EXPERT ASSESSMENT OF UNCERTAINTIES IN DETECTION AND ATTRIBUTION OF

BY JAMES S. RISBEY AND MILIND KANDLIKAR

The problem of detection of climate change and attribution of causes of change has been formalized as a series of discrete probability judgements by experts

he study of detection and attribution of climate and further on "attributing" any detected signal to change addresses the issue of whether, and to increases in greenhouse gases or other possible causes Twhat extent, human-induced increases in green- (Hasselmann 1998; Hegerl et al. 1997; Santer et al. house gases have caused climate changes. Communi- 1996a; Zwiers 1999). Summary assessments of the cation of the science and role of uncertainties on this detection and attribution issue have tended to take a issue has been hindered by the lack of explicit formal qualitative approach to characterizing uncertainties approaches for making overall conclusions on detec- (Santer et al. 1996a; Barnett et al. 1999; Mitchell et al. tion and attribution. We have developed a protocol 2001). This study outlines some of the major uncer- to quantify uncertainties in each component of the de- tainties in detection and attribution, uses expert tection and attribution process and to provide a struc- judgements to quantify them, and gives an overall tured way to make overall conclusions (Risbey et al. quantitative assessment of detection and attribution 2000). Here we describe results from use of the pro- of climate change. tocol with a set of climate experts. In making overall assessments on detection and Studies of detection and attribution of climate attribution of climate change, a variety of scientific change have focused first on "detecting" climate judgements are called for. These relate to the quality change against the background of natural variability, of the underlying data needed to monitor climate changes, to the quality of models used to assess pos- sible causes of those changes, to the ability to moni- AFFILIATIONS: RISBEY—School of Mathematical Sciences, Monash tor the forcing of the earth system, and to model fu- University, Melbourne, Australia; KANDLIKAR—Faculty of Graduate ture consequences. The probability-based protocol Studies, University of British Columbia, Vancouver, Canada employed here makes many of these underlying CORRESPONDING AUTHOR: Dr. James S. Risbey, School of judgements explicit. The protocol breaks the detec- Mathematical Sciences, P.O. Box 28M, Monash University, Clayton, Vic 3800, Australia tion and attribution problem up into its component E-mail: [email protected] steps and allows for uncertainties on each of these judgements to be represented quantitatively via prob- In final form 31 May 2002 ©2002 American Meteorological Society ability density functions (pdfs). The protocol was completed by a set of 19 experts working in the field

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC tainty deriving from incomplete and TABLE 1. List of experts and their institutional affiliations. biased datasets. This is done by eliciting the pdf, /(£.)> for the observed change Expert Affiliation over a specified period for each line of evidence from the expert. The detection Myles Allen Oxford University step entails discriminating forced cli- Robert Balling mate changes (Shine and Forster Curt Covey Lawrence Livermore National Laboratory 1999) from internal natural variability Chris Folland Hadley Centre (Pelletier 1997). This requires an elici- Klaus Hasselmann Deutsches Klimarechenzentrum tation of the pdf,/(AT), for natural vari- ability in each line of evidence over the Gabi Hegerl Texas A&M University same time period. The distributions, Phil Jones University of East Anglia /(£.) and /(N.) can then be compared David Karoly Monash University using statistical tests to see how similar Haroon Kheshgi Exxon Corporate Research they are. Michael MacCracken U.S. Global Change Research Program Following (Bell 1986) and (Zwiers 1999), the detector, D., is defined as Nathan Mantua University of Washington D. = N{0, cfe) when S = 0 (null hypoth- University of Virginia esis); D. = N(S, eg) when S* 0 (alterna- John Mitchell Hadley Centre tive hypothesis), where D ~ &) in- Neville Nicholls Australian Bureau of Meteorology dicates that D is normally distributed Stephen Schneider Stanford University with the mean, ju, and variance, The null hypothesis of no climate change is John Wallace University of Washington rejected at the p x 100% significance Warren Washington National Center for Atmospheric Research level using a one-sided z test if the value Tom Wigley National Center for Atmospheric Research of the detector exceeds a critical value

