) or no speci

c long-term constraint

has been agreed upon (as in the Kyoto Protocol). Either metric
formulation requires knowledge of the contribution to climate
change from emissions of various components over time. The
metrics assessed in this report are purely physically based.
However, it should be noted that many economists have argued
that emission metrics need also to account for the economic
dimensions of the problem they are intended to address (e.g.,
Bradford, 2001; Manne and Richels, 2001; Godal, 2003;
O’Neill, 2003). Substitution of gases within an international
climate policy with a long-term target that includes economic
factors is discussed in Chapter 3 of IPCC WGIII AR4. Metrics
based on this approach will not be discussed in this report.

A very general formulation of an emission metric is given by

(e.g., Kandlikar, 1996):

where

∆

(t))

is a function describing the impact (damage and

bene

t) of change in climate (

∆

at time

. The expression

g(t)

is a weighting function over time (e.g.,

g(t) =

–kt

is a simple

discounting giving short-term impacts more weight) (Heal,
1997; Nordhaus, 1997). The subscript

refers to a baseline

emission path. For two emission perturbations

and

the

absolute metric values

and

can be calculated to provide

a quantitative comparison of the two emission scenarios. In the
special case where the emission scenarios consist of only one
component (as for the assumed pulse emissions in the de

nition

of GWP), the ratio between

and

can be interpreted as

a relative emission index for component

versus a reference

component

(such as CO

in the case of GWP).

There are several problematic issues related to de

ning

a metric based on the general formulation given above
(Fuglestvedt

et al., 2003). A major problem is to de

appropriate impact functions, although there have been some
initial attempts to do this for a range of possible climate impacts
(Hammitt

et al., 1996; Tol, 2002; den Elzen et al., 2005). Given

that impact functions can be de

ned,

calculations would

require regionally resolved climate change data (temperature,
precipitation, winds, etc.) that would have to be based on GCM
results with their inherent uncertainties (Shine et al., 2005a).
Other problematic issues include the de

nition of the temporal

weighting function

g(t)

and the baseline emission scenarios.

Due to these dif

culties, the simpler and purely physical

GWP index, based on the time-integrated global mean RF of a
pulse emission of 1 kg of some compound (

) relative to that of

1 kg of the reference gas CO

, was developed (IPCC, 1990) and

adopted for use in the Kyoto Protocol. The GWP of component

is de

ned by

∫

∞

[(I(

(r + i)

(t)) – I (

(t)))

g(t)]dt

∫

(t) dt

∫

· [C

(t)] dt

∫

(t) dt

∫

· [C

(t)] dt

GWP

211

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

where

is the time horizon,

is the global mean RF of

component

is the RF per unit mass increase in atmospheric

abundance of component

(radiative ef

ciency), [

(

)] is the

time-dependent abundance of

, and the corresponding quantities

for the reference gas (

) in the denominator. The numerator and

denominator are called the absolute global warming potential
(AGWP) of

and

respectively. All GWPs given in this report

use CO

as the reference gas. The simpli

cations made to

derive the standard GWP index include (1) setting

(

) = 1 (i.e.,

no discounting) up until the time horizon (

) and then

g(t)

= 0

thereafter, (2) choosing a 1-kg pulse emission, (3) de

ning the

impact function,

∆

to be the global mean RF, (4) assuming

that the climate response is equal for all RF mechanisms and
(5) evaluating the impact relative to a baseline equal to current
concentrations (i.e., setting

∆

(t)) =

0). The criticisms of the

GWP metric have focused on all of these simpli

cations (e.g.,

O’Neill, 2000; Smith and Wigley, 2000; Bradford, 2001; Godal,
2003). However, as long as there is no consensus on which
impact function (

∆

) and temporal weighting functions to

use (both involve value judgements), it is dif

cult to assess the

implications of the simpli

cations objectively (O’Neill, 2000;

Fuglestvedt et al., 2003).

