8Anthropogenic and Natural
Radiative Forcing
Coordinating Lead Authors:
Gunnar Myhre (Norway), Drew Shindell (USA)

Lead Authors:
François-Marie Bréon (France), William Collins (UK), Jan Fuglestvedt (Norway), Jianping Huang(China), Dorothy Koch (USA), Jean-François Lamarque (USA), David Lee (UK), Blanca Mendoza(Mexico), Teruyuki Nakajima (Japan), Alan Robock (USA), Graeme Stephens (USA), Toshihiko
Takemura (Japan), Hua Zhang (China)

Contributing Authors:
Borgar Aamaas (Norway), Olivier Boucher (France), Stig B. Dalsøren (Norway), John S. Daniel(USA), Piers Forster (UK), Claire Granier (France), Joanna Haigh (UK), Øivind Hodnebrog(Norway), Jed O. Kaplan (Switzerland/Belgium/USA), George Marston (UK), Claus J. Nielsen(Norway), Brian C. O’Neill (USA), Glen P. Peters (Norway), Julia Pongratz (Germany), Michael
Prather (USA), Venkatachalam Ramaswamy (USA), Raphael Roth (Switzerland), Leon Rotstayn(Australia), Steven J. Smith (USA), David Stevenson (UK), Jean-Paul Vernier (USA), Oliver Wild
(UK), Paul Young (UK)

Review Editors:
Daniel Jacob (USA), A.R. Ravishankara (USA), Keith Shine (UK)This chapter should be cited as:
Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza,
T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen,
J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA.

659

Table of Contents
Executive Summary...................................................................... 661

8

8.1

Radiative Forcing............................................................. 664

8.1.1

The Radiative Forcing Concept................................... 664

Box 8.1: Definition of Radiative Forcing and Effective
Radiative Forcing........................................................................ 665
Box 8.2: Grouping Forcing Compounds by Common
Properties .................................................................................. 668
8.1.2
			

8.2

Calculation of Radiative Forcing due toConcentration or Emission Changes.......................... 668

Atmospheric Chemistry.................................................. 669

8.7

Emission Metrics.............................................................. 710

8.7.1

Metric Concepts......................................................... 710

Box 8.4: Choices Required When Using Emission Metrics..... 711
8.7.2

Application of Metrics................................................ 716

References .................................................................................. 721
Appendix 8.A: Lifetimes, Radiative Efficiencies andMetric Values.................................................................................. 731
Frequently Asked Questions

8.2.1

Introduction............................................................... 669

FAQ 8.1
			How Important Is Water Vapour to
Climate Change?..................................................... 666

8.2.2
			

Global Chemistry Modelling in Coupled Model
Intercomparison Project Phase 5................................ 670

FAQ 8.2
			Do Improvements in Air Quality Have an
Effect on Climate Change?.................................... 684

8.2.3

Chemical Processes and Trace Gas Budgets............... 670

8.3

Present-Day Anthropogenic Radiative Forcing....... 675

8.3.1
			

Updated Understanding of the Spectral Properties ofGreenhouse Gases and Radiative Transfer Codes....... 675

8.3.2

Well-mixed Greenhouse Gases.................................. 676

8.3.3

Ozone and Stratospheric Water Vapour...................... 679

8.3.4

Aerosols and Cloud Effects........................................ 682

8.3.5

Land Surface Changes................................................ 686

8.4		

Natural Radiative Forcing Changes: Solarand Volcanic....................................................................... 688

8.4.1

Solar Irradiance.......................................................... 688

8.4.2

Volcanic Radiative Forcing......................................... 691

Box 8.3: Volcanic Eruptions as Analogues............................... 693

8.5
		Synthesis of Global Mean Radiative Forcing,
Past and Future................................................................. 693

8.5.1
			

Summary of Radiative Forcing by Species andUncertainties.............................................................. 694

8.5.2

Time Evolution of Historical Forcing........................... 698

8.5.3

Future Radiative Forcing............................................ 700

8.6

Geographic Distribution of Radiative Forcing........ 702

8.6.1

Spatial Distribution of Current Radiative Forcing....... 702

8.6.2
			

Spatial Evolution of Radiative Forcing and Responseover the Industrial Era................................................ 705

8.6.3
			

Spatial Evolution of Radiative Forcing and Response
for the Future............................................................. 708

660

Supplementary Material
Supplementary Material is available in online versions of the report.

Anthropogenic and Natural Radiative Forcing

Executive Summary
It is unequivocal that anthropogenic increases in the well-mixed
greenhouse gases (WMGHGs) have substantially enhanced
the greenhouse effect, and the resulting forcing continues toincrease. Aerosols partially offset the forcing of the WMGHGs anddominate the uncertainty associated with the total anthropogenic
driving of climate change.
As in previous IPCC assessments, AR5 uses the radiative forcing1(RF) concept, but it also introduces effective radiative forcing2(ERF). The RF concept has been used for many years and in previous
IPCC assessments for evaluating and comparing the strength of thevarious mechanisms affecting the Earth’s radiation balance and thus
causing climate change. Whereas in the RF concept all surface andtropospheric conditions are kept fixed, the ERF calculations presented
here allow all physical variables to respond to perturbations except
for those concerning the ocean and sea ice. The inclusion of theseadjustments makes ERF a better indicator of the eventual temperature
response. ERF and RF values are significantly different for anthropogenic aerosols owing to their influence on clouds and on snow cover.
These changes to clouds are rapid adjustments and occur on a timescale much faster than responses of the ocean (even the upper layer) to
forcing. RF and ERF are estimated over the Industrial Era from 1750 to2011 if other periods are not explicitly stated. {8.1, Box 8.1, Figure 8.1}

Industrial-Era Anthropogenic Forcing
The total anthropogenic ERF over the Industrial Era is 2.3 (1.1 to3.3) W m–2.3 It is certain that the total anthropogenic ERF is positive.
Total anthropogenic ERF has increased more rapidly since 1970 thanduring prior decades. The total anthropogenic ERF estimate for 2011 is
43% higher compared to the AR4 RF estimate for the year 2005 owing
to reductions in estimated forcing due to aerosols but also to continued growth in greenhouse gas RF. {8.5.1, Figures 8.15, 8.16}
Due to increased concentrations, RF from WMGHGs has
increased by 0.20 (0.18 to 0.22) W m–2 (8%) since the AR4 estimate for the year 2005. The RF of WMGHG is 2.83 (2.54 to 3.12)
W m–2. The majority of this change since AR4 is due to increases in the
carbon dioxide (CO2) RF of nearly 10%. The Industrial Era RF for CO2
alone is 1.82 (1.63 to 2.01) W m–2, and CO2 is the component with thelargest global mean RF. Over the last decade RF of CO2 has an average
growth rate of 0.27 (0.24 to 0.30) W m–2 per decade. Emissions of CO2
have made the largest contribution to the increased anthropogenic
forcing in every decade since the 1960s. The best estimate for ERF of

Chapter 8

WMGHG is the same as the RF but with a larger uncertainty (±20%).
{8.3.2, 8.5.2, Figures 8.6, 8.18}
The net forcing by WMGHGs other than CO2 shows a small
increase since the AR4 estimate for the year 2005. A small growthin the CH4 concentration has increased its RF by 2% to an AR5 valueof 0.48 (0.43 to 0.53) W m–2. RF of nitrous oxide (N2O) has increased
by 6% since AR4 and is now 0.17 (0.14 to 0.20) W m–2. N2O concentrations continue to rise while those of dichlorodifluoromethane (CFC12), the third largest WMGHG contributor to RF for several decades, isfalling due to its phase-out under the Montreal Protocol and amendments. Since 2011 N2O has become the third largest WMGHG contributor to RF. The RF from all halocarbons (0.36 W m–2) is very similar tothe value in AR4, with a reduced RF from chlorofluorocarbons (CFCs)
but increases from many of their substitutes. Four of the halocarbons(trichlorofluoromethane (CFC-11), CFC-12, trichlorotrifluoroethane(CFC-113) and chlorodifluoromethane (HCFC-22)) account for around
85% of the total halocarbon RF. The first three of these compounds
have declining RF over the last 5 years but their combined decreaseis compensated for by the increased RF from HCFC-22. Since AR4, theRF from all HFCs has nearly doubled but still only amounts to 0.02
W m–2. There is high confidence4 that the overall growth rate in RFfrom all WMGHG is smaller over the last decade than in the 1970s and1980s owing to a reduced rate of increase in the combined non-CO2
RF. {8.3.2; Figure 8.6}
Ozone and stratospheric water vapour contribute substantiallyto RF. The total RF estimated from modelled ozone changes is 0.35
(0.15 to 0.55) W m–2, with RF due to tropospheric ozone changes of
0.40 (0.20 to 0.60) W m–2 and due to stratospheric ozone changes of–0.05 (–0.15 to +0.05) W m–2. Ozone is not emitted directly into theatmosphere but is formed by photochemical reactions. Troposphericozone RF is largely attributed to anthropogenic emissions of methane(CH4), nitrogen oxides (NOx), carbon monoxide (CO) and non-methane
volatile organic compounds (NMVOCs), while stratospheric ozone RF
results primarily from ozone depletion by halocarbons. Estimates arealso provided attributing RF to emitted compounds. Ozone-depleting
substances (ODS) cause ozone RF of –0.15 (–0.30 to 0.0) W m–2, someof which is in the troposphere. Tropospheric ozone precursors cause
ozone RF of 0.50 (0.30 to 0.70) W m–2, some of which is in the stratosphere; this value is larger than that in AR4. There is robust evidence
that tropospheric ozone also has a detrimental impact on vegetation
physiology, and therefore on its CO2 uptake, but there is a low confidence on quantitative estimates of the RF owing to this indirect effect.
RF for stratospheric water vapour produced by CH4 oxidation is 0.07
(0.02 to 0.12) W m–2. The RF best estimates for ozone and stratospheric

1

Change in net downward radiative flux at the tropopause after allowing for stratospheric temperatures to readjust to radiative equilibrium, while holding surface and tropospheric temperatures and state variables fixed at the unperturbed values.

2

Change in net downward radiative flux at the top of the atmosphere (TOA) after allowing for atmospheric temperatures, water vapour, clouds and land albedo to adjust, butwith global mean surface temperature or ocean and sea ice conditions unchanged (calculations presented in this chapter use the fixed ocean conditions method).

3

Uncertainties are given associated with best estimates of forcing. The uncertainty values represent the 5–95% (90%) confidence range.

4

In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high.
A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence andagreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (seeSection 1.4 and Box TS.1 for more details).

661

8

Chapter 8

water vapour are either identical or consistent with the range in AR4.
{8.2, 8.3.3, Figure 8.7}

8The magnitude of the aerosol forcing is reduced relative to AR4.
The RF due to aerosol–radiation interactions, sometimes referred to as
direct aerosol effect, is given a best estimate of –0.35 (–0.85 to +0.15)
W m–2, and black carbon (BC) on snow and ice is 0.04 (0.02 to 0.09)
W m–2. The ERF due to aerosol–radiation interactions is –0.45 (–0.95 to+0.05) W m–2. A total aerosol–cloud interaction5 is quantified in termsof the ERF concept with an estimate of –0.45 (–1.2 to 0.0) W m–2. The
total aerosol effect (excluding BC on snow and ice) is estimated as ERF
of –0.9 (–1.9 to –0.1) W m–2. The large uncertainty in aerosol ERF is thedominant contributor to overall net Industrial Era forcing uncertainty. Since AR4, more aerosol processes have been included in models,and differences between models and observations persist, resulting insimilar uncertainty in the aerosol forcing as in AR4. Despite the large
uncertainty range, there is a high confidence that aerosols have offset
a substantial portion of WMGHG global mean forcing. {8.3.4, 8.5.1,
Figures 8.15, 8.16}
There is robust evidence that anthropogenic land use change
has increased the land surface albedo, which leads to an RF of–0.15 ± 0.10 W m–2. There is still a large spread of estimates owing todifferent assumptions for the albedo of natural and managed surfaces
and the fraction of land use changes before 1750. Land use change
causes additional modifications that are not radiative, but impact thesurface temperature, in particular through the hydrologic cycle. These
are more uncertain and they are difficult to quantify, but tend to offset
the impact of albedo changes. As a consequence, there is low agreement on the sign of the net change in global mean temperature as aresult of land use change. {8.3.5}
Attributing forcing to emissions provides a more direct linkfrom human activities to forcing. The RF attributed to methane
emissions is very likely6 to be much larger (~1.0 W m–2) than that
attributed to methane concentration increases (~0.5 W m–2) as concentration changes result from the partially offsetting impact of emissionsof multiple species and subsequent chemical reactions. In addition,
emissions of CO are virtually certain to have had a positive RF, whileemissions of NOX are likely to have had a net negative RF at the global
scale. Emissions of ozone-depleting halocarbons are very likely to havecaused a net positive RF as their own positive RF has outweighed thenegative RF from the stratospheric ozone depletion that they haveinduced. {8.3.3, 8.5.1, Figure 8.17, FAQ 8.2}

Anthropogenic and Natural Radiative Forcing

20th century, extending to Asia, South America and central Africa by
1980. Emission controls have since reduced aerosol pollution in North
America and Europe, but not in much of Asia. Ozone forcing increased
throughout the 20th century, with peak positive amplitudes around
15°N to 30°N due to tropospheric pollution but negative values over
Antarctica due to stratospheric loss late in the century. The patternand spatial gradients of forcing affect global and regional temperature
responses as well as other aspects of climate response such as thehydrologic cycle. {8.6.2, Figure 8.25}

Natural Forcing
Satellite observations of total solar irradiance (TSI) changes
from 1978 to 2011 show that the most recent solar cycle minimum was lower than the prior two. This very likely led to a small
negative RF of –0.04 (–0.08 to 0.00) W m–2 between 1986 and 2008.
The best estimate of RF due to TSI changes representative for the 1750
to 2011 period is 0.05 (to 0.10) W m–2. This is substantially smaller
than the AR4 estimate due to the addition of the latest solar cycle
and inconsistencies in how solar RF has been estimated in earlier IPCC
assessments. There is very low confidence concerning future solar forcing estimates, but there is high confidence that the TSI RF variations
will be much smaller than the projected increased forcing due to GHG
during the forthcoming decades. {8.4.1, Figures 8.10, 8.11}
The RF of volcanic aerosols is well understood and is greatest
for a short period (~2 years) following volcanic eruptions. There
have been no major volcanic eruptions since Mt Pinatubo in 1991, butseveral smaller eruptions have caused a RF for the years 2008–2011 of–0.11 (–0.15 to –0.08) W m–2 as compared to 1750 and –0.06 (–0.08
to –0.04) W m–2 as compared to 1999–2002. Emissions of CO2 fromvolcanic eruptions since 1750 have been at least 100 times smaller
than anthropogenic emissions. {8.4.2, 8.5.2, Figures 8.12, 8.13, 8.18}
There is very high confidence that industrial-era natural forcing
is a small fraction of the anthropogenic forcing except for brief
periods following large volcanic eruptions. In particular, robust
evidence from satellite observations of the solar irradiance and volcanic aerosols demonstrates a near-zero (–0.1 to +0.1 W m–2) change in
the natural forcing compared to the anthropogenic ERF increase of 1.0(0.7 to 1.3) W m–2 from 1980 to 2011. The natural forcing over the last
15 years has likely offset a substantial fraction (at least 30%) of theanthropogenic forcing. {8.5.2; Figures 8.18, 8.19, 8.20}

Future Anthropogenic Forcing and Emission Metrics
Forcing agents such as aerosols, ozone and land albedo changes
are highly heterogeneous spatially and temporally. These patterns generally track economic development; strong negative aerosol
forcing appeared in eastern North America and Europe during the early

Differences in RF between the emission scenarios consideredhere7 are relatively small for year 2030 but become very large by
2100 and are dominated by CO2. The scenarios show a s­ ubstantial

5

The aerosol–cloud interaction represents the portion of rapid adjustments to aerosols initiated by aerosol-cloud interactions, and is defined here as the total aerosol ERF minus
the ERF due to aerosol-radiation-interactions (the latter includes cloud responses to the aerosol–radiation interaction RF)

6

In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%,
Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely
than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1
for more details).

7

Chapter 1 describes the Representative Concentration Pathways (RCPs) that are the primary scenarios discussed in this report.

662

Anthropogenic and Natural Radiative Forcing

Chapter 8

weakening of the negative total aerosol ERF. Nitrate aerosols are an
exception to this reduction, with a substantial increase, which is arobust feature among the few available models for these scenarios.
The scenarios emphasized in this assessment do not span the range of
future emissions in the literature, however, particularly for near-termclimate forcers. {8.2.2, 8.5.3, Figures 8.2, 8.21, 8.22}

8Emission metrics such as Global Warming Potential (GWP) andGlobal Temperature change Potential (GTP) can be used toquantify and communicate the relative and absolute contributions to climate change of emissions of different substances,and of emissions from regions/countries or sources/sectors. The
metric that has been used in policies is the GWP, which integrates the
RF of a substance over a chosen time horizon, relative to that of CO2.
The GTP is the ratio of change in global mean surface temperature ata chosen point in time from the substance of interest relative to thatfrom CO2. There are significant uncertainties related to both GWP and
GTP, and the relative uncertainties are larger for GTP. There are also
limitations and inconsistencies related to their treatment of indirect
effects and feedbacks. The values are very dependent on metric type
and time horizon. The choice of metric and time horizon depends on
the particular application and which aspects of climate change areconsidered relevant in a given context. Metrics do not define policies
or goals but facilitate evaluation and implementation of multi-component policies to meet particular goals. All choices of metric contain
implicit value-related judgements such as type of effect considered
and weighting of effects over time. This assessment provides updated
values of both GWP and GTP for many compounds. {8.7.1, 8.7.2, Table
8.7, Table 8.A.1, Supplementary Material Table 8.SM.16}
Forcing and temperature response can also be attributed to sectors. From this perspective and with the GTP metric, a single year’s
worth of current global emissions from the energy and industrial sectors have the largest contributions to global mean warming over the
next approximately 50 to 100 years. Household fossil fuel and biofuel,biomass burning and on-road transportation are also relatively large
contributors to warming over these time scales, while current emissions from sectors that emit large amounts of CH4 (animal husbandry,
waste/landfills and agriculture) are also important over shorter time
horizons (up to 20 years). {8.7.2, Figure 8.34}

663

Chapter 8

8.1

8

Radiative Forcing

There are a variety of ways to examine how various drivers contributeto climate change. In principle, observations of the climate response
to a single factor could directly show the impact of that factor, or climate models could be used to study the impact of any single factor.
In practice, however, it is usually difficult to find measurements thatare influenced by only a single cause, and it is computationally prohibitive to simulate the response to every individual factor of interest.
Hence various metrics intermediate between cause and effect are used
to provide estimates of the climate impact of individual factors, withapplications both in science and policy. Radiative forcing (RF) is oneof the most widely used metrics, with most other metrics based on RF.
In this chapter, we discuss RF from natural and anthropogenic components during the industrial period, presenting values for 2011 relative
to 1750 unless otherwise stated, and projected values through 2100
(see also Annex II). In this section, we present the various definitionsof RF used in this chapter, and discuss the utility and limitations of
RF. These definitions are used in the subsequent sections quantifying
the RF due to specific anthropogenic (Section 8.3) and natural (Section 8.4) causes and integrating RF due to all causes (Sections 8.5 and
8.6). Atmospheric chemistry relevant for RF is discussed in Section 8.2
and used throughout the chapter. Emission metrics using RF that aredesigned to facilitate rapid evaluation and comparison of the climate
effects of emissions are discussed in Section 8.7.

8.1.1

Anthropogenic and Natural Radiative Forcing

excluded from this definition of forcing. The assumed relation between
a sustained RF and the equilibrium global mean surface temperature
response (DT) is DT = lRF where l is the climate sensitivity parameter.
The relationship between RF and DT is an expression of the energy­balance of the climate system and a simple reminder that the steadystate global mean climate response to a given forcing is determinedboth by the forcing and the responses inherent in l.
Implicit in the concept of RF is the proposition that the change in net
irradiance in response to the imposed forcing alone can be separated from all subsequent responses to the forcing. These are not in fact
always clearly separable and thus some ambiguity exists in what may
be considered a forcing versus what is part of the climate response.
In both the Third Assessment Report (TAR) and AR4, the term radiative
forcing (RF, also called stratospherically adjusted RF, as distinct frominstantaneous RF) was defined as the change in net irradiance at thetropopause after allowing for stratospheric temperatures to readjust toradiative equilibrium, while holding surface and tropospheric temperatures and state variables such as water vapour and cloud cover fixed at
the unperturbed values8. RF is generally more indicative of the surface
and tropospheric temperature responses than instantaneous RF, especially for agents such as carbon dioxide (CO2) or ozone (O3) changethat substantially alter stratospheric temperatures. To be consistent
with TAR and AR4, RF is hereafter taken to mean the stratosphericallyadjusted RF.

The Radiative Forcing Concept
8.1.1.2 Defining Effective Radiative Forcing

RF is the net change in the energy balance of the Earth system due tosome imposed perturbation. It is usually expressed in watts per square
meter averaged over a particular period of time and quantifies theenergy imbalance that occurs when the imposed change takes place.
Though usually difficult to observe, calculated RF provides a simple
quantitative basis for comparing some aspects of the potential climate
response to different imposed agents, especially global mean temperature, and hence is widely used in the scientific community. Forcing isoften presented as the value due to changes between two particular
times, such as pre-industrial to present-day, while its time evolution
provides a more complete picture.

8.1.1.1 Defining Radiative Forcing

For many forcing agents the RF gives a very useful and appropriate way
to compare the relative importance of their potential climate effect.
Instantaneous RF or RF is not an accurate indicator of the temperature response for all forcing agents, however. Rapid adjustments inthe troposphere can either enhance or reduce the flux perturbations,leading to substantial differences in the forcing driving long-term climate change. In much the same way that allowing for the relatively
rapid adjustment of stratospheric temperatures provides a more usefulcharacterization of the forcing due to stratospheric constituent changes, inclusion of rapid tropospheric adjustments has the potential toprovide more useful characterization for drivers in the troposphere (seealso Section 7.1.3).

Alternative definitions of RF have been developed, each with its own
advantages and limitations. The instantaneous RF refers to an instantaneous change in net (down minus up) radiative flux (shortwave plus
longwave; in W m–2) due to an imposed change. This forcing is usually
defined in terms of flux changes at the top of the atmosphere (TOA)or at the climatological tropopause, with the latter being a better indicator of the global mean surface temperature response in cases when
they differ.

Many of the rapid adjustments affect clouds and are not readily included into the RF concept. For example, for aerosols, especially absorbing
ones, changes in the temperature distribution above the surface occur
due to a variety of effects, including cloud response to changing atmospheric stability (Hansen et al., 2005; see Section 7.3.4.2) and cloudabsorption effects (Jacobson, 2012), which affect fluxes but are notstrictly part of RF. Similar adjustments take place for many forcings,
including CO2 (see Section 7.2.5.6).

Climate change takes place when the system responds in order tocounteract the flux changes, and all such responses are explicitly

Aerosols also alter cloud properties via microphysical interactions
leading to indirect forcings (referred to as aerosol–cloud interactions;8

Tropospheric variables were fixed except for the impact of aerosols on cloud albedo due to changes in droplet size with constant cloud liquid water which was considered an
RF in AR4 but is part of ERF in AR5.

664

Anthropogenic and Natural Radiative Forcing

see Section 7.4). Although these adjustments are complex and not fully
quantified, they occur both on the microphysical scale of the cloudparticles as well as on a more macroscopic scale involving whole cloudsystems (e.g., Shine et al., 2003; Penner et al., 2006; Quaas et al., 2009).
A portion of these adjustments occurs over a short period, on cloud life
cycle time scales, and is not part of a feedback arising from the surface temperature changes. Previously these type of adjustments weresometimes termed ‘fast feedbacks’ (e.g., Gregory et al., 2004; Hansen
et al., 2005), whereas in AR5 they are denoted ‘rapid adjustments’ toemphasize their distinction from feedbacks involving surface temperature changes. Atmospheric chemistry responses have typically been
included under the RF framework, and hence could also be included ina forcing encompassing rapid adjustments, which is important whenevaluating forcing attributable to emissions changes (Section 8.1.2)
and in the calculation of emission metrics (Section 8.7).
Studies have demonstrated the utility of including rapid adjustment incomparison of forcing agents, especially in allowing quantification offorcing due to aerosol-induced changes in clouds (e.g., effects previously denoted as cloud lifetime or semi-direct effects; see Figure 7.3)
that are not amenable to characterization by RF (e.g., Rotstayn andPenner, 2001; Shine et al., 2003; Hansen et al., 2005; Lohmann et al.,2010; Ban-Weiss et al., 2012). Several measures of forcing have been
introduced that include rapid adjustments. We term a forcing that
accounts for rapid adjustments the effective radiative forcing (ERF).
Conceptually, ERF represents the change in net TOA downward radiative flux after allowing for atmospheric temperatures, water vapour
and clouds to adjust, but with global mean surface temperature or a
portion of surface conditions unchanged. The primary methods in use
for such calculations are (1) fixing sea surface temperatures (SSTs) andsea ice cover at climatological values while allowing all other parts ofthe system to respond until reaching steady state (e.g., Hansen et al.,2005) or (2) analyzing the transient global mean surface temperature
response to an instantaneous perturbation and using the regression of
the response extrapolated back to the start of the simulation to derive

Chapter 8

the initial ERF (Gregory et al., 2004; Gregory and Webb, 2008). The
ERF calculated using the regression technique has an uncertainty of
about 10% (for the 5 to 95% confidence interval) for a single 4 × CO2
simulation (ERF ~7 W m–2) due to internal variability in the transient
climate (Andrews et al., 2012a), while given a similar length simulation
the uncertainty due to internal variability in ERF calculated using the
fixed-SST technique is much smaller and hence the latter may be moresuitable for very small forcings. Analysis of both techniques shows thatthe fixed-SST method yields a smaller spread across models, even incalculations neglecting the uncertainty in the regression fitting procedure (Andrews et al., 2012a). As a portion of land area responses areincluded in the fixed-SST technique, however, that ERF is slightly lessthan it would be with surface temperature held fixed everywhere. It is
possible to adjust for this in the global mean forcing, though we do notinclude such a correction here as we examine regional as well as global
ERF, but the land response will also introduce artificial gradients inland–sea temperatures that could cause small local climate responses.
In contrast, there is no global mean temperature response included in
the regression method. Despite the low bias in fixed-SST ERF due toland responses, results from a multi-model analysis of the forcing due
to CO2 are 7% greater using this method than using the regression
technique (Andrews et al., 2012a) though this is within the uncertainty
range of the calculations. Although each technique has advantages,
forcing diagnosed using the fixed-SST method is available for many
more forcing agents in the current generation of climate models than
forcing diagnosed using the regression method. Hence for practical
purposes, ERF is hereafter used for results from the fixed-SST techniqueunless otherwise stated (see also Box 8.1).
The conceptual relation between instantaneous RF, RF and ERF is illustrated in Figure 8.1. It implies the adjustments to the instantaneous RF
involve effects of processes that occur more rapidly than the time scaleof the response of the global mean surface temperature to the forcing.
However, there is no a priori time scale defined for adjustments to be
rapid with the fixed-SST method. The majority take place on time scales

Box 8.1 | Definition of Radiative Forcing and Effective Radiative Forcing
The two most commonly used measures of radiative forcing in this chapter are the radiative forcing (RF) and the effective radiative
forcing (ERF). RF is defined, as it was in AR4, as the change in net downward radiative flux at the tropopause after allowing for stratospheric temperatures to readjust to radiative equilibrium, while holding surface and tropospheric temperatures and state variables suchas water vapor and cloud cover fixed at the unperturbed values.
ERF is the change in net TOA downward radiative flux after allowing for atmospheric temperatures, water vapour and clouds to adjust,
but with surface temperature or a portion of surface conditions unchanged. Although there are multiple methods to calculate ERF, wetake ERF to mean the method in which sea surface temperatures and sea ice cover are fixed at climatological values unless otherwise
specified. Land surface properties (temperature, snow and ice cover and vegetation) are allowed to adjust in this method. Hence ERF
includes both the effects of the forcing agent itself and the rapid adjustments to that agent (as does RF, though stratospheric temperature is the only adjustment for the latter). In the case of aerosols, the rapid adjustments of clouds encompass effects that havebeen referred to as indirect or semi-direct forcings (see Figure 7.3 and Section 7.5), with some of these same cloud responses also
taking place for other forcing agents (see Section 7.2). Calculation of ERF requires longer simulations with more complex models thancalculation of RF, but the inclusion of the additional rapid adjustments makes ERF a better indicator of the eventual global mean temperature response, especially for aerosols. When forcing is attributed to emissions or used for calculation of emission metrics, additional
responses including atmospheric chemistry and the carbon cycle are also included in both RF and ERF (see Section 8.1.2). The generalterm forcing is used to refer to both RF and ERF.
665

8

Chapter 8

Anthropogenic and Natural Radiative Forcing

Frequently Asked Questions

FAQ 8.1 | How Important Is Water Vapour to Climate Change?
As the largest contributor to the natural greenhouse effect, water vapour plays an essential role in the Earth’s
climate. However, the amount of water vapour in the atmosphere is controlled mostly by air temperature, rather
than by emissions. For that reason, scientists consider it a feedback agent, rather than a forcing to climate change.
Anthropogenic emissions of water vapour through irrigation or power plant cooling have a negligible impact on
the global climate.

8

Water vapour is the primary greenhouse gas in the Earth’s atmosphere. The contribution of water vapour to thenatural greenhouse effect relative to that of carbon dioxide (CO2) depends on the accounting method, but canbe considered to be approximately two to three times greater. Additional water vapour is injected into the atmosphere from anthropogenic activities, mostly through increased evaporation from irrigated crops, but also throughpower plant cooling, and marginally through the combustion of fossil fuel. One may therefore question why there
is so much focus on CO2, and not on water vapour, as a forcing to climate change.
Water vapour behaves differently from CO2 in one fundamental way: it can condense and precipitate. When air
with high humidity cools, some of the vapour condenses into water droplets or ice particles and precipitates. The
typical residence time of water vapour in the atmosphere is ten days. The flux of water vapour into the atmospherefrom anthropogenic sources is considerably less than from ‘natural’ evaporation. Therefore, it has a negligible
impact on overall concentrations, and does not contribute significantly to the long-term greenhouse effect. This is
the main reason why tropospheric water vapour (typically below 10 km altitude) is not considered to be an anthropogenic gas contributing to radiative forcing.
Anthropogenic emissions do have a significant impact on water vapour in the stratosphere, which is the part of
the atmosphere above about 10 km. Increased concentrations of methane (CH4) due to human activities lead toan additional source of water, through oxidation, which partly explains the observed changes in that atmosphericlayer. That stratospheric water change has a radiative impact, is considered a forcing, and can be evaluated. Stratospheric concentrations of water have varied significantly in past decades. The full extent of these variations is notwell understood and is probably less a forcing thana feedback process added to natural variability. The
T -4 T -2 T
T +2 T +4 Temperature change
contribution of stratospheric water vapour to warm1.4
ing, both forcing and feedback, is much smaller than
1.2
from CH4 or CO2.
0

666

0

0

0

1.0
0.8
0.6

Water vapour

The maximum amount of water vapour in the air
is controlled by temperature. A typical column of
air extending from the surface to the stratospherein polar regions may contain only a few kilogramsof water vapour per square metre, while a similar column of air in the tropics may contain up to
70 kg. With every extra degree of air temperature,
the atmosphere can retain around 7% more water
vapour (see upper-left insert in the FAQ 8.1, Figure1). This increase in concentration amplifies the greenhouse effect, and therefore leads to more warming.
This process, referred to as the water vapour feedback, is well understood and quantified. It occurs inall models used to estimate climate change, where
its strength is consistent with observations. Although
an increase in atmospheric water vapour has been
observed, this change is recognized as a climate feedback (from increased atmospheric temperature) andshould not be interpreted as a radiative forcing fromanthropogenic emissions. (continued on next page)0

FAQ 8.1, Figure 1 | Illustration of the water cycle and its interaction with thegreenhouse effect. The upper-left insert indicates the relative increase of potential water vapour content in the air with an increase of temperature (roughly
7% per degree). The white curls illustrate evaporation, which is compensated by
precipitation to close the water budget. The red arrows illustrate the outgoing
infrared radiation that is partly absorbed by water vapour and other gases, a process that is one component of the greenhouse effect. The stratospheric processes
are not included in this figure.

Anthropogenic and Natural Radiative Forcing

Chapter 8

FAQ 8.1 (continued)

Currently, water vapour has the largest greenhouse effect in the Earth’s atmosphere. However, other greenhousegases, primarily CO2, are necessary to sustain the presence of water vapour in the atmosphere. Indeed, if these othergases were removed from the atmosphere, its temperature would drop sufficiently to induce a decrease of water
vapour, leading to a runaway drop of the greenhouse effect that would plunge the Earth into a frozen state. So
greenhouse gases other than water vapour provide the temperature structure that sustains current levels of atmospheric water vapour. Therefore, although CO2 is the main anthropogenic control knob on climate, water vapour
is a strong and fast feedback that amplifies any initial forcing by a typical factor between two and three. Water
vapour is not a significant initial forcing, but is nevertheless a fundamental agent of climate change.

of seasons or less, but there is a spectrum of adjustment times. Changes in land ice and snow cover, for instance, may take place over many
years. The ERF thus represents that part of the instantaneous RF that ismaintained over long time scales and more directly contributes to thesteady-state climate response. The RF can be considered a more limited
version of ERF. Because the atmospheric temperature has been allowed
to adjust, ERF would be nearly identical if calculated at the tropopause
instead of the TOA for tropospheric forcing agents, as would RF. Recent
work has noted likely advantages of the ERF framework for understanding model responses to CO2 as well as to more complex forcing
agents (see Section 7.2.5.6).
The climate sensitivity parameter λ derived with respect to RF can vary
substantially across different forcing agents (Forster et al., 2007). The
response to RF from a particular agent relative to the response to RF
from CO2 has been termed the efficacy (Hansen et al., 2005). By including many of the rapid adjustments that differ across forcing agents,
the ERF concept includes much of their relative efficacy and therefore
leads to more uniform climate sensitivity across agents. For example,
the influence of clouds on the interaction of aerosols with sunlight andthe effect of aerosol heating on cloud formation can lead to very large
differences in the response per unit RF from black carbon (BC) locatedat different altitudes, but the response per unit ERF is nearly uniformwith altitude (Hansen et al., 2005; Ming et al., 2010; Ban-Weiss et al.,2012). Hence as we use ERF in this chapter when it differs significantly
from RF, efficacy is not used hereinafter. For inhomogeneous forcings,
we note that the climate sensitivity parameter may also depend on thehorizontal forcing distribution, especially with latitude (Shindell andFaluvegi, 2009; Section 8.6.2).
A combination of RF and ERF will be used in this chapter with RF provided to keep consistency with TAR and AR4, and ERF used to allow
quantification of more complex forcing agents and, in some cases, provide a more useful metric than RF.

8.1.1.3 Limitations of Radiative Forcing
Both the RF and ERF concepts have strengths and weaknesses in
addition to those discussed previously. Dedicated climate model simulations that are required to diagnose the ERF can be more computationally demanding than those for instantaneous RF or RF because
many years are required to reduce the influence of climate variability.
The presence of meteorological variability can also make it difficult to8isolate the ERF of small forcings that are easily isolated in the pair ofradiative transfer calculations performed for RF (Figure 8.1). For RF, on
the other hand, a definition of the tropopause is required, which canbe ambiguous.
In many cases, however, ERF and RF are nearly equal. Analysis of 11
models from the current Coupled Model Intercomparison Project Phase
5 (CMIP5) generation finds that the rapid adjustments to CO2 cause
fixed-SST-based ERF to be 2% less than RF, with an intermodel standard deviation of 7% (Vial et al., 2013). This is consistent with an earlier
study of six GCMs that found a substantial inter-model variation in
the rapid tropospheric adjustment to CO2 using regression analysis inslab ocean models, though the ensemble mean adjustment was lessthan 5% (Andrews and Forster, 2008). Part of the large uncertainty
range arises from the greater noise inherent in regression analyses ofsingle runs in comparison with fixed-SST experiments. Using fixed-SST
simulations, Hansen et al. (2005) found that ERF is virtually identicalto RF for increased CO2, tropospheric ozone and solar irradiance, andwithin 6% for methane (CH4), nitrous oxide (N2O), stratospheric aerosols and for the aerosol–radiation interaction of reflective aerosols.
Shindell et al. (2013b) also found that RF and ERF are statistically equal
for tropospheric ozone. Lohmann et al. (2010) report a small increasein the forcing from CO2 using ERF instead of RF based on the fixed-SST
technique, while finding no substantial difference for CH4, RF due toaerosol–radiation interactions or aerosol effects on cloud albedo. In
the fixed-SST simulations of Hansen et al. (2005), ERF was about 20%
less than RF for the atmospheric effects of BC aerosols (not including
microphysical aerosol–cloud interactions), and nearly 300% greaterfor the forcing due to BC snow albedo forcing (Hansen et al., 2007).
ERF was slightly greater than RF for stratospheric ozone in Hansen
et al. (2005), but the opposite is true for more recent analyses (Shindell et al., 2013b), and hence it seems most appropriate at present to
use RF for this small forcing. The various studies demonstrate that RFprovides a good estimate of ERF in most cases, as the differences arevery small, with the notable exceptions of BC-related forcings (Bondet al., 2013). ERF provides better characterization of those effects, aswell as allowing quantification of a broader range of effects including
all aerosol–cloud interactions. Hence while RF and ERF are generally
quite similar for WMGHGs, ERF typically provides a more useful indication of climate response for near-term climate forcers (see Box 8.2). As
the rapid adjustments included in ERF differ in strength across climate
models, the uncertainty range for ERF estimates tends to be larger than
the range for RF estimates.
667

Chapter 8

Anthropogenic and Natural Radiative Forcing

Box 8.2 | Grouping Forcing Compounds by Common Properties
As many compounds cause RF when their atmospheric concentration is changed, it can be useful to refer to groups of compounds withsimilar properties. Here we discuss two primary groupings: well-mixed greenhouse gases (WMGHGs) and near-term climate forcers(NTCFs).

8

We define as ‘well-mixed’ those greenhouse gases that are sufficiently mixed throughout the troposphere that concentration measurements from a few remote surface sites can characterize the climate-relevant atmospheric burden; although these gases may still have
local variation near sources and sinks and even small hemispheric gradients. Global forcing per unit emission and emission metrics
for these gases thus do not depend on the geographic location of the emission, and forcing calculations can assume even horizontal
distributions. These gases, or a subset of them, have sometimes been referred to as ‘long-lived greenhouse gases’ as they are well
mixed because their atmospheric lifetimes are much greater than the time scale of a few years for atmospheric mixing, but the physicalproperty that causes the aforementioned common characteristics is more directly associated with their mixing within the atmosphere.
WMGHGs include CO2, N2O, CH4, SF6, and many halogenated species. Conversely, ozone is not a WMGHG.
We define ‘near-term climate forcers’ (NTCFs) as those compounds whose impact on climate occurs primarily within the first decade
after their emission. This set of compounds is composed primarily of those with short lifetimes in the atmosphere compared to WMGHGs,
and has been sometimes referred to as short-lived climate forcers or short-lived climate pollutants. However, the common property
that is of greatest interest to a climate assessment is the time scale over which their impact on climate is felt. This set of compounds
includes methane, which is also a WMGHG, as well as ozone and aerosols, or their precursors, and some halogenated species thatare not WMGHGs. These compounds do not accumulate in the atmosphere at decadal to centennial time scales, and so their effect on
climate is predominantly in the near term following their emission.

Whereas the global mean ERF provides a useful indication of the eventual change in global mean surface temperature, it does not reflect
regional climate changes. This is true for all forcing agents, but is especially the case for the inhomogeneously distributed forcings because
they activate climate feedbacks based on their regional distribution.
For example, forcings over Northern Hemisphere (NH) middle and highlatitudes induce snow and ice albedo feedbacks more than forcings at
lower latitudes or in the Southern Hemisphere (SH) (e.g., Shindell andFaluvegi, 2009).

temperatures, which can differ greatly from the global mean. Hence
although they are quite useful for understanding the factors driving
global mean temperature change, they provide only an imperfect andlimited perspective on the factors driving broader climate change. In
addition, a metric based solely on radiative perturbations does notallow comparison of non-RFs, such as effects of land cover change
on evapotranspiration or physiological impacts of CO2 and O3 except
where these cause further impacts on radiation such as through cloud
cover changes (e.g., Andrews et al., 2012b).

In the case of agents that strongly absorb incoming solar radiation(such as BC, and to a lesser extent organic carbon (OC) and ozone) the
TOA forcing provides little indication of the change in solar radiation
reaching the surface which can force local changes in evaporation andalter regional and general circulation patterns (e.g., Ramanathan andCarmichael, 2008; Wang et al., 2009). Hence the forcing at the surface,or the atmospheric heating, defined as the difference between surface and tropopause/TOA forcing, might also be useful metrics. Global
mean precipitation changes can be related separately to ERF within
the atmosphere and to a slower response to global mean temperaturechanges (Andrews et al., 2010; Ming et al., 2010; Ban-Weiss et al.,2012). Relationships between surface forcing and localized aspects ofclimate response have not yet been clearly quantified, however.

