Causes of the reduction in uncertainty in the anthropogenic radiative forcing of climate between IPCC (2001) and IPCC (2007)

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Causes of the reduction in uncertainty in the

anthropogenic radiative forcing of climate between

IPCC (2001) and IPCC (2007)

Jim Haywood, Michael Schulz

To cite this version:

Jim Haywood, Michael Schulz. Causes of the reduction in uncertainty in the anthropogenic radiative forcing of climate between IPCC (2001) and IPCC (2007). Geophysical Research Letters, American Geophysical Union, 2007, 34 (20), �10.1029/2007GL030749�. �hal-03191249�

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Causes of the reduction in uncertainty in the anthropogenic radiative

forcing of climate between IPCC (2001) and IPCC (2007)

Jim Haywood1and Michael Schulz2

Received 21 May 2007; revised 27 July 2007; accepted 19 September 2007; published 18 October 2007.

[1] Mechanisms that drive climate change are quantified by the radiative forcing which is the perturbation to the global energy balance of the Earth/atmosphere system. These mechanisms may be of anthropogenic or natural origins and each has an associated level of scientific uncertainty. Until recently, even the sign of the anthropogenic radiative forcing has been in doubt because strong, poorly quantified negative radiative forcings such as those from aerosols act to oppose the strong, well quantified positive radiative forcings from well mixed greenhouse gases. We present an analysis of the probability distribution function of the anthropogenic radiative forcing for the individual forcing mechanisms identified by IPCC (2001) and IPCC (2007). We conclude that significant progress in reducing the uncertainty of the anthropogenic radiative forcing has been made since IPCC (2001). The single most important contributor to this conclusion appears to be the reduction in the uncertainty associated with the aerosol direct effect, followed by the provision of a best estimate for the aerosol cloud albedo indirect effect. Citation: Haywood, J., and M. Schulz (2007), Causes of the reduction in uncertainty in the anthropogenic radiative forcing of climate between IPCC (2001) and IPCC (2007), Geophys. Res. Lett., 34, L20701, doi:10.1029/2007GL030749.

1. Introduction

[2] The drivers of human induced global mean temperature change since the pre-industrial period are quantified by changes in the energy balance of the Earth/atmosphere system via the radiative forcing, DFglobal [e.g., Intergovernmental Panel on Climate Change (IPCC), 2001].DFglobal, is a useful indicator of a mechanisms potential impact on global surface temperatures,DTglobal, because:

DFglobal¼ lDTglobal

holds for many radiative forcing mechanisms wherel is the model dependent climate sensitivity [Forster et al., 2007; Intergovernmental Panel on Climate Change (IPCC), 2007]. Radiative forcing occurs through increases in concentrations of well mixed greenhouse gases (WMGHGs) such as carbon dioxide and other complex human induced processes. Both positive DFglobal processes leading to warming (e.g. increases in atmospheric concentrations of WMGHGs, tropospheric ozone) and negative DFglobal

processes leading to cooling (e.g. stratospheric ozone depletion, aerosols effects, and surface albedo changes) have been quantified. The uncertainty in DFglobal due to WMGHGs (carbon dioxide, methane, nitrous oxide, and the halocarbons) may be relatively well quantified to10% but other components remain more uncertain.

[3] We reproduce the radiative forcing probability distri-bution functions (PDFs) from Boucher and Haywood [2001] based upon the radiative forcings documented by IPCC [2001] and show the large uncertainty in the strong negative radiative forcing processes leads to uncertainty in the sign of the overallDF_global. We then extend the analysis to the radiative forcing from IPCC [2007], showing that a positive DF_global is now highly probable. Causes for this change are then investigated.

2. Method

[4] Schwartz and Andreae [1996] proposed a systematic method for determining the total DFglobal from individual radiative forcing estimates and for estimating the uncertainty. This methodology was further developed by Boucher and Haywood [2001] who assumed that each individual radia-tive forcing estimates could be represented by a normal, a log-normal, or a flat probability distribution function (PDF) and ran a Monte-Carlo simulation to estimate the mean and uncertainty in the total DFglobal. Here, we isolate the anthropogenicDFglobali.e. we exclude the solar and volca-nicDFglobalcomponents which are of natural origin.

