2

Changes in Atmospheric Constituents

and in Radiative Forcing

Coordinating Lead Authors:

Piers Forster (UK), Venkatachalam Ramaswamy (USA)

Lead Authors:

Paulo Artaxo (Brazil), Terje Berntsen (Norway), Richard Betts (UK), David W. Fahey (USA), James Haywood (UK), Judith Lean (USA),

David C. Lowe (New Zealand), Gunnar Myhre (Norway), John Nganga (Kenya), Ronald Prinn (USA, New Zealand),

Graciela Raga (Mexico, Argentina), Michael Schulz (France, Germany), Robert Van Dorland (Netherlands) 

Contributing Authors:

G. Bodeker (New Zealand), O. Boucher (UK, France), W.D. Collins (USA), T.J. Conway (USA), E. Dlugokencky (USA), J.W. Elkins (USA), 

D. Etheridge (Australia), P. Foukal (USA), P. Fraser (Australia), M. Geller (USA), F. Joos (Switzerland), C.D. Keeling (USA), R. Keeling (USA), 

S. Kinne (Germany), K. Lassey (New Zealand), U. Lohmann (Switzerland), A.C. Manning (UK, New Zealand), S. Montzka (USA), 

D. Oram (UK), K. O’Shaughnessy (New Zealand), S. Piper (USA), G.-K. Plattner (Switzerland), M. Ponater (Germany), 

N. Ramankutty (USA, India), G. Reid (USA), D. Rind (USA), K. Rosenlof (USA), R. Sausen (Germany), D. Schwarzkopf (USA), 

S.K. Solanki (Germany, Switzerland), G. Stenchikov (USA), N. Stuber (UK, Germany), T. Takemura (Japan), C. Textor (France, Germany), 

R. Wang (USA), R. Weiss (USA), T. Whorf (USA)

Review Editors:

Teruyuki Nakajima (Japan), Veerabhadran Ramanathan (USA)

This chapter should be cited as:

Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, 

G. Raga, M. Schulz and R. Van Dorland, 2007: Changes in Atmospheric Constituents and in Radiative Forcing. In: Climate Change 2007: 

The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate 

Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University 

Press, Cambridge, United Kingdom and New York, NY, USA.
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 Infl uences? .............................................. 135 

References........................................................................ 217
131

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

Executive Summary

Radiative forcing (RF)1 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 confi 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 fi rst time, the combined RF for all anthropogenic 

agents is derived. Estimates are also made for the fi 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]2 W m–2, indicating that, since 1750, it is extremely 

likely3 that humans have exerted a substantial warming 

infl uence on climate. This RF estimate is likely to be at least 

fi 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 infl uence comparable to that of the combined 

anthropogenic RF.

Increasing concentrations of the long-lived greenhouse 

gases (carbon dioxide (CO2), methane (CH4), nitrous oxide 

(N2O), halocarbons and sulphur hexafl uoride (SF6); hereinafter 

LLGHGs) have led to a combined RF of +2.63 [±0.26] W m–2. 

Their RF has a high level of scientifi c understanding.4 The 9% 

increase in this RF since the TAR is the result of concentration 

changes since 1998.

— The global mean concentration of CO2 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 CO2 in the atmosphere was 1.9 ppm yr–1 and the 

CO2 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 CH4 in 2005 was 1,774 

ppb, contributing an RF of +0.48 [±0.05] W m–2. Over the past 

two decades, CH4 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 CH4 emissions 

are not increasing.

— The Montreal Protocol gases (chlorofl uorocarbons (CFCs), 

hydrochlorofl 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 

infl uencing the observed spatial concentration gradients. 

— Concentrations of many of the fl uorine-containing Kyoto 

Protocol gases (hydrofl uorocarbons (HFCs), perfl uorocarbons, 

SF6) 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 

infl uences the lifetimes and thus RF values of CH4, 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 scientifi c understanding. There 

are indications of signifi 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 

CH4 is estimated to be +0.07 [± 0.05] W m–2, with a low level 

of scientifi 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 

1 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 mentioned, 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.

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).

3 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).

4 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 scientifi c understanding. The RF due to the 

cloud albedo effect (also referred to as fi 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 scientifi c 

understanding. 

— Atmospheric models have improved and many now 

represent all aerosol components of signifi cance. Improved in 

situ, satellite and surface-based measurements have enabled 

verifi 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 infl uenced by aerosol overlying 

clouds. For the fi 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 scientifi 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 

a low level of scientifi c understanding. Other surface property 

changes can affect climate through processes that cannot be 

quantifi ed by RF; these have a very low level of scientifi c 

understanding.

Persistent linear contrails from aviation contribute an RF 

of +0.01 [–0.007, +0.02] W m–2, with a low level of scientifi c 

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 scientifi 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 signifi 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 profi 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 signifi 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 

confi 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 forcing5 is very likely negative. 

By reducing the shortwave radiative fl 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. 

5 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 signifi 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) Scientifi 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 

(CO2), methane (CH4), nitrous oxide (N2O), chlorofl uorocarbons (CFCs), hydrochlorofl uorocarbons (HCFCs), 

hydrofl uorocarbons (HFCs), perfl uorocarbons (PFCs) and 

sulphur hexafl uoride (SF6), hereinafter collectively referred 

to as the LLGHGs; Section 2.3), the chapter makes use of 

new global measurement capabilities and combines longterm measurements from various networks to update trends 

through 2005. Compared to other RF agents, these trends are 

considerably better quantifi 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 suffi 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 briefl y discussed in Chapter 10. 

2.2 Concept of Radiative Forcing

The defi nition of RF from the TAR and earlier IPCC 

assessment reports is retained. Ramaswamy et al. (2001) defi ne 

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 

fi 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 CO2, 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 

(ΔTs): ΔTs = λRF, where λ 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 infl 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 quantifi 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 fi 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 fi elds along with their associated climate change. 

In such models, some ‘response’ is necessary to evaluate the 
134

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

RF. This ‘response’ is most signifi cant for aerosolrelated cloud changes, where the tropospheric 

state needs to change signifi cantly in order to 

create a radiative perturbation of the climate 

system (Jacob et al., 2005).

Over the palaeoclimate time scales that are 

discussed in Chapter 6, long-term changes in 

forcing agents arise due to so-called ‘boundary 

condition’ changes to the Earth’s climate system 

(such as changes in orbital parameters, ice sheets 

and continents). For the purposes of this chapter, 

these ‘boundary conditions’ are assumed to be 

invariant and forcing agent changes are considered 

to be external to the climate system. The natural 

RFs considered are solar changes and volcanoes; 

the other RF agents are all attributed to humans. 

For the LLGHGs it is appropriate to assume 

that forcing agent concentrations have not been 

signifi cantly altered by biogeochemical responses 

(see Sections 7.3 and 7.4), and RF is typically 

calculated in off-line radiative transfer schemes, 

using observed changes in concentration (i.e., 

humans are considered solely responsible for their 

increase). For the other climate change drivers, RF 

is often estimated using general circulation model 

(GCM) data employing a variety of methodologies 

(Ramaswamy et al., 2001; Stuber et al., 2001b; 

Tett et al., 2002; Shine et al., 2003; Hansen et 

al., 2005; Section 2.8.3). Often, alternative RF calculation 

methodologies that do not directly follow the TAR defi nition of 

a stratospheric-adjusted RF are used; the most important ones 

are illustrated in Figure 2.2. For most aerosol constituents (see 

Section 2.4), stratospheric adjustment has little effect on the RF, 

and the instantaneous RF at either the top of the atmosphere 

or the tropopause can be substituted. For the climate change 

drivers discussed in Sections 7.5 and 2.5, that are not initially 

radiative in nature, an RF-like quantity can be evaluated by 

allowing the tropospheric state to change: this is the zerosurface-temperature-change RF in Figure 2.2 (see Shine et al., 

2003; Hansen et al., 2005; Section 2.8.3). Other water vapour 

and cloud changes are considered climate feedbacks and are 

evaluated in Section 8.6. 

Climate change agents that require changes in the 

tropospheric state (temperature and/or water vapour amounts) 

prior to causing a radiative perturbation are aerosol-cloud 

lifetime effects, aerosol semi-direct effects and some surface 

Figure 2.1. Diagram illustrating how RF is linked to other aspects of climate change assessed 

by the IPCC. Human activities and natural processes cause direct and indirect changes in climate 

change drivers. In general, these changes result in specifi c RF changes, either positive or negative, 

and cause some non-initial radiative effects, such as changes in evaporation. Radiative forcing and 

non-initial radiative effects lead to climate perturbations and responses as discussed in Chapters 6, 

7 and 8. Attribution of climate change to natural and anthropogenic factors is discussed in Chapter 

9. The coupling among biogeochemical processes leads to feedbacks from climate change to its 

drivers (Chapter 7). An example of this is the change in wetland emissions of CH4 that may occur in 

a warmer climate. The potential approaches to mitigating climate change by altering human activities (dashed lines) are topics addressed by IPCC’s Working Group III.

Figure 2.2. Schematic comparing RF calculation methodologies. Radiative forcing, defi ned as the net fl ux imbalance at the tropopause, is shown by an arrow. The horizontal 

lines represent the surface (lower line) and tropopause (upper line). The unperturbed temperature profi le is shown as the blue line and the perturbed temperature profi le as 

the orange line. From left to right: Instantaneous RF: atmospheric temperatures are fi xed everywhere; stratospheric-adjusted RF: allows stratospheric temperatures to adjust; 

zero-surface-temperature-change RF: allows atmospheric temperatures to adjust everywhere with surface temperatures fi xed; and equilibrium climate response: allows the 

atmospheric and surface temperatures to adjust to reach equilibrium (no tropopause fl ux imbalance), giving a surface temperature change (∆Ts). 
135

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

Frequently Asked Question 2.1

How do Human Activities Contribute to Climate Change 

and How do They Compare with Natural Influences?

Human activities contribute to climate change by causing 

changes in Earth’s atmosphere in the amounts of greenhouse gases, aerosols (small particles), and cloudiness. The largest known 

contribution comes from the burning of fossil fuels, which releases 

carbon dioxide gas to the atmosphere. Greenhouse gases and aerosols affect climate by altering incoming solar radiation and outgoing infrared (thermal) radiation that are part of Earth’s energy 

balance. Changing the atmospheric abundance or properties of 

these gases and particles can lead to a warming or cooling of the 

climate system. Since the start of the industrial era (about 1750), 

the overall effect of human activities on climate has been a warming infl uence. The human impact on climate during this era greatly 

exceeds that due to known changes in natural processes, such as 

solar changes and volcanic eruptions.

Greenhouse Gases

Human activities result in emissions of four principal greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide 

(N2O) and the halocarbons (a group of gases containing fl uorine, 

chlorine and bromine). These gases accumulate in the atmosphere, 

causing concentrations to increase with time. Signifi cant increases 

in all of these gases have occurred in the industrial era (see Figure 

1). All of these increases are attributable to human activities.

• Carbon dioxide has increased from fossil fuel use in transportation, building heating and cooling and the manufacture of 

cement and other goods. Deforestation releases CO2 and reduces its uptake by plants. Carbon dioxide is also released in 

natural processes such as the decay of plant matter.

• Methane has increased as a result of human activities related 

to agriculture, natural gas distribution and landfi lls. Methane 

is also released from natural processes that occur, for example, 

in wetlands. Methane concentrations are not currently increasing in the atmosphere because growth rates decreased over the 

last two decades.

• Nitrous oxide is also emitted by human activities such as fertilizer use and fossil fuel burning. Natural processes in soils and 

the oceans also release N2O. 

• Halocarbon gas concentrations have increased primarily due 

to human activities. Natural processes are also a small source. 

Principal halocarbons include the chlorofl uorocarbons (e.g., 

CFC-11 and CFC-12), which were used extensively as refrigeration agents and in other industrial processes before their 

presence in the atmosphere was found to cause stratospheric 

ozone depletion. The abundance of chlorofl uorocarbon gases is 

decreasing as a result of international regulations designed to 

protect the ozone layer.

• Ozone is a greenhouse gas that is continually produced and 

destroyed in the atmosphere by chemical reactions. In the troposphere, human activities have increased ozone through the 

release of gases such as carbon monoxide, hydrocarbons and 

nitrogen oxide, which chemically react to produce ozone. As 

mentioned above, halocarbons released by human activities 

destroy ozone in the stratosphere and have caused the ozone 

hole over Antarctica. 

• Water vapour is the most abundant and important greenhouse 

gas in the atmosphere. However, human activities have only 

a small direct infl uence on the amount of atmospheric water vapour. Indirectly, humans have the potential to affect 

water vapour substantially by changing climate. For example, 

a warmer atmosphere contains more water vapour. Human 

activities also infl uence water vapour through CH4 emissions, 

because CH4 undergoes chemical destruction in the stratosphere, producing a small amount of water vapour.

• Aerosols are small particles present in the atmosphere with 

widely varying size, concentration and chemical composition. 

Some aerosols are emitted directly into the atmosphere while 

others are formed from emitted compounds. Aerosols contain 

both naturally occurring compounds and those emitted as a result of human activities. Fossil fuel and biomass burning have 

increased aerosols containing sulphur compounds, organic 

compounds and black carbon (soot). Human activities such as 

FAQ 2.1, Figure 1. Atmospheric concentrations of important long-lived greenhouse gases over the last 2,000 years. Increases since about 1750 are attributed to 

human activities in the industrial era. Concentration units are parts per million (ppm) 

or parts per billion (ppb), indicating the number of molecules of the greenhouse gas 

per million or billion air molecules, respectively, in an atmospheric sample. (Data 

combined and simplifi ed from Chapters 6 and 2 of this report.)

(continued)
136

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

FAQ 2.1, Box 1: What is Radiative Forcing? 

What is radiative forcing? The infl uence of a factor that can cause climate change, such as a greenhouse gas, is often evaluated in 

terms of its radiative forcing. Radiative forcing is a measure of how the energy balance of the Earth-atmosphere system is infl uenced 

when factors that aff ect climate are altered. The word radiative arises because these factors change the balance between incoming solar 

radiation and outgoing infrared radiation within the Earth’s atmosphere. This radiative balance controls the Earth’s surface temperature. 

The term forcing is used to indicate that Earth’s radiative balance is being pushed away from its normal state. 

Radiative forcing is usually quantifi ed as the ‘rate of energy change per unit area of the globe as measured at the top of the atmosphere’, and is expressed in units of ‘Watts per square metre’ (see Figure 2). When radiative forcing from a factor or group of factors 

is evaluated as positive, the energy of the Earth-atmosphere system will ultimately increase, leading to a warming of the system. In 

contrast, for a negative radiative forcing, the energy will ultimately decrease, leading to a cooling of the system. Important challenges 

for climate scientists are to identify all the factors that aff ect climate and the mechanisms by which they exert a forcing, to quantify the 

radiative forcing of each factor and to evaluate the total radiative forcing from the group of factors. 

FAQ 2.1, Figure 2. Summary of the principal components of the radiative forcing of climate change. All these 

radiative forcings result from one or more factors that affect climate and are associated with human activities or 

natural processes as discussed in the text. The values represent the forcings in 2005 relative to the start of the 

industrial era (about 1750). Human activities cause signifi cant changes in long-lived gases, ozone, water vapour, 

surface albedo, aerosols and contrails. The only increase in natural forcing of any signifi cance between 1750 and 

2005 occurred in solar irradiance. Positive forcings lead to warming of climate and negative forcings lead to a 

cooling. The thin black line attached to each coloured bar represents the range of uncertainty for the respective 

value. (Figure adapted from Figure 2.20 of this report.)

surface mining and industrial processes 

have increased dust in the atmosphere. 

Natural aerosols include mineral dust released from the surface, sea salt aerosols, 

biogenic emissions from the land and 

oceans and sulphate and dust aerosols 

produced by volcanic eruptions. 

Radiative Forcing of Factors Affected by 

Human Activities

The contributions to radiative forcing 

from some of the factors infl uenced by human activities are shown in Figure 2. The 

values refl ect the total forcing relative to the 

start of the industrial era (about 1750). The 

forcings for all greenhouse gas increases, 

which are the best understood of those due 

to human activities, are positive because each 

gas absorbs outgoing infrared radiation in the 

atmosphere. Among the greenhouse gases, 

CO2 increases have caused the largest forcing 

over this period. Tropospheric ozone increases have also contributed to warming, while 

stratospheric ozone decreases have contributed to cooling. 

Aerosol particles infl uence radiative forcing directly through refl ection and absorption 

of solar and infrared radiation in the atmosphere. Some aerosols cause a positive forcing 

while others cause a negative forcing. The direct radiative forcing summed over all aerosol 

types is negative. Aerosols also cause a negative radiative forcing indirectly through the 

changes they cause in cloud properties. 

Human activities since the industrial era 

have altered the nature of land cover over 

the globe, principally through changes in 

(continued)
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 defi 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 

‘effi 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 

quantifi 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 specifi ed 

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

to CO2 (see Section 2.10).

croplands, pastures and forests. They have also modifi ed the refl ective 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 (‘contrails’) in regions that have suitably low temperatures and high 

humidity. Contrails are a form of cirrus cloud that refl ect solar radiation and absorb infrared radiation. Linear contrails from global 

aircraft operations have increased Earth’s cloudiness and are estimated 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 differences 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 CO2, 

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 CO2 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 CO2 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 confi rm 

that the atmospheric mixing ratio of CO2 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 CO2 in the atmosphere increased 

substantially: the fi 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 CO2 increased by 

about 19 ppm; the highest average growth rate recorded for any 

decade since direct atmospheric CO2 measurements began in 

the 1950s. The average rate of increase in CO2 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 CO2 concentrations and emissions. (a) CO2 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 CO2 are 

larger there than in the SH. In the lower right of the panel, atmospheric oxygen (O2) 

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 O2 concentration 

is measured as ‘per meg’ deviations in the O2/N2 ratio from an arbitrary reference, 

analogous to the ‘per mil’ unit typically used in stable isotope work, but where the ratio is multiplied by 106 instead of 103 because much smaller changes are measured. 