8.1.2

In general, most widely used definitions of forcing and most forcing-based metrics are intended to be proportional to the eventual
temperature response, and most analyses to date have explored theglobal mean temperature response only. These metrics do not explicitly include impacts such as changes in precipitation, surface sunlightavailable for photosynthesis, extreme events, and so forth, or regional
668

Calculation of Radiative Forcing due toConcentration or Emission Changes

Analysis of forcing due to observed or modelled concentration changes
between pre-industrial, defined here as 1750, and a chosen later year
provides an indication of the importance of different forcing agents toclimate change during that period. Such analyses have been a mainstay of climate assessments. This perspective has the advantage thatobservational data are available to accurately quantify the concentration changes for several of the largest forcing components. Atmospheric concentration changes, however, are the net result of variations inemissions of multiple compounds and any climate changes that haveinfluenced processes such as wet removal, atmospheric chemistry or
the carbon cycle. Characterizing forcing according to concentration
changes thus mixes multiple root causes along with climate feedbacks.
Policy decisions are better informed by analysis of forcing attributableto emissions, which the IPCC first presented in AR4. These analyses canbe applied to historical emissions changes in a ‘backward-looking’ perspective, as done for example, for major WMGHGs (den Elzen et al.,2005; Hohne et al., 2011) and NTCFs (Shindell et al., 2009), or to current

Anthropogenic and Natural Radiative ForcingaClimatologicalTropopause

Radiative
forcing tries
to modify
original
temperature

Chapter 8Stratospheric b
T adjust
RF: Net Flux
IRF: Netchange atFlux
tropopausechangeTemperatureﬁxed
everywherecd

Net Fluxchange at
TOA

ERF: Net Fluxchange at
TOA

Groundtemperature
ﬁxed

Tropospherictemperature
ﬁxed

Net Flux = 0
Temperatureadjusts
everywhere

Ocean
ﬁxed
T0

Calculation Methodology
Online or ofﬂine pair ofradiative transfer
calculations within one
simulationeDifference between
two ofﬂine radiativetransfer calculations
with prescribed surface
and troposphericconditions allowing
stratospherictemperature to adjust

Difference between
two full atmosphericmodel simulations
with prescribedsurface conditions
everywhere or
estimate based on
regression of
response in full
coupled atmosphereocean simulation

Difference between
two full atmosphericmodel simulations
with prescribed ocean
conditions (SSTs andsea ice)8TsDifference between
two full coupledatmosphere-ocean
model simulations

Figure 8.1 | Cartoon comparing (a) instantaneous RF, (b) RF, which allows stratospheric temperature to adjust, (c) flux change when the surface temperature is fixed over the whole
Earth (a method of calculating ERF), (d) the ERF calculated allowing atmospheric and land temperature to adjust while ocean conditions are fixed and (e) the equilibrium responseto the climate forcing agent. The methodology for calculation of each type of forcing is also outlined. DTo represents the land temperature response, while DTs is the full surface
temperature response. (Updated from Hansen et al., 2005.)

or projected future emissions in a ‘forward-looking’ view (see Section
8.7). Emissions estimates through time typically come from the scientificcommunity, often making use of national reporting for recent decades.
With the greater use of emission-driven models, for example, in CMIP5,
it is becoming more natural to estimate ERF resulting from emissionsof a particular species rather than concentration-based forcing. Suchcalculations typically necessitate model simulations with chemicaltransport models or chemistry–climate models, however, and require
careful consideration of which processes are included, especially whencomparing results to concentration-based forcings. In particular, simulation of concentration responses to emissions changes requires incorporating models of the carbon cycle and atmospheric chemistry (gasand aerosol phases). The requisite expansion of the modelling realm foremissions-based forcing or emission metrics should in principle be consistent for all drivers. For example, as the response to aerosol or ozone
precursor emissions includes atmospheric chemistry, the response to
CO2 emissions should as well. In addition, if the CO2 concentration
responses to CO2 emissions include the impact of CO2-induced climatechanges on carbon uptake, then the effect of climate changes causedby any other emission on carbon uptake should also be included. Similarly, if the effects of atmospheric CO2 concentration change on carbon
uptake are included, the effects of other atmospheric composition or
deposition changes on carbon uptake should be included as well (seealso Section 6.4.1). Comparable issues are present for other forcing
agents. In practice, the modelling realm used in studies of forcing
attributable to emissions has not always been consistent. Furthermore,climate feedbacks have sometimes been included in the calculationof forcing due to ozone or aerosol changes, as when concentrations
from a historical transient climate simulation are imposed for an ERF
calculation. In this chapter, we endeavour to clarify which processes

have been included in the various estimates of forcing attributed toemissions (Sections 8.3 and 8.7).
RF or ERF estimates based on either historical emissions or concentrations provide valuable insight into the relative and absolute contribution of various drivers to historical climate change. Scenarios ofchanging future emissions and land use are also developed based on
various assumptions about socioeconomic trends and societal choices.
The forcing resulting from such scenarios is used to understand thedrivers of potential future climate changes (Sections 8.5.3 and 8.6).
As with historical forcings, the actual impact on climate depends on
both the temporal and spatial structure of the forcings and the rate of
response of various portions of the climate system.

8.2

Atmospheric Chemistry

8.2.1

Introduction

Most radiatively active compounds in the Earth’s atmosphere arechemically active, meaning that atmospheric chemistry plays a largerole in determining their burden and residence time. In the atmosphere,a gaseous chemically active compound can be affected by (1) interaction with other species (including aerosols and water) in its immediate
vicinity and (2) interaction with solar radiation (photolysis). Physicalprocesses (wet removal and dry deposition) act on some chemicalcompounds (gas or aerosols) to further define their residence timein the atmosphere. Atmospheric chemistry is characterized by many
interactions and patterns of temporal or spatial variability, leading tosignificant nonlinearities (Kleinman et al., 2001) and a wide range oftime scales of importance (Isaksen et al., 2009).
669

Chapter 8This section assesses updates in understanding of processes, modellingand observations since AR4 (see Section 2.3) on key reactive species
contributing to RF. Note that aerosols, including processes responsible
for the formation of aerosols, are extensively described in Section 7.3.

8.2.2

8

Global Chemistry Modelling in Coupled Model
Intercomparison Project Phase 5

Because the distribution of NTCFs cannot be estimated from observations alone, coupled chemistry-climate simulations are required todefine their evolution and associated RF. While several CMIP5 modeling groups performed simulations with interactive chemistry (i.e., computed simultaneously within the climate model), many models used
as input pre-computed distributions of radiatively active gases and/or aerosols. To assess the distributions of chemical species and theirrespective RF, many research groups participated in the Atmospheric
Chemistry and Climate Model Intercomparison Project (ACCMIP).
The ACCMIP simulations (Lamarque et al., 2013) were defined to provide information on the long-term changes in atmospheric composition with a few, well-defined atmospheric simulations. Because of thenature of the simulations (pre-industrial, present-day and future climates), only a limited number of chemistry-transport models (models
which require a full definition of the meteorological fields needed tosimulate physical processes and transport) participated in the ACCMIP
project, which instead drew primarily from the same General Circulation Models (GCMs) as CMIP5 (see Lamarque et al., 2013 for a listof the participating models and their configurations), with extensivemodel evaluation against observations (Bowman et al., 2013; Lee etal., 2013; Shindell et al., 2013c; Voulgarakis et al., 2013; Young et al.,2013).
In all CMIP5/ACCMIP chemistry simulations, anthropogenic and biomass burning emissions are specified. More specifically, a single set of
historical anthropogenic and biomass burning emissions (Lamarque etal., 2010) and one set of emissions for each of the RCPs (van Vuuren
et al., 2011) was defined (Figure 8.2). This was designed to increase
the comparability of simulations. However, these uniform emission
specifications mask the existing uncertainty (e.g., Bond et al., 2007; Lu
et al., 2011), so that there is in fact a considerable range in the estimates and time evolution of recent anthropogenic emissions (Granier
et al., 2011). Historical reconstructions of biomass burning (wildfires
and deforestation) also exhibit quite large uncertainties (Kasischke andPenner, 2004; Ito and Penner, 2005; Schultz et al., 2008; van der Werf
et al., 2010). In addition, the RCP biomass burning projections do notinclude the feedback between climate change and fires discussed inBowman et al. (2009), Pechony and Shindell (2010) and Thonicke et al.
(2010). Finally, the RCP anthropogenic precursor emissions of NTCFs
tend to span a smaller range than available from existing scenarios(van Vuuren et al., 2011). The ACCMIP simulations therefore provide anestimate of the uncertainty due to range of representation of physicaland chemical processes in models, but do not incorporate uncertainty
in emissions.

670

Anthropogenic and Natural Radiative Forcing

8.2.3

Chemical Processes and Trace Gas Budgets

8.2.3.1 Tropospheric Ozone
The RF from tropospheric ozone is strongly height- and latitude-dependent through coupling of ozone change with temperature, water
vapour and clouds (Lacis et al., 1990; Berntsen et al., 1997; Worden
et al., 2008, 2011; Bowman et al., 2013). Consequently, it is necessary
to accurately estimate the change in the ozone spatio-temporal structure using global models and observations. It is also well establishedthat surface ozone detrimentally affects plant productivity (Ashmore,
2005; Fishman et al., 2010), albeit estimating this impact on climate,although possibly significant, is still limited to a few studies (Sitch etal., 2007; UNEP, 2011).
Tropospheric ozone is a by-product of the oxidation of carbon monoxide (CO), CH4, and non-CH4 hydrocarbons in the presence of nitrogenoxides (NOx). As emissions of these precursors have increased (Figure8.2), tropospheric ozone has increased since pre-industrial times (Volzand Kley, 1988; Marenco et al., 1994) and over the last decades (Parrish
et al., 2009; Cooper et al., 2010; Logan et al., 2012), but with importantregional variations (Section 2.2). Ozone production is usually limitedby the supply of HOx (OH + HO2) and NOX (NO + NO2) (Levy, 1971;
Logan et al., 1981). Ozone’s major chemical loss pathways in the troposphere are through (1) photolysis (to O(1D), followed by reaction withwater vapour) and (2) reaction with HO2 (Seinfeld and Pandis, 2006).
The former pathway leads to couplings between stratospheric ozone(photolysis rate being a function of the overhead ozone column) andclimate change (through water vapour). Observed surface ozone abundances typically range from less than 10 ppb over the tropical Pacific
Ocean to more than 100 ppb downwind of highly emitting regions. The
lifetime of ozone in the troposphere varies strongly with season andlocation: it may be as little as a few days in the tropical boundary layer,
or as much as 1 year in the upper troposphere. Two recent studies give
similar global mean lifetime of ozone: 22.3 ± 2 days (Stevenson et al.,2006) and 23.4 ± 2.2 days (Young et al., 2013).
For present (about 2000) conditions, the various components of thebudget of global mean tropospheric ozone are estimated from the
ACCMIP simulations and other model simulations since AR4 (Table
8.1). In particular, most recent models define a globally and annually
averaged tropospheric ozone burden of (337 ± 23 Tg, 1-σ). Differencesin the definition of the tropopause lead to inter-model variations ofapproximately 10% (Wild, 2007). This multi-model mean estimate ofglobal annual tropospheric ozone burden has not significantly changed
since the Stevenson et al. (2006) estimates (344 ± 39 Tg, 1-σ), andis consistent with the most recent satellite-based Ozone Monitoring
Instrument–Microwave Limb Sounder (OMI-MLS; Ziemke et al., 2011)
and Tropospheric Emission Spectrometer (TES; Osterman et al., 2008)
climatologies.
Estimates of the ozone chemical sources and sinks (uncertainty estimates are quoted here as 1-σ) are less robust, with a net chemical
production (production minus loss) of 618 ± 275 Tg yr–1 (Table 8.1),
larger than the Atmospheric Composition Change: a European Network (ACCENT) results (442 ± 309 Tg yr–1; Stevenson et al., 2006). Estimates of ozone deposition (1094 ± 264 Tg yr–1) are slightly increased

Anthropogenic and Natural Radiative Forcing

Black Carbon(TgC year )-1

Chapter 8

CH4 (TgCH4 year-1)
Historical
RCP2.6
RCP4.5
RCP6.0
RCP8.5SRES A2
SRES B1
IS92a
GAINS-CLE
GAINS-MFR

NH3(TgNH3 year )(TgNMVOC year )Organic Carbon

SO2(TgC year-1)-1(TgSO2 year-1)(TgCO year-1)

8

NMVOC
-1

CO

NOx
(TgNO2 year-1)

Post-SRES scenarios
CH4: Rogelj et al. (2011)
Others: van Vuuren et al. (2008)
policy (upper bound)reference (upper bound)policy (lower bound)reference (lower bound)
Asia Modeling Exercise (Calvin
et al., 2012)
2.6 W m-2 (mean)
reference (mean)

Figure 8.2 | Time evolution of global anthropogenic and biomass burning emissions 1850–2100 used in CMIP5/ACCMIP following each RCP. Historical (1850–2000) values arefrom Lamarque et al. (2010). RCP values are from van Vuuren et al. (2011). Emissions estimates from Special Report on Emission Scenarios (SRES) are discussed in Annex II; note thatblack carbon and organic carbon estimates were not part of the SRES and are shown here only for completeness. The Maximum Feasible Reduction (MFR) and Current Legislation(CLE) are discussed in Cofala et al. (2007); as biomass burning emissions are not included in that publication, a fixed amount, equivalent to the value in 2000 from the RCP estimates, is added (see Annex II for more details; Dentener et al., 2006). The post-SRES scenarios are discussed in Van Vuuren et al. (2008) and Rogelj et al. (2011). For those, only therange (minimum to maximum) is shown. Global emissions from the Asian Modelling Exercise are discussed in Calvin et al. (2012). Regional estimates are shown in Supplementary
Material Figure 8.SM.1 and Figure 8.SM.2 for the historical and RCPs.

since ACCENT (1003 ± 200 Tg yr–1) while estimates of the net influx ofozone from the stratosphere to the troposphere (477 ± 96 Tg yr–1) haveslightly decreased since ACCENT (552 ± 168 Tg yr–1). Additional model
estimates of this influx (Hegglin and Shepherd, 2009; Hsu and Prather,2009) fall within both ranges, as do estimates based on observations(Murphy and Fahey, 1994; Gettelman et al., 1997; Olsen et al., 2002),
all estimates being sensitive to their choice of tropopause definition
and interannual variability.
Model simulations for present-day conditions or the recent past areevaluated (Figure 8.3) against frequent ozonesonde measurements(Logan, 1999; Tilmes et al., 2012) and additional surface, aircraft andsatellite measurements. The ACCMIP model simulations (Figure 8.3)

indicate 10 to 20% negative bias at 250 hPa in the SH tropical region,
and a slight underestimate in NH tropical region. Comparison withsatellite-based estimates of tropospheric ozone column (Ziemke et al.,2011) indicates an annual mean bias of –4.3 ± 29 Tg (with a spatial
correlation of 0.87 ± 0.07, 1-σ) for the ACCMIP simulations (Young etal., 2013). Overall, our ability to simulate tropospheric ozone burden
for present (about 2000) has not substantially changed since AR4.
Evaluation (using a subset of two ACCMIP models) of simulated trends(1960s to present or shorter) in surface ozone against observations atremote surface sites (see Section 2.2) indicates an underestimation,
especially in the NH (Lamarque et al., 2010). Although this limits theability to represent recent ozone changes, it is unclear how this translates into an uncertainty on changes since pre-industrial times.
671

Chapter 8

Anthropogenic and Natural Radiative Forcing

Table 8.1 | Summary of tropospheric ozone global budget model and observation estimates for present (about 2000) conditions. Focus is on modelling studies published since
AR4. STE stands for stratosphere–troposphere exchange. All uncertainties quoted as 1 standard deviation (68% confidence interval).

BurdenProduction

Loss

Deposition

STE

Tg

Tg yr–1

Tg yr–1

Tg yr–1

Tg yr–1

337 ± 234877 ± 853

4260 ± 645

1094 ± 264

477 ± 96

Reference

Modelling Studies

8

Young et al. (2013); ACCMIP

323

N/A

N/A

N/A

N/A

Archibald et al. (2011)

330

4876

4520

916

560

Kawase et al. (2011)

312

4289

3881

829

421

Huijnen et al. (2010)

334

3826

3373

1286

662

Zeng et al. (2010)

324

4870

4570

801

502

Wild and Palmer (2008)

314

N/A

N/A

1035

452

Zeng et al. (2008)

319

4487

3999

N/A

500

Wu et al. (2007)

372

5042

4507

884

345

Horowitz (2006)

349

4384

3972

808

401

Liao et al. (2006)

344 ± 39

5110 ± 606

4668 ± 727

1003 ± 200

552 ± 168

Stevenson et al. (2006); ACCENT

314 ± 334465 ± 514

4114 ± 409

949 ± 222529 ± 105

Wild (2007) (post-2000 studies)

N/A

N/A

N/A

N/A

515

Hsu and Prather (2009)

N/A

N/A

N/A

N/A

655

Hegglin and Shepherd (2009)

N/A

N/A

N/A

N/A

383–451

Clark et al. (2007)

Observational Studies
333

N/A

N/A

N/A

N/A

Fortuin and Kelder (1998)

327

N/A

N/A

N/A

N/A

Logan (1999)

325

N/A

N/A

N/A

N/A

Ziemke et al. (2011); 60S–60N

319–351

N/A

N/A

N/A

N/A

Osterman et al. (2008); 60S–60N

N/A

N/A

N/A

N/A

449 (192–872)

Murphy and Fahey (1994)

N/A

N/A

N/A

N/A

510 (450–590)

Gettelman et al. (1997)

N/A

N/A

N/A

N/A

500 ± 140

In most studies ‘pre-industrial’ does not identify a specific year but is
usually assumed to correspond to 1850s levels; no observational information on ozone is available for that time period. Using the Lamarqueet al. (2010) emissions, the ACCMIP models (Young et al., 2013) areunable to reproduce the low levels of ozone observed at Montsouris
1876–1886 (Volz and Kley, 1988). The other early ozone measurements
using the Schönbein paper are controversial (Marenco et al., 1994)
and assessed to be of qualitative use only. The main uncertainty inestimating the pre-industrial to present-day change in ozone therefore remains the lack of constraint on emission trends because of thevery incomplete knowledge of pre-industrial ozone concentrations, ofwhich no new information is available. The uncertainty on pre-industrial conditions is not confined to ozone but applies to aerosols as well(e.g., Schmidt et al., 2012), although ice and lake core records provide
some constraint on pre-industrial aerosol concentrations.
The ACCMIP results provide an estimated tropospheric ozone increase(Figure 8.4) from 1850 to 2000 of 98 ± 17 Tg (model range), similar
to AR4 estimates. Skeie et al. (2011a) found an additional 5% increasein the anthropogenic contribution to the ozone burden between 2000
and 2010, which translates into an approximately 1.5% increase intropospheric ozone burden. A best estimate of the change in ozone
since 1850 is assessed at 100 ± 25 Tg (1-σ). Attribution simulations
672

Olsen et al. (2001)(Stevenson et al., 2013) indicate unequivocally that anthropogenic
changes in ozone precursor emissions are responsible for the increase
between 1850 and present or into the future.

8.2.3.2 Stratospheric Ozone and Water Vapour
Stratospheric ozone has experienced significant depletion since the1960s due to bromine and chlorine-containing compounds (Solomon,1999), leading to an estimated global decrease of stratospheric ozone
of 5% between the 1970s and the mid-1990s, the decrease being
largest over Antarctica (Fioletov et al., 2002). Most of the ozone loss
is associated with the long-lived bromine and chlorine-containing
compounds (chlorofluorocarbons and substitutes) released by humanactivities, in addition to N2O. This is in addition to a background levelof natural emissions of short-lived halogens from oceanic and volcanic
sources.
With the advent of the Montreal Protocol and its amendments, emissions of chlorofluorocarbons (CFCs) and replacements have strongly
declined (Montzka et al., 2011), and signs of ozone stabilization andeven possibly recovery have already occurred (Mader et al., 2010; Salbyet al., 2012). A further consequence is that N2O emissions (Section
8.2.3.4) likely dominate all other emissions in terms of ozone-depleting

Anthropogenic and Natural Radiative Forcing

Chapter 8100

r = 0.90, 0.96
mnbe = −12.2%, 3.0%

80

r = 0.61, 0.45
mnbe = −1.1%, 19.3%

250 hPa

80

60

60

40
20

20

Ozone volume mixing ratio (ppb)

100

80

F

M

A

M

J

J

A

S

O

N

D

J

100

J

F

M

A

M

J

J

A

S

O

N

D

100

J

r = 0.71, 0.59
mnbe = 5.0%, 15.0%

r = 0.94, 0.91
mnbe = −10.0%, −1.5%

r = 0.95, 0.97
mnbe = −2.2%, 10.9%
80

80

80

60

60

60

40

40

40

500 hPa

60
40
20

20

J

80

J

8

Ozonesondes
ACCMIP mean
ACCMIP models
ACCENT mean

40

F

M

A

M

J

J

A

S

O

N

D

J

100

20

J

F

M

A

M

J

J

A

S

O

N

D

J

100

r = 0.97, 0.89
mnbe = −8.6%, −3.1%

r = 0.97, 0.98
mnbe = −2.6%, −8.5%

20

J

F

M

A

M

J

J

A

S

O

N

D

100

J

r = 0.89, 0.81
mnbe = 7.8%, 10.8%80

r = 0.99, 0.84
mnbe = 12.8%, 9.9%
J

F

M

A

M

J

J

A

S

O

N

D

J

r = 0.95, 0.84
mnbe = 13.2%, 3.0%

80

80

60

60

60

40

40

40

20

20

20

750 hPa

60
40
20
J FMAMJ J ASOND J

90°S - 30°S

J FMAMJ J ASOND J

30°S - EQ

J FMAMJ J ASOND J

EQ - 30°N

J FMAMJ J ASOND J

30°N - 90°N

Figure 8.3 | Comparisons between observations and simulations for the monthly mean ozone for ACCMIP results (Young et al., 2013). ACCENT refers to the model results in
Stevenson et al. (2006). For each box, the correlation of the seasonal cycle is indicated by the r value, while the mean normalized bias estimated is indicated by mnbe value.

Modeled Past

200

RCP6.0

RCP8.5

Osterman et al.

Logan

300

RCP4.52000
mean

Ziemke et al.

400

Modeled Projections
RCP2.6

Fortuin and Kelder

Burden (Tg)

500

Historical
ACCENT

600

Observations

100
01850 1930 1980 2000 2000

2030 2100 2030 2100 2030 2100 2030 2100

Figure 8.4 | Time evolution of global tropospheric ozone burden (in Tg(O3)) from 1850 to 2100 from ACCMIP results, ACCENT results (2000 only), and observational estimates(see Table 8.1). The box, whiskers, line and dot show the interquartile range, full range, median and mean burdens and differences, respectively. The dashed line indicates the 2000
ACCMIP mean. (Adapted from Young et al., 2013.)

673

Chapter 88Anthropogenic and Natural Radiative Forcing

potential (Ravishankara et al., 2009). Chemistry-climate models withresolved stratospheric chemistry and dynamics recently predicted an
estimated global mean total ozone column recovery to 1980 levels tooccur in 2032 (multi-model mean value, with a range of 2024 to 2042)
under the A1B scenario (Eyring et al., 2010a). Increases in the stratospheric burden and acceleration of the stratospheric circulation leads
to an increase in the stratosphere–troposphere flux of ozone (Shindell
et al., 2006c; Grewe, 2007; Hegglin and Shepherd, 2009; Zeng et al.,2010). This is also seen in recent RCP8.5 simulations, with the impact
of increasing tropospheric burden (Kawase et al., 2011; Lamarque etal., 2011). However, observationally based estimates of recent trends
in age of air (Engel et al., 2009; Stiller et al., 2012) do not appear
to be consistent with the acceleration of the stratospheric circulation
found in model simulations, possibly owing to inherent difficulties withextracting trends from SF6 observations (Garcia et al., 2011).
Oxidation of CH4 in the stratosphere (see Section 8.2.3.3) is a significant source of water vapour and hence the long-term increase in CH4leads to an anthropogenic forcing (see Section 8.3) in the stratosphere.
Stratospheric water vapour abundance increased by an average of 1.0± 0.2 (1-σ) ppm during 1980–2010, with CH4 oxidation explaining
approximately 25% of this increase (Hurst et al., 2011). Other factors
contributing to the long-term change in water vapour include changes
in tropical tropopause temperatures (see Section 2.2.2.1).

other main source of OH is through secondary reactions (Lelieveld etal., 2008), although some of those reactions are still poorly understood(Paulot et al., 2009; Peeters et al., 2009; Taraborrelli et al., 2012). A
recent estimate of the CH4 tropospheric chemical lifetime with respect
to OH constrained by methyl chloroform surface observations is 11.2
± 1.3 years (Prather et al., 2012). In addition, bacterial uptake in soils
provides an additional small, less constrained loss (Fung et al., 1991);
estimated lifetime = 120 ± 24 years (Prather et al., 2012), with anothersmall loss in the stratosphere (Ehhalt and Heidt, 1973); estimated lifetime = 150 ± 50 years (Prather et al., 2012). Halogen chemistry in the
troposphere also contributes to some tropospheric CH4 loss (Allan etal., 2007), estimated lifetime = 200 ± 100 years (Prather et al., 2012).
The ACCMIP estimate for present CH4 lifetime with respect to tropospheric OH varies quite widely (9.8 ± 1.6 years (Voulgarakis et al.,2013)), slightly shorter than the 10.2 ± 1.7 years in (Fiore et al. (2009),
but much shorter than the methyl chloroform-based estimate of 11.2
± 1.3 years (Prather et al., 2012). A partial explanation for the range in
CH4 lifetime changes can be found in the degree of representation ofchemistry in chemistry–climate models. Indeed, Archibald et al. (2010)
showed that the response of OH to increasing nitrogen oxides strongly
depends on the treatment of hydrocarbon chemistry in a model. The
impact on CH4 distribution in the ACCMIP simulations is, however,rather limited because most models prescribed CH4 as a time-varying
lower-boundary mixing ratio (Lamarque et al., 2013).

8.2.3.3 Methane
The surface mixing ratio of CH4 has increased by 150% since pre-industrial times (Sections 2.2.1.1.2 and 8.3.2.2), with some projections indicating a further doubling by 2100 (Figure 8.5). Bottom-up estimates ofpresent CH4 emissions range from 542 to 852 TgCH4 yr–1 (see Table 6.8),
while a recent top-down estimate with uncertainty analysis is 554 ±56 TgCH4 yr–1 (Prather et al., 2012). All quoted uncertainties in Section
8.2.3.3 are defined as 1-σ.
The main sink of CH4 is through its reaction with the hydroxyl radical(OH) in the troposphere (Ehhalt and Heidt, 1973). A primary sourceof tropospheric OH is initiated by the photodissociation of ozone, followed by reaction with water vapour (creating sensitivity to humidity, cloud cover and solar radiation) (Levy, 1971; Crutzen, 1973). The

CH4 (ppm)

CO2 (ppm)

The chemical coupling between OH and CH4 leads to a significant
amplification of an emission impact; that is, increasing CH4 emissions
decreases tropospheric OH which in turn increases the CH4 lifetimeand therefore its burden. The OH-lifetime sensitivity for CH4, s_OH =–δln(OH)/δln(CH4), was estimated in Chapter 4 of TAR to be 0.32,
implying a 0.32% decrease in tropospheric mean OH (as weighted byCH4 loss) for a 1% increase in CH4. The Fiore et al. (2009) multi-model (12 models) study provides a slightly smaller value (0.28 ± 0.03).
Holmes et al. (2013) gives a range 0.31 ± 0.04 by combining Fiore et al.
(2009), Holmes et al. (2011) and three new model results (0.36, 0.31,
0.27). Only two ACCMIP models reported values (0.19 and 0.26; Voulgarakis et al., 2013). The projections of future CH4 in Chapter 11 use
the Holmes et al. (2013) range and uncertainty, which at the 2-σ level
covers all but one model result. The feedback factor f, the ratio of theN2O (ppm)

SRES A2
SRES B1
IS92a
Historical
RCP2.6
RCP4.5
RCP6.0
RCP8.5Figure 8.5 | Time evolution of global-averaged mixing ratio of long-lived species1850–2100 following each RCP; blue (RCP2.6), light blue (RCP4.5), orange (RCP6.0) and red(RCP8.5). (Based on Meinshausen et al., 2011b.)674

Anthropogenic and Natural Radiative Forcing

Chapter 8

lifetime of a CH4 perturbation to the lifetime of the total CH4 burden, iscalculated as f = 1/(1-s). Other CH4 losses, which are relatively insensitive to CH4 burden, must be included so that f = 1.34 ± 0.06, (slightly
larger but within the range of the Stevenson et al. (2006) estimate of
1.29 ± 0.04, based on six models), leading to an overall perturbation
lifetime of 12.4 ± 1.4 years, which is used in calculations of metrics
in Section 8.7. Additional details are provided in the Supplementary
Material Section 8.SM.2.

of non-methane hydrocarbons (and their products) with the hydroxyl
radical (OH), ozone, nitrate (NO3) or photolysis (Hallquist et al., 2009).
Thus although many hydrocarbons in the atmosphere are of biogenic
origin, anthropogenic pollutants can have impacts on their conversion
to SOAs. There is tremendous complexity and still much uncertainty inthe processes involved in the formation of SOAs (Hallquist et al., 2009;
Carslaw et al., 2010). Additional information can be found in Section
7.3.2.

8.2.3.4 Nitrous Oxide

Once generated, the size and composition of aerosol particles can be
modified by additional chemical reactions, condensation or evaporation of gaseous species and coagulation (Seinfeld and Pandis, 2006).
It is this set of processes that defines their physical, chemical and optical properties, and hence their impact on radiation and clouds, withlarge regional and global differences (see Section 7.3.3). Furthermore,their distribution is affected by transport and deposition, defining a
residence time in the troposphere of usually a few days (Textor et al.,2006).

Nitrous oxide (N2O) in 2011 has a surface concentration 19% aboveits 1750 level (Sections 2.2.1.1.3 and 8.3.2.3). Increases in N2O lead todepletion of mid- to upper-stratospheric ozone and increase in mid-latitude lower stratospheric ozone (as a result of increased photolysis
rate from decreased ozone above). This impacts tropospheric chemistry
through increase in stratosphere–troposphere exchange of ozone andodd nitrogen species and increase in tropospheric photolysis rates andOH formation (Prather and Hsu, 2010). Anthropogenic emissions represent around 30 to 45% of the present-day global total, and are mostly
from agricultural and soil sources (Fowler et al., 2009) and fossil-fuelactivities. Natural emissions come mostly from microbial activity in thesoil. The main sink for N2O is through photolysis and oxidation reactions in the stratosphere, leading to an estimated lifetime of 131 ±10 years (Prather et al., 2012), slightly larger than previous estimates(Prather and Hsu, 2010; Montzka et al., 2011). The addition of N2O
to the atmosphere changes its own lifetime through feedbacks that
couple N2O to stratospheric NOy and ozone depletion (Prather, 1998;
Ravishankara et al., 2009; Prather and Hsu, 2010), so that the lifetimeof a perturbation is less than that of the total burden, 121 ± 10 years(1-σ; Prather et al., 2012) and is used in calculations of metrics (Section 8.7).

8.2.3.5 Halogenated Species
Halogenated species can be powerful greenhouse gases (GHGs). Those
containing chlorine and bromine also deplete stratospheric ozone
and are referred to as ozone-depleting substances (ODSs). Most of
those compounds do not have natural emissions and, because of theimplementation of the Montreal Protocol and its amendments, total
emissions of ODSs have sharply decreased since the 1990s (Montzka
et al., 2011). For CFCs, perfluorocarbons (PFCs) and SF6 the main loss
is through photolysis in the stratosphere. The CFC substitutes (hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs)) aredestroyed by OH oxidation in the troposphere. Their global concentration has steadily risen over the recent past (see Section 2.2.1.1.4).

8.3

Present-Day Anthropogenic Radiative
Forcing

Human activity has caused a variety of changes in different forcing
agents in the atmosphere or land surface. A large number of GHGs
have had a substantial increase over the Industrial Era and some ofthese gases are entirely of anthropogenic origin. Atmospheric aerosols
have diverse and complex influences on the climate. Human activity
has modified the land cover and changed the surface albedo. Some of
the gases and aerosols are directly emitted to the atmosphere whereas others are secondary products from chemical reactions of emitted
species. The lifetimes of these different forcing agents vary substantially. This section discusses all known anthropogenic forcing agentsof non-negligible importance and their quantification in terms of RF
or ERF based on changes in abundance over the 1750–2011 period.
In this section we determine the RFs for WMGHGs and heterogeneously distributed species in fundamentally different ways. As describedin Box 8.2, the concentrations of WMGHGs can be determined fromobservations at a few surface sites. For the pre-industrial concentrations these are typically from trapped air in polar ice or firn (see Section 2.2.1). Thus the RFs from WMGHGs are determined entirely fromobservations (Section 8.3.2). In contrast, we do not have sufficient
pre-industrial or present-day observations of heterogeneously distributed forcing agents (e.g., ozone and aerosols) to be able to characterize their RF; therefore we instead have to rely on chemistry–climate
models (Sections 8.3.3 and 8.3.4).

8.2.3.6 Aerosols
8.3.1
Aerosol particles are present in the atmosphere with size ranges
from a few nanometres to tens of micrometres. They are the resultsof direct emission (primary aerosols: BC, OC, sea salt, dust) into theatmosphere or as products of chemical reactions (secondary inorganic
aerosols: sulphate, nitrate, ammonium; and secondary organic aerosols (SOAs)) occurring in the atmosphere. Secondary inorganic aerosols are the products of reactions involving sulphur dioxide, ammonia
and nitric oxide emissions. SOAs are the result of chemical reactions

Updated Understanding of the Spectral Propertiesof Greenhouse Gases and Radiative Transfer Codes

RF estimates are performed with a combination of radiative transfer
codes typical for GCMs as well as more detailed radiative transfer
codes. Physical properties are needed in the radiative transfer codessuch as spectral properties for gases. The HITRAN (HIgh Resolution
TRANsmission molecular absorption) database (Rothman, 2010) is
widely used in radiative transfer models. Some researchers studied
675

8

Chapter 88

the difference among different editions of HITRAN databases fordiverse uses (Feng et al., 2007; Kratz, 2008; Feng and Zhao, 2009;
Fomin and Falaleeva, 2009; Lu et al., 2012). Model calculations haveshown that modifications of the spectroscopic characteristics tend tohave a modest effect on the determination of RF estimates of order
2 to 3% of the calculated RF attributed to the combined doubling ofCO2, N2O and CH4. These results showed that even the largest overall
RF induced by differences among the HITRAN databases is considerably smaller than the range reported for the modelled RF estimates;
thus the line parameter updates to the HITRAN database are not asignificant source for discrepancies in the RF calculations appearing
in the IPCC reports. However, the more recent HITRAN data set isstill recommended, as the HITRAN process offers internal verificationand tends to progress closer to the best laboratory measurements.
It is found that the differences among the water vapour continuum
absorption formulations tend to be comparable to the differencesamong the various HITRAN databases (Paynter and Ramaswamy,2011); but use of the older Robert continuum formula produces significantly larger flux differences, thus, replacement of the older continuum is warranted (Kratz, 2008) and there are still numerous unresolved
issues left in the continuum expression, especially related to shortwave radiative transfer (Shine et al., 2012). Differences in absorption
data from various HITRAN versions are very likely a small contributor
to the uncertainty in RF of GHGs.
Line-by-line (LBL) models using the HITRAN data set as an input are
the benchmark of radiative transfer models for GHGs. Some researchers compared different LBL models (Zhang et al., 2005; Collins et al.,2006) and line-wing cutoff, line-shape function and gas continuum
absorption treatment effects on LBL calculations (Zhang et al., 2008;
Fomin and Falaleeva, 2009). The agreement between LBL codes has
been investigated in many studies and found to generally be withina few percent (e.g., Collins et al., 2006; Iacono et al., 2008; Forster etal., 2011a) and to compare well to observed radiative fluxes undercontrolled situations (Oreopoulos et al., 2012). Forster et al. (2011a)
evaluated global mean radiatively important properties of chemistryclimate models (CCMs) and found that the combined WMGHG global
annual mean instantaneous RF at the tropopause is within 30% of LBL
models for all CCM radiation codes tested. The accuracies of the LW RF
due to CO2 and tropospheric ozone increase are generally very goodand within 10% for most of the participation models, but problems
remained in simulating RF for stratospheric water vapour and ozone
changes with errors between 3% and 200% compared to LBL models.
Whereas the differences in the results from CCM radiation codes werelarge, the agreement among the LW LBL codes was within 5%, except
for stratospheric water vapour changes.
Most intercomparison studies of the RF of GHGs are for clear-sky andaerosol-free conditions; the introduction of clouds would greatly complicate the targets of research and are usually omitted in the intercomparison exercises of GCM radiation codes and LBL codes (e.g., Collins
et al., 2006; Iacono et al., 2008). It is shown that clouds can reducethe magnitude of RF due to GHGs by about 25% (Forster et al., 2005;
Worden et al., 2011; Zhang et al., 2011), but the influence of clouds
on the diversity in RF is found to be within 5% in four detailed radiative transfer schemes with realistic cloud distributions (Forster et al.,2005). Estimates of GHG RF are based on the LBL codes or the r­ adiative
676

Anthropogenic and Natural Radiative Forcingt­ransfer codes compared and validated against LBL models, and theuncertainty range from AR4 in the RF of GHG of 10% is retained. We
underscore that uncertainty in RF calculations in many GCMs is substantially higher owing both to radiative transfer codes and meteorological data such as clouds adopted in the simulations.

8.3.2

Well-mixed Greenhouse Gases

AR4 assessed the RF from 1750 to 2005 of the WMGHGs to be 2.63
W m–2. The four most important gases were CO2, CH4, dichlorodifluoromethane (CFC-12) and N2O in that order. Halocarbons, comprising
CFCs, HCFCs, HFCs, PFCs and SF6, contributed 0.337 W m–2 to the total.
Uncertainties (90% confidence ranges) were assessed to be approximately 10% for the WMGHGs. The major changes to the science sinceAR4 are the updating of the atmospheric concentrations, the inclusionof new species (NF3 and SO2F2) and discussion of ERF for CO2. Since
AR4 N2O has overtaken CFC-12 as the third largest contributor to RF.
The total WMGHG RF is now 2.83 (2.54 to 3.12) W m–2.
The RFs in this section are derived from the observed differences inconcentrations of the WMGHGs between 1750 and 2011. The concentrations of CO2, CH4 and N2O vary throughout the pre-industrial
era, mostly due to varying climate, with a possible small contribution
from anthropogenic emissions (MacFarling Meure et al., 2006). Thesevariations do not contribute to uncertainty in the RF as strictly defined
here, but do affect the RF attribution to anthropogenic emissions. On
centennial time scales, variations in late Holocene concentrations of
CO2 are around 10 ppm (see note to Table 2.1), much larger thanthe uncertainty in the 1750 concentration. This would equate to avariation in the RF of 10%. For CH4 and N2O the centennial variations
are comparable to the uncertainties in the 1750 concentrations andso do not significantly affect the estimate of the 1750 value used in
calculating RF.

8.3.2.1 Carbon Dioxide
The tropospheric mixing ratio of CO2 has increased globally from 278(276–280) ppm in 1750 to 390.5 (390.3 to 390.7) ppm in 2011 (seeSection 2.2.1.1.1). Here we assess the RF due to changes in atmospheric concentration rather than attributing it to anthropogenic emissions. Section 6.3.2.6 describes how only a fraction of the historical
CO2 emissions have remained in the atmosphere. The impact of land
use change on CO2 from 1850 to 2000 was assessed in AR4 to be 12 to
35 ppm (0.17 to 0.51 W m–2).
Using the formula from Table 3 of Myhre et al. (1998), and see Supplementary Material Table 8.SM.1, the CO2 RF (as defined in Section 8.1)
from 1750 to 2011 is 1.82 (1.63 to 2.01) W m–2. The uncertainty is dominated by the radiative transfer modelling which is assessed to be 10%(Section 8.3.1). The uncertainty in the knowledge of 1750 concentrations contributes only 2% (see Supplementary Material Table 8.SM.2)
Table 8.2 shows the concentrations and RF in AR4 (2005) and 2011 forthe most important WMGHGs. Figure 8.6 shows the time evolution of
RF and its rate of change. Since AR4, the RF of CO2 has increased by
0.16 W m–2 and continues the rate noted in AR4 of almost 0.3 W m–2
per decade. As shown in Figure 8.6(d) the rate of increase in the RFAnthropogenic and Natural Radiative Forcing

from the WMGHGs over the last 15 years has been dominated by CO2.
Since AR4, CO2 has accounted for more than 80% of the WMGHG RF
increase. The interannual variability in the rate of increase in the CO2
RF is due largely to variation in the natural land uptake whereas thetrend is driven by increasing anthropogenic emissions (see Figure 6.8
in Section 6.3.1).

Chapter 8

As described in Section 8.1.1.3, CO2 can also affect climate throughphysical effects on lapse rates and clouds, leading to an ERF that will
be different from the RF. Analysis of CMIP5 models (Vial et al., 2013)
found a large negative contribution to the ERF (20%) from the increasein land surface temperatures which was compensated for by positive
contributions from the combined effects on water vapour, lapse rate,
albedo and clouds. It is therefore not possible to conclude with thecurrent information whether the ERF for CO2 is higher or lower thanthe RF. Therefore we assess the ratio ERF/RF to be 1.0 and assess our
uncertainty in the CO2 ERF to be (–20% to 20%). We have medium
confidence in this based on our understanding that the physical processes responsible for the differences between ERF and RF are smallenough to be covered within the 20% uncertainty.
There are additional effects mediated through plant physiology, reducing the conductance of the plant stomata and hence the transpirationof water. Andrews et al. (2012b) find a physiological enhancement of
the adjusted forcing by 3.5% due mainly to reductions in low cloud.
This is smaller than a study with an earlier model by Doutriaux-Boucher et al. (2009) which found a 10% effect. Longer-term impacts of
CO2 on vegetation distributions also affect climate (O’ishi et al., 2009;
Andrews et al., 2012b) but because of the longer time scale we choose
to class these as feedbacks rather than rapid adjustments.

8.3.2.2 Methane
Globally averaged surface CH4 concentrations have risen from 722 ±25 ppb in 1750 to 1803 ± 2 ppb by 2011 (see Section 2.2.1.1.2). Overthat time scale the rise has been due predominantly to changes inanthropogenic-related CH4. Anthropogenic emissions of other compounds have also affected CH4 concentrations by changing its removal rate (Section 8.2.3.3). Using the formula from Myhre et al. (1998)(see Supplementary Material Table 8.SM.1) the RF for CH4 from 1750
to 2011 is 0.48 ± 0.05 W m–2, with an uncertainty dominated by theradiative transfer calculation. This increase of 0.01 W m–2 since AR4 isdue to the 29 ppb increase in the CH4 mixing ratio. This is much larger
than the 11 ppb increase between TAR and AR4, and has been driven
by increases in net natural and anthropogenic emissions, but the relative contributions are not well quantified. Recent trends in CH4 andtheir causes are discussed in Sections 2.2.1.1.2 and 6.3.3.1. CH4 concentrations do vary with latitude and decrease above the tropopause;
however, this variation contributes only 2% to the uncertainty in RF(Freckleton et al., 1998).
In this section only the direct forcing from changing CH4 concentrations
is addressed. CH4 emissions can also have indirect effects on climate
through impacts on CO2, stratospheric water vapour, ozone, sulphateaerosol and lifetimes of HFCs and HCFCs (Boucher et al., 2009; Shindell
et al., 2009; Collins et al., 2010). Some of these are discussed further in
Sections 8.3.3, 8.5.1 and 8.7.2.

8.3.2.3 Nitrous Oxide
Figure 8.6 | (a) Radiative forcing (RF) from the major well-mixed greenhouse gases(WMGHGs) and groups of halocarbons from 1850 to 2011 (data from Tables A.II.1.1
and A.II.4.16), (b) as (a) but with a logarithmic scale, (c) RF from the minor WMGHGs
from 1850 to 2011 (logarithmic scale). (d) Rate of change in forcing from the major
WMGHGs and groups of halocarbons from 1850 to 2011.

Concentrations of nitrous oxide have risen from 270 ± 7 ppb in 1750 to324.2 ± 0.1 ppb in 2011, an increase of 5 ppb since 2005 (see Section
2.2.1.1.3). N2O now has the third largest forcing of the anthropogenic
gases, at 0.17 ± 0.03 W m–2 an increase of 6% since 2005 (see Table
677

8

Chapter 8

Anthropogenic and Natural Radiative Forcing

8.2) where the uncertainty is due approximately equally to the pre-industrial concentration and radiative transfer. Only the direct RF fromchanging nitrous oxide concentrations is included. Indirect effects ofN2O emissions on stratospheric ozone are not taken into account here
but are discussed briefly in Section 8.7.2.

8

8.3.2.4 Other Well-mixed Greenhouse Gases
RFs of the other WMGHG are shown in Figure 8.6 (b and c) and Table
8.2. The contribution of groups of halocarbons to the rate of changeof WMGHG RF is shown in Figure 8.6 (d). Between 1970 and 1990
halocarbons made a significant contribution to the rate of change ofRF. The rate of change in the total WMGHG RF was higher in 1970 to1990 with high confidence compared to the present owing to higher
contribution from non-CO2 gases especially the halocarbons. Since theMontreal Protocol and its amendments, the rate of change of RF fromhalocarbons and related compounds has been much less, but still justpositive (total RF of 0.360 W m–2 in 2011 compared to 0.351 W m–2 in2005) as the growth of HCFCs, HFCs, PFCs and other halogens (SF6,
SO2F2, NF3) RFs (total 0.022 W m–2 since 2005) more than compensates

Table 8.2 | Present-day mole fractions (in ppt(pmol mol–1) except where specified) and RF (in W m–2) for the WMGHGs. Concentration data are averages of National Oceanic andAtmospheric Administration (NOAA) and Advanced Global Atmospheric Gases Experiment (AGAGE) observations where available. CO2 concentrations are the average of NOAA
and SIO. See Table 2.1 for more details of the data sources. The data for 2005 (the time of the AR4 estimates) are also shown. Some of the concentrations vary slightly from thosereported in AR4 owing to averaging different data sources. Radiative efficiencies for the minor gases are given in Table 8.A.1. Uncertainties in the RF for all gases are dominatedby the uncertainties in the radiative efficiencies. We assume the uncertainties in the radiative efficiencies to be perfectly correlated between the gases, and the uncertainties in thepresent day and 1750 concentrations to be uncorrelated.

