Pattern scaling: Its strengths and limitations,and an update on the latest model simulations

Claudia Tebaldi & Julie M. Arblaster

Received: 21 January 2013 /Accepted: 6 December 2013 /Published online: 8 January 2014# Springer Science+Business Media Dordrecht 2013

Abstract We review the ideas behind the pattern scaling technique, and focus on its value andlimitations given its use for impact assessment and within integrated assessment models. Wepresent estimates of patterns for temperature and precipitation change from the latest transientsimulations available from the Coupled Model Inter-comparison Project Phase 5 (CMIP5),focusing on multi-model mean patterns, and characterizing the sources of variability of thesepatterns across models and scenarios. The patterns are compared to those obtained from theprevious set of experiments, under CMIP3. We estimate the significance of the emergingdifferences between CMIP3 and CMIP5 results through a bootstrap exercise, while also takinginto account the fundamental differences in scenario and model ensemble composition. All inall, the robustness of the geographical features in patterns of temperature and precipitation,when computed as multi-model means, is confirmed by this comparison. The intensity of thechange (in both the warmer and cooler areas with respect to global temperature change, and thedrier and wetter regions) is overall heightened per degree of global warming in the ensemblemean of the new simulations. The presence of stabilized scenarios in the new set of simulationsallows investigation of the performance of the technique once the system has gotten close toequilibrium. Overall, the well established validity of the technique in approximating the forcedsignal of change under increasing concentrations of greenhouse gases is confirmed.

Climatic Change (2014) 122:459–471DOI 10.1007/s10584-013-1032-9

This article is part of the Special Issue on “A Framework for the Development of New Socio-economic Scenariosfor Climate Change Research” edited by Nebojsa Nakicenovic, Robert Lempert, and Anthony Janetos.

Electronic supplementary material The online version of this article (doi:10.1007/s10584-013-1032-9)contains supplementary material, which is available to authorized users.

C. Tebaldi (*)Climate and Global Dynamics, National Center for Atmospheric Research (NCAR),1850 Table Mesa dr., Boulder, CO 80305, USAe-mail: tebaldi@ucar.edu

J. M. ArblasterNCAR and Bureau of Meteorology, Melbourne, Australia

1 Introducing the method, its literature and the focus of the present study

Moss et al. (2010) describe the new set of Representative Concentration Pathways (RCPs)constituting the scenarios for the Coupled Model Intercomparison Project Phase 5 (CMIP5)future climate simulations (Taylor et al. 2012). The discussion explicitly refers to the methodof pattern scaling as a way of determining regionally differentiated projections of futureclimate change under further scenarios, when simulations with fully coupled climate models(GCMs) are not available. Such projections would be used for impact assessment, especiallywhen assessing the effects of mitigation policies compared to reference scenarios or viceversa.

Pattern scaling was first proposed by Santer et al. (1990). Spatial features of the externallyforced change, standardized by global average temperature warming, were estimated on thebasis of 2xCO2 equilibrium simulations by mixed-layer ocean GCMs. These patterns wereassumed to remain stable also during a transient simulation where the main external forcing isan increase in well-mixed greenhouse gases. These common features explain a large portion ofthe variability of the externally forced changes in temperature and precipitation over time andacross scenarios within a given model (the hypothesis driving the development of thetechnique). The advent of coordinated experiments run by multiple models in recent yearshas offered the opportunity of testing the stability of these patterns also across an ensemble ofmodels, and this will be the main focus of this paper. In this multi-model setting, as in thesingle model framework, we will show that temperature change patterns conform better to thisapproximation than precipitation patterns. This result is not uniquely due to model uncer-tainties but may be explained by a larger component of natural variability affecting theestimates of the forced signal of precipitation change compared to temperature (Tebaldiet al. 2011; Hawkins and Sutton 2011). Nonetheless, precipitation patterns have been shownto scale linearly with global average temperature to a good degree of accuracy within manymodel experiments (Neelin et al. 2006; Shiogama et al. 2010).