(b) Annual global CO2 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 13C/12C ratio measured in atmospheric CO2 at 

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

data are expressed as δ13C(CO2) ‰ (per mil) deviation from a calibration standard. 

Note that this scale is inverted to improve clarity. 

High-precision measurements of atmospheric CO2 are 

essential to the understanding of the carbon cycle budgets 

discussed in Section 7.3. The fi rst in situ continuous 

measurements of atmospheric CO2 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 fi rst time that not only was CO2 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 CO2 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 CO2 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 CO2 and provided the fi rst 

data from which the global increase of atmospheric CO2 was 

documented. Because CO2 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 CO2 measurements over continental areas was 

required to provide the basis for estimating sources and sinks of 

atmospheric CO2 over land as well as ocean regions. Because 

continuous CO2 analysers are relatively expensive to maintain 

and require meticulous on-site calibration, these records are 

now widely supplemented by air sample fl ask programmes, 

where air is collected in glass and metal containers at a large 

number of continental and marine sites. After collection, the 

fi lled fl 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 CO2 from six continuous 

analyser locations as well as weekly fl ask air samples from a 

global network of almost 50 surface sites. Many international 

laboratories make atmospheric CO2 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.6

The increases in global atmospheric CO2 since the industrial 

revolution are mainly due to CO2 emissions from the combustion 

of fossil fuels, gas fl 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, 

CO2 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 

6 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 

CO2 remaining in the atmosphere, are presented in Section 7.3, 

where carbon cycle budgets are discussed.

The increase in CO2 mixing ratios continues to yield the 

largest sustained RF of any forcing agent. The RF of CO2 is a 

function of the change in CO2 in the atmosphere over the time 

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

CO2 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 

CO2 as well as high-precision measurements of atmospheric 

oxygen (O2) content. The carbon contained in CO2 has two 

naturally occurring stable isotopes denoted 12C and 13C. The 

fi rst of these, 12C, is the most abundant isotope at about 99%, 

followed by 13C at about 1%. Emissions of CO2 from coal, gas 

and oil combustion and land clearing have 13C/12C isotopic 

ratios that are less than those in atmospheric CO2, and each 

carries a signature related to its source. Thus, as shown in 

Prentice et al. (2001), when CO2 from fossil fuel combustion 

enters the atmosphere, the 13C/12C isotopic ratio in atmospheric 

CO2 decreases at a predictable rate consistent with emissions 

of CO2 from fossil origin. Note that changes in the 13C/12C 

ratio of atmospheric CO2 are also caused by other sources and 

sinks, but the changing isotopic signal due to CO2 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 13C/12C in atmospheric CO2 to better 

than 1 part in 105 (Ferretti et al., 2000). Data presented in Figure 

2.3 for the 13C/12C ratio of atmospheric CO2 at Mauna Loa show 

a decreasing ratio, consistent with trends in both fossil fuel CO2

emissions and atmospheric CO2 mixing ratios (Andres et al., 

2000; Keeling et al., 2005). 

Atmospheric O2 measurements provide a powerful and 

independent method of determining the partitioning of CO2

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

O2 and CO2 changes are inversely coupled during plant 

respiration and photosynthesis. In addition, during the process 

of combustion O2 is removed from the atmosphere, producing 

a signal that decreases as atmospheric CO2 increases on a 

molar basis (Figure 2.3). Measuring changes in atmospheric 

O2 is technically challenging because of the diffi culty of 

resolving changes at the part-per-million level in a background 

mixing ratio of roughly 209,000 ppm. These diffi culties were 

fi 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 O2 in the atmosphere at the 

part-per-million level (Figure 2.3). Recent work by Manning 

and Keeling (2006) indicates that atmospheric O2 is decreasing 

at a faster rate than CO2 is increasing, which demonstrates 

the importance of the oceanic carbon sink. Measurements of 

both the 13C/12C ratio in atmospheric CO2 and atmospheric O2

levels are valuable tools used to determine the distribution of 

fossil-fuel derived CO2 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 CO2 increases, O2 decreases, fossil fuel 

consumption and the 13C/12C ratio of atmospheric CO2.

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 or 

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 CO2 mixing ratios and emissions 

has been tracked using a scaling factor known as the apparent 

‘airborne fraction’, defi ned as the ratio of the annual increase 

in atmospheric CO2 to the CO2 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 

CO2 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 CO2 growth rate 

(Brovkin et al., 2004). Prentice et al. (2001) cited an inventorybased 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 CO2

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 

CO2 budget over the 1980s and 1990s is discussed further in 

Section 7.3. 

In 2005, the global mean average CO2 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 highaltitude 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 fi nal reference gas 

calibrations used to measure the samples.
140

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

New measurements of CO2 from Antarctic ice and fi rn 

(MacFarling Meure et al., 2006) update and extend those from 

Etheridge et al. (1996) to AD 0. The CO2 mixing ratio in 1750 

was 277 ± 1.2 ppm.7 This record shows variations between 

272 and 284 ppm before 1800 and also that CO2 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 CO2, CH4 and N 2O 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 CO2 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 CO2 mixing ratio. (The formula 

used for the CO2 RF calculation in this chapter is the IPCC 

(1990) expression as revised in the TAR. Note that for CO2, RF 

increases logarithmically with mixing ratio.) Collins et al. (2006) 

performed a comparison of fi 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% confi 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 

CO2 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 CO2 and is also much larger 

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

2005, the RF due to CO2 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 CO2 and that from the other LLGHGs have a high level 

of scientifi 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 CO2 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 CH4, 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 CH4 required as the accepted 

reference level for these calculations. Detailed analyses of CH4

budgets and its biogeochemistry are presented in Section 7.4.

Methane has the second-largest RF of the LLGHGs after 

CO2 (Ramaswamy et al., 2001). Over the last 650 kyr, ice 

core records indicate that the abundance of CH4 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 CH4 measured at 

the network of 40 surface air fl ask sampling sites operated by 

NOAA/GMD in both hemispheres was 1,774.62 ± 1.22 ppb.8

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 CH4 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 CH4 is also monitored at fi ve sites in the NH 

and SH by the AGAGE network. This group uses automated 

systems to make 36 CH4 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% confi 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 CH4 measurements for these and other CH4

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 CH4 are unprecedented in at 

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

8 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).

7 For consistency with the TAR, the pre-industrial value of 278 ppm is retained in the CO2 RF calculation.
141

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

Notes:

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

b 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 CO2 are combined 

measurements from the NOAA Earth System Research Laboratory (ESRL) and SIO networks (see Section 2.3.1); CH4 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).

c Pre-industrial values are zero except for CO2 (278 ppm), CH4 (715 ppb; 700 ppb was used in the TAR), N2O (270 ppb) and CF4 (40 ppt).

d 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.

e Data available from AGAGE network only.

f

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

g Data from UEA only.

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

i

 Data from ESRL only.

j

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

k 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 ppt

Concentrationsb and their changesc Radiative Forcingd

 Change since Change since

Speciesa 2005 1998 2005 (W m–2) 1998 (%)

CO2 379 ± 0.65 ppm +13 ppm 1.66 +13

CH4 1,774 ± 1.8 ppb +11 ppb 0.48 -

N2O 319 ± 0.12 ppb +5 ppb 0.16 +11

CFC-11 251 ± 0.36 –13 0.063 –5

CFC-12 538 ± 0.18 +4 0.17 +1

CFC-113 79 ± 0.064 –4 0.024 –5

HCFC-22 169 ± 1.0 +38 0.033 +29

HCFC-141b 18 ± 0.068 +9 0.0025 +93

HCFC-142b 15 ± 0.13 +6 0.0031 +57

CH3CCl3 19 ± 0.47 –47 0.0011 –72

CCl4 93 ± 0.17 –7 0.012 –7

HFC-125 3.7 ± 0.10e +2.6f 0.0009 +234

HFC-134a 35 ± 0.73 +27 0.0055 +349

HFC-152a 3.9 ± 0.11e +2.4f 0.0004 +151

HFC-23 18 ± 0.12g,h +4 0.0033 +29

SF6 5.6 ± 0.038i +1.5 0.0029 +36

CF4 (PFC-14) 74 ± 1.6j - 0.0034 -

C2F6 (PFC-116) 2.9 ± 0.025g,h +0.5 0.0008 +22

CFCs Totalk 0.268 –1

HCFCs Total 0.039 +33

Montreal Gases 0.320 –1

Other Kyoto Gases

(HFCs + PFCs + SF6) 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 CH4 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 CH4 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 CH4

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 CH4

sources and sinks. Most CH4 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 CH4 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 CH4 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 CH4. New source estimates published 

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

Bergamaschi et al.(2005), national inventories based on ‘bottomup’ studies can grossly underestimate emissions and ‘topdown’ measurement-based assessments of reported emissions 

will be required for verifi cation. Keppler et al. (2006) reported 

the discovery of emissions of CH4 from living vegetation and 

estimated that this contributed 10 to 30% of the global CH4

source. This work extrapolates limited measurements to a global 

source and has not yet been confi rmed by other laboratories, 

but lends some support to space-borne observations of CH4

plumes above tropical rainforests reported by Frankenberg et 

al. (2005). That such a potentially large source of CH4 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 CH4 growth rate and its 

variability. For example, Hansen et al. (2000) considered that 

economic incentives have led to a reduction in anthropogenic 

CH4 emissions. The negligible long-term change in its main 

sink (OH; see Section 2.3.5 and Figure 2.8) implies that CH4

emissions are not increasing. Similarly, Dlugokencky et al. 

(1998) and Francey et al. (1999) suggested that the slowdown 

in the growth rate refl ects a stabilisation of CH4 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) SO2 into the lower stratosphere of the tropics with 

subsequent impacts on tropical photochemistry and the removal 

of CH4 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 

CH4 emissions from wetlands. At this time, and in parallel with 

the growth rate anomaly in the CH4 mixing ratio, an anomaly 

was observed in the 13C/12C ratio of CH4 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 confi rmed 

by lower frequency measurements from the same period made 

by Francey et al. (1999). 

Figure 2.4. Recent CH4 concentrations and trends. (a) Time series of global CH4

abundance mole fraction (in ppb) derived from surface sites operated by NOAA/GMD 

(blue lines) and AGAGE (red lines). The thinner lines show the CH4 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 CH4 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 differences 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 CH4 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 CH4 to constrain 

the relative contributions of biomass burning (one-third) and 

wetlands (two-thirds) to the increase. 

Based on ice core measurements of CH4 (Etheridge et al., 

1998), the pre-industrial global value for CH4 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 CH4 as measured from 

Greenland and Antarctic ice cores. 

The RF due to changes in CH4 mixing ratio is calculated 

with the simplifi ed yet still valid expression for CH4 given in 

Ramaswamy et al. (2001). The change in the CH4 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 CH4 as the second highest RF 

of the LLGHGs after CO2 (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 CH4 is the same as in Ramaswamy et 

al. (2001) despite the small increase in its 

mixing ratio. The spectral absorption by 

CH4 is overlapped to some extent by N2O 

lines (taken into account in the simplifi ed 

expression). Taking the overlapping lines 

into account using current N2O mixing 

ratios instead of pre-industrial mixing 

ratios (as in Ramaswamy et al., 2001) 

reduces the current RF due to CH4 by 

1%. 

Collins et al. (2006) confi rmed that 

line-by-line models agree extremely 

well for the calculation of clear-sky 

instantaneous RF from CH4 and N2O 

when the same atmospheric background 

profi 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 

CH4, N2O 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, N2O had the fourth largest RF among 

the LLGHGs behind CO2, CH4 and CFC-12. The TAR quoted 

an atmospheric N2O 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 CH4 levels due to overlapping absorptions. As N2O 

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

and nitrogen dioxide (NO2) 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 N2O. The TAR pointed out large uncertainties 

in the major soil, agricultural, combustion and oceanic sources 

of N2O. Given these emission uncertainties, its observed rate of 

increase of 0.2 to 0.3% yr–1 was not inconsistent with its betterquantifi ed major sinks (principally stratospheric destruction). 

The primary driver for the industrial era increase of N2O was 

concluded to be enhanced microbial production in expanding 

and fertilized agricultural lands.

Ice core data for N2O have been reported extending back 

2,000 years and more before present (MacFarling Meure et 

al., 2006; Section 6.6). These data, as for CO2 and CH4, show 

relatively little changes in mixing ratios over the fi rst 1,800 

years of this record, and then exhibit a relatively rapid rise (see 

FAQ 2.1, Figure 1). Since 1998, atmospheric N2O 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 N2O mixing ratio 

Figure 2.5. Hemispheric monthly mean N2O mole fractions (ppb) (crosses for the NH and triangles for the 

SH). Observations (in situ) of N2O 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 simplifi 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), N2O should, with its current 

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

Since the TAR, understanding of regional N2O fl uxes has 

improved. The results of various studies that quantifi ed the 

global N2O 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 N2O 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 N2O emissions of 0.9 ± 0.1 TgN yr–1 that 

agree well with the United Nations Framework Convention 

on Climate Change (UNFCCC) N2O 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 N2O relative to the original forest. They also 

deduced that Brazilian forest soils alone contribute about 10% 

of total global N2O production. Estimates of N2O sources 

and sinks using observations and inverse methods had earlier 

implied that a large fraction of global N2O emissions in 1978 

to 1988 were tropical: specifi 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 signifi cant 

sensitivity to assumed troposphere-stratosphere exchange rates 

that strongly infl uence inter-hemispheric gradients. Hirsch et al.

(2006) used inverse modelling to estimate signifi 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 N2O (Nevison et al., 2004a). For example, its well-defi ned 

seasonal cycle in the SH has been interpreted as resulting from 

the net effect of seasonal oceanic outgassing of microbially 

produced N2O, stratospheric intrusion of low-N2O air and other 

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

a Southern Ocean (30°S–90°S) N2O source of 0.9 TgN yr–1, 

or about 5% of the global total. The complex seasonal cycle in 

the NH is more diffi 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 N2O 

in the stratosphere causes enrichment of its heavier isotopomers 

and isotopologues, providing a potential method to differentiate 

stratospheric and surface fl ux infl uences on tropospheric N2O 

(Morgan et al., 2004).

Human-made PFCs, HFCs and SF6 are very effective 

absorbers of infrared radiation, so that even small amounts of 

these gases contribute signifi cantly to the RF of the climate 

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

PFCs and SF6 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 fl 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 HFC23 (2003 value), 3.7 ppt for HFC-125 and 3.9 ppt for HFC152a (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 HFC134a 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. Hydrofl 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 differences exist, these network measurements agree reasonably well with each other 

(except for CCl4 (differences of 2 – 4% between networks) and HCFC-142b (differences 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 HFC143a, which are both refrigerants.

The PFCs, mainly CF4 (PFC-14) and C2F6 (PFC-116), and 

SF6 have very large radiative effi 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 SF6 and 

C2F6 concentrations and RFs have increased by over 20% since 

the TAR (Table 2.1 and Figure 2.6), but CF4 concentrations 

have not been updated since 1997. Both anthropogenic and 

natural sources of CF4 are important to explain its observed 

atmospheric abundance. These PFCs are produced as byproducts of traditional aluminium production, among other 

activities. The CF4 concentrations have been increasing linearly 

since about 1960 and CF4 has a natural source that accounts for 

about one-half of its current atmospheric content (Harnisch et 

al., 1996). Sulphur hexafl uoride (SF6) is produced for use as an 

electrical insulating fl 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 Scientifi 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 CO2-equivalent emissions of CFCs, HCFCs 

and HFCs decreased from a peak of about 7.5 GtCO2-eq yr–1

in the late 1980s to about 2.5 GtCO2-eq yr–1 by the year 2000, 

corresponding to about 10% of that year’s CO2 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 et 

al., 2000, 2005b) and the GMD network (Montzka et al., 1999 

updated; Thompson et al., 2004). Certain fl 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 fi 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 CFC113 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 (CH3CCl3), 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 CH4, 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 et 

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 et 

al., 2001; Hurst et al., 2004). 

Carbon tetrachloride (CCl4) 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 signifi cant inter-hemispheric CCl4 gradient 

still exists in 2005 in spite of its moderately long lifetime of 

20 to 30 years, resulting from a persistence of signifi 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-CO2

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

relative to CO2, 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-CO2 gases in GtC equivalents 

and the signifi cant roles of CH4, N2O 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 expressed 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 CO2 to carbon (C) masses (44/12). These rates are positive 

or negative whenever the mole fractions are increasing or decreasing, respectively. Use of these common 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 CF4 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 CH4 and all HFCs and HCFCs, each year 

(Ehhalt, 1999). It therefore has a very signifi 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 14CO, 

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

2002). While the accuracy of the 14CO cosmic ray and other 

14CO source estimates and the frequency and spatial coverage 

of its measurements do not match those for methyl chloroform, 

the 14CO lifetime (2 months) is much shorter than that of methyl 

chloroform (4.9 years). As a result, 14CO 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 14CO source variability is better 

defi 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 106 radicals per 

cubic centimetre (Prinn et al., 2001; Krol and Lelieveld, 2003). 

A similar average concentration is derived using 14CO (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 defi ned 

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

diffi 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 signifi 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 suffi 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 reconfi 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 wildfi res and an intense El Niño at that 

time. The 1997 Indonesian fi 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 pre1992 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 
148

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

were caused by model shortcomings and that models need, 

in particular, to include observationally-based, interannually 

varying meteorology to provide accurate annual OH estimates. 

Neither the two-dimensional Prinn et al. (2001) nor the 

three-dimensional Krol et al. (2003) inversion models used 

interannually varying circulation. However, the Bousquet et al. 