Concentrations (ppt)
Species

2011

Radiative forcinga (W m–2)
2005

2011

2005

CO2 (ppm)

391 ± 0.2379

1.82 ± 0.19

1.66

CH4 (ppb)

1803 ± 2

1774

0.48 ± 0.05

0.47e

N2O (ppb)

324 ± 0.1319

0.17 ± 0.03

0.16

CFC-11

238 ± 0.8

251

0.062

0.065

CFC-12528 ± 1

542

0.17

0.17

CFC-132.7

CFC-11374.3 ± 0.1

78.6

0.022

0.024

CFC-115

8.37

8.36

0.00170.0017

HCFC-22213 ± 0.1

169

0.04470.0355

HCFC-141b

21.4 ± 0.1

17.7

0.00340.0028HCFC-142b

21.2 ± 0.2

15.5

0.00400.0029

HFC-2324.0 ± 0.3

18.8

0.00430.0034

HFC-324.92

1.15

0.00050.0001

HFC-125

9.58 ± 0.04

3.69

0.00220.0008

HFC-134a

62.7 ± 0.3

34.3

0.01000.0055

HFC-143a12.0 ± 0.1

5.6

0.00190.00090.0007

HFC-152a

6.4 ± 0.1

3.4

0.00060.0003SF6

7.28 ± 0.03

5.64

0.00410.0032

SO2F2

1.71

1.35

0.00030.0003

NF3

0.9

0.4

0.00020.0001CF4

79.0 ± 0.1

75.0

0.00400.0036

C2F6

4.16 ± 0.02

3.66

0.00100.0009CH3CCl3

6.32 ± 0.07

18.32

0.00040.0013CCl485.8 ± 0.8

93.1

0.01460.0158CFCs

0.263 ± 0.026b

0.273cHCFCs

0.052 ± 0.005

0.041

Montreal gases

0.330 ± 0.033

0.331

Total halogens

0.360 ± 0.036

0.351fTotal

2.83 ± 0.029

2.64

d

Notes:aPre-industrial values are zero except for CO2 (278 ppm), CH4 (722 ppb), N2O (270 ppb) and CF4 (35 ppt).

b

Total includes 0.007 W m–2 to account for CFC-114, Halon-1211 and Halon-1301.

c

Total includes 0.009 W m–2 forcing (as in AR4) to account for CFC-13, CFC-114, CFC-115, Halon-1211 and Halon-1301.

d

Defined here as CFCs + HCFCs + CH3CCl3 + CCl4.

e

The value for the 1750 methane concentrations has been updated from AR4 in this report, thus the 2005 methane RF is slightly lower than reported in AR4.

f

Estimates for halocarbons given in the table may have changed from estimates reported in AR4 owing to updates in radiative efficiencies and concentrations.

678

Anthropogenic and Natural Radiative Forcing

for the decline in the CFCs, CH3CCl3 and CCl4 RFs (–0.013 W m–2 since2005). The total halocarbon RF is dominated by four gases, namely
CFC-12, trichlorofluoromethane (CFC-11), chlorodifluoromethane(HCFC-22) and trichlorofluoroeethane (CFC-113) in that order, which
account for about 85% of the total halocarbon RF (see Table 8.2) . The
indirect RF from the impacts of ODSs is discussed in Section 8.3.3.2.
8.3.2.4.1

Chlorofluorocarbons and hydrochlorofluorocarbons

The CFCs and HCFCs contribute approximately 11% of the WMGHG
RF. Although emissions have been drastically reduced for CFCs, theirlong lifetimes mean that reductions take substantial time to affect theirconcentrations. The RF from CFCs has declined since 2005 (mainly due
to a reduction in the concentrations of CFC-11 and CFC-12), whereas
the RF from HCFCs is still rising (mainly due to HCFC-22).
8.3.2.4.2

Hydrofluorocarbons

The RF of HFCs is 0.02 W m–2 and has close to doubled since AR4 (2005
concentrations). HFC-134a is the dominant contributor to RF of theHFCs, with an RF of 0.01 W m–2.
8.3.2.4.3

Perfluorocarbons and sulphur hexafluoride

These gases have lifetimes of thousands to tens of thousands of years(Table 8.A.1); therefore emissions essentially accumulate in the atmosphere on the time scales considered here. CF4 has a natural source anda 1750 concentration of 35 ppt (see Section 2.2.1.1.4). These gases
currently contribute 0.01 W m–2 of the total WMGHG RF.
8.3.2.4.4

New species

Nitrogen trifluoride (NF3) is used in the electronics industry and sulfuryl
fluoride (SO2F2) is used as a fumigant. Both have rapidly increasing
emissions and high GWPs, but currently contribute only around 0.0002
W m–2 and 0.0003 W m–2 to anthropogenic RF, respectively (Weiss etal., 2008; Andersen et al., 2009; Muhle et al., 2009; Arnold et al., 2013).

8.3.3

Ozone and Stratospheric Water Vapour

Unlike for the WMGHGs, the estimate of the tropospheric and stratospheric ozone concentration changes are almost entirely model based
for the full pre-industrial to present-day interval (though, especially forthe stratosphere, more robust observational evidence on changes isavailable for recent decades; see Section 2.2).
AR4 assessed the RF (for 1750–2005) from tropospheric ozone to be
0.35 W m–2 from multi-model studies with a high 95th percentile of
0.65 W m–2 to allow for the possibility of model overestimates of the
pre-industrial tropospheric ozone levels. The stratospheric ozone RF
was assessed from observational trends from 1979 to 1998 to be –0.05± 0.1 W m–2, with the 90% confidence range increased to reflect uncertainty in the trend prior to 1979 and since 1998. In AR4 the RF fromstratospheric water vapour generated by CH4 oxidation was assessed
to be +0.07 ± 0.05 W m–2 based on Hansen et al. (2005).

Chapter 8

Since AR4, there have been a few individual studies of troposphericor stratospheric ozone forcing (Shindell et al., 2006a, 2006c, 2013a;
Skeie et al., 2011a; Søvde et al., 2011), a multi-model study of stratospheric ozone RF in the 2010 WMO stratospheric ozone assessment(Forster et al., 2011b), and the ACCMIP multi-model study of tropospheric and tropospheric + stratospheric chemistry models (Conley etal., 2013; Stevenson et al., 2013). There is now greater understandingof how tropospheric ozone precursors can affect stratospheric ozone,
and how ODSs can affect tropospheric ozone (Shindell et al., 2013a).
We assess the total ozone RF to be +0.35 (0.15 to 0.55) W m–2. Thiscan be split according to altitude or by emitted species (Shindell etal., 2013a). We assess these contributions to be 0.40 (0.20 to 0.60) W
m–2 for ozone in the troposphere and –0.05 ± 0.10 W m–2 for ozone in
the stratosphere based on the studies presented in Table 8.3. Alternatively, the contributions to the total ozone forcing can be attributed
as 0.50 (0.30 to 0.70) W m–2 from ozone precursors and –0.15 (–0.3
to 0.0) W m–2 from the effect of ODSs. The value attributed to ODSs is
assessed to be slightly smaller in magnitude than in the two studies
quoted in Table 8.3 (Søvde et al., 2011; Shindell et al., 2013a) because
the models used for these had stratospheric ozone RFs with higher
magnitudes than the ACCMIP mean (Conley et al., 2013). Differences
between the ERFs and RFs for tropospheric and stratospheric ozone
are likely to be small compared to the uncertainties in the RFs (Shindell et al., 2013b), so the assessed values for the ERFs are the sameas those for the RFs.
The influence of climate change is typically included in ozone RF estimates as those are based on modelled concentration changes, but theavailable literature provides insufficient evidence for the sign and magnitude of the impact and we therefore refrain from giving an estimate
except to assess that it is very likely to be smaller than the overall
uncertainty in the total RF. Unlike the WMGHGs, there are significant
latitudinal variations in the RFs from changes in tropospheric and stratospheric ozone. The implications of inhomogeneous RFs are exploredin more detail in Section 8.6.
There has been one study since AR4 (Myhre et al., 2007) on the RF fromwater vapour formed from the stratospheric oxidation of CH4 (Section
8.3.3.3). This is consistent with the AR4 value and so has not led to
any change in the recommended value of 0.07 (0.02 to 0.12) W m–2
since AR4.

8.3.3.1 Tropospheric Ozone
Ozone is formed in the troposphere by photochemical reactions of natural and anthropogenic precursor species (Section 8.2.3.1). Changes inozone above the tropopause due to emissions of stratospheric ODSs
can also affect ozone in the troposphere either by transport acrossthe tropopause or modification of photolysis rates. Changes in climate
have also affected tropospheric ozone concentrations (medium evidence, low agreement) through changes in chemistry, natural emissions and transport from the stratosphere (Isaksen et al., 2009).
The most recent estimates of tropospheric ozone RF come from­multi-model studies under ACCMIP (Conley et al., 2013; Lamarque etal., 2013; Stevenson et al., 2013). The model ensemble reported only
1850–2000 RFs (0.34 W m–2) so the single-model results from Skeie et679

8Chapter 8

Because we have low confidence in the pre-industrial ozone observations, and these were extremely limited in spatial coverage, it is notpossible to calculate a purely observationally based ozone RF. However,modern observations can be used to assess the performance of thechemistry models. Bowman et al. (2013) used satellite retrievals fromthe TES instrument to constrain the RF from the ACCMIP models. This
reduced the inter-model uncertainty by 30%; however, we still maintain overall the (–50% to +50%) 5 to 95% confidence range for AR5.

non-methane volatile organic compounds (NMVOCs). These results
were calculated by reducing the precursor emissions individually from2000 to pre-industrial levels. The results were scaled by the total ozone
RFs attributed to ozone precursors (0.50 W m–2) to give the contributions to the full 1750–2010 RF. Because of the nonlinearity of thechemistry an alternative method of starting from pre-industrial conditions and increasing precursor emissions singly may give a different
result. Note that as well as inducing an ozone RF, these ozone precursor species can also strongly affect the concentrations of CH4 andaerosols, adding extra terms (both positive and negative) to their total
indirect forcings. The contributions to the 1750–2010 CH4 RF are again
based on Stevenson et al. (2013) and Shindell et al. (2009). The Stevenson et al. (2013) values are for 1850–2000 rather than 1750 to 2011
so for these we distribute the CH4 RF for 1750–1850 and 2000–2011(0.06 W m–2) by scaling the CH4 and CO contributions (assuming thesewere the most significant contributors over those time periods). This
gives contributions of 0.58 ± 0.08, –0.29 ± 0.18, 0.07 ± 0.02 and 0.02
± 0.02 W m–2 for changes from historical to present day emissions ofCH4 (inferred emissions), NOX, CO and VOCs respectively (uncertainties
are 5 to 95% confidence intervals). The difference between the total
CH4 RF attributed to ozone precursors here (0.38 W m–2) and the value
calculated from CH4 concentration changes in Table 8.2 (0.48 W m–2)
is due to nonlinearities in the CH4 chemistry because large single-stepchanges were used. To allow an easier comparison between the concentration-based and emission-based approaches in Section 8.5.1 thenonlinear term (+0.1 W m–2) is distributed between the four emitted
species according to their absolute magnitude so that they total 0.48
W m–2. The scaled results still lie within the uncertainty bounds of thevalues quoted above. The impact of climate change over the historicalperiod on CH4 oxidation is not accounted for in these calculations.

The time evolution of the tropospheric ozone forcing is shown in Figure8.7. There is a noticeable acceleration in the forcing after 1950 and a
deceleration in the 1990s reflecting the time evolution of anthropogenic precursor emissions. Observational evidence for trends in ozone
concentrations is discussed in Section 2.2.2.3.

Tropospheric ozone can also affect the natural uptake of CO2 bydecreasing plant productivity (see Sections 6.4.8.2 and 8.2.3.1) and
it is found that this indirect effect could have contributed to the total
CO2 RF (Section 8.3.2.1; Sitch et al., 2007), roughly doubling the overall RF attributed to ozone precursors. Although we assess there to be

It can be useful to calculate a normalized radiative forcing (NRF) which
is an RF per change in ozone column in W m–2 DU–1 or W mol–1. This is
only an approximation as the NRF is sensitive to the vertical profileof the ozone change and to the latitudinal profile to a smaller extent.
From Table 8.3 we assess the NRF to be 0.042 (0.037 to 0.047) W m–2
DU–1 (94 (83 to 105) W mol–1) similar to the value of 0.042 W m–2 DU–1
(94 W mol–1) in TAR (Ramaswamy et al., 2001).
A small number of studies have looked at attributing the ozone changes among the anthropogenically emitted species. Søvde et al. (2011)
report a tropospheric ozone RF of 0.38 W m–2, 0.44 W m–2 from ozone
precursors and –0.06 W m–2 from the impact of stratospheric ozone
depletion on the troposphere. Shindell et al. (2013a) also calculatethat ODSs are responsible for about –0.06 W m–2 of the tropospheric ozone RF, and ozone precursors for about 0.41 W m–2. Six of themodels in Stevenson et al. (2013) and Shindell et al. (2009) performedexperiments to attribute the ozone RF to the individual precursor emissions. An average of these seven model results leads to attributions of
0.24 ± 0.13 W m–2 due to CH4 emissions, 0.14 ± 0.09 W m–2 from NOX
emissions, 0.07 ± 0.03 W m–2 from CO, and 0.04 ± 0.03W m–2 from
680

Ozone Radiative Forcing ( W m-2 )

8al. (2011a) were used to expand the timespan to 1750–2010, adding
0.04 W m–2, and 0.02 W m–2 to account for the periods 1750–1850 and
2000–2010 respectively. The best estimate of tropospheric ozone RF
taking into account the ACCMIP models and the Søvde et al. (2011)
results (the Skeie et al. (2011a) and Shindell et al. (2013a) models areincluded in ACCMIP) is 0.40 (0.20 to 0.60) W m–2. The quantifiable
uncertainties come from the inter-model spread (–0.11 to 0.11 W m–2)
and the differences between radiative transfer models (–0.07 to 0.07
W m–2); all 5 to 95% confidence interval. Additional uncertainties arise
from the lack of knowledge of pre-industrial emissions and the representation of chemical and physical processes beyond those includedin the current models. The tropospheric ozone RF is sensitive to theassumed ‘pre-industrial’ levels. As described in Section 8.2.3.1, very
limited late 19th and early 20th century observations of surface ozone
concentrations are lower than the ACCMIP models for the same period;
however, we assess that those observations are very uncertain. Skeie
et al. (2011a) and Stevenson et al. (2013) increase their uncertainty
ranges to 30% for 1 standard deviation which is equivalent to (–50%
to +50%) for the 5 to 95% confidence range and we adopt this forAR5. The overall confidence in the tropospheric ozone RF is assessedas high.

Anthropogenic and Natural Radiative Forcing

0.6

Tropospheric
0.4

0.2

Total
Stratospheric

0.0
-0.2

1750

1800

1850

1900

1950

2000

Figure 8.7 | Time evolution of the radiative forcing from tropospheric and stratosphericozone from 1750 to 2010. Tropospheric ozone data are from Stevenson et al. (2013)
scaled to give 0.40 W m–2 at 2010. The stratospheric ozone RF follow the functional
shape of the Effective Equivalent Stratospheric Chlorine assuming a 3-year age of air(Daniel et al., 2010) scaled to give –0.05 W m–2 at 2010.

Anthropogenic and Natural Radiative Forcing

Chapter 8

Table 8.3 | Contributions of tropospheric and stratospheric ozone changes to radiative forcing (W m–2) from 1750 to 2011.

Troposphere
Longwave

Shortwave

Total

Stratosphere
Normalized
Radiative Forcing
m W m–2 DU–1

Longwave

Shortwave

Total

AR4 (Forster
et al. (2007)

0.35(0.25 to 0.65)–0.05(–0.15 to 0.05)

Shindell et al.
(2013a)f

0.33(0.31 to 0.35)–0.08(–0.10 to –0.06)–0.03a(–0.23 to +0.17)

WMO (Forster
et al., 2011b)+0.03b

Søvde et al. (2011)

0.45c

40

–0.12

0.38

39

–0.12

0.41
(0.21 to 0.61)

38

0.41
(0.21 to 0.61)

42(37 to 47)

0.40(0.20 to 0.60)

42(37 to 47)dSkeie et al. (2011a)
ACCMIPe80.33(0.24 to 0.42)

0.08(0.06 to 0.10)

AR5

–0.13
(–0.26 to 0)

0.11(0.03 to 0.19)–0.02(–0.09 to 0.05)–0.05(-0.15 to 0.05)

Notes:a

From multi-model results.

b

From Randel and Wu (2007) observation-based data set.

cUsing the REF chemistry, see Søvde et al. (2011).

dUsing the R2 chemistry.

e

The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) tropospheric ozone RFs are from Stevenson et al. (2013). The stratospheric ozone values are from
Conley et al. (2013) calculations for 1850–2005 disregarding the Modèle de Chimie Atmosphérique a Grande Echelle (MOCAGE) model which showed excessive ozone depletion.

f

Only the Goddard Institute for Space Studies (GISS)-E2-R results (including bias correction) from the Shindell et al. (2013a) study are shown here rather than the multi-model result
presented in that paper.

robust evidence of an effect, we make no assessment of the magnitude
because of lack of further corroborating studies.

8.3.3.2 Stratospheric Ozone
The decreases in stratospheric ozone due to anthropogenic emissionsof ODSs have a positive RF in the shortwave (increasing the flux into
the troposphere) and a negative RF in the longwave. This leaves aresidual forcing that is the difference of two larger terms. In the lower
stratosphere the longwave effect tends to be larger, whereas in theupper stratosphere the shortwave dominates. Thus whether stratospheric ozone depletion has contributed an overall net positive or
negative forcing depends on the vertical profile of the change (Forster
and Shine, 1997). WMO (2011) assessed the RF from 1979 to 2005
from observed ozone changes (Randel and Wu, 2007) and results from
16 models for the 1970s average to 2004. The observed and modelledmean ozone changes gave RF values of different signs (see Table 8.3).
Negative net RFs arise from models with ozone decline in the lowermost stratosphere, particularly at or near the tropopause.
The ACCMIP study also included some models with stratosphericchemistry (Conley et al., 2013). One model in that study stood out as
having excessive ozone depletion. Removing that model leaves a stratospheric ozone RF of –0.02 (–0.09 to 0.05) W m–2. These results are ingood agreement with the model studies from WMO (2011). Forster etal. (2007) in AR4 calculated a forcing of –0.05 W m–2 from observations over the period 1979–1998 and increased the uncertainty to 0.10
W m–2 to encompass changes between the pre-industrial period and2005. The RF from stratospheric ozone due to changes in emissionsof ozone precursors and ODSs is here assessed to be –0.05 (–0.15 to0.05) taking into account all the studies listed in Table 8.3. This is inagreement with AR4, although derived from different data. The timeline of stratospheric ozone forcing is shown in Figure 8.7, making theassumption that it follows the trajectory of the changes in EESC. It
reaches a minimum in the late 1990s and starts to recover after that.
The net global RF from ODSs taking into account the compensating
effects on ozone and their direct effects as WMGHGs is 0.18 (0.03 to0.33) W m–2. The patterns of RF for these two effects are different so
the small net global RF comprises areas of positive and negative RF.

8.3.3.3 Stratospheric Water Vapour
Stratospheric water vapour is dependent on the amount entering fromthe tropical troposphere and from direct injection by volcanic plumes(Joshi and Jones, 2009) and aircraft, and the in situ chemical production from the oxidation of CH4 and hydrogen. This contrasts withtropospheric water vapour which is almost entirely controlled by thebalance between evaporation and precipitation (see FAQ 8.1). We consider trends in the transport (for instance, due to the Brewer–Dobson
circulation or tropopause temperature changes) to be climate feedback
rather than a forcing so the anthropogenic RFs come from oxidation of
CH4 and hydrogen, and emissions from stratospheric aircraft.
Myhre et al. (2007) used observations of the vertical profile of CH4 todeduce a contribution from oxidation of anthropogenic CH4 of 0.083
681

Chapter 88

W m–2 which compares with the value of 0.07 W m–2 from calculations in a 2D model in Hansen et al. (2005). Both of these values are
consistent with AR4 which obtained the stratospheric water vapour
forcing by scaling the CH4 direct forcing by 15%. Thus the time evolution of this forcing is also obtained by scaling the CH4 forcing by 15%.
The best estimate and uncertainty range from AR4 of 0.07 (0.02 to0.12) W m–2 remain unchanged and the large uncertainty range is due
to large differences found in the intercomparison studies of radiativetransfer modelling for changes in stratospheric water vapour (see Section 8.3.1).
RF from the current aircraft fleet through stratospheric water vapour
emissions is very small. Wilcox et al. (2012) estimate a contribution
from civilian aircraft in 2005 of 0.0009 (0.0003 to 0.0013) W m–2
with high confidence in the upper limit. Water vapour emissions fromaircraft in the troposphere also contribute to contrails which are discussed in Section 8.3.4.5.

8.3.4

Aerosols and Cloud Effects

8.3.4.1 Introduction and Summary of AR4
In AR4 (Forster et al., 2007), RF estimates were provided for three aerosol effects. These were the RF of aerosol–radiation interaction (previously denoted as direct aerosol effect), RF of the aerosol–cloud interaction (previously denoted as the cloud albedo effect), and the impact
of BC on snow and ice surface albedo. See Chapter 7 and Figure 7.3 foran explanation of the change in terminology between AR4 and AR5.
The RF due to aerosol–radiation interaction is scattering and absorption of shortwave and longwave radiation by atmospheric aerosols.
Several different aerosol types from various sources are present in theatmosphere (see Section 8.2). Most of the aerosols primarily scattersolar radiation, but some components absorb solar radiation to various
extents with BC as the most absorbing component. RF of aerosols in
the troposphere is often calculated at the TOA because it is similar to
tropopause values (Forster et al., 2007). A best estimate RF of –0.5 ±0.4 W m–2 was given in AR4 for the change in the net aerosol–radiation interaction between 1750 and 2005 and a medium to low level ofscientific understanding (LOSU).
An increase in the hygroscopic aerosol abundance may enhance theconcentration of cloud condensation nuclei (CCN). This may increase
the cloud albedo and under the assumption of fixed cloud water content this effect was given a best estimate of –0.7 W m–2 (range from
–1.8 to –0.3) in AR4 and a low LOSU.
BC in the snow or ice can lead to a decrease of the surface albedo.
This leads to a positive RF. In AR4 this mechanism was given a best RF
estimate of 0.1 ± 0.1 W m–2 and a low LOSU.
Impacts on clouds from the ERF of aerosol–cloud interaction (including both effects previously denoted as cloud lifetime and cloud albedo
effect) and the ERF of aerosol–radiation interaction (including both
effects previously denoted as direct aerosol effect and semi-direct
effect) were not strictly in accordance with the RF concept, because
they involve tropospheric changes in variables other than the forcing
agent at least in the available model estimates, so no best RF e­ stimates
682

Anthropogenic and Natural Radiative Forcing

were provided in AR4 (see Section 8.1). However, the ERF of aerosol–
cloud and aerosol–radiation interactions were included in the discussion of total aerosol effect in Chapter 7 in AR4 (Denman et al., 2007).
The mechanisms influenced by anthropogenic aerosol including theaerosol cloud interactions are discussed in detail in this assessment in
Section 7.5 and summarized in the subsections that follow.

8.3.4.2 Radiation Forcing of the Aerosol–Radiation
Interaction by Component
Based on a combination of global aerosol models and observation-based methods, the best RF estimate of the aerosol–radiationinteraction in AR5 is –0.35 (–0.85 to +0.15) W m–2 (see Section 7.5).
This estimate is thus smaller in magnitude than in AR4, however; withlarger uncertainty range. Overall, the estimate compared to AR4 ismore robust because the agreement between estimates from models
and observation-based methods is much greater (see Section 7.5). The
larger range arises primarily from analysis by observation-based methods (see Section 7.5).
The main source of the model estimate is based on updated simulations in AeroCom (Myhre et al., 2013), which is an intercomparison
exercise of a large set of global aerosol models that includes extensive
evaluation against measurements. The assessment in Chapter 7 relies
to a large extent on this study for the separation in the various aerosol
components, except for BC where the assessment in Chapter 7 relies in
addition on Bond et al. (2013). The RF of aerosol–radiation interaction
is separated into seven components in this report; namely sulphate,
BC from fossil fuel and biofuel, OA from fossil fuel and biofuel, BC andOA combined from biomass burning (BB), nitrate, SOA and mineraldust. BC and OA from biomass burning are combined due to the joint
sources, whereas treated separately for fossil fuel and biofuel because
there is larger variability in the ratio of BC to OA in the fossil fueland biofuel emissions. This approach is consistent with TAR and AR4.
Table 8.4 compares the best estimates of RF due to aerosol–radiationinteraction for various components in this report with values in SAR,
TAR and AR4. In magnitude the sulphate and BC from use of fossil fueland biofuel dominate. It is important to note that the BB RF is small inmagnitude but consists of larger, offsetting terms in magnitude from
OA and BC (see Section 7.5.2). Changes in the estimates of RF due toaerosol–radiation interaction of the various components have beenrather modest compared to AR4, except for BC from fossil fuel andbiofuel (see Section 7.5). SOA is a new component compared to AR4.
Anthropogenic SOA precursors contribute only modestly to the anthropogenic change in SOA. The increase in SOA is mostly from biogenic
precursors and enhanced partitioning of SOA into existing particles
from anthropogenic sources and changes in the atmospheric oxidation(Carlton et al., 2010). This change in SOA is therefore of anthropogenic
origin, but natural emission of SOA precursors is important (Hoyle etal., 2011).
Note that the best estimate and the uncertainty for the total is notequal to the sum of the aerosol components because the total isestimated based on a combination of methods (models and observation-based methods), whereas the estimates for the components rely
mostly on model estimates.

Anthropogenic and Natural Radiative Forcing

Chapter 8

Table 8.4 | Global and annual mean RF (W m–2) due to aerosol–radiation interaction between 1750 and 2011 of seven aerosol components for AR5. Values and uncertainties
from SAR, TAR, AR4 and AR5 are provided when available. Note that for SAR, TAR and AR4 the end year is somewhat different than for AR5 with 1993, 1998 and 2005, respectively.

Global Mean Radiative Forcing (W m–2)
SAR

TAR

AR4

AR5

Sulphate aerosol–0.40 (–0.80 to –0.20)–0.40 (–0.80 to –0.20)–0.40 (–0.60 to –0.20)–0.40 (–0.60 to –0.20)

Black carbon aerosol fromfossil fuel and biofuel+0.10 (+0.03 to +0.30)+0.20 (+0.10 to +0.40)+0.20 (+0.05 to +0.35)+0.40 (+0.05 to +0.80)

Primary organic aerosol
from fossil fuel and biofuel

Not estimated–0.10 (–0.30 to –0.03)–0.05 (0.00 to –0.10)–0.09 (–0.16 to –0.03)–0.20 (–0.60 to –0.07)–0.20 (–0.60 to –0.07)+0.03(–0.09 to +0.15)–0.0 (–0.20 to +0.20)

Secondary organic aerosol

Not estimatedNot estimatedNot estimated–0.03 (–0.27 to +0.20)

NitrateNot estimatedNot estimated–0.10 (–0.20 to 0.00)–0.11 (–0.30 to –0.03)

Dust

Not estimated–0.60 to +0.40–0.10 (–0.30 to +0.10)–0.10 (–0.30 to +0.10)

Total

Not estimatedNot estimated–0.50 (–0.90 to –0.10)–0.35 (–0.85 to +0.15)

Biomass burning

The RF due to aerosol–radiation interaction during some time periods ismore uncertain than the current RF. Improvements in the observationsof aerosols have been substantial with availability of remote sensing
from the ground-based optical observational network AErosol RObotic
NETwork (AERONET) and the launch of the Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments (starting in 2000) as well as other satellite
data. This has contributed to constraining the current RF using aerosol
observations. The aerosol observations are very limited backward intime, although there is growing constraint coming from new ice and

0.80

Radiative Forcing (W m-2)

0.4
BC

0.2

BC on snow

0.0

OC

-0.2

Sulfate

1900

1950

SOA
NitrateTotal Aerosols

2000 -0.60

lake core records, and uncertainties in the historical emission of aerosols and their precursors used in the global aerosol modeling are larger
than for current conditions. Emissions of carbonaceous aerosols are
particularly uncertain in the 1800s due to a significant biofuel source inthis period, in contrast to the SO2 emissions which were very small until
the end of the 1800s. The uncertainty in the biomass burning emissions
also increases backward in time. Note that, for 1850, the biomass burning emissions from Lamarque et al. (2010) are quite different from theprevious estimates, but RF due to aerosol–radiation interaction is close
to zero for this component. Figure 8.8 shows an example of the time
evolution of the RF due to aerosol–radiation interaction as a total andseparated into six aerosol components. From 1950 to 1990 there wasa strengthening of the total RF due to aerosol–radiation interaction,
mainly due to a strong enhancement of the sulphate RF. After 1990 thechange has been small with even a weakening of the RF due to aerosol–radiation interaction, mainly due to a stronger BC RF as a result of
increased emissions in East and Southeast Asia.

8.3.4.3 Aerosol–Cloud Interactions

BioBurn

-0.4
1850

8

-0.81

Figure 8.8 | Time evolution of RF due to aerosol–radiation interaction and BC on
snow and ice. Multi-model results for 1850, 1930, 1980 and 2000 from ACCMIP for
aerosol–radiation interaction (Shindell et al., 2013c) and BC on snow and ice (Lee et al.,2013) are combined with higher temporal-resolution results from the Goddard Institutefor Space Studies (GISS)-E2 and Oslo-Chemical Transport Model 2 (OsloCTM2) models(aerosol–radiation interaction) and OsloCTM2 (BC on snow and ice). Uncertainty ranges(5 to 95%) for year 2010 are shown with vertical lines. Values next to the uncertainty
lines are for cases where uncertainties go beyond the scale. The total includes the RF
due to aerosol–radiation interaction for six aerosol components and RF due to BC on
snow and ice. All values have been scaled to the best estimates for 2011 given in Table
8.4. Note that time evolution for mineral dust is not included and the total RF due toaerosol–radiation interaction is estimated based on simulations of the six other aerosol
components.

The RF by aerosol effects on cloud albedo was previously referred toas the Twomey or cloud albedo effect (see Section 7.1). Although thisRF can be calculated, no estimate of this forcing is given because it
has heuristic value only and does not simply translate to the ERF due
to aerosol–cloud interaction. The total aerosol ERF, namely ERF due
to aerosol–radiation and aerosol–cloud interactions (excluding BC on
snow and ice) provided in Chapter 7 is estimated with a 5 to 95%
uncertainty between –1.9 and –0.1 W m–2 with a best estimate valueof –0.9 W m–2 (medium confidence). The likely range of this forcing
is between –1.5 and –0.4 W m–2. The estimate of ERF due to aerosol–radiation and aerosol-cloud interaction is lower (i.e., less negative)
than the corresponding AR4 RF estimate of –1.2 W m–2 because thelatter was based mainly on GCM studies that did not take secondary
processes (such as aerosol effects on mixed-phase and/or convective
clouds and effects on longwave radiation) into account. This new best
estimate of ERF due to aerosol–radiation and aerosol–cloud interaction is also consistent with the studies allowing cloud-scale processes and related responses and with the lower estimates of this forcing
inferred from satellite observations.

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Chapter 8

Anthropogenic and Natural Radiative Forcing

Frequently Asked Questions

FAQ 8.2 | Do Improvements in Air Quality Have an Effect on Climate Change?
Yes they do, but depending on which pollutant(s) they limit, they can either cool or warm the climate. For example,
whereas a reduction in sulphur dioxide (SO2) emissions leads to more warming, nitrogen oxide (NOx) emission
control has both a cooling (through reducing of tropospheric ozone) and a warming effect (due to its impact on
methane lifetime and aerosol production). Air pollution can also affect precipitation patterns.

8

Air quality is nominally a measure of airborne surface pollutants, such as ozone, carbon monoxide, NOx and aerosols(solid or liquid particulate matter). Exposure to such pollutants exacerbates respiratory and cardiovascular diseases,
harms plants and damages buildings. For these reasons, most major urban centres try to control discharges of airborne pollutants.
Unlike carbon dioxide (CO2) and other well-mixed greenhouse gases, tropospheric ozone and aerosols may last in
the atmosphere only for a few days to a few weeks, though indirect couplings within the Earth system can prolong
their impact. These pollutants are usually most potent near their area of emission or formation, where they canforce local or regional perturbations to climate, even if their globally averaged effect is small.
Air pollutants affect climate differently according to their physical and chemical characteristics. Pollution-generated
greenhouse gases will impact climate primarily through shortwave and longwave radiation, while aerosols can in
addition affect climate through cloud–aerosol interactions.
Controls on anthropogenic emissions of methane (FAQ 8.2, Figure 1) to lower surface ozone have been identified as‘win–win’ situations. Consequences of controlling other ozone precursors are not always as clear. NOx emission controls, for instance, might be expected to have a cooling effect as they reduce tropospheric ozone, but their impact
on CH4 lifetime and aerosol formation is more likely instead to cause overall warming.
Satellite observations have identified increasing atmospheric concentrations of SO2 (the primary precursor to scattering sulphate aerosols) from coal-burning power plants over eastern Asia during the last few decades. The mostrecent power plants use scrubbers to reduce such emissions (albeit not the concurrent CO2 emissions and associatedlong-term climate warming). This improves air quality, but also reduces the cooling effect of sulphate aerosols andtherefore exacerbates warming. Aerosol cooling occurs through aerosol–radiation and aerosol–cloud interactions
and is estimated at –0.9 W m–2 (all aerosols combined, Section 8.3.4.3) since pre-industrial, having grown especiallyduring the second half of the 20th century when anthropogenic emissions rose sharply. (continued on next page)
Ozone pollution
controls

MethaneCarbonmonoxide

NitrogenoxidesCooling

Particulatematter controls

VOCs

AmmoniaBlack
carbonOrganic
carbonSulfurdioxideWarming

FAQ 8.2, Figure 1 | Schematic diagram of the impact of pollution controls on specific emissions and climate impact. Solid black line indicates known impact; dashedline indicates uncertain impact.

684

Anthropogenic and Natural Radiative Forcing

Chapter 8

FAQ 8.2 (continued)

Black carbon or soot, on the other hand, absorbs heat in the atmosphere (leading to a 0.4 W m–2 radiative forcing
from anthropogenic fossil and biofuel emissions) and, when deposited on snow, reduces its albedo, or ability toreflect sunlight. Reductions of black carbon emissions can therefore have a cooling effect, but the additional interaction of black carbon with clouds is uncertain and could lead to some counteracting warming.

8

Air quality controls might also target a specific anthropogenic activity sector, such as transportation or energy production. In that case, co-emitted species within the targeted sector lead to a complex mix of chemistry and climate
perturbations. For example, smoke from biofuel combustion contains a mixture of both absorbing and scattering
particles as well as ozone precursors, for which the combined climate impact can be difficult to ascertain.
Thus, surface air quality controls will have some consequences on climate. Some couplings between the targetedemissions and climate are still poorly understood or identified, including the effects of air pollutants on precipitation patterns, making it difficult to fully quantify these consequences. There is an important twist, too, in thepotential effect of climate change on air quality. In particular, an observed correlation between surface ozone
and temperature in polluted regions indicates that higher temperatures from climate change alone could worsen
summertime pollution, suggesting a ‘climate penalty’. This penalty implies stricter surface ozone controls will be
required to achieve a specific target. In addition, projected changes in the frequency and duration of stagnation
events could impact air quality conditions. These features will be regionally variable and difficult to assess, butbetter understanding, quantification and modelling of these processes will clarify the overall interaction between
air pollutants and climate.

One reason an expert judgment estimate of ERF due to aerosol–radiation and aerosol–cloud interaction is provided rather than ERF due
to aerosol–cloud interaction specifically is that the individual contributions are very difficult to disentangle. These contributions are theresponse of processes that are the outputs from a system that is constantly readjusting to multiple nonlinear forcings. Assumptions of independence and linearity are required to deduce ERF due to aerosol–radiation interaction and ERF due to aerosol–cloud interaction (although
there is no a priori reason why the individual ERFs should be simply
additive). Under these assumptions, ERF due to aerosol–cloud interaction is deduced as the difference between ERF due to aerosol–radiation and aerosol–cloud interaction and ERF due to aerosol–radiationinteraction alone. This yields an ERF due to aerosol–cloud interaction
estimate of –0.45 W m–2 which is much smaller in magnitude than the–1.4 W m–2 median forcing value of the models summarized in Figure
7.19 and is also smaller in magnitude than the AR4 estimates of –0.7
W m–2 for RF due to aerosol–cloud interaction.

8.3.4.4 Black Carbon Deposition in Snow and Ice
Because absorption by ice is very weak at visible and ultraviolet (UV)
wavelengths, BC in snow makes the snow darker and increases absorption. This is not enough darkening to be seen by eye, but it is enoughto be important for climate (Warren and Wiscombe, 1980; Clarke andNoone, 1985). Several studies since AR4 have re-examined this issue
and find that the RF may be weaker than the estimates of Hansen
and Nazarenko (2004) in AR4 (Flanner et al., 2007; Koch et al., 2009a;
Rypdal et al., 2009; Lee et al., 2013). The anthropogenic BC on snow/
ice is assessed to have a positive global and annual mean RF of +0.04
W m−2, with a 0.02–0.09 W m−2 5 to 95% uncertainty range (see fur-ther description in Section 7.5.2.3). This RF has a two to four times
larger global mean surface temperature change per unit forcing thana change in CO2.
In Figure 8.8, the time evolution of global mean RF due to BC on snow
and ice is shown based on multi-model simulations in ACCMIP (Lee etal., 2013) for 1850, 1930, 1980 and 2000. The results show a maximum
in the RF in 1980 with a small increase since 1850 and a 20% lower
RF in 2000 compared to 1980. Those results are supported by observations. The BC concentration in the Arctic atmosphere is observed to
be declining since 1990, at least in the Western Hemisphere portion(Sharma et al., 2004), which should lead to less deposition of BC on
the snow surface. Surveys across Arctic during 1998 and 2005 to 2009
showed that the BC content of Arctic snow appears to be lower thanin 1984 (Doherty et al., 2010) and found BC concentrations in Canada,
Alaska and the Arctic Ocean (e.g., Hegg et al., 2009), about a factor of
2 lower than measured in the 1980s (e.g., Clarke and Noone, 1985).
Large-area field campaigns (Huang et al., 2011; Ye et al., 2012) foundthat the BC content of snow in northeast China is comparable to values
found in Europe. The steep drop off in BC content of snow with latitude
in northeast China may indicate that there is not much BC in the Arctic
coming from China (Huang et al., 2011; Ye et al., 2012; Wang et al.,2013). The change in the spatial pattern of emission of BC is a maincause for the difference in the temporal development of RF due to BC
on snow and ice compared to the BC from RF due to aerosol–radiationinteraction over the last decades.

685

Chapter 8

8.3.4.5 Contrails and Contrail-Induced Cirrus

8

AR4 assessed the RF of contrails (persistent linear contrails) as +0.01(–0.007 to +0.02) W m–2 and provided no estimate for contrail induced
cirrus. In AR5, Chapter 7 gives a best estimate of RF due to contrails of+0.01 (+0.005 to +0.03) W m–2 and an ERF estimate of the combined
contrails and contrail-induced cirrus of +0.05 (+0.02 to +0.15) W
m–2. Since AR4, the evidence for contrail-induced cirrus has increased
because of observational studies (for further details see Section 7.2.7).

8.3.5

Land Surface Changes

8.3.5.1 Introduction
Anthropogenic land cover change has a direct impact on the Earth radiation budget through a change in the surface albedo. It also impacts
the climate through modifications in the surface roughness, latent heat
flux and river runoff. In addition, human activity may change the water
cycle through irrigation and power plant cooling, and also generate
direct input of heat to the atmosphere by consuming energy. Land usechange, and in particular deforestation, also has significant impacts on
WMGHG concentration, which are discussed in Section 6.3.2.2. Potential geo-engineering techniques that aim at increasing the surface
albedo are discussed in Section 7.7.2.3.
AR4 referenced a large number of RF estimates resulting from a change
in land cover albedo. It discussed the uncertainties due to the reconstruction of historical vegetation, the characterization of present-day
vegetation and the surface radiation processes. On this basis, AR4 gavea best estimate of RF relative to 1750 due to land use related surface
albedo at –0.2 ± 0.2 W m–2 with a level of scientific understanding at
medium-low.

8.3.5.2 Land Cover Changes
Hurtt et al. (2006) estimates that 42 to 68% of the global land surface was impacted by land use activities (crop, pasture, wood harvest)
during the 1700–2000 period. Until the mid-20th century most land
use change took place over the temperate regions of the NH. Since
then, reforestation is observed in Western Europe, North America andChina as a result of land abandonment and afforestation efforts, while
deforestation is concentrated in the tropics. After a rapid increase of
the rate of deforestation during the 1980s and 1990s, satellite data
indicate a slowdown in the past decade (FAO, 2012).
Since AR4, Pongratz et al. (2008) and Kaplan et al. (2011) extended
existing reconstructions on land use back in time to the past millennium, accounting for the progress of agriculture technique and historicalevents such as the black death or war invasions. As agriculture wasalready widespread over Europe and South Asia by 1750, the RF, which
is defined with respect to this date, is weaker than the radiative fluxchange from the state of natural vegetation cover (see Figure 8.9).
Deforestation in Europe and Asia during the last millennium led to asignificant regional negative forcing. Betts et al. (2007) and Goosse
et al. (2006) argue that it probably contributed to the ‘Little Ice Age’,together with natural solar and volcanic activity components, before
the increase in GHG concentration led to temperatures similar to those686

Anthropogenic and Natural Radiative Forcing

experienced in the early part of the second millennium. There is still
significant uncertainty in the anthropogenic land cover change, and inparticular its time evolution (Gaillard et al., 2010).