The adequacy of the GWP concept has been widely debated

since its introduction (O’Neill, 2000; Fuglestvedt et al., 2003).
By its de

nition, two sets of emissions that are equal in terms

of their total GWP-weighted emissions will not be equivalent in
terms of the temporal evolution of climate response (Fuglestvedt
et al., 2000; Smith and Wigley, 2000). Using a 100-year
time horizon as in the Kyoto Protocol, the effect of current
emissions reductions (e.g., during the

rst commitment period

under the Kyoto Protocol) that contain a signi

cant fraction

of short-lived species (e.g., CH

) will give less temperature

reductions towards the end of the time horizon, compared to
reductions in CO

emissions only. Global Warming Potentials

can really only be expected to produce identical changes in one
measure of climate change – integrated temperature change
following emissions impulses – and only under a particular
set of assumptions (O’Neill, 2000). The Global Temperature
Potential (GTP) metric (see Section 2.10.4.2) provides an
alternative approach by comparing global mean temperature
change at the end of a given time horizon. Compared to the
GWP, the GTP gives equivalent climate response at a chosen
time, while putting much less emphasis on near-term climate

uctuations caused by emissions of short-lived species (e.g.,

). However, as long as it has not been determined, neither

scienti

cally, economically nor politically, what the proper time

horizon for evaluating ‘dangerous anthropogenic interference in
the climate system’ should be, the lack of temporal equivalence
does not invalidate the GWP concept or provide guidance as to
how to replace it. Although it has several known shortcomings, a
multi-gas strategy using GWPs is very likely to have advantages
over a CO

-only strategy (O’Neill, 2003). Thus, GWPs remain

the recommended metric to compare future climate impacts of
emissions of long-lived climate gases.

Globally averaged GWPs have been calculated for short-

lived species, for example, ozone precursors and absorbing

aerosols (Fuglestvedt

et al., 1999; Derwent

et al., 2001; Collins

et al., 2002; Stevenson

et al., 2004; Berntsen

et al., 2005; Bond

and Sun, 2005). There might be substantial co-bene

ts realised

in mitigation actions involving short-lived species affecting
climate and air pollutants (Hansen and Sato, 2004); however,
the effectiveness of the inclusion of short-lived forcing agents
in international agreements is not clear (Rypdal et al., 2005).
To assess the possible climate impacts of short-lived species
and compare those with the impacts of the LLGHGs, a metric
is needed. However, there are serious limitations to the use of
global mean GWPs for this purpose. While the GWPs of the
LLGHGs do not depend on location and time of emissions, the
GWPs for short-lived species will be regionally and temporally
dependent. The different response of precipitation to an aerosol
RF compared to a LLGHG RF also suggests that the GWP
concept may be too simplistic when applied to aerosols.

2.10.2 Direct Global Warming Potentials

All GWPs depend on the AGWP for CO

(the denominator

in the de

nition of the GWP). The AGWP of CO

again depends

on the radiative ef

ciency for a small perturbation of CO

from

the current level of about 380 ppm. The radiative ef

ciency per

kilogram of CO

has been calculated using the same expression

as for the CO

RF in Section 2.3.1, with an updated background

mixing ratio of 378 ppm. For a small perturbation from 378

ppm, the RF is 0.01413 W m

–2

ppm

–1

(8.7% lower than the TAR

value). The CO

response function (see Table 2.14) is based on

an updated version of the Bern carbon cycle model (Bern2.5CC;
Joos et al. 2001), using a background CO

concentration of

378 ppm. The increased background concentration of CO

means that the airborne fraction of emitted CO

(Section 7.3)

is enhanced, contributing to an increase in the AGWP for
CO

. The AGWP values for CO

for 20, 100, and 500 year

time horizons are 2.47 × 10

–14

, 8.69 × 10

–14

, and 28.6 × 10

–14

W m

–2

yr (kg CO

)

–1

, respectively. The uncertainty in the AGWP

for CO

is estimated to be ±15%, with equal contributions from

the CO

response function and the RF calculation.

Updated radiative ef

ciencies for well-mixed greenhouse

gases are given in Table 2.14. Since the TAR, radiative
ef

ciencies have been reviewed by Montzka et al. (2003)

and Velders et al. (2005). Gohar et al. (2004) and Forster et
al. (2005) investigated HFC compounds, with up to 40%
differences from earlier published results. Based on a variety
of radiative transfer codes, they found that uncertainties could
be reduced to around 12% with well-constrained experiments.
The HFCs studied were HFC-23, HFC-32, HFC-134a and HFC-
227ea. Hurley et al. (2005) studied the infrared spectrum and
RF of per

uoromethane (C

) and derived a 30% higher

GWP value than given in the TAR. The RF calculations for
the GWPs for CH

, N

O and halogen-containing well-mixed

greenhouse gases employ the simpli

ed formulas given in

Ramaswamy et al. (2001; see Table 6.2 of the TAR). Table 2.14
gives GWP values for time horizons of 20, 100 and 500 years.