(2005) analysis, which uses observationally based meteorology 

and estimates OH on monthly time scales, yields interannual 

OH variations that agree very well with the Prinn et al. (2001) 

and equivalent Krol and Lelieveld (2003) estimates (see Figure 

2.8). However, when Bousquet et al. (2005) estimated both OH 

concentrations and methyl chloroform emissions (constrained 

by their uncertainties as reported by McCulloch and Midgley, 

2001), the OH variations were reduced by 65% (dashed line 

in Figure 2.8). The error bars on the Prinn et al. (2001, 2005a) 

OH estimates, which account for these emission uncertainties 

using Monte Carlo ensembles of inversions, also easily allow 

such a reduction in OH variability (thin vertical bars in Figure 

2.8). This implies that these interannual OH variations are real, 

but only their phasing and not their amplitude, is well defi ned. 

Bousquet et al. (2005) also deduced that OH in the SH shows a 

zero to small negative trend, in qualitative agreement with Prinn 

et al. (2001). Short-term variations in OH were also recently 

deduced by Manning et al. (2005) using 13 years of 14CO 

measurements in New Zealand and Antarctica. They found no 

signifi cant long-term trend between 1989 and 2003 in SH OH 

but provided evidence for recurring multi-month OH variations 

of around 10%. They also deduced even larger (20%) OH 

decreases in 1991 and 1997, perhaps triggered by the 1991 Mt. 

Pinatubo eruption and the 1997 Indonesian fi res. The similarity 

Figure 2.8. Estimates used to evaluate trends in weighted global average OH concentrations. (A) and (B): comparison of 1980 to 1999 OH anomalies (relative to their longterm means) inferred by Bousquet et al. (2005), Prinn et al. (2001) and Krol et al. (2003) from AGAGE methyl chloroform observations, and by Bousquet et al. (2005) when methyl 

chloroform emissions as well as OH are inferred; error bars for Bousquet et al. (2005) refer to 1 standard deviation inversion errors while yellow areas refer to the envelope of 

their 18 OH inversions. (C) OH concentrations for 1979 to 2003 inferred by Prinn et al. (2005a) (utilising industry emissions corrected using recent methyl chloroform observations), showing the recovery of 2003 OH levels to 1979 levels; also shown are results assuming uncorrected emissions and estimates of recent oceanic re-emissions. Error bars 

in Prinn et al. (2001, 2005a) are 1 standard deviation and include inversion, model, emission and calibration errors from large Monte Carlo ensembles (see Section 2.3.5 for 

details and references).
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 (NOx). 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 Scientifi 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 pre1980 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 signifi 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 signifi 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 signifi 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 Chipperfi 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 (Chipperfi 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 fi 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 scientifi c 

understanding is medium, unchanged from the TAR (see Section 

2.9, Table 2.11).

2.3.6.2 Tropospheric Ozone

The TAR identifi 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 (NOx, CO, nonmethane volatile organic compounds (NMVOCs), and CH4) 

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 infl 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 defi 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 

specifi c regions, there is signifi cant variability over the period 

due to variations in the emissions from open biomass burning 

sources. For all components (NOx, 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 signifi cant growth 

in emissions. Recently, the tropospheric burdens of CO and 

NO2 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 

diffi 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 signifi cantly more infl uenced 

by air originating over China, while Sapporo and Tsukuba are 

more infl 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 insignifi 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 (fl 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 NOx 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 DU–1, which is signifi cantly lower 

than the TAR estimate of +0.042 W m–2 DU–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 instantaneous 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 Atmospheric 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 GCMINteraction 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 insignifi 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 signifi 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 signifi 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 

Pacifi c indicate an increased frequency of nearzero 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 

confi 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 CH4 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 (SO2; 

Notholt et al., 2005) and d) changes in CH4 oxidation rates from 

changes in stratospheric chlorine, ozone and OH (Rockmann et 

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 quantifi 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 CH4 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 CH4 increases being 

considered a forcing. Two related CTM studies have calculated 

the RF associated with increases in CH4 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 CH4 concentration 

since 2000 (see Section 2.3.2); therefore the best estimate of 

the stratospheric water vapour RF from CH4 oxidation (+0.07 

W m–2) is based on the Hansen et al. (2005) calculation. The 

90% confi 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 scientifi c understanding in this estimate, as 

there is only a partial understanding of the vertical profi le of 

CH4-induced stratospheric water vapour change (Section 2.9, 

Table 2.11). Other human causes of stratospheric water vapour 

change are unquantifi ed and have a very low level of scientifi c 

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 NOx 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 NOx

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 

NOx 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% confi 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 scientifi 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 signifi cant radiative impact. 

However, it only considered the stratospheric water vapour 

increase expected from CH4 increases as an RF, and this was 

estimated to contribute 2 to 5% of the total CH4 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 balloonbased 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 defi ned here. 

Instead, they yield a perspective on the infl 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 profi les in the atmosphere 

such that they do not conform to the conditions underlying the 

RF defi 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 Pacifi 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 CO2 and CH4. 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 signifi cant increase in the clear-sky 

longwave downward fl 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 infl 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 identifi ed 

as having a signifi cant anthropogenic component and exerting 

a signifi cant direct RF. Key parameters for determining 

the direct RF are the aerosol optical properties (the single 

scattering albedo, ωo, specifi c extinction coeffi cient, ke 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-theatmosphere (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 fi xed has been called the ‘fi 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 fi 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 infl uence of 

increased aerosol concentration on the cloud optical properties 

while holding the liquid water content of the cloud fi 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 
154

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

7.5). The TAR also discussed the impact of anthropogenic 

aerosols on the formation and modifi cation of the physical 

and radiative properties of ice clouds (Penner et al., 2001), 

although quantifi cation of an RF from this mechanism was 

not considered appropriate given the host of uncertainties and 

unknowns surrounding ice cloud nucleation and physics.

The TAR did not include any assessment of the semi-direct 

effect (e.g., Hansen et al., 1997; Ackerman et al., 2000a; 

Jacobson, 2002; Menon et al., 2003; Cook and Highwood, 

2004; Johnson et al., 2004), which is the mechanism by which 

absorption of shortwave radiation by tropospheric aerosols 

leads to heating of the troposphere that in turn changes the 

relative humidity and the stability of the troposphere and 

thereby infl uences cloud formation and lifetime. In this report, 

the semi-direct effect is not strictly considered an RF because of 

modifi cations to the hydrological cycle, as discussed in Section 

7.5 (see also Sections 2.2, 2.8 and 2.4.5).

Since the TAR, there have been substantial developments in 

observations and modelling of tropospheric aerosols; these are 

discussed in turn in the following sections.

2.4.2 Developments Related to Aerosol 

Observations

Surface-based measurements of aerosol properties such 

as size distribution, chemical composition, scattering and 

absorption continue to be performed at a number of sites, either 

at long-term monitoring sites, or specifi cally as part of intensive 

fi eld campaigns. These in situ measurements provide essential 

validation for global models, for example, by constraining 

aerosol concentrations at the surface and by providing highquality information about chemical composition and local 

trends. In addition, they provide key information about 

variability on various time scales. Comparisons of in situ

measurements against those from global atmospheric models 

are complicated by differences in meteorological conditions and 

because in situ measurements are representative of conditions 

mostly at or near the surface while the direct and indirect RFs 

depend on the aerosol vertical profi le. For example, the spatial 

resolution of global model grid boxes is typically a few degrees 

of latitude and longitude and the time steps for the atmospheric 

dynamics and radiation calculations may be minutes to hours 

depending on the process to be studied; this poses limitations 

when comparing with observations conducted over smaller 

spatial extent and shorter time duration.

Combinations of satellite and surface-based observations 

provide near-global retrievals of aerosol properties. These are 

discussed in this subsection; the emissions estimates, trends 

and in situ measurements of the physical and optical properties 

are discussed with respect to their infl uence on RF in Section 

2.4.4. Further detailed discussions of the recent satellite 

observations of aerosol properties and a satellite-measurement 

based assessment of the aerosol direct RF are given by Yu et 

al. (2006).

2.4.2.1 Satellite Retrievals

Satellite retrievals of aerosol optical depth in cloud-free 

regions have improved via new generation sensors (Kaufman et 

al., 2002) and an expanded global validation program (Holben et 

al., 2001). Advanced aerosol retrieval products such as aerosol 

fi ne-mode fraction and effective particle radius have been 

Figure 2.10. Schematic diagram showing the various radiative mechanisms associated with cloud effects that have been identifi ed as signifi cant in relation to aerosols 

(modifi ed from Haywood and Boucher, 2000). The small black dots represent aerosol particles; the larger open circles cloud droplets. Straight lines represent the incident and 

refl ected solar radiation, and wavy lines represent terrestrial radiation. The fi lled white circles indicate cloud droplet number concentration (CDNC). The unperturbed cloud contains larger cloud drops as only natural aerosols are available as cloud condensation nuclei, while the perturbed cloud contains a greater number of smaller cloud drops as both 

natural and anthropogenic aerosols are available as cloud condensation nuclei (CCN). The vertical grey dashes represent rainfall, and LWC refers to the liquid water content.
155

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

developed and offer potential for improving estimates of the 

aerosol direct radiative effect. Additionally, efforts have been 

made to determine the anthropogenic component of aerosol and 

associated direct RF, as discussed by Kaufman et al. (2002) and 

implemented by Bellouin et al. (2005) and Chung et al. (2005). 

However, validation programs for these advanced products 

have yet to be developed and initial assessments indicate some 

systematic errors (Levy et al., 2003; Anderson et al., 2005a; Chu 

et al., 2005), suggesting that the routine differentiation between 

natural and anthropogenic aerosols from satellite retrievals 

remains very challenging.

2.4.2.1.1 Satellite retrievals of aerosol optical depth

Figure 2.11 shows an example of aerosol optical depth τaer

(mid-visible wavelength) retrieved over both land and ocean, 

together with geographical positions of aerosol instrumentation. 

Table 2.2 provides a summary of aerosol data currently 

available from satellite instrumentation, together with acronyms 

for the instruments. τaer from the Moderate Resolution Imaging 

Spectrometer (MODIS) instrument for the January to March 

2001 average (Figure 2.11, top panel) clearly differs from that for 

the August to October 2001 average (Figure 2.11, bottom panel) 

(Kaufman et al., 1997; Tanré et al., 1997). Seasonal variability 

in τaer can be seen; biomass burning aerosol is most strongly 

evident over the Gulf of Guinea in Figure 2.11 (top panel) but 

shifts to southern Africa in Figure 2.11 (bottom panel). Likewise, 

the biomass burning in South America is most evident in Figure 

2.11 (bottom panel). In Figure 2.11 (top panel), transport of 

mineral dust from Africa to South America is discernible while 

in Figure 2.11 (bottom panel) mineral dust is transported over 

the West Indies and Central America. Industrial aerosol, which 

consists of a mixture of sulphates, organic and black carbon, 

Figure 2.11. Aerosol optical depth, τaer, at 0.55 μm (colour bar) as determined by the MODIS instrument for the January to March 2001 mean (top panel) and for the August 

to October 2001 mean (bottom panel). The top panel also shows the location of AERONET sites (white squares) that have been operated (not necessary continuously) since 

1996. The bottom panel also shows the location of different aerosol lidar networks (red: EARLINET, orange: ADNET, black: MPLNET).
156

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2 Satellite Instrument Period of Operation Spectral Bands Productsa Comment and Reference AVHRR (Advanced 1979 to present 5 bands (0.63, τaer, α 1-channel retrieval gives τλ=0.63 over ocean (Husar et al., 1997; Ignatov and Very High Resolution 0.87, 3.7, 10.5 Stowe, 2002) Radiometer) 

α and 11.5 μm) 2-channel using 0.63 μm and 0.86 μm gives τλ=0.55 and 

α over ocean assuming 

 mono-modal aerosol size distribution (Mishchenko et al., 1999) 2-channel using 0.63 μm and 0.86 μm gives τλ=0.55 and 

α over dark forests and lake 

 surfaces (Souffl et et al., 1997) 2-channel using 0.64 μm and 0.83 μm gives τλ=0.55 and 

α over ocean assuming a 

 bimodal aerosol size distribution (Higurashi and Nakajima, 1999; Higurashi et al., 2000)TOMSb (Total Ozone 1979 to present 0. 33 μm, 0.36 μm Aerosol Index, Aerosol index to τaer conversion sensitive to the altitude of the 8 mono-modal

Mapping Spectrometer) τaer aerosol models used in the retrieval (Torres et al., 2002).

POLDER (Polarization Nov 1996 to 8 bands τaer, α, DRE Multiple view angles and polarization capabilities.

and Directionality 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 aerosol models overEarth’s Refl ectances) to Oct 2003; ocean (Goloub et al., 1999; Deuzé et al., 2000).

 Jan 2005 to present Polarization allows fi ne particle retrieval over land (Herman et al., 1997; Goloub and Arino, 2000). DRE determined over ocean (Boucher and Tanré, 2000; Bellouin et al., 2003). OCTS (Ocean Colour Nov 1996 to 9 bands τaer, α 0.67 μm and 0.86 μm retrieval gives τλ=0.50 and 

α over ocean. Bi-modal aerosol

and Temperature 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 present 12 bands τaer, α, DRE Retrievals developed over ocean surfaces using bi-modal size distributions

Resolution Imaging (0.41 to 2.1 μm) (Tanré et al., 1997; Remer et al., 2002).Spectrometer) 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 present 4 bands τaer, α 9 different viewing angles. Five climatological mixing groups composed of fourSpectro-Radiometer) (0.47 to 0.86 μm) component particles are used in the retrieval algorithm (Kahn et al., 2001; Kahn et al., 2005). Retrievals over bright surfaces are possible (Martonchik et al., 2004).CERES (Clouds and the 1998 to present DRE DRE determined by a regression of, for example, Visible Infrared Scanner (VIRS;Earth’s Radiant Energy System) AVHRR-like) τaer against upwelling irradiance (Loeb and Kato; 2002).

GLAS (Geoscience 2003 to present Active lidar Aerosol vertical Lidar footprint roughly 70 m at 170 m intervals. 8-day repeat orbiting cycle (Spinhirne

Laser Altimeter System) (0.53, 1.06 μm) profi le et al., 2005).ATSR-2/AATSR 1996 to present 4 bands τaer, α Nadir and 52° forward viewing geometry. 40 aerosol climatological mixtures

(Along Track Scanning (0.56 to 1.65 μm) containing up to six aerosol species are used in the retrievals (Veefkind et al., 1998; Radiometer/Advanced ATSR) Holzer-Popp et al., 2002).SeaWiFS (Sea-Viewing Wide 1997 to present 0.765 and 0.865 μm τaer, α 2-channel using 0.765 μm and 0.856 μm gives τλ=0.856 and 

α over ocean. Bi-modal

Field-of-View Sensor) (ocean) aerosol size distribution assumed (M. Wang et al., 2005). Retrievals over land and 0.41 to 0.67 μm ocean using six visible channels from 0.41 to 0.67μm (von Hoyningen-Huene, 2003; (land) Lee et al., 2004) also developed.

Notes: a DRE is the direct radiative effect and includes both natural and anthropogenic sources (see Table 2.3). The Angstrom exponent, α, is the wavelength dependence of 

τaer and is defi ned by α = –ln(τaerλ1/τaerλ2) / ln(λ1 / λ2) where λ1 = wavelength 1 and λ2 = wavelength 2.

 b TOMS followed up by the Ozone Monitoring Instrument (OMI) on the Earth Observing System (EOS) Aura satellite, launched July 2004.Table 2.2. Periods of operation, spectral bands and products available from various different satellite sensors that have been used to retrieve 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 refl ective surfaces such as deserts, snow cover, ice 

and areas affected by ocean glint, or over high-latitude regions 

when the solar insolation is insuffi cient. 

Early retrievals for estimating τaer 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 refl ectance characteristics 

are relatively well known, although retrievals are also possible 

over dark land surfaces such as boreal forests and lakes (Souffl et

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

surface refl 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 Refl ectance 

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

μm) with several viewing angles, and measures polarization 

of radiation. Aerosol optical depth and Ångstrom exponent (α) 

over ocean (Deuzé et al., 2000), τaer 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 τaer is necessarily higher over land (Chu 

et al., 2002) than over oceans (Remer et al., 2002) owing to 

uncertainties in land surface refl 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. Multiangle 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 refl 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 μm), and two viewing directions and 

aerosol climatologies from the Optical Parameters of Aerosols 

and Clouds (OPAC) database (Hess et al., 1998) to make τaer

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 

μm to derive τaer and α 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 μm and 0.856 

μm radiances to provide τλ=0.856 and α over ocean using a bimodal 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 

τaer 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 refl 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 fi xed aerosol models. While some systematic 

biases in specifi 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 τaer and 

the DRE (e.g., Zhang and Christopher, 2003; Zhang et al., 2005), 

which enables estimation of the DRE effi ciency, that is, the 

DRE divided by τaer (W m–2 τaer–1). Because the DRE effi ciency 

removes the dependence on the geographic distribution of 

τaer it is a useful parameter for comparison of models against 

observations (e.g., Anderson et al., 2005b); however, the DRE 

effi ciency thus derived is not a linear function of τaer at high 

τaer 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 fi 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 

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

System (CERES) broadband TOA fl uxes and estimated an 

identical value of –1.4 ± 0.9 W m–2. Bellouin et al. (2005) used 

a combination of MODIS τaer and fi 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 fi 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 signifi 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 Instrumenta Data Analysed Brief Description (W m–2) ocean

Bellouin et al. (2005) MODIS; TOMS; 2002 MODIS fi ne and total τaer 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-NESDISb 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. τaer 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 τaer 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 τaer 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:

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

b 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 τaer 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 τaer is high enough (τaer > 0.5), 

the aerosol single scattering albedo, ωo, 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 ωo 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 fi eld campaigns (e.g., Campbell et al., 2003). 