8.3.5.3 Surface Albedo and Radiative Forcing
Surface albedo is the ratio between reflected and incident solar flux
at the surface. It varies with the surface cover. Most forests are darker(i.e., lower albedo) than grasses and croplands, which are darker
than barren land and desert. As a consequence, deforestation tends
to increase the Earth albedo (negative RF) while cultivation of somebright surfaces may have the opposite effect. Deforestation also leads
to a large increase in surface albedo in case of snow cover as low vegetation accumulates continuous snow cover more readily in early winterallowing it to persist longer in spring. This causes average winteralbedo in deforested areas to be generally much higher than that of atree-covered landscape (Bernier et al., 2011).
The pre-industrial impact of the Earth albedo increase due to land usechange, including the reduced snow masking by tall vegetation, is estimated to be on the order of –0.05 W m–2 (Pongratz et al., 2009). Since
then, the increase in world population and agriculture development
led to additional forcing. Based on reconstruction of land use since thebeginning of the Industrial Era, Betts et al. (2007) and Pongratz et al.
(2009) computed spatially and temporally distributed estimates of theland use RF. They estimate that the shortwave flux change induced by
the albedo variation, from fully natural vegetation state to 1992, is on
the order of –0.2 W m–2 (range –0.24 to –0.21W m–2). The RF, definedwith respect to 1750, is in the range –0.17 to –0.18 W m–2. A slightly
stronger value (–0.22 W m–2) was found by Davin et al. (2007) for theperiod 1860–1992.
In recent years, the availability of global scale MODIS data (Schaaf etal., 2002) has improved surface albedo estimates (Rechid et al., 2009).
These data have been used by Myhre et al (2005a) and Kvalevag et al.
(2010). They argue that the observed albedo difference between natural vegetation and croplands is less than usually assumed in climate
simulations, so that the RF due to land use change is weaker than inestimates that do not use the satellite data. On the other hand, Nair etal. (2007) show observational evidence of an underestimate of the surface albedo change in land use analysis in southwest Australia. Overall,there is still a significant range of RF estimates for the albedo component of land use forcing. This is mostly due to the range of albedo
change as a result of land use change, as shown in an inter-comparisonof seven atmosphere–land models (de Noblet-Ducoudre et al., 2012).
Deforestation has a direct impact on the atmospheric CO2 concentration and therefore contributes to the WMGHG RF as quantified in
Section 8.3.2. Conversely, afforestation is a climate mitigation strategy to limit the CO2 concentration increase. Several authors have compared the radiative impact of deforestation/afforestation that resultsfrom the albedo change with the greenhouse effect of CO2 released/sequestered. Pongratz et al. (2010) shows that the historic land usechange has had a warming impact (i.e., greenhouse effect dominates)
at the global scale and over most regions with the exception of Europe
and India. Bala et al. (2007) results show latitudinal contrast where
the greenhouse effect dominates for low-latitude deforestation while

Anthropogenic and Natural Radiative Forcing

Chapter 8

pears within one to a few years (Jin et al., 2012). Myhre et al. (2005b)­estimates a global albedo-related radiative effect due to African firesof 0.015 W m–2.
Over semi-arid areas, the development of agriculture favours the generation of dust. Mulitza et al. (2010) demonstrates a very large increaseof dust emission and deposition in the Sahel concomitant with thedevelopment of agriculture in this area. This, together with the analysis of dust sources (Ginoux et al., 2010), suggests that a significant
fraction of the dust that is transported over the Atlantic has an anthropogenic origin and impacts the Earth albedo. There is no full estimateof the resulting RF, however. The dust RF estimate in Section 8.3.4.2
includes both land use contributions and change in wind-driven emissions. Both dust and biomass burning aerosol may impact the Earthsurface albedo as these particles can be deposed on snow, which has
a large impact on its absorption, in particular for soot. This is discussed
in Section 8.3.4.4.

1750

1900

Urban areas have an albedo that is 0.01 to 0.02 smaller than adjacentcroplands (Jin et al., 2005). There is the potential for a strong increase
through white roof coating with the objective of mitigating the heatisland effect (Oleson et al., 2010). Although the global scale impact issmall, local effects can be very large, as shown by Campra et al. (2008)
that reports a regional (260 km2) 0.09 increase in albedo and –20 W
m–2 RF as a consequence of greenhouse horticulture development.
0.0

0.0

-4.5 -3-1 0 1(W m-2)

3 4.5

1750

-0.1
-0.2
1400

1600

1800

Rad. forc. (W m-2)

SW flux change(W m-2)

1992
-0.1
2000

Figure 8.9 | Change in top of the atmosphere (TOA) shortwave (SW) flux (W m–2)
following the change in albedo as a result of anthropogenic Land Use Change for three
periods (1750, 1900 and 1992 from top to bottom). By definition, the RF is with respect
to 1750, but some anthropogenic changes had already occurred in 1750. The lower
right inset shows the globally averaged impact of the surface albedo change to the TOA
SW flux (left scale) as well as the corresponding RF (right scale) after normalization to
the 1750 value. Based on simulations by Pongratz et al. (2009).

the combined effect of albedo and evapotranspiration impact does athigh latitude. These results are also supported by Bathiany et al. (2010).
Similarly, Lohila et al. (2010) shows that the afforestation of boreal
peatlands results in a balanced RF between the albedo and greenhouse effect. Overall, because of the opposite impacts, the potential ofafforestation to mitigate climate change is limited (Arora and Montenegro, 2011) while it may have undesired impacts on the atmosphericcirculation, shifting precipitation patterns (Swann et al., 2012).

8.3.5.4 Other Impacts of Land Cover Change on the
Earth’s Albedo
Burn scars resulting from agriculture practices, uncontrolled fires ordeforestation (Bowman et al., 2009) have a lower albedo than unperturbed vegetation (Jin and Roy, 2005). On the other hand, at high latitude, burnt areas are more easily covered by snow, which may resultin an overall increase of the surface albedo. Surface blackening of natural vegetation due to fire is relatively short lived and typically disap-

8.3.5.5 Impacts of Surface Change on Climate
Davin et al. (2007) argues that the climate sensitivity to land use forcing is lower than that for other forcings, due to its spatial distribution
but also the role of non-radiative processes. Indeed, in addition to theimpact on the surface albedo, land use change also modifies the evaporation and surface roughness, with counterbalancing consequenceson the lower atmosphere temperature. There is increasing evidence
that the impact of land use on evapotranspiration—a non-RF on climate—is comparable to, but of opposite sign than, the albedo effect,
so that RF is not as useful a metric as it is for gases and aerosols. For
instance, Findell et al. (2007) climate simulations show a negligible
impact of land use change on the global mean temperature, although
there are some significant regional changes.
Numerical climate experiments demonstrate that the impact of land use
on climate is much more complex than just the RF. This is due in partto the very heterogeneous nature of land use change (Barnes and Roy,
2008), but mostly due to the impact on the hydrological cycle throughevapotranspiration, root depth and cloudiness (van der Molen et al.,2011). As a consequence, the forcing on climate is not purely radiative
and the net impact on the surface temperature may be either positiveor negative depending on the latitude (Bala et al., 2007). Davin and deNoblet-Ducoudre (2010) analyses the impact on climate of large-scale
deforestation; the albedo cooling effect dominates for high latitude
whereas reduced evapotranspiration dominates in the tropics. This latitudinal trend is confirmed by observations of the temperature difference between open land and nearby forested land (Lee et al., 2011).
Irrigated areas have continuously increased during the 20th century
although a slowdown has been observed in recent decades (­Bonfils
687

8

Chapter 8

and Lobell, 2007). There is clear evidence that irrigation leads to local
cooling of several degrees (Kueppers et al., 2007). Irrigation also
affects cloudiness and precipitation (Puma and Cook, 2010). In the
United States, DeAngelis et al. (2010) found that irrigation in the Great
Plains in the summer produced enhanced precipitation in the Midwest
1000 km to the northeast.

8

8.3.5.6 Conclusions
There is still a rather wide range of estimates of the albedo change
due to anthropogenic land use change, and its RF. Although mostpublished studies provide an estimate close to –0.2 W m–2, there isconvincing evidence that it may be somewhat weaker as the albedo
difference between natural and anthropogenic land cover may havebeen overestimated. In addition, non-radiative impact of land use havea similar magnitude, and may be of opposite sign, as the albedo effect(though these are not part of RF). A comparison of the impact of land
use change according to seven climate models showed a wide range ofresults (Pitman et al., 2009), partly due to difference in the implementation of land cover change, but mostly due to different assumptions
on ecosystem albedo, plant phenology and evapotranspiration. Thereis no agreement on the sign of the temperature change induced byanthropogenic land use change. It is very likely that land use change
led to an increase of the Earth albedo with a RF of –0.15 ± 0.10 W m–2,
but a net cooling of the surface—accounting for processes that are notlimited to the albedo—is about as likely as not.

8.4

Natural Radiative Forcing Changes:
Solar and Volcanic

Several natural drivers of climate change operate on multiple timescales. Solar variability takes place at many time scales that include
centennial and millennial scales (Helama et al., 2010), as the radiantenergy output of the Sun changes. Also, variations in the astronomicalalignment of the Sun and the Earth (Milankovitch cycles) induce cyclical changes in RF, but this is substantial only at millennial and longer
time scales (see Section 5.2.1.1). Volcanic forcing is highly episodic,
but can have dramatic, rapid impacts on climate. No major asteroid
impacts occurred during the reference period (1750–2012) and thus
this effect is not considered here. This section discusses solar andvolcanic forcings, the two dominant natural contributors of climatechange since the pre-industrial time.

8.4.1

Solar Irradiance

In earlier IPCC reports the forcing was estimated as the instantaneous
RF at TOA. However, due to wavelength-albedo dependence, solar radiation-wavelength dependence and absorption within the stratosphereand the resulting stratospheric adjustment, the RF is reduced to about
78% of the TOA instantaneous RF (Gray et al., 2009). There is low confidence in the exact value of this number, which can be model and timescale dependent (Gregory et al., 2004; Hansen et al., 2005). AR4 gives
an 11-year running mean instantaneous TOA RF between 1750 andthe present of 0.12 W m–2 with a range of estimates of 0.06 to 0.30 W
m–2, equivalent to a RF of 0.09 W m–2 with a range of 0.05 to 0.23 W
m–2. For a consistent treatment of all forcing agents, hereafter we use
688

Anthropogenic and Natural Radiative Forcing

RF while numbers quoted from AR4 will be provided both as RF andinstantaneous RF at TOA.

8.4.1.1 Satellite Measurements of Total Solar IrradianceTotal solar irradiance (TSI) measured by the Total Irradiance Monitor(TIM) on the spaceborne Solar Radiation and Climate Experiment(SORCE) is 1360.8 ± 0.5 W m–2 during 2008 (Kopp and Lean, 2011)
which is ~4.5 W m–2 lower than the Physikalisch-Meteorologisches
Observatorium Davos (PMOD) TSI composite during 2008 (Frohlich,2009).The difference is probably due to instrumental biases in measurements prior to TIM. Measurements with the PREcision MOnitor
Sensor (PREMOS) instrument support the TIM absolute values (Koppand Lean, 2011). The TIM calibration is also better linked to national
standards which provides further support that it is the most accurate(see Supplementary Material Section 8.SM.6). Given the lower TIM TSI
values relative to currently used standards, most general circulation
models are calibrated to incorrectly high values. However, the few
tenths of a percent bias in the absolute TSI value has minimal consequences for climate simulations because the larger uncertainties incloud properties have a greater effect on the radiative balance. As the
maximum-to-minimum TSI relative change is well-constrained fromobservations, and historical variations are calculated as changes relative to modern values, a revision of the absolute value of TSI affects RF
by the same fraction as it affects TSI. The downward revision of TIM TSIwith respect to PMOD, being 0.3%, thus has a negligible impact on RF,
which is given with a relative uncertainty of several tenths of a percent.
Since 1978, several independent space-based instruments have directly measured the TSI. Three main composite series were constructed,
referred to as the Active Cavity Radiometer Irradiance Monitor (ACRIM)(Willson and Mordvinov, 2003), the Royal Meteorological Institute ofBelgium (RMIB) (Dewitte et al., 2004) and the PMOD (Frohlich, 2006)
series. There are two major differences between ACRIM and PMOD.
The first is the rapid drift in calibration between PMOD and ACRIM
before 1981. This arises because both composites employ the Hickey–
Frieden (HF) radiometer data for this interval, while a re-evaluation of
the early HF degradation has been implemented by PMOD but not by
ACRIM. The second one, involving also RMIB, is the bridging of the gap
between the end of ACRIM I (mid-1989) and the beginning of ACRIM
II (late 1991) observations, as it is possible that a change in HF data
occurred during this gap. This possibility is neglected in ACRIM andthus its TSI increases by more than 0.5 W m–2 during solar cycle (SC)22. These differences lead to different long-term TSI trends in the threecomposites (see Figure 8.10): ACRIM rises until 1996 and subsequently
declines, RMIB has an upward trend through 2008 and PMOD shows adecline since 1986 which unlike the other two composites, follows thesolar-cycle-averaged sunspot number (Lockwood, 2010). Moreover, the
ACRIM trend implies that the TSI on time scales longer than the SC ispositively correlated with the cosmic ray variation indicating a decline
in TSI throughout most of the 20th century (the opposite to most TSI
reconstructions produced to date; see Section 8.4.1.2). Furthermore,
extrapolating the ACRIM TSI long-term drift would imply a brighter
Sun in the Maunder minimum (MM) than now, again opposite to most
TSI reconstructions (Lockwood and Frohlich, 2008). Finally, analysis
of instrument degradation and pointing issues (Lee et al., 1995) andindependent modeling based on solar magnetograms (Wenzler et al.,2009; Ball et al., 2012), confirm the need for correction of HF data, andwe conclude that PMOD is more accurate than the other composites.
TSI variations of approximately 0.1% were observed between the
maximum and minimum of the 11-year SC in the three composites
mentioned above (Kopp and Lean, 2011). This variation is mainly due
to an interplay between relatively dark sunspots, bright faculae andbright network elements (Foukal and Lean, 1988; see Section 5.2.1.2).
A declining trend since 1986 in PMOD solar minima is evidenced in
Figure 8.10. Considering the PMOD solar minima values of 1986 and2008, the RF is –0.04 W m–2. Our assessment of the uncertainty rangeof changes in TSI between 1986 and 2008 is –0.08 to 0.0 W m–2 andthus very likely negative, and includes the uncertainty in the PMOD
data (Frohlich, 2009; see Supplementary Material Section 8.SM.6) but
is extended to also take into account the uncertainty of combining thesatellite data.
For incorporation of TIM data with the previous and overlapping data,
in Figure 8.10 we have standardized the composite time series to the
TIM series (over 2003–2012, the procedure is explained in Supplementary Material Section 8.SM.6. Moreover as we consider annual averages, ACRIM and PMOD start at 1979 because for 1978 both composites
have only two months of data.

on physical modeling of the evolution of solar surface magnetic flux,and its relationship with sunspot group number (before 1974) andsunspot umbra and penumbra and faculae afterwards. This provides
a more detailed reconstruction than other models (see the time seriesin Supplementary Material Table 8.SM.3). The best estimate from ourassessment of the most reliable TSI reconstruction gives a 7-year running mean RF between the minima of 1745 and 2008 of 0.05 W m–2.
Our assessment of the range of RF from TSI changes is 0.0 to 0.10
W m–2 which covers several updated reconstructions using the same
7-year running mean past-to-present minima years (Wang et al., 2005;
Steinhilber et al., 2009; Delaygue and Bard, 2011), see Supplementary Material Table 8.SM.4. All reconstructions rely on indirect proxies
that inherently do not give consistent results. There are relatively large
discrepancies among the models (see Figure 8.11).With these considerations, we adopt this value and range for AR5. This RF is almost halfof that in AR4, in part because the AR4 estimate was based on theprevious solar cycle minimum while the AR5 estimate includes the dropof TSI in 2008 compared to the previous two SC minima (see 8.4.1).
Concerning the uncertainty range, in AR4 the upper limit corresponded
to the reconstruction of Lean (2000), based on the reduced brightnessof non-cycling Sun-like stars assumed typical of a Maunder minimum(MM) state. The use of such stellar analogues was based on the workof Baliunas and Jastrow (1990), but more recent surveys have not
reproduced their results and suggest that the selection of the original
set was flawed (Hall and Lockwood, 2004; Wright, 2004); the lower
limit from 1750 to present in AR4 was due to the assumed increasein the amplitude of the 11-year cycle only. Thus the RF and uncertainty range have been obtained in a different way in AR5 compared toAR4. Maxima to maxima RF give a higher estimate than minima tominima RF, but the latter is more relevant for changes in solar activity.
Given the medium agreement and medium evidence, this RF value has
a medium confidence level (although confidence is higher for the last
three decades). Figure 8.11 shows several TSI reconstructions modelledusing sunspot group numbers (Wang et al., 2005; Krivova et al., 2010;)

Figure 8.10 | Annual average composites of measured total solar irradiance: The
Active Cavity Radiometer Irradiance Monitor (ACRIM) (Willson and Mordvinov, 2003),
the Physikalisch-Meteorologisches Observatorium Davos (PMOD) (Frohlich, 2006) and
the Royal Meteorological Institute of Belgium (RMIB) (Dewitte et al., 2004).These composites are standardized to the annual average (2003–2012) Total Irradiance Monitor(TIM) (Kopp and Lean, 2011) measurements that are also shown.

Chapter 8(()

Anthropogenic and Natural Radiative Forcing

8.4.1.2 Total Solar Irradiance Variations Since Preindustrial TimeThe year 1750, which is used as the preindustrial reference for estimating RF, corresponds to a maximum of the 11-year SC. Trend analysis areusually performed over the minima of the solar cycles that are morestable. For such trend estimates, it is then better to use the closest
SC minimum, which is in 1745. To avoid trends caused by comparing different portions of the solar cycle, we analyze TSI changes using
multi-year running means. For the best estimate we use a recent TSI
reconstruction by Krivova et al. (2010) between 1745 and 1973 andfrom 1974 to 2012 by Ball et al. (2012). The reconstruction is based

Figure 8.11 | Reconstructions of total solar irradiance since1745; annual resolution
series from Wang et al. (2005) with and without an independent change in the background level of irradiance, Krivova et al. (2010) combined with Ball et al. (2012) and5-year time resolution series from Steinhilber et al. (2009) and Delaygue and Bard
(2011). The series are standardized to the Physikalisch-Meteorologisches Observatorium Davos (PMOD) measurements of solar cycle 23 (1996–2008) (PMOD is alreadystandardized to Total Irradiance Monitor).

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Ball et al., 2012) and sunspot umbra and penumbra and faculae (Ballet al., 2012), or cosmogenic isotopes (Steinhilber et al., 2009; Delaygue
and Bard, 2011). These reconstructions are standardized to PMOD SC
23 (1996–2008) (see also Supplementary Material Section 8.SM.6).

8

For the MM-to-present AR4 gives a TOA instantaneous RF range of
0.1 to 0.28 W m–2, equivalent to 0.08 to 0.22 W m–2 with the RF definition used here. The reconstructions in Schmidt et al. (2011) indicatea MM-to-present RF range of 0.08 to 0.18 W m–2, which is within
the AR4 range although narrower. As discussed above, the estimates
based on irradiance changes in Sun-like stars are not included in thisrange because the methodology has been shown to be flawed. A moredetailed explanation of this is found in Supplementary Material Section
8.SM.6. For details about TSI reconstructions on millennia time scales
see Section 5.2.1.2.

8.4.1.3 Attempts to Estimate Future Centennial Trends ofTotal Solar IrradianceCosmogenic isotope and sunspot data (Rigozo et al., 2001; Solanki andKrivova, 2004; Abreu et al., 2008) reveal that currently the Sun is in a
grand activity maximum that began about 1920 (20th century grandmaximum). However, SC 23 showed an activity decline not previously seen in the satellite era (McComas et al., 2008; Smith and Balogh,
2008; Russell et al., 2010). Most current estimations suggest that theforthcoming solar cycles will have lower TSI than those for the past 30
years (Abreu et al., 2008; Lockwood et al., 2009; Rigozo et al., 2010;
Russell et al., 2010). Also there are indications that the mean magnetic
field in sunspots may be diminishing on decadal level. A linear expansion of the current trend may indicate that of the order of half thesunspot activity may disappear by about 2015 (Penn and Livingston,2006). These studies only suggest that the Sun may have left the 20th
century grand maximum and not that it is entering another grand minimum. But other works propose a grand minimum during the 21st century, estimating an RF within a range of -0.16 to 0.12 W m–2 between
this future minimum and the present-day TSI (Jones et al., 2012). However, much more evidence is needed and at present there is very low
confidence concerning future solar forcing estimates.
Nevertheless, even if there is such decrease in the solar activity, there
is a high confidence that the TSI RF variations will be much smaller
in magnitude than the projected increased forcing due to GHG (seeSection 12.3.1).

8.4.1.4 Variations in Spectral Irradiance8.4.1.4.1

Impacts of ultraviolet variations on the stratosphereOzone is the main gas involved in stratospheric radiative heating.
Ozone production rate variations are largely due to solar UV irradiance changes (HAIGH, 1994), with observations showing statistically significant variations in the upper stratosphere of 2 to 4% along
the SC (Soukharev and Hood, 2006). UV variations may also produce
transport-induced ozone changes due to indirect effects on circulation(Shindell et al., 2006b). In addition, statistically significant evidence foran 11-year variation in stratospheric temperature and zonal winds is
attributed to UV radiation (Frame and Gray, 2010). The direct UV heat690

ing of the background ozone is dominant and over twice as large as
the ozone heating in the upper stratosphere and above, while indirect
solar and terrestrial radiation through the SC-induced ozone change
is dominant below about 5 hPa (Shibata and Kodera, 2005). The RF
due to solar-induced ozone changes is a small fraction of the solar RF
discussed in Section 8.4.1.1 (Gray et al., 2009).
8.4.1.4.2

Measurements of spectral irradiance

Solar spectral irradiance (SSI) variations in the far (120 to 200 nm)
and middle (200 to 300 nm) ultraviolet (UV) are the primary driver forheating, composition, and dynamic changes of the stratosphere, andalthough these wavelengths compose a small portion of the incoming
radiation they show large relative variations between the maximum
and minimum of the SC compared to the corresponding TSI changes. As UV heating of the stratosphere over a SC has the potential toinfluence the troposphere indirectly, through dynamic coupling, andtherefore climate (Haigh, 1996; Gray et al., 2010), the UV may have amore significant impact on climate than changes in TSI alone would
suggest. Although this indicates that metrics based only on TSI are notappropriate, UV measurements present several controversial issues
and modelling is not yet robust.
Multiple space-based measurements made in the past 30 years indicated that UV variations account for about 30% of the SC TSI variations, while about 70% were produced within the visible and infrared(Rottman, 2006). However, current models and data provide the rangeof 30 to 90% for the contribution of the UV variability below 400 nm to
TSI changes (Ermolli et al., 2013), with a more probable value of ~60%
(Morrill et al., 2011; Ermolli et al., 2013). The Spectral Irradiance Monitor (SIM) on board SORCE (Harder et al., 2009) shows, over the SC 23
declining phase, measurements that are rather inconsistent with priorunderstanding, indicating that additional validation and uncertainty
estimates are needed (DeLand and Cebula, 2012; Lean and Deland,2012). A wider exposition can be found in Supplementary Material
Section 8.SM.6.
8.4.1.4.3

Reconstructions of preindustrial ultraviolet variations

The Krivova et al. (2010) reconstruction is based on what is known
about spectral contrasts of different surface magnetic features and therelationship between TSI and magnetic fields. The authors interpolated
backwards to the year 1610 based on sunspot group numbers andmagnetic information. The Lean (2000) model is based on historicalsunspot number and area and is scaled in the UV using measurementsfrom the Solar Stellar Irradiance Comparison Experiment (SOLSTICE)
on board the Upper Atmosphere Research Satellite (UARS). The results
show smoothed 11-year UV SSI changes between 1750 and the present of about 25% at about 120 nm, about 8% at 130 to 175 nm, ~4%
at 175 to 200 nm, and about 0.5% at 200 to 350 nm. Thus, the UV SSI
appears to have generally increased over the past four centuries, withlarger trends at shorter wavelengths. As few reconstructions are available, and recent measurements suggest a poor understanding of UV
variations and their relationship with solar activity, there is very low
confidence in these values.

Anthropogenic and Natural Radiative Forcing

Chapter 88.4.1.5 The Effects of Cosmic Rays on Clouds

twice the magnitude of the 1999–2002 RF of –0.06 (–0.08 to –0.04)
W m–2, consistent with the trends noted in Solomon et al. (2011).
However, the CMIP5 simulations discussed elsewhere in this report
did not include the recent small volcanic forcing in their calculations.
New work has also produced a better understanding of high latitude
eruptions, the hydrological response to volcanic eruptions (Trenberthand Dai, 2007; Anchukaitis et al., 2010), better long-term records ofpast volcanism and better understanding of the effects of very large
eruptions.

Changing cloud amount or properties modify the Earth’s albedo andtherefore affect climate. It has been hypothesized that cosmic ray flux
create atmospheric ions which facilitates aerosol nucleation and newparticle formation with a further impact on cloud formation (Dickinson,
1975; Kirkby, 2007). High solar activity means a stronger heliosphericmagnetic field and thus a more efficient screen against cosmic rays.
Under the hypothesis underlined above, the reduced cosmic ray flux
would promote fewer clouds amplifying the warming effect expectedfrom high solar activity. There is evidence from laboratory, field andmodelling studies that ionization from cosmic ray flux may enhanceaerosol nucleation in the free troposphere (Merikanto et al., 2009;
Mirme et al., 2010; Kirkby et al., 2011). However, there is high confidence (medium evidence and high agreement) that the cosmic ray–ionization mechanism is too weak to influence global concentrationsof cloud condensation nuclei or their change over the last century or
during a SC in a climatically significant way (Harrison and Ambaum,
2010; Erlykin and Wolfendale, 2011; Snow-Kropla et al., 2011). A
detailed exposition is found in Section 7.4.6.

8.4.2

Volcanic Radiative Forcing

8.4.2.1 Introduction
Volcanic eruptions that inject substantial amounts of SO2 gas into
the stratosphere are the dominant natural cause of externally forced
climate change on the annual and multi-decadal time scales, bothbecause of the multi-decadal variability of eruptions and the timescale of the climate system response, and can explain much of the
pre-industrial climate change of the last millennium (Schneider etal., 2009; Brovkin et al., 2010; Legras et al., 2010; Miller et al., 2012).
Although volcanic eruptions inject both mineral particles (called ashor tephra) and sulphate aerosol precursor gases (predominantly SO2)
into the atmosphere, it is the sulphate aerosols, which because of theirsmall size are effective scatterers of sunlight and have long lifetimes,that are responsible for RF important for climate. Global annually averaged emissions of CO2 from volcanic eruptions since 1750 have beenat least 100 times smaller than anthropogenic emissions and inconsequential for climate on millennial and shorter time scales (Gerlach,2011). To be important for climate change, sulphur must be injected
into the stratosphere, as the lifetime of aerosols in the troposphere is
only about one week, whereas sulphate aerosols in the stratospherefrom tropical eruptions have a lifetime of about one year, and thosefrom high-latitude eruptions last several months. Most stratosphericaerosols are from explosive eruptions that directly put sulphur into thestratosphere, but Bourassa et al. (2012, 2013) showed that sulphur
injected into the upper troposphere can then be lifted into the stratosphere over the next month or two by deep convection and large scale
Asian summer monsoon circulation, although Vernier et al. (2013) andFromm et al. (2013) suggested that direct injection was also important.
Robock (2000), AR4 (Forster et al., 2007) and Timmreck (2012) provide
summaries of this relatively well understood forcing agent.
There have been no major volcanic eruptions since Mt Pinatubo in
1991 (Figure 8.12), but several smaller eruptions have caused a RF forthe years 2008–2011 of –0.11 (–0.15 to –0.08) W m–2, approximately

There are several ways to measure both the SO2 precursor and sulphate aerosols in the stratosphere, using balloons, airplanes, and both
ground- and satellite-based remote sensing. Both the infrared andultraviolet signals sensed by satellite instruments can measure SO2,
and stratospheric aerosol measurements by space-based sensors havebeen made on a continuous basis since 1978 by a number of instruments employing solar and stellar occultation, limb scattering, limb
emission, and lidar strategies (Thomason and Peter, 2006; Kravitz et al.,2011; Solomon et al., 2011).
Forster et al. (2007) described four mechanisms by which volcanic
forcing influences climate: RF due to aerosol–radiation interaction;differential (vertical or horizontal) heating, producing gradients andchanges in circulation; interactions with other modes of circulation,such as El Niño-Southern Oscillation (ENSO); and ozone depletion with
its effects on stratospheric heating, which depends on anthropogenic
chlorine (stratospheric ozone would increase with a volcanic eruption
under low-chlorine conditions). In addition, the enhanced diffuse lightfrom volcanic aerosol clouds impacts vegetation and hence the carbon
cycle (Mercado et al., 2009) and aerosol–cloud interaction of sulphateaerosols on clouds in the troposphere can also be important (Schmidt
et al., 2010), though Frolicher et al. (2011) showed that the impacts of
the 1991 Mt Pinatubo eruption on the carbon cycle were small.

8.4.2.2 Recent Eruptions
The background stratospheric aerosol concentration was affected byseveral small eruptions in the first decade of the 21st century (Nagai etal., 2010; Vernier et al., 2011; Neely et al., 2013; see also Figure 8.13),
with a very small contribution from tropospheric pollution (Siddaway
and Petelina, 2011; Vernier et al., 2011), and had a small impact on RF(Solomon et al., 2011). Two recent high-latitude eruptions, of Kasatochi Volcano (52.1°N, 175.3°W) on August 8, 2008 and of Sarychev
Volcano (48.1°N, 153.2°E) on June 12–16, 2009, each injected ~1.5
Tg(SO2) into the stratosphere, but did not produce detectable climate
response. Their eruptions, however, led to better understanding of thedependence of the amount of material and time of year of high-latitude injections to produce climate impacts (Haywood et al., 2010;
Kravitz et al., 2010, 2011). The RF from high-latitude eruptions is afunction of seasonal distribution of insolation and the 3- to 4-month
lifetime of high-latitude volcanic aerosols. Kravitz and Robock (2011)
showed that high-latitude eruptions must inject at least 5 Tg(SO2) into
the lower stratosphere in the spring or summer, and much more in fallor winter, to have a detectible climatic response.
On April 14, 2010 the Eyjafjallajökull volcano in Iceland (63.6°N,
19.6°W) began an explosive eruption phase that shut down air traffic
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Chapter 88Anthropogenic and Natural Radiative Forcing

in Europe for 6 days and continued to disrupt it for another month. The
climatic impact of Eyjafjallajökull was about 10,000 times less thanthat of Mt Pinatubo; however, because it emitted less than 50 ktonnes
SO2 and its lifetime in the troposphere was 50 times less than if it had
been injected into the stratosphere, and was therefore undetectable
amidst the chaotic weather noise in the atmosphere (Robock, 2010).
2011 saw the continuation of a number of small eruptions with significant tropospheric SO2 and ash injections, including Puyehue-Cordón
Caulle in Chile, Nabro in Eritrea, and Grimsvötn in Iceland. None havebeen shown to have produced an important RF, but the June 13, 2011
Nabro eruption resulted in the largest stratospheric aerosol cloud sincethe 1991 Mt Pinatubo eruption (Bourassa et al., 2012), more than 1.5
Tg(SO2).
Figure 8.12 shows reconstructions of volcanic aerosol optical depth
since 1750. Figure 8.13 shows details of the vertical distribution ofstratospheric aerosols in the tropics since 1985. The numerous small
eruptions in the past decade are evident, but some of them were athigher latitudes and their full extent is not captured in this plot.

Figure 8.12 | Volcanic reconstructions of global mean aerosol optical depth (at 550
nm). Gao et al. (2008) and Crowley and Unterman (2013) are from ice core data, andend in 2000 for Gao et al. (2008) and 1996 for Crowley and Unterman (2013). Sato etal. (1993) includes data from surface and satellite observations, and has been updated
through 2011. (Updated from Schmidt et al., 2011.)

Figure 8.13 | (Top) Monthly mean extinction ratio (525 nm) profile evolution in the tropics [20°N to 20°S] from January 1985 through December 2012 derived from Stratospheric
Aerosol and Gas Experiment (SAGE) II extinction in 1985–2005 and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) scattering ratio in 2006–2012,
after removing clouds below 18 km based on their wavelength dependence (SAGE II) and depolarization properties (CALIPSO) compared to aerosols. Black contours represent theextinction ratio in log-scale from 0.1 to 100. The position of each volcanic eruption occurring during the period is displayed with its first two letters on the horizontal axis, where
tropical eruptions are noted in red. The eruptions were Nevado del Ruiz (Ne), Augustine (Au), Chikurachki (Ch), Kliuchevskoi (Kl), Kelut (Ke), Pinatubo (Pi), Cerro Hudson (Ce), Spur(Sp), Lascar (La), Rabaul (Ra), Ulawun (Ul), Shiveluch (Sh), Ruang (Ru), Reventador (Re), Manam (Ma), Soufrière Hills (So), Tavurvur (Ta), Okmok (Ok), Kasatochi (Ka), Victoria (Vi*—forest fires with stratospheric aerosol injection), Sarychev (Sa), Merapi (Me), Nabro (Na). (Updated from Figure 1 from Vernier et al., 2011.) (Bottom) Mean stratospheric aerosol
optical depth (AOD) in the tropics [20°N to 20°S] between the tropopause and 40 km since 1985 from the SAGE II (black line), the Global Ozone Monitoring by Occultation of Stars(GOMOS) (red line), and CALIPSO (blue line). (Updated from Figure 5 from Vernier et al., 2011.)

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Anthropogenic and Natural Radiative Forcing

Chapter 8

Box 8.3 | Volcanic Eruptions as Analogues
Volcanic eruptions provide a natural experiment of a stratospheric aerosol cloud that can serve to inform us of the impacts of the proposed production of such a cloud as a means to control the climate, which is one method of geoengineering (Rasch et al., 2008); see
Section 7.7. For example, Trenberth and Dai (2007) showed that the Asian and African summer monsoon, as well as the global hydrological cycle, was weaker for the year following the 1991 Mt Pinatubo eruption, which is consistent with climate model simulations(Robock et al., 2008). MacMynowski et al. (2011) showed that because the climate system response of the hydrological cycle is rapid,
forcing from volcanic eruptions, which typically last about a year, can serve as good analogues for longer-lived forcing. The formationof sulphate aerosols, their transport and removal, their impacts on ozone chemistry, their RF, and the impacts on whitening skies all
also serve as good analogues for geoengineering proposals. Volcanic impacts on the carbon cycle because of more diffuse radiation(Mercado et al., 2009) and on remote sensing can also be useful analogues, and the impacts of contrail-generated sub-visual cirrus(Long et al., 2009) can be used to test the long-term impacts of a permanent stratospheric cloud.

8

Smoke from fires generated by nuclear explosions on cities and industrial areas, which could be lofted into the stratosphere, would
cause surface cooling and a reduction of stratospheric ozone (Mills et al., 2008). Volcanic eruptions that produce substantial stratospheric aerosol clouds also serve as an analogue that supports climate model simulations of the transport and removal of stratosphericaerosols, their impacts on ozone chemistry, their RF, and the climate response. The use of the current global nuclear arsenal still has
the potential to produce nuclear winter, with continental temperatures below freezing in summer (Robock et al., 2007a; Toon et al.,2008), and the use of only 100 nuclear weapons could produce climate change unprecedented in recorded human history (Robock etal., 2007b), with significant impacts on global agriculture (Özdoğan et al., 2013; Xia and Robock, 2013).

8.4.2.3 Records of Past Volcanism and Effects of Very
Large Eruptions
Although the effects of volcanic eruptions on climate are largest in
the 2 years following a large stratospheric injection, and the winterwarming effect in the NH has been supported by long-term records(Fischer et al., 2007), there is new work indicating extended volcanic
impacts via long-term memory in the ocean heat content and sea level(Stenchikov et al., 2009; Gregory, 2010; Otterä et al., 2010). Zanchettin et al. (2012) found changes in the North Atlantic Ocean circulationthat imply strengthened northward oceanic heat transport a decade
after major eruptions, which contributes to the emergence of extensive
winter warming over the continental NH along with persistent coolingover Arctic regions on decadal time scales, in agreement with Zhong etal. (2011) and Miller et al. (2012).
New work on the mechanisms by which a supereruption (Self andBlake, 2008) could force climate has focused on the 74,000 BP eruptionof the Toba volcano (2.5°N, 99.0°E). Robock et al. (2009) used simulations of up to 900 times the 1991 Mt Pinatubo sulphate injection toshow that the forcing is weaker than that predicted based on a linear
relationship with the sulphate aerosol injection. The results agreed
with a previous simulation by Jones et al. (2005). They also showedthat chemical interactions with ozone had small impacts on the forcing
and that the idea of Bekki et al. (1996) that water vapour would limit
and prolong the growth of aerosols was not supported. Timmreck et al.
(2010) however, incorporating the idea of Pinto et al. (1989) that aerosols would grow and therefore both have less RF per unit mass andfall out of the atmosphere more quickly, found much less of a radiative
impact from such a large stratospheric input.

8.4.2.4 Future Effects
We expect large eruptions over the next century but cannot predict
when. Ammann and Naveau (2003) and Stothers (2007) suggested an
80-year periodicity in past eruptions, but the data record is quite shortand imperfect, and there is no mechanism proposed that would cause
this. While the period 1912–1963 was unusual for the past 500 years inhaving no large volcanic eruptions, and the period 1250–1300 had themost globally climatically significant eruptions in the past 1500 years(Gao et al., 2008), current knowledge only allows us to predict such
periods on a statistical basis, assuming that the recent past distributions are stationary. Ammann and Naveau (2003), Gusev (2008), andDeligne et al. (2010) studied these statistical properties and Ammann
and Naveau (2010) showed how they could be used to produce a statistical distribution for future simulations. Although the future forcing
from volcanic eruptions will depend only on the stratospheric aerosol
loading for most forcing mechanisms, the future effects on reducing
ozone will diminish as ozone depleting substances diminish in the
future (Eyring et al., 2010b).

8.5

Synthesis of Global Mean Radiative
Forcing, Past and Future

The RF can be used to quantify the various agents that drive climatechange over the Industrial Era or the various contributions to futureclimate change. There are multiple ways in which RF can be attributed to underlying causes, each providing various perspectives on theimportance of the different factors driving climate change. This section
evaluates the RF with respect to emitted component and with respect
to the ultimate atmospheric concentrations. The uncertainties in the RF

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agents vary and the confidence levels for these are presented in thissection. Finally, this section shows historical and scenarios of future
time evolution of RF.

8.5.1

8

Summary of Radiative Forcing by Species andUncertainties

Table 8.5 has an overview of the RF agents considered here and each ofthem is given a confidence level for the change in RF over the Industrial
Era to the present day. The confidence level is based on the evidence(robust, medium, and limited) and the agreement (high, medium, andlow; see further description in Chapter 1). The confidence level of theforcing agents goes beyond the numerical values available in estimatesand is an assessment for a particular forcing agent to have a realvalue within the estimated range. Some of the RF agents have robust­evidence such as WMGHG with well documented increases based onhigh precision measurements as well as contrails as additional clouds
which can be seen by direct observations. However, for some forcing
agents the evidence is more limited regarding their existence such as
aerosol influence on cloud cover. The consistency in the findings fora particular forcing agent determines the evaluation of the evidence.
A combination of different methods, for example, observations andmodeling, and thus the understanding of the processes causing theforcing is important for this evaluation. The agreement is a qualitative
judgment of the difference between the various estimates for a particular RF agent. Figure 1.11 shows how the combined evidence andagreement results in five levels for the confidence level.

Table 8.5 | Confidence level for the forcing estimate associated with each forcing agent for the 1750–2011 period. The confidence level is based on the evidence and the agreement as given in the table. The basis for the confidence level and change since AR4 is provided. See Figure 1.11 for further description of the evidence, agreement and confidence
level. The colours are adopted based on the evidence and agreement shown in Figure 1.11. Dark green is “High agreement and Robust evidence”, light green is either “Highagreement and Medium evidence” or “Medium agreement and Robust evidence”, yellow is either “High agreement and limited evidence” or “Medium agreement and Medium
evidence” or “Low agreement and Robust evidence”, orange is either “Medium agreement and Limited evidence” or “Low agreement and Medium evidence” and finally red is“Low agreement and Limited evidence”. Note, that the confidence levels given in Table 8.5 are for 2011 relative to 1750 and for some of the agents the confidence level may bedifferent for certain portions of the Industrial Era.

Evidence

Agreement

Confidence
Level

Basis for Uncertainty Estimates(more certain / less certain)

Change in Understanding Since AR4

Well-mixed
greenhouse gases

RobustHigh

Very highMeasured trends from different observed data sets and differences
between radiative transfer models

No major change

Troposphericozone

RobustMediumHigh

Observed trends of ozone in the troposphere and model results forthe industrial era/Differences between model estimates of RF

No major change

Stratosphericozone

RobustMediumHigh

Observed trends in stratospheric and total ozone and modelling of ozone depletion/Differences between estimates of RF

No major change

Stratosphericwater vapour
from CH4

RobustLow

MediumSimilarities in results of independent methods to estimate the
RF/Known uncertainty in RF calculations

Aerosol–radiationinteractions

RobustMediumHigh

A large set of observations and converging independent estimates of
RF/Differences between model estimates of RF

Elevated owing to more robust estimates from independent methods

Aerosol–cloudinteractions

MediumLow

Low

Variety of different observational evidence and modelling activities/
Spread in model estimates of ERF and differences between
observations and model results

ERF in AR5 has a similar
confidence level to RF in AR4

Rapid adjustment
aerosol–radiationinteractions

MediumLow

Low

Observational evidence combined with results from different types ofmodels/Large spread in model estimates

Total aerosol
effectMediumMediumA large set of observations and model results, independent methods
to derive ERF estimates/Aerosol–cloud interaction processes
and anthropogenic fraction of CCN still fairly uncertain

Not provided previously

MediumSurface albedo(land use)

RobustMediumHigh

Estimates of deforestation for agricultural purposes and well known
physical processes/Spread in model estimates of RF

Elevated owing to the availabilityof high-quality satellite data

Surface albedo(BC aerosol onsnow and ice)

MediumLow

Low

Observations of snow samples and the link between BC content
in snow and albedo/Large spread in model estimates of RF

Contrails

RobustLow

MediumContrail- induced
cirrusMediumLow

Solar irradiance

MediumVolcanic aerosol

RobustElevated owing to more studies

Elevated owing toincreased evidence

No major change

Contrails observations , large number of model estimates/Spread inmodel estimates of RF and uncertainties in contrail optical properties

Elevated owing to more studies

Low

Observations of a few events of contrail induced cirrus/Extent of
events uncertain and large spread in estimates of ERF

Elevated owing to additional
studies increasing the evidence

MediumMediumSatellite information over recent decades and small uncertainty
in radiative transfer calculations/Large relative spread inreconstructions based on proxy data

Elevated owing to better
agreement of a weak RF

MediumHigh

Observations of recent volcanic eruptions/Reconstructions ofpast eruptions

Elevated owing to improved
understanding

Notes:
The confidence level for aerosol–cloud interactions includes rapid adjustments (which include what was previously denoted as cloud lifetime effect or second indirect aerosol effect). The separate
confidence level for the rapid adjustment for aerosol–cloud interactions is very low. For aerosol–radiation interaction the table provides separate confidence levels for RF due to aerosol–radiationinteraction and rapid adjustment associated with aerosol–radiation interaction.

694

Anthropogenic and Natural Radiative Forcing

Chapter 8

Evidence is robust for several of the RF agents because of long term
observations of trends over the industrial era and well defined links
between atmospheric or land surfaced changes and their radiative
effects. Evidence is medium for a few agents where the anthropogenic
changes or the link between the forcing agent and its radiative effectare less certain. Medium evidence can be assigned in cases whereobservations or modelling provide a diversity of information and thus
not a consistent picture for a given forcing agent. We assess the evidence to be limited only for rapid adjusment associated with aerosol–
cloud interaction where model studies in some cases indicate changes
but direct observations of cloud alterations are scarce. High agreement
is given only for the WMGHG where the relative uncertainties in the RF
estimates are much smaller than for the other RF agents. Low agreement can either be due to large diversity in estimates of the magnitudeof the forcing or from the fact that the method to estimate the forcing
has a large uncertainty. Stratospheric water vapour is an example ofthe latter with modest difference in the few available estimates but
a known large uncertainty in the radiative transfer calculations (seefurther description in Section 8.3.1).

used instead of confidence level. For comparison with previous IPCC
assessments the LOSU is converted approximately to confidence level.
Note that LOSU and confidence level use different terms for their rankings. The figure shows generally increasing confidence levels but alsothat more RF mechanisms have been included over time. The confidence levels for the RF due to aerosol–radiation interactions, surface
albedo due to land use and volcanic aerosols have been raised andare now at the same ranking as those for change in stratospheric andtropospheric ozone. This is due to an increased understanding of key
parameteres and their uncertainties for the elevated RF agents. For
tropospheric and stratospheric ozone changes, research has shown further complexities with changes primarily influencing the troposphereor the stratosphere being linked to some extent (see Section 8.3.3). The
rapid adjustment associated with aerosol–cloud interactions is given
the confidence level very low and had a similar level in AR4. For rapid
adjustment associated with aerosol–radiation interactions (previously
denoted as semi-direct effect) the confidence level is low and is raised
compared to AR4, as the evidence is improved and is now medium (seeSection 7.5.2).