Forcing by regionally differentiated, time varying species, like anthropogenic aerosols orblack carbon, hinder the validity of the pattern scaling approximation (Shiogama et al. 2012)when the simple approach is used, i.e., when a single pattern is estimated on the basis of GHG-dominated changes, and then rescaled. The effects of short lived and regionally differentiatedagents, however, can be estimated by constructing patterns specific to these types of emissions(Schlesinger et al. 2000), which are found to add linearly to the patterns from long-lived andwell-mixed greenhouse gas forcing (Meehl et al. 2004). MAGICC-SCENGEN, a popularsoftware tool which implements the pattern scaling technique, allows scenarios that assumetime-varying, regional sources of aerosols by utilizing this approach1. In this paper we focuson results from the simple pattern scaling approach.

Analytically, pattern scaling is based on the validity of the model-specific linear relation

P t; x; y; sð Þ ¼ T tð Þp x; y; sð Þwhere x,y identify spatial coordinates (e.g., latitude and longitude of a model grid point) and,possibly, s identifies a specific time of year (e.g., a June-July-August average). t indexes thelength of the forcing scenario, referring usually to the central year in a multi-decadal average.P(t,x,y,s) is the actual field of change for the climate parameter (temperature, say, or precip-itation) at time t under a specific scenario. T(t) indicates global annual average temperaturechange at time t under this scenario; p(x,y,s) is the time-invariant spatial pattern of change ofthe climate parameter. Units in the case of temperature are expressed as degrees*degrees-1,

1 MAGICC/SCENGEN user manual, p. 42, http://www.cgd.ucar.edu/cas/wigley/magicc/UserMan5.3.v2.pdf

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while for precipitation the pattern is usually calculated as percent change per degree of globalwarming. Therefore, regionally and temporally differentiated changes for specific scenarioscan be approximated by the product of a constant spatial pattern and a time evolving globalmean change in temperature, capturing the scenario evolution. The dependence of the changeon the scenario, but also on model parameters on which the magnitude of the warmingdepends, like sensitivity to greenhouse gases and aerosols, ocean heat uptake and feedbackson the carbon cycle, are thus incorporated in the global mean temperature response only.Therefore, simulations under any combination of these factors can be done inexpensively, byclimate models of reduced complexity tuned to represent the global response of fully coupledGCMs.

The pattern under simple pattern scaling can be estimated on the basis of GCM simulationsunder scenarios of transient increases in anthropogenic greenhouse gas concentrations. Use ofmultiple models produces a “library” of patterns that can be used in analysis concerned withmodel uncertainty. One of the prevalent interpretations of multi-model ensembles sees themembers of the ensemble as exchangeable, or indistinguishable, with the real world (Annan andHargreaves 2011; Sanderson and Knutti 2012). Thus, considering a range of individual modelpatterns is a simple way of considering a range of possibilities among which the real world mayfall. MAGICC-SCENGEN provides a library of patterns from the CMIP3 ensemble, andCMIP5 patterns are being implemented.

Our analysis focuses on multi-model average patterns, but we characterize the variabilityaround those originating from model and scenario choice.

Because of length constraints, we do not perform a thorough review but we do point atseveral studies that have addressed either methodological choices or applications of patternscaling in impact studies or scenario construction.

Simple pattern scaling estimation is most commonly implemented by

a) computing local changes, defined usually as the difference of two multi-decadal averagesseparated by a significant interval e.g., a baseline period at the end of the 20th century anda future period at the end of the 21st century, thus ensuring that the signal of forced changehas emerged from internal variability, and

b) normalizing them by the corresponding change in global average temperature.

This is the approach we take here for our computations, and it is essentially howMAGICC-SCENGEN implements pattern scaling. It is also the most common method found in theimpacts literature.