The European-wide lidar network EARLINET currently has 15 

aerosol lidars making routine retrievals of vertical profi 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 profi 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 τaer, 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 fi 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 fi 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 fl 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 

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

1 standard deviation of inter-model range), and consequently 

in the dry deposition fl 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 humidifi 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 effi cient wet removal schemes. Tropospheric 

residence times, defi ned here as the ratio of burden over sinks 

established for an equilibrated one-year simulation, vary by 20 

to 30% for the fi 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 τaer at a wavelength of 0.55 

μm 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 signifi 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 effi 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 fi 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 presentday aerosol fi elds consistent with observations. Collins et al. 

(2001) showed that assimilation of satellite-derived fi elds of 

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

daily mean τaer measured with a sun photometer at the Indian 

Ocean Experiment (INDOEX) station Kaashidhoo. Liu et al. 

(2005) demonstrated similar effi cient reduction of errors in τaer. 

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 Pacifi 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% 

confi 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 SO2 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 SO2 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 SO2 emissions from volcanoes (about 

7%). Estimates of global SO2 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 SO2

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 SO2 emissions have been increasing 

signifi 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 SO2 from the USA, Europe, Russia, Northern 

Atlantic Ocean and parts of Africa to Southeast Asia and the 

Indian and Pacifi 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 (ωo = 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 

specifi c extinction coeffi cient, fRH (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 signifi cant fraction of the sub-micron 

aerosol mass, anthropogenic τaer 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 τaer is substantial 

due to differing representations of aerosol mixing state, optical 

properties, cloud, surface refl ectance, hygroscopic growth, subgrid scale effects, radiative transfer codes, etc. (Ramaswamy et 

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 fi 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% confi 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 defi 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 (NO3) (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 signifi cant contributor 

to τaer. 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 τaer 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 Modela

 LOAD τaer τaer ant RF NRFM NRF 

Reference (mg(SO4) m–2) (0.55 μm) (%) (W m–2) (W g–1) (W m–2 τaer–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 72 –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 58 –0.58 –220 –28 (Liu and Penner, 2002)

N UIO_CTM 1.70 0.019 57 –0.35 –208 –19 (Myhre et al., 2003)

O LOA 3.64 0.035 64 –0.49 –136 –14 (Reddy and Boucher, 2004)

P LSCE 3.01 0.023 59 –0.42 –138 –18 (Schulz et al., 2006)

Q ECHAM5-HAM 2.47 0.016 60 –0.46 –186 –29 (Stier et al., 2005)

R GISS 1.34 0.006 41 –0.19 –139 –31 (Koch, 2001)

S UIO_GCM 1.72 0.012 59 –0.25 –145 –21 (Iversen and Seland, 2002; 

 Kirkevag and Iversen, 2002)

T SPRINTARS 1.19 0.013 59 –0.16 –137 –13 (Takemura et al., 2005)

U ULAQ 1.62 0.020 42 –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 55 –0.35 –161 –20 

Minimum A to U 1.19 0.006 41 –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 75 9 

Std. dev. M to U 0.83 0.008 8 0.15 34 7

Notes:

a 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 (τaer ) refer to the anthropogenic sulphate; τaer ant is the fraction of anthropogenic sulphate to total sulphate τaer for present 

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

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

9 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 

simplifi 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% confi dence interval.9 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 signifi cant decreases were 

recorded in the UK, Germany, the former Soviet Union and the 

USA over the period 1950 to 2000, while signifi 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 signifi cantly stronger than the 

local RF at the TOA (–7 W m–2). Additionally, the presence 

of BC in the atmosphere above highly refl ective surfaces such 

as snow and ice, or clouds, may cause a signifi cant positive 

RF (Ramaswamy et al., 2001). The vertical profi 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-tohydrophobic 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 Table 2.5. Estimates of anthropogenic carbonaceous aerosol forcing derived from models published since the TAR and from the AeroCom simulations where different models used identical emissions. POM: particulate organic matter; BC: black carbon; BCPOM: BC and POM; FFBC: fossil fuel black carbon; FFPOM: fossil fuel particulate organic matter; BB: biomass burning sources included. Notes: a MOZGN: MOZART (Model for OZone and Related chemical Tracers-GFDL(Geophysical Fluid Dynamics Laboratory)-NCAR (National Center for Atmospheric Research); for other models see Note (a) in Table 2.4. b Models A to C are used to provide a split in sources derived from total POM and total BC: FFPOM = POM × 0.25; FFBC = BC × 0.5; BB = (BCPOM) – (FFPOM + FFBC); BC = 2 × FFBC; POM = 4 × FFPOM. c Models L, O and Q to T are used to provide a split in components: POM = BCPOM × (–1.16); BC = BCPOM × 2.25. No Modela LOAD POM (mgPOM m–2) τaer POM τaer POMant (%) LOAD BC (mg m–2) RF BCPOM (W m–2) RF POM (W m–2) RF BC (W m–2) RF FFPOM (W m–2) RF FFBC (W m–2) RF BB (W m–2) Reference Published since IPCC, 2001 A SPRINT 0.12 –0.24 0.36 –0.05 0.15 –0.01 (Takemura et al., 2001) B LOA 2.33 0.016 0.37 0.30 –0.25 0.55 –0.02 0.19 0.14 (Reddy et al., 2005b) C GISS 1.86 0.017 0.29 0.35 –0.26 0.61 –0.13 0.49 0.065 (Hansen et al., 2005) D GISS 1.86 0.015 0.29 0.05 –0.30 0.35 –0.08b 0.18b –0.05b (Koch, 2001) E GISS 2.39 0.39 0.32 –0.18 0.50 –0.05b 0.25b 0.12b (Chung and Seinfeld., 2002) F GISS 2.49 0.43 0.30 –0.23 0.53 –0.06b 0.27b 0.09b (Liao and Seinfeld, 2005) G SPRINTARS 2.67 0.029 82 0.53 0.15 –0.27 0.42 –0.07b 0.21b 0.01b (Takemura et al., 2005) H GATORG 2.55 0.39 0.47 –0.06 0.55 –0.01b 0.27b 0.22b (Jacobson, 2001b) I MOZGN 3.03 0.018 –0.34 (Ming et al., 2005a) J CCM 0.33 0.34 (Wang, 2004) K UIO-GCM 0.30 0.19 (Kirkevag and Iversen, 2002) AeroCom: identical emissions used for year 1750 and 2000 (Schulz et al., 2006) L UMI 1.16 0.0060 53 0.19 0.02 –0.23 0.25 –0.06b 0.12b –0.01 (Liu and Penner, 2002) M UIO_CTM 1.12 0.0058 55 0.19 0.02 –0.16b 0.22b –0.04 0.11 –0.05 (Myhre et al., 2003) N LOA 1.41 0.0085 52 0.25 0.14 –0.16c 0.32c –0.04b 0.16b 0.02b (Reddy and Boucher, 2004) O LSCE 1.50 0.0079 46 0.25 0.13 –0.17 0.30 –0.04b 0.15b 0.02b (Schulz et al., 2006) P ECHAM5-HAM 1.00 0.0077 0.16 0.09 –0.10c 0.20c –0.03b 0.10b 0.01 (Stier et al., 2005) Q GISS 1.22 0.0060 51 0.24 0.08 –0.14 0.22 –0.03b 0.11b 0.01b (Koch, 2001) R UIO_GCM 0.88 0.0046 59 0.19 0.24 –0.06 0.36 –0.02b 0.18b 0.08b (Iversen and Seland, 2002) S SPRINTARS 1.84 0.0200 49 0.37 0.22 –0.10 0.32 –0.01 0.13 0.06 (Takemura et al., 2005) T ULAQ 1.71 0.0075 58 0.38 –0.01 –0.09 0.08 –0.02b 0.04b –0.03b (Pitari et al., 2002) Average A–K 2.38 0.019 0.38 0.26 –0.24 0.44 –0.06 0.25 0.07 Average L–T 1.32 0.008 53 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 4 0.08 0.09 0.05 0.08 0.01 0.04 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% confi 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 signifi 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 

diffi 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 et 

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

et al., 2003b; Ichoku et al., 2003) and to study the infl 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 in 

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 ωo at 0.55 μm 

derived from near-source AERONET sites ranged from 0.85 to 

0.89 (Eck et al., 2003), while ωo at 0.55 μm for aged aerosol 

was less absorbing at approximately 0.91 (Haywood et al., 

2003b). Abel et al. (2003) showed evidence that ωo at 0.55 

μm 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 nonabsorbing organic gases onto existing aerosol particles. Fresh 

biomass burning aerosols produced by boreal forest fi res appear 

to have weaker absorption than those from tropical fi res, with 

ωo at 0.55 μm greater than 0.9 (Wong and Li 2002). Boreal fi res 

may not exert a signifi cant direct RF because a large proportion 

of the fi res are of natural origin and no signifi cant change over 

the industrial era is expected. However, Westerling et al. (2006) 

showed that earlier spring and higher temperatures in USA have 

increased wildfi 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 profi le of biomass 

burning aerosol (Myhre et al., 2003; Penner et al., 2003; Section 

2.4.5). These modifi cations have had important consequences 

for estimates of the RF due to biomass burning aerosols 

because the RF is signifi 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 

refl 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 
166

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

Figure 2.12. Characteristic aerosol properties related to their radiative effects, derived as the mean of the results from the nine AeroCom models listed in Table 2.5. All panels 

except (b) relate to the combined anthropogenic aerosol effect. Panel (b) considers the total (natural plus anthropogenic) aerosol optical depth from the models. (a) Aerosol 

optical depth. (b) Difference in total aerosol optical depth between model and MODIS data. (c) Shortwave RF. (d) Standard deviation of RF from the model results. (e) Shortwave 

forcing of the atmosphere. (f) Shortwave surface forcing.
167

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

correlations of τaer and cloud effective radius in estimating the 

indirect radiative effect of aerosols.

Since the biomass burning aerosols can exert a signifi cant 

positive RF when above clouds, the aerosol vertical profi 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 signifi cant differences in aerosol vertical profi 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% confi 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 NOx

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, fi nemode nitrate burden of 0.58 mg NO3 m–2, which would imply an 

equivalent of 20% of the mean anthropogenic sulphate burden. 

Surface observations of fi 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 fl 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 τaer. 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 

insuffi 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 diffi 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 profi 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 CO2 variations on various time scales can 

change vegetation cover in semi-arid regions. Such processes 

dominate over land use changes as defi 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 effi 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 diffi 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 (ωo) of pure dust at a wavelength 

of 0.67 μm 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 ωo based on the haematite content 

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

Analyses of ωo from long-term AERONET sites infl uenced by 

Saharan dust suggest an average ωo of 0.95 at 0.67 μm (Dubovik

et al., 2002), while unpolluted Asian dust during the Aeolian 

Dust Experiment on Climate (ADEC) had an average ωo of 

0.93 at 0.67 μm (Mikami et al., 2006 and references therein). 

These high ωo 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 μm 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 μm) 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 refl ects the fact that dust burden and 

τaer 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% confi dence level is estimated to be ±0.2 

W m–2, refl 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 intensifi ed 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 τaer. 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 fi 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 infl 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 specifi c emission source categories. 

Estimating the combined aerosol RF is a fi 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-specifi c 

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 fi eld. An 

error in accounting for cloud cover would affect 

the all-sky RF from all aerosol components. 

Such errors result in correlated RF effi 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 

signifi cant differences in the modelled local and 

regional composition of the aerosol (see also 

Figure 2.12), but an overall reproduction of the 

total τaer 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% confi 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 τaer

is similar between model and measurement based studies. 

Kaufman et al. (2005a) used satellite-observed fi ne-mode τaer

to estimate the anthropogenic τaer. Correcting for fi ne-mode τaer

Figure 2.13. Estimates of the direct aerosol RF from observationally based studies, independent modelling 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 Table 2.6. Quantities related to estimates of the aerosol direct RF. Recent estimates of anthropogenic aerosol load (LOAD), anthropogenic aerosol optical depth (τaer), its fraction of the present-day total aerosol optical depth (τaer ant), cloud cover in aerosol model, total aerosol direct radiative forcing (RF) for clear sky and all sky conditions, surface forcing and atmospheric all-sky forcing. No Modela LOAD τaer (0.55 μm) τaer ant (0.55 μm) Cloud Cover RF top clear sky RF top all sky Surface Forcing all sky Atmospheric Forcing all sky Reference (mg m–2) (%) (%) (W m–2) (W m–2) (W m–2) (W m–2) Published since IPCC, 2001 A GISS 5.0 79% –0.39b +0.01c –1.98b –2.42c 1.59b 2.43c (Liao and Seinfeld, 2005) B LOA 6.0 0.049 34% 70% –0.53 –0.09 (Reddy and Boucher, 2004) C SPRINTARS 4.8 0.044 50% 63% –0.77 –0.06 –1.92 1.86 (Takemura et al., 2005) D UIO-GCM 2.7 57% –0.11 (Kirkevag and Iversen, 2002) E GATORG 6.4d 62% –0.89 –0.12 –2.5 2.38 (Jacobson, 2001a) F GISS 6.7 0.049 –0.23 (Hansen et al., 2005) G GISS 5.6 0.040 –0.63 (Koch, 2001) AeroCom: identical emissions used for year 1750 and 2000 (Schulz et al., 2006) H UMI 4.0 0.028 25% 63% –0.80 –0.41 –1.24 0.84 (Liu and Penner, 2002) I UIO_CTM 3.0 0.026 19% 70% –0.85 –0.34 –0.95 0.61 (Myhre et al., 2003) J LOA 5.3 0.046 28% 70% –0.80 –0.35 –1.49 1.14 (Reddy and Boucher, 2004) K LSCE 4.8 0.033 40% 62% –0.94 –0.28 –0.93 0.66 (Schulz et al., 2006) L ECHAM5 4.3 0.032 30% 62% –0.64 –0.27 –0.98 0.71 (Stier et al., 2005) M GISS 2.8 0.014 11% 57% –0.29 –0.11 –0.81 0.79 (Koch, 2001) N UIO_GCM 2.8 0.017 11% 57% –0.01 –0.84 0.84 (Kirkevag and Iversen, 2002) O SPRINTARS 3.2 0.036 44% 62% –0.35 +0.04 –0.91 0.96 (Takemura et al., 2005) P ULAQ 3.7 0.030 23% –0.79 –0.24 (Pitari et al., 2002) Average A–G 5.1 0.046 42% 67% –0.73 –0.23 –2.21 2.07 Average 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% 5% 0.24 0.16 0.23 0.17 Average A–P 4.3 0.035 29% 64% –0.70 –0.22 –1.21 1.24 Stddev A–P 1.3 0.012 13% 7% 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: a See Note (a) in Table 2.4 for model information. b External mixture. c Internal mixture. d The load excludes that of mineral dust, some of which was considered anthropogenic 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 

τaer to be anthropogenic, while Table 2.6 suggests that 29% of 

τaer 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 τaer 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 

τaer 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 signifi 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 τaer

from models against those from AERONET must be performed 

very carefully, reducing the discrepancy between their model 

and AERONET derived aerosol absorption τaer 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 confi 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 τaer (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 τaer shows that regionally τaer 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% confi 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% confi 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 

scientifi 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 fi 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 fi 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 fi 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 modifi cation of clouds provided by the 

ship track observations reported in the TAR has been further 

confi 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 

refl ectance in continental stratocumulus cases, utilising remote 

sensing techniques at specifi c fi eld sites. The estimates in 

Feingold et al. (2003) confi rm that the relationship between 

aerosol and cloud droplet number concentrations is nonlinear, 

that is Nd ≈ (Na)b, where Nd is the cloud drop number density 

and Na is the aerosol number concentration. The parameter b in 

this relationship can vary widely, with values ranging from 0.06 

to 0.48 (low values of b 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 b (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 fi rm 