The species in Table 2.14 are those for which either signi

cant

concentrations or large trends in concentrations have been

212

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

Table 2.14.

Lifetimes, radiative effi ciencies and direct (except for CH

) GWPs relative to CO

. For ozone-depleting substances and their replacements, data are taken from

IPCC/TEAP (2005) unless otherwise indicated.

Global Warming Potential for

Given Time Horizon

Industrial Designation

Radiative

or Common Name

Lifetime

ciency

SAR

‡

(years)

Chemical Formula

(years)

(W m

–2

ppb

–1)

(100-yr)

20-yr

100-yr

500-yr

Carbon dioxide

See below

1.4x10

–5

Methane

3.7x10

–4

7.6

Nitrous oxide

114

3.03x10

–3

310

289

298

153

Substances controlled by the Montreal Protocol

CFC-11

CCl

0.25

3,800

6,730

4,750

1,620

CFC-12

CCl

100

0.32

8,100

11,000

10,900

5,200

CFC-13

CClF

640

0.25

10,800

14,400

16,400

CFC-113

CCl

FCClF

0.3

4,800

6,540

6,130

2,700

CFC-114

CClF

300

0.31

8,040

10,000

8,730

CFC-115

CClF

1,700

0.18

5,310

7,370

9,990

Halon-1301

CBrF

0.32

5,400

8,480

7,140

2,760

Halon-1211

CBrClF

0.3

4,750

1,890

575

Halon-2402

CBrF

0.33

3,680

1,640

503

Carbon tetrachloride

CCl

0.13

1,400

2,700

1,400

435

Methyl bromide

0.7

0.01

Methyl chloroform

CCl

0.06

506

146

HCFC-22

CHClF

0.2

1,500

5,160

1,810

549

HCFC-123

CHCl

1.3

0.14

273

HCFC-124

CHClFCF

5.8

0.22

470

2,070

609

185

HCFC-141b

CCl

9.3

0.14

2,250

725

220

HCFC-142b

CClF

17.9

0.2

1,800

5,490

2,310

705

HCFC-225ca

CHCl

1.9

0.2

429

122

HCFC-225cb

CHClFCF

CClF

5.8

0.32

2,030

595

181

Hydrofl uorocarbons

HFC-23

CHF

270

0.19

11,700

12,000

14,800

12,200

HFC-32

4.9

0.11

650

2,330

675

205

HFC-125

CHF

0.23

2,800

6,350

3,500

1,100

HFC-134a

FCF

0.16

1,300

3,830

1,430

435

HFC-143a

0.13

3,800

5,890

4,470

1,590

HFC-152a

CHF

1.4

0.09

140

437

124

HFC-227ea

CHFCF

34.2

0.26

2,900

5,310

3,220

1,040

HFC-236fa

240

0.28

6,300

8,100

9,810

7,660

HFC-245fa

CHF

7.6

0.28

3,380

1030

314

HFC-365mfc

8.6

0.21

2,520

794

241

HFC-43-10mee

CHFCHFCF

15.9

0.4

1,300

4,140

1,640

500

Perfl uorinated compounds

Sulphur hexa

uoride

3,200

0.52

23,900

16,300

22,800

32,600

Nitrogen tri

uoride

740

0.21

12,300

17,200

20,700

PFC-14

50,000

0.10

6,500

5,210

7,390

11,200

PFC-116

10,000

0.26

9,200

8,630

12,200

18,200

213

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

Notes:

The CO

response function used in this report is based on the revised version of the Bern Carbon cycle model used in Chapter 10 of this report (Bern2.5CC; Joos et

al. 2001) using a background CO

concentration value of 378 ppm. The decay of a pulse of CO

with time t is given by

Where

= 0.217, a

= 0.259, a

= 0.338, a

= 0.186,

= 172.9 years,

= 18.51 years, and

= 1.186 years.