Pattern scaling has alternatively been implemented by choosing the future period as thatstraddling the time when global average temperature change reaches a given size (for example2 °C with respect to pre-industrial average). Other approaches estimate the pattern by fitting aregression between local and global temperature at each grid point and define the pattern as thefield of regression coefficients (Solomon et al. 2009); some methods use empirical orthogonalfunctions (Holden and Edwards 2010) thus formally maximizing the variance explained by thepattern along the length of the simulation.

The application of pattern scaling has a rich literature behind it. Murphy et al. (2007),Watterson (2008), Giorgi (2008), Harris et al. (2006) and Harris et al. (2010), May (2008),Ruosteenoja et al. (2007), Raisanen and Ruokolainen (2006), Cabre et al. (2010) andWatterson and Whetton (2011) use it to produce regional climate change projections, andDessai et al. (2005) and Fowler et al. (2007) for impact studies. It has been recently used todescribe the regional effects of global temperatures reaching high thresholds of change, e.g.,4 °C (May 2008; Sanderson et al. 2011) and its performance has been tested using the RCPs(Ishizaki et al. 2012).

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The validity of pattern scaling has been assessed and discussed starting in Mitchell et al.(1999) and Mitchell (2003). Some applications addressing uncertainty in future projectionshave sought to quantify the errors introduced (Harris et al. 2006) by this approximation.Process-based studies have highlighted the better performance of the methodology for tem-perature rather than precipitation. Very recently, for example, Shiogama et al. (2010) usedoutput from the MIROC3.2 model to highlight a dependence of the global average precipita-tion change per 1ºC of global warming on the scenario, if significant differences in black andorganic carbon aerosol forcing are present across scenarios. This behaviour is a consequence ofthe more general limitations of simple pattern scaling in the presence of highly regional andshort-lived forcings. This is indeed the case for the RCP scenarios, which all assume distinctaerosol emission patterns. Thus, if future scenarios include significant changes over time in thestrength of regional sources of pollution, the pattern scaling approach needs to be modified(May 2008).

Pattern scaling has also been shown to be less accurate for strongly mitigated stabilizationscenarios (May 2012). The physical mechanisms for this are identified as far back as ManabeandWetherald (1980), where it is pointed out that the spatial characteristics of warming changeas the temperatures of the deep oceans reach equilibrium over many centuries, one featurebeing a more uniform warming between the two hemispheres. A recent study (Wu et al. 2010)also showed that precipitation change behaves non-linearly under strong mitigation scenarios,with changes continuing to intensify even after temperatures peak and start decreasing. Thanksto idealized experiments within the CMIP5 suite, evolution in the spatial patterns of warmingdue to different timescales of response (like delayed warming over the southern ocean) arebeing studied (Good et al. 2013; Chadwick et al. 2013). These are still categorized as linearresponses, but non-linear effects may also contribute to the approximation errors (Good et al.2012; Chadwick and Good 2013; Bouttes et al. 2013).

There are regions of the globe where the application of pattern scaling is particularlychallenging. One example is the edge of the polar ice caps, where the gradient of temperaturechange is typically larger earlier in the simulation than later, after the ice has melted. Differentrepresentations of sea-ice in different models add to the variability of patterns across models.

The goal of this study is to document the patterns emerging from the new CMIP5simulations, testing the hypothesis of robustness across models and scenarios, and comparingthe results to the patterns emerging from the older CMIP3 set of experiments. We will try whenpossible to identify the sources of variation and their significance. In this paper we do notinvestigate in depth the processes that may lead to the emergence of peculiar geographicfeatures when analyzing differences. In particular we maintain a “multi-model ensembleperspective” which per se limits the possibility of separating these sources with respect tomodel and scenario development.

