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Attributionofextremeweatherandclimate‐relatedevents

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Overview

Attribution of extreme weatherand climate-related eventsPeter A. Stott,1* Nikolaos Christidis,1 Friederike E. L. Otto,2 Ying Sun,3

Jean-Paul Vanderlinden,4 Geert Jan van Oldenborgh,5 Robert Vautard,6

Hans von Storch,7 Peter Walton,2 Pascal Yiou8 and Francis W. Zwiers9

Edited by Eduardo Zorita, Domain Editor, and Mike Hulme, Editor-in-Chief

Extreme weather and climate-related events occur in a particular place, by defi-nition, infrequently. It is therefore challenging to detect systematic changes intheir occurrence given the relative shortness of observational records. However,there is a clear interest from outside the climate science community in the extentto which recent damaging extreme events can be linked to human-induced cli-mate change or natural climate variability. Event attribution studies seek todetermine to what extent anthropogenic climate change has altered the probabil-ity or magnitude of particular events. They have shown clear evidence for humaninfluence having increased the probability of many extremely warm seasonaltemperatures and reduced the probability of extremely cold seasonal tempera-tures in many parts of the world. The evidence for human influence on the prob-ability of extreme precipitation events, droughts, and storms is more mixed.Although the science of event attribution has developed rapidly in recent years,geographical coverage of events remains patchy and based on the interests andcapabilities of individual research groups. The development of operational eventattribution would allow a more timely and methodical production of attributionassessments than currently obtained on an ad hoc basis. For event attributionassessments to be most useful, remaining scientific uncertainties need to berobustly assessed and the results clearly communicated. This requires the conti-nuing development of methodologies to assess the reliability of event attributionresults and further work to understand the potential utility of event attributionfor stakeholder groups and decision makers. © 2015 The Authors. WIREs Climate Changepublished by Wiley Periodicals, Inc.

How to cite this article:WIREs Clim Change 2016, 7:23–41. doi: 10.1002/wcc.380

*Correspondence to: peter.stott@metoffice.gov.uk1Hadley Centre, Met Office, Exeter, UK2Centre for the Environment, Oxford University, Oxford, UK3National Climate Center, China Meteorological Adminstration,Beijing, China4Observatoire de Versailles Saint-Quentin-en-Yvelines for Univer-sity of Versailles, Versailles, France5Weather and Climate Modeling, Koninklijk Nederlands Meteoro-logisch Instituut, De Bilt, Netherlands

6Laboratoire des Sciences du Climat et de l’Environnement forCentre National de la recherche scientifique (CNRS), Paris, France7Institut für Küstenforschung, Geesthacht, Germany8Extrèmes : Statistiques, Impacts et Régionalisation in the Labora-toire des Sciences du Climat et de l’Environment, Gif-sur-Yvette,France9Pacific Climate Impacts Consortium, Victoria, Canada

Conflict of interest: The authors have declared no conflicts of inter-est for this article.

Volume 7, January/February 2016 23© 2015 The Authors. WIREs Climate Change published by Wiley Periodicals, Inc.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution inany medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

INTRODUCTION

Anthropogenic climate change provides a keychallenge for mankind. The Fifth Assessment

report (AR5) of the Intergovernmental Panel on Cli-mate Change (IPCC) concluded that ‘human influ-ence on the climate system is clear’ and that ‘changesin many extreme weather and climate events havebeen observed since about 1950’.1 Societies aroundthe world are faced with increasing climate changerisks. Reducing such risks, which requires a consider-ation of vulnerability and exposure to climate-relatedhazards, can be achieved through a combination ofadaptation to those hazards that are unavoidableand climate change mitigation by reduction of green-house gas emissions.

Attribution of climate change has been definedas ‘the process of evaluating the relative contributionsof multiple causal factors to a change or event with anassignment of statistical confidence.’2 Therefore attri-bution is a key aspect of the understanding of climatechange risks, many of which are associated with theoccurrence of extreme weather or climate events. Suchevents have been defined as ‘discrete episodes ofextreme weather or unusual climate conditions, oftenassociated with deleterious impacts on society or natu-ral systems, defined using some metric to characterizeeither the meteorological characteristics of the eventor the consequent impacts.’3 Events can occur on awide range of timescales from minutes to seasons orlonger and on a wide range of spatial scales from afew kilometers to the size of continents. Thereforeattribution can be applied to an extreme that could beclassed as a weather extreme, such as a very high dailyrainfall total, or an extreme that could be classed as aclimate-related extreme, such as a very high seasonalmean temperature.

Often in the immediate aftermath of extremeevents, there is great media and public interest inwhat caused them. There can be a tendency in somequarters to want to confidently attribute extremes toanthropogenic climate change in the absence of scien-tific consensus or to argue that it isn’t possible to linkindividual extreme events with anthropogenic climatechange, neither of which is correct. Given that manyextreme weather and climate events have occurredbefore substantial anthropogenic modification of theclimate system has been clearly detected in manyregions, an over simplistic attribution to humancauses could be costly. For example, based on theoccurrence of a particularly damaging extreme event,plans could made to adapt to an increasing frequencyof such events in future when in fact this is not whatis expected.