The radiative effi ciency of CO

is calculated using the IPCC (1990) simplifi ed expression as revised in the TAR, with an updated background concentration value of

378 ppm and a perturbation of +1 ppm (see Section 2.10.2).

The perturbation lifetime for methane is 12 years as in the TAR (see also Section 7.4). The GWP for methane includes indirect effects from enhancements of ozone

and stratospheric water vapour (see Section 2.10.3.1).

Shine et al. (2005c), updated by the revised AGWP for CO

. The assumed lifetime of 1,000 years is a lower limit.

Hurley et al. (2005)

Robson et al. (2006)

Young et al. (2006)

i =

•

-t/

Table 2.14 (continued)

Global Warming Potential for

Given Time Horizon

Industrial Designation

Radiative

or Common Name

Lifetime

ciency

SAR

‡

(years) Chemical

Formula

(years)

–2

ppb

–1)

(100-yr)

20-yr

100-yr

500-yr

Perfl uorinated compounds

(continued)

PFC-218

2,600

0.26

7,000

6,310

8,830

12,500

PFC-318

c-C

3,200

0.32

8,700

7,310

10,300

14,700

PFC-3-1-10

2,600

0.33

7,000

6,330

8,860

12,500

PFC-4-1-12

4,100

0.41

6,510

9,160

13,300

PFC-5-1-14

3,200

0.49

7,400

6,600

9,300

13,300

PFC-9-1-18

>1,000

0.56

>5,500

>7,500

>9,500

trifl uoromethyl

800

0.57

13,200

17,700

21,200

sulphur pentafl uoride

Fluorinated ethers

HFE-125

CHF

OCF

136

0.44

13,800

14,900

8,490

HFE-134

CHF

OCHF

0.45

12,200

6,320

1,960

HFE-143a

OCF

4.3

0.27

2,630

756

230

HCFE-235da2

CHF

OCHClCF

2.6

0.38

1,230

350

106

HFE-245cb2

OCF

CHF

5.1

0.32

2,440

708

215

HFE-245fa2

CHF

OCH

4.9

0.31

2,280

659

200

HFE-254cb2

OCF

CHF

2.6

0.28

1,260

359

109

HFE-347mcc3

OCF

5.2

0.34

1,980

575

175

HFE-347pcf2

CHF

OCH

7.1

0.25

1,900

580

175

HFE-356pcc3

OCF

CHF

0.33

0.93

386

110

HFE-449sl
(HFE-7100)

OCH

3.8

0.31

1,040

297

HFE-569sf2

0.77

0.3

207

(HFE-7200)

HFE-43-10pccc124

CHF

OCF

OCHF

6.3

1.37

6,320

1,870

569

(H-Galden 1040x)

HFE-236ca12

CHF

OCF

OCHF

12.1

0.66

8,000

2,800

860

(HG-10)

HFE-338pcc13

CHF

OCF

OCHF

6.2

0.87

5,100

1,500

460

(HG-01)

Perfl uoropolyethers

PFPMIE

OCF(CF

)CF

OCF

800

0.65

7,620

10,300

12,400

Hydrocarbons and other compounds – Direct Effects

Dimethylether

OCH

0.015

0.02

<<1

Methylene chloride

0.38

0.03

8.7

2.7

Methyl chloride

1.0

0.01

214

Changes in Atmospheric Constituents and in Radiative Forcing

Chapter 2

observed or a clear potential for future

emissions has been

identi

ed. The uncertainties of these direct GWPs are taken to

be ±35% for the 5 to 95% (90%) con

dence range.

2.10.3 Indirect

GWPs

Indirect radiative effects include the direct effects of

degradation products or the radiative effects of changes in
concentrations of greenhouse gases caused by the presence of
the emitted gas or its degradation products. Direct effects of
degradation products for the greenhouse gases are not considered
to be signi

cant (WMO, 2003). The indirect effects discussed

here are linked to ozone formation or destruction, enhancement
of stratospheric water vapour, changes in concentrations of the
OH radical with the main effect of changing the lifetime of CH

and secondary aerosol formation. Uncertainties for the indirect
GWPs are generally much higher than for the direct GWPs.
The indirect GWP will in many cases depend on the location
and time of the emissions. For some species (e.g., NO