2 Patterns of temperature and precipitation change from CMIP5

We start by showing patterns of warming and precipitation changes from CMIP5 simulations.In the following we always use the definition of simple pattern scaling, computing differencesbetween 20 year averages over 2081–2100 and 1986–2005, the latter the historical baseline inthe Fifth Assessment Report by Working Group 1 of the Intergovernmental Panel on ClimateChange (IPCC 2013). We then divide by the corresponding change in global average temper-ature. We compute patterns first within individual model simulations (using one simulationfrom each of the models, even when more initial condition ensemble members are available),then average over the ensemble. Patterns for precipitation change are always expressed as

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percentage change per degree C of global warming with respect to the baseline average. Thesingle ensemble member used for each model is interpolated to a T42 (approximately 2.8°resolution) grid. There are currently 45 models in the CMIP5 archive with historical and RCPexperiments available, although not all RCP experiments are available for all models. Also,some of the models are variations from a basic development by a modelling group, thereforeare likely not independent of one another. RCPs describe pathways of radiative forcing overthe 21st Century and beyond, comprising rising, overshoot and stabilization scenarios. VanVuuren et al. (2011) contains a comprehensive overview of RCPs’ characteristics. Briefly,RCP 2.6 assumes strong mitigation measures and reaches a peak in radiative forcings ofapproximately 3Wm−2 early in the first half 21st century. The radiative forcings then decline toreach 2.6Wm−2 by the end of the century and continue to decline afterwards. The other threeRCPs see a continuous growth of greenhouse gas forcings as a whole, albeit at different rates.RCP 8.5 stabilizes the radiative forcing only after 2200, while all other RCP extensionsassume a flattening of the radiative forcing starting early after 2100. All RCPs assume animmediate decrease in the emission of short-lived pollutants, like aerosols.

We first compute scenario-specific patterns to validate the assumption that patterns arescenario independent, even when considering multi-model average patterns. This implicitlytests the validity of pattern scaling across time, since the different radiative forcings at the endof the 21st century across RCPs could be seen as different radiative forcings across time in asteadily increasing forcing scenario, at least when well-mixed, long-lived greenhouse gasesconstitute the primary forcing. The similarity of the patterns can be assessed by eye in Fig. 1,which shows temperature and precipitation patterns (under RCP 2.6, 4.5 and 8.5). Patterncorrelations range between 0.95 and 0.99 for temperature and between 0.85 and 0.97 forprecipitation. The lower values are the correlation between RCP 2.6 and the two higher RCPs,and may originate from the fact that the climate under the lower RCP has begun to stabilize bythe end of the 21st century, or the signal has not yet cleanly emerged because of low externalforcings. A comparison with the expected loss in pattern correlation due only to the effects ofnatural variability (Fig. S2 and its explanation in the Supplementary Material) suggests that thetwo lower correlation values in the case of temperature could be considered significant at the5 % level. In the case of precipitation, the loss in correlation would be significant only at the10 % level.

Fig. 1 Patterns of Temperature (top) and Precipitation (bottom) changes according to three RCPs (we exclude6.0 because only a small number of models ran it). The numbers between adjacent maps show the patterncorrelation between the two maps. The lower number in the center shows the pattern correlation between the firstand third map

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Comparisons of patterns at different times in the course of multi-century simulations underthe strongly mitigated RCP 2.6 (using 20 years at the end of the 21st, 22nd and 23rd centurycompared to the historical baseline), are shown in Fig. 2. The use of RCP 2.6, in whichradiative forcings are on a decreasing trajectory below 2.6 Wm-2 after 2100 (Moss et al. 2010and Fig. S1) is an effective test of the validity of pattern scaling around scenarios other than thetraditional, which assume increasing forcings (we show results for the stabilization path ofRCP 4.5 and RCP 8.5 in Figs. S3 and S4). As can be assessed by the patterns in Fig. 2 thestrongly mitigated scenario affects the correlation between the temperature patterns of the twoperiods that are farther apart (0.97, thus significantly lower), mainly because of the relaxationof the polar amplification gradient and the relative warming of the Southern Hemisphere overtime. None of the correlation values for the precipitation patterns is significantly lower at the5 % level, but the correlation between the last two periods and the first and third are, at the10 % level. Figures S3 and S4 show that there is no significant loss of pattern correlation underthe stronger forcings of the two higher RCPs, even when stabilized earlier in the 22nd century,as is the case for RCP4.5.