There is a basic expectation that climate changewill alter the occurrence of some extremes. Extremelyhot temperatures are expected to become more fre-quent where mean temperatures increase. A simpleshift in the mean without any change in the distribu-tion will suffice although changes in the distributioncan enhance or reverse this tendency.4 For example,land surface feedbacks can exacerbate temperatureextremes as soils dry out and fail to provide evapora-tive cooling to moderate temperatures, therebybroadening the distribution of summer maximumdaily temperatures in continental interiors.5 Atmos-pheric warming increases the moisture holding capac-ity of the atmosphere potentially increasing theprevalence of extreme rainfall events. These changesin temperature and rainfall extremes expected fromthermodynamic considerations have been detected inthe observed record6,7 and climate models show thatan increased occurrence of extreme temperature andrainfall events worldwide can be attributed toanthropogenic forcings.7–9

At regional scales and for individual extremeevents, global statistics and thermodynamic argu-ments may no longer apply if the occurrence andevolution of climate extremes in a particular place isinfluenced by the atmospheric or oceanic circulationor when there are large external forcings on regionalclimate such as from tropospheric aerosols. While cli-mate models appear to capture thermodynamicchanges well, they may struggle to simulate circula-tion changes10 and questions remain about what con-trols convection, changes in which can affectextremes,11 and the position of the storm tracks andthe tropical rain belts.12 In the light of these difficul-ties, it could be decided to ignore dynamical changesand concentrate instead on how human-induced ther-modynamic changes have affected extremes.13 How-ever, many event attribution studies consider howthe probability of an event is changing. This forcesconsideration of both dynamical and thermodynamicinfluences because both can play a role in the chan-ging probability of an event. Taking account ofdynamical changes requires physical understandingto support attribution assessments and climate mod-els used in such analyses need to be able to capturethe salient physical features. Testing our understand-ing and our models against observed events helps toimprove predictions of future changes throughimproved models and a deeper appreciation of whychanges are occurring. The science of event attribu-tion has developed considerably in recent years inresponse to a growing demand to explain recentextreme events from a climate perspective. Eventattribution studies have sought to determine whether

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anthropogenic climate change has altered the proba-bility or the magnitude of a particular event. Earlyexamples include the European summer heat wave of2003 that killed many thousands of people14,15 andthe flooding in the UK in autumn, 2000.16

A position paper presented to the World Cli-mate Research Programme (WCRP) Open ScienceConference in 2011 argued that there was a need tofurther develop carefully calibrated physically basedassessments of observed weather and climate events.3

Since then many other extreme events from aroundthe world have been investigated including in anannual report explaining extreme events of the previ-ous year from a climate perspective.17–20 Such eventsare often selected for their severe and widespreadimpacts and the interest and capability of scientificgroups in investigating them. Studies show clear evi-dence for human influence on some events and littleevidence for human influence on others19,20 and insome cases draw seemingly conflicting conclusionsdue to differences in the way the attribution questionhas been framed.21

This article discusses the challenges facing thisnewly emerging science of event attribution. Many ofthe current studies have focused on the meteorologi-cal nature of events, which we focus on here, ratherthan their impacts.

Methodologies section reviews the event attri-bution and Evaluation section considers the evalua-tion of attribution results in order to ensure they arereliable. The following section considers the influenceof framing on attribution results. Over time, theregional coverage of event attribution studies hasincreased and this is discussed in the fifth section.Operational weather services have long provided cur-rent weather and weather forecasts to a variety ofcustomers. More recently the operational provisionof seasonal forecasts providing probabilistic predic-tions of future seasonal climate anomalies hasbecome established. In a similar way there is thepotential to deliver routine assessments of climaterisks in an operational attribution system,3 which isdiscussed in the sixth section. Stakeholder Perspec-tives section provides details of some stakeholder per-spectives whereas the following section concludeswith a brief summary.

METHODOLOGIES

Event attribution assessments seek to quantify towhat extent anthropogenic or natural influences havealtered the probability or magnitude of a particulartype of event having occurred. Any climate event

under consideration, for example, a heat wave,drought, or flood, has evolved in its own unique wayand is therefore, in principle, attributable to a uniqueset of causes that is not applicable to any other event.However, event attribution assessments typicallyhave wider applicability by considering some metricto characterize the extreme nature of the event inquestion. Therefore, event attribution assessmentstypically have relevance for the occurrence of similartypes of events in future.

The concept of fraction attributable risk(FAR)22 was first applied in 2004 in an analysis ofthe European heat wave of 2003.14 This was the firstinstance of an event attribution study providing adirect link between anthropogenic climate changeand an individual extreme climate event. To achievethis result, the probability (P1) of a record warmsummer in a particular European region was com-pared with its probability (P0) had anthropogenicinfluences on climate been absent. This approach isshown schematically in Figure 1. These probabilitieswere determined from coupled climate model simula-tions calibrated to observations using optimal detec-tion techniques.14 The study concluded that humaninfluence had very likely (probability >90%) morethan doubled the probability of a record warm sum-mer. Therefore, having calculated the probabilities ofthe event in the presence and absence of anthropo-genic climate change, P1 and P0, the results can beexpressed as a probability ratio, P1/P0, i.e., in thiscase a doubling of probability. Alternatively they canbe expressed as a FAR, calculated as 1-P0/P1 where a

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FIGURE 1 | An illustration of the Probability Density Functions(PDFs) of a climatic variable with (solid red line) and without (greenline) the effect of human influence on the climate. The correspondingprobabilities of exceeding a prespecified threshold (P1 and P0) arerepresented by the hatched areas of the same color. The red-dashedline illustrates how the PDF of the actual world may change in achanging climate.