[5] Four different assumptions for individual radiative forcing PDFs are applied. (1) Normal distribution: which are applied to those DFglobal mechanisms which are de-scribed in the IPCC reports by the form xWm2± yWm2. (2) Log-normal distribution: which are applied to the DFglobal of contrails which is described by IPCC [2007] by the form xWm2 with an uncertainty of a factor of y. (3) Flat-distribution: an equal probability between the range boundaries is assumed if a range is stated and if no best estimate is available. (4) Discrete values: where a larger number of specific tabulated estimates of DFglobal are explicitly quoted by IPCC [2007], these discrete values are randomly sampled instead of applying normal distribu-tions. This removes the assumptions of a particular shape of PDF.

[6] The individual component DFglobal quoted by IPCC [2001] were guided by the range in the estimates rather than from rigorous statistics. As described by Boucher and Haywood [2001], we perform three, 1-million point Monte-Carlo simulations assuming the individual radiative forcing uncertainties quoted by IPCC [2001] encompass 1, 1.5, and 2 standard deviations. We also assume the most reasonable component PDFs of the ‘‘C scenario’’ of Boucher and

1

Met Office, Exeter, UK.

2

Laboratoire des Sciences du Climat et l’Environnement, CEA, CNRS, Gif-sur-Yvette, France.

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Haywood [2001] as summarised in Table 1. In IPCC [2007] the uncertainty in the individual DFglobalcomponents rep-resents the 90% confidence interval justifying a single assumption: the uncertainty spans 1.645 standard devia-tions. The component PDFs are summarised in Table 1. The calculations are identical to those presented by Forster et al. [2007] and IPCC [2007].

[7] We assess the effect of the updates of the individual DFglobalmechanisms upon the composite PDF by neglect-ing updates of the individualDFglobalupdates in turn. In all cases a one million point Monte-Carlo simulation is per-formed to estimate the composite PDF of the totalDFglobal.

3. Results

[8] The three PDFs derived from IPCC [2001] are shown by the black lines on Figure 1. Assuming that the uncer-tainty range represents one standard deviation (SD = 1) shows the largest resulting uncertainty, while the SD = 2 curve shows the smallest uncertainty. The mean, median, and mode of the compositeDFglobalranges from 0.59 Wm2 to 0.81 Wm2 (Table 2). All three PDFs indicate that DFglobal has a significant chance of being negative and ranges from 13.7% to 25.5% depending on which simula-tion is considered most reasonable. A positive DFglobal is therefore considered likely [IPCC, 2007], and a negative DFglobal unlikely. Thus, the negative DFglobal from the indirect and direct aerosol effects, significantly offset the DFglobal due to WMGHGs. Note here that the means summarised in Table 2 are not simply the sum of those shown in Table 1. This is explained by considering the properties of the log-normal distribution for the IPCC [2001] sulphate aerosol where the radiative forcing is 0.4 Wm2 with an uncertainty of a factor of two. Assuming that 1, 1.5, and 2 standard deviations encompass the range of 0.2 Wm2 to 0.8 Wm2 leads to mean/ median values of 0.51/0.40 0.44/0.40, and 0.42/ 0.40 Wm2 respectively. Similar comments apply when discrete values are used.

[9] The PDF derived from IPCC [2007] is shown in the grey line on Figure 1. The mean and median composite DFglobalis significantly more positive than those of IPCC

[2001] at 1.55 Wm2and 1.59 Wm2. The probability that theDFglobalis negative is calculated to be just 0.2%, with a probability of positive DFglobal of 99.8%. Thus, a positive DFglobal can be considered virtually certain and the possi-bility of a negative DFglobal exceptionally unlikely. Of course, there are inherent structural uncertainties associated with the assumptions used in determining the DFglobal which temper the conclusions leading to the statement that we have a very high (>90%) confidence of a positive DFglobal; these factors are more fully discussed in section 4. [10] Next we consider which of the updates causes the change in the composite DFglobal PDF; changes in atmo-spheric concentrations of WMGHGs, revision of estimates/ addition of new forcing mechanisms, and reduction in uncertainty of the direct effect of aerosols. We make

Table 1. Radiative Forcing of Individual Components for IPCC [2001] and IPCC [2007]a