On the basis of these results, we now combine all unique model runs under each RCPexcept 2.6 to compute the main multi-model average temperature and precipitation changepatterns from CMIP5. Figure 3 shows the two patterns of annual average changes in temper-ature and percent changes in precipitation. We show seasonal patterns in Figs. S5 and S6.

For temperature changes, high latitudes warm more than low latitudes, continents morethan oceans and the northern hemisphere more than the southern. There are regions ofrelatively lower warming in the North Atlantic, which have been ascribed to the weakeningof the Atlantic Meridional Overturning Circulation, and Southern Oceans due to deep oceanmixed layers there, a feature present in most models, but model dependent in magnitude.Precipitation changes show the familiar wetting of the high latitudes and equatorial Pacific,and the drying of the subtropics, hot spots of future drought-prone areas around theMediterranean basin and over Australia, particularly its western regions.

The representativeness of these patterns across the ensemble of models and scenarios isexplored by computing standard deviations from the mean patterns.

Figure 4maps values of the standard deviation of temperature and precipitation patterns whenconsidering all models and the three higher scenarios (left column), or when quantifying thedeparture from the mean pattern due to the “model effect” (borrowing an ANOVA terminology,

Fig. 2 Patterns of Temperature (top) and Precipitation (bottom) changes calculated by using 20 year means at theend of the 21st, 22nd and 23rd century and the mean over the 1986−2005 baseline period. Only modelsimulations under RCP 2.6

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obtained by first averaging across scenarios), or when quantifying the “scenario effect” (by firstaveraging across models). Standard deviation values for the overall ensemble are less than 0.5 °Cfor most of the temperature pattern, being larger than 1 °C only at higher latitudes of bothhemispheres. Standard deviation values for precipitation are much less than 10 % for most areasof the globe except for the tropical Pacific, where they are as high as 50%, and the arid regions of

Fig. 4 Maps of standard deviations around the mean patterns for Temperature (top) and Precipitation (bottom),combining all models and scenarios (left panels) or first averaging across scenarios (centre) or across models(right panels)

Fig. 3 Patterns of Temperature and Precipitation change obtained by combining all RCP simulations fromCMIP5 except RCP 2.6. RCP 2.6 simulations were excluded because of the scenario’s low forcing andstabilization path achieved early in the century (2081−2100 vs. 1986−2005)

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Africa and the Arabian peninsula, where, however, large percentage values are sensitive to thesmall climatological baseline.

It is easy to gauge that the dominant source of variability resides across the models, as thepatterns of standard deviation in the center column look almost identical in feature andmagnitude to the patterns in the left column. Only a small additional portion of the totalvariation within the CMIP5 ensemble is explained by the variability of patterns acrossscenarios (see also Fig. 1).

Interestingly, the relative magnitude of the standard deviation values over the globe hasapproximately an inverted behavior when comparing temperature and precipitation maps.Larger approximation errors appear at the very high latitudes for temperature, and at the verylow latitudes around the equator for precipitation. Sea-ice retreat and the associated polaramplification have been already identified as sources of variability for temperature changepatterns. Large uncertainty in precipitation projections for the low-latitude not only stem fromthe challenge of representing convective processes that mostly drive precipitation in thetropical regions, but also from the smaller signal-to-noise affecting precipitation changes,due to large internal variability. (Hawkins and Sutton 2009; Hawkins and Sutton 2011;Tebaldi et al. 2011; Mahlstein et al. 2011; Mahlstein et al. 2012; Deser et al. 2012a, b).Even if in lesser measure, we can expect contributions from internal variability of temperaturestoo, since we know that internal variability is larger at higher latitudes than at lower latitudes(and over land than over ocean). From this perspective, therefore, model disagreement can bedue to the confounding effect of internal variability, not completely erased when averaging20 year-long segments of the simulations.