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FAR of more than 0.5, as in this case, indicates itsprobability has more than doubled. This is equivalentto half of the instances of such events being attributa-ble to anthropogenic climate change. An analogyis with a loaded die in which throwing a six is twiceas likely as for an unloaded die. On repeated throws,a FAR of 0.5 corresponds to half the sixes beingattributed to the loading of the die rather than tochance.

Subsequent research has shown that continuedwarming in Europe has increased the probability ofsuch an extreme seasonal temperature event as seenin 2003 further (dashed line in Figure 1) and demon-strated the robustness of the earlier findings.23

Despite this reaffirmation of the robustness ofthe result, this early attribution finding shares withlater studies using climate models a reliance on thefidelity of the models used. Climate models haveerrors that could invalidate attribution results. Forexample, tropospheric aerosol concentrations, whichin recent years have reduced in Europe and increasedin Asia, are potentially very important for regionalclimate change but also highly uncertain. There isfurther discussion of the evaluation of attributionresults in Evaluation section. Clearly, all attributionassessments are contingent on our current under-standing and are therefore liable to be updated as sci-entific understanding develops.

Confidence in attribution results can beenhanced where independent methods lead to similarconclusions. A variety of approaches have been takento event attribution, differing in their use of observa-tions and models, and in their framing of the attribu-tion question being asked. We now describe the mainmethods used in event attribution.

Coupled Model ApproachesCoupled general circulation models (GCMs) ofincreasing complexity, which often include not onlyatmosphere, ocean, and land but also biological andchemical processes, provide the most comprehensivesimulations of the climate system. Data from modelexperiments with different forcing combinations arereadily available from the archive of the World Cli-mate Research Programme’s Coupled Model Inter-comparison Project phase 5 (CMIP5)24 and can beutilized in analyses of extreme events. This typicallyinvolves pooling data from multimodel ensembles ofsimulations with and without anthropogenic influ-ences and so generating large samples of the relevantclimatic variable (e.g. temperature, if investigating aheat-wave event). The distribution of the variable inthe ‘actual’ world and the counterfactual ‘natural’

world without human influence on climate can thusbe constructed, from which estimates of the FAR forthe event under investigation are obtained. A numberof studies of recent temperature and rainfall extremeevents in Australia have employed this approach25–28

and have shown that anthropogenic forcings haveled to manifold increases in the likelihood ofAustralian heat waves, although their influence onrainfall extremes is less robustly identified. For suchattribution assessments, it is important that the mod-els employed in studies are rigorously evaluatedagainst observations29 (see Evaluation section).

Coupled model approaches have beenemployed to provide fast-track assessments, availableas soon as an extreme event is observed. The chan-ging likelihood of extremes is estimated with refer-ence to prespecified thresholds, e.g., the temperatureassociated with a heat-wave event. By precomputingsuch estimates over a range of thresholds, attributioninformation becomes readily available when a newevent occurs. This approach has been applied toannual and seasonal mean temperature extremesin subcontinental regions around the world by usingan improved representation of the regional tempera-ture distributions and introducing observationalconstraints from a global optimal fingerprintinganalysis.30

An application of such a fast-track attributionmethodology enabled the Met Office to issue an attri-bution statement on the record temperatures seen in2014 in the UK (back to 1910) and Central England(back to 1659). Precomputed estimates of the FARmeasuring the human-induced change in the likeli-hood of getting annual-mean temperatures in the UKabove certain thresholds from the climatologicalmean to five standard deviations above it are illus-trated in Figure 2. It is estimated that human influ-ence has increased the likelihood of record-breakingtemperatures in the UK by a factor of about 10 (bestestimate of the FAR ~0.9). In a complementarystudy, the chances of the smaller region of Englandexperiencing a record-breaking warm year, as seen in2014, were found to have been made more than13 times more likely as a result of anthropogenic cli-mate change.31

In some cases, investigations are carried outinto the attribution of events conditional on particu-lar features of the climatic conditions present at thetime of the event. For example, event attributionstudies have investigated how anthropogenic influ-ence under La Niña conditions has affected the likeli-hood of extreme rainfall seen in 2011–2012 oversouth-Eastern Australia32 and the likelihood ofextreme drought seen in 2011 over Texas.33

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Sea Surface Temperature ForcedAtmosphere Only Model ApproachesAnother way of conditioning results on aspects of theclimatic conditions present at the time of the event isto prescribe observed sea surface temperature (SST)anomalies into an atmosphere only climate model.Thus, many event attribution studies contrastatmosphere-only general circulation model (AGCM)simulations representing the ‘actual’ world includingthe observed evolution of SSTs with simulations ofthe counterfactual ‘natural’ world, a ‘world thatmight have been,’ had there been no human influenceon climate.16,34

As with coupled model approaches, this meth-odology also requires the availability of sufficientlylarge model datasets to simulate the statistics of theevents in question, and relies on the model’s abilityto reliably simulate the climate conditions generatingthe extreme event. Prescribing SSTs in an AGCMrather than using coupled models can reduce modelbiases and enables more ensemble members to besimulated because they are cheaper to run, poten-tially resulting in a better representation of extremeevents, and improved signal-to-noise ratio. However,this approach does not represent atmosphere–oceancoupling and so could lead to a worse representationof extreme events strongly affected by such coupling.