Figure 8.14 shows the development of the confidence level over thelast four IPCC assessments for the various RF mechanisms. In the previous IPCC reports level of scientific understanding (LOSU) has been

Table 8.6 shows the best estimate of the RF and ERF (for AR5 only)
for the various RF agents from the various IPCC assessments. The RF
due to WMGHG has increased by 16% and 8% since TAR and AR4,

Figure 8.14 | Confidence level of the forcing mechanisms in the 4 last IPCC assessments. In the previous IPCC assessments the level of scientific understanding (LOSU) has been
adopted instead of confidence level, but for comparison with previous IPCC assessments the LOSU is converted approximately to confidence level. The thickness of the bars represents the relative magnitude of the current forcing (with a minimum value for clarity of presentation). LOSU for the RF mechanisms was not available in the first IPCC Assessment(Houghton et al., 1990). Rapid adjustments associated with aerosol–cloud interactions (shown as RA aero. –cloud interac.) which include what was previously referred to as thesecond indirect aerosol effect or cloud lifetime effect whereas rapid adjustments associated with aerosol–radiation interactions (shown as RA aero.-rad. interac.) were previously
referred to as the semi-direct effect (see Figure 7.3). In AR4 the confidence level for aerosol–cloud interaction was given both for RF due to aerosol–cloud interaction and rapid
adjustment associated with aerosol–cloud interaction. Generally the aerosol–cloud interaction is not separated into various components in AR5, hence the confidence levels for
ERF due to aerosol–cloud interaction in AR5 and for RF due to aerosol–cloud interaction from previous IPCC reports are compared. The confidence level for the rapid adjustment
associated with aerosol–cloud interaction is comparable for AR4 and AR5. The colours are adopted based on the evidence and agreement shown in Figure 1.11. Dark green is “Highagreement and Robust evidence”, light green is either “High agreement and Medium evidence” or “Medium agreement and Robust evidence”, yellow is either “High agreement
and limited evidence” or “Medium agreement and Medium evidence” or “Low agreement and Robust evidence”, orange is either “Medium agreement and Limited evidence” or“Low agreement and Medium evidence” and finally red is “Low agreement and Limited evidence”.

695

8

Chapter 88Anthropogenic and Natural Radiative Forcing

r­espectively. This is due mainly to increased concentrations (see Section 8.3.2), whereas the other changes for the anthropogenic RF
agents compared to AR4 are due to re-evaluations and in some cases
from improved understanding. An increased number of studies, additional observational data and better agreement between models andobservations can be the causes for such re-evaluations. The best estimates for RF due to aerosol–radiation interactions, BC on snow andsolar irradiance are all substantially decreased in magnitude comparedto AR4; otherwise the modifications to the best estimates are rather
small. For the RF due to aerosol–radiation interaction and BC on snow
the changes in the estimates are based on additional new studies sinceAR4 (see Section 8.3.4 and Section 7.5). For the change in the estimateof the solar irradiance it is a combination on how the RF is calculated,
new evidence showing some larger earlier estimates were incorrect,
and a downward trend observed during recent years in the solar activity that has been taken into account (see Section 8.4.1). The estimate
for ERF due to to aerosol–cloud interaction includes rapid adjustment
but still this ERF is smaller in magnitude than the AR4 RF estimate due
to aerosol–cloud interactions without rapid adjustments (a theoretical construct not quantified in AR5). The uncertainties for ERF due to
CO2 increase when compared to RF (see Section 8.3.2). We assume therelative ERF uncertainties for CO2 apply to all WMGHG. For the shortlived GHG we do not have sufficient information to include separate
ERF uncertainty to each of these forcing agents (see Section 8.1.1.3).

­ owever, for these forcing mechanisms the RF uncertainties are larger
Hthan for the WMGHG and thus it is unlikely that rapid adjustmentschange the uncertainties substantially.
Figure 8.15 shows the RF for agents listed in Table 8.6 over the
1750–2011 period. The methods for calculation of forcing estimates
are described in Section 8.3 and 8.4. For some of the components theforcing estimates are based on observed abundance whereas someare estimated from a combination of model simulations and observations and for others are purely model based. Solid bars are given for
ERF, whereas RF values are given as (additional) hatched bars. Similarly
the uncertainties are given for ERF in solid lines and dotted lines for
RF. An important assumption is that different forcing mechanisms canbe treated additively to calculate the total forcing (see Boucher andHaywood, 2001; Forster et al., 2007; Haywood and Schulz, 2007). Total
ERF over the Industrial Era calculated from Monte Carlo simulations
are shown in Figure 8.16, with a best estimate of 2.29 W m–2. For eachof the forcing agents a probability density function (PDF) is generated
based on uncertainties provided in Table 8.6. The combination of theindividual RF agents to derive total forcing follows the same approachas in AR4 (Forster et al., 2007) which is based on the method in Boucher and Haywood (2001). The PDF of the GHGs (sum of WMGHG, ozone
and stratospheric water vapour) has a more narrow shape than the
PDF for the aerosols owing to the much lower relative uncertainty.

Table 8.6 | Summary table of RF estimates for AR5 and comparison with the three previous IPCC assessment reports. ERF values for AR5 are included. For AR5 the values are given
for the period 1750–2011, whereas earlier final years have been adopted in the previous IPCC assessment reports.

Global Mean Radiative Forcing (W m–2)

ERF (W m–2)

SAR
(1750–1993)

TAR(1750–1998)

AR4(1750–2005)

AR5(1750–2011)

Well-mixed
greenhouse gases(CO2, CH4, N2O, andhalocarbons)

2.45 (2.08 to 2.82)

2.43 (2.19 to 2.67)

2.63 (2.37 to 2.89)

2.83 (2.54 to 3.12)

Change due to increasein concentrations

Tropospheric ozone+0.40 (0.20 to 0.60)+0.35 (0.20 to 0.50)+0.35 (0.25 to 0.65)+0.40 (0.20 to 0.60)

Slightly modified estimate

Stratospheric ozone–0.1 (–0.2 to –0.05)–0.15 (–0.25 to –0.05)–0.05 (–0.15 to +0.05)–0.05 (–0.15 to +0.05)

Estimate unchanged

Stratospheric water
vapour from CH4

Not estimated+0.01 to +0.03+0.07 (+0.02, +0.12)+0.07 (+0.02 to +0.12)

Estimate unchanged

Aerosol–radiation interactions

Not estimatedNot estimated–0.50 (–0.90 to –0.10)–0.35 (–0.85 to +0.15)

Re-evaluated to besmaller in magnitude–0.45 (–0.95 to +0.05)

Aerosol–cloudinteractions

0 to –1.5(sulphate only)

0 to –2.0(all aerosols)–0.70 (–1.80 to –0.30)(all aerosols)

Not estimatedReplaced by ERF and re-evaluatedto be smaller in magnitude–0.45 (–1.2 to 0.0)

Surface albedo(land use)

Not estimated–0.20 (–0.40 to 0.0)

–0.20 (–0.40 to 0.0)

–0.15 (–0.25 to –0.05)

Re-evaluated to be slightly
smaller in magnitude

Surface albedo(black carbon aerosol on snow and ice)

Not estimatedNot estimated+0.10 (0.0 to +0.20)+0.04 (+0.02 to +0.09)

Re-evaluated to be weaker

Not estimated+0.02 (+0.006
to +0.07)+0.01 (+0.003
to +0.03)+0.01 (+0.005
to +0.03)

Not estimated0 to +0.04

Not estimatedNot estimatedNot estimatedNot estimated

1.6 (0.6 to 2.4)

Not estimated+0.30 (+0.10
to +0.50)+0.30 (+0.10
to +0.50)+0.12 (+0.06
to +0.30)+0.05 (0.0 to +0.10)

Contrails
Combined contrails
and contrailinduced cirrus
Total anthropogenic
Solar irradiance

Comment

2.83 (2.26 to 3.40)

No major change
0.05 (0.02 to 0.15)

Stronger positive due to changes
in various forcing agents
Re-evaluated to be weakerNotes:
Volcanic RF is not added to the table due to the periodic nature of volcanic eruptions, which makes it difficult to compare to the other forcing mechanisms.

696

AR5

2.3 (1.1 to 3.3)

Anthropogenic and Natural Radiative Forcing

Chapter 8

Radiative forcing of climate between 1750 and 2011

Forcing agent

CO2

Well Mixed
Greenhouse Gases

Other WMGHG

Anthropogenic

Ozone

CH4 N2O

Stratospheric

Halocarbons

8
Tropospheric

Stratospheric water
vapour from CH4

Surface Albedo

Black carbon
on snow

Land Use

Contrails

Contrail induced cirrus

Aerosol-Radiation Interac.
Aerosol-Cloud Interac.

Natural

Total anthropogenic
Solar irradiance

-1

0

1
Radiative Forcing (W m-2)

2

3

Figure 8.15 | Bar chart for RF (hatched) and ERF (solid) for the period 1750–2011, where the total ERF is derived from Figure 8.16. Uncertainties (5 to 95% confidence range)
are given for RF (dotted lines) and ERF (solid lines).

AR4 RF

Probability density function

1.2
Greenhouse
gases

1.0
0.8

Aerosols

Total
anthropogenic

0.6
0.4
0.2
0.0

-2

0

2

Effective radiative forcing (Wm-2)

4

Figure 8.16 | Probability density function (PDF) of ERF due to total GHG, aerosol
forcing and total anthropogenic forcing. The GHG consists of WMGHG, ozone andstratospheric water vapour. The PDFs are generated based on uncertainties provided inTable 8.6. The combination of the individual RF agents to derive total forcing over the
Industrial Era are done by Monte Carlo simulations and based on the method in Boucher
and Haywood (2001). PDF of the ERF from surface albedo changes and combined contrails and contrail-induced cirrus are included in the total anthropogenic forcing, but
not shown as a separate PDF. We currently do not have ERF estimates for some forcing
mechanisms: ozone, land use, solar, etc. For these forcings we assume that the RF isrepresentative of the ERF and for the ERF uncertainty an additional uncertainty of 17%
has been included in quadrature to the RF uncertainty. See Supplementary Material Section 8.SM.7 and Table 8.SM.4 for further description on method and values used in thecalculations. Lines at the top of the figure compare the best estimates and uncertainty
ranges (5 to 95% confidence range) with RF estimates from AR4.

Therefore, the large uncertainty in the aerosol forcing is the maincause of the large uncertainty in the total anthropogenic ERF. The total
anthropogenic forcing is virtually certain to be positive with the probability for a negative value less than 0.1%. Compared to AR4 the total
anthropogenic ERF is more strongly positive with an increase of 43%.
This is caused by a combination of growth in GHG concentration, andthus strengthening in forcing of WMGHG, and weaker ERF estimates ofaerosols (aerosol–radiation and aerosol–cloud interactions) as a resultof new assessments of these effects.
Figure 8.17 shows the forcing over the Industrial Era by emitted compounds (see Supplementary Material Tables 8.SM.6 and 8.SM.7 foractual numbers and references). It is more complex to view the RF
by emitted species than by change in atmospheric abundance (Figure8.15) since the number of emitted compounds and changes leading to
RF is larger than the number of compounds causing RF directly (seeSection 8.3.3). The main reason for this is the indirect effect of several compounds and in particular components involved in atmosphericchemistry (see Section 8.2). To estimate the RF by the emitted compounds in some cases the emission over the entire Industrial Era isneeded (e.g., for CO2) whereas for other compounds (such as ozone
and CH4) quite complex simulations are required (see Section 8.3.3).
CO2 is the dominant positive forcing both by abundance and by emitted compound. Emissions of CH4, CO, and NMVOC all lead to excess
CO2 as one end product if the carbon is of fossil origin and is the reason
why the RF of direct CO2 emissions is slightly lower than the RF ofabundance change of CO2. For CH4 the contribution from emission isestimated to be almost twice as large as that from the CH4 concen697

Chapter 88Anthropogenic and Natural Radiative Forcing

tration change, 0.97 (0.80 to 1.14) W m–2 versus 0.48 (0.43 to 0.53)
W m–2, respectively. This is because emission of CH4 leads to ozone
production, stratospheric water vapour, CO2 (as mentioned above), andimportantly affects its own lifetime (Section 8.2). Actually, emissions of
CH4 would lead to a stronger RF via the direct CH4 greenhouse effect(0.64 W m–2) than the RF from abundance change of CH4 (0.48 W m–2).
This is because other compounds have influenced the lifetime of CH4
and reduced the abundance of CH4, most notably NOx. Emissions of CO(0.23 (0.18 to 0.29) W m–2) and NMVOC (0.10 (0.06 to 0.14) W m–2)
have only indirect effects on RF through ozone production, CH4 andCO2 and thus contribute an overall positive RF. Emissions of NOX, on
the other hand, have indirect effects that lead to positive RF throughozone production and also effects that lead to negative RF throughreduction of CH4 lifetime and thus its c­ oncentration, and through contributions to nitrate aerosol formation. The best estimate of the overall
effect of anthropogenic emissions of NOX is a negative RF (-0.15 (-0.34
to +0.02) W m–2). Emissions of ammonia also contribute to nitrate aerosol formation, with a small offset due to compensating changes insulphate aerosols. Additionally indirect effects from sulphate on atmospheric compounds are not included here as models typically simulatea small effect, but there are large relative differences in the response
between models. Impacts of emissions other than CO2 on the carbon
cycle via changes in atmospheric composition (ozone or aerosols) arealso not shown owing to the limited amount of available information.
For the WMGHG, the ERF best estimate is the same as the RF. The
uncertainty range is slightly larger, however. The total emission-based
ERF of WMGHG is 3.00 (2.22 to 3.78) W m–2. That of CO2 is 1.68 (1.33
to 2.03) W m–2; that of CH4 is 0.97 (0.74 to 1.20) W m–2; that of stratospheric ozone-depleting halocarbons is 0.18 (0.01 to 0.35) W m–2.
Emissions of BC have a positive RF through aerosol–radiation interactions and BC on snow (0.64 W m–2, see Section 8.3.4 and Section 7.5).
The emissions from the various compounds are co-emitted; this is inparticular the case for BC and OC from biomass burning aerosols. The
net RF of biomass burning emissions for aerosol–radiation interactions
is close to zero, but with rather strong positive RF from BC and negative
RF from OC (see Sections 8.3.4 and 7.5). The ERF due to aerosol–cloudinteractions is caused by primary anthropogenic emissions of BC, OC
and dust as well as secondary aerosol from anthropogenic emissionsof SO2, NOX and NH3. However, quantification of the contribution fromthe various components to the ERF due to aerosol–cloud interactions
has not been attempted in this assessment.

8.5.2

(

)

Figure 8.17 | RF bar chart for the period 1750–2011 based on emitted compounds(gases, aerosols or aerosol precursors) or other changes. Numerical values and theiruncertainties are shown in Supplementary Material Tables 8.SM.6 and 8.SM.7. Notethat a certain part of CH4 attribution is not straightforward and discussed further in
Section 8.3.3. Red (positive RF) and blue (negative forcing) are used for emitted components which affect few forcing agents, whereas for emitted components affecting
many compounds several colours are used as indicated in the inset at the upper partthe figure. The vertical bars indicate the relative uncertainty of the RF induced by eachcomponent. Their length is proportional to the thickness of the bar, that is, the full lengthis equal to the bar thickness for a ±50% uncertainty. The net impact of the individual
contributions is shown by a diamond symbol and its uncertainty (5 to 95% confidence
range) is given by the horizontal error bar. ERFaci is ERF due to aerosol–cloud interaction. BC and OC are co-emitted, especially for biomass burning emissions (given as
Biomass Burning in the figure) and to a large extent also for fossil and biofuel emissions(given as Fossil and Biofuel in the figure where biofuel refers to solid biomass fuels).
SOA have not been included because the formation depends on a variety of factors not
currently sufficiently quantified.

698

Time Evolution of Historical Forcing

The time evolution of global mean forcing is shown in Figure 8.18 for
the Industrial Era. Over all time periods during the Industrial Era CO2
and other WMGHG have been the dominant term, except for shorter periods with strong volcanic eruptions. The time evolution showsan almost continuous increase in the magnitude of anthropogenic
ERF. This is the case both for CO2 and other WMGHGs as well as several individual aerosol components. The forcing from CO2 and other
WMGHGs has increased somewhat faster since the 1960s. Emissionsof CO2 have made the largest contribution to the increased anthropogenic forcing in every decade since the 1960s. The total aerosol ERF(aerosol–radiation interaction and aerosol–cloud interaction) has thestrongest negative forcing (except for brief periods with large volcanic
forcing), with a strengthening in the magnitude similar to many of theother anthropogenic forcing mechanisms with time. The global mean
forcing of aerosol–radiation interactions was rather weak until 1950
but strengthened in the latter half of the last century and in particularin the period between 1950 and 1980. The RF due to aerosol–radiationinteraction by aerosol component is shown in Section 8.3.4 (Figure 8.8).
Although there is high confidence for a substantial enhancement in thenegative aerosol forcing in the period 1950–1980, there is much more
uncertainty in the relative change in global mean aerosol forcing overthe last two decades (1990–2010). Over the last two decades there
has been a strong geographic shift in aerosol and aerosol precursor

The volcanic RF has a very irregular temporal pattern and for certain
years has a strongly negative RF. There has not been a major volcanic
eruption in the past decade, but some weaker eruptions give a current
RF that is slightly negative relative to 1750 and slightly stronger inmagnitude compared to 1999–2002 (see Section 8.4.2).
Figure 8.19 shows linear trends in forcing (anthropogenic, natural andtotal) over four different time periods. Three of the periods are thesame as chosen in Box 9.2 (1984–1998, 1998–2011 and 1951–2011)
and the period 1970–2011 is shown in Box 13.1. Monte Carlo simulations are performed to derive uncertainties in the forcing basedon ranges given in Table 8.6 and the derived linear trends. Further,
these uncertainties are combined with uncertainties derived fromshifting time periods ±2 years and the full 90% confidence range isshown in Figure 8.19 (in Box 9.2 only the total forcing is shown withuncertainties derived from the forcing uncertainty without sensitivity
to time period). For the anthropogenic forcing sensitivity to the selection of time periods is very small with a maximum contribution to theuncertainties shown in Figure 8.19 of 2%. However, for the naturalforcing the sensitivity to time periods is the dominant contributor tothe overall uncertainty (see Supplementary Material Figure 8.SM.3) for
the relatively short periods 1998–2011 and 1984–1998, whereas thisis not the case for the longer periods. For the 1998–2011 period thenatural forcing is very likely negative and has offset 2 to 89% of theanthropogenic forcing. It is likely that the natural forcing change has
offset at least 30% of the anthropogenic forcing increase and very
likely that it has offset at least 10% of the anthropogenic increase. For
the 1998–2011 period both the volcanic and solar forcings contributeto this negative natural forcing, with the latter dominating. For theother periods shown in Figure 8.19 the best estimate of the natural ismuch smaller in magnitude than the anthropogenic forcing, but notethat the natural forcing is very dependent on the selection of time
period near the 1984–1998 interval. Over the period 1951–2011 thetrend in anthropogenic forcing is almost 0.3 W m–2 per decade andthus anthropogenic forcing over this period is more than 1.5 W m–2. The
anthropogenic forcing for 1998–2011 is 30% higher and with smaller
uncertainty than for the 1951–2011 period. Note that due to large
WMGHG forcing (Section 8.3.2) the anthropogenic forcing was similarin the late 1970s and early 1980s to the 1998–2011 period. The reason
for the reduced uncertainty in the 1998–2011 anthropogenic forcing(e­ missions (see Section 2.2.3), and there are some uncertainties in theseemissions (Granier et al., 2011). In addition to the regional changes in
the aerosol forcing there is also likely a competition between various
aerosol effects. Emission data indicate a small increase in the BC emissions (Granier et al., 2011) but model studies also indicate a weakenhancement of other aerosol types. Therefore, the net aerosol forcing depends on the balance between absorbing and scattering aerosols for aerosol–radiation interaction as well as balance between thechanges in aerosol–radiation and aerosol–cloud interactions. In the
ACCMIP models, for example, the RF due to aerosol–radiation interaction becomes less negative during 1980 to 2000, but total aerosol
ERF becomes more negative (Shindell et al., 2013c). There is a very low
confidence for the trend in the total aerosol forcing during the past twoto three decades, even the sign; however, there is high confidence thatthe offset from aerosol forcing to WMGHG forcing during this period
was much smaller than over the 1950–1980 period.

Chapter 8)

Anthropogenic and Natural Radiative Forcing

8

Figure 8.18 | Time evolution of forcing for anthropogenic and natural forcing mechanisms. Bars with the forcing and uncertainty ranges (5 to 95% confidence range) atpresent are given in the right part of the figure. For aerosol the ERF due to aerosol–
radiation interaction and total aerosol ERF are shown. The uncertainty ranges are forpresent (2011 versus 1750) and are given in Table 8.6. For aerosols, only the uncertainty
in the total aerosol ERF is given. For several of the forcing agents the relative uncertainty
may be larger for certain time periods compared to present. See Supplementary Material
Table 8.SM.8 for further information on the forcing time evolutions. Forcing numbers
provided in Annex II. The total antropogenic forcing was 0.57 (0.29 to 0.85) W m–2
in 1950, 1.25 (0.64 to 1.86) W m–2 in 1980 and 2.29 (1.13 to 3.33) W m–2 in 2011.

is the larger domination of WMGHG forcing and smaller contribution
from aerosol forcing compared to previous periods. Similar to theresults for 1970–2011 in Figure 8.19, Box 13.1 shows that the global
energy budget is dominated by anthropogenic forcing compared to
the natural forcing, except for the two major volcanic eruption in thisperiod as can be easily seen in Figure 8.18.
Figure 8.20 shows the forcing between 1980 and 2011. Comparedto the whole Industrial Era the dominance of the CO2 is larger forthis recent period both with respect to other WMGHG and the total­anthropogenic RF. The forcing due to aerosols is rather weak leading()

Figure 8.19 | Linear trend in anthropogenic, natural and total forcing for the indicatedtime periods. The uncertainty ranges (5 to 95% confidence range) are combined fromuncertainties in the forcing values (from Table 8.6) and the uncertainties in selectionof time period. Monte Carlo simulations were performed to derive uncertainties in theforcing based on ranges given in Table 8.6 and linear trends in forcing. The sensitivity totime periods has been derived from changing the time periods by ±2 years.

699

Chapter 8

Anthropogenic and Natural Radiative Forcing

Radiative forcing of climate between 1980 and 2011

Forcing agent

Well Mixed
Greenhouse Gases

CO2
Other WMGHG

N2O
CH4

Halocarbons

Anthropogenic

8

Ozone

StratosphericTropospheric

Stratospheric water
vapour from CH4
Surface Albedo

BC on snow
+ Land Use

Contrails

Contrail induced cirrus

Aero.-Rad. Interac.
Aero.-Cloud Interac.

Natural

Total anthropogenic
Solar irradiance

-0.5
0.0
0.5
1.0
Radiative Forcing (W m-2)

Figure 8.20 | Bar chart for RF (hatched) and ERF (solid) for the period 1980–2011,
where the total anthropogenic ERF are derived from Monte-Carlo simulations similar to
Figure 8.16. Uncertainties (5 to 95% confidence range) are given for RF (dotted lines)
and ERF (solid lines).

to a very strong net positive ERF for the 1980–2011 period. More than
40% of the total anthropogenic ERF has occurred over the 1980–2011
period with a value close to 1.0 (0.7 to 1.3) W m–2. The major contribution to the uncertainties in the time evolution of the anthropogenic
forcing is associated with the aerosols (see Section 8.5.1). Despite this,anthropogenic ERF is very likely considerably more positive than thenatural RF over the decadal time periods since 1950. This is in particular the case after 1980, where satellite data are available that provide important measurements to constrain the natural RF mechanisms(e.g., the volcanic RF change between 2007–2011 and 1978–1982 is
0.06 W m–2 and the representative change in solar irradiance over the
1980–2011 period is –0.06 W m–2) with total natural RF of 0.0 (-0.1 to+0.1) W m–2.

8.5.3

Future Radiative Forcing

Projections of global mean RF are assessed based on results from multiple sources examining the RF due to RCP emissions: the ACCMIP initiative (see Section 8.2) provides analysis of the RF or ERF due to aerosols and ozone (Shindell et al., 2013c), while WMGHG, land use andstratospheric water RFs are taken from the results of calculations withthe reduced-complexity Model for the Assessment of Greenhouse-gas
Induced Climate Change 6 (MAGICC6) driven by the RCP emissions
and land use (Meinshausen et al., 2011a). While MAGICC6 also estimated ozone and aerosol RF, those values differ substantially from the
ACCMIP values and are considered less realistic. Additional discussionof biases in the MAGICC6 results due to the simplified representations700

of atmospheric chemistry and the carbon cycle, along with further discussion on the representativeness of the RCP projections in context
with the broader set of scenarios in the literature, is presented in Section 11.3.5 and Section 12.3 (also see Section 8.2). As the ACCMIP
project provided projected forcings primarily at 2030 and 2100, we
hereafter highlight those times. Although understanding the relative
contributions of various processes to the overall effect of aerosols on
forcing is useful, we emphasize the total aerosol ERF, which includes
all aerosol–radiation and aerosol–cloud interactions, as this is themost indicative of the aerosol forcing driving climate change. We alsopresent traditional RF due to aerosol–radiation interaction (previously
called direct aerosol effect) but do not examine further the various
components of aerosol ERF. Aerosol forcing estimates, both mean anduncertainty ranges, are derived from the 10 ACCMIP models, 8 of which
are also CMIP5 models. We analyze forcing during the 21st century(relative to 2000), and hence the WMGHG forcing changes are in addition to persistent forcing from historical WMGHG increases.
Analysis of forcing at 2030 relative to 2000 shows that under RCP2.6,
total ozone (tropospheric and stratospheric) forcing is near zero, RF
due to aerosol–radiation interaction is positive but small, and hence
WMGHG forcing dominates changes over this time period (Figure 8.21).
WMGHG forcing is dominated by increasing CO2, as declining CH4 andincreasing N2O have nearly offsetting small contributions to forcing.
Aerosol ERF was not evaluated for this RCP under ACCMIP, and values
cannot be readily inferred from RF due to aerosol–radiation interaction
as these are not directly proportional. Under RCP8.5, RF due to aerosol–
radiation interaction in 2030 is weakly negative, aerosol ERF is positive
with a fairly small value and large uncertainty range, total ozone forcing is positive but small (~0.1 W m–2), and thus WMGHG forcing again
dominates with a value exceeding 1 W m–2. As with RCP2.6, WMGHG
forcing is dominated by CO2, but under this scenario the other WMGHGs
all contribute additional positive forcing. Going to 2100, ozone forcing
diverges in sign between the two scenarios, consistent with changes inthe tropospheric ozone burden (Figure 8.4) which are largely attributable to projected CH4 emissions, but is small in either case. Ozone RF
is the net impact of a positive forcing from stratospheric ozone recovery owing to reductions in anthropogenic ozone-depleting halocarbon
emissions in both scenarios and a larger impact from changes in tropospheric precursors (Shindell et al., 2013c) which have a negative forcing
in RCP2.6 and a positive forcing in RCP8.5.
The two scenarios are fairly consistent in their trends in RF due to aerosol–radiation interaction by component (Figure 8.21). There is positive
RF due to aerosol–radiation interaction due to reductions in sulfate
aerosol. This is largely offset by negative RF due to aerosol–radiationinteraction by primary carbonaceous aerosols and especially by nitrate(though nearly all CMIP5 models did not include nitrate), leaving net
aerosol RF due to aerosol–radiation interaction values that are verysmall, 0.1 W m–2 or less in magnitude, in either scenario at 2030 and
2100. Nitrate aerosols continue to increase through 2100 as ammonia emissions rise steadily due to increased use of agricultural fertilizer even as all other aerosol precursor emissions decrease (Figure8.2), including sulphur dioxide which drives the reduction in sulphate­aerosol that also contributes to additional formation of nitrate aerosolsin the future (Bauer et al., 2007; Bellouin et al., 2011). Aerosol ERF is
likely similar at this time in all scenarios given that they all have greatly

Anthropogenic and Natural Radiative Forcing

Although aerosol ERF becomes less negative by nearly 1 W m–2 from2000 to 2100, this change is still small compared with the increased
WMGHG forcing under RCP8.5, which is roughly 6 W m–2 during thistime (Figure 8.21). Roughly 5 W m–2 of this WMGHG forcing comesfrom CO2, with substantial additional forcing from increases in both
CH4 and nitrous oxide and only a very small negative forcing fromreductions in halocarbons. Under RCP2.6, the WMGHG forcing isonly about 0.5 W m–2 during this time, as relatively strong decreases
in CH4 and halocarbon forcing offset roughly 40% of the increased
CO2 forcing, which is itself far less than under RCP8.5. Hence under
this scenario, the projected future forcing due to aerosol reductions is
actually stronger than the WMGHG forcing. Viewing the timeseries ofthe various forcings, however, indicates that aerosol ERF is returning
to its pre-industrial levels, so that net forcing becomes increasingly
dominated by WMGHGs regardless of scenario during the 21st century (Figure 8.22). As the forcing is so heavily dominated by WMGHGs
at 2100, and the WMGHG concentrations (CO2) or emissions (others)
were chosen to match forcing targets, all the scenarios show net forcing values at that time that are fairly close to the scenarios’ target
values. The reduced aerosol forcing, with its large uncertainty, leads
to a pronounced decrease in the uncertainty of the total net forcing
by 2100. Based on the spread across ACCMIP models (using ERF foraerosols and converting to ERF for GHGs), the 90% confidence interval(CI) is about 20% for the 2100 net forcing, versus 26% for 2030 under
RCP8.5 and 45–61% for 1980 and 2000 (Shindell et al., 2013c). The
total ERF due to all causes has been independently estimated basedon the transient response in the CMIP5 models and a linear forcing-response relationship derived through regression of the modelled
response to an instantaneous increase in CO2 (Forster et al., 2013).
Uncertainties based on model spread behave similarly, with the 90%
CI for net total ERF decreasing from 53% for 2003 to only 24 to 34%
for 2100. Forcing relative to 2000 due to land use (via albedo only) andstratospheric water vapor changes are not shown separately as theirprojected values under the four RCPs are quite small: –0.09 to 0.00 and–0.03 to 0.10 W m–2, respectively.
The CMIP5 forcing estimates (Forster et al., 2013) for the total projected 2030 and 2100 ERF are slightly smaller than the results obtained
from the ACCMIP models (or the RCP targets; see Section 12.3.3).
Examining the subset of models included in both this regression analysis and in ACCMIP shows that the ACCMIP subset show forcings on
the low side of the mean value obtained from the full set of CMIP5

Effective radiative forcing (W m-2)

8

0.6

RCP8.5
RCP2.6
RCP8.5
RCP2.60.4
0.2

6} 2100 vs 2000
} 2030 vs 2000

0.0
-0.2

4

-0.4

Sulphate Carbonaceous Nitrate

8

SOA

2
0

CO2

O3+Strat H2O
Aerosol ERF
Other WMGHG
Total RFari

Total

Figure 8.21 | Radiative forcing relative to 2000 due to anthropogenic compositionchanges based on ACCMIP models for aerosols (with aerosol ERF scaled to matchthe best estimate of present-day forcing) and total ozone and RCP WMGHG forcings.
Ranges are one standard deviation in the ACCMIP models and assessed relative uncertainty for WMGHGs and stratospheric water vapor. Carbonaceous aerosols refer to primary carbonaceous, while SOA are secondary organic aerosols. Note that 2030 ERF for
RCP2.6 was not available, and hence the total shown for that scenario is not perfectlycomparable to the other total values. RFari is RF due to aerosol–radiation interaction.

analyzed, indicating that the discrepancy between the methods is not
related to analysis of a different set of models. Instead, it may reflect
nonlinearities in the response to forcing that are not represented by
the regression analysis of the response to abrupt CO2 increase experiments (Long and Collins, 2013) or differences in the response to otherforcing agents relative to the response to CO2 used in deriving theCMIP5 estimates (see also 12.3.3).
Natural forcings will also change in the future. The magnitudes cannot
be reliably projected, but are likely to be small at multi-decadal scales(see Section 8.4). Brief episodic volcanic forcing could be large, ­however.

Effective radiative forcing (W m -2)

reduced emissions of all aerosols and aerosol precursors other thanammonia. Aerosol ERF shows a large positive value at 2100 relative
to 2000, nearly returning to its 1850 levels (the 2100 versus 1850 ERF
represents a decrease in ERF of 91% relative to the 2000 versus 1850
value), as is expected given the RCP emissions. Thus although somemodels project large increases in nitrate RF in the future, the reduction in overall aerosol loading appears to lead to such a strong reduction in aerosol ERF that the impact of aerosols becomes very small
under these RCPs. Of course the projections of drastic reductions inprimary aerosol as well as aerosol and ozone precursor emissions may
be overly optimistic as they assume virtually all nations in the world
become wealthy and that emissions reductions are directly dependent
on wealth. The RCPs also contain substantially lower projected growthin HFC emissions than in some studies (e.g., Velders et al., 2009).

Chapter 8864

RCP8.5
RCP6.0
RCP4.5
RCP2.6
WMGHG

2

Net

0
1850

Ozone

Aerosol
1900

1950

2000

2050

2100

Figure 8.22 | Global mean anthropogenic forcing with symbols indicating the times atwhich ACCMIP simulations were performed (solid lines with circles are net; long dashes
with squares are ozone; short dashes with diamonds are aerosol; dash-dot are WMGHG;
colours indicate the RCPs with red for RCP8.5, orange RCP6.0, light blue RCP4.5, anddark blue RCP2.6). RCPs 2.6, 4.5 and 6.0 net forcings at 2100 are approximate values
using aerosol ERF projected for RCP8.5 (modified from Shindell et al., 2013c). Some
individual components are omitted for some RCPs for visual clarity.

701

Chapter 88.6

8Geographic Distribution of Radiative
Forcing

The forcing spatial pattern of the various RF mechanisms varies substantially in space and in time, especially for the NTCFs. The spatial pattern is of interest to the extent that it may influence climate response(Section 8.6.2.2) as is being particularly investigated in the ACCMIP
simulations.

8.6.1

Spatial Distribution of Current Radiative Forcing

The WMGHGs such as CO2 have the largest forcing in the subtropics,
decreasing toward the poles, with the largest forcing in warm and dry
regions and smaller values in moist regions and in high-altitude regions(Taylor et al., 2011). For the NTCFs (Box 8.2) their concentration spatial
pattern and therefore their RF pattern are highly inhomogeneous, andagain meteorological factors such as temperature, humidity, clouds,
and surface albedo influence how concentration translates to RF.
Figure 8.23 shows the RF spatial distribution of the major NTCFs together with standard deviation among the ACCMIP models (Shindell et al.,2013c) the net anthropogenic composition (WMGHG+ozone+aerosol)
forcing is also shown (lower left panel). These models used unified
anthropogenic emissions of aerosol and ozone precursors (Supplementary Material Figure 8.SM.2), so that the model diversity in RF is due
only to differences in model chemical and climate features and natural emissions, and would be larger if uncertainty in the anthropogenic
emissions were also included. In general, the confidence in geographical distribution is lower than for global mean, due to uncertainties inchemistry, transport and removal of species.
The negative RF due to aerosol–radiation interaction (first row; definedin Figure 7.3) is greatest in the NH and near populated and biomassburning regions. The standard deviation for the net RF due to aerosol–
radiation interaction is typically largest over regions where vegetation
changes are largest (e.g., South Asia and central Africa), due to uncertainties in biomass burning aerosol optical properties and in treatment
of secondary organic aerosols. Carbonaceous aerosol forcing (secondrow) is greatest in South and East Asia and can be negative in biomassburning regions due to large weakly absorbing organic components.
Absorbing aerosols also have enhanced positive forcing when they
overlie high albedo surfaces such as cryosphere, desert or clouds, withas much as 50% of BC RF resulting from BC above clouds (Zarzycki
and Bond, 2010).
Figure 8.24 compares the aerosol RFs for ACCMIP (Shindell et al.,2013c), which are representative of the CMIP5 experiments, with thosefrom the AeroCom model intercomparison (Myhre et al., 2013) which
includes sixteen models that used unified meteorology and are more
extensively compared to measurements (e.g., Koch et al., 2009b; Koffi
et al., 2012). The forcing results are very similar, establishing the representativeness and validity of the ACCMIP aerosol simulations.
The net aerosol ERF (Figure 8.23; third row), includes both aerosol–
radiation and aerosol–cloud interactions. The spatial pattern correlates with the RF (first row), except with stronger effect in the outflow regions over oceans. The flux change is larger in the NH than the702

Anthropogenic and Natural Radiative Forcing

SH (e.g., by nearly a factor of 3; Ming et al., 2007). Rapid adjustment
associated with aerosol–radiation and aerosol–cloud interactions may
enhance or reduce cloud cover depending on the region, cloud dynamics and aerosol loading (e.g., Randles and Ramaswamy, 2008; Kochand Del Genio, 2010; Persad et al., 2012). In general, the ocean-land
forcing pattern differs from that reported in AR4, where the forcing due
to aerosol–cloud interaction were larger over land than ocean (Forster
et al., 2007), and this continues to be a source of uncertainty. Since
AR4, Quaas et al. (2009) showed using satellite retrievals that the correlation between AOD changes and droplet number changes is stronger over oceans than over land and that models tend to overestimate
the strength of the relation over land. Penner et al. (2011) showedthat satellite retrievals, due to their dependence on present-day conditions, may underestimate the forcing due to aerosol–cloud interaction,
especially over land, although this model analysis may overestimate
the cloud condensation nucleus to AOD relation (Quaas et al., 2011).
Wang and Penner (2009) also showed that if models include boundary
layer nucleation and increase the fraction of sulphur emitted as a primary particle, the effect over land is increased relative to over ocean(see also Section 7.5.3). The aerosol ERF standard deviation is large
in biomass burning regions, as for the RF, and in regions where cloud
effects differ among models (e.g., northern North America, northeast
Asia, Amazonia). The spread in aerosol ERF is much larger than for the
RF alone, although the relative standard deviation is no larger (Shindell
et al., 2013c).
For components that primarily scatter radiation, the radiative effect at
the surface is similar to the RF (according to the definition in Section
8.1.1). However for components that absorb radiation in the atmosphere the radiation reaching the surface is reduced (Forster et al., 2007;
Ramanathan and Carmichael, 2008; Andrews et al., 2010). This absorption of incoming solar radiation alters the vertical temperature profilein the atmospheric column and can thus change atmospheric circulation and cloud formation. The aerosol atmospheric absorption (Figure
8.23, bottom right), or the difference between ERF and the analogous
radiative flux reaching the surface including rapid adjustments, has aspatial pattern that to lowest order tracks the carbonaceous aerosol
forcing, but is also affected by cloud changes, where e.g., cloud loss
could enhance atmospheric absorption. Atmospheric aerosol absorption patterns thus mirror the ERF due to aerosol–cloud interaction pattern, with larger forcing over continents.
Ozone RF is calculated using the methodology described in Shindell etal. (2013c), but applied to the larger set of models in ACCMIP (Stevenson et al., 2013). The net ozone RF (Figure 8.23; fourth row) is largest
in subtropical latitudes, and is more positive in the NH than the SH.
Pollution in the NH accounts for positive tropospheric forcing; stratospheric ozone loss has caused negative SH polar forcing. Model standard deviation is largest in the polar regions where lower stratosphere/
upper troposphere changes differ in the models (Young et al., 2013).
Overall, the confidence in aerosol and ozone RF spatial patterns is
medium and lower than that for the global mean due to the large
regional standard deviations (Figure 8.23), and is exacerbated in aerosol ERF patterns due to uncertainty in cloud responses.

Anthropogenic and Natural Radiative Forcing

Chapter 8

Preindustrial to Present-Day Forcing
Standard deviation-0.26 W m-2

0.14 (0.30) W m-2

All aerosol RF

Multi-model mean

8

.12

.38

.62 .88
0.21 W m-2

0

.35

.70

1.05

1.40 1.75 2.10 2.45
0.08 (0.18) W m-2-.88 -.62 -.38 -.12

.12

.38

.62 .88-1.17 W m-2

0

.35

.70

1.05

1.40 1.75 2.10 2.45
0.29 (1.27) W m-2-3.5 -2.5 -1.5

-.5

.25

.75 1.25 1.75
0.29 W m-2

0

.35

.70

1.05

1.40 1.75 2.10 2.45
0.16 (0.19) W m-2-.88 -.62 -.38 -.12

.12

.38

0

.06

.12

.18

.24

Ozone RFAll aerosol ERF

Carbonaceous aerosol RF-.88 -.62 -.38 -.12

.88

.30

Multi-model mean

1.46 W m-2

.36

.42

1.42 W m-2

All aerosol effective
atmospheric forcing

Total anthropogenic
composition forcing

Multi-model mean

.62

-3.0 -2.0 -1.0

-0.5

0.5

1.0 2.0

3.0-3.5 -2.5 -1.5

-.5

.5

1.5

2.5

3.5

Figure 8.23 | Spatial pattern of ACCMIP models 1850 to 2000 forcings, mean values (left) and standard deviation (right) for aerosols and ozone (top four rows). Values above are
the average of the area-weighted global means, with the area weighted mean of the standard deviation of models at each point provided in parenthesis. Shown are net aerosol RF
due to aerosol–radiation interaction (top, 10 models), carbonaceous aerosol RF due to aerosol-radiation interaction (2nd row, 7 models), aerosol ERF (3rd row, 8 models), ozone(4th row, 11 models), total anthropogenic composition forcing (WMGHG+ozone+aerosols; bottom left), aerosol atmospheric absorption including rapid adjustment (bottom right,
6 models). Note that RF and ERF means are shown with different colour scales, and standard deviation colour scales vary among rows.

703

Chapter 8

Anthropogenic and Natural Radiative Forcing

Preindustrial to present-day forcing

Multi-model mean

0.14 (0.30) W m-2-0.27 W m-2

0.15 (0.27) W m-2

0.25 W m-2

0.10 (0.12) W m-2

0.18 W m-2

0.07 (0.12) W m-2

ACCMIP
AeroCom

Fossil & biofuel Black Carbon RF

AeroCom

All aerosol RF

ACCMIP

8

Standard deviation-0.26 W m-2-.88 -.62 -.38 -.12

.12

.38

.62

.88

0

.35

.70

1.05

1.40 1.75 2.10

2.45

Figure 8.24 | Spatial pattern of ACCMIP and 16 AeroCom models 1850 to 2000 RF due to aerosol–radiation interaction, mean values (left) and standard deviation (right). Notethat different carbonaceous aerosol diagnostics are used here compared to Figure 8.23, due to available AeroCom fields. Values above are the average of the area-weighted global
means, with the area weighted mean of the standard deviation of models at each point provided in parentheses.