3 Patterns of temperature and precipitation change from CMIP3: were they different?

We have documented the main spatial features of temperature and precipitation changes perunit degree C of global warming and the main characteristics of their robustness and,conversely, their uncertainties, in the CMIP5 ensemble, in particular as we average patternsacross models and we characterize variability around them. How different are these resultsfrom what we obtain from the previous generation of coupled model experiments, CMIP3(Meehl et al. 2007)? Figure 5 shows multi-model average patterns from CMIP3 and CMIP5,and the difference between the two, together with a measure of its significance. It is worth

Fig. 5 Temperature (top) and Precipitation (bottom) change patterns fromCMIP3/SRES experiment (left panels),CMIP5/RCPs experiment (centre) and their difference (right panels). Numbers indicate pattern correlations.Stippling in the difference maps indicates statistical significance at the 5% level on the basis of a bootstrap analysis

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noting that CMIP3 future simulations used a different set of scenarios of future greenhouse gasemission pathways, specifically the SRES B1, A2, and A1B (Nakicenovic et al. 2000),although under pattern scaling this aspect should be irrelevant. More importantly, the set ofCMIP3 models was smaller, and even the groups contributing to both projects have meanwhilechanged the version of their model.

Figure 5 shows that the large scale features of the patterns obtained by combining the entireensemble of simulations from CMIP3 (21 models and 3 scenarios) are very similar to the onesobtained from CMIP5 experiments (Knutti and Sedláček 2013). The correlation between thetwo temperature pattern is 0.98, between the two precipitation patterns 0.92. These values arenot significantly lower than what we would expect from the effects of natural variability(Fig. S2) but we estimate significant differences in the intensity of the patterns by a bootsrapexercise (see supplementary material). We detect a diminished polar amplification, and,conversely, warmer patterns over the southern oceans in the CMIP3 temperature response,and lesser wetting of the tropical Pacific, plus overall less intense drying of the subtropics in theCMIP3 precipitation response. Overall the differences seem to suggest an amplification of thefeatures of faster vs. slower warming, and wetting vs. drying in the new simulations, rather thandifferent geographic features, with the possible exception of the eastmost area of Africa and theArabian peninsula, where the precipitation pattern in CMIP5 suggests a localized intensificationof the wetting, as well as of the tropical Pacific region (which preliminary investigation suggestscould be attributed to a few models’ intensified response, particularly that of the CSIRO-Mk3.6model). We have not conducted in depth analysis of these results, but a possible cause, at leastfor the differences over the eastern portion of Africa, could be due to local forcing from blackcarbon and sulfate aerosols in some of the CMIP5 scenarios, absent in CMIP3.

It is not possible to straightforwardly identify the sources of these large scale differences inintensity, because both models and scenarios have changed from CMIP3 to CMIP5 andaltogether new models are included in the latter.

As an initial exploration, we show in Fig. S7 a similar analysis, using only the subset ofmodels that contributed to both CMIPs (albeit in different versions) and using only SRES A2and RCP 8.5 simulations, whose temporal evolution and end of the century radiative forcingare similar (Fig. S1). The similarity of the areas of disagreement in Fig. 5 to those in Fig. S7,suggests that, at least according to this simple analysis, the source of the differences is modeldevelopment rather than scenario differences. Furthermore, differences appear as a result ofgeneral model development rather than from the use of a larger set of different models. Asimilar exercise conducted on the entire ensembles, but only considering RCP 8.5 and SRESA2 simulations gives us very similar results (not shown), further downweighting the effects ofscenario differences. The only aspect affected by considering only the subset of 11 models isthe significance of the changes, not surprisingly since smaller sample size decreases the powerof the test, all else being equal.

As an exception, the localized wetting in East Africa seems to be linked to scenariodifferences rather than model formulation, since it disappears when comparing the two similarscenarios A2 and RCP 8.5.