While removing the anthropogenic greenhousegas forcing from the modeled atmosphere is straight-forward, estimating the pattern of warming to beremoved from the observed SSTs and sea ice is not.

In the majority of studies, the warming patterns to beremoved are obtained from coupled GCM simula-tions by subtracting ‘Historical’ SST simulationswhich include both anthropogenic and natural for-cings (e.g., volcanoes and solar fluctuations) and the‘Natural’ simulations which include only natural for-cings. Figure 3 shows an example in which theassessment of the anthropogenic influence on theevent is sensitive to differences in these patterns,hence in this case, it is important to use more thanone counterfactual SST pattern. This can be a majoruncertainty in attribution assessments and an alterna-tive is to use SST patterns based on observationsrather than models.36

Further experiments can be made usingAGCMs to diagnose in more detail the anthropo-genic influence on extreme events. For example, diag-nostic simulations have been carried out in whichSSTs and anthropogenic forcings from greenhousegases and tropospheric aerosols have been varied sep-arately to show that the hot dry summer in westernEurope in 2013 was influenced substantially byanthropogenic forcing, whereas North Atlantic SSTswere shown to be an important factor explaining thecontrast between the very dry summer of 2013 andthe very wet summer of 2012.37

Analogue-Based ApproachesA further way of conditioning results on aspects ofthe climatic conditions present at the time of theevent is to consider the observed circulation charac-teristics. Circulation analogs38 have been designed toestimate climatic conditions in previous times underthe same large-scale circulation as today.39,40 Poten-tially it is one way to investigate how secular climatechange has affected unusual climatic events.13 Anillustration of this approach is applied here to theFall/Winter of 2006/2007, which was one of thewarmest in Europe since 1500,41 and the secondwarmest in France since the beginning of the 20thcentury. Euro-Atlantic sea-level pressure (SLP)anomalies from the 20CR reanalysis42 for the1900–2011 period and over the (80�W–60�E; 30�–70�N) domain are used to characterize the circulationtype for each day starting from the beginning of the20th century. The ‘analogues’ are found from the 20days over the observed record which have similar cir-culation characteristics and the temperatures overthese analogues are averaged. From a statistical per-spective, the analogue temperatures are random‘replicates’ of the temperature at the day conditionedby the atmospheric circulation. This allows a

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FIGURE 2 | Estimates of the fraction attributable risk (FAR) in theUK region (10�W–5�E, 48–60�N) over a range of temperaturethresholds in units of standard deviation above the climatological(1961–1990) annual mean temperature. The colored area marks the5–95% uncertainty range of the estimated FAR. The black vertical linecorresponds to the UK record temperature.

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determination of the probability distributions of tem-perature variability driven by the atmosphericcirculation.

Having derived circulation-dependent tempera-ture distributions one can calculate the probability ofthe event in question, as measured in this illustrationby the frequency of days within the cold season forwhich the observed temperature is above all analoguetemperatures. This statistic was a record in2006–2007 (Figure 4).

The probability P0 of observing the record dur-ing the period 1900–1960 is estimated to be about0.0007 compared to a probability P1 during theperiod 1970–2011 of about 0.03, which indicates amore than 40-fold increase in probability betweenthe two periods, implying a FAR of about 0.97 (esti-mated to be between 0.87 and 0.98 with a bootstrapestimate of uncertainty). Unlike the methodsdescribed in Coupled Model Approaches section andthe following section, which explicitly model theworld that might have been absent anthropogenicforcings, these analogue-based approaches provide a

probabilistic attribution of an extreme event to theoverall climate change over the period considered,howsoever caused, with the assumption that periodsconsidered are long enough to cancel any low-frequency natural climate variability.

Empirical ApproachesEmpirical approaches applied to observations directlyhave been used to estimate how climate change isaffecting the probability or return times of particularclasses of events. The odds of record-breaking tem-peratures can be related to increasing mean tempera-tures43,44 and the odds of record-breaking dailyrainfall events can be related to atmospheric warmingand the associated increased water holding capac-ity.45 Resultant estimates that long-term warminghas caused a fivefold increase in the number of localrecord-breaking monthly temperature extremesworldwide43,44 and has led to 12% more record-breaking rainfall events45 are broadly consistent withestimates using climate models that about 18% of

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FIGURE 3 | Return time (years) for the maximum 4-day precipitation average during May to June in the HadRM3P model for the upper Elbecatchment. The red dots indicate May to June possible 4-day maximum precipitation events in a large ensemble of HadRM3P simulations of theyear 2013, while the light blue dots indicate possible May to June 4-day maximum precipitation events in 25 different large ensembles ofsimulations of the year 2013 as it might have been without climate change where each of the 25 ensembles has a different sea surfacetemperature (SST) pattern to represent the anthropogenic change in SSTs. The dark blue dots represent the 25 natural ensembles aggregatedtogether. The error bars correspond to the 5–95% confidence interval estimated with a nonparametric bootstrap. (Reprinted with permission fromRef 35. Copyright 2014 American Meteorological Society)

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moderate daily precipitation extremes, and about75% of moderate daily hot extremes, currentlyoccurring over land, are attributable to warming.9,46

Empirical approaches can thus serve as a check onmodel results and are often appreciated by the userswho are skeptical of the veracity of climate models,even though it necessarily also makes assumptions.They can also be employed for events that cannot yetbe represented well by climate models used for attri-bution, such as extreme summer convective events.