704

Anthropogenic and Natural Radiative Forcing

8.6.2

Chapter 8

Spatial Evolution of Radiative Forcing andResponse over the Industrial Era

8.6.2.1 Regional Forcing Changes During the Industrial Era
The spatial distribution of the WMGHG RF has shifted only slightly overthe industrial period; however the RF spatial distributions for NTCFs
has shifted with emissions, due to the timing of regional development
and implementation of pollution standards (Supplementary Material
Figures 8.SM.1 and 8.SM.2 show regional trends and emissions maps;
Lamarque et al., 2013). Figure 8.25 shows how the distributions ofaerosol and ozone forcings are modelled to have changed up to 1930,
1980 and 2000. Substantial industrial coal-burning in the early part ofthe 20th century occurred in the northeastern United States and Western Europe, leading to stronger sulphate and BC forcing near those-0.06 W m-2

1980 vs 1850

-0.29 W m-2

2000 vs 1850

-0.26 W m-2

0.07 W m-2

0.17 W m-2

0.21 W m-2

0.09 W m-2

0.27 W m-2

0.29 W m-2

Ozone RF

Carbonaceous aerosol RF

All aerosol RF

1930 vs 1850

regions (Figure 8.25, left). Between 1950 and 1970, coal burning for
power generation increased while coal burning for other purposes was
replaced by oil and natural gas and motor vehicle usage grew rapidly inthese regions, leading to more sulphate and less BC. Peak aerosol forcing in North America and Europe occurred around 1970–1980 (Figure
8.25, second column), while Asian development led to increased biofuel and fossil fuel sources of aerosols and ozone precursors toward
the end of the century. During the final decades of the century, desulphurization controls reduced sulphur emissions from North America
and Europe, resulting in reduced negative forcing in these regions andpositive Arctic aerosol forcing. The SH ozone hole developed during thefinal three decades, with negative forcing over high latitudes. Biomassburning generated ozone and carbonaceous aerosols in NH high-latitudes early in the century, with increased tropical burning from mid tolate century.

-.88 -.62 -.38 -.12.12 .38 .62 .88-1.03 W m-2-1.17 W m-2

All aerosol ERF-0.27 W m-2-3.5 -2.5 -1.5 -.5.25 .75 1.25 1.75

Figure 8.25 | Multi-model mean RF due to aerosol–radiation interaction of all aerosols, carbonaceous aerosols, ozone, and aerosol ERF (W m–2) for the indicated times based on
the ACCMIP simulations. Global area-weighted means are given in the upper right.

705

8

Chapter 8

8

Aerosol ERF grew rapidly from 1930 to 1980, as did RF due to aerosol–radiation interaction, with a spatial structure reflecting both the
influence of aerosol–radiation and aerosol–cloud interactions that areespecially strong over pollution outflow regions and over areas withhigh surface albedo. From 1980 to 2000, aerosol ERF continued to
become more negative even as negative RF due to aerosol–radiationinteraction grew weaker, with the spatial pattern showing strengthening of aerosol ERF over Asia and weakening of aerosol ERF over North
America and Europe.
Soil dust has changed since the pre-industrial due to land disturbanceand resulting desertification (a forcing) and to changes in climate (afeedback). Mahowald et al. (2010) showed approximate doubling indust loading over the 20th century (–0.1 W m–2; consistent with thebest estimate in Section 7.5.2; Section 8.3.4.2), primarily from the
Saharan and Middle Eastern Deserts, with largest increase from the
1950s to the 1980s (–0.3 W m–2), followed by a leveling. The increased
dustiness reduces model precipitation within the Saharan source
region, improving agreement with observed precipitation.
Aerosol loading changes during the past century have impacted radiation at the surface (Section 2.3.3), with peak radiation reductionsin North America and Europe in the 1980s, and ongoing reduction inSouth and East Asia (Wild, 2009). The AR4 and CMIP5 models simulated these trends but underestimated their magnitude, the decadaltemperature variations and the diurnal temperature range over land
(Wild, 2009; see Chapter 9).
Changes in spatial patterns of species and their forcing over the century are difficult to validate due to sparse observations of short-lived
species. Some constraint comes from limited historical observationsin ice core records and from shorter trends beginning in late century
from satellite and surface-based site measurements. The emissions
estimates for historical species are very uncertain, especially for carbonaceous aerosols and dust. Therefore, the confidence in the historical forcing pattern changes is low for RF due to aerosol–radiationinteraction and ozone, and very low for ERF, carbonaceous aerosolsand dust.

8.6.2.2 Relationship Between Regional Forcing Patterns andClimate Response During the Industrial Era
An increasing body of research considers how spatial variations in RF
affect climate response. Detection and attribution methods have had
limited success in discerning statistically significant regional climate
signals from regional forcing, due to large internal climate variability
at regional scales, uncertainty in model processes and sparse regional observational records (Chapter 10). Meanwhile, research including
model sensitivity studies for NTCFs, which vary strongly in space intime, explores climate response patterns.
In AR4 (Forster et al., 2007; Knutti et al., 2008) it was argued that the
spatial pattern of forcing is not indicative of the pattern of climate
response. Rather, the response is linked more closely to TOA flux resulting from the climate feedback spatial patterns (Boer and Yu, 2003;
Taylor et al., 2011; Ming and Ramaswamy, 2012), with the lapse rate,surface albedo and cloud feedbacks explaining most of the ­temperature
706

Anthropogenic and Natural Radiative Forcing

response. Yet Crook and Forster (2011) showed that both the spatial
distribution of climate feedbacks and of heterogeneous forcing played
important roles in the patterns of 20th century temperature changes.
Other studies since AR4 have probed relationships between forcing
patterns and climate responses.
Broad links between forcing and climate response have been identified. Shindell et al. (2010) used multiple models to show that surface
temperature changes are much more sensitive to latitudinal than longitudinal variations in forcing. Shindell and Faluvegi (2009) used a model
inverse approach to infer that NH aerosol reduction was associatedwith more than 70% of Arctic warming from the 1970s to the 2000s,
and that Arctic and much of the SH surface temperature changes arestrongly affected by remote forcing changes (alsoSection 10.3.1.1.4).
Voulgarakis and Shindell (2010) defined a regional transient temperature sensitivity parameter, or temperature response per unit forcing foreach 4-degree latitude band. Using observed surface air temperaturechanges they showed that the parameter is best constrained from 50°S
to 25°N, where the value is 0.35°C (W m–2)–1, smaller than at northernhigher latitudes, and 35% smaller than in AR4 models.
Some aerosol model studies have demonstrated highly localized climate response to regional forcing. Significant regional cooling andhydrological shifts in the eastern USA and in Eastern Asia during thelast half of the 20th century were modelled and attributed to local
aerosols (Leibensperger et al., 2008, 2012a, 2012b; Chang et al., 2009)and localized warming projected for aerosol reductions (Mickley et al.,2012). Observations have also linked historical trends in aerosols andtemperature (Ruckstuhl et al., 2008; Philipona et al., 2009).
Since AR4, there has been new research on aerosol influences on thehydrologic cycle (also Sections 7.4, 7.6.4, 10.3.3.1 and 11.3.2.4.3).
Increased aerosol loading, with greater surface energy flux reductionin the NH, has been implicated in the observed southward shift of theIntertropical Convergence Zone (ITCZ) towards the hemisphere withsmaller surface energy reduction: southward up to the 1980s with areversal since (e.g., Denman et al., 2007; Zhang et al., 2007). Several
studies have modelled an associated reduction in NH precipitation
and associated shifts in the Hadley circulation (e.g., Rotstayn et al.,
2000; Williams et al., 2001; Ming et al., 2011). The ITCZ shift may inturn be responsible for broad regional precipitation changes, including drying of the Sahel (e.g., Rotstayn and Lohmann, 2002; Biasutti
and Giannini, 2006; Kawase et al., 2010; Ackerley et al., 2011) andnorthwestern Brazil (Cox et al., 2008), both of which peaked in the
1980s. These hemispheric asymmetric ITCZ effects are overlaid on
thermodynamic aerosol effects which moisten subtropical regions,countering GHG-induced drying of these regions (Ming et al., 2011).
Studies indicate that aerosols are more effective than an equivalent
WMGHG forcing for shifting precipitation, and that historical trends
in several areas cannot be explained without including aerosol forcing(Bollasina et al., 2011; Booth et al., 2012; Shindell et al., 2012a; Shindell et al., 2012b). However, confidence in attribution of any human
influence on zonal shifts in precipitation distribution is only medium(Section 10.3.2.2).
There is increasing evidence but limited agreement that absorbing­aerosols influence cloud distributions (Section 7.3.4.2). Absorbing

Anthropogenic and Natural Radiative Forcing

Chapter 8a­ erosols apparently have complex influences on precipitation in monsoon regions. Model studies of Stephens et al. (2004) and Miller etal. (2004) showed that dust absorption over Africa enhances low-level convergence, vertical velocities and therefore local monsoon circulation and precipitation. On the other hand, Kawase et al. (2010)
showed that biomass burning BC may cause the decreasing precipitation trend seen in tropical Africa during austral summer, due to reduction in evaporation and enhanced subsidence. The aerosol effects on
the Indian monsoon are similarly complex, and have been the subject of numerous studies (e.g., Ramanathan et al., 2005; Chung andRamanathan, 2006; Lau et al., 2006; Wang et al., 2009; Bollasina etal., (2011), but a clear picture of how the regional aerosol forcing
correlates with responses has not yet fully emerged. Attribution ofchanges in monsoon to human influence generally has low confidence(Section 10.3).

albedo feedback. These forcings can also have non-local impacts that
result from enhanced land-ocean temperature contrast, increasing surface convergence over land and divergence over oceans. A poleward
intensification of the high pressure patterns and subtropical jet may
also result (Fletcher et al., 2009). BC contributions to snow darkening reduces snow cover, however the magnitude of the effect is very
uncertain (see Sections 7.5.2.3 and 8.3.4.4). A model study calculated
BC-albedo reduction to cause about 20% Arctic snow/ice cover reduction and 20% of Arctic warming over the previous century (Koch et al.,2011). However, reductions in Arctic soot during the past two decades(e.g., Hegg et al., 2009) have likely reversed that trend (e.g., Koch et al.,2011; Skeie et al., 2011b; Lee et al., 2013). Cryospheric feedbacks andatmospheric dynamical responses in models have an associated poleward shift in the temperature response to aerosol–cloud interactions(Kristjansson et al., 2005; Koch et al., 2009a; Chen et al., 2010).

Stratospheric ozone loss modelling has demonstrated an effect on the
SH stratosphere similar to increased GHGs, cooling stratospheric temperatures, strengthening the polar vortex and shifting the westerly jetpoleward; however causing cooler Antarctic surface temperatures, withlarger influence on austral summer conditions (Son et al., 2009; McLandress et al., 2011; Thompson et al., 2011; see also Sections 10.3.3 and11.3.2.4.3.) In the troposphere, models indicate that increased tropospheric ozone has caused warming, proportionally more in the NH andnotably to the Arctic during winter, mainly during the second half of the20th century (Shindell et al., 2006a).

Solar spectral (UV) irradiance variations along the solar cycle induceozone responses by modifying the ozone production rate through photolysis of molecular oxygen (Section 8.4.1.4.1), and the resulting differential heating can drive circulation anomalies that lead to regionaltemperature and precipitation changes (Haigh, 1999; Shindell et al.,2006b; Frame and Gray, 2010; Gray et al., 2010). Such solar forcing
may influence natural modes of circulation such as the Northern Annular Mode (e.g., Shindell et al., 2001; de la Torre et al., 2006; Ineson et al.,2011), the South Asian Summer Monsoon (Fan et al., 2009), the Southern Annular Mode (Kuroda and Kodera, 2005; Roscoe and Haigh, 2007)or the ENSO (Mann et al., 2005). The pattern of temperature response
is less uniform than the forcing, for example, warming in the NH, butlittle response in the SH due to temperature moderation by wind speed
enhancement effects on ocean circulation (Swingedouw et al., 2011).
Regional responses to solar forcing are mediated by the stratosphere,
so that reproducing such change requires spectrally varying solar forcing rather than TSI forcing (Lee et al., 2009; Section 8.4.1.4).

Albedo changes due to land use and land cover changes exert a heterogeneous climate forcing (Figure 8.9). The surface albedo brightenedon the one hand due to a shift from forest to brighter croplands, causing local cooling (e.g., Eliseev and Mokhov, 2011; Lee et al., 2011), butalso darkened due to the re-expansion of forests to higher latitudes(Esper and Schweingruber, 2004) and increased vegetation height insnowy regions (Bonfils et al., 2012; also Section 8.3.5). Model studies
have shown cooling from land use and land cover changes, especially over NH continents, although without demonstrating a detectablesignal in observations (Matthews et al., 2004).
In addition to land use and climate-induced vegetation changes, CO2
affects vegetation forcing indirectly, reducing transpiration from plants
as stomata open less with increasing CO2, resulting in localized atmospheric drying and warming (Section 11.3.2.3.1; Joshi and Gregory,2008). These are not included in the standard RF (Section 8.1) and may
be considered feedbacks (Section 8.3.2). This is modelled to be largest
over the Amazon, the central African forest, and to some extent overboreal and temperate forests (Andrews et al., 2011). In the coupledclimate modelling study of Lawrence and Chase (2010), the vegetation
changes caused significant reduction in evapotranspiration, drying andwarming in tropical and subtropical regions, with insignificant cooling at higher latitudes. Overall, vegetation changes may have caused
modest cooling at high latitudes and warming at low latitudes, but theuncertainties are large and confidence is very low.
Deposition of BC on snow and ice, and loss of snow and ice darken
the surface, reduces albedo, and enhances climate warming. Substantial snow-cover reduction of North America leads to warmer North
American summertime temperature in models having a strong snow

Stratospheric aerosol clouds (also Section 8.4.2.2) from tropical eruptions spread poleward and can cover an entire hemisphere or the
globe, depending on the initial latitudinal spread. The aerosol eruption
cloud from the 1963 Agung was confined mainly to the SH; the 1982 El
Chichón mainly to the NH; and the 1991 Pinatubo covered the globe,
all with an e-folding lifetime of about 1 year (e.g., Antuña et al., 2003).
High-latitude eruptions typically stay confined to the high-latitude
regions with shorter lifetimes of 2 to 4 months (Kravitz and Robock,2011). Volcanic aerosols primarily scatter solar radiation back to space,but also absorb longwave radiation with the former larger by an orderof magnitude. Stratospheric aerosol absorption heats the layer where
they reside and produces distinct vertical and horizontal distributionsof the heating rate. The temperature and chemical effects of the aerosols also enhance ozone destruction, which somewhat counteracts
the radiative heating (Stenchikov et al., 2002). For tropical eruptions,this may affect atmospheric dynamics, with a stronger polar vortex, apositive mode of the Arctic Oscillation, and winter warming over NH
continents (Robock, 2000). Climate responses to solar and volcanic
forcings are further discussed in the context of detection and attribution of millennial climate change (see Section 10.7).
The study of how climate responds to regionally varying patterns offorcing is critical for understanding how local activities impact regional
707

8

Chapter 8

climate; however, the studies are exploratory and generally evoke very
low confidence. However there is medium to high confidence in somequalitative but robust features, such as the damped warming of the
NH and shifting of the ITCZ from aerosols, and positive feedbacks fromhigh-latitude snow and ice albedo changes.

8

8.6.3

Spatial Evolution of Radiative Forcing andResponse for the Future

Most components of aerosols and ozone precursors are estimated todecrease toward the end of this century in the RCPs except CH4 in
RCP8.5 (Figure 8.2) and nitrate aerosols, though some species reachthe maximum amounts of emissions around the mid-21st century(Figure 8.2). The RCPs therefore contrast with the emission scenarios
for TAR and AR4, which were based on Special Report on Emissions
Scenarios (SRES) and have future projections of larger increase in thenear-term climate forcers (NTCFs). It has been questioned whether
such low emission of NTCFs is possible in the future given the current
policies (Pozzer et al., 2012). This section surveys spatial differences in
the RF of aerosols and ozone for the future based on the RCPs.
Figure 8.26 shows the global distributions of changes in aerosol andozone forcings in 2030 and 2100 relative to 2000 for RCP2.6 and 8.5(Shindell et al., 2013c). Both scenarios indicate reduced aerosol loading, and thus positive forcing over Europe, North America and Asia by
2100 where RF is above +0.5 W m–2 because of substantial reductionof scattering aerosols. The global mean RF due to aerosol–radiationinteraction is estimated to be +0.12 and +0.08 W m–2 for RCP2.6 and
8.5, respectively, in 2100. Though the RF by total anthropogenic aerosols is positive, reduced BC contributes substantial negative forcing
especially over the similar regions. The global mean carbonaceous RF
including both the effects of BC and OC is estimated to be –0.20 and–0.11 W m–2 for RCP2.6 and 8.5, respectively, in 2100. Early in the
century, on the other hand, both scenarios indicate increased negative
aerosol forcing over South Asia, with reversal between 2030 and 2100.
Emissions of BC, OC and SO2 will reach their maximums early andmiddle in the century for RCP2.6 and 8.5, respectively in India. In RCP6,
high emission levels of SO2 in China persist until the mid-21st century(Supplementary Material Figure 8.SM.1), and then it is predicted tokeep a high negative RF due to aerosol–radiation interaction over East
Asia. The RF due to aerosol–radiation interaction for carbonaceous aerosol is positive over East and South Asia in 2030 relative to 2000 for
RCP8.5 because BC emission is also larger in 2030. Over central andsouthern Africa, a change in the future RF due to aerosol–radiationinteraction based on RCPs is not clear mainly because of uncertainties
in the wildfires emissions (see Section 7.3.5.3). The global mean total
RF due to aerosol–radiation interaction in the future is rather small due
to offsetting effects, with reductions in BC, increases in nitrate aerosols, and reductions in scattering aerosols each causing substantiallymore forcing than the net.
Emissions and atmospheric loadings of natural aerosols are affectedby climate change. There is, however, no consensus among studies on
future trends of their changes for major natural aerosols, mineral dust
and sea salt, as indicated in Section 7.3.5.1. The spatial pattern of theaerosol forcing may be influenced by natural aerosols due to ­reduction

708

Anthropogenic and Natural Radiative Forcing

in sea ice cover leading to increased emission of high-latitude sea
salt (Struthers et al., 2011; Takemura, 2012) and SOA from vegetation
changes (Tsigaridis and Kanakidou, 2007).
The simulations applying the RCPs indicate that the latitude of maximum emission of NTCFs, and therefore of maximum RF, is projected
to shift somewhat southward for the next few decades (in 2030 of
Figure 8.26). The shift of peak aerosol loading southward is expected tocause the ITCZ to continue to shift northward. This, in combination withwarming and drying over tropical land, has been modelled to lead togreatly enhanced drought conditions in the Amazon (Cox et al., 2008).
On the other hand, if the low-latitude aerosol is sufficiently absorbing,broadening of the ITCZ convergence region and enhanced cloud cover
could result, as modelled for dust (Perlwitz and Miller, 2010).
Reductions in high-latitude BC are expected to contribute to reducing
Arctic forcing (e.g., Koch et al., 2011), due to reduction in BC deposition on snow as well as in absorption of sunlight over bright surface.
On the other hand, reduction in mid-high-latitude scattering aerosols
may offset all or part of the impact of the local Arctic forcing change(Shindell et al., 2010; Koch et al., 2011).
Figure 8.26 also shows the ozone RF in 2030 and 2100 relative to 2000,
which includes changes both in tropospheric and stratospheric ozone.
Recovery of ozone in the stratosphere in the 21st century will result in
positive forcing in the SH high latitudes in comparison with the year
2000 for both the pathways. This is because of the reduced emissionsof ozone-depleting substances controlled under the Montreal Protocol,
with a small additional effect from a feedback of changes in temperature and in the vertical circulation due to changes in stratosphericcompositions (Kawase et al., 2011; Lamarque et al., 2011). In the troposphere, on the other hand, a large difference in the CH4 emissions
between RCP8.5 and the other pathways shown in Figure 8.2 leads
to a different RF trend outside the SH high latitudes. Ozone recovery
in the stratosphere and ozone increase in the troposphere leads to a
positive RF all over the globe in RCP8.5 with a mean of +0.26 W m–2
in 2100. The cancellation between positive RF due to ozone increase in
the stratosphere and negative RF due to ozone decrease in the troposphere results in a global mean RF of –0.12 W m–2 in RCP2.6.
Figure 8.26 also shows the global distributions of changes in ERF due
to both aerosol–radiation and aerosol–cloud interactions in 2030 and
2100 relative to 2000 for RCP8.5. Although the ERF includes rapid
adjustments and therefore its magnitude is much larger than that of
RF due to aerosol–radiation interaction, the spatial pattern is generally
similar to RF. The ERF in 2100 shows positive values relative to 2000
in North America, Europe and Asia even with RCP8.5, which indicatesthe aerosol forcing is projected to approach to the pre-industrial level.

Anthropogenic and Natural Radiative Forcing

Chapter 8

2100

0.09 W m-2

0.12 W m-2

All aerosol RF
RCP 2.62030

80.08 W m-2-0.03 W m-2-0.20 W m-2-0.02 W m-2-0.11 W m-2

0.00 W m-2-0.12 W m-2

0.11 W m-2

0.26 W m-2

Ozone RFRCP 8.5

Ozone RFRCP 2.6

Carbonaceous aerosol RF Carbonaceous aerosol RF
RCP 8.5
RCP 2.6

All aerosol RF
RCP 8.5

0.01 W m-2-.88 -.62 -.38 -.12.12 .38 .62 .88

0.90 W m-2

All aerosol ERF
RCP 8.5

0.27 W m-2-1.75 -1.25 -.75 -.25 .5

1.5 2.5 3.5Figure 8.26 | Multi-model mean RF (W m ) due to aerosol–radiation interaction of all anthropogenic aerosols (first and second rows) and anthropogenic carbonaceous (BC+OC)
aerosols (third and fourth rows), and total ozone (fifth and sixth rows) in 2030 (left) and 2100 (right) relative to 2000 for RCP2.6 (top each) and RCP8.5 (bottom each) based on
the ACCMIP simulations. The seventh row shows multi-model mean ERF (W m–2) by all anthropogenic aerosols in 2030 (left) and 2100 (right) relative to 2000 for RCP8.5. Globalarea-weighted means are given in the upper right of each panel.
–2709

Chapter 8

8.7

Emission Metrics

8.7.1

Metric Concepts

8.7.1.1 Introduction

8

To quantify and compare the climate impacts of various emissions,
it is necessary to choose a climate parameter by which to measure
the effects; that is, RF, temperature response, and so forth. Thus, various choices are needed for the steps down the cause–effect chain
from emissions to climate change and impacts (Figure 8.27 and Box8.4). Each step in the cause effect chain requires a modelling framework. For assessments and evaluation one may—as an alternative tomodels that explicitly include physical processes resulting in forcing
and responses—apply simpler measures or metrics that are based on
results from complex models. Metrics are used to quantify the contributions to climate change of emissions of different substances and canthus act as ‘exchange rates’ in multi-component policies or comparisons of emissions from regions/countries or sources/sectors. Metrics
are also used in areas such as Life Cycle Assessments and Integrated
Assessment Modelling (e.g., by IPCC WGIII).
Metrics can be given in absolute terms (e.g., K kg–1) or in relative terms
by normalizing to a reference gas — usually CO2. To transform theeffects of different emissions to a common scale — often called ‘CO2
equivalent emissions’—the emission (Ei) of component i can be multiplied with the adopted normalized metric (Mi): Mi × Ei = CO2-eqi.
Ideally, the climate effects of the calculated CO2 equivalent emissions
should be the same regardless of the mix of components emitted.
However, different components have different physical properties, anda metric that establishes equivalence with regard to one effect cannot
guarantee equivalence with regard to other effects and over extended
time periods, for example, Lauder et al. (2013), O’Neill (2000), Smithand Wigley (2000), Fuglestvedt et al. (2003).

Anthropogenic and Natural Radiative Forcing

Metrics do not define goals and policy—they are tools that enableevaluation and implementation of multi-component policies (i.e.,
which emissions to abate). The most appropriate metric will depend
on which aspects of climate change are most important to a particular application, and different climate policy goals may lead to different conclusions about what is the most suitable metric with which
to implement that policy, for example, Plattner et al. (2009); Tol et al.
(2012). Metrics that have been proposed include physical metrics aswell as more comprehensive metrics that account for both physical andeconomic dimensions (see 8.7.1.5 and WGIII, Chapter 3).
This section provides an assessment that focuses on the scientificaspects and utility of emission metrics. Extending such an assessment
to include more policy-oriented aspects of their performance andusage such as simplicity, transparency, continuity, economic implications of usage of one metric over another, and so forth, is not given
here as this is beyond the scope of WGI. However, consideration ofsuch aspects is vital for user-assessments. In the following, the focus ison the more well-known Global Warming Potential (GWP) and Global
Temperature change Potential (GTP), though other concepts are also
briefly discussed.

8.7.1.2 The Global Warming Potential Concept
The Global Warming Potential (GWP) is defined as the time-integrated RF due to a pulse emission of a given component, relative to apulse emission of an equal mass of CO2 (Figure 8.28a and formula).
The GWP was presented in the First IPCC Assessment (Houghton et al.,1990), stating ‘It must be stressed that there is no universally accepted
methodology for combining all the relevant factors into a single global
warming potential for greenhouse gas emissions. A simple approachhas been adopted here to illustrate the difficulties inherent in theconcept, ...’. Further, the First IPCC Assessment gave no clear physicalinterpretation of the GWP.

Figure 8.27 | The cause–effect chain from emissions to climate change and impacts showing how metrics can be defined to estimate responses to emissions (left) and for development of multi-component mitigation (right). The relevance of the various effects increases downwards but at the same time the uncertainty also increases. The dotted line on theleft indicates that effects and impacts can be estimated directly from emissions, while the arrows on the right side indicate how these estimates can be used in development ofstrategies for reducing emissions. (Adapted from Fuglestvedt et al., 2003, and Plattner et al., 2009.)

710

Anthropogenic and Natural Radiative Forcing

Chapter 8

Box 8.4 | Choices Required When Using Emission Metrics
Time frames: One can apply a backward-looking (i.e., historical) or a forward-looking perspective on the responses to emissions. In
the forward-looking case one may use pulses of emissions, sustained emissions or emission scenarios. All choices of emission perturbations are somewhat artificial and idealized, and different choices serve different purposes. One may use the level (e.g., degrees Celsius)or rate of change (e.g., degrees Celsius per decade). Furthermore, the effects of emissions may be estimated at a particular time or be
integrated over time up to a chosen time horizon. Alternatively, discounting of future effects may be introduced (i.e., a weighting ofeffects over time).

8

Type of effect or end-point: Radiative forcing, temperature change or sea level change, for example, could be examined (Figure8.27). Metrics may also include eco/biological or socioeconomic damages. The choice of climate impact parameters is related to whichaspects of climate change are considered relevant for interpretation of ‘dangerous anthropogenic interference with the climate system’(UNFCCC Article 2).
Spatial dimension for emission and response: Equal-mass emissions of NTCFs from different regions can induce varying global
mean climate responses, and the climate response also has a regional component irrespective of the regional variation in emissions.
Thus, metrics may be given for region of emission as well as region of response.
Some of the choices involved in metrics are scientific (e.g., type of model, and how processes are included or parameterized in themodels). Choices of time frames and climate impact are policy-related and cannot be based on science alone, but scientific studies canbe used to analyse different approaches and policy choices.

A direct interpretation is that the GWP is an index of the total energy
added to the climate system by a component in question relative tothat added by CO2. However, the GWP does not lead to equivalencewith temperature or other climate variables (Fuglestvedt et al., 2000,
2003; O’Neill, 2000; Daniel et al., 2012; Smith and Wigley, 2000;
Tanaka et al., 2009). Thus, the name ‘Global Warming Potential’ may besomewhat misleading, and ‘relative cumulative forcing index’ would
be more appropriate. It can be shown that the GWP is approximately
equal to the ratio (normalizing by the similar expression for CO2) of theequilibrium temperature response due to a sustained emission of thespecies or to the integrated temperature response for a pulse emission(assuming efficacies are equal for the gases that are compared; O’Neill,
2000; Prather, 2002; Shine et al., 2005a; Peters et al., 2011a; Azar andJohansson, 2012).
The GWP has become the default metric for transferring emissions ofdifferent gases to a common scale; often called ‘CO2 equivalent emissions’ (e.g., Shine, 2009). It has usually been integrated over 20, 100
or 500 years consistent with Houghton et al. (1990). Note, howeverthat Houghton et al. presented these time horizons as ‘candidates fordiscussion [that] should not be considered as having any special significance’. The GWP for a time horizon of 100 years was later adopted
as a metric to implement the multi-gas approach embedded in the
United Nations Framework Convention on Climate Change (UNFCCC)
and made operational in the 1997 Kyoto Protocol. The choice of time
horizon has a strong effect on the GWP values — and thus also on thecalculated contributions of CO2 equivalent emissions by component,
sector or nation. There is no scientific argument for selecting 100 years
compared with other choices (Fuglestvedt et al., 2003; Shine, 2009).
The choice of time horizon is a value judgement because it depends

Figure 8.28 | (a) The Absolute Global Warming Potential (AGWP) is calculated byintegrating the RF due to emission pulses over a chosen time horizon; for example, 20
and 100 years (vertical lines). The GWP is the ratio of AGWP for component i over AGWP
for the reference gas CO2. The blue hatched field represents the integrated RF from apulse of CO2, while the green and red fields represent example gases with 1.5 and 13
years lifetimes, respectively. (b) The Global Temperature change Potential (GTP) is basedon the temperature response at a selected year after pulse emission of the same gases;
e.g., 20 or 100 years (vertical lines). See Supplementary Material Section 8.SM.11 forequations for calculations of GWP and GTP.

711

Chapter 8

Anthropogenic and Natural Radiative Forcingt­ emperature effects of emissions relative to that of CO2 for the chosen
time horizon. As for GWP, the choice of time horizon has a strong effecton the metric values and the calculated contributions to warming.
In addition, the AGTP can be used to calculate the global mean temperature change due to any given emission scenario (assuming linearity)using a convolution of the emission scenarios and AGTPi:

8∆𝑇𝑇 𝑡𝑡 =!
! ! 𝐸𝐸! 𝑠𝑠 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴! 𝑡𝑡 − 𝑠𝑠 𝑑𝑑𝑑𝑑

(8.1)

where i is component, t is time, and s is time of emission (Berntsen andFuglestvedt, 2008; Peters et al., 2011b; Shindell et al., 2011).
()

Figure 8.29 | Development of AGWP-CO2, AGWP-CH4 and GWP-CH4 with time horizon. The yellow and blue curves show how the AGWPs changes with increasing time
horizon. Because of the integrative nature the AGWP for CH4 (yellow curve) reaches aconstant level after about five decades. The AGWP for CO2 continues to increase for centuries. Thus the ratio which is the GWP (black curve) falls with increasing time horizon.

on the relative weight assigned to effects at different times. Other
important choices include the background atmosphere on which the
GWP calculations are superimposed, and the way indirect effects andfeedbacks are included (see Section 8.7.1.4).
For some gases the variation in GWP with time horizon mainly reflects
properties of the reference gas, not the gas for which the GWP is calculated. The GWP for NTCFs decreases with increasing time horizon, as
GWP is defined with the integrated RF of CO2 in the denominator. As
shown in Figure 8.29, after about five decades the development in the
GWP for CH4 is almost entirely determined by CO2. However, for longlived gases (e.g., SF6) the development in GWP is controlled by both theincreasing integrals of RF from the long-lived gas and CO2.

8.7.1.3 The Global Temperature change Potential Concept
Compared to the GWP, the Global Temperature change Potential (GTP;
Shine et al., 2005a) goes one step further down the cause–effectchain (Figure 8.27) and is defined as the change in global mean surface temperature at a chosen point in time in response to an emission
pulse—relative to that of CO2. Whereas GWP is integrated in time
(Figure 8.28a), GTP is an end-point metric that is based on temperature change for a selected year, t, (see Figure 8.28b with formula). Likefor the GWP, the impact from CO2 is normally used as reference, hence,
for a component i, GTP(t)i = AGTP(t)i / AGTP(t)CO2 = ∆T((t)i /∆T(t)CO2,
where AGTP is the absolute GTP giving temperature change per unit
emission (see Supplementary Material Section 8.SM.11 for equations
and parameter values). Shine et al. (2005a) presented the GTP for bothpulse and sustained emission changes based on an energy balance
model as well as analytical equations. A modification was later introduced (Shine et al., 2007) in which the time horizon is determined by
the proximity to a target year as calculated by using scenarios andclimate models (see Section 8.7.1.5).
Like GWP, the GTP values can be used for weighting the emissions
to obtain ‘CO2 equivalents’ (see Section 8.7.1.1). This gives the712

By accounting for the climate sensitivity and the exchange of heat
between the atmosphere and the ocean, the GTP includes physical processes that the GWP does not. The GTP accounts for the slow responseof the (deep) ocean, thereby prolonging the response to emissions
beyond what is controlled by the decay time of the atmospheric concentration. Thus the GTP includes both the atmospheric adjustment
time scale of the component considered and the response time scaleof the climate system.
The GWP and GTP are fundamentally different by construction and different numerical values can be expected. In particular, the GWPs for
NTCFs, over the same time frames, are higher than GTPs due to theintegrative nature of the metric. The GTP values can be significantly
affected by assumptions about the climate sensitivity and heat uptake
by the ocean. Thus, the relative uncertainty ranges are wider for the
GTP compared to GWP (see Section 8.7.1.4). The additional uncertainty
is a typical trade-off when moving along the cause–effect chain to aneffect of greater societal relevance (Figure 8.27). The formulation of theocean response in the GTP has a substantial effect on the values; thus
its characterization also represents a trade-off between simplicity andaccuracy. As for GWP, the GTP is also influenced by the backgroundatmosphere, and the way indirect effects and feedbacks are included(see Section 8.7.1.4).

8.7.1.4 Uncertainties and Limitations related to Global Warming
Potential and Global Temperature change Potential
The uncertainty in the numerator of GWP; that is, the AGWPi (see formula in Figure 8.28a) is determined by uncertainties in lifetimes (orperturbation lifetimes) and radiative efficiency. Inclusion of indirect
effects increases uncertainties (see below). For the reference gas CO2,
the uncertainty is dominated by uncertainties in the impulse response
function (IRF) that describes the development in atmospheric concentration that follows from an emission pulse (Joos et al., 2013); see Box
6.2 and Supplementary Material Section 8.SM.12. The IRF is sensitive
to model representation of the carbon cycle, pulse size and backgroundCO2 concentrations and climate.
Based on a multi-model study, Joos et al. (2013) estimate uncertainty ranges for the time-integrated IRF for CO2 to be ±15% and ±25%
(5 to 95% uncertainty range) for 20- and 100-year time horizons,
respectively. Assuming quadratic error propagation, and ±10% uncertainty in radiative efficiency, the uncertainty ranges in AGWP for CO2
were estimated to be ±18% and ±26% for 20 and 100 years. TheseAnthropogenic and Natural Radiative Forcingu­ ncertainties affect all metrics that use CO2 as reference. Reisinger etal. (2010) and Joos et al. (2013) show that these uncertainties increase
with time horizon.
The same factors contribute to uncertainties in the GTP, with an additional contribution from the parameters describing the ocean heatuptake and climate sensitivity. In the first presentation of the GTP,
Shine et al. (2005a) used one time constant for the climate response in
their analytical expression. Improved approaches were used by Boucher and Reddy (2008), Collins et al. (2010) and Berntsen and Fuglestvedt(2008) that include more explicit representations of the deep oceanthat increased the long-term response to a pulse forcing. Over therange of climate sensitivities from AR4, GTP50 for BC was found to varyby a factor of 2, the CH4 GTP50 varied by about 50%, while for N2O
essentially no dependence was found (Fuglestvedt et al., 2010). AGTPs
for CO2 were also calculated in the multi-model study by Joos et al.
(2013). They found uncertainty ranges in AGTP that are much larger
than for AGWP; ±45% and ±90% for 20 and 100 years (5 to 95%
uncertainty range). These uncertainty ranges also reflect the signal-tonoise ratio, and not only uncertainty in the physical mechanisms.
There are studies combining uncertainties in various input parameters.
Reisinger et al. (2011) estimated the uncertainty in the GWP for CH4
and found an uncertainty of –30 to +40% for the GWP100 and –50 to+75% for GTP100 of CH4 (for 5 to 95% of the range). Boucher (2012)
performed a Monte Carlo analysis with uncertainties in perturbation
lifetime and radiative efficiency, and for GWP100 for CH4 (assuming a
constant background atmosphere) he found ±20%, and –40 to +65 for
GTP100 (for 5 to 95% uncertainty range).
Here we estimate uncertainties in GWP values based on the uncertainties given for radiative efficiencies (Section 8.3.1), perturbation
lifetimes, indirect effects and in the AGWP for the reference gas CO2(see Supplementary Material Section 8.SM.12). For CH4 GWP we estimate an uncertainty of ±30% and ±40% for 20- and 100-year time
horizons, respectively (for 5 to 95% uncertainty range). The uncertainty
is dominated by AGWP for CO2 and indirect effects. For gases with lifetimes of a century or more the uncertainties are of the order of ±20%
and ±30% for 20- and 100-year horizons. The uncertainty in GWPs forgases with lifetimes of a few decades is estimated to be of the orderof ±25% and ±35% for 20 and 100 years. For shorter-lived gases, theuncertainties in GWPs will be larger (see Supplementary Material Section 8.SM.12 for a discussion of contributions to the total uncertainty.)
For GTP, few uncertainty estimates are available in the literature. Basedon the results from Joos et al. (2013), Reisinger et al. (2010) and Boucher (2012) we assess the uncertainty to be of the order of ±75% for
the CH4 GTP100.
The metric values are also strongly dependent on which processes
are included in the definition of a metric. Ideally all indirect effects(Sections 8.2 and 8.3) should be taken into account in the calculationof metrics. The indirect effects of CH4 on its own lifetime, tropospheric ozone and stratospheric water have been traditionally included in
its GWP. Boucher et al. (2009) have quantified an indirect effect on
CO2 when fossil fuel CH4 is oxidized in the atmosphere. Shindell etal. (2009) estimated the impact of reactive species emissions on both
gaseous and aerosol forcing species and found that ozone precursors,

Chapter 8

including CH4, had an additional substantial climate effect because
they increased or decreased the rate of oxidation of SO2 to sulphateaerosol. Studies with different sulphur cycle formulations have foundlower sensitivity (Collins et al., 2010; Fry et al., 2012). Collins et al.
(2010) postulated an additional component to their GWPs and GTPs
for ozone precursors due to the decreased productivity of plants underhigher levels of surface ozone. This was estimated to have the same
magnitude as the ozone and CH4 effects. This effect, however, has
so far only been examined with one model. In a complex and interconnected system, feedbacks can become increasingly complex, anduncertainty of the magnitude and even direction of feedback increases
the further one departs from the primary perturbation, resulting in a
trade-off between completeness and robustness, and hence utility fordecision-making.
Gillett and Matthews (2010) included climate–carbon feedbacks incalculations of GWP for CH4 and N2O and found that this increased
the values by about 20% for 100 years. For GTP of CH4 they foundan increase of ~80%. They used numerical models for their studies
and suggest that climate–carbon feedbacks should be considered andparameterized when used in simple models to derive metrics. Collins et al. (2013) parameterize the climate-carbon feedback based on
Friedlingstein et al. (2006) and Arora et al. (2013) and find that thismore than doubles the GTP100 for CH4. Enhancement of the GTP forCH4 due to carbon–climate feedbacks may also explain the higher GTP
values found by Reisinger et al. (2010).
The inclusion of indirect effects and feedbacks in metric values has
been inconsistent in the IPCC reports. In SAR and TAR, a carbon model
without a coupling to a climate model was used for calculation of IRF
for CO2 (Joos et al., 1996), while in AR4 climate-carbon feedbacks were
included for the CO2 IRF (Plattner et al., 2008). For the time horizons
20 and 100 years, the AGWPCO2 calculated with the Bern3D-LPJ modelis, depending on the pulse size, 4 to 5% and 13 to 15% lower, respectively, when carbon cycle–climate feedbacks are not included (Jooset al., 2013). While the AGWP for the reference gas CO2 included climate–carbon feedbacks, this is not the case for the non-CO2 gas in the
numerator of GWP, as recognized by Gillett and Matthews (2010), Jooset al. (2013), Collins et al. (2013) and Sarofim (2012). This means that
the GWPs presented in AR4 may underestimate the relative impacts
of non-CO2 gases. The different inclusions of feedbacks partially represent the current state of knowledge, but also reflect inconsistent andambiguous definitions. In calculations of AGWP for CO2 in AR5 we use
the IRF for CO2 from Joos et al. (2013) which includes climate–carbon
feedbacks. Metric values in AR5 are presented both with and without
including climate–carbon feedbacks for non-CO2 gases. This feedback
is based on the carbon-cycle response in a similar set of models (Aroraet al., 2013) as used for the reference gas (Collins et al., 2013).
The effect of including this feedback for the non-reference gas increases with time horizon due to the long-lived nature of the initiated CO2
perturbation (Table 8.7). The relative importance also increases withdecreasing lifetime of the component, and is larger for GTP than GWP
due to the integrative nature of GWP. We calculate an increase in the
CH4 GWP100 of 20%. For GTP100, however, the changes are much larger;of the order of 160%. For the shorter time horizons (e.g., 20 years)
the effect of including this feedback is small (<5%) for both GWP
713

8Chapter 8

Anthropogenic and Natural Radiative Forcing

Table 8.7 | GWP and GTP with and without inclusion of climate–carbon feedbacks (cc fb) in response to emissions of the indicated non-CO2 gases (climate-carbon feedbacks inresponse to the reference gas CO2 are always included).

Lifetime (years)
CH4b

8

HFC-134a

CFC-11

12.4

a

13.4

45.0

N2O

121.0aCF4

50,000.0

GWP20

GWP100

GTP20

GTP100No cc fb

84

28

67

4

With cc fb

86

34

70

11

No cc fb

3710

1300

3050

201

With cc fb

3790

1550

3170

530

No cc fb

6900

4660

6890

2340

With cc fb

7020

5350

7080

3490

No cc fb

264

265

277

234

With cc fb

268

298

284

297

No cc fb

4880

6630

5270

8040

With cc fb

4950

7350

5400

9560

Notes:
Uncertainties related to the climate–carbon feedback are large, comparable in magnitude to the strength of the feedback for a single gas.
aPerturbation lifetime is used in the calculation of metrics.
b
These values do not include CO2 from methane oxidation. Values for fossil methane are higher by 1 and 2 for the 20 and 100 year metrics, respectively (Table 8.A.1).

and GTP. For the more long-lived gases the GWP100 values increaseby 10 to 12%, while for GTP100 the increase is 20 to 30%. Table 8.A.1
gives metric values including the climate–carbon feedback for CO2
only, while Supplementary Material Table 8.SM.16 gives values for all
halocarbons that include the climate–carbon feedback. Though uncertainties in the carbon cycle are substantial, it is likely that including
the climate–carbon feedback for non-CO2 gases as well as for CO2
provides a better estimate of the metric value than including it only
for CO2.
Emission metrics can be estimated based on a constant or variablebackground climate and this influences both the adjustment times andthe concentration–forcing–temperature relationships. Thus, all metricvalues will need updating due to changing atmospheric conditions
as well as improved input data. In AR5 we define the metric values
with respect to a constant present-day condition of concentrations andclimate. However, under non-constant background, Joos et al. (2013)
found decreasing CO2 AGWP100 for increasing background levels (up to
23% for RCP8.5). This means that GWP for all non-CO2 gases (except
CH4 and N2O) would increase by roughly the same magnitude. Reisinger et al. (2011) found a reduction in AGWP for CO2 of 36% for RCP8.5
from 2000 to 2100 and that the CH4 radiative efficiency and AGWP
also decrease with increasing CH4 concentration. Accounting for both
effects, the GWP100 for CH4 would increase by 10 to 20% under low
and mid-range RCPs by 2100, but would decrease by up to 10% by
mid-century under the highest RCP. While these studies have focusedon the background levels of GHGs, the same issues apply for temperature. Olivié et al. (2012) find different temperature IRFs depending on
the background climate (and experimental set up).
User related choices (see Box 8.4) such as the time horizon can greatly
affect the numerical values obtained for CO2 equivalents. For a change
in time horizon from 20 to 100 years, the GWP for CH4 decreases by
a factor of approximately 3 and its GTP by more than a factor of 10.
Short-lived species are most sensitive to this choice. Some approaches
have removed the time horizon from the metrics (e.g., Boucher, 2012),
but discounting is usually introduced which means that a discount rate714

r (for the weighting function e–rt) must be chosen instead. The choice of
discount rate is also value based (see WGIII, Chapter 3).
For NTCFs the metric values also depend on the location and timingof emission and whether regional or global metrics are used for thesegases is also a choice for the users. Metrics are usually calculated forpulses, but some studies also give metric values that assume constantemissions over the full time horizon (e.g., Shine et al., 2005a; Jacobson,2010). It is important to be aware of the idealized assumption about
constant future emissions (or change in emissions) of the compound
being considered if metrics for sustained emissions are used.