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 signifi 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 diffi 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 refl ectance of low-level, warm clouds with increasing 

cloud optical thickness, while liquid water path (LWP) remained 

unmodifi 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 refl ectance and an 

enhanced ambient aerosol concentration has been confi 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 refl 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 confi 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 diffi culty of devising observational studies that can isolate 

the albedo effect from other effects (e.g., meteorological 

variability, cloud dynamics) that infl 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 μm down to 6 μm (Bréon et al., 2002). This confi 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 diffi 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 signifi cant changes (Hashvardhan et al., 2002; Schwartz

et al., 2002; Krüger and Graβl, 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 signifi cant 

infl uence on the radiation transfer. The inclusions of scattering 

and absorbing particles within large ice crystals (Macke et al., 

1996) suggest a signifi 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 refl 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 fi 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 fi 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 diffi 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 Table 2.7. Published model studies of the RF due to cloud albedo effect, in the context of liquid water clouds, with a listing of the relevant modelling details. Model Model Aerosol Aerosol Cloud types Microphysics Radiative Forcing typea speciesb mixturesc included (W m–2)d Lohmann et al. (2000) AGCM + S, OC, BC, I warm and Droplet number concentration and LWC, Beheng (1994); –1.1 (total) sulphur cycle SS, D mixed phase Sundqvist et al. (1989). Also, mass and number from –0.45 (albedo) (ECHAM4) fi eld observations E –1.5 (total) Jones et al. (2001) AGCM + S, SS, D E stratiform and Droplet number concentration and LWC, Wilson and –1.89 (total) sulphur cycle, (a crude attempt shallow Ballard (1999); Smith (1990); Tripoli and Cotton (1980); –1.34 (albedo) fi xed SST for D over land, cumulus Bower et al. (1994). Warm and mixed phase, radiative (Hadley) no radiation) treatment of anvil cirrus, non-spherical ice particles Williams et al. (2001b) GCM with slab S, SS E stratiform and Jones et al. (2001) –1.69 (total) ocean + sulphur shallow cumulus –1.37 (albedo) cycle (Hadley) AGCM, fi xed SST –1.62 (total) –1.43(albedo) Rotstayn and Penner AGCM (CSIRO), S n.a. warm and Rotstayn (1997); Rotstayn et al. (2000) –1.39 (albedo) (2001) fi xed SST and mixed phase sulphur loading Rotstayn and Liu (2003) Interactive sulphur Inclusion of dispersion 12 to 35% decrease cycle –1.12 (albedo, mid value decreased) Ghan et al. (2001) AGCM (PNNL) + S, OC, BC, E (for different warm and Droplet number concentration and LWC, crystal –1.7 (total) chemistry (MIRAGE), SS, N, D modes); I mixed phase concentration and ice water content. Different processes –0.85 (albedo) fi xed SST (within modes) affecting the various modes Chuang et al. (2002) CCM1 (NCAR) + S, OC, BC, E (for emitted warm and Modifi ed from Chuang and Penner (1995), –1.85 (albedo) chemistry SS, D particles); I: mixed phase no collision/coalescence (GRANTOUR), when growing fi xed SST by condensation Menon et al. (2002a) GCM (GISS) + S,OC, SS E warm Droplet number concentration and LWC, Del Genio et al. –2.41 (total) sulphur cycle, (1996), Sundqvist et al. (1989). Warm and mixed phase, –1.55 (albedo) fi xed SST improved vertical distribution of clouds (but only nine layers).Global aerosol burdens poorly constrained Kristjansson (2002) CCM3 (NCAR) S, OC, BC, E (for nucleation warm and Rasch and Kristjánsson (1998). Stratiform and detraining –1.82 (total) fi 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, E stratiform Berry(1967), Sundqvist(1978) 0.54 (albedo) fi xed SST SS Quaas et al. (2004) AGCM (LMDZ) + S n.a. warm and Aerosol mass and cloud droplet 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 Model Aerosol Aerosol Cloud types Microphysics Radiative Forcing typea speciesb mixturesc included (W m–2)d Hansen et al. (2005) GCM (GISS) + 3 S, OC, BC, E warm and Schmidt et al. (2005), 20 vertical layers. Droplet number –0.77 (albedo) different ocean SS, N, D shallow (below concentration (Menon and Del Genio, 2007) parametrizations (D not included 720hPa) in 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, E warm and Aerosol mass and cloud droplet number concentration, –0.9 (albedo) (2005) interactive sulphur SS, D mixed phase Boucher and Lohmann (1995); Boucher et al. (1995) cycle, fi xed SST control run fi t to POLDER data –0.5 (albedo)e fi t to MODIS data –0.3 (albedo)e Quaas et al. (2005) AGCM (LMDZ S, OC, BC, E warm and Aerosol mass and cloud droplet number concentration, –0.84 (total and ECHAM4) SS, D mixed phase Boucher and Lohmann, (1995), control runs (ctl) LMDZ-ctl) –1.54 (total (ECHAM4-ctl) Aerosol mass and cloud droplet number concentration –0.53 (total fi tted to MODIS data LMDZ)e –0.29 (total (ECHAM4)e Dufresne et al. (2005) AGCM (LMDZ) + S n.a. warm Aerosol mass and cloud droplet number concentration, –0.22 (albedo)e interactive sulphur Boucher and Lohmann, (1995), fi tted to POLDER data cycle, fi xed SST Takemura et al. (2005) AGCM (SPRINTARS) S, OC, BC, E (50% BC warm Activation based on Kohler theory and updraft velocity –0.94 (total) + slab ocean SS, D from fossil fuel); –0.52 (albedo) I (for OC and BC) Chen and Penner AGCM (UM) + S, SS, D, I warm and Aerosol mass and cloud droplet number concentration (2005) fi xed SST OC, BC mixed phase (lognormal) Control (Abdul-Razzak and Ghan, 2002) –1.30 (albedo, UM_ctrl)f Relationship between droplet concentration and –0.75 (albedo, dispersion coeffi cient: High UM_1)f Relationship between droplet concentration and –0.86 (albedo, dispersion coeffi cient: Medium UM_2)f Updraft velocity –1.07 (albedo, UM_3)f Relationship between droplet concentration and –1.10 (albedo, dispersion coeffi cient: Low UM_4)f Chuang et al. (1997) –1.29 (albedo, UM_5)f Nenes and Seinfeld (2003) –1.79 (albedo, UM_6)f

Table 2.7 (continued)
176

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2 Notes: a AGCM: Atmospheric GCM; SST: sea surface temperature; CSIRO: Commonwealth Scientifi c and Industrial Research Organisation; MIRAGE: Model for Integrated Research on Atmospheric Global Exchanges; GRANTOUR: Global Aerosol Transport and Removal model; GFDL: Geophysical Fluid Dynamics Laboratory; CCSR: Centre for Climate System Research; see Table 2.4, Note (a) for listing of other models and modelling centres listed in this column. b S: sulphate; SS: sea salt; D: mineral dust; BC: black carbon; OC: organic carbon; N: nitrate. c E: external mixtures; I: internal mixtures. d Only the bold numbers were used to construct Figure 2.16. e These simulations have been constrained by satellite observations, using the same empirical fi t to relate aerosol mass and cloud droplet number concentration. f The notation UM corresponds to University of Michigan, as listed in Figure 2.14. Model Model Aerosol Aerosol Cloud types Microphysics Radiative Forcing typea speciesb mixturesc included (W m–2)d Ming et al. (2005b) AGCM (GFDL), S n.a. warm Rotstayn et al. (2000), Khainroutdinov and Kogan (2000). –2.3 (total) fi xed SST and Aerosols off-line –1.4 (albedo) sulphur loading Penner et al. (2006) LMDZ, Oslo and S, SS, D, E warm and Aerosol mass and cloud droplet number concentration; –0.65 (albedo Oslo) results from 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) Table 2.7 (continued)
177

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

Figure 2.14. Radiative forcing due to the cloud albedo effect, in the context of liquid water clouds, 

from the global climate models that appear in Table 2.7. The labels next to the bars correspond to the 

published study; the notes of Table 2.7 explain the species abbreviations listed on the left hand side. Top 

panel: results for models that consider a limited number of species, primarily anthropogenic sulphate 

(S). Bottom panel: results from studies that include a variety of aerosol compositions and mixtures; the 

estimates here cover a larger range than those in the top panel. Chen and Penner (2005) presented a 

sensitivity study obtained by changing parametrizations in their model, so the results can be considered 

independent and are thus listed separately. Penner et al. (2006) is an intercomparison study, so the 

results of the individual models are listed separately.

well as naturally produced sea salt, dust and 

continuously outgassing volcanic sulphate 

aerosols. Lohmann et al. (2000) and Chuang 

et al. (2002) included internally mixed 

sulphate, black and organic carbon, sea 

salt and dust aerosols, resulting in the most 

negative estimate of the cloud albedo indirect 

effect. Takemura et al. (2005) used a global 

aerosol transport-radiation model coupled 

to a GCM to estimate the direct and indirect 

effects of aerosols and their associated 

RF. The model includes a microphysical 

parametrization to diagnose the cloud 

droplet number concentration using Köhler 

theory, which depends on the aerosol particle 

number concentration, updraft velocity, size 

distributions and chemical properties of each 

aerosol species. The results indicate a global 

decrease in cloud droplet effective radius 

caused by anthropogenic aerosols, with 

the global mean RF calculated to be –0.52 

W m–2; the land and oceanic contributions 

are –1.14 and –0.28 W m–2, respectively. 

Other modelling results also indicate that the 

mean RF due to the cloud albedo effect is on 

average somewhat larger over land than over 

oceans; over oceans there is a more consistent 

response from the different models, resulting 

in a smaller inter-model variability (Lohmann 

and Feichter, 2005). 

Chen and Penner (2005), by systematically 

varying parameters, obtained a less negative 

RF when the in-cloud updraft velocity was 

made to depend on the turbulent kinetic 

energy. Incorporating other cloud nucleation 

schemes, for example, changing from AbdulRazzak and Ghan (2002) to the Chuang et 

al. (1997) parametrization resulted in no RF 

change, while changing to the Nenes and 

Seinfeld (2003) parametrization made the RF 

more negative. Rotstayn and Liu (2003) found 

a 12 to 35% decrease in the RF when the size 

dispersion effect was included in the case of 

sulphate particles. Chen and Penner (2005) 

further explored the range of parameters used 

in Rotstayn and Liu (2003) and found the 

RF to be generally less negative than in the 

standard integration.

A model intercomparison study (Penner et al., 2006) 

examined the differences in cloud albedo effect between 

models through a series of controlled experiments that allowed 

examination of the uncertainties. This study presented results 

from three models, which were run with prescribed aerosol 

mass-number concentration (from Boucher and Lohmann, 1995), 

aerosol fi eld (from Chen and Penner, 2005) and precipitation 

effi ciency (from Sundqvist, 1978). The cloud albedo RFs in 

the three models do not vary widely: –0.65, –0.68 and –0.74 

W m–2, respectively. Nevertheless, changes in the autoconversion 

scheme led to a differing response of the LWP between the 

models, and this is identifi ed as an uncertainty. 

A closer inspection of the treatment of aerosol species in the 

models leads to a broad separation of the results into two groups: 

models with only a few aerosol species and those that include 

a more complex mixture of aerosols of different composition. 

Thus, in Figure 2.14, RF results are grouped according to the 
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 diffi 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 (Nd ≈ (Na)b) 

obtained is consistent with Twomey’s hypothesis; however, the 

parameter b 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 Na lead to larger changes in Nd 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 infl 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 infl 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 fi 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, modifi 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 

diffi 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 fi rst principles has 

proven diffi 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 signifi 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 diffi culty in accurately estimating 

the amount of cloud liquid and ice water content in a grid box.

It has proven diffi cult to compare directly the results from 

the different models, as uncertainties are not well identifi ed 

and quantifi ed. All models could be suffering from similar 

biases, and modelling studies do not often quote the statistical 

signifi 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% confi 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 insuffi 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 diffi 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 infl 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 DoutriauxBoucher, 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 preindustrial 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 verifi 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 confi 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 insuffi 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 diffi 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 suffi 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 insuffi 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 aerosolcloud 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 scientifi c 

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 fl 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 refl 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 refl ective, while trees can 

remain exposed above the snow (Betts, 2000). Even a snowcovered canopy exhibits a relatively low albedo as a result of 

multiple refl ections within the canopy (Harding and Pomeroy, 

1996). Surface albedo change may therefore provide the 

dominant infl 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 modifi ed by the 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 defi 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 fl uxes of moisture through 

evaporation and transpiration, the ratio of latent to sensible 

heat fl uxes (the Bowen ratio) and the aerodynamic roughness, 

which exerts frictional drag on the atmosphere and also affects 
181

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

turbulent transfer of heat and moisture. All these processes 

can affect the air temperature near the ground, and also 

modify humidity, precipitation and wind speed. Direct human 

perturbations to the water cycle, such as irrigation, can affect 

surface moisture fl uxes and hence the surface energy balance. 

Changes in vegetation cover can affect the production of dust, 

which then exerts an RF. Changes in certain gases, particularly 

CO2 and ozone, can also exert an additional infl uence on 

climate by affecting the Bowen ratio, through plant responses 

that affect transpiration. These processes are discussed in detail 

in Section 7.2. While such processes will act as anthropogenic 

perturbations to the climate system (Pielke et al., 2002) and 

will fall at least partly within the ‘forcing’ component of the 

forcing-feedback-response conceptual model, it is diffi cult to 

unequivocally quantify the pure forcing component as distinct 

from feedbacks and responses. The term ‘non-radiative forcing’ 

has been proposed (Jacob et al., 2005) and this report adopts the 

similar term ‘non-initial radiative effect’, but no quantitative 

metric separating forcing from feedback and response has yet 

been implemented for climatic perturbation processes that do 

not act directly on the radiation budget (see Section 2.2). 

Energy consumption by human activities, such as heating 

buildings, powering electrical appliances and fuel combustion 

by vehicles, can directly release heat into the environment. This 

was not discussed in the TAR. Anthropogenic heat release is not 

an RF, in that it does not directly perturb the radiation budget; 

the mechanisms are not well identifi ed and so it is here referred 

to as a non-initial radiative effect. It can, however, be quantifi ed 

as a direct input of energy to the system in terms of W m–2.

Figure 2.15. Anthropogenic modifi cations of land cover up to 1990. Top panel: Reconstructions of potential natural vegetation (Haxeltine and Prentice, 1996). Lower panels: 

reconstructions of croplands and pasture for 1750 and 1990. Bottom left: fractional cover of croplands from Centre for Sustainability and the Global Environment (SAGE; 

Ramankutty and Foley, 1999) at 0.5° resolution. Bottom right: reconstructions from the HistorY Database of the Environment (HYDE; Klein Goldewijk, 2001), with one land cover 

classifi cation per 0.5° grid box.
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 km2 (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 intensifi 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 km2 (35% to 

39% of global land), and forest cover had decreased by roughly 

11 million km2 (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 classifi 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 Table 2.8. Estimates of forest area, contribution to CO2 increase from anthropogenic land cover change, and RF due to the land use change-induced CO2 increase and surface albedo change, relative to pre-industrial vegetation and PNV. The CO2 RFs are for 2000 relative to 1850, calculated from the land use change contribution to the total increase in CO2 from 1850 to 2000 simulated with both land use and fossil fuel emissions by the carbon cycle models. Carbon emissions from land cover change for the 1980s and 1990s are discussed in Section 7.3 and Table 7.2. Notes: a The available literature simulates CO2 rises with and without land use relative to 1850. b 1750 forest area reported as 51.85 x 106 km2. c 1992 forest area. d Land use contribution CO2 rise from Brovkin et al. (2004). e Albedo RF from Betts et al. (2007). Land cover data combined from Ramankutty and Foley (1999), Klein Goldelwijk (2001) and Wilson and Henderson-Sellers (1985). f Albedo RF from Myhre and Myhre (2003). Range of estimate for each land cover data set arises from use of different albedo values. g Albedo RF from model of Goosse et al. (2005) in Brovkin et al. (2006). h RF relative to 1750 estimated here as 0.75 of RF relative to PNV following Betts et al. (2007) and Brovkin et al. (2006). i Estimate relative to 1700. j Albedo RF from Matthews et al. (2003). Main Source of Land Cover Data Forest Area PNV 106 km2 Forest Area circa 1700 106 km2 Forest Area circa 1990 106 km2 Contribution to CO2 Increase 1850–2000a (ppm) CO2 RF (W m–2) Albedo RF vs. PNV (W m–2) Albedo RF vs. 1750 (W m–2) Ramankutty and Foley (1999) 55.27 52.77b 43.97c 16d 0.27 –0.24e –0.29 to +0.02f –0.2g –0.18e –0.22 to +0.02h –0.14g,i –0.15 to –0.28i,j –0.15k –0.075 to –0.325i,l Klein Goldewijk (2001) 58.6 54.4 41.5 12d 0.20 –0.66 to +0.1f –0.50 to +0.08h –0.275i,l Houghton (1983m, 2003) 62.15 50.53n 35d 26o 0.57 0.44 MODIS (Schaaf et al., 2002) –0.09p –0.07h Wilson and Henderson-Sellers (1985) –0.2q –0.29f –0.15h –0.22h SARBr –0.11 to –0.55f –0.08 to –0.41h Matthews (1983) –0.12f –0.4s –0.08t –0.09h –0.3h –0.06h k Albedo RF from Hansen et al. (2005). l Albedo RF from Matthews et al. (2004). m Forest areas aggregated by Richards (1990). n 1980 forest area. o Land use contribution to CO2 rise from Matthews et al. (2004). Estimate only available relative to 1850 not 1750. p Albedo RF from Myhre et al. (2005a). q Albedo RF from Betts (2001). r Surface and Atmosphere Radiation Budget; http://www-surf.larc.nasa.gov/surf/. s Albedo RF from Hansen et al. (1997). t Albedo RF from 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 confl 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 scientifi 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 refi 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 CO2

RF, that is, the ‘effi 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 

scientifi 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 CO2, 

CH4, 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 fl 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 et 

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 et 

al., 2001) to –0.25°C (Govindasamy et al., 2001a; Brovkin et 

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 signifi cant in recent decades, warming due to 

reduced evaporation may become more signifi 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 fl ow to 

the atmosphere from irrigation are signifi 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 

fl ow to the atmosphere from deforestation, most importantly 

in tropical areas. This reduced water vapour fl 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 signifi 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 signifi 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 fl 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 fl 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 infl 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 CO2 can perturb the climate 

system through direct effects on plant physiology. Plant stomatal 

apertures open less under higher CO2 concentrations (Field et 
186

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

al., 1995), which directly reduces the fl ux of moisture from the 

surface to the atmosphere through transpiration (Sellers et al., 

1996). A decrease in moisture fl ux modifi es the surface energy 

balance, increasing the ratio of sensible heat fl ux to latent heat 

fl ux and therefore warming the air near the surface (Sellers et 

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 quantifi ed the presentday 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 

CO2 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 CO2 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 scientifi 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 fl 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:

a Values for contrails are best estimates. Values in parentheses give the 

uncertainty range.

b Values from IPCC-1999 (IPCC, 1999).

c Values interpolated from 1992 and 2015 estimates in IPCC-1999 (Sausen et 

al., 2005).

d 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)a

1992 IPCCb 2000 IPCCc 2000d

CO2

d 0.018 0.025 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 refl 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 modifi ed cirrus cloud parametrization in a GCM (Ponater et 

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 fl own by the global fl eet if fl 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 traffi 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 effi cacy or climate 

sensitivity of contrail RF would need to be much greater than 

that of other larger RF terms, (e.g., CO2). Instead, contrail RF 

is found to have a smaller effi cacy than an equivalent CO2 RF 

(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 infl 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 traffi 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 scientifi 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 defi 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, 

200510), 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 signifi cant positive trends in cirrus cloudiness 

in some regions of high air traffi c and found lower to negative 

trends outside air traffi c regions (Zerefos et al., 2003; Stordal et 

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, signifi 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 

10 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 

infl 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 signifi cant cirrus modifi cations by aviation caused 

by increased numbers of BC particles. The modifi cations would 

occur in fl ight corridors as well as in regions far away from fl 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 aerosolcloud 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 fl 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 signifi 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 signifi 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 signifi 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 fl ux and the aa 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., Sofi 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 fi 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 defi 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 
190

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

which variations arise, in part, from heliospheric modulation. 