An empirical approach to event attribution byfitting observed data to statistical distributions hasbeen used to show that climate change did not play amajor role in the 2011 floods in Thailand.47 A non-significant downward trend in rainfall in the upperChao Praya basin in Thailand found in this analysis,agreed with a similar lack of increasing trends in cli-mate models.

Other such analyses show positive results. Forexample, the cold waves in the United Statesobserved in early 2014 are found to be significantlyless likely48 although they are still not uncommon,with a return time in 2014 of about 12 years

compared to a return time in 1950 of about every 4years (Figure 5). For spatially small events such assummer thunderstorms data from stations that areclose enough to be identically distributed and farenough apart to be reasonably independent can bepooled. The methods used to derive the results shownin Figure 5 have been incorporated into the publicclimate analysis website KNMI Climate Explorer.

Broad-Scale ApproachesWhile the analogue and statistical methods describedin sections Analogue-Based Approaches and Empiri-cal Approaches consider the overall effects of climatetrends, attributing such changes to anthropogenic ornatural causes requires the use of a climate model asdescribed in the section Coupled Model Approachesand the following section.

Climate models additionally come into theirown in describing the global statistics of similarlydefined events over the globe. Several studies haveadapted detection and attribution methods49 used toattribute changes in the mean state of the climate

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FIGURE 4 | Example of an analogue-based approach. (a) Temperature mean anomalies of minimum daily temperature (Paris, Toulouse, andBesançon) between September 2006 and February 2007 (black line). Maxima of analogue temperatures (red line). (b) variations of the fraction ofobserved temperatures above all analogue temperatures between September and February. The red circle indicates the record of 2006/2007. Thehorizontal-dashed lines indicate the quantile values of a binomial distribution that is fitted to an unperturbed period (1900–1960) and perturbedperiod (1970–2011).

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system50 to analyses of changes in the frequency orintensity of specific types of extreme events. Suchstudies are sometimes able to provide informationthat is relevant to event attribution, particularly forpredefined events and on larger spatial scales.

One such example considered observedchanges in the annual minimum and maximumextremes of daily minimum and maximum tempera-ture over 1961–2000 to show that, globally, extremeannual minimum daily minimum and maximum tem-peratures (i.e., the temperatures of the coldest nightand coldest day annually) that would have beenexpected to recur once every 20 years on average inthe 1960’s had become substantially more unlikely tooccur due to human influence on the climate system.6

Expected recurrence times were estimated to haveincreased to 35 and 30 years, respectively, in the1990s. In contrast, anthropogenic forcing was esti-mated to have increased the likelihood of extremewarm events (hottest night and hottest day of theyear) by a similar factor, with expected recurrencetimes having decreased to 10 and 15 years,respectively.

Changes in the extremes of annual maximum1-day and 5-day precipitation accumulations havealso been shown to have been caused by humaninfluence.7,8 Over the period 1950–2005 for aNorthern Hemisphere land domain, the increase inthe intensity of annual extreme 1-day precipitationattributed to human influence has a sensitivity ofchange per degree of warming in global mean tem-perature that is consistent with the Clausius–Clapeyron relation (that expresses how moisture

increases in a warming atmosphere when relativehumidity stays constant). By interpreting the attribu-ted change in terms of a reduction in the waiting timefor a 20-year extreme event in the 1950s to approxi-mately 15-years now, the FAR of such extremeevents is 0.25. In contrast, for annual and seasonalmean regional temperatures that would have been 1-in-10-year events in an unperturbed climate, esti-mates of FAR have been found that are often well inexcess of 0.75 or higher, both for annual and JJAmean temperatures.30 For comparison, an analysis ofextreme thresholds of daily temperatures and dailyrainfall totals in models including those expected tooccur once in 1000 days (about once every 3 years)in an unperturbed climate, finds that about 75% ofthese moderate daily hot extremes and about 18% ofmoderate daily precipitation extremes over land areattributable to warming.9

These approaches demonstrate the strong linkbetween conventional detection and attribution andevent attribution. They avoid selection bias sinceevent definitions are not driven by impacts that havejust been experienced (and may have received highmedia profiles). Being based on detection and attribu-tion methods, they also allow projections of futurerisk to be observationally constrained.51 However,they also have a number of limitations. Detectionand attribution techniques for extremes are not yetfully developed, and detection and attributionremains difficult on regional and smaller scales, par-ticularly for variables other than temperature, suchas precipitation, for which evidence of a human influ-ence is only just beginning to accumulate.