Francis Zwiers Canadian Climate Center D = Z^cfe, where Zlp is the (1 - p) quantile of the standard normal distri- bution. The signal is "detected" when of detection and attribution studies (see Table 1). Note the null hypothesis is rejected. The power of the test that the order of experts has been scrambled in pre- or probability of detecting the signal when it is present senting results to prevent identification with particu- is given by lar experts. Twenty-three experts were approached to complete the protocol and four declined to do so. The 1 foo - —(D-S)2 / (J2; experts were selected to provide depth and diversity P(D > D )= _ exp 2 dD. (1) c in a range of different areas. For example, some were V2/ro"si data specialists, some modelers, some focused on methods in detection and attribution, some were from The conventional form of detection described above the Intergovernmental Panel on Climate Change uses only information on the critical percentile of (IPCC) detection and attribution groups (Santer et al. /(N.), and may be sensitive to the choice of critical 1996b; Mitchell et al. 2001), and some were not in percentile. For that reason we perform detection tests those groups. Further, they entertained a range of for a range of values of the critical percentile (1%, 5%, views about the likely magnitude of greenhouse cli- 10%, 20%). Detection implies rejection of the null mate change. This sample of 19 experts is considered hypothesis at the critical percentile. This does not to be indicative, but not exhaustive. identify the cause of any climate change or the con- The first step in the detection and attribution pro- tribution of natural variability to observed changes. cess is the determination of a set of lines of evidence, It merely identifies the likelihood that the change ex- E. i =1,1, which serve as signifiers of climate change. y ceeds the critical threshold. For each expert the protocol elicits lines of evidence that are relatively independent in order to minimize 1 redundant information. The expert must then evalu- 1 For example, changes in global mean sea level are not consid- ate the magnitude and uncertainty in each line of evi- ered because they are a fairly direct consequence of changes in dence, since each will be associated with some uncer- global mean temperature, which is a chosen line of evidence.

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC For the attribution step, responsibility in explain- many realizations of climate change over the recent ing observed trends (E) is partitioned out among period, the expected contribution from natural vari- competing causes. These causes can include natural ability to the observed change would be zero. But since variability as well as a range of various forcing pro- we have only one real observed period to draw from, cesses. The attribution step requires the experts to it is possible that there were long-term excursions of identify this set of forcings, and to characterize their natural variability during this particular period that patterns and magnitude. The set of forcings consid- contributed to the observed change. As for the forc- ered include changes in forcing, aero- ing, natural variability can only contribute when it has sol forcing, solar forcing, volcanic forcing, and the same sign as the expected change. The fractional forcing. The uncertainty in each of the j forcings rel- contribution of natural variability is calculated from evant to a line of evidence, i, can also be represented those cases where natural variability makes a contri- by a pdf,/(F ). Once the forcings have been charac- bution to the expected change and is given by the ra- terized, the next step is to elicit pdfs attributing frac- tio N./E.. Sampling from /(£.) and/(IV.) in this way tional responsibilities from 0 (no responsibility) to 1 leads to an expected contribution from natural vari-