) the

indirect effects can be of opposite sign, further increasing the
uncertainty of the net GWP. This can be because background
levels of reactive species (e.g., NO

) can affect the chemical

response nonlinearly, and/or because the lifetime or the
radiative effects of short-lived secondary species formed can be
regionally dependent. Thus, the usefulness of the global mean
GWPs to inform policy decisions can be limited. However, they
are readily calculable and give an indication of the total potential
of mitigating climate change by including a certain forcing
agent in climate policy. Following the approach taken by the
SAR and the TAR, the CO

produced from oxidation of CH

CO and NMVOCs of fossil origin is not included in the GWP
estimates since this carbon has been included in the national
CO

inventories. This issue may need to be reconsidered as

inventory guidelines are revised.

2.10.3.1 Methane

Four indirect radiative effects of CH

emissions have been

identi

ed (see Prather et al., 2001; Ramaswamy et al., 2001).

Methane enhances its own lifetime through changes in the
OH concentration: it leads to changes in tropospheric ozone,
enhances stratospheric water vapour levels, and produces CO

The GWP given in Table 2.14 includes the

rst three of these

effects. The lifetime effect is included by adopting a perturbation
lifetime of 12 years (see Section 7.4). The effect of ozone
production is still uncertain, and as in the TAR, it is included
by enhancing the net of the direct and the lifetime effect by
25%. The estimate of RF caused by an increase in stratospheric
water vapour has been increased signi

cantly since the TAR

(see Section 2.3.7). This has also been taken into account in the
GWP estimate for CH

by increasing the enhancement factor

from 5% (TAR) to 15%. As a result, the 100-year GWP for CH

has increased from 23 in the TAR to 25.

2.10.3.2 Carbon

Monoxide

The indirect effects of CO occur through reduced OH levels

(leading to enhanced concentrations of CH

) and enhancement

of ozone. The TAR gave a range of 1.0 to 3.0 for the 100-year
GWP. Since the TAR, Collins

et al. (2002) and Berntsen et al.

(2005) have calculated GWPs for CO emissions that range
between 1.6 and 2.0, depending on the location of the emissions.
Berntsen et al. (2005) found that emissions of CO from Asia
had a 25% higher GWP compared to European emissions.
Averaging over the TAR values and the new estimates give a
mean of 1.9 for the 100-year GWP for CO.

2.10.3.3 Non-methane Volatile Organic Compounds

Collins et al. (2002) calculated indirect GWPs for 10

NMVOCs with a global three-dimensional Lagrangian
chemistry-transport model. Impacts on tropospheric ozone,
CH

(through changes in OH) and CO

have been considered,

using either an ‘anthropogenic’ emission distribution or a
‘natural’ emission distribution depending on the main sources
for each gas. The indirect GWP values are given in Table 2.15.
Weighting these GWPs by the emissions of the respective
compounds gives a weighted average 100-year GWP of 3.4. Due
to their short lifetimes and the nonlinear chemistry involved in
ozone and OH chemistry, there are signi

cant uncertainties in

the calculated GWP values. Collins et al. (2002) estimated an
uncertainty range of –50% to +100%.

2.10.3.4 Nitrogen

Oxides

The short lifetime and complex nonlinear chemistry, which

cause two opposing indirect effects through ozone enhancements
and CH

reductions, make calculations of GWP for NO

emissions very uncertain (Shine et al., 2005a). In addition, the
effect of nitrate aerosol formation (see Section 2.4.4.5), which
has not yet been included in model studies calculating GWPs
for NO

, can be signi

cant. Due to the nonlinear chemistry, the

net RF of NO

emissions will depend strongly on the location

of emission and, with a strict de

nition of a pulse emission

for the GWP, also on timing (daily, seasonal) of the emissions
(Fuglestvedt

et al., 1999; Derwent

et al., 2001; Wild

et al., 2001;

Stevenson

et al., 2004; Berntsen

et al., 2005, 2006). Due to the

lack of agreement even on the sign of the global mean GWP for
NO

among the different studies and the omission of the nitrate

aerosol effect, a central estimate for the 100-year GWP for NO

is not presented.