8.7.1.5 New Metric Concepts
New metric concepts have been developed both to modify physicalmetrics to address shortcomings as well as to replace them with metrics that account for economic dimensions of problems to which metrics are applied. Modifications to physical metrics have been proposed
to better represent CO2 emissions from bioenergy, regional patterns ofresponse, and for peak temperature limits.
Emissions of CO2 from the combustion of biomass for energy in national emission inventories are currently assumed to have no net RF, basedon the assumption that these emissions are compensated by biomass
regrowth (IPCC, 1996). However, there is a time lag between combustion and regrowth, and while the CO2 is resident in the atmosphereit leads to an additional RF. Modifications of the GWP and GTP forbioenergy (GWPbio, GTPbio) have been developed (Cherubini et al., 2011;
Cherubini et al., 2012). The GWP­bio give values generally between zero(current default for bioenergy) and one (current for fossil fuel emissions)(Cherubini et al., 2011), and negative values are possible for GTPbio
due to the fast time scale of atmospheric–ocean CO2 exchange relative
to the growth cycle of biomass (Cherubini et al., 2012). GWPbio and
GTPbio have been used in only a few applications, and more research isneeded to assess their robustness and applicability. Metrics for biogeophysical effects, such as albedo changes, have been proposed (Betts,
2000; Rotenberg and Yakir, 2010) , but as for NTCFs regional variations

Anthropogenic and Natural Radiative Forcing

are important (Claussen et al., 2001) and the RF concept may not beadequate (Davin et al., 2007).
New concepts have also been developed to capture information
about regional patterns of responses and cancelling effects that are
lost when global mean metrics are used. The use of nonlinear damage
functions to capture information on the spatial pattern of responses
has been explored (Shine et al., 2005b; Lund et al., 2012). In addition, the Absolute Regional Temperature Potential (ARTP) (Shindell,2012; Collins et al., 2013) has been developed to provide estimatesof impacts at a sub-global scale. ARTP gives the time-dependent temperature response in four latitude bands as a function of the regional
forcing imposed in all bands. These metrics, as well as new regional
precipitation metrics (Shindell et al., 2012b), require additional studies
to determine their robustness.
Alternatives to the single basket approach adopted by the Kyoto Protocol are a component-by-component approach or a multi-basket
approach (Rypdal et al., 2005; Daniel et al., 2012; Sarofim, 2012; Jackson, 2009). Smith et al. (2012) show how peak temperature change isconstrained by cumulative emissions (see 12.5.4) for gases with long
lifetimes and emissions rates for shorter-lived gases (including CH4).
Thus, they divide gases into two baskets and present two metrics thatcan be used for estimating peak temperature for various emission scenarios. This division of gases into the two baskets is sensitive to thetime of peak temperature in the different scenarios. The approach uses
time invariant metrics that do not account for the timing of emissions
relative to the target year. The choice of time horizon is implicit in the
scenario assumed and this approach works only for a peak scenario.
A number of new metrics have been developed to add economic
dimensions to purely physically based metrics such as the GWP and
GTP. The use of physical metrics in policy contexts has been criticizedby economists (Reilly and Richards, 1993; Schmalensee, 1993; Hammitt
et al., 1996; Reilly et al., 1999; Bradford, 2001; De Cara et al., 2008). A
prominent use of metrics is to set relative prices of gases when implementing a multi-gas policy. Once a particular policy has been agreed
on, economic metrics can address policy goals more directly than physical metrics by accounting not only for physical dimensions but also
for economic dimensions such as mitigation costs, damage costs anddiscount rates (see WGIII, Chapter 3; Deuber et al., 2013).
For example, if mitigation policy is set within a cost-effectivenessframework with the aim of making the least cost mix of emissions
reductions across components to meet a global temperature target,the ‘price ratio’ (Manne and Richels, 2001), also called the Global Cost
Potential (GCP) (Tol et al., 2012), most directly addresses the goal. The
choice of target is a policy decision; metric values can then be calculated based on an agreed upon target. Similarly, if policy is set withina cost–benefit framework, the metric that directly addresses the policy
goal is the ratio of the marginal damages from the emission of a gas(i.e., the damage costs to society resulting from an incremental increasein emissions) relative to the marginal damages of an emission of CO2,
known as the Global Damage Potential (GDP) (Kandlikar, 1995). Bothtypes of metrics are typically determined within an integrated climate–economy model, since they are affected both by the response of theclimate system as well as by economic factors.

Chapter 8

If other indexes, such as the GWP, are used instead of an economic
cost-minimizing index, costs to society will increase. Cost implications
at the project or country level could be substantial under some circumstances (Godal and Fuglestvedt, 2002; Shine, 2009; Reisinger etal., 2013). However, under idealized conditions of full participation in
mitigation policy, the increase is relatively small at the global level,
particularly when compared to the cost savings resulting from a multi(as opposed to single-) gas mitigation strategy even when based on
an imperfect metric (O’Neill, 2003; Aaheim et al., 2006; Johansson etal., 2006; Johansson, 2012; Reisinger et al., 2013; Smith et al., 2013).
Purely physical metrics continue to be used in many contexts due atleast in part to the added uncertainties in mitigation and damage
costs, and therefore in the values of economic metrics (Boucher, 2012).
Efforts have been made to view purely physical metrics such as GWPsand GTPs as approximations of economic indexes. GTPs, for example,
can be interpreted as an approximation of a Global Cost Potential
designed for use in a cost-effectiveness setting (Shine et al., 2007; Tolet al., 2012). Quantitative values for time-dependent GTPs reproduce
in broad terms several features of the Global Cost Potential such as therising value of metrics for short-lived gases as a climate policy target isapproached (Tanaka et al., 2013). Figure 8.30 shows how contributionsof N2O, CH4 and BC to warming in the target year changes over time.
The contributions are given relative to CO2 and show the effects ofemission occurring at various times. Similarly, GWPs can be interpreted as approximations of the Global Damage Potential designed for acost–benefit framework (Tol et al., 2012). These interpretations of the
GTP and GWP imply that using even a purely physical metric in an economic policy context involves an implicit economic valuation.
In both cases, a number of simplifying assumptions must be madefor these approximations to hold (Tol et al., 2012). For example, inthe case of the GWP, the influence of emissions on RF, and therefore
implicitly on costs to society, beyond the time horizon is not taken
into account, and there are substantial numerical differences between
GWP and GDP values (Marten and Newbold, 2012). In the case of the
GTP, the influence of emissions on temperature change (and costs) is

Figure 8.30 | Global Temperature change Potential (GTP(t)) for CH4, nitrous oxide
and BC for each year from year of emission to the time at which the temperaturechange target is reached. The (time-invariant) GWP100 is also shown for N2O and CH4for ­comparison.

715

8Chapter 88i­ncluded only at the time the target is reached, but not before nor
after. Other metrics have been developed to more closely approximate
GCPs or GDPs. The Cost-Effective Temperature Potential (CETP) reproduces values of the GCP more closely than does the GTP (Johansson,2012). It is similar to the GTP but accounts for post-target temperatureeffects based on an assumption about how to value costs beyond thetime the target is reached. Metrics have also been proposed that take
into account forcing or temperature effects that result from emissions
trajectories over broad time spans, and that behave similarly to GCP
and GTP (Tanaka et al., 2009; Manning and Reisinger, 2011) or to GWP(e.g., O’Neill, 2000; Peters et al., 2011a; Gillett and Matthews, 2010;
Azar and Johansson, 2012).

8.7.1.6 Synthesis
In the application and evaluation of metrics, it is important to distinguish between two main sources of variation in metric values. Whilescientific choices of input data have to be made, there are also choices involving value judgements. For some metrics such choices are notalways explicit and transparent. The choice of metric type and time
horizon will for many components have a much larger effect than
improved estimates of input parameters and can have strong effects
on perceived impacts of emissions and abatement strategies.
In addition to progress in understanding of GWP, new concepts havebeen introduced or further explored since AR4. Time variant metrics
introduce more dynamical views of the temporal contributions that
accounts for the proximity to a prescribed target (in contrast to the traditional static GWP). Time variant metrics can be presented in a format
that makes changing metric values over time predictable.
As metrics use parameters further down the cause effect chain the metrics become in general more policy relevant, but at the same time theuncertainties increase. Furthermore, metrics that account for regionalvariations in sensitivity to emissions or regional variation in response
could give a very different emphasis to various emissions. Many species, especially NTCFs, produce distinctly regionally heterogeneous RFand climate response patterns. These aspects are not accounted for in
the commonly used global scale metrics.
The GWPs and GTPs have had inconsistent treatment of indirect effects
and feedbacks. The GWPs reported in AR4 include climate–carbon
feedbacks for the reference gas CO2 but not for the non-CO2 gases.
Such feedbacks may have significant impacts on metrics and should betreated consistently. More studies are needed to assess the importanceof consistent treatment of indirect effects/feedbacks in metrics.
The weighting of effects over time—choice of time horizon in thecase of GWP and GTP—is value based. Discounting is an alternative,
which also includes value judgements and is equally controversial. The
weighting used in the GWP is a weight equal to one up to the time horizon and zero thereafter, which is not in line with common approaches
for evaluation of future effects in economics (e.g., as in WGIII, Chapter
3). Adoption of a fixed horizon of e.g., 20, 100 or 500 years will inevitably put no weight on the long-term effect of CO2 beyond the time
horizon (Figure 8.28 and Box 6.1). While GWP integrates the effects up
to a chosen time horizon the GTP gives the temperature just for one
716

Anthropogenic and Natural Radiative Forcing

chosen year with no weight on years before or after. The most appropriate metric depends on the particular application and which aspectof ­climate change is considered relevant in a given context. The GWP
is not directly related to a temperature limit such as the 2°C target(Manne and Richels, 2001; Shine et al., 2007; Manning and Reisinger,
2011; Smith et al., 2012; Tol et al., 2012; Tanaka et al., 2013), whereassome economic metrics and physical end-point metrics like the GTP
may be more suitable for this purpose.
To provide metrics that can be useful to the users and policymakers
a more effective dialog and discussion on three topics is needed: (1)which applications particular metrics are meant to serve; (2) how comprehensive metrics need to be in terms of indirect effects and feedbacks, and economic dimensions; and—related to this (3) how important it is to have simple and transparent metrics (given by analyticalformulations) versus more complex model-based and thus model-dependent metrics. These issues are also important to consider in a wider
disciplinary context (e.g., across the IPCC Working Groups). Finally, it
is important to be aware that all metric choices, even ‘traditional’ or‘widely used’ metrics, contain implicit value judgements as well aslarge uncertainties.

8.7.2

Application of Metrics

8.7.2.1 Metrics for Carbon Dioxide, Methane, Nitrous Oxide,
Halocarbons and Related Compounds
Updated (A)GWP and (A)GTP values for CO2, CH4, N2O, CFCs, HCFCs,
bromofluorocarbons, halons, HFCs, PFCs, SF6, NF3, and related halogencontaining compounds are given for some illustrative and tentative
time horizons in Tables 8.7, 8.A.1 and Supplementary Material Table
8.SM.16. The input data and methods for calculations of GWPs and
GTPs are documented in the Supplementary Material Section 8.SM.13.
Indirect GWPs that account for the RF caused by depletion of stratospheric ozone (consistent with Section 8.3.3) are given for selectedgases in Table 8.A.2.
The confidence in the ability to provide useful metrics at time scales ofseveral centuries is very low due to nonlinear effects, large uncertainties for multi-century processes and strong assumptions of constantbackground conditions. Thus, we do not give metric values for longer
time scales than 100 years (see discussion in Supplementary Material
Section 8.SM.11). However, these time scales are important to consider
for gases such as CO2, SF6 and PFCs. For CO2, as much as 20 to 40% of
the initial increase in concentration remains after 500 years. For PFC14, 99% of an emission is still in the atmosphere after 500 years. The
effects of emissions on these time scales are discussed in Chapter 12.
The GWP values have changed from previous assessments due to
new estimates of lifetimes, impulse response functions and radiative
efficiencies. These are updated due to improved knowledge and/orchanged background levels. Because CO2 is used as reference, anychanges for this gas will affect all metric values via AGWP changes.
Figure 8.31 shows how the values of radiative efficiency (RE), integrated impulse response function (IRF) and consequentially AGWP for CO2
have changed from earlier assessments relative to AR5 values. The net
effect of change in RE and IRF is an increase of approximately 1% andAnthropogenic and Natural Radiative Forcing

Chapter 8

is reduced by one third. Among the hydrofluoroethers (HFEs) there arealso several large changes in lifetimes. In addition, substantial updatesof radiative efficiencies are made for several important gases; CFC11, CFC-115, HCFC-124, HCFC-225cb, HFC-143a, HFC-245fa, CCl4,
CHCl3, and SF6. The radiative efficiency for carbon tetrachloride (CCl4)
is higher now and the GWP100 has increased by almost 25% from
AR4. Uncertainties in metric values are given in Section 8.7.1.4. See
also Supplementary Material Section 8.SM.12 and footnote to Table
8.A.1. As can be seen from Table 8.A.2, some ODS have strong indirect effects through stratospheric ozone forcing, which for some of thegases reduce their net GWP100 values substantially (and for the halons,
to large negative values). Note that, consistent with Section 8.3.3, theuncertainties are large; ±100% for this indirect effect.

Figure 8.31 | Changes in the radiative efficiency (RE), integrated impulse response
function (IRF) and Absolute Global Warming Potential (AGWP) for CO2 for 100 years
from earlier IPCC Assessment Reports normalized relative to the values given in AR5.
The ‘original’ values are calculated based on the methods explained or value reported
in each IPCC Assessment Report. The ‘updated’ values are calculated based on themethods used in AR5, but the input values from each Assessment Report. The difference is primarily in the formula for the RE, which was updated in TAR. The different
integrated IRF in TAR relates to a different parameterisation of the same IRF (WMO,
1999). Changes represent both changes in scientific understanding and a changing
background atmospheric CO2 concentration (note that y-axis starts from 0.8). The lines
connecting individual points are meant as a visual guide and not to represent the values
between different Assessment Reports.

6% from AR4 to AR5 in AGWP for CO2 for 20 and 100 years, respectively (see Supplementary Material Section 8.SM.12). These increases in
the AGWP of the reference gas lead to corresponding decreases in the
GWPs for all non-CO2 gases. Continued increases in the atmosphericlevels of CO2 will lead to further changes in GWPs (and GTPs) in the
future.
To understand the factors contributing to changes relative to AR4,comparisons are made here using the AR5 values that include climate–carbon feedbacks for CO2 only. Relative to AR4 the CH4 AGWP haschanged due to changes in perturbation lifetime, a minor change in RE
due to an increase in background concentration, and changes in theestimates of indirect effects. The indirect effects on O3 and stratospheric
H2O are accounted for by increasing the effect of CH4 by 50% and 15%,
respectively (see Supplementary Material Table 8.SM.12). The ozone
effect has doubled since AR4 taking into account more recent studies
as detailed in Sections 8.3.3 and 8.5.1. Together with the changes in
AGWP for CO2 the net effect is increased GWP values of CH4.
The GWPs for N2O are lower here compared to AR4. A longer perturbation lifetime is used in AR5, while the radiative efficiency is lower due
to increased abundances of CH4 and N2O. In addition, the reduction in
CH4 via stratospheric O3, UV fluxes and OH levels due to increased N2O
abundance is included in GWPs and GTP. Owing to large uncertainties
related to altitude of changes, we do not include the RF from stratospheric ozone changes as an indirect effect of N2O.
Lifetimes for most of the halocarbons are taken from WMO (2011) andmany of these have changed from AR4. The lifetimes of CFC-114, CFC115 and HCF-161 are reduced by approximately 40%, while HFC-152

When climate-carbon feedbacks are included for both the non-CO2 andreference gases, all metric values increase relative to the methodology used in AR4, sometimes greatly (Table 8.7, Supplementary Material
Table 8.SM.16). Though the uncertainties range for these metric values
is greater, as uncertainties in climate-carbon feedbacks are substantial,
these calculations provide a more consistent methodology.

8.7.2.2 Metrics for Near-Term Climate Forcers
The GWP concept was initially used for the WMGHGs, but later for
NTCFs as well. There are, however, substantial challenges related to
calculations of GWP (and GTP) values for these components, whichis reflected in the large ranges of values in the literature. Below we
present and assess the current status of knowledge and quantificationof metrics for various NTCFs.
8.7.2.2.1

Nitrogen oxides

Metric values for NOX usually include the short-lived ozone effect,
CH4 changes and the CH4-controlled O3 response. NOX also causes RF
through nitrate formation, and via CH4 it affects stratospheric H2O andthrough ozone it influences CO2. In addition, NOx affects CO2 throughnitrogen deposition (fertilization effect). Due to high reactivity andthe many nonlinear chemical interactions operating on different timescales, as well as heterogeneous emission patterns, calculation of net
climate effects of NOX is difficult. The net effect is a balance of large
opposing effects with very different temporal behaviours. There is alsoa large spread in values among the regions due to variations in chemical and physical characteristics of the atmosphere.
As shown in Table 8.A.3 the GTP and GWP values are very different.
This is due to the fundamentally different nature of these two metrics(see Figure 8.28) and the way they capture the temporal behaviour of
responses to NOx emissions. Time variation of GTP for NOX is complex,
which is not directly seen by the somewhat arbitrary choices of time
horizon, and the net GTP is a fine balance between the contributing
terms. The general pattern for NOX is that the short-lived ozone forcing is always positive, while the CH4-induced ozone forcing and CH4
forcing are always negative (see Section 8.5.1). Nitrate aerosols fromNOx emission are not included in Table 8.A.3. For the GTP, all estimatesfor NOX from surface sources give a negative net effect. As discussed
in Section 8.7.1.4 Collins et al. (2010) and Shindell et al. (2009) implemented further indirect effects, but these are not included in Table
717

8

Chapter 88Anthropogenic and Natural Radiative Forcing

8.A.3 due to large uncertainties. The metric estimates for NOX reflect
the level of knowledge, but they also depend on experimental design,treatment of transport processes, and modelling of background levels.
The multi-model study by Fry et al. (2012) shows the gaseous chemistry
response to NOX is relatively robust for European emissions, but thatthe uncertainty is so large that for some regions of emissions it is notpossible to conclude whether NOX causes cooling or warming.
8.7.2.2.2

Carbon monoxide and volatile organic compounds

Emissions of carbon monoxide (CO) and volatile organic compounds(VOCs) lead to production of ozone on short time scales. By affecting
OH and thereby the levels of CH4 they also initiate a positive long-termozone effect. With its lifetime of 2 to 3 months, the effect of CO emissions is less dependent on location than is the case for NOX (see Table
8.A.4). There is also less variation across models. However, Collins etal. (2010) found that inclusion of vegetation effects of O3 increased the
GTP values for CO by 20 to 50%. By including aerosol responses Shindell et al. (2009) found an increase in GWP100 by a factor of ~2.5. CO offossil origin will also have a forcing effect by contributing to CO2 levels.
This effect adds 1.4 to 1.6 to the GWP100 for CO (Daniel and Solomon,
1998; Derwent et al., 2001). (The vegetation and aerosol effects are notincluded in the numbers in Table 8.A.4.)
VOC is not a well-defined group of hydrocarbons. This group of gaseswith different lifetimes is treated differently across models by lumping or using representative key species. However, the spread in metricvalues in Table 8.A.5 is moderate across regions, with highest values
for emissions in South Asia (of the four regions studied). The effects
via ozone and CH4 cause warming, and the additional effects via interactions with aerosols and via the O3–CO2 link increase the warming
effect further. Thus, the net effects of CO and VOC are less uncertain
than for NOX for which the net is a residual between larger terms of
opposite sign. However, the formation of SOAs is usually not included
in metric calculations for VOC, which introduces a cooling effect andincreased uncertainty.
8.7.2.2.3

Black carbon and organic carbon

Most of the metric values for BC in the literature include the aerosol–radiation interaction and the snow/ice albedo effect of BC, thoughwhether external or internal mixing is used varies between the studies.
Bond et al. (2011) calculate GWPs and find that when the albedo effectis included the values increase by 5 to 15%. Studies have shown, however, that the climate response per unit forcing to this mechanism isstronger than for WMGHG (see Section 7.5).
Bond et al. (2013) assessed the current understanding of BC effects
and calculated GWP and GTP for BC that includes aerosol–radiationinteraction, aerosol–cloud interactions and albedo. As shown in Table
8.A.6 the uncertainties are wide for both metrics (for 90% uncertainty range) reflecting the current challenges related to understandingand quantifying the various effects (see Sections 7.5, 8.3.4 and 8.5.1).
Their aerosol–radiation interaction effect is about 65% of the total
effect while the albedo effect is approximately 20% of the aerosol–
radiation interaction effect. Based on two studies (Rypdal et al., 2009;
Bond et al., 2011), the GWP and GTP metrics were found to vary with718

the region where BC is emitted by about ±30% . For larger regionsof emissions, Collins et al. (2013) calculated GWPs and GTPs for the
direct effect of BC and found somewhat lower variations among theregions.
Several studies have focused on the effects of emissions of BC and
OC from different regions (Bauer et al., 2007; Koch et al., 2007; Naiket al., 2007; Reddy and Boucher, 2007; Rypdal et al., 2009). However,examination of results from these models (Fuglestvedt et al., 2010)
reveals that there is not a robust relationship between the region ofemission and the metric value — hence, regions that yield the highest
metric value in one study, do not, in general, do so in the other studies.
The metric values for OC are quite consistent across studies, but fewerstudies are available (see Table 8.A.6). A brief overview of metricvalues for other components is given in the Supplementary Material
Section 8.SM.14.
8.7.2.2.4

Summary of status of metrics for near-term climate forcers

The metrics provide a format for comparing the magnitudes of thevarious emissions as well as for comparing effects of emissions fromdifferent regions. They can also be used for comparing results fromdifferent studies. Much of the spread in results is due to differences inexperimental design and how the models treat physical and chemical
processes. Unlike most of the WMGHGs, many of the NTCFs are tightly
coupled to the hydrologic cycle and atmospheric chemistry, leading toa much larger spread in results as these are highly complex processes
that are difficult to validate on the requisite small spatial and short
temporal scales. The confidence level is lower for many of the NTCF
compared to WMGHG and much lower where aerosol–cloud interactions are important (see Section 8.5.1). There are particular difficulties
for NOX, because the net impact is a small residual of opposing effects
with quite different spatial distributions and temporal behaviour.
Although climate–carbon feedbacks for non-CO2 emissions have notbeen included in the NTCF metrics (other than CH4) presented here,
they can greatly increase those values (Collins et al., 2013) and likely
provide more realistic results.

8.7.2.3 Impact by Emitted Component
We now use the metrics evaluated here to estimate climate impactsof various components (in a forward looking perspective). Figure 8.32
shows global anthropogenic emissions of some selected components
weighted by the GWP and GTP. The time horizons are chosen as examples and illustrate how the perceived impacts of components—relative
to the impact of the reference gas—vary strongly as function of impact
parameter (integrated RF in GWP or end-point temperature in GTP)
and with time horizon.
We may also calculate the temporal development of the temperature
responses to pulse or sustained emissions using the AGTP metric.
Figure 8.33 shows that for a one-year pulse the impacts of NTCF decay
quickly owing to their atmospheric adjustment times even if effects areprolonged due to climate response time (in the case of constant emissions the effects reach approximately constant levels since the emissions are replenished each year, except for CO2, which has a ­fraction

Anthropogenic and Natural Radiative Forcing

Chapter 8

GTP

10

20

5

0

CO2

CH4
N2O
NOX
CO
SO2
BC
OC

-20

10 yrs

20 yrs

100 yrs

20 yrs

10 yrs0-5

CO2 equivalent emissions (PgC-eq)

CO2 equivalent emissions (Pg CO2-eq)

GWP

8100 yrs

Figure 8.32 | Global anthropogenic emissions weighted by GWP and GTP for chosen time horizons (aerosol–cloud interactions are not included). Emission data for 2008 aretaken from the EDGAR database. For BC and OC emissions for 2005 are from Shindell et al. (2012a). The units are ‘CO2 equivalents’ which reflects equivalence only in the impact
parameter of the chosen metric (integrated RF over the chosen time horizon for GWP; temperature change at the chosen point in time for GTP), given as Pg(CO2)eq (left axis) andgiven as PgCeq (right axis). There are large uncertainties related to the metric values and consequentially also to the calculated CO2 equivalents (see text).

These examples show that the outcome of comparisons of effects ofemissions depends strongly on choice of time horizon and metric type.
Such end-user choices will have a strong influence on the calculated contributions from NTCFs versus WMGHGs or non-CO2 versus CO2
emissions. Thus, each specific analysis should use a design chosen inlight of the context and questions being asked.

8.7.2.4 Metrics and Impacts by Sector
While the emissions of WMGHGs vary strongly between sectors, the climate impacts of these gases are independent of sector. The latter is not
the case for chemically active and short-lived components, due to thedependence of their impact on the emission location. Since most sectors
have multiple co-emissions, and for NTCFs some of these are warming while others are cooling, the net impact of a given sector requires
explicit calculations. Since AR4, there has been significant progress inthe understanding and quantification of climate impacts of NTCFs fromsectors such as transportation, power production and biomass burning(Berntsen and Fuglestvedt, 2008; Skeie et al., 2009; Stevenson and Derwent, 2009; Lee et al., 2010; Unger et al., 2010; Dahlmann et al., 2011).
Supplementary Material Table 8.SM.18 gives an overview of recent published metric values for various components by sector.
The impact from sectors depends on choice of metric, time horizon,
pulse versus sustained emissions and forward versus backward looking
perspective (see Section 8.7.1 and Box 8.4). Unger et al. (2010) calculated RF for a set of components emitted from each sector. RF at chosenpoints in time (20 and 100 years) for sustained emissions was used byUnger et al. (2010) as the metric for comparison. This is ­comparable

20
Temperature impact (10-3 K)

remaining in the atmosphere on time scales of centuries). Figure 8.33
also shows how some components have strong short-lived effects ofboth signs while CO2 has a weaker initial effect but one that persists
to create a long-lived warming effect. Note that there are large uncertainties related to the metric values (as discussed in Section 8.7.1.4);
especially for the NTCFs.

10
0
-10
-20
-30
0

20

40
Time Horizon (yr)6080Figure 8.33 | Temperature response by component for total anthropogenic emissions
for a 1-year pulse. Emission data for 2008 are taken from the EDGAR database and for
BC and OC for 2005 from Shindell et al. (2012a). There are large uncertainties related
to the AGTP values and consequentially also to the calculated temperature responses(see text).

to using integrated RF up to the chosen times for pulse emissions (asin GWPs). Such studies are relevant for policymaking that focuses onregulating the total activity of a sector or for understanding the contribution from a sector to climate change. On the other hand, the fixed
mix of emissions makes it less general and relevant for emission scenarios. Alternatively, one may adopt a component-by-component viewwhich is relevant for policies directed towards specific components (orsets of components, as controlling an individual pollutant in isolation
is usually not practical). But this view will not capture interactions andnon-linearities within the suite of components emitted by most sectors.
The effects of specific emission control technologies or policies or projected societal changes on the mix of emissions is probably the most
relevant type of analysis, but there are an enormous number of possible actions and regional details that could be investigated. Henze et al.
(2012) demonstrate a method for providing highly spatially resolved
719

Chapter 8

estimates of forcing per component, and caution that RF aggregatedover regions or sectors may not represent the impacts of emissionschanges on finer scales.

8

Metrics for individual land-based sectors are often similar to the global
mean metric values (Shindell et al., 2008). In contrast, metrics for emissions from aviation and shipping usually show large differences fromglobal mean metric values (Table 8.A.3 versus Table 8.SM.18). Though
there can sometimes be substantial variation in the impact of landbased sectors across regions, and for a particular region even from one
sector to another, variability between different land-based sources isgenerally smaller than between land, sea and air emissions.
NOx from aviation is one example where the metric type is especially important. GWP20 values are positive due to the strong responseof short-lived ozone. Reported GWP100 and GTP100 values are of either
sign, however, due to the differences in balance between the individual effects modelled. Even if the models agree on the net effect ofNOX, the individual contributions can differ significantly, with large
uncertainties stemming from the relative magnitudes of the CH4 andO3 responses (Myhre et al., 2011) and the background troposphericconcentrations of NOX (Holmes et al., 2011; Stevenson and Derwent,2009). Köhler et al. (2013), find strong regional sensitivity of ozone
and CH4 to NOX particularly at cruise altitude. Generally, they find thestrongest effects at low latitudes. For the aviation sector contrails andcontrail induced cirrus are also important. Based on detailed studies
in the literature, Fuglestvedt et al. (2010) produced GWP and GTP for
contrails, water vapor and contrail-induced cirrus.

Anthropogenic and Natural Radiative Forcing

in terms of temperature change. The AGTP concept can be used to
study the effects of the various components for chosen time horizons.
A single year’s worth of current global emissions from the energy andindustrial sectors have the largest contributions to warming after 100
years (see Figure 8.34a). Household fossil fuel and biofuel, biomassburning and on-road transportation are also relatively large contributors to warming over 100-year time scales. Those same sectors, alongwith sectors that emit large amounts of CH4 (animal husbandry, waste/
landfills and agriculture), are most important over shorter time horizons (about 20 years; see Figure 8.34b).
Analysing climate change impacts by using the net effect of particular
activities or sectors may—compared to other perspectives—provide
more insight into how societal actions influence climate. Owing tolarge variations in mix of short- and long-lived components, as wellas cooling and warming effects, the results will also in these cases
depend strongly on choice of time horizon and climate impact parameter. Improved understanding of aerosol–cloud interactions, and how
those are attributed to individual components is clearly necessary torefine estimates of sectoral or emitted component impacts.
(a)

The GWP and GTPs for NOX from shipping are strongly negative forall time horizons. The strong positive effect via O3 due to the low-NOX
environment into which ships generally emit NOX is outweighed by thestronger effect on CH4 destruction due to the relatively lower latitudesof these emissions compared to land-based sources.
In addition to having large emissions of NOX the shipping sector haslarge emission of SO2. The direct GWP100 for shipping ranges from –11
to –43 (see Supplementary Material Table 8.SM.18). Lauer et al. (2007)
reported detailed calculations of the indirect forcing specifically for this
sector and found a wide spread of values depending on the emission
inventory. Righi et al. (2011) and Peters et al. (2012) calculate indirect
effects that are 30 to 50% lower than the indirect forcing reported byLauer et al. (2007). The values from Shindell and Faluvegi (2010) for
SO2 from power generation are similar to those for shipping.
Although the various land transport sectors often are treated as one
aggregate (e.g., road transport) there are important subdivisions. For
instance, Bond et al. (2013) points out that among the BC-rich sectors they examined, diesel vehicles have the most clearly positive net
impact on forcing. Studies delving even further have shown substantial
differences between trucks and cars, gasoline and diesel vehicles, andlow-sulphur versus high-sulphur fuels. Similarly, for power production
there are important differences depending on fuel type (coal, oil, gas;e.g., Shindell and Faluvegi, 2010).
In the assessment of climate impacts of current emissions by sectors
we give examples and apply a forward-looking perspective on effects
720

(

)

(

)

(b)

Figure 8.34 | Net global mean temperature change by source sector after (a) 100
and (b) 20 years (for 1-year pulse emissions). Emission data for 2008 are taken fromthe EDGAR database. For BC and OC anthropogenic emissions are from Shindell et al.
(2012a) and biomass burning emissions are from Lamarque et al. (2010), see Supplementary Material Section 8.SM.17. There are large uncertainties related to the AGTP
values and consequentially also to the calculated temperature responses (see text).

Anthropogenic and Natural Radiative Forcing

Chapter 8

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CH4

CH4

N2O

Fossil methane‡

Nitrous Oxide

CClF3

CCl2FCClF2

CClF2CClF2

CClF2CF3

CFC-12

CFC-13

CFC-113

CFC-114

CFC-115(continued on next page)trans-CF3CH=CHCl

CHClFCF2CClF2

HCFC-225cb(E)-1-Chloro-3,3,3trifluoroprop-1-ene

CH3CClF2

CHCl2CF2CF3

HCFC-225ca

CH3CCl2F

HCFC-141b

HCFC-142b

CHClFCF3

CH2FCFCl2

HCFC-132c

HCFC-123a

HCFC-124

CHCl2CF3

CHClFCF2Cl

HCFC-123

CHCl2CF2Cl

CHFClCFCl2

HCFC-122a

CHClF2

HCFC-22

HCFC-122

CHCl2F

HCFC-21

Hydrochlorofluorocarbons

CCl3F

CCl2F2

CFC-11Chlorofluorocarbons

CO2

MethaneChemical
FormulaCarbon dioxide

Acronym, Common
Name or Chemical Name

45.0

26.0 days

5.9

1.9

17.2

9.2

4.3

5.9

4.0

1.3

3.4

1.0

11.9

1.7

1,020.0

190.0

85.0

640.0

100.0

0.04

0.29

0.22

0.19

0.16

0.17

0.20

0.23

0.15

0.21

0.17

0.21

0.15

0.20

0.31

0.30

0.25

0.32

0.26

1.37e-13

4.65e-11

1.17e-11

1.25e-10

6.36e-11

3.07e-11

4.67e-11

3.37e-11

7.28e-12

2.36e-11

5.43e-12

1.32e-10

1.35e-11

1.46e-10

1.92e-10

1.62e-10

2.71e-10

2.69e-10

1.72e-10

6.58e-12

2.11e-12

121†

3.00e-3

3.63e-4†

12.4

2.09e-12

2.49e-14

AGWP
20-year(W m–2yr kg–1)

3.63e-4

1.37e-5

Radiative
Efficiency(W m–2
ppb–1)

12.4†

see*

Lifetime(Years)2.37e-11
7.28e-12
3.39e-11
4.83e-11
3.10e-11
7.17e-11
1.82e-10
1.17e-11
4.81e-11

945
292
1350
1870
1230
2550
5020
469
1860

1.37e-13

5.43e-12218

51.62e-10

7.03e-105860

1.35e-11

7.88e-10

7710

543

5.34e-10

6490

5280

1.27e-09

10,9002.43e-11

9.39e-10

2.73e-1285264

4.28e-10

2.61e-126900

9.17e-14

1
84

10,800AGWP
100-year(W m–2yr kg–1)

GWP
20-year

1

525

127

1980

782

338

527

370

79

258

59

1760

148

7670

8590

5820

13,900

10,200

4660

265

30

28

1

GWP
100-year

1.09e-15

7.61e-13

1.17e-13

3.01e-12

1.27e-12

4.27e-13

7.63e-13

4.51e-13

6.71e-14

2.91e-13

4.81e-14

2.87e-12

1.31e-13

4.32e-125.60e-12

4.60e-12

7.99e-12

7.71e-12

4.71e-12

1.89e-13

4.68e-14

4.62e-14

6.84e-16AGTP
20-year(K kg–1)

2

1110

170

4390

1850

624

1120

659

98

426

70

4200

192

6310

8190

6730

11,70011,300

6890

277

68

67

1

GTP
20-year1.54e-16

7.43e-14

1.38e-14

8.46e-13

1.67e-13

4.14e-14

7.46e-14

4.44e-14

8.45e-15

2.99e-14

6.25e-15

5.13e-13

1.59e-14

4.81e-12

5.56e-12

3.85e-12

8.77e-12

6.75e-12

3.01e-12

1.74e-13

9.55e-15

8.69e-15

6.17e-16AGTP
50-year(K kg–1)

<1

120

22

1370

271

67

121

72

14

48

10

832

26

7810

9020

6250

14,20011,000

4890

282

15

14

1

GTP
50-year

356
18
73
<1

1.95e-13
9.65e-15
4.01e-14
1.12e-16

4.03e-1447

74

2.81e-14

111

51

6.00e-15

6.09e-14

11

1.96e-14

2.58e-14836

4.47e-15

20

8980
4.91e-12262

8550
4.68e-12

1.43e-13

4470
2.45e-12

1.12e-14

8450
15,9008.71e-122340
1.28e-12
4.62e-126234

42.34e-15

1.28e-131
5.47e-16

3.11e-15

GTP
100-yearAGTP
100-year(K kg–1)

Table 8.A.1 | Radiative efficiencies (REs), lifetimes/adjustment times, AGWP and GWP values for 20 and 100 years, and AGTP and GTP values for 20, 50 and 100 years. Climate–carbon feedbacks are included for CO2 while no climate
feedbacks are included for the other components (see discussion in Sections 8.7.1.4 and 8.7.2.1, Supplementary Material and notes below the table; Supplementary Material Table 8.SM.16 gives analogous values including climate–carbon
feedbacks for non-CO2 emissions). For a complete list of chemical names and CAS numbers, and for accurate replications of metric values, see Supplementary Material Section 8.SM.13 and references therein.