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FIGURE 5 | Generalized extreme value (GEV) distribution fit to the coldest temperature of the year at Chicago Midway station 1928–2013compared to the value observed in 2014. The distribution of the temperature is assumed to shift with the smoothed global mean temperature. Thered lines indicate the fit for the climate of 2014, the blue lines indicate the fit for an earlier climate. The observations have been drawn twice,once shifted up with the fitted trend to the current climate, once down to the climate of 1951.

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EVALUATION

As many of the methods for attribution of extremeevents rely largely on climate model simulations, pro-viding evidence that the models employed in a studyare fit for purpose is essential in order to demonstratethe degree of confidence one can have in the results.A model is likely to have different skill in reprodu-cing different types of extremes in different regionsand therefore its evaluation assessment needs to betailored to the event under consideration. The synop-tic circulation prevalent at the time of an extremeevent may play a key role in its development andmodels need to be able to reproduce the same kindof circulation patterns with a realistic frequency tobe suitable for an attribution study. For example,the heat wave in Russia in July 2010 was associatedwith a quasi-stationary anticyclonic circulation whichneeds to be reproduced by models used in an

attribution analysis of the heat wave.34 The ability ofmodels to accurately simulate the physical processesand mechanisms linked to extremes is also crucial.For example, attribution studies of the summer heatwave of 2012 in the United States, needs to accountfor the effect of the stable atmosphere on the surfaceenergy budget, which led to a decrease in soil mois-ture and surface evapotranspiration and an increasein temperature.52 Finally, models need to be assessedin terms of their representation of modes of internalvariability that are known to be primary drivers ofregional extremes.

Evaluation assessments are typically based oncomparisons between model data from a smallensemble of multidecadal simulations of the actualclimate during a recent climatological period andobservations or reanalysis data. Figure 6 illustratessome common tests34 to examine whetherHadGEM3-A would be a suitable model for studies

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FIGURE 6 | Evaluation assessments of HadGEM3-A to determine whether the model is suitable for attribution studies of extreme winterrainfall events in the UK. (a) Reliability diagram for wet events in the UK, defined relative to terciles of the 1960–2010 climatology. (b) Normalizeddistributions of the winter rainfall in the UK during 1960–2010 produced with data from the National Center for Environmental Prediction/NationalCenter for Atmospheric Research (NCEP/NCAR) reanalysis (red) and five HadGEM3-A experiments (blue). (c) Power spectra corresponding to winterrainfall time series based on data from reanalysis (red) and five model simulations (blue). (d) Return time of winter rainfall events in the UKestimated with order statistics, or, for extreme thresholds, with the generalized Pareto distribution. Results plotted in red correspond to the NCEP/NCAR reanalysis and results plotted in blue to the five model simulations.

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of extreme winter rainfall in the UK region, similarto the recent extreme event during the catastrophicstorms and floods of 2013/2014.53 Five model simu-lations of the climate in 1960–2010 are used for thisassessment which were run with observed SST andsea-ice data as boundary conditions and include all(both natural and anthropogenic) major external for-cings. A seasonal forecast reliability diagram54 indi-cates whether the model is able to capture thepredictable features of the event under consideration(Figure 6(a)). Although the use of reliability is wellestablished for forecasting, its meaning for attribu-tion is less clear55 given that reliable attribution isstill possible when there is no inherent real-worldpredictability.

For the example of extreme winter rainfall inthe UK, further tests can be undertaken to establishwhether the modeled statistics for this kind of eventare reliably reproduced with HadGEM3-A. Figure 6(b)–(d) illustrates different aspects of the winter rain-fall distribution and show how the model compareswith the reanalysis. A Kolmogorov–Smirnov testindicates that there is no significant differencebetween the distributions constructed withHadGEM3-A and reanalysis data over period1960–2010 (Figure 6(b)). The power spectra of rain-fall timeseries over the same period (Figure 6(c)) alsosuggest that the simulated variability is generally con-sistent with the observations, albeit possibly higher atmultidecadal timescales. Focussing on the warm tailof the distributions, the return time of high rainfallevents has been estimated with a generalized-Paretodistribution (Figure 6(d)) and again the model isfound to agree well with the reanalysis. Although thelength of the simulations does not allow extrapola-tions to rarer events, the above assessments providesome evidence that HadGEM3-A would be suitablefor attribution studies of extreme rainfall events inthe UK region.

Model evaluation is subject to the availabilityand quality of observations. Identification of a clearsignal of change in the observations often requireslong time series of homogenous data but an evalua-tion of a model’s representation of the most relevantprocesses for a particular event may be achieved withhigh spatial resolution data that are not availableover long time frames. Thus, homogenized multide-cadal datasets may be needed for some applicationssuch as signal detection whereas reanalyses or satel-lite data may be appropriate for others such as modelverification.

Clear evidence for human influence on extremetemperature events seen in many studies20 benefitsfrom robust observational support, whereas mixed

evidence for human influence on extreme precipitationevents20 can be affected by inadequacies in observa-tions in many parts of the world,56 as well as limita-tions in models’ representation of cloud processes.57

Evaluation methodologies similar to those dis-cussed in this section have been routinely employed instudies of extremes. In some cases, bias correctionsmethods need to be applied to adjust modeled distri-butions although such corrections can strongly influ-ence the attribution assessment and hence should beapplied with caution.58 Further development of evalu-ation methodologies will support additional evidenceto stakeholders of the value of attribution products.