(complete responsibility) for each forcing in explain- ability, A. nv, which can range from 0 to 1. Note that ing the detected signal. Note that any given forcing when/(£.) and/(N.) are identical distributions, A.nv could explain a vanishingly small fraction of the de- = 1, and natural variability is responsible for all of the tected signal with high probability or almost all of the observed change. signal with vanishingly low probability. Calculation of The results presented here for each step in the the attribution to each forcing requires a convolu- above process were obtained in a set of detailed per- tion of the forcing pdf and attribution pdf. This step sonal interviews (lasting between 4 and 8 h each) can be simplified for the expert elicitation process by where the experts offered probabilistic judgements as discretizing the range of values that each forcing well as the underlying rationale for their judgements. can take, and by eliciting just the expected fraction, Though results included attributions to all potentially Q.., of the detected signal for line of evidence, i, ac- significant forcings, we isolate only outcomes for counted for by each forcing, j. These fractions pro- greenhouse forcing here. Greenhouse forcing is po- vide a breakdown of the contributions of only those tentially most problematic among the forcings be- forcings that could have caused a change in E. in the cause of its magnitude and expected persistence direction expected. For example, when the change in (Hansen and Lacis 1990; Hansen et al. 1998; Kasting E. is positive, the Q fractions provide a breakdown 1998). of the total positive forcing (X Q = 1). The attribution model described here presupposes RESULTS. Lines of evidence. Four primary lines of a linear-additive model of the forcing, which is not evidence resulting from the protocol are the necessarily the case (Ramaswamy et al. 2001). Our following: approach differs from more conventional approaches to attribution described in (Zwiers 1999) in that we M: The century-long trend in global mean surface do not explicitly compare model outputs to observa- temperature (Jones et al. 1999); tions to make attributions to given forcings. That ap- V: The past 30-yr trend in vertical pattern of tem- proach measures consistency between forced model perature (Santer et al. 1996a; Tett et al. 1996; runs and observations, but could still yield high at- Folland et al. 2001); tributions in the case where there was no signal G: The past 30-yr trend in geographical pattern of present—that is, the forced run is similar to observa- surface temperature (Santer et al. 1996b); tions, but the observations are dominated by natural D: The past 30-yr trend in diurnal temperature range variability. In the present approach, the experts pro- over land (Karl et al. 1993; Folland et al. 2001). vide assessments of contributions from different forcings, conditioned on the uncertainties in those The latter three lines of evidence use the shorter time forcings. That is, the signal is partitioned on the basis period because that is the period over which more of estimates of the relevant forcing. reliable observations are available and on which most The final share of responsibility accorded to each detection studies of these lines of evidence have been of the forcings (A.p in partitioning changes in E. in carried out (Santer et al. 1996b; Mitchell et al. 2001). the expected direction is adjusted to allow for contri- For the detection and attribution exercise each line butions from natural variability (A.nv), such that X.A^. of evidence is quantified using a single metric. The + A = 1. In a hypothetical world in which we had vertical pattern of temperature is quantified as the