Appendix 8.A: Lifetimes, Radiative Efficiencies and Metric Values

Anthropogenic and Natural Radiative Forcing
Chapter 8

8

731

732

CH2FCHF2

HFC-143

CH3CF2CH3

CHF2CF2CF2CF3

CH3CF2CH2CF3

CF3CHFCHFCF2CF3

HFC-272ca

HFC-329p

HFC-365mfc

HFC-43-10mee(Z)-HFC-1234ze

CF3CH=CHCF3(Z)(Z)-HFC-1336(continued on next page)trans-CF3CH=CHF(E)-HFC-1234ze

CF3CF=CH2

CF3CF=CHF(E)

CF3CH=CHF(Z)(E)-HFC-1225ye

HFC-1234yf

CF3CF=CHF(Z)(Z)-HFC-1225ye

CH2=CF2

CH3CH2CF3

HFC-263fb

CH2=CHF

CHF2CH2CF3

HFC-245fa

HFC-1141

CH2FCHFCF3

HFC-245eb

HFC-1132a

CF3CF2CH3

CHF2CHFCHF2

CH2FCF2CHF2

HFC-245ca

HFC-245ea

CF3CH2CF3

HFC-236fa

HFC-245cb

CH2FCF2CF3

CHF2CHFCF3

HFC-227ea

HFC-236ea

CF3CHFCF3

HFC-227ca

HFC-236cb

CH3CH2F

CF3CF2CHF2

HFC-161

CH3CHF2

CH2FCF3

HFC-134a

HFC-152a

CHF2CHF2

HFC-134

CH3CF3

CHF2CF3

HFC-125

CH2FCH2F

CH3F

HFC-41

HFC-152

CH2F2

HFC-32

HFC-143a

CHF3

HFC-23

Hydrofluorocarbons

Chemical
Formula22.0 days

16.4 days

10.5 days

10.0 days

4.9 days

8.5 days

2.1 days

4.0 days

16.1

8.7

28.4

2.6

1.2

7.7

3.1

3.2

47.1

6.5

242.0

11.0

13.1

38.9

28.2

66.0 days

1.5

0.4

47.1

3.5

13.4

9.7

28.2

2.8

5.2

222.0

Lifetime(Years)6.56e-11
4.24e-10
2.16e-11
2.66e-11
7.87e-11
6.93e-12
1.32e-11
2.16e-10
7.38e-11

2510
6680
863
1070
2920
278
530
4510
2660

1.54e-13

6
1.54e-13d

0.07

8.74e-14

48.74e-14

3.22e-14

2.61e-14

7.25e-15

2.14e-14

1.54e-15

0.04

0.02

0.02

0.01

0.02

0.002d0.004

3.22e-14

7.39e-106940

11.22e-104110

2.61e-14

1.11e-103480

7.25e-15

3.07e-105360

12.42e-10<1

3.33e-13135080

2.14e-14

1.26e-11506<11.51e-1260

1.54e-15

4.41e-106940

3.87e-15

3.01e-11

1200<11.19e-103710<11.02e-103580

3.87e-15

2.91e-106090

d1.07e-11427

1.51e-10

6.21e-11

2430

4310

1.14e-09

AGWP
100-year(W m–2yr kg–1)

10,800GWP
20-year1.08e-10

6.64e-11

1.13e-10

1.32e-11

6.93e-12

7.29e-11

2.66e-11

2.15e-11

1.67e-10

6.26e-11

1.73e-10

1.03e-10

8.67e-11

1.34e-10

1.27e-10

3.33e-13

1.26e-11

1.51e-12

1.73e-10

3.00e-11

9.26e-11

8.93e-11

1.52e-10

1.07e-11

6.07e-11

2.70e-10AGWP
20-year(W m–2yr kg–1)

0.42b

0.22

0.31

0.07

0.10c

0.24

0.20c

0.16c

0.24

0.24b

0.24

0.30a

0.23

0.26

0.27

0.02

0.10

0.04

0.16

0.13

0.16

0.19

0.23

0.02

0.11

0.18

Radiative
Efficiency(W m–2
ppb–1)

2

<1

<1

<1

<1

<1

<1

<1

1650

804

2360

144

76

858

290

235

4620

716

8060

1330

1210

3350

2640

4

138

16

4800

328

1300

1120

3170

116

677

12,400GWP
100-year

1.23e-15

6.98e-16

2.57e-16

2.08e-16

5.77e-17

1.70e-16

1.23e-17

3.08e-17

2.54e-121.30e-12

2.94e-12

1.46e-13

6.31e-14

1.35e-12

3.15e-13

2.59e-13

4.58e-12

1.07e-12

5.06e-12

2.18e-12

1.94e-12

3.61e-12

3.31e-122.70e-15

1.19e-13

1.25e-14

4.76e-12

3.76e-13

2.09e-12

1.82e-12

3.97e-12

1.21e-139.32e-13

7.88e-12AGTP
20-year(K kg–1)

2

<1

<1

<1

<1

<1

<1

<1

3720

1890

4290

213

92

1970

460

378

6690

1570

7400

3190

2840

5280

4830

4

174

18

6960

549

3050

2660

5800

177

1360

11,500GTP
20-year1.73e-16

9.82e-17

3.62e-17

2.93e-17

8.14e-18

2.40e-17

1.73e-18

4.35e-18

6.62e-13

1.62e-13

1.37e-12

1.61e-14

8.02e-15

1.51e-13

3.31e-14

2.70e-14

3.00e-12

1.09e-13

5.18e-12

3.53e-13

3.92e-13

2.12e-12

1.53e-12

3.76e-16

1.47e-14

1.71e-15

3.12e-12

3.82e-14

4.33e-13

2.54e-13

1.84e-12

1.31e-14

8.93e-14

7.99e-12AGTP
50-year(K kg–1)

<1

<1

<1

<1

<1

<1

<1

<1

1070

262

2220

26

13

245

54

44

4870

176

8400

573

636

3440

2480

<1

24

3

5060

62

703

412

2980

21

145

13,000GTP
50-year1.26e-16

7.18e-17

2.65e-17

2.14e-17

5.95e-18

1.76e-17

1.27e-18

3.18e-18

1.54e-13

6.24e-14

3.96e-13

1.09e-14

5.70e-15

6.62e-14

2.20e-14

1.79e-14

1.32e-12

5.49e-14

4.58e-12

1.06e-13

1.01e-13

7.98e-13

4.41e-13

2.74e-16

1.04e-14

1.24e-15

1.37e-12

2.49e-14

1.10e-13

8.73e-14

5.29e-13

8.82e-15

5.17e-14

6.95e-12

AGTP
100-year(K kg–1)

8

Acronym, Common Nameor Chemical Name

Table 8.A.1 (continued)

<1

<1

<1

<1

<1

<1

<1

<1

281

114

725

20

10

121

40

33

2410

100

8380

195

185

1460

806

<1

19

2

2500

46

201

160

967

16

94

12,700GTP
100-year

Chapter 8
Anthropogenic and Natural Radiative Forcing

C4F9CH=CH2
C6F13CH=CH2
C8F17CH=CH2

3,3,4,4,5,5,6,6,6-Nonafluorohex-1-ene

3,3,4,4,5,5,6,6,7,7,8,8,8-Tridecafluorooct-1-ene

3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-Heptadecafluorodec-1-ene

CH3Cl
CH2Cl2
CHCl3

Methyl chloride

Methylene chloride

Chloroform

CBrF2CBrF2

Halon-2402

c-C3F6
C3F8
c-C4F8

PFC-116

PFC-c216

PFC-218

PFC-318(continued on next page)

CF4
C2F6

PFC-14SO2F2Sulphuryl fluoride

SF6
SF5CF3Sulphur hexafluoride(Trifluoromethyl) sulphur pentafluoride

NF3

Nitrogen trifluoride

Fully Fluorinated Species

CHFBrCF3

Halon-1301

Halon-2401

CBrF3

Halon-1211

CH2BrCF3

CBrClF2

Halon-1202

CHBrClCF3

CBr2F2

Halon-1201

Halon-2311 / Halothane

CHBrF2

Methylene bromide

Halon-2301

CH3Br
CH2Br2

Methyl bromide

Bromocarbons, Hydrobromocarbons and Halons

CH2ClCH2Cl

CCl4

Carbon tetrachloride

1,2-Dichloroethane

CH3CCl3

Methyl chloroform

Chlorocarbons and Hydrochlorocarbons

CF3CH=CH2
C2F5CH=CH2

HFC-1345zfc

Chemical FormulaHFC-1243zf

Acronym, Common Name or Chemical Name

Table 8.A.1 (continued)

3,200.02,600.0

3,000.0

10,000.0

50,000.0

36.0

800.0

3,200.0

500.0

20.0

2.9

1.0

3.4

65.0

16.0

2.9

5.2

0.3

0.8

65.0 days

0.4

0.4

1.0

26.0

5.0

7.6 days

7.6 days

7.6 days

7.6 days

7.0 days

Lifetime(Years)1.15e-14
1.25e-14
9.89e-15

<1
<1

0.32

0.28

0.23e

0.25

0.09

0.20

0.59

0.57

0.20

0.31

0.19

0.13

0.14

0.30

0.29

0.27

0.15

0.01

0.004

1.77e-10

1.66e-10

1.71e-10

2.05e-10

1.22e-10

1.71e-10

3.36e-10

4.37e-10

3.19e-10

8.59e-11

1.68e-11

3.77e-12

1.59e-11

1.95e-10

1.15e-10

2.12e-11

3.37e-119.31e-14

2.16e-13

8.24e-14

6.08e-10
1.02e-09
8.44e-10
8.16e-10
8.75e-10

8210
6850
6640
7110

1.35e-10

3440

4880

1.68e-11674

3.76e-10

3.77e-12

151

6840

1.59e-11635

1.60e-09

5.77e-107800

2.16e-09

1.60e-104590

13,5002.12e-11848

17,5003.45e-111350

1.47e-09

9.31e-14

12,8002.16e-13

8.24e-14

3

0.01

4

1.50e-1260

98.18e-1333

1.50e-128.18e-13

0.08

0.03

1.12e-1245

1.59e-10

1.12e-12

1.47e-11578
3480

8.52e-15

1.37e-14

1
<1

<1

AGWP
100-year(W m–2yr kg–1)

GWP
20-yearb

8.69e-11

1.44e-11

8.52e-15

9.89e-15

1.25e-14

1.15e-14

1.37e-14

AGWP
20-year(W m–2yr kg–1)

0.01a

0.17

0.07

0.03

0.03

0.03

0.01

0.01

Radiative
Efficiency(W m–2
ppb–1)

9540

8900

9200

11,1006630

4090

17,40023,500

16,100

1470

184

41

173

6290

1750

231

376

1

2

<1

16

9

12

1730

160

<1

<1

<1

<1

<1

GWP
100-year

5.25e-12

4.91e-12

5.06e-12

6.07e-12

3.61e-12

4.58e-12

9.93e-12

1.29e-11

9.39e-122.12e-12

1.94e-13

3.35e-14

1.96e-13

5.46e-122.70e-122.43e-13

5.17e-13

7.66e-16

1.87e-15

6.67e-16

1.25e-14

6.78e-15

9.93e-15

2.24e-12

2.17e-13

6.79e-17

7.87e-17

9.92e-17

9.19e-17

1.09e-16AGTP
20-year(K kg–1)

7680

7180

7400

8880

5270

6690

14,50018,90013,700

3100

283

49

286

7990

3950

356

756

1

3

<1

18

10

15

3280

317

<1

<1

<1

<1

<1

GTP
20-year

5.96e-12

5.56e-12

5.74e-12

6.93e-12

4.12e-12

2.55e-12

1.10e-11

1.47e-11

1.02e-11

7.08e-13

2.07e-14

4.34e-15

2.01e-14

4.16e-12

6.98e-13

2.61e-14

4.96e-14

1.05e-16

2.47e-16

9.29e-17

1.70e-15

9.26e-16

1.29e-15

9.68e-13

2.07e-14

9.57e-18

1.11e-17

1.40e-17

1.30e-17

1.53e-17AGTP
50-year(K kg–1)

9660

9010

9310

11,2006690

4140

17,80023,80016,500

1150

34

7

33

6750

1130

42

80

<1

<1

<1

3

2

2

1570

34

<1

<1

<1

<1

<1

GTP
50-year11,00010,70011,5005.83e-12
6.27e-12

1650
9.01e-13

6.03e-12

20,2001.11e-11

8040

28,2001.54e-11

13,50018,1009.91e-12

7.36e-12304
1.66e-13

4.40e-12625

24
1.32e-14

1.39e-14

4170
2.28e-12

3.10e-15

32

52
2.87e-14297<1
7.65e-17

1.62e-13<1
1.78e-16

1.75e-142<1
6.77e-171
6.72e-16
1.24e-152<17.00e-18

9.20e-16<1
8.12e-18

22<11.02e-17479<1
9.48e-18

2.62e-13<1

1.12e-17

1.22e-14

GTP
100-yearAGTP
100-year(K kg–1)

Anthropogenic and Natural Radiative Forcing
Chapter 8

8

733

734
CF3CF=CFCF3

CHF2OCH2CF3
CF3CF2CH2OH
CH3OCF2CHF2
CF3CH2OCH3
CF3OCH2CH3

HFE-245fa2

2,2,3,3,3-Pentafluoropropan-1-ol

HFE-254cb1

HFE-263fb2

HFE-263m1(continued on next page)(CF3)2CHOCHF2

CHF2CH2OCF3

HFE-245fa1

HFE-338mmz1

CF3CF2OCH3

HFE-245cb2

CF3CH2CH2OH

CF3CH2OCF3

HFE-236fa

CHF2CF2OCF2CF3

CHF2OCHFCF3

HFE-236ea2 (desflurane)

HFE-329mcc2

CHF2OCF2CHF2

HFE-236ca

3,3,3-Trifluoropropan-1-ol

CHF2OCHClCF3

HFE-227ea

HCFE-235da2 (isoflurane)

CF3CHFOCF3

HFE-143a

CHF2OCF2CHFCl

CH3OCF3

HFE-134 (HG-00)

HCFE-235ca2 (enflurane)

CHF2OCF3
CHF2OCHF2

HFE-125

Halogenated Alcohols and Ethers

CF3CF2CF=CF2

PFC-1216

Perfluorobut-2-ene

CF2=CF2
CF3CF=CF2

PFC-1114

Perfluorobut-1-ene

E-C10F18

Perfluorodecalin (trans)

CF2=CFCF=CF2

Z-C10F18

Perfluorodecalin (cis)Perfluorobuta-1,3-diene

C8F18
C10F18n-C7F16

PFC-61-16

PFC-91-18

n-C6F14

PFC-51-14

PFC-71-18

n-C5F12

PFC-41-12

21.2

22.5

12.0 days

0.4

23.0 days

2.5

0.3

5.5

6.6

4.9

7.5

10.8

20.8

3.5

4.3

51.6

4.8

24.4

119.0

31.0 days

6.0 days

1.1 days

4.9 days

1.1 days

2,000.0

2,000.0

2,000.0

3,000.0

3,000.0

3,100.0

4,100.02,600.0
31.0 days

C4F10
c-C5F8Perfluorocyclopentene

Chemical Formula

Lifetime(Years)2.68e-16
6.42e-15
3.29e-16
8.38e-15
1.62e-13

<1
<1
<1
<1
6

1.72e-12
2.76e-11
1.22e-13
2.70e-12

69
1110
5
108

0.44

0.53

0.02

0.13

0.04

0.26

0.14

1.48e-10

1.68e-10

3.57e-14

2.70e-12

1.22e-13

2.76e-11

1.72e-122.40e-10

7.45e-112910
7.25e-11

7.22e-115940

7.59e-11

2900

0.36

0.31

5.90e-11

3.57e-14

6.00e-11

2360

0.33

8.35e-11

2.81e-10

8.98e-11

3350

0.36

1

1.64e-10

5550

6720

3.89e-10

9710

0.45

1.39e-102.42e-10g

0.56

4.50e-11

1800

5.35e-11

5.77e-104720

5.92e-10

6.64e-10

5430

2120

6.59e-10

5390

8900

6.99e-105680

4.80e-11

7.17e-10

5830

1890

7.26e-105890

5.10e-10

7.84e-106350

1.14e-09

1.71e-13

7

12,4008.44e-10

6870

11,600AGWP
100-year(W m–2yr kg–1)

GWP
20-year4.49e-115.30e-11

2.22e-10

4.72e-11

2.90e-10

3.10e-10

1.62e-13

8.38e-15

3.29e-16

6.42e-15

2.68e-16

1.18e-10

1.35e-10

1.34e-10

1.42e-10

1.45e-10

1.47e-10

1.58e-10

1.71e-13

1.71e-10AGWP
20-year(W m–2yr kg–1)

0.42

0.41

0.44

0.18

0.44

0.41

0.07

0.02

0.003

0.01

0.002

0.48

0.56

0.55

0.55

0.50

0.44

0.41

0.08f

0.36

Radiative
Efficiency(W m–2
ppb–1)

2620

3070

<1

29

1

301

19

812

828

654

979

1790

4240

491

583

6450

523

5560

12,400

2

<1

<1

<1

<1

6290

7240

7190

7620

7820

7910

8550

2

9200

GWP
100-year

3.70e-12

4.23e-12

2.85e-16

2.25e-14

9.72e-16

2.99e-13

1.42e-14

1.15e-12

1.25e-12

8.77e-13

1.53e-12

2.93e-12

6.03e-125.62e-13

7.36e-13

6.15e-12

6.95e-13

7.42e-12

8.91e-121.30e-15

6.67e-17

2.61e-18

5.11e-17

2.13e-18

3.48e-12

4.01e-12

3.98e-12

4.20e-12

4.31e-12

4.35e-12

4.69e-12

1.37e-15

5.08e-12AGTP
20-year(K kg–1)

5410

6180

<1

33

1

438

21

1670

1820

1280

2240

4280

8820

822

1080

8980

1020

10,80013,000

2

<1

<1

<1

<1

5090

5860

5820

6130

6290

6370

6860

2

7420

GTP
20-year1.31e-12

1.59e-12

4.01e-17

3.06e-15

1.37e-16

3.34e-14

1.95e-15

1.10e-13

1.27e-13

8.40e-14

1.68e-13

4.64e-13

2.10e-125.72e-14

7.14e-14

4.22e-12

6.66e-14

3.02e-12

8.14e-12

1.82e-16

9.41e-18

3.69e-19

7.21e-18

3.00e-19

3.93e-12

4.52e-12

4.49e-12

4.76e-12

4.88e-12

4.94e-12

5.33e-12

1.92e-16

5.75e-12AGTP
50-year(K kg–1)

2130

2580

<1

5

<1

54

3

179

206

136

273

753

3400

93

116

6850

108

4900

13,200

<1

<1

<1

<1

<1

6380

7340

7290

7710

7920

8010

8650

<1

9320

GTP
50-year3.14e-13

3.93e-13

2.93e-17

2.22e-15

9.98e-17

2.28e-14

1.42e-15

6.21e-14

6.35e-14

4.99e-14

7.54e-14

1.42e-13

4.98e-13

3.73e-14

4.44e-14

1.98e-12

3.99e-14

7.83e-13

5.97e-12

1.33e-16

6.88e-18

2.70e-19

5.27e-18

2.20e-19

4.10e-12

4.72e-12

4.68e-12

5.00e-12

5.13e-12

5.19e-125.62e-12

1.40e-16

6.02e-12

AGTP
100-year(K kg–1)

8

PFC-31-10

Acronym, Common Name or Chemical Name

Table 8.A.1 (continued)

575

718

<1

4

<1

42

3

114

116

91

138

260

912

68

81

3630

73

1430

10,900

<1

<1

<1

<1

<1

7500

8630

8570

9140

9380

9490

10,300<1

11,000GTP
100-year

Chapter 8
Anthropogenic and Natural Radiative Forcing

CH3OCF2CF2CF3
CHF2CH2OCF2CF3
CHF2CF2OCH2CF3(CF3)2CFOCH3
CH3OCF2CHFCF3
CF3CH2OCH2CF3
CHF2CH2OCF2CHF2CHF2OCH2CF2CHF2CH3OCF2CF2CHF2(CF3)2CHOCH3
CF3CF2CH2OCH3
CF3CF2OCH2CH3
CHF2CF2OCH2CH3
CF3(CH2)2CH2OH-(CF2)4CH(OH)-

HFE-347mcc3 (HFE-7000)

HFE-347mcf2

HFE-347pcf2

HFE-347mmy1

HFE-356mec3

HFE-356mff2

HFE-356pcf2

HFE-356pcf3

HFE-356pcc3

HFE-356mmz1

HFE-365mcf3

HFE-365mcf2

HFE-374pc2

4,4,4-Trifluorobutan-1-ol

2,2,3,3,4,4,5,5-Octafluorocyclopentanol

i-C4F9OC2H5

n-HFE-7200

i-HFE-7200

12.9
12.9
25.0
13.5

HF2C–(OCF2CF2)2–
OCF2H
HF2C–(OCF2CF2)3–
OCF2H
HF2C–(OCF2)2–OCF2H
HF2C–OCF2CF2OCF2OCF2O–CF2H

HG-02

HG-03

HG-20

HG-21

(continued on next page)

1.9

12.9

25.0

0.8

0.8

0.8

4.7

4.7

4.7

13.5

0.3

4.0 days

5.0

0.6

19.3 days

97.1 days

3.8

3.5

7.83e-11
8.15e-11
3.33e-11
3.55e-11

2990
3150
1330
1410

1.71i

0.92i

1.76i

1.24i

2.76e-10

2.73e-10

2.06e-10

1.97e-10

3.57e-10

4.86e-10

10,900
11,1002.62e-108270

2.50e-10

1.67e-117900

2.67e-10
1.67e-11

2.10e-10

2.75e-10

4.06e-12

668
0.26

0.86

0.65

0.24

0.35

8430

3.73e-111480

4.91e-10

4.45e-111760

11,0003.86e-111530

4.06e-12

2.58e-108010

5.92e-12

1.18e-1247

163

1.73e-15<1237

5.75e-112260

5.92e-12

5.35e-123215

i

8.51e-1450

5.21e-12

1.25e-12

1510

209

4.09e-11
3.79e-111640

6.59e-11

4.86e-11

1910

2560

1.98e-11795

1.54e-12

8.52e-11

3180

62

AGWP
100-year(W m–2yr kg–1)

GWP
20-year5.21e-12

3.68e-11

4.39e-11

3.80e-11

2.00e-10

1.18e-12

1.73e-15

5.65e-11

5.35e-128.51e-14

1.25e-12

3.77e-11

4.08e-11

6.40e-11

1.54e-12

3.53e-11

3.32e-11

7.86e-11

7.45e-11

4.78e-11

1.98e-11

7.93e-11

AGWP
20-year(W m–2yr kg–1)

0.30

0.35

0.42

0.36

1.02

0.16

0.01

0.30

0.26i

0.05

0.15

0.32

0.38

0.37

0.17

5.7

0.30

3.8

0.32

0.48h

0.42

0.35

0.32

0.44

Radiative
Efficiency(W m–2
ppb–1)

105.0
days

3.7

6.0

6.6

5.0

2.2

7.5

Lifetime(Years)(CF3)2CHOH

1,1,1,3,3,3-Hexafluoropropan-2-ol

CHF2OCF2CF2OCHF2

n-C4F9OC2H5

HFE-569sf2 (HFE-7200)

HFE-338pcc13 (HG-01)

C4F9OC2H5

i-HFE-7100

CHF2OCF2OCHF2

i-C4F9OCH3

n-HFE-7100

HFE-236ca12 (HG-10)

C4F9OCH3
n-C4F9OCH3

HFE-449s1 (HFE-7100)

CHF2OCF2OC2F4OCHF2(CF3)2CHOCH2F

HFE-43-10pccc124 (H-Galden 1040x, HG-11)

CF3CH2OCF2CF3

Sevoflurane (HFE-347mmz1)

Chemical FormulaHFE-338mcf2

Acronym, Common Name or Chemical Name

Table 8.A.1 (continued)

3890

5300

2850

2730

182

2910

5350

44

65

57

407

486

421

2820

13

<1

627

58

<1

14

413

446

719

17

387

363

889

854

530

216

929

GWP
100-year

6.23e-12

7.00e-12

4.60e-12

4.40e-12

1.66e-13

4.69e-12

7.06e-12

3.52e-14

5.14e-14

4.52e-14

5.35e-13

6.39e-13

5.54e-13

4.52e-12

9.67e-15

1.38e-17

8.48e-13

4.53e-14

6.80e-16

1.02e-14

4.91e-13

5.11e-13

1.03e-12

1.26e-14

4.60e-13

4.27e-13

1.30e-12

1.29e-12

7.18e-13

2.06e-13

1.45e-12AGTP
20-year(K kg–1)

9110

10,200

6730

6430

243

6860

10,300

52

75

66

783

934

809

6600

14

<1

1240

66

<1

15

718

747

1500

18

673

624

1900

1880

1050

302

2120

GTP
20-year1.31e-12

2.91e-129.10e-13

8.70e-13

1.97e-14

9.28e-13

2.94e-12

4.65e-15

6.78e-15

5.97e-15

5.14e-14

6.14e-14

5.32e-14

9.46e-13

1.33e-15

1.94e-18

8.12e-14

6.10e-15

9.56e-17

1.41e-15

4.89e-14

5.20e-14

9.97e-14

1.74e-15

4.58e-14

4.28e-14

1.27e-13

1.31e-13

6.87e-14

2.37e-14

1.60e-13AGTP
50-year(K kg–1)

2120

4730

1480

1410

32

1500

4770

8

11

10

83

99

86

1530

2

<1

132

10

<1

2

79

84

162

3

74

69

206

212

111

38

259

GTP
50-year

51
54
2
101
62
57
2
<1

2.76e-14
2.94e-14
1.26e-15
5.50e-14
3.39e-14
3.14e-14
1.02e-15
6.99e-17

442
25
415
434
1400
602

2.27e-13
2.37e-13
7.68e-13
3.29e-13

4.87e-15

1.38e-1494.29e-15

2.42e-138

3.10e-14

657

3.70e-141420

68

3.21e-14

7.75e-1359

2.38e-13

3.34e-152436

9.69e-16<1

124
6.81e-14

1.42e-18

120
6.55e-14874
4.05e-148830
1.64e-14

4.79e-14131

7.16e-14

4.40e-15

GTP
100-yearAGTP
100-year(K kg–1)

Anthropogenic and Natural Radiative Forcing
Chapter 8

8

735

736
CH3COOCF2CF2CF3
CH3COOCF2CF3
CH3COOCF3

Perfluoropropyl acetate

Perfluoroethyl acetate

Trifluoromethyl acetate(continued on next page)

CH3COOCF2CF2CF2CF3

1,1,1,3,3,3-Hexafluoropropan-2-yl formate

Perfluorobutyl acetate

HCOOCHFCF3
HCOOCH(CF3)2

1,2,2,2-Tetrafluoroethyl formate

HCOOCH2CH2CF3

3,3,3-Trifluoropropyl formate

HCOOCF2CF2CF2CF3

Perfluorobutyl formate
HCOOCH2CF3

HCOOCF2CF2CF3

Perfluoropropyl formate

2,2,2-Trifluoroethyl formate

HCOOCF3
HCOOCF2CF3

Perfluoroethyl formate

CF3OCF=CF2

HFE-216

Trifluoromethyl formate

800.0

PFPMIE (perfluoropolymethylisopropyl ether)

21.9 days

21.9 days

21.9 days

21.9 days

3.2

3.2

0.3

0.4

3.0

2.6

3.5

3.5

8.4 days

1.4

20.0 days

CF3OCF(CF3)
CF2OCF2OCF3

CF3(CF2)8CH2CH2OH

3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,11-Nonadecafluoroundecan-1-ol

20.0 days

20.0 days

CH3OCF2CHFCl

CF3(CF2)6CH2CH2OH

3,3,4,4,5,5,6,6,7,7,8,8,9,9,9-Pentadecafluorononan-1-ol

40.0

2.0

2.0

2.0

4.4

3.3

0.9

1.1

73.0 days

1.0

6.2

3.6 days

0.8

25.0

2-Chloro-1,1,2-trifluoro-1-methoxyethane

CF3(CF2)4CH2CH2OH

3,3,4,4,5,5,6,6,7,7,7-Undecafluoroheptan-1-ol

CF3CFHCF2OCF3

CH3O(CF2CF2O)3CH3

HFE-329me3

CH3O(CF2CF2O)2CH3

HG’-03CH2FOCF3

Trifluoro(fluoromethoxy)methane

HG’-02CH2FOCHF2

Difluoro(fluoromethoxy)methane

CH3OCF2CF2OCH3

CH2FOCH2F

Fluoro(fluoromethoxy)methane

HG’-01

CH3OCHF2

Difluoro(methoxy)methane

CH2FOCF2CF2H

1,1,2,2-Tetrafluoro-1-(fluoromethoxy)ethane

CH3OCH2F

CF3CH2OCH=CH2

Fluoroxene

Fluoro(methoxy)methane

CF3CF2CF2OCH2CH3

1-Ethoxy-1,1,2,2,3,3,3-heptafluoropropane

C12H5F19O2

HF2C–(OCF2)3–OCF2H

HG-30

2-Ethoxy-3,3,4,4,5-pentafluorotetrahydro-2,5-bis[1,2,2,2tetrafluoro-1-(trifluoromethyl)ethyl]-furan

Chemical Formula

Lifetime(Years)2.03e-11
4.17e-10
3.91e-14
3.00e-14

812
7170
2
1

5.39e-11
5.32e-11

2150
2130

3.59e-11
3.07e-121440
123

4.31e-11
3.05e-11
1.52e-13

1720
1220
6

1.89e-13
1.90e-13881.90e-13

1.89e-13
0.07i

0.10

1.59e-13

6
1.59e-13i

1.52e-13
0.11i

0.12i

0.33i

3.05e-11

4.30e-11i

0.35

1.60e-1264
1.60e-12

0.13i

0.16i

3.07e-12

3.59e-11i

0.56

3.45e-111380

3.45e-11

0.50i

5.30e-11

5.37e-11

0.44i

0.31

1.92e-14<1

1.92e-14

8.90e-107500

i

1.87e-10

1.12e-11

1.72e-14

3.00e-14

3.91e-14

1.79e-10

0.02

0.65

0.21

0.05

0.07

0.06

0.48

2.03e-11

2.16e-11

1.12e-11

2.16e-11

868

0.76

0.56

2.03e-11449

2.03e-11

815

0.29

1.72e-14

6.89e-112730<15.66e-112260

6.82e-11

5.65e-11

0.33g

0.30

1.20e-11479

1.32e-11

1.20e-11

1.15e-1246g

1.32e-11

1.15e-12

5.09e-12

0.19g

0.17g

0.07g

0.49j

7.68e-11528

7.99e-11

0.34i

0.01

5.09e-12

4.97e-15<13080

4.97e-15i

204

6.73e-10
5.56e-12

3.77e-10
223

1.65i

15,100AGWP
100-year(W m–2yr kg–1)

GWP
20-year5.56e-12AGWP
20-year(W m–2yr kg–1)

0.28i

Radiative
Efficiency(W m–2
ppb–1)

2

2

2

2

333

470

17

33

392

376

580

588

<1

9710

122

<1

<1

<1

4550

221

236

222

751

617

130

144

13

56

871

<1

61

7330

GWP
100-year

1.52e-15

1.51e-15

1.27e-15

1.21e-15

3.66e-13

5.17e-13

1.31e-14

2.55e-14

4.19e-13

3.80e-13

6.64e-13

6.73e-13

1.53e-16

5.52e-12

1.05e-13

1.37e-16

2.40e-16

3.12e-16

4.85e-12

2.05e-13

2.19e-13

2.06e-13

9.59e-13

6.88e-13

1.05e-13

1.18e-13

9.34e-15

4.53e-14

1.29e-12

3.95e-17

4.80e-14

9.68e-12AGTP
20-year(K kg–1)

2

2

2

2

535

755

19

37

613

555

971

984

<1

8070

153

<1

<1

<1

7090

299

320

301

1400

1010

153

173

14

66

1880

<1

70

14,100GTP
20-year

2.14e-16

2.12e-16

1.78e-16

1.71e-16

3.81e-14

5.39e-14

1.80e-15

3.48e-15

4.45e-14

4.19e-14

6.76e-14

6.85e-14

2.15e-17

6.11e-12

1.31e-14

1.93e-17

3.37e-17

4.39e-17

2.89e-12

2.41e-14

2.57e-14

2.42e-14

9.27e-14

7.11e-14

1.37e-14

1.52e-14

1.30e-15

5.86e-15

1.28e-13

5.58e-18

6.36e-15

4.03e-12AGTP
50-year(K kg–1)

<1

<1

<1

<1

62

87

3

6

72

68

110

111

<1

9910

21

<1

<1

<1

4690

39

42

39

150

115

22

25

2

10

207

<1

10

6530

GTP
50-year1.56e-16

1.55e-16

1.30e-16

1.25e-16

2.53e-14

3.57e-14

1.31e-15

2.52e-15

2.97e-14

2.85e-14

4.41e-14

4.47e-14

1.58e-17

6.15e-129.24e-15

1.41e-17

2.46e-17

3.21e-17

1.12e-12

1.67e-14

1.79e-14

1.68e-14

5.72e-14

4.69e-14

9.84e-15

1.08e-14

9.46e-16

4.19e-15

6.68e-14

4.08e-18

4.57e-15

1.06e-12

AGTP
100-year(K kg–1)

8

Acronym, Common Name or Chemical Name

Table 8.A.1 (continued)

<1

<1

<1

<1

46

65

2

5

54

52

81

82

<1

11,300

17

<1

<1

<1

2040

31

33

31

105

86

18

20

2

8

122

<1

8

1940

GTP
100-year

Chapter 8
Anthropogenic and Natural Radiative Forcing

C3F7CH2OH

2,2,3,3,4,4,4-Heptafluorobutan-1-ol

HCF2O(CF2CF2O)4CF2H

1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12,13,13,15,15-eicosafluoro-2,5,8,11,14-Pentaoxapentadecane
26.0

26.0

26.0

0.3

40.0 days

20.4 days

2.0 days

7.0 days

14.2 days

0.3

94.9 days

91.3 days

67.0

0.4

9.8

0.6

0.3

40.1 days

0.6

54.8 days

21.9 days

0.3

0.3

1.8

Lifetime(Years)1.26e-13
6.27e-13

5
25

1.46k

1.43k

1.15k

0.10

0.04

0.02

0.004

0.03

0.03

0.16

0.19

0.11

0.58

0.19

0.35

0.20

1.83e-10

2.26e-10

2.47e-10

1.83e-12

2.78e-13

8.07e-14

9.86e-16

9.14e-15

4.82e-14

1.49e-12

1.56e-12

1.19e-12

1.98e-10

2.14e-12

9.91e-11

3.10e-12

1.49e-12
4.82e-14
9.14e-15
9.86e-16
8.07e-14
2.78e-13
1.83e-12
4.51e-10

60
2
<1
<1
3
11
73
9910

7320

3.33e-10

4.12e-10

1.56e-129050

1.19e-12

63

86

48

1.14e-10
2.14e-12

3970

5.95e-10

3.10e-12

124

7940

3.00e-13
2.48e-121299

3.00e-13

0.24i

2.48e-12i

0.05

4.80e-12

192

4.80e-12

0.18i

6.27e-13

1.26e-13

0.15i

0.05
i

2.46e-1299
2.83e-128.74e-12

350

113

AGWP
100-year(W m–2yr kg–1)

GWP
20-year2.83e-12

2.46e-128.74e-12AGWP
20-year(W m–2yr kg–1)

0.27i

0.17i

0.07i

Radiative
Efficiency(W m–2
ppb–1)

3630

4490

4920

20

3

<1

<1

<1

<1

16

17

13

6490

23

1240

34

27

3

52

7

1

31

27

95

GWP
100-year

4.71e-12

5.83e-12

6.38e-12

1.50e-14

2.23e-15

6.45e-16

7.84e-18

7.27e-17

3.84e-16

1.23e-14

1.27e-14

9.72e-15

5.57e-12

1.77e-14

2.03e-12

2.61e-14

2.04e-14

2.41e-15

4.08e-14

5.06e-15

1.00e-15

2.33e-14

2.02e-14

8.60e-14AGTP
20-year(K kg–1)

9.07e-17
3.12e-16
2.07e-15
2.75e-12

<1
3
22
9320

6880

2.03e-12

2.51e-12

1.11e-18<18520

1.03e-17

2.43e-1526

5.41e-17

2.88e-132960<13.52e-15

38<12.81e-15

1.69e-15

3.38e-16430

18

5.47e-1560

1.76e-15

7.07e-16

7

1.35e-15

1.41e-16

119

3.20e-15

34

14

2.78e-1530

4.29e-12

1.03e-14

126

8140

AGTP
50-year(K kg–1)GTP
20-year

3300

4080

4460

3

<1

<1

<1

<1

<1

3

3

2

6960

4

467

6

5

<1

9

1

<1

5

5

17

GTP
50-year

4
5
178
3
4380
2
2
2
<1
<1
<1
<1
<1

2.04e-15
2.55e-15
9.74e-14
1.76e-15
2.39e-12
9.79e-16
1.28e-15
1.23e-15
3.96e-17
7.51e-18
8.10e-19
6.63e-17
2.28e-161010<12.47e-16

5.50e-13

7
3.95e-151250<1
5.15e-16

6.81e-13<11.03e-16342.32e-151360

42.02e-15

7.45e-13

13

7.21e-15

1.50e-15

GTP
100-yearAGTP
100-year(K kg–1)(continued on next page)† Perturbation lifetime is used in calculation of metrics, not the lifetime of the atmospheric burden.

* No single lifetime can be given. The impulse response function for CO2 from Joos et al. (2013) has been used. See also Supplementary Material Section 8.SM.11.

For CH4 we estimate an uncertainty of ±30% and ±40% for 20- and 100-year time horizon, respectively (for 90% uncertainty range). The uncertainty is dominated by AGWP for CO2 and indirect effects. The uncertainty in GWP for N2O is estimated to ±20% and±30% for 20- and 100-year time horizon, with the largest contributions from CO2. The uncertainty in GWP for HFC-134a is estimated to ±25% and ±35% for 20- and 100-year time horizons while for CFC-11 the GWP the corresponding numbers are approximately±20% and ±35% (not accounting for the indirect effects). For CFC-12 the corresponding numbers are ±20 and ±30. The uncertainties estimated for HFC-134a and CFC-11 are assessed as representative for most other gases with similar or longer lifetimes. For
shorter-lived gases, the uncertainties will be larger. For GTP, few estimates are available in the literature. The uncertainty is assessed to be of the order of ±75% for the methane GTP100.

Notes:HCF2O(CF2CF2O)3CF2H

1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12-hexadecafluoro-2,5,8,11-Tetraoxadodecane

CF3CH2OH

2,2,2-Trifluoroethanol
HCF2O(CF2CF2O)2CF2H

CHF2CH2OH

1,1’-Oxybis[2-(difluoromethoxy)-1,1,2,2-tetrafluoroethane

CH2FCH2OH

2,2-Difluoroethanol

perfluoro-2-methyl-3-pentanone

2-Fluoroethanol

CHF2CF2CH2OCH3
CF3CF2C(O)CF(CF3)2

1,1,2,2-Tetrafluoro-3-methoxy-propane

CF3CH2CHO

CF3CF2CF2CH2OH

2,2,3,3,4,4,4-Heptafluoro-1-butanol

3,3,3-Trifluoro-propanal

CHF2CF2CH2OH
CF3CHFCF2CH2OH

2,2,3,4,4,4-Hexafluoro-1-butanol

CF3CF2CF2OCHFCF3

1,1,1,2,2,3,3-Heptafluoro-3-(1,2,2,2tetrafluoroethoxy)-propane

2,2,3,3-Tetrafluoro-1-propanol

CF3CHFCF2OCH2CH3

1-Ethoxy-1,1,2,3,3,3-hexafluoropropane

CHF2CHFOCF3

CF3COOCHF2

1,1,2-Trifluoro-2-(trifluoromethoxy)-ethane

HCF2COOCH3

Difluoromethyl 2,2,2-trifluoroacetate

CF3COOCH2CF3

2,2,2-Trifluoroethyl 2,2,2-trifluoroacetate

Methyl 2,2-difluoroacetate

CF3COOCH2CH3

Ethyl 2,2,2-trifluoroacetate

CF3COOCH3

CF3COOCF2CH3

1,1-Difluoroethyl 2,2,2-trifluoroacetate

Methyl 2,2,2-trifluoroacetate

FCOOCF2CH3

FCOOCH3

Chemical Formula

1,1-Difluoroethyl carbonofluoridate

Methyl carbonofluoridate

Acronym, Common Name or Chemical Name

Table 8.A.1 (continued)

Anthropogenic and Natural Radiative Forcing
Chapter 8

8

737

8

738

Based on instantaneous RE from Heathfield et al. (1998) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing).

Note that calculation of RE is based on calculated (ab initio) absorption cross-section and uncertainties are therefore larger than for calculations using experimental absorption cross section.

Based on instantaneous RE from Javadi et al. (2007) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing).

Based on instantaneous RE from Andersen et al. (2010) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing).

g

h

i

j

k

The GTP values are calculated with a temperature impulse response function taken from Boucher and Reddy (2008). See also Supplementary Material Section 8.SM.11.

Based on average instantaneous RE reported in literature (Vasekova et al., 2006; Bravo et al., 2010) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing).

Based on instantaneous RE from ab initio studies of Blowers et al. (2007, 2008)(correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing).

f

Based on instantaneous RE from Baasandorj et al. (2010); Baasandorj et al. (2011) (correction factors have been applied to account for stratospheric temperature adjustment and non-homogeneous horizontal and vertical mixing).

Based on instantaneous RE from ab initio study of Bravo et al. (2010) (a factor 1.10 has been applied to account for stratospheric temperature adjustment).

ceBased on Rajakumar et al. (2006) (lifetime correction factor has been applied to account for non-homogeneous horizontal and vertical mixing).

bd

RE is unchanged since AR4.

RE is unchanged since AR4 except the absolute forcing is increased by a factor of 1.04 to account for the change in the recommended RE of CFC-11.

a‡ Metric values for CH4 of fossil origin include the oxidation to CO2 (based on Boucher et al., 2009). In applications of these values, inclusion of the CO2 effect of fossil methane must be done with caution to avoid any double-counting because CO2 emissions
numbers are often based on total carbon content. Methane values without the CO2 effect from fossil methane are thus appropriate for fossil methane sources for which the carbon has been accounted for elsewhere, or for biospheric methane sources for which
there is abalance between CO2 taken up by the biosphere and CO2 produced from CH4 oxidization. The addition effect on GWP and GTP represents lower limits from Boucher et al. (2009) and assume 50% of the carbon is deposited as formaldehyde to thesurface and is then lost. The upper limit in Boucher et al. (2009) made the assumption that this deposited formaldehyde was subsequently further oxidized to CO2 .

Table 8.A.1 Notes (continued)

Chapter 8
Anthropogenic and Natural Radiative Forcing

Anthropogenic and Natural Radiative Forcing

Chapter 8

Table 8.A.2 | Halocarbon indirect GWPs from ozone depletion using the EESC-based
method described in WMO (2011), adapted from Daniel et al. (1995). A radiative forcing
in year 2011 of –0.15 (–0.30 to 0.0) W m–2 relative to preindustrial times is used (seeSection 8.3.3). Uncertainty on the indirect AGWPs due to the ozone forcing uncertainty
is ±100%.

Gas

GWP100

CFC-11–2640

CFC-12–2100

CFC-113–2150

CFC-114–914

CFC-115–223

HCFC-22–98

HCFC-123–37

HCFC-124–46

HCFC-141b–261

HCFC-142b–152

CH3CCl3

–319

CCl4

–2110

CH3Br

–1250

Halon-1211–19,000

Halon-1301–44,500

Halon-2402–32,000

HCFC-225ca–40

HCFC-225cb–60

8Table 8.A.3 | GWP and GTP for NOX from surface sources for time horizons of 20 and 100 years from the literature. All values are on a per kilogram of nitrogen basis. Uncertainty
for numbers from Fry et al. (2012) and Collins et al. (2013) refer to 1-s. For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values
can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. For 20 years the changes are negligible.

GWP

GTP

H = 20H = 100

H = 20H = 100

NOX East Asiaa

6.4 (±38.1)–5.3 (±11.5)–55.6 (±23.8)–1.3 (±2.1)

NOX EU + North Africaa–39.4 (±17.5)–15.6 (±5.8)–48.0 (±14.9)–2.5 (±1.3)

NOX North Americaa–2.4 (±30.3)–8.2 (±10.3)–61.9 (±27.8)–1.7 (±2.1)

NOX South Asia

–40.7 (±88.3)–25.3 (±29.0)–124.6 (±67.4)–4.6 (±5.1)

NOX four above regionsa–15.9 (±32.7)–11.6 (±10.7)–62.1 (±26.2)–2.2 (±2.1)

Mid-latitude NOxc

–43 to +23–18 to +1.6

–55 to –37–2.9 to –0.02

Tropical NOxc

43 to 130

–28 to –10–260 to –220

–6.6 to –5.4

NOX globalb

19

–11

–87

–2.9

NOX globald–108 ± 35–335 ± 110–560 ± 279–31 ± 10–95 ± 31–159 ± 79a

Notes:aFry et al. (2012) (updated by including stratospheric H2O) and Collins et al. (2013).

b

Fuglestvedt et al. (2010); based on Wild et al. (2001).

c

Fuglestvedt et al. (2010).

d

Shindell et al. (2009). Three values are given: First, without aerosols, second, direct aerosol effect included (sulfate and nitrate), third, direct and indirect aerosol effects included. Uncertainty
ranges from Shindell et al. (2009) are given for 95% confidence levels.

739

Chapter 8

Anthropogenic and Natural Radiative Forcing

Table 8.A.4 | GWP and GTP for CO for time horizons of 20 and 100 years from the literature. Uncertainty for numbers from Fry et al. (2012) and Collins et al. (2013) refer to 1-s.
For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated
values for the reference gas CO2. For 20 years the changes are negligible.

GWP

8

GTP

H = 20H = 100

H = 20H = 100

CO East Asiaa

5.4 (±1.7)1.8 (±0.6)

3.5 (±1.3)0.26 (±0.12)

CO EU + North Africaa

4.9 (±1.5)1.6 (±0.5)

3.2 (±1.2)0.24 (±0.11)

CO North Americaa

5.6 (±1.8)

1.8 (±0.6)

3.7 (±1.3)0.27 (±0.12)

CO South Asiaa

5.7 (±1.3)

1.8 (±0.4)

3.4 (±1.0)0.27 (±0.10)

CO four regions abovea

5.4 (±1.6)1.8 (±0.5)

3.5 (±1.2)0.26 (±0.11)

CO globalb

6 to 9.3

2 to 3.3

3.7 to 6.1

0.29 to 0.55

CO globalc

7.8 ± 2.0
11.4 ± 2.9
18.6 ± 8.3

2.2 ± 0.6
3.3 ± 0.8
5.3 ± 2.3

Notes:aFry et al. (2012) (updated by including stratospheric H2O) and Collins et al. (2013).

b

Fuglestvedt et al. (2010).

c

Shindell et al. (2009). Three values are given: First, without aerosols, second, direct aerosol effect included, third, direct and indirect aerosol effects included. Uncertainty ranges from Shindell etal. (2009) are given for 95% confidence levels.

Table 8.A.5 | GWP and GTP for VOCs for time horizons of 20 and 100 years from the literature. Uncertainty for numbers from Fry et al. (2012) and Collins et al. (2013) refer to
1-s. For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated
values for the reference gas CO2. For 20 years the changes are negligible.

GWP

GTP

H = 20H = 100

H = 20H = 100

16.3 (±6.4)

5.0 (±2.1)8.4 (±4.6)

0.7 (±0.4)

VOC EU + North Africa

18.0 (±8.5)5.6 (±2.8)9.5 (±6.5)

0.8 (±0.5)

VOC North Americaa

16.2 (±9.2)

5.0 (±3.0)8.6 (±6.4)

0.7 (±0.5)

VOC South Asiaa

27.8 (±5.6)8.8 (±1.9)15.7 (±5.0)

1.3 (±0.5)

VOC four regions above

18.7 (±7.5)5.8 (±2.5)

10.0 (±5.7)

0.9 (±0.5)

14

4.5

7.5

0.66

VOC East Asiaaa

VOC globalb

Notes:aFry et al. (2012) (updated by including stratospheric H2O) and Collins et al. (2013).

b

Fuglestvedt et al. (2010) based on Collins et al. (2002).

The values are given on a per kilogram of C basis.

Table 8.A.6 | GWP and GTP from the literature for BC and OC for time horizons of 20 and 100 years. For the reference gas CO2, RE and IRF from AR4 are used in the calculations.
The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. For 20 years the changes are negligible.

GWP

GTP

H = 20H = 100

H = 20H = 100

BC total, globalc

3200 (270 to 6200)

900 (100 to 1700)

920 (95 to 2400)

130 (5 to 340)

BC (four regions)d1200 ± 720

345 ± 207420 ± 190

56 ± 25

470

64

–71

–10

–55 ± 16–7.3±2.1

BC global

1600

460

2900 ± 1500

830 ± 440

OC globala

–240

–69

OC global–160 (–60 to –320)–46 (–18 to –19)–160 ± 68–46 ± 20aBC aerosol–radiation interaction +albedo, globalbb

OC (4 regions)d
Notes:
aFuglestvedt et al. (2010).

b

Bond et al. (2011). Uncertainties for OC are asymmetric and are presented as ranges.

c

Bond et al. (2013). Metric values are given for total effect.

dCollins et al. (2013). The four regions are East Asia, EU + North Africa, North America and South Asia (as also given in Fry et al., 2012). Only aerosol-radiation interaction is included.

740