Component IPCC [2001] PDF IPCC [2007] PDF

WMGHGs 2.43 ± 0.24 Normal 2.63 ± 0.26 Normal Trop Ozone 0.35 ± 0.15 Normal 0.35 [0.25 to 0.65] Discrete Strat Ozone 0.15 ± 0.10 Normal 0.05 ± 0.10 Normal

Strat H2O N/A N/A 0.07 ± 0.05 Normal

Alb (land use) 0.2 ± 0.2 Normal 0.2 ± 0.2 Normal

Alb (BC on snow) N/A N/A 0.1 ± 0.1 Normal

Aerosol Direct effect

Sulphate 0.40 {2} Log 0.4 ± 0.2 All aerosol types combined = 0.5 ± 0.4 Discrete BB 0.20 {3} Log +0.03 ± 0.12 Discrete FF BC 0.20 {2} Log 0.20 ± 0.15 Discrete FF OC 0.10 {3} Log 0.05 ± 0.05 Discrete

Dust [0.6 to 0.4] Flat 0.10 ± 0.20 Normal

Nitrate N/A N/A 0.10 ± 0.10 Normal

Aerosol 1st indirect effect [0 to2.0] Flat 0.7 [0.3 to 1.8] Discrete Linear Contrails 0.02 {3.5} Log 0.01 [0.007 to +0.02] Log

a

BB, biomass burning; FF BC, fossil fuel black carbon; FF OC, fossil-fuel organic carbon. N/A indicates estimates were not available or too speculative to be included in the IPCC [2001] forcing bar chart. Normal, normal distributions assumed; Log, log-normal distributions assumed; Flat, equally probable assumed. The numbers within square brackets represent the range (no specific statistical meaning for IPCC [2001]), and 90% confidence for IPCC [2007]. {2} represents an uncertainty of a factor of 2.

Figure 1. Probability density functions (PDFs) ofDFglobal derived from information given by IPCC [2001] (black curves) and IPCC [2007] (thick grey curve). The solid, dotted, and dashed black lines correspond to the uncertainty range quoted by IPCC [2001] being encompassed by 1/1.5/ 2 standard deviations. The grey line is based on the 90% confidence level quoted by IPCC [2007] which corresponds to the uncertainty range being encompassed by 1.645 standard deviations.

L20701 HAYWOOD AND SCHULZ: PDF OF ANTHROPOGENIC RADIATIVE FORCING L20701

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perturbations to the PDF derived by IPCC [2007], as this PDF is more rigorously statistically defined meaning we investigate the effect of neglecting a particular update on the IPCC [2007] PDF, rather than the effect of including the particular update on the IPCC [2001] PDF.

[11] Atmospheric concentrations of WMGHGs such as carbon dioxide, nitrous oxide, CFCs and HCFCs have increased since IPCC [2001], while others (e.g. methane) have remained approximately constant. DF_global has increased since IPCC [2001] due primarily to change in concentrations of carbon dioxide (Table 1). Neglecting changes in concentrations of WMGHGs changes the mean DF_global by 0.2 Wm2, and simulation of the PDF suggests that the probability of negative DFglobal changes from 0.2% to 0.7% (see Table 3 and Figure 2).

[12] The DFglobal due to tropospheric and stratospheric ozone given by IPCC [2001] and IPCC [2007] are sum-marised in Table 1. The changes in the PDF due to changes in ozone are estimated by assuming that the IPCC [2001] uncertainties encompass 1, 1.5, and 2 standard deviations and taking a mean of the results. The mean and the median DFglobal neglecting these change shows a change in the mean, median and mode radiative forcing by approximately 0.10 Wm2(Table 3 and Figure 2).

[13] Since IPCC [2001], two additional forcing mecha-nisms have been quantified; stratospheric water vapour from methane oxidation, and black carbon on snow/ice albedo (Table 1). The change in the mean radiative forcing of 0.17 Wm2 caused by neglecting these DFglobal mecha-nisms does not greatly change the likelihood that the compositeDFglobalis negative (Table 3 and Figure 2).