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC temperature change at 50 hPa. This choice was made portant in the Tropics, where ozone-induced cooling because the stratospheric component is the aspect of is likely to be less important than that due to green- the vertical pattern that discriminates it from the sur- house gases. While many experts note that the geo- face temperature change, and because we found that graphical pattern, G, could be an important discrimi- experts had difficulty operating on the difference of nator of greenhouse climate change, it is usually temperature between the stratosphere and lower tro- ranked below V. This is because the observational posphere. The geographical pattern was quantified record for which broadscale global coverage exists is as the temperature difference between land and relatively short, there is an overlap of data with the ocean areas (Karoly and Braganza 2001). This choice global mean temperature, and there are few model does not capture all features of the geographical pat- runs exploring the geographical pattern response to tern, but it is one of its more salient components. a wide range of different forcing combinations. The These lines of evidence are fairly distinct (as judged diurnal cycle, D, generally ranks last among the pri- by expert assessments of the dependence among mary lines of evidence. This is because there is no clear them) and rely on a range of underlying physical expectation about what signal to expect in response processes in response to greenhouse and other cli- to greenhouse or other forcing. Virtually all experts mate forcing. cite the critical role of cloudiness in controlling Most experts are comfortable with the selection of changes in D, and point to the fact that little is known M, V, G, and D as core lines of evidence. While there about controls on long-term (low frequency) cloudi- is a high level of agreement among the experts on the ness changes. appropriate lines of evidence to use to detect climate Among the additional lines of evidence cited by change and differentiate greenhouse forcing, the se- only a few experts, no single selection dominates. lection of lines of evidence is still a potential source Some cite changes in the annual cycle over the last of bias. That is because lines of evidence were chosen century, which shows more warming in winter than partly on the basis that they would be useful for find- summer (Karoly and Braganza 2001). This may help ing evidence of greenhouse climate change. Experts discriminate between greenhouse and solar forcings, tend to select lines of evidence that are already in the since greenhouse forcing tends to reduce the annual literature, and such lines of evidence become estab- cycle, whereas solar forcing is concentrated in sum- lished in the literature because they are considered mer and would act to enhance it. Some experts sug- indicative of greenhouse forcing. Were lines of evi- gested that extreme events may eventually provide dence chosen more generically to differentiate opportunity for increasing signal-to-noise ratios for nonspecified sources of climate change, a different set detection, but that the existing observational records might have been selected and results may be less in- are too short (Wigley 1999). Examples cited included dicative of a greenhouse forcing signal. increases in precipitation intensity in the upper decile Some experts did add other lines of evidence, but of the precipitation distribution (Karl and Knight the additional lines are rarely ranked high in terms 1998). Another line of evidence cited is the concen- of utility for detection and attribution of greenhouse tration of warming in dry, cold anticyclones in north- climate change. The global mean, M, is generally ern winter (Michaels et al. 2000), which act as a ranked first on the basis that it provides a good fit to marker of greenhouse warming in regions with re- a priori estimates of greenhouse and aerosol-induced duced water vapor feedback. climate change for a relatively long data record. It is often coupled with the millennial timescale proxy Sources and assessments of natural variability. Experts temperature record (Mann et al. 1998; Briffa and tended to cite similar processes when asked to account Osborn 1999) on the basis that the latter highlights for the major sources of natural internal variability in the uniqueness of the trend in the century-scale each line of evidence. For example, the major sources record. The vertical pattern, V, is generally ranked of variability cited for M were: upper-ocean air-sea second on the basis that it provides some ability to exchanges (e.g., ENSO); deep-ocean circulation in- discriminate between greenhouse and solar forcing at cluding the thermohaline circulation; large-scale os- upper levels where the expected signal direction is cillations (natural modes) in the atmosphere [e.g., the opposed. The importance of V is diminished on the North Atlantic Oscillation (NAO)]; land surface pro- basis that the data record is short (less than 40 yr), and cesses and changes (soil moisture, vegetation, land the stratospheric temperature signal is strongly con- use, hydrology); and snow and sea ice cover changes. founded by changes in ozone concentration over the Experts were also asked to assess confidence in un- period. Some point out that this confound is less im- derstanding and modeling of each of these processes

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC as they relate to characterization of each line of evi- most experts tended to allocate nearly equal probabil- dence. Responses were made on a qualitative five- ity masses to models underestimating and being point scale following Moss and Schneider's (2000) about right. In general, the responses indicate that characterization from "very high confidence" to "very model estimates of natural variability would be biased low confidence." There was little agreement among on the underestimate side of about right (consistent experts, with responses (for each process) ranging with Barnett 1999). Depending on the expert, this from high to low confidence on assessments of un- bias may be moderate, or quite severe. The more se- derstanding and the ability to model these processes. vere this bias, the more the detection studies would In some cases experts noted that while understand- overestimate levels of detection when drawing esti- ing of processes was reasonable, the appropriate mates of natural variability from climate model mechanisms had not yet been incorporated in climate simulations. models, thereby diminishing confidence in the model simulations. Detection. The probability distributions characteriz- For each line of evidence, respondents were asked ing century-long changes in global mean surface tem- to make judgements about whether the natural vari- perature and natural variability for each expert are ability was typically underestimated in models (less shown in Fig. la. Experts are in good agreement on than half the true natural variability), about right in the size of the century-long trend, with the smallest models (between half and one and a half times the 95% confidence band expressed encompassing the true natural variability), or overestimated in models mean of almost all the other experts. The agreement (greater than one and a half times the true natural on the expected value of the trend in internal natural variability). For the global mean, M, little probabil- variability over the period is not meaningful as that ity mass was given to possible model overestimates would be close to zero by definition—the expected of natural variability. Experts typically assigned about value of any large ensemble of 100-yr periods should seven-tenths of the probability mass to "about right" tend toward zero in the absence of external forcing. and the balance to "model underestimated." The excep- tions were one expert who reversed this allocation, and one who assigned almost all the probability mass to about right. For the vertical pat- tern, V, experts were less consistent with one another in assigning probability masses, and tended to in- crease the allocation of prob- ability mass to the model un- derestimate category. Some experts placed the majority of the probability mass in this category, citing the fact that climate models have truncated stratospheres (re- duced in vertical extent and capped by a lid) with coarse resolution. These experts had low confidence in mod- els simulating sufficient vari- FIG. I. The 95% confidence bands for each expert on the trend and natural ability near the lid, and cited variability in (a) global mean surface temperature, (b) vertical temperature the failure to generate a pattern (50 hPa), (c) geographical pattern, and (d) diurnal temperature range. The solid lines refer to the trend and the dashed lines refer to natural vari- quasi-biennial oscillation ability. Some distributions are missing because the expert felt the relevant (QBO) in the models. For available data was too sparse to allow a reasonable assessment of the the geographical pattern, G, distribution.