This gives confi dence that the approach is plausible. A small 

accumulation of total fl 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 14C and 10Be cosmogenic isotope records 

in tree rings and ice cores, respectively, which exhibit solarrelated 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 fi elds modulate the heliosphere, in turn 

altering the penetration of the galactic cosmic rays, the fl 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 14C 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 

signifi 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). Other 

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 fl ux 

on the Sun and propagation of open fl ux into the heliosphere 

indicate that ‘open’ magnetic fl ux (which modulates geomagnetic 

activity and cosmogenic isotopes) can accumulate on intercycle time scales even when closed fl 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 fl 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 signifi cantly less than previously assumed, specifi cally 

0.27 times that of Lean (2000). This estimate results from 

simulations of the eruption, transport and accumulation of 

magnetic fl ux during the past 300 years using a fl ux transport 

model with variable meridional fl ow. Variations in both the total 

fl ux and in just the fl ux that extends into the heliosphere (the 

open fl 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 fl ux 

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 brightness 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.
191

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

Table 2.10. Comparison of the estimates of the increase in RF from the 17th-century Maunder Minimum (MM) to contemporary solar minima, documenting new understanding since the TAR.

Notes: 

a The RF is the irradiance change divided by 4 (geometry) and multiplied by 0.7 (albedo). The solar activity cycle, which was negligible during the Maunder Minimum 

and is of order 1 W m–2 (minimum to maximum) during recent cycles, is superimposed on the irradiance changes at cycle minima. When smoothed over 20 years, 

this cycle increases the net RF in the table by an additional 0.09 W m–2.

b These reconstructions extend only to 1713, the end of the Maunder Minimum. 

Reference

Assumptions

and Technique

RF Increase from

the Maunder Minimum

to Contemporary

Minima (W m–2)a

Comment on

Current Understanding

Schatten and

Orosz (1990) 

Extrapolation of the 11-year irradiance cycle to 

the MM, using the sunspot record.

~ 0 Irradiance levels at cycle minima remain 

approximately constant.

Lean et al. (1992) No spots, plage or network in Ca images 

assumed during MM.

0.26 Maximum irradiance increase from a 

non-magnetic sun, due to changes in 

known bright features on contemporary 

solar disk.

Lean et al. (1992) No spots, plage or network and reduced 

basal emission in cell centres in Ca images to 

match reduced brightness in non-cycling stars, 

assumed to be MM analogues.

0.45 New assessment of stellar data (Hall 

and Lockwood, 2004) does not support 

original stellar brightness distribution, 

or the use of the brightness reduction in 

the Baliunas and Jastrow (1990) ‘noncycling’ stars as MM analogues.

Hoyt and Schatten 

(1993)b

Convective restructuring implied by changes 

in sunspot umbra/penumbra ratios from MM 

to present: amplitude of increase from MM to 

present based on brightness of non-cycling 

stars, from Lean et al. (1992).

0.65 As above

Lean et al. (1995) Reduced brightness of non-cycling stars, 

relative to those with active cycles, assumed 

typical of MM.

0.45 As above

Solanki and Fligge 

(1999)b

Combinations of above. 0.68 As above

Lean (2000) Reduced brightness of non-cycling stars 

(revised solar-stellar calibration) assumed 

typical of MM.

0.38 As above

Foster (2004)

Model

Non-magnetic sun estimates by removing

bright features from MDI images assumed for 

MM. 

0.28 Similar approach to removal of spots, 

plage and network by Lean et al. (1992).

Y. Wang et al.

(2005)b

Flux transport simulations of total magnetic fl ux 

evolution from MM to present.

0.1 Solar model suggests that modest 

accumulation of magnetic fl ux from 

one solar cycle to the next produces a 

modest increase in irradiance levels at 

solar cycle minima.

Dziembowski et al. 

(2001)

Helioseismic observations of solar interior 

oscillations suggest that the historical Sun 

could not have been any dimmer than current 

activity minima.

~ 0
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 RF11 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 fl 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 signifi cant spectral dependence. 

For example, the Y. Wang et al. (2005) fl 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 signifi cantly to changes in total solar 

irradiance (15% of the total irradiance cycle; Lean et al., 1997) 

and creates and modifi 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 confi rmed and advanced the 

plausibility of indirect effects involving the modifi 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 fl 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 infl 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 infl uences on climate, Gray et al. (2005) noted 

that updated observational analyses have confi rmed earlier 11-

year cycle signals in zonally averaged stratospheric temperature, 

ozone and circulation with increased statistical confi 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 signifi 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 

confi guration of the heliosphere reduces the fl ux of galactic 

cosmic rays in the Earth’s atmosphere. Various scenarios have 

been proposed whereby solar-induced galactic cosmic ray 

fl uctuations might infl uence climate (as surveyed by Gray et 

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 electrifi 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 infl 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 diffi culty in tracking 

the infl uence of one particular modifi cation brought about by 

11 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 solarozone 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 

fl uxes (e.g., Marsh and Svensmark, 2000a,b). Hypothesised 

to result from changing ionization of the atmosphere from 

solar-modulated cosmic ray fl 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 signifi 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 fl ux. However, there appears to be a 

small but statistically signifi cant positive correlation between 

cloud over the UK and galactic cosmic ray fl 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 

fl 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 fl 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% 

confi 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 scientifi 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 

infl 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 μm 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 et 

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 groundbased 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 SO2

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 signifi 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 fi lled by lidar measurements and fi 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 signifi 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 refl 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 refl 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 refl 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. Altitudedependent stratospheric optical observations at a 

few wavelengths, together with columnar optical 

and physical measurements, have been used to 

construct the time-dependent global fi eld of 

stratospheric aerosol size distribution formed in 

the aftermath of volcanic events. The wavelengthdependent 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 simplifi ed parametrization 

of atmospheric transport, and employs a fi xed aerosol effective 

radius (0.42 μm) 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 SO2) 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 quantifi 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 fi ll gaps 

(Randall et al., 2001). While the aerosol characteristics are 

Figure 2.18. Visible (wavelength 0.55 μm) 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; Chipperfi 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’ 

simplifi 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 diffi cult to 

give a best estimate for the volcanic RF, unlike the other agents. 

Neither a best estimate nor a level of scientifi c understanding 

was given in the TAR. For the well-documented case of the 

explosive 1991 Mt. Pinatubo eruption, there is a good scientifi c 

understanding. However, the limited knowledge of the RF 

associated with prior episodic, explosive events indicates a low 

level of scientifi 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 fi 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; 

Chipperfi 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, QuasiBiennial 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 (Chipperfi 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 wellmixed 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 fl 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 signifi 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 fl ux 

(Joshi and Shine, 2003; see Section 2.3.7).

Anomalies in the volcanic-aerosol induced global radiative 

heating distribution can force signifi 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 defi 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) fi 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) fi 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-temperaturechange 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 fi 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 defi ning the tropopause in 

the stratospherically adjusted RF defi 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 effi 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 fl 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, signifi 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 fi 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). Identifi 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 fi rst order, all 

methods are comparable and all prove useful for understanding 

climate response. 

2.8.4 Linearity of the Forcing-Response 

Relationship

Reporting fi 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 fi 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 fi 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 confi dence in a linear relationship 

between global mean RF and global mean surface 

temperature response.

2.8.5 Effi cacy and Effective Radiative 

Forcing

Effi cacy (E) is defi ned as the ratio of the climate 

sensitivity parameter for a given forcing agent 

(λi

) to the climate sensitivity parameter for CO2

changes, that is, Ei = λi / λCO2 (Joshi et al., 2003; 

Hansen and Nazarenko, 2004). Effi cacy can then 

be used to defi ne an effective RF (= Ei RFi) (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, ΔTs = λCO2 x Ei x RFi Preliminary studies have 

found that effi 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 effi cacies 

closer to unity, may be another way of achieving similar goals 

(see Section 2.8.3). This section assesses the effi cacy associated 

with stratospherically adjusted RF, as this is the defi nition of 

RF adopted in this chapter (see Section 2.2). Therefore, cloudaerosol interaction effects beyond the cloud albedo RF are 

included in the effi cacy term. The fi 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 effi 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 

effi cacies and a general understanding is beginning to emerge 

as to how and why effi cacies vary between mechanisms. The 

initial climate state, and the sign and magnitude of the RF have 

less importance but can still affect effi 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 effi cacy estimate. Overall, there 

is medium confi dence that the direct solar effi cacy is within the 

0.7 to 1.0 range.

2.8.5.4 Ozone

Stratospheric ozone effi 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 effi 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 effi 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 

confi dence that the effi cacy is within a 0.5 to 2.0 range and 

(2) established but incomplete physical understanding of how 

and why the effi cacy could be larger than 1.0. There is medium 

confi dence that for realistic tropospheric ozone perturbations 

the effi cacy is within the 0.6 to 1.1 range.

2.8.5.5 Scattering Aerosol

For idealised global perturbations, the effi 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 effi cacy than 

solar changes (Hansen et al., 2005). Although the number of 

modelling results is limited, it is expected that effi cacies would 

be similar to other solar effects; thus there is medium confi dence 

that effi cacies for scattering aerosol would be in the 0.7 to 1.1 

range. Effi cacies are likely to be similar for scattering aerosol in 

the troposphere and stratosphere. 

With the formulation of RF employed in this chapter, the 

effi cacy of the cloud albedo RF accounts for cloud lifetime 

effects (Section 2.8.3). Only two studies contained enough 

information to calculate effi cacy in this way and both found 

effi cacies higher than 1.0. However, the uncertainties in 

quantifying the cloud lifetime effect make this effi cacy very 

uncertain. If cloud lifetime effects were excluded from the 

effi cacy term, the cloud albedo effi 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 forcingresponse relationship and effi 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 effi cacy can be negative. The surface albedo and 

developed useful conceptual models to help explain variations 

in effi cacy with forcing mechanism. The effi 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 effi 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 fi nd that both the geographical 

and vertical distribution of the forcing can have the most 

signifi cant effect on effi cacy (in particular see Boer and Yu, 

2003b; Joshi et al., 2003; Stuber et al., 2005; Sokolov, 2006). 

Nearly all studies that examine it fi nd that high-latitude forcings 

have higher effi cacies than tropical forcings. Effi 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 effi 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 et 

al., 2001b; Joshi et al., 2003; Sokolov, 2006). 

2.8.5.2 Long-Lived Greenhouse Gases

The few models that have examined effi cacy for combined 

LLGHG changes generally fi nd effi 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 effi cacy of over 1.2 with no clear 

reason of why this changed from earlier versions of the same 

model. Individual LLGHG effi cacies have only been analysed 

in two or three models. Two GCMs suggest higher effi cacies 

from individual components (over 30% for CFCs in Hansen et 

al., 2005). In contrast another GCM gives effi cacies for CFCs 

(Forster and Joshi, 2005) and CH4 (Berntsen et al., 2005) that are 

slightly less than one. Overall there is medium confi dence that 

the observed changes in the combined LLGHG changes have 

an effi cacy close to 1.0 (within 10%), but there are not enough 

studies to constrain the effi cacies for individual species.

2.8.5.3 Solar 

Solar changes, compared to CO2, 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 effi cacies close to 1.0. All models with a 

positive solar RF fi nd effi cacies of 1.0 or smaller. One study 

fi nds a smaller effi cacy than other models (0.63: Gregory et 

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 effi cacies signifi cantly closer to 1.0 for 

all mechanisms. It should be noted that effi cacies have only 

been evaluated in GCMs and actual climate effi 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 scientifi c understanding (LOSU) for each RF, 

which was a subjective judgment of the estimate’s reliability. 

The concept of LOSU has been slightly modifi 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 fi nd effi cacies 

much larger than 1.0 (Cook and Highwood, 2004; Roberts and 

Jones, 2004; Hansen et al., 2005). These studies also fi nd that 

effi cacies are considerably smaller than 1.0 when BC aerosol is 

changed above the boundary layer. These changes in effi cacy 

are at least partly attributable to a semi-direct effect whereby 

absorbing aerosol modifi es the background temperature profi le 

and tropospheric cloud (see Section 7.5). Another possible 

feedback mechanism is the modifi 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 fi nd 

that overall the effi 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 fi nds an effi cacy of 1.3 (Sokolov, 

2006). Further, Penner et al. (2007) also modelled BC changes 

and found effi cacies very much larger and very much smaller 

than 1.0 for biomass and fossil fuel carbon, respectively (Hansen 

et al. (2005) found similar effi cacies for biomass and fossil fuel 

carbon). In summary there is no consensus as to BC effi cacy 

and this may represent problems with the stratospherically 

adjusted defi nition of RF (see Section 2.8.3).

2.8.5.7 Other Forcing Agents

Effi 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 effi cacy of roughly 1.0, while the BCsnow albedo RF had an effi cacy of 1.7. Ponater et al. (2005) 

found an effi 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 effi cacies. The results of Hansen et al. 

(2005) and Forster and Shine (1999) suggest that stratospheric 

water vapour effi cacies are roughly one. 

2.8.6 Effi cacy and the Forcing-Response 

Relationship

Effi 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 confi dence that effi 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 confi dence that 

realistic aerosol and ozone changes have effi cacies within the 
200

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

confi dence range (see Box TS.1). Only ‘well-established’ 

RFs are quantifi ed. ‘Well established’ implies that there is 

qualitatively both suffi cient evidence and suffi 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 insuffi cient evidence. 

Strong evidence implies that observations have verifi 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 insuffi 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% 

confi dence range of RF quantifi 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% confi 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 (effi 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 

quantifi ed, either because they do not have enough evidence 

or because their quantifi cation lacks consensus. These include 

certain mechanisms associated with land use, stratospheric 

water vapour and cosmic rays. Cloud lifetime and the semidirect 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 effi 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 effi 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 effi 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 

effi cacies are now better understood and quantifi ed (see Section 

2.8.5), and as the linear additivity assumption has been more 

thoroughly tested (see Section 2.8.4), it becomes scientifi cally 

justifi 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% confi 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% confi 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% confi 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 quantifi ed to 

90% confi dence levels, enabling a more defi 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 Table 2.11. Uncertainty assessment of forcing agents discussed in this chapter. Evidence for the forcing is given a grade (A to C), with A implying strong evidence and C insuffi cient evidence. The degree of consensus among forcing estimates is given a 1, 2 or 3 grade, where grade 1 implies a good deal of consensus and grade 3 implies an insuffi cient consensus. From these two factors, a level of scientifi c understanding is determined (LOSU). Uncertainties are in approximate order of importance with fi rst-order uncertainties listed fi rst. Evidence Consensus LOSU Certainties Uncertainties Basis of RF range LLGHGs A 1 High Past and present concentrations; spectroscopy Pre-industrial concentrations of some species; vertical profi le in stratosphere; spectroscopic strength of minor gases Uncertainty assessment of measured trends from different observed data sets and differences between radiative transfer models Stratospheric ozone A 2 Medium Measured trends and its vertical profi le since 1980; cooling of stratosphere; spectroscopy Changes prior to 1970; trends near tropopause; effect of recent trends Range of model results weighted to calculations employing trustworthy observed ozone trend data Tropospheric ozone A 2 Medium Present-day concentration at surface and some knowledge of vertical and spatial structure of concentrations and emissions; spectroscopy Pre-industrial values and role of changes in lightning; vertical structure of trends near tropopause; aspects of emissions and chemistry Range of published model results, upper bound increased to account for anthropogenic trend in lightning Stratospheric water vapour from CH4 A 3 Low Global trends since 1990; CH4 contribution to trend; spectroscopy Global trends prior to 1990; radiative transfer in climate models; CTM models of CH4 oxidation Range based on uncertainties in CH4 contribution to trend and published RF estimates Direct aerosol A 2 to 3 Medium to Low Ground-based and satellite observations; some source regions and modelling Emission sources and their history vertical structure of aerosol, optical properties, mixing and separation from natural background aerosol Range of published model results with allowances made for comparisons with satellite data Cloud albedo effect (all aerosols) B 3 Low Observed in case studies – e.g., ship tracks; GCMs model an effect Lack of direct observational evidence of a global forcing Range of published model results and published results where models have been constrained by satellite data Surface albedo (land use) A 2 to 3 Medium to Low Some quantifi cation of deforestation and desertifi cation Separation of anthropogenic changes from natural Based on range of published estimates and published uncertainty analyses Surface albedo (BC aerosol on snow) B 3 Low Estimates of BC aerosol on snow; some model studies suggest link Separation of anthropogenic changes from natural; mixing of snow and BC aerosol; quantifi cation of RF Estimates based on a few published model studies Persistent linear Contrails A 3 Low Cirrus radiative and microphysical properties; aviation emissions; contrail coverage in certain regions Global contrail coverage and optical properties Best estimate based on recent work and range from published model results
202

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2 Solar irradiance B 3 Low Measurements over last 25 years; proxy indicators of solar activity Relationship between proxy data and total solar irradiance; indirect ozone effects Range from available reconstructions of solar irradiance and their qualitative assessment Volcanic aerosol A 3 Low Observed aerosol changes from Mt. Pinatubo and El Chichón; proxy data for past eruptions; radiative effect of volcanic aerosol Stratospheric aerosol concentrations from pre-1980 eruptions; atmospheric feedbacks Past reconstructions/estimates of explosive volcanoes and observations of Mt. Pinatubo aerosol Stratospheric water vapour from causes other than CH4 oxidation C 3 Very Low Empirical and simple model studies suggest link; spectroscopy Other causes of water vapour trends poorly understood Not given Tropospheric water vapour from irrigation C 3 Very Low Process understood; spectroscopy; some regional information Global injection poorly quantifi ed Not given Aviation-induced cirrus C 3 Very Low Cirrus radiative and microphysical properties; aviation emissions; contrail coverage in certain regions Transformation of contrails to cirrus; aviation’s effect on cirrus clouds Not given Cosmic rays C 3 Very Low Some empirical evidence and some observations as well as microphysical models suggest link to clouds General lack/doubt regarding physical mechanism; dependence on correlation studies Not given Other surface effects C 3 Very Low Some model studies suggest link and some evidence of relevant processes Quantifi cation of RF and interpretation of results in forcing feedback context diffi cult Not given Evidence Consensus LOSU Certainties Uncertainties Basis of RF range Table 2.11 (continued)
203

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

Figure 2.20. (A) Global mean RFs from the agents and mechanisms discussed in this chapter, grouped by agent type. Anthropogenic RFs and the natural direct solar RF are 

shown. The plotted RF values correspond to the bold values in Table 2.12. Columns indicate other characteristics of the RF; effi cacies are not used to modify the RFs shown. 