[14] IPCC [2001] suggests that the aerosol first indirect DFgloballies somewhere in the range 0 Wm2to2 Wm2, but was unable to give a best estimate owing to the many

gaps in knowledge, the scarcity of model studies, and the lack of validation data. The update given by IPCC [2007] suggests a best estimate of around0.7 Wm2 with a 5 – 95% confidence interval spanning the range0.3 Wm2to 1.8 Wm2. Neglecting the update to the first indirect effect produces a much flatter distribution (thick black curve) which has a significant influence on the mode of the composite PDF (Figure 2 and Table 3).

[15] Examination of Figure 2 shows that neglect of each effect always results in a small negative shift in the mean of the PDF. When all of the effects are considered together (Figure 2 and Table 3), the probability of a negativeDFglobal changes from 0.2% for IPCC [2007] to 5.2% when the updates are excluded and thus from virtually certain to very likely. The mean, median and mode are in the range 0.95 Wm2to 1.04 Wm2when the updates are neglected but 1.55 Wm2 to 1.73 Wm2 when the updates are included. This indicates that, while each of the updates to DF_global are small when considered separately, when summed these differences explain some (but not all) of the discrepancy between the IPCC [2001] estimates and those from IPCC [2007] (Figure 1, Tables 2 and 3).

[16] Now we consider the updates to estimates of the direct effect. The DFglobal radiative of five individual components were quantified by IPCC [2001] (Table 1). IPCC [2007] added an estimate for nitrate, and a mean composite direct forcing for all aerosols of 0.5 Wm2 (Table 1) based on a combination of model and satellite estimates.

[17] The effect of neglecting updates in the aerosol direct DFglobalare demonstrated in Figure 3, where three different IPCC [2001] PDF curves are shown corresponding to aerosol direct DFglobal uncertainties encompassing 1, 1.5, and 2 standard deviations. The statistics shown in Table 3

Table 2. Statistics Associated With the Distributions Shown in Figure 1a

Simulation Mean RF, Wm2 Median RF, Wm2 Mode* RF, Wm2 Probability RF Less Than 0 Wm2, % Likelihood of More Than 0 Wm2 IPCC 2001: SD = 1 0.59 0.66 0.72 25.5 Likely IPCC 2001: SD = 1.5 0.76 0.77 0.79 16.9 Likely IPCC 2001: SD = 2 0.81 0.81 0.80 13.7 Likely IPCC 2007 1.55 1.59 1.73 0.2 Virtually certain

a

Virtually certain >99% probability; extremely likely >95% probability; very likely >90% probability; likely >66% probability; about as likely as not 33 – 66% probability; unlikely <33% probability; very unlikely <10% probability; extremely unlikely <5% probability; exceptionally unlikely <1% probability.

Table 3. As for Table 2, but for the Distributions Shown in Figures 2 and 3a

Simulated Effect by Neglecting Mean RF, Wm2 Median RF, Wm2 Mode RF, Wm2 Probability RF Less Than 0 Wm2, % Base case 1.55 1.59 1.73 0.2 a) WMGHG concentration changes 1.35 (0.20) 1.39 (0.20) 1.54 (0.19) 0.7 b) Strat and trop water vapour 1.46 (0.09) 1.49 (0.10) 1.62 (0.11) 0.3 c) BC on snow and strat water vapour 1.38 (0.17) 1.42 (0.17) 1.58 (0.15) 0.5 d) Aerosol indirect effect 1.50 (0.05) 1.50 (0.09) 1.46 (0.27) 0.5 a) + b) + c) + d) 1.04 (0.51) 1.04 (0.55) 0.95 (0.78) 5.2 e) Aerosol direct effect

SD = 1 1.05 (0.50) 1.14 (0.45) 1.30 (0.43) 11.1 SD = 1.5 1.23 (0.32) 1.26 (0.33) 1.34 (0.39) 3.8 SD = 2 1.28 (0.27) 1.31 (0.28) 1.37 (0.36) 2.2

Average 1.19 1.24 1.34 5.7

a

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indicate that the aerosol directDFglobalupdates produce the largest changes in the total PDF with mean, median, and mode estimates of the totalDFglobalin the range 1.05 Wm2 to 1.37 Wm2 when the updates are neglected. Thus, despite the fact that nitrate aerosol is included in the IPCC [2007] radiative forcing estimates (0.10 Wm2 ± 0.1 Wm2), the overall effect of including the updates makes the composite total DFglobal significantly more positive. Not including the updates also has a large effect on the probability of the total radiative forcing being negative. This is due to IPCC [2001] defining the uncer-tainty of each aerosol direct radiative forcing by the form xWm2with an uncertainty of a factor of y, and thus a log-normal distribution with a significant skew and a long tail was used by Boucher and Haywood [2001] as the most appropriate direct radiative forcing PDF for each aerosol species.