FRAMING OF THEATTRIBUTION QUESTION

The various approaches described in Methodologiessection are scientifically legitimate methods of fram-ing attribution questions. But applying differentmethods can lead to very different assessmentsof change in risk even if the event considered isthe same. Hence, it is paramount to clearly statethe exact framing of the research question beingasked.59

Attribution results can depend strongly on thedefinition of the event. Any particular event willnever occur exactly the same way again so for anattribution statement to say something relevant tothe future it has to be constructed for a class ofevents of which the one that occurred is a representa-tive. Typical definitions of an event are for tempera-tures or rainfall averaged over a certain area andtime to be above or below a particular threshold.Defining an event over a large area and long time-scale reduces the natural variability and thereforetends to give larger FARs than more impact-baseddefinitions focusing on small-scale extremes that maybe more closely related to damage. Defining a classof events very close to the observed one tends togives very low probabilities of occurrence. For moregeneralizable statements, a wider class of eventsneeds to be considered for which details such asexact location or timing can differ.

Attribution results can also differ depending onwhether an event is attributed to the overall climatechange as in Analogue-Based Approaches and Empiri-cal Approaches sections or to the change attributableto anthropogenic factors (sections Coupled ModelApproaches and SST Forced Atmosphere).

The attribution of an anthropogenic contribu-tion can be conditioned on the natural variabilitybeing in a certain state. If the probability of this

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condition is itself changing, then the overall probabil-ity of the event could be very different than the con-ditional probability. For example, a study of theextreme flooding in Colorado in September, 2013concluded that the probability of such an extreme5-day September rainfall event had likely decreaseddue to climate change as a result of changes inatmospheric circulation and vertical stability60 inapparent contradiction to an analysis focusing on theeffects of human influence (via increased SSTs andincreased atmospheric moisture) conditioned on theatmospheric circulation regime during the event13

Some attribution assessments that link events todynamically driven changes in circulation have beencriticized on the grounds that small signal-to-noiseratios, modeling deficiencies, and uncertainties in theeffects of climate forcings on circulation render con-clusions unreliable and prone to downplaying therole of anthropogenic climate change.13 Instead, it isargued, it is more useful to consider how changes inthe climate’s thermodynamic state have affected theimpact of a particular event.13 The analogue-basedapproaches are consistent with this approach in con-sidering how climate change has affected events givenparticular circulation characteristics. But a widervariety of approaches as described in the rest ofMethodologies section are needed to tackle the wholeattribution problem which is important given thatchanges in global SLP patterns and correspondingcirculations have been detected.61 By always findinga role for human-induced effects, attribution assess-ments that only consider thermodynamics couldoverstate the role of anthropogenic climate change,when its role may be small in comparison with thatof natural variability, and do not say anything abouthow the risk of such events has changed.

The importance of such framing issues meanthat clear communication of results from such attri-bution studies is vital else apparently contradictoryfindings can result. For example, whereas theRussian heat wave of 2010 has been found tohave been made much more likely by anthropogenicclimate change,43 its magnitude has been found tobe largely attributable to natural variability62 anapparent contradiction that can be resolved by con-sidering the changing distribution of temperatureextremes under a warming climate21 as shown inFigure 7.

REGIONAL PERSPECTIVE

As the science of event attribution has developed,there has been an increase in the geographical

coverage of such studies. This is important for thedevelopment of the field, not only in applying suchscience in new areas but also in developing scientificunderstanding by considering regions with differentdominant modes of internal variability and differentresponses to external climate forcings.

Since 2012, a series of annual reports have beenpublished in the Bulletin of the American Meteoro-logical Society (BAMS) explaining extreme events ofthe previous year from a climate perspective.17–20

They provide an early example of how the develop-ment of the underpinning science of event attributionis being applied to answer topical questions aboutreal-life events around the world. Geographical cov-erage in the first three reports was far from uniform(Figure 8) with a greater concentration of studies intoevents in Europe and North America and some gapsin South America and much of Africa. However, thelatest report explaining extreme events of 2014includes a much wider geographical spread in its32 studies with events from South America andAfrica being considered in additional to events fromEurope, North America, Australasia, and Asia.20

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Asia is one region where there has been astrong growth in interest in event attribution.

As an example, the 2013 summer saw extraor-dinarily high temperatures in the region with recordwarm temperatures observed over an extendedregion including Eastern China, Korea, and Japan.There was interest therefore in placing this event inthe context of climate variability and change giventhat anthropogenic influence has been detected inchanges in annual extremes of daily maximum andminimum temperatures in China.6,63 An event attri-bution study of the hot summer of 2013 in EasternChina found that anthropogenic influence caused amore than 60-fold increase in its probability.51 Thisresult indicates that the increasing frequency ofextreme summer heat in Eastern China is primarilyattributable to the anthropogenic emission of green-house gases with rapid urbanization and the expan-sion of urban heat islands contributing as asecondary factor. With continued emissions 50% ofsummers could be hotter than the 2013 summer inonly two decades (Figure 9).

Complementary studies have analyzed thisevent from different perspectives using differentapproaches and focusing on different countries.Results show consistent and clear anthropogenic

Arctic sea ice minimum

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FIGURE 8 | Events considered in the three reports explaining extreme events of 2011, 2012, 2013 (Ref 17–19) indicating whether they areheat, cold, high precipitation, low precipitation or drought, storms, or sea-ice events.