Time scales represent the length of time that a given RF term would persist in the atmosphere after the associated emissions and changes ceased. No CO2 time scale is given, 

as its removal from the atmosphere involves a range of processes that can span long time scales, and thus cannot be expressed accurately with a narrow range of lifetime 

values. The scientifi c understanding shown for each term is described in Table 2.11. (B) Probability distribution functions (PDFs) from combining anthropogenic RFs in (A). 

Three cases are shown: the total of all anthropogenic RF terms (block fi lled red curve; see also Table 2.12); LLGHGs and ozone RFs only (dashed red curve); and aerosol direct 

and cloud albedo RFs only (dashed blue curve). Surface albedo, contrails and stratospheric water vapour RFs are included in the total curve but not in the others. For all of the 

contributing forcing agents, the uncertainty is assumed to be represented by a normal distribution (and 90% confi dence intervals) with the following exceptions: contrails, for 

which a lognormal distribution is assumed to account for the fact that the uncertainty is quoted as a factor of three; and tropospheric ozone, the direct aerosol RF (sulphate, 

fossil fuel organic and black carbon, biomass burning aerosols) and the cloud albedo RF, for which discrete values based on Figure 2.9, Table 2.6 and Table 2.7 are randomly 

sampled. Additional normal distributions are included in the direct aerosol effect for nitrate and mineral dust, as these are not explicitly accounted for in Table 2.6. A one-million 

point Monte Carlo simulation was performed to derive the PDFs (Boucher and Haywood, 2001). Natural RFs (solar and volcanic) are not included in these three PDFs. Climate 

effi cacies are not accounted for in forming the PDFs.
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: a 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. 

 b 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)a

 Summary comments on changes 

 SAR (1750–1993) TAR (1750–1998) AR4 (1750–2005) since the TAR

Combined Not evaluated 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 (CO2 1.56; CH4 (CO2 1.46; CH4 0.48; (CO2 1.66 [±0.17]; upward trends, particularly in

(Comprising CO2, 0.47; N2O 0.14; N2O 0.15; CH4 0.48 [±0.05]; CO2. Halocarbon RF trend

CH4, N2O, and Halocarbons 0.28) Halocarbons 0.34b) N2O 0.16 [±0.02]; is positiveb

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

 be possible

Stratospheric Not evaluated +0.01 to +0.03 +0.07 [±0.05] Re-evaluated to be higher

water vapour

from CH4

Total direct aerosol Not evaluated Not evaluated –0.50 [±0.40] Newly evaluated

Direct sulphate aerosol –0.40 [2x] –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] –0.20 [3x] +0.03 [±0.12] Re-evaluated and sign changed.

burning aerosol Response affected by semi-direct 

 effects

Direct nitrate aerosol Not evaluated 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) (all aerosols) 

Surface albedo Not evaluated –0.20 [100%] –0.20 [±0.20] Additional studies

(land use) 

Surface albedo Not evaluated 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%] +0.30 [67%] +0.12 [–0.06, +0.18] Re-evaluated to be less than half
205

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

the PDF and the assumptions describing the 

component uncertainties. Normal distributions 

are assumed for most RF mechanisms (with the 

exceptions noted in the caption); this may not 

accurately capture extremes. Additionally, as in 

Boucher and Haywood (2001), all of the individual 

RF mechanisms are given equal weighting, even 

though the level of scientifi c understanding differs 

between forcing mechanisms. Note also that 

variation in effi cacy and hence the semi-direct 

and cloud lifetime effects are not accounted for, 

as these are not considered to be RFs in this report 

(see Section 2.2). Adding these effects, together 

with other potential mechanisms that have so far 

not been defi ned as RFs and quantifi ed, would 

introduce further uncertainties but give a fuller 

picture of the role of anthropogenic drivers. 

Introducing effi cacy would give a broader PDF 

and a large cloud lifetime effect would reduce 

the median estimate. Despite these caveats, from 

the current knowledge of individual forcing 

mechanisms presented here it remains extremely 

likely that the combined anthropogenic RF is 

both positive and substantial (best estimate: +1.6

W m–2).

2.9.3 Global Mean Radiative Forcing by 

Emission Precursor

The RF due to changes in the concentration of 

a single forcing agent can have contributions from 

emissions of several compounds (Shindell et al., 

2005). The RF of CH4, for example, is affected 

by CH4 emissions, as well as NOx emissions. 

The CH4 RF quoted in Table 2.12 and shown in 

Figure 2.20 is a value that combines the effects 

of both emissions. As an anthropogenic or natural 

emission can affect several forcing agents, it is 

useful to assess the current RF caused by each 

primary emission. For example, emission of NOx

affects CH4, tropospheric ozone and tropospheric 

aerosols. Based on a development carried forward 

from the TAR, this section assesses the RF terms 

associated with each principal emission including 

indirect RFs related to perturbations of other 

forcing agents, with the results shown in Figure 

2.21. The following indirect forcing mechanisms 

are considered:

• fossil carbon from non-CO2 gaseous compounds, which 

eventually increase CO2 in the atmosphere (from CO, CH4, 

and NMVOC emissions);

• changes in stratospheric ozone (from N2O and halocarbon 

(CFCs, HCFC, halons, etc.) emissions);

• changes in tropospheric ozone (from CH4, NOx, CO, and 

NMVOC emissions);

• changes in OH affecting the lifetime of CH4 (from CH4, 

CO, NOx, and NMVOC emissions); and 

• changing nitrate and sulphate aerosols through changes in 

NOx and SO2 emissions, respectively.

For some of the principal RFs (e.g., BC, land use and mineral 

dust) there is not enough quantitative information available to 

assess their indirect effects, thus their RFs are the same as those 

Figure 2.21. Components of RF for emissions of principal gases, aerosols and aerosol precursors 

and other changes. Values represent RF in 2005 due to emissions and changes since 1750. (S) and 

(T) next to gas species represent stratospheric and tropospheric changes, respectively. The uncertainties are given in the footnotes to Table 2.13. Quantitative values are displayed in Table 2.13.
206

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

Figure 2.22. Integrated RF of year 2000 emissions over two time horizons (20 and 100 years). The 

fi gure gives an indication of the future climate impact of current emissions. The values for aerosols and 

aerosol precursors are essentially equal for the two time horizons. It should be noted that the RFs of 

short-lived gases and aerosol depend critically on both when and where they are emitted; the values 

given in the fi gure apply only to total global annual emissions. For organic carbon and BC, both fossil 

fuel (FF) and biomass burning emissions are included. The uncertainty estimates are based on the 

uncertainties in emission sources, lifetime and radiative effi ciency estimates.

presented in Table 2.12. Table 2.5 gives the 

total (fossil and biomass burning) direct 

RFs for BC and organic carbon aerosols 

that are used to obtain the average shown 

in Figure 2.21. Table 2.13 summarises 

the direct and indirect RFs presented in 

Figure 2.21, including the methods used 

for estimating the RFs and the associated 

uncertainty. Note that for indirect effects 

through changes in chemically active 

gases (e.g., OH or ozone), the emissionbased RF is not uniquely defi ned since the 

effect of one precursor will be affected by 

the levels of the other precursors. The 

RFs of indirect effects on CH4 and ozone 

by NOx, CO and VOC emissions are 

estimated by removing the anthropogenic 

emissions of one precursor at a time. 

A sensitivity analysis by Shindell et 

al. (2005) indicates that the nonlinear 

effect induced by treating the precursors 

separately is of the order of 10% or less. 

Very uncertain indirect effects are not 

included in Table 2.13 and Figure 2.21. 

These include ozone changes due to solar 

effects, changes in secondary organic 

aerosols through changes in the ozone/OH 

ratio and apportioning of the cloud albedo 

changes to each aerosol type (Hansen et 

al., 2005).

2.9.4 Future Climate Impact of 

Current Emissions

The changes in concentrations since 

pre-industrial time of the long-lived 

components causing the RF shown in 

Figure 2.20 are strongly infl uenced by 

the past history of emissions. A different 

perspective is obtained by integrating RF 

over a future time horizon for a one-year 

‘pulse’ of global emissions (e.g., Jacobson 

(2002) used this approach to compare 

fossil fuel organic and BC aerosols to 

CO2). Comparing the contribution from 

each forcing agent as shown in Figure 2.22 gives an indication 

of the future climate impact for current (year 2000) emissions 

of the different forcing agents. For the aerosols, the integrated 

RF is obtained based on the lifetimes, burdens and RFs from the 

AeroCom experiments, as summarised in Tables 2.4 and 2.5. 

For ozone precursors (CO, NOx and NMVOCs), data are taken 

from Derwent et al. (2001), Collins et al. (2002), Stevenson et 

al. (2004) and Berntsen et al. (2005), while for the long-lived 

species the radiative effi ciencies and lifetimes are used, as well 

as a response function for CO2 (see Section 2.10.2, Table 2.14). 

Uncertainties in the estimates of the integrated RF originate 

from uncertainties in lifetimes, optical properties and current 

global emissions. 

Figure 2.22 shows the integrated RF for both a 20- and 

100-year time horizon. Choosing the longer time horizon 

of 100 years, as was done in the GWPs for the long-lived 

species included in the Kyoto Protocol, reduces the apparent 

importance of the shorter-lived species. It should be noted that 

the compounds with long lifetimes and short emission histories 

will tend to contribute more to the total with this ‘forward 

looking’ perspective than in the standard ‘IPCC RF bar chart 

diagram’ (Figure 2.20). 
207

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing Table. 2.13. Emission-based RFs for emitted components with radiative effects other than through changes in their atmospheric abundance. Minor effects where the estimated RF is less than 0.01 W m–2 are not included. Effects on sulphate aerosols are not included since SO2 emission is the only signifi cant factor affecting sulphate aerosols. Method of calculation and uncertainty ranges are given in the footnotes. Values represent RF in 2005 due to emissions and changes since 1750. See Figure 2.21 for graphical presentation of these values. Notes: a tropospheric ozone. b stratospheric ozone. c stratospheric water vapour. d Derived from the total RF of the observed CO2 change (Table 2.12), with the contributions from CH4, CO and VOC emissions from fossil sources subtracted. Historical emissions of CH4, CO and VOCs from Emission Database for Global Atmospheric Research (EDGAR)-HistorY Database of the Environment (HYDE) (Van Aardenne et al., 2001), CO2 contribution from these sources calculated with CO2 model described by Joos et al. (1996). e Derived from the total RF of the observed CH4 change (Table 2.12). Subtracted from this were the contributions through lifetime changes caused by emissions of NOx, CO and VOC that change OH concentrations. The effects of NOx, CO and VOCs are from Shindell et al. (2005). There are signifi cant uncertainties related to these relations. Following Shindell et al. (2005) the uncertainty estimate is taken to be ±20% for CH4 emissions, and ±50% for CO, VOC and NOx emissions. f All the radiative forcing from changes in stratospheric water vapour is attributed to CH4 emissions (Section 2.3.7 and Table 2.12). g RF calculated based on observed concentration change, see Table 2.12 and Section 2.3 h 80% of RF from observed ozone depletion in the stratosphere (Table 2.12) is attributed to CFCs/HFCs, remaining 20% to N2O (Based on Nevison et al., 1999 and WMO, 2003). i RF from Table 2.12, uncertainty ±0.10 W m–2. j Uncertainty too large to apportion the indirect cloud albedo effect to each aerosol type (Hansen et al., 2005). k Mean of all studies in Table 2.5, includes fossil fuel, biofuel and biomass burning. Uncertainty (90% confi dence ranges) ±0.25 W m–2 (BC) and ±0.20 W m–2 (organic carbon) based on range of reported values in Table 2.5. l RF from Table 2.12, uncertainty ±0.10 W m–2. CO2 CH4 CFC/ HCFC N2O HFC/ PFC/SF6 BC- direct BC snow albedo Organic carbon O3(T)a O3(S)b H2O(S)c Nitrate aerosols Indirect cloud albedo effect Component emitted Atmospheric or surface change directly causing radiative forcing CO2 1.56d CH4 0.016d 0.57e 0.2e 0.07f CFC/HCFC/halons 0.32g –0.04h N2O 0.15g –0.01h HFC/PFC/SF6 0.017g CO/VOC 0.06d 0.08e 0.13e NOx –0.17e 0.06e –0.10i Xj BC 0.34k 0.1l Xj OC –0.19k Xj SO2 Xj
208

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

2.9.5 Time Evolution of Radiative Forcing and 

Surface Forcing

There is a good understanding of the time evolution of the 

LLGHG concentrations from in situ measurements over the last 

few decades and extending further back using fi rn and ice core 

data (see Section 2.3, FAQ 2.1, Figure 1 and Chapter 6). Increases 

in RF are clearly dominated by CO2. Halocarbon RF has grown 

rapidly since 1950, but the RF growth has been cut dramatically 

by the Montreal Protocol (see Section 2.3.4). The RF of CFCs is 

declining; in addition, the combined RF of all ozone-depleting 

substances (ODS) appears to have peaked at 0.32 W m–2 during 

2003. However, substitutes for ODS are growing at a slightly 

faster rate, so halocarbon RF growth is still positive (Table 2.1). 

Although the trend in halocarbon RF since the time of the TAR 

has been positive (see Table 2.1), the halocarbon RF in this 

report, as shown in Table 2.12, is the same as in the TAR; this is 

due to a re-evaluation of the TAR results. 

Radiative forcing time series for the natural (solar, volcanic 

aerosol) forcings are reasonably well known for the past 25 

years; estimates further back are prone to uncertainties (Section 

2.7). Determining the time series for aerosol and ozone RF is far 

more diffi cult because of uncertainties in the knowledge of past 

emissions and chemical-microphysical modelling. Several time 

series for these and other RFs have been constructed (e.g., Myhre

et al., 2001; Ramaswamy et al., 2001; Hansen et al., 2002). 

General Circulation Models develop their own time evolution 

of many forcings based on the temporal history of the relevant 

concentrations. As an example, the temporal evolution of the 

global and annual mean, instantaneous, all-sky RF and surface 

forcing due to the principal agents simulated by the Model for 

Interdisciplinary Research on Climate (MIROC) + Spectral 

Radiation-Transport Model for Aerosol Species (SPRINTARS) 

GCM (Nozawa et al., 2005; Takemura et al., 2005) is illustrated 

in Figure 2.23. Although there are differences between models 

with regards to the temporal reconstructions and thus presentday forcing estimates, they typically have a qualitatively similar 

temporal evolution since they often base the temporal histories 

on similar emissions data.

General Circulation Models compute the climate response 

based on the knowledge of the forcing agents and their temporal 

evolution. While most current GCMs incorporate the trace gas 

RFs, aerosol direct effects, solar and volcanoes, a few have in 

addition incorporated land use change and cloud albedo effect. 

While LLGHGs have increased rapidly over the past 20 years 

and contribute the most to the present RF (refer also to Figure 

2.20 and FAQ 2.1, Figure 1), Figure 2.23 also indicates that 

the combined positive RF of the greenhouse gases exceeds the 

contributions due to all other anthropogenic agents throughout 

the latter half of the 20th century. 

The solar RF has a small positive value. The positive solar 

irradiance RF is likely to be at least fi ve times smaller than the 

combined RF due to all anthropogenic agents, and about an order 

of magnitude less than the total greenhouse gas contribution 

(Figures 2.20 and 2.23 and Table 2.12; see also the Foukal et 

al., 2006 review). The combined natural RF consists of the solar 

Figure 2.23. Globally and annually averaged temporal evolution of the instantaneous all-sky RF (top panel) and surface forcing (bottom panel) due to various 

agents, as simulated in the MIROC+SPRINTARS model (Nozawa et al., 2005; 

Takemura et al., 2005). This is an illustrative example of the forcings as implemented 

and computed in one of the climate models participating in the AR4. Note that there 

could be differences in the RFs among models. Most models simulate roughly similar 

evolution of the LLGHGs’ RF.

RF plus the large but transitory negative RF from episodic, 

explosive volcanic eruptions of which there have been several 

over the past half century (see Figure 2.18). Over particularly 

the 1950 to 2005 period, the combined natural forcing has been 

either negative or slightly positive (less than approximately 

0.2 W m–2), reaffi rming and extending the conclusions in the 
209

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

TAR. Therefore, it is exceptionally unlikely that natural RFs 

could have contributed a positive RF of comparable magnitude 

to the combined anthropogenic RF term over the period 1950 

to 2005 (Figure 2.23). Attribution studies with GCMs employ 

the available knowledge of the evolution of the forcing over 

the 20th century, and particularly the features distinguishing the 

anthropogenic from the natural agents (see also Section 9.2).

The surface forcing (Figure 2.23, top panel), in contrast to 

RF, is dominated by the strongly negative shortwave effect 

of the aerosols (tropospheric and the episodic volcanic ones), 

with the LLGHGs exerting a small positive effect. Quantitative 

values of the RFs and surface forcings by the agents differ 

across models in view of the differences in model physics 

and in the formulation of the forcings due to the short-lived 

species (see Section 10.2, Collins et al. (2006) and Forster 

and Taylor (2006) for further discussion on uncertainties in 

GCMs’ calculation of RF and surface forcing). As for RF, it is 

diffi cult to specify uncertainties in the temporal evolution, as 

emissions and concentrations for all but the LLGHGs are not 

well constrained.