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC The variation in expert assessment of the size of the what sensitive to the significance level selected, with 95% confidence band in natural variability spans a detections increasing slightly as the level relaxes, as factor of 5. would be expected. Bearing that in mind, we discuss The probability of detection for the global mean results for the 5% significance level. For the global

line of evidence M [denoted P(DM)] calculated from mean, M, a majority of experts reject the null hypoth-

these distributions [f(EM) and f(NM)] is given in esis of no climate change, thereby suggesting a near Table 2 for the 5% level of significance. Detection consensus among experts for detection of climate probabilities were also calculated for the 1%, 10%, and change as measured by the change in global mean 20% significance levels (not shown). Results are some- surface air temperature. The median probability of detection for experts is high (-0.95) and the spread across experts is relatively low. For the vertical pat- TABLE 2. Probability of detection for each line of tern, V, (Fig. lb) detection probabilities are generally evidence [P(D,)]. In cases where the null higher (than for M) with even less spread across ex- hypothesis is rejected (detection), the probabil- perts, though two experts did not provide data for this ity of detection is given. Otherwise, acceptance line of evidence, citing low confidence in existing of the null hypothesis is indicated by "0." A "—" indicates that the expert declined to observations and model results. provide an assessment. For the diurnal cycle Results for the geographical pattern, G, and diur- (D) this was typically because the expert did nal cycle, D, show far less support for detection of not feel sufficiently well acquainted with the climate change based on these lines of evidence. For data and/or did not have clear expectations G, less than half the experts reject the null hypoth- about what signal to expect. esis (detection) at 5% and 10% levels of significance. For the diurnal cycle there is even less support for Expert P(D ) P(DG) P(DD) PV>J V detection. Five of the experts declined to provide an assessment on the diurnal cycle. The rest are almost 1 0 0 0 evenly split between accepting the null hypothesis and 2 0.97 0 0 0 rejecting it. For the experts who do reject the null 3 0.99 0.99 0 0 hypothesis, the probabilities of detection are lower than for M and V. For the geographical pattern and 4 0.71 0.99 0.81 0.71 diurnal cycle lines of evidence, expert judgements 5 0.99 0.70 0 0 tend to reflect both a lack of confidence in the avail-

6 0.99 0.99 0.70 0.95 able data and uncertainty on their role in detection of climate change. 7 0.99 0.99 0 8 0.77 0.99 0 0.72 Attribution. The first part of the attribution process

9 0.99 0.99 0.95 0.64 entailed an assessment of uncertainty in the key forcings for each line of evidence. The pdfs represent- 10 0.91 0 0.80 ing forcing uncertainty are discretized and then at- 1 1 0.80 0.99 0 0 tributions are made for each of the combinations of discrete forcing cases. To keep the number of such 12 0.78 0.87 0 0 combinations manageable, solar and aerosol forcing 13 0.99 0.99 0.75 — pdfs are represented by a "low" and "high" range only.