4 Conclusions

We have briefly described the methodology of simple pattern scaling, highlighting its strengthsand limitations and pointing at studies and software implementations that address some of thelatter, in particular the effects of localized, time-varying forcings (Fordham et al. 2011). Thecomputational efficiency of the technique allows impact modelers to use results from the few

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scenarios run with AOGCMs to approximate alternative scenarios, simply through the simu-lation of global average temperature responses by simple climate models. This approximationhas been shown to work robustly for annual and seasonal averages of temperature and (even ifin relatively lesser measure) precipitation. The main source of uncertainty appears to be modelrather than scenario differences. We have not discussed the spatial resolution aspect explicitly,but similarly to what can be said about the robustness of results from ensemble of GCMs ingeneral, we should expect the value of the pattern scaling approximation to decrease when thespatial scale addressed is small and its climate affected by local dynamics. Our paper onlyaddresses patterns of mean temperature and precipitation change. Pattern scaling is likely to bemore limited for extreme events (Lustenberger et al. 2013), or in cases where certain feedbacks(e.g. the drying of the Mediterranean) lead to an amplification of some types of events (e.g.heat waves) in certen areas but not others (Fischer and Schär 2009; Seneviratne et al. 2006).For certain quantities it will inevitably fail, e.g. frost days which are decreasing but arebounded at zero. Snowfall patterns in cold regions, may first increase as a result of highercapacity of warm air to hold moisture, but eventually decrease because the snow falls as rain ina warmer climate. By definition, patterns represent multi-decadal averages of a climatevariable, and some impact studies likely need projections at different time-scales, or addressingaspects related to variability rather than mean behavior.

The patterns of change from CMIP5 under transient increases in CO2 have been shown tobe fairly robust across models and scenarios. Robustness across time, within the 21st Century(Fig. 1) and over a multi-century period (Fig. 2) under a strong mitigation scenario has beenshown to degrade, however. Future work will address this behavior further. For now, therobustness of the results under another mitigation pathway, RCP4.5, would suggest thatperhaps the low forcings in RCP2.6 could contribute to the low performance, rather than thestabilization profile itself..

We have also shown that the new patterns from CMIP5 do not present large-scale geograph-ical features that are different from those derived from CMIP3. What appears significant in thedifferences is the strength of the patterns, in both directions (warming and cooling fortemperature, wetting and drying for precipitation). We have tried to reckon with both modelsand scenarios changes between the two CMIPs. At least according to our first exploration, andfor temperature, the differences do not seem to be attributable to the change in scenarios, and donot seem to be traceable to the addition of altogether newmodels. Rather they can apparently beexplained by the mere development from the old to the new generation of climate models.

Lastly, we call the attention of the reader to the fact that this analysis, like all analyses basedon ensembles of opportunity of the CMIP type, is hampered by a fundamental challenge ininterpreting the idiosyncratic nature of these collection of models as a statistical sample. Thereasons for, and repercussions of, this peculiarity have been amply discussed (Tebaldi andKnutti 2007; Knutti et al. 2008, 2010; Masson and Knutti 2011; Knutti et al. 2013; Sandersonand Knutti 2012) and can be summarized in the lack of independence among models(sometimes simply different versions of the same model) and the lack of systematic samplingof the uncertainties affecting their projections2.

Acknowledgments We thank the editors, Dr. Tom Wigley, Dr. Reto Knutti and two anonymous reviewers fortheir comments and suggestions.

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison, andthe Working Group on Coupled Modeling of the World Climate Research Programme (WCRP) for their roles in

2 The reader is referred to the IPCC Good Practice Guidance Paper on Multimodel Ensembles (http://www.ipcc-wg2.gov/meetings/EMs/IPCC_EM_MME_GoodPracticeGuidancePaper.pdf) for discussions andrecommendations in interpreting multi-model results.

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making available the WCRP CMIP3 and CMIP5 multimodel datasets. Support of these datasets is provided bythe Office of Science, US Department of Energy (DOE). Portions of this study were supported by the Office ofScience, Biological, and Environmental Research, US DOE (Grant DE-SC0004956 and Cooperative AgreementNo. DE-FC0297ER62402). The National Center for Atmospheric Research is funded by the National ScienceFoundation.

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