4. Discussion and Conclusions

[18] One of the most important advances of the IPCC [2007] DFglobal bar chart compared to that from IPCC [2001] is that the uncertainty in the individual estimates ofDFglobalare now representative of the 5 – 95% confidence intervals. This allows a more definitive estimate of the total DFglobal which is assessed as virtually certainly positive, and conversely exceptionally unlikely negative [Forster et al., 2007]. The most significant update appears to be in the aerosol directDFglobalwhere the uncertainty is significantly reduced. This is due to improvements in modelling and measurement efforts and to a concerted effort to constrain modelling estimates of the aerosol optical depth, and the associated DFglobal with satellite and surface based obser-vations [e.g., Schulz et al., 2006; Kinne et al., 2006]. Aerosol optical depth (AOD) has been intercompared between ground based sun photometers (Aeronet) and

several new generation satellite sensors (MODIS, POLDER, MISR) making it a reliable benchmark for global model simulations [Myhre et al., 2004; Dubovik et al., 2002]. Additionally, the fraction of the AOD that is due to the fine mode (submicron sizes) is a powerful intermediate param-eter linking anthropogenic emissions and radiative forcing. Detailed comparisons and/or the synergy between model and measurement methods has increased confidence in radiative forcing estimates [Kaufman et al., 2005; Anderson et al., 2005; Bellouin et al., 2005; Schulz et al., 2006; Yu et al., 2006]. The inclusion of a best estimate and a 5 – 95% confidence interval for the aerosol indirect effect rather than the flat distribution assumed by Boucher and Haywood [2001] leads to an increase in the mode of the PDF and changes the shape of the composite PDF (Figure 2). While other updates have relatively minor effects upon the com-posite PDF when considered alone, the sum of these effects combined with the direct and indirect effect updates explain the difference between the conclusions derived using IPCC [2001] and IPCC [2007] data.

[19] As stated by IPCC [2007], while the formulation of the PDF is reasonably logical, there are some structural uncertainties associated with the assumptions used in the construction of the PDF which are not accounted for. Additionally, equal weighting is given to each of the DFglobal mechanisms even though the level of scientific understanding varies. Nevertheless, based on our current knowledge of the drivers of climate change, it appears at least very unlikely that the positiveDFglobaldue to increases in concentrations in WMGHGs can be entirely offset by the negativeDFglobaldue to other mechanisms.

[20] The magnitude of anthropogenicDFglobalcan also be quantitatively compared against the magnitude of the natu-ral DFglobalinduced by changes in solar output since pre-industrial times. The mean, median and mode radiative forcings from the PDF shown in Figure 1 can be compared against the estimate for solar forcing of +0.12 Wm2 (5 – 95% confidence interval +0.06 Wm2to +0.30 Wm2). The magnitude of the anthropogenicDFglobalappears to be some

Figure 3. The effects of neglecting updates in the aerosol direct effect. The solid, dotted, and dashed black lines correspond to the uncertainty range quoted by IPCC [2001] being encompassed by 1/1.5/2 standard deviations. The grey line is statistically identical to that shown in Figure 1. Figure 2. The effects of neglecting updates between IPCC

[2001] and IPCC [2007] in concentrations of WMGHGs, tropospheric and stratospheric ozone, black carbon on snow/ice surfaces and increases in stratospheric water vapour, and a best estimate for the indirect effect of aerosols. The thick black line represents the sum of the aforementioned. The grey line is statistically identical to that shown in Figure 1.

L20701 HAYWOOD AND SCHULZ: PDF OF ANTHROPOGENIC RADIATIVE FORCING L20701

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ten times that of the naturalDFglobaland therefore anthro-pogenic drivers of climate change far outweigh those from natural sources.

[21] Acknowledgments. Olivier Boucher and Piers Forster are thanked for their comments. Jim Haywood was funded under the Defra Climate Prediction Contract, PECD 7/12/37.

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