14 0 — 0 0.89 In the case of the global mean temperature line of evidence, M, solar forcing low and high are defined 15 0 0.99 0 by less or more than 1 W m~2 over the century and 16 0.64 0.98 0 0 aerosol low and high are defined by less or more nega- 2 17 0.98 0.99 0.94 tive forcing than 1.5 W m~ . Experts produced a wide range of estimates of the probability mass in each cat- 18 0.82 0.99 0 0.75 egory for each of the forcings. There is more expert 19 0.93 0.91 0.72 agreement in the case of solar forcing, with only five experts allocating more than 30% of the probability median 0.95 0.99 0.80 0.73 mass to solar forcing > 1 W m~2, and all experts allo- cating 50% or more probability mass to solar forcing c.v. (f) 0.13 0.08 0.12 0.15 < 1 W m~2. For aerosol forcing, all of the experts allo-

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC cate 50% or more of the probability mass to aerosol TABLE 3. Expected fraction attributed to forcing < 1.5 W m~2, but fewer than half of the experts greenhouse forcing for each line of evidence allocate more than 70% of the probability mass to this (Q| gh). The Q. . = l,kQ. j kPk, where Pk = W,.„ P,k, range. The low ranges of aerosol and solar forcing and Pj k are the probability masses assigned to above encompass most of the uncertainty range for each of the k discrete forcing cases. estimates of these forcings in (Shine and Forster 1999) and (Ramaswamy et al. 2001)—that is, the so- Expert called low ranges here correspond to the more con- Q/Vl.gh ^V.gh Q^gh ventional views of the forcings. The considerable 1 0.84 0.04 0.77 0.78 spread of probability mass across the low and high forcing ranges for most experts is consistent with the 2 0.75 0.15 0.77 0.23

"low confidence" afforded aerosol and solar forcing 3 0.75 0.25 0.72 0.50 estimates in (Shine and Forster 1999) and (Ramaswamy et al. 2001). 4 0.63 0.20 0.44 0.40 The fraction of the signal attributed to greenhouse 5 0.71 0.40 0.71 0.78

forcing, Q. , for each line of evidence, i, is shown in 6 0.70 0.20 0.70 0.39 Table 3. The fraction 1-Q. . is the fraction attributed to all other forcings. For both the global mean, M, and 7 0.72 0.20 0.61 — geographical pattern, G, greenhouse forcing explains 8 0.65 0.20 0.56 0.30 more than half of the signal for most experts. For the 9 0.82 0.25 0.60 0.30 vertical pattern, V, and diurnal temperature range, D, the fraction of responsibility attributed to greenhouse 10 0.75 0.20 0.70 0.60

forcings is much lower and the spread across experts 1 1 0.60 — 0.60 0.30 is higher. The median fraction of greenhouse respon- 12 0.61 0.15 0.47 0.59 sibility ascribed for V is only 0.2, reflecting the belief that changes in ozone are more directly responsible 13 0.61 0.45 0.50 — for recent stratospheric temperature changes. 14 0.50 — 0.50 0.80 The fractional attributions to greenhouse gases 15 0.80 0.20 0.80 — reported in Table 3 represent averages over the un- certainty in the relevant forcings. For example, the 16 0.40 0.40 0.42 0.60 attribution of greenhouse forcing to global mean tem- 17 0.74 0.20 0.75 — perature, M, is a probability weighted average of the attributions for low and high forcing combinations of 18 0.77 0.50 0.49 0.30