2.10.3.5 Halocarbons

Chlorine- and bromine-containing halocarbons lead to ozone

depletion when the halocarbon molecules are broken down in
the stratosphere and chlorine or bromine atoms are released.
Indirect GWPs for ozone-depleting halocarbons are estimated
in Velders et al. (2005; their Table 2.7). These are based on

215

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

observed ozone depletion between 1980 and 1990 for 2005
emissions using the Daniel et al. (1995) formulation. Velders
et al. (2005) did not quote net GWPs, pointing out that the
physical characteristics of the CFC warming effect and ozone
cooling effect were very different from each other.

2.10.3.6 Hydrogen

The main loss of hydrogen (H

) is believed to be through

surface deposition, but about 25% is lost through oxidation
by OH. In the stratosphere, this enhances the water vapour
concentrations and thus also affects the ozone concentrations. In
the troposphere, the chemical effects are similar to those of CO,
leading to ozone production and CH

enhancements (Prather,

2003). Derwent et al. (2001) calculated an indirect 100-year
GWP for the tropospheric effects of H

of 5.8, which includes

the effects of CH

lifetime and tropospheric ozone.

2.10.4 New Alternative Metrics for Assessing

Emissions

While the GWP is a simple and straightforward index

to apply for policy makers to rank emissions of different
greenhouse gases, it is not obvious on what basis ‘equivalence’
between emissions of different species is obtained (Smith and

Wigley, 2000; Fuglestvedt et al., 2003). The GWP metric is
also problematic for short-lived gases or aerosols (e.g., NO

or BC aerosols), as discussed above. One alternative, the RF
index (RFI) introduced by IPCC (1999), should not be used as
an emission metric since it does not account for the different
residence times of different forcing agents.

2.10.4.1 Revised GWP Formulations

2.10.4.1.2 Including the climate ef

cacy in the GWP

As discussed in Section 2.8.5, the climate ef

cacy can vary

between different forcing agents (within 25% for most realistic
RFs). Fuglestvedt et al. (2003) proposed a revised GWP
concept that includes the ef

cacy of a forcing agent. Berntsen

et al. (2005) calculated GWP values in this way for NO

and

CO emissions in Europe and in South East Asia. The ef

cacies

are less uncertain than climate sensitivities. However, Berntsen
et al. (2005) showed that for ozone produced by NO

emissions

the climate ef

cacies will also depend on the location of the

emissions.

2.10.4.2 The Global Temperature Potential

Shine et al. (2005b) proposed the GTP as a new relative

emission metric. The GTP is de

ned as the ratio between the

Organic Compound/Study

GWP

CH4

GWP

Ethane (C

)

2.9

2.6

5.5

Propane (C

)

2.7

0.6

3.3

Butane (C

)

2.3

1.7

4.0

Ethylene (C

)

1.5

2.2

3.7

Propylene (C

)

–2.0

3.8

1.8

Toluene (C

)

0.2

2.5

2.7

Isoprene (C

)

1.1

1.6

2.7

Methanol (CH

OH)

1.6

1.2

2.8

Acetaldehyde (CH

CHO)

–0.4

1.7

1.3

Acetone (CH

COCH

)

0.3

0.2

0.5

Derwent et al. NH surface NO

a,b

–24

–12

Derwent et al. SH surface NO

a,b

–64

–31

Wild et al., industrial NO

–44

–12

Berntsen et al., surface NO

Asia

–31 to –42

55 to 70

25 to 29

Berntsen et al., surface NO

Europe

–8.6 to –11

8.1 to 12.7

–2.7 to +4.1

Derwent et al., Aircraft NO

a,b

–145

246

100

Wild et al., Aircraft NO

–210

340

130

Stevenson et al. Aircraft NO

–159

155

–3

Table 2.15.

Indirect GWPs (100-year) for 10 NMVOCs from Collins et al. (2002) and for NO

emissions (on N-basis) from Derwent et al. (2001), Wild et al. (2001), Berntsen et al.

(2005) and Stevenson et al. (2004). The second and third columns respectively represent the methane and ozone contribution to the net GWP and the fourth column represents
the net GWP.

Notes:

Corrected values as described in Stevenson et al. (2004).

For January pulse emissions.

Range from two three-dimensional chemistry transport models and two radiative transfer models.

217

Chapter 2

Changes in Atmospheric Constituents and in Radiative Forcing

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