2.9.6 Spatial Patterns of Radiative Forcing and 

 Surface Forcing

Figure 6.7 of Ramaswamy et al. (2001) presented examples 

of the spatial patterns for most of the RF agents discussed in 

this chapter; these examples still hold. Many of the features 

seen in Figure 6.7 of Ramaswamy et al. (2001) are generic. 

However, additional uncertainties exist for the spatial patterns 

compared to those for the global-mean RF. Spatial patterns of 

the aerosol RF exhibit some of the largest differences between 

models, depending on the specifi cation of the aerosols and their 

properties, and whether or not indirect cloud albedo effects 

are included. The aerosol direct and cloud albedo effect RF 

also depend critically on the location of clouds, which differs 

between the GCMs. Figure 2.24 presents illustrative examples 

of the spatial pattern of the instantaneous RF between 1860 and 

present day, due to natural plus anthropogenic agents, from two 

GCMs. Volcanic aerosols play a negligible role in this calculation 

owing to the end years considered and their virtual absence 

during these years. The MIROC+SPRINTARS model includes 

Figure 2.24. Instantaneous change in the spatial distribution of the net (solar plus longwave) radiative fl ux (W m–2) due to natural plus anthropogenic forcings between 

the years 1860 and 2000. Results here are intended to be illustrative examples of these quantities in two different climate models. (a) and (c) correspond to tropopause and 

surface results using the GFDL CM 2.1 model (adapted from Knutson et al., 2006). (b) and (d) correspond to tropopause and surface results using the MIROC+SPRINTARS model 

(adapted from Nozawa et al., 2005 and Takemura et al., 2005). Note that the MIROC+SPRINTARS model takes into account the aerosol cloud albedo effect while the CM 2.1 

model does not.
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 infl 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 fl 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 infl 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 costeffective policies (Article 3 of the UNFCCC) in a decentralised 

manner so that multi-gas emitters (nations, industries) can 

compose mitigation measures, according to a specifi 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 specifi 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 I(∆Ci

(t)) is a function describing the impact (damage and 

benefi t) of change in climate (∆C) at time t. The expression g(t)

is a weighting function over time (e.g., g(t) = e–kt is a simple 

discounting giving short-term impacts more weight) (Heal, 

1997; Nordhaus, 1997). The subscript r refers to a baseline 

emission path. For two emission perturbations i and j the 

absolute metric values AMi and AMj 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 defi nition 

of GWP), the ratio between AMi and AMj can be interpreted as 

a relative emission index for component i versus a reference 

component j (such as CO2 in the case of GWP). 

There are several problematic issues related to defi ning 

a metric based on the general formulation given above 

(Fuglestvedt et al., 2003). A major problem is to defi ne 

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 defi ned, AM 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 defi nition of the temporal 

weighting function g(t) and the baseline emission scenarios. 

Due to these diffi 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 (i) relative to that of 

1 kg of the reference gas CO2, was developed (IPCC, 1990) and 

adopted for use in the Kyoto Protocol. The GWP of component 

i is defi ned by

AMi = ∫

∞

0 

[(I(ΔC (r + i) (t)) – I (ΔCr (t))) x g(t)]dt

∫

TH

0

RFi (t) dt ∫

TH

0

ai · [Ci (t)] dt

∫

TH

0

RFr (t) dt ∫

TH

0

ar · [Cr (t)] dt

GWPi = =
211

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

where TH is the time horizon, RFi is the global mean RF of 

component i, ai is the RF per unit mass increase in atmospheric 

abundance of component i (radiative effi ciency), [Ci(t)] is the 

time-dependent abundance of i, and the corresponding quantities 

for the reference gas (r) in the denominator. The numerator and 

denominator are called the absolute global warming potential 

(AGWP) of i and r respectively. All GWPs given in this report 

use CO2 as the reference gas. The simplifi cations made to 

derive the standard GWP index include (1) setting g(t) = 1 (i.e., 

no discounting) up until the time horizon (TH) and then g(t) = 0 

thereafter, (2) choosing a 1-kg pulse emission, (3) defi ning the 

impact function, I(∆C), 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 I(∆Cr(t)) = 0). The criticisms of the 

GWP metric have focused on all of these simplifi 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 (I(∆C)) and temporal weighting functions to 

use (both involve value judgements), it is diffi cult to assess the 

implications of the simplifi 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 defi 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 fi rst commitment period 

under the Kyoto Protocol) that contain a signifi cant fraction 

of short-lived species (e.g., CH4) will give less temperature 

reductions towards the end of the time horizon, compared to 

reductions in CO2 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 

fl uctuations caused by emissions of short-lived species (e.g., 

CH4). However, as long as it has not been determined, neither 

scientifi 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 CO2-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 shortlived 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-benefi 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 CO2 (the denominator 

in the defi nition of the GWP). The AGWP of CO2 again depends 

on the radiative effi ciency for a small perturbation of CO2 from 

the current level of about 380 ppm. The radiative effi ciency per 

kilogram of CO2 has been calculated using the same expression 

as for the CO2 RF in Section 2.3.1, with an updated background 

CO2 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 CO2 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 CO2 concentration of 

378 ppm. The increased background concentration of CO2

means that the airborne fraction of emitted CO2 (Section 7.3) 

is enhanced, contributing to an increase in the AGWP for 

CO2. The AGWP values for CO2 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 CO2)–1, respectively. The uncertainty in the AGWP 

for CO2 is estimated to be ±15%, with equal contributions from 

the CO2 response function and the RF calculation. 

Updated radiative effi ciencies for well-mixed greenhouse 

gases are given in Table 2.14. Since the TAR, radiative 

effi 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 HFC227ea. Hurley et al. (2005) studied the infrared spectrum and 

RF of perfl uoromethane (CΩF4) and derived a 30% higher 

GWP value than given in the TAR. The RF calculations for 

the GWPs for CH4, N2O and halogen-containing well-mixed 

greenhouse gases employ the simplifi 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 signifi 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 CH4) GWPs relative to CO2. 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 Effi ciency SAR‡

(years) Chemical Formula (years) (W m–2 ppb–1) (100-yr) 20-yr 100-yr 500-yr

Carbon dioxide CO2 See belowa b1.4x10–5 1 1 1 1

Methanec CH4 12c 3.7x10–4 21 72 25 7.6

Nitrous oxide N2O 114 3.03x10–3 310 289 298 153

Substances controlled by the Montreal Protocol 

CFC-11 CCl3F 45 0.25 3,800 6,730 4,750 1,620

CFC-12 CCl2F2 100 0.32 8,100 11,000 10,900 5,200

CFC-13 CClF3 640 0.25 10,800 14,400 16,400

CFC-113 CCl2FCClF2 85 0.3 4,800 6,540 6,130 2,700

CFC-114 CClF2CClF2 300 0.31 8,040 10,000 8,730

CFC-115 CClF2CF3 1,700 0.18 5,310 7,370 9,990

Halon-1301 CBrF3 65 0.32 5,400 8,480 7,140 2,760

Halon-1211 CBrClF2 16 0.3 4,750 1,890 575

Halon-2402 CBrF2CBrF2 20 0.33 3,680 1,640 503

Carbon tetrachloride CCl4 26 0.13 1,400 2,700 1,400 435

Methyl bromide CH3Br 0.7 0.01 17 5 1

Methyl chloroform CH3CCl3 5 0.06 506 146 45

HCFC-22 CHClF2 12 0.2 1,500 5,160 1,810 549

HCFC-123 CHCl2CF3 1.3 0.14 90 273 77 24

HCFC-124 CHClFCF3 5.8 0.22 470 2,070 609 185

HCFC-141b CH3CCl2F 9.3 0.14 2,250 725 220

HCFC-142b CH3CClF2 17.9 0.2 1,800 5,490 2,310 705

HCFC-225ca CHCl2CF2CF3 1.9 0.2 429 122 37

HCFC-225cb CHClFCF2CClF2 5.8 0.32 2,030 595 181

Hydrofl uorocarbons

HFC-23 CHF3 270 0.19 11,700 12,000 14,800 12,200

HFC-32 CH2F2 4.9 0.11 650 2,330 675 205

HFC-125 CHF2CF3 29 0.23 2,800 6,350 3,500 1,100

HFC-134a CH2FCF3 14 0.16 1,300 3,830 1,430 435

HFC-143a CH3CF3 52 0.13 3,800 5,890 4,470 1,590

HFC-152a CH3CHF2 1.4 0.09 140 437 124 38

HFC-227ea CF3CHFCF3 34.2 0.26 2,900 5,310 3,220 1,040

HFC-236fa CF3CH2CF3 240 0.28 6,300 8,100 9,810 7,660

HFC-245fa CHF2CH2CF3 7.6 0.28 3,380 1030 314

HFC-365mfc CH3CF2CH2CF3 8.6 0.21 2,520 794 241

HFC-43-10mee CF3CHFCHFCF2CF3 15.9 0.4 1,300 4,140 1,640 500

Perfl uorinated compounds 

Sulphur hexafl uoride SF6 3,200 0.52 23,900 16,300 22,800 32,600

Nitrogen trifl uoride NF3 740 0.21 12,300 17,200 20,700

PFC-14 CF4 50,000 0.10 6,500 5,210 7,390 11,200

PFC-116 C2F6 10,000 0.26 9,200 8,630 12,200 18,200
213

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

Notes:

a The CO2 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 CO2 concentration value of 378 ppm. The decay of a pulse of CO2 with time t is given by

 Where a0 = 0.217, a1 = 0.259, a2 = 0.338, a3 = 0.186, τ1 = 172.9 years, τ2 = 18.51 years, and τ3 = 1.186 years.

b The radiative effi ciency of CO2 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). 

c 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). 

d Shine et al. (2005c), updated by the revised AGWP for CO2. The assumed lifetime of 1,000 years is a lower limit.

e Hurley et al. (2005)

f

 Robson et al. (2006)

g Young et al. (2006)

i = 1

3

 a0 + Σ ai • e -t/τi

Table 2.14 (continued)

 

 Global Warming Potential for

 Given Time Horizon Industrial Designation Radiative

or Common Name Lifetime Effi ciency SAR‡

(years) Chemical Formula (years) (W m–2 ppb–1) (100-yr) 20-yr 100-yr 500-yr

Perfl uorinated compounds (continued) 

PFC-218 C3F8 2,600 0.26 7,000 6,310 8,830 12,500

PFC-318 c-C4F8 3,200 0.32 8,700 7,310 10,300 14,700

PFC-3-1-10 C4F10 2,600 0.33 7,000 6,330 8,860 12,500

PFC-4-1-12 C5F12 4,100 0.41 6,510 9,160 13,300

PFC-5-1-14 C6F14 3,200 0.49 7,400 6,600 9,300 13,300

PFC-9-1-18 C10F18 >1,000d 0.56 >5,500 >7,500 >9,500

trifl uoromethyl SF5CF3 800 0.57 13,200 17,700 21,200

sulphur pentafl uoride

Fluorinated ethers 

HFE-125 CHF2OCF3 136 0.44 13,800 14,900 8,490

HFE-134 CHF2OCHF2 26 0.45 12,200 6,320 1,960

HFE-143a CH3OCF3 4.3 0.27 2,630 756 230

HCFE-235da2 CHF2OCHClCF3 2.6 0.38 1,230 350 106

HFE-245cb2 CH3OCF2CHF2 5.1 0.32 2,440 708 215

HFE-245fa2 CHF2OCH2CF3 4.9 0.31 2,280 659 200

HFE-254cb2 CH3OCF2CHF2 2.6 0.28 1,260 359 109

HFE-347mcc3 CH3OCF2CF2CF3 5.2 0.34 1,980 575 175

HFE-347pcf2 CHF2CF2OCH2CF3 7.1 0.25 1,900 580 175

HFE-356pcc3 CH3OCF2CF2CHF2 0.33 0.93 386 110 33

HFE-449sl 

(HFE-7100) C4F9OCH3 3.8 0.31 1,040 297 90

HFE-569sf2 C4F9OC2H5 0.77 0.3 207 59 18

(HFE-7200) 

HFE-43-10pccc124 CHF2OCF2OC2F4OCHF2 6.3 1.37 6,320 1,870 569

(H-Galden 1040x) 

HFE-236ca12 CHF2OCF2OCHF2 12.1 0.66 8,000 2,800 860

(HG-10)

HFE-338pcc13 CHF2OCF2CF2OCHF2 6.2 0.87 5,100 1,500 460

(HG-01) 

Perfl uoropolyethers 

PFPMIE CF3OCF(CF3)CF2OCF2OCF3 800 0.65 7,620 10,300 12,400

Hydrocarbons and other compounds – Direct Effects 

Dimethylether CH3OCH3 0.015 0.02 1 1 <<1

Methylene chloride CH2Cl2 0.38 0.03 31 8.7 2.7

Methyl chloride CH3Cl 1.0 0.01 45 13 4
214

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

observed or a clear potential for future emissions has been 

identifi ed. The uncertainties of these direct GWPs are taken to 

be ±35% for the 5 to 95% (90%) confi 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 signifi 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 CH4, 

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., NOx) 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., NOx) 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 CO2 produced from oxidation of CH4, 

CO and NMVOCs of fossil origin is not included in the GWP 

estimates since this carbon has been included in the national 

CO2 inventories. This issue may need to be reconsidered as 

inventory guidelines are revised.

2.10.3.1 Methane

Four indirect radiative effects of CH4 emissions have been 

identifi 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 CO2. 

The GWP given in Table 2.14 includes the fi 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 signifi cantly since the TAR 

(see Section 2.3.7). This has also been taken into account in the 

GWP estimate for CH4 by increasing the enhancement factor 

from 5% (TAR) to 15%. As a result, the 100-year GWP for CH4

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 CH4) 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, 

CH4 (through changes in OH) and CO2 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 signifi 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 CH4 reductions, make calculations of GWP for NOx

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 NOx, can be signifi cant. Due to the nonlinear chemistry, the 

net RF of NOx emissions will depend strongly on the location 

of emission and, with a strict defi 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 

NOx among the different studies and the omission of the nitrate 

aerosol effect, a central estimate for the 100-year GWP for NOx

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 (H2) 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 CH4 enhancements (Prather, 

2003). Derwent et al. (2001) calculated an indirect 100-year 

GWP for the tropospheric effects of H2 of 5.8, which includes 

the effects of CH4 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., NOx

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 effi cacy in the GWP

As discussed in Section 2.8.5, the climate effi 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 effi cacy of a forcing agent. Berntsen 

et al. (2005) calculated GWP values in this way for NOx and 

CO emissions in Europe and in South East Asia. The effi cacies 

are less uncertain than climate sensitivities. However, Berntsen 

et al. (2005) showed that for ozone produced by NOx emissions 

the climate effi 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 defi ned as the ratio between the 

Organic Compound/Study GWPCH4 GWPO3 GWP

Ethane (C2H6) 2.9 2.6 5.5

Propane (C3H8) 2.7 0.6 3.3

Butane (C4H10) 2.3 1.7 4.0

Ethylene (C2H4) 1.5 2.2 3.7

Propylene (C3H6) –2.0 3.8 1.8

Toluene (C7H8) 0.2 2.5 2.7

Isoprene (C5H8) 1.1 1.6 2.7

Methanol (CH3OH) 1.6 1.2 2.8

Acetaldehyde (CH3CHO) –0.4 1.7 1.3

Acetone (CH3COCH3) 0.3 0.2 0.5

Derwent et al. NH surface NOx

a,b –24 11 –12

Derwent et al. SH surface NOx

a,b –64 33 –31

Wild et al., industrial NOx –44 32 –12

Berntsen et al., surface NOx Asia –31 to –42c 55 to 70c 25 to 29c

Berntsen et al., surface NOx Europe –8.6 to –11c 8.1 to 12.7 –2.7 to +4.1c

Derwent et al., Aircraft NOx

a,b –145 246 100

Wild et al., Aircraft NOx –210 340 130

Stevenson et al. Aircraft NOx –159 155 –3

Table 2.15. Indirect GWPs (100-year) for 10 NMVOCs from Collins et al. (2002) and for NOx 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:

a Corrected values as described in Stevenson et al. (2004). 

b For January pulse emissions.

c Range from two three-dimensional chemistry transport models and two radiative transfer models. 
216

Changes in Atmospheric Constituents and in Radiative Forcing Chapter 2

global mean surface temperature change at a given future time 

horizon (TH) following an emission (pulse or sustained) of a 

compound x relative to a reference gas r (e.g., CO2): 

where ΔT

H

x denotes the global mean surface temperature change 

after H years following an emission of compound x. The GTPs 

do not require simulations with AOGCMs, but are given as 

transparent and simple formulas that employ a small number 

of input parameters required for calculation. Note that while 

the GWP is an integral quantity over the time horizon (i.e., the 

contribution of the RF at the beginning and end of the time 

horizon is exactly equal), the GTP uses the temperature change 

at time H (i.e., RF closer to time H contributes relatively more). 

The GTP metric requires knowledge of the same parameters 

as the GWP metric (radiative effi ciency and lifetimes), but in 

addition, the response times for the climate system must be 

known, in particular if the lifetime of component x is very 

different from the lifetime of the reference gas. Differences 

in climate effi cacies can easily be incorporated into the GTP 

metric. Due to the inclusion of the response times for the climate 

system, the GTP values for pulse emissions of gases with 

shorter lifetimes than the reference gas will be lower than the 

corresponding GWP values. As noted by Shine et al. (2005b), 

there is a near equivalence between the GTP for sustained 

emission changes and the pulse GWP. The GTP metric has the 

potential advantage over GWP that it is more directly related to 

surface temperature change. 

GTPTH

x =

ΔT

H

x

ΔT

H

r
217

Chapter 2 Changes in Atmospheric Constituents and in Radiative Forcing

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