solar and aerosol forcing (described above). The ef- 19 0.74 0.50 0.77 — fect of forcing uncertainty on attribution can be de- termined by placing the entire probability mass in median 0.72 0.20 0.61 0.45 either the low or high forcing case (i.e., assuming one of these cases is true) and comparing the resulting C.V. (£) 0.16 0.51 0.20 0.41 attribution values to those in Table 3. The results of such an analysis for M are discussed below. must be balanced by greater positive forcing. This The effect of solar forcing uncertainty on global tends to increase attribution to nongreenhouse mean temperature, M, is consistent across all experts. forcings because they are typically less well known Fractional attribution values assessed for the low so- than greenhouse forcing, and many believe they are lar forcing assumption are within a few percent of the more plausible candidates for adjusting upward to weighted-average values in Table 3 for most experts. balance the increased negative forcing. If uncertainty For the high solar forcing case, most experts increased in climate sensitivity is considered more important the fractional attribution to solar forcing and de- than uncertainty in forcing, then higher negative forc- creased the attribution to greenhouse forcing, typi- ing does not need to be balanced by higher positive cally by about 10%. The results for aerosol forcing forcing to explain the observed trend. In this case the uncertainty are more subtle and depend on whether trend can be explained by lower net positive forcing the expert considers attribution to be more sensitive and higher climate sensitivity. Experts making this to uncertainty in forcing or to climate sensitivity. In argument typically did not adjust attributions for the the former case, higher aerosol forcing (negative) high aerosol case. In either case the effect of aerosol

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC forcing uncertainty on greenhouse attributions is lines of evidence for some experts. For the global mean modest. For these experts then, the attributions set by surface temperature line of evidence the threshold is weighting the fractional attributions by the probabil- exceeded for all but three experts, while for the verti- ity masses assigned to each forcing range (as in cal temperature pattern it is not satisfied for any of Table 3) are not substantially different from those that the experts (see Table 4). The level at which such would be obtained if the high or low forcing cases thresholds are set can be modified to match the level alone were considered. of responsibility deemed important, which is in part Overall attributions to each cause take into account a value judgement (Schneider 1989). Attribution the role of natural variability as well as climate studies can evaluate the potential contributions from forcings. Figure 2 provides a bar plot summary of the different causes, but they cannot set the burden of re- expected contributions of greenhouse forcing, all sponsibility required, since that entails broader social other forcing, and natural variability to the change in judgements. each line of evidence. It shows the consistent (across experts) high attributions to greenhouse forcing for CONCLUSIONS. Among a varied sample of 19 ex- M (and to a lesser extent, G), the dominant role of perts engaged in studies of detection and attribution "all other forcings" in explaining changes in V, and of climate change, there are a number of issues on the large spread of expert assessments of the green- which there is general agreement and a number on house forcing contribution to changes in D. The fig- which there is a broad spread of expert judgement. ure also indicates a typically small role for contribu- Caution must be used in interpreting these results, tions of natural variability to explain changes in M and since expert agreement does not necessarily imply low V, with more substantial contributions across experts uncertainty (all experts may be wrong), just as expert for G and D. There are some exceptions to this pat- disagreement does not necessarily imply high uncer- tern in each case. While most experts attribute nearly tainty (some experts may simply know better than all the responsibility for changes in M (and V) to forc- others). Nonetheless, on a contentious issue like de- ing, a few of the experts apportion larger roles for natural variability. These experts allow broader distributions for natural variability (see Fig. 1). For both G and D there are substantial contributions from each of greenhouse forcing, all other forc- ing, and natural variability across experts such that no one cause dominates. After levels of attribution have been determined for each line of evidence, it is desirable to assess the implications of that for a more general picture of climate change than afforded by each line of evi- dence alone. One such picture of climate change is obtained by considering the set of lines of evidence.2 These can be aggre- gated in a variety of different ways. One way is to set thresholds for the overall at- tributions to each potential cause. For example, a threshold of 0.5 would imply that the cause must be responsible for half FIG. 2. Responsibility attributed to greenhouse forcing (black seg-

or more of the change in each given line ment) [Agh], all other forcing (medium gray segment) [A oth], and of evidence. With that threshold, inspec- natural variability (light gray segment) [A nv] in explaining the tion of Fig. 2 indicates that greenhouse change in (a) global mean surface temperature, (b) vertical tem- forcing exceeds the threshold for some perature pattern (50 hPa), (c) geographical pattern, and (d) di- urnal temperature range for each expert. Note that A. gh = (I - N /E where 1 where = M/NZ A,„v)Qi.gh» A.nv ,N>OE>O , /> represents ran- ! The process of combining lines of evidence is dom draws from f(N) and f(£), and m and n are the number of implicit in statements such as the "balance of draws where N and £ are greater than 0, respectively, for posi- evidence suggests ..." (Santer et al. 1996b). tive trends.

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Unauthenticated | Downloaded 10/05/21 07:05 PM UTC tection and attribution, it is useful to know where ex- perts agree or disagree and why, along with some in- TABLE 4. Cases where the fractional contribu- tion of greenhouse forcing to changes in each dication of whether the disagreements are conse- given line of evidence exceeds an arbitrary quential or not. threshold of 0.5—indicated by "0." Expert agreement is strongest on some aspects of the detection phase of the detection and attribution M V G D problem. The characterization of natural variability Expert AT surfac. e AT50hPa AT,and-ocean A DTR is the largest source of uncertainty at the detection step, and there is a general belief that it has been un- 1 0 derestimated in model-based detection studies. Experts are in agreement on selection of the key lines 2 0 of evidence that signify climate change, and there is 3 0 — 0 — broad agreement that changes in the global mean (M) A A V and vertical pattern (V) are unlikely to be the result of natural variability alone. For the geographical pat- 5 0 — — 0 tern and diurnal cycle lines of evidence, there is much 6 0 - 0 - more disagreement across experts. 7 0 — — — Expert assessments of uncertainty in key forcings A of the different lines of evidence vary quite a bit. For 8 V the global mean temperature (M) line of evidence, the 9 17 1 main forcing uncertainties assessed were aerosol and 10 0 - 0 - solar forcing. One consistency that emerges is that most experts allocate substantial probability mass 1 1 0 — — — to values of the forcings outside the conventional un- 12 0 — — — certainty ranges for these forcings. The assessed ef- fect of uncertainty in these forcings on greenhouse at- 13 0 — — — tribution values is fairly modest, however. 14 —• — — 0

In attributing causes to the observed changes, ex- 15 perts allocate the lion's share (typically about 60%) of the responsibility to greenhouse forcing for the glo- 16 bal mean surface temperature. Solar and all other 17 0 7 I forcings and natural variability together account for 18 0 — — — the minority of responsibility for this line of evidence. For both the vertical pattern of temperature change 19 0 0 and the change in diurnal temperature range the re- sponsibility allocated to greenhouse forcing is typi- cally much lower. However, for these lines of evidence Some of the details of the results reported here will the spread across experts is higher and several experts undoubtedly change as the evidence and science un- declined to make assessments. In the case of the ver- derlying detection and attribution progresses. Expert tical pattern, the low greenhouse forcing attributions levels of confidence will change, new lines of evidence are largely due to ozone forcing in the stratosphere. will be added, and some will fall from favor. However, While these results suggest that the vertical pattern it is interesting to note that as of circa 2000, there is a is not very useful for discriminating a greenhouse sig- high level of confidence in detection of climate change nal at present, it may play a bigger role in the future based on changes in global mean temperature and in as the confounding effects of ozone forcing in the the vertical pattern of temperature change. There is stratosphere gradually diminish. For the diurnal tem- also general agreement that points to a substantial role perature range, experts point to higher uncertainty in for greenhouse forcing in contributing to global mean the expected response to greenhouse forcing, citing surface temperature changes, and scattered support key unknowns related to changes in cloudiness. This for a greenhouse role for the other lines of evidence high uncertainty may allow for a greater role for the examined. The change in global mean surface air tem- diurnal temperature in discriminating among differ- perature appears to be the single most important line ent forcings as more is known about the processes of evidence in the detection of climate change and governing its response. attribution to anthropogenic causes.

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