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FIGURE 9 | Time evolution of the frequency of summertemperature anomalies above 1.1�C, relative to the 1955–1984 mean,in the reconstructed observations (1955–2013) and in theobservationally constrained projections (2014–2072) under RCP4.5(plus) and RCP8.5 (cross) emission scenarios (left-hand scale). Thesolid smooth curves are LOESS (local regression) fitting. The dashedcurves represent projected ensemble mean temperature changes underthe relevant emission scenarios (right-hand scale) and are shown herefor reference. Results for RCP4.5 and RCP8.5 are represented by redand green, respectively. (Reprinted with permission from 51. Copyright2014 Nature Publishing Group)

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influence on the probability of extreme temperaturesin Korea64 and Japan65 associated with this verylarge-sale high temperature event, as well as alsohighlighting the role of natural variability in contri-buting to the magnitude of the extreme temperaturesrecorded.

DEVELOPMENT OF OPERATIONALEVENT ATTRIBUTION

Given that there are often conflicting messages givenby scientists in the immediate aftermath of damagingclimate events about whether there is a link to cli-mate change, well communicated assessments basedon carefully calibrated operational attribution sys-tems have been proposed as a way forward toaddress this confusion.3 As well as providing moretimely assessments of events, operational systemscould address the selection effects inherent in the cur-rent ad hoc nature of attribution studies, events oftenbeing chosen based on scientists’ individual preferencesrather than any more objective criteria which hasresulted in uneven geographical coverage and whichhas limited the potential to draw widespread conclu-sions from the current collection of ad hoc studies.

An operational event attribution service wouldprovide regular updates using predetermined selec-tion criteria for events and previously establishedmethodologies. It could provide assessments on arange of timescales, during and immediately follow-ing an event, monthly or seasonally, and for publica-tion in annual assessments.17–20 Operationalattribution assessments should aim to synthesize theavailable information including results from a rangeof methods and incorporating physical understandingin addition to models and statistics. A fast-trackcapability for assessment on media timescales wouldrequire the use of empirical statistical methods asdescribed in Empirical Approaches section, ensem-bles of AGCMs with forecast SSTs or precomputedresults from coupled models30 (see Coupled ModelApproaches section). More detailed assessments, con-ditioned on details of the observed climate evolution,and seeking to determine how aspects of the event inquestion have been affected by particular compo-nents of natural and anthropogenic influence, couldbe made monthly based on operational modeling sys-tems that prescribe relevant features, such asobserved SSTs34 (see SST Forced Atmosphere sec-tion). Studies that adopt more tailored approachescould be published in the annual reports explainingextreme events from a climate perspective17–20 or thegeneral literature.

An important requirement for any operationalattribution system is a clear communication of therobustness of results and of how attribution ques-tions have been framed (Attribution Question sec-tion). Evidence with potential users of suchinformation has shown that clear communication ofscientific uncertainties supports rather than impedesthe credibility of assessments for decision making.66

Furthermore the comprehensive approach of an oper-ational system, no longer dependent on ad hocchoices of research teams and subject to accusationsof selectivity in choice of events to study, is attractiveto users in enabling them to see how individualevents fit in to a wider picture of climate change.66

While human influence on the climate system is clear,carefully designed operational attribution systemsshould help societies understand how they are beingaffected by climate change and how to avoid theworst outcomes.46

STAKEHOLDER PERSPECTIVES

The high volume of media enquiries received by cli-mate scientists in the aftermath of many extreme cli-mate events shows that there is a demand for eventattribution but that the whole range of possible usesfor such information is not yet fully understood.66,67

Better information about climate risks could be ofpotential use to the insurance industry, to regionalmanagers developing climate adaptation strategies, tolitigators, to policy makers and for disaster riskreduction.3 But profitable use of such informationrequires a dialogue between stakeholders and scien-tists that allows the development of trust as a way todevelop the credibility, saliency, and legitimacy ofscientific findings.66,68

The credibility of event attribution is aided byobjective communication of the links between cli-matic changes and the impacts of extreme events.66

Successful communication of scientific findingsrequires the use of language adapted to lay under-standing, noting that some terms such as extremeevent may not be widely understood or such as ‘attri-bution’ may not translate easily into other languagessuch as German.69,70 The legitimacy of event attribu-tion for stakeholders is affected by their values andbeliefs,66,71 which may be influenced by vested inter-ests in welcoming or rejecting climate of anthropo-genic climate change and may reflect different riskcultures. The saliency of event attribution assess-ments for users depends on whether they provideknowledge that is relevant to them.66 If the statisticalrarity of attaining a threshold for a specific physical

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measure makes a specific event salient to a climatescientist, saliency for a stakeholder may be associatedwith impact-related dimensions as well. A shift of theprobability of extremes in terms of casualties, eco-nomic losses, or redistribution of wealth, could be‘attributed’ to multiple factors associated with cli-mate, vulnerability, and exposure. Extreme eventidentification by stakeholders may therefore bedependent upon the causal chain they are mobilizing(see Figure 10).

When stakeholders consider that attribution ismultifactorial, this potentially complicates the ‘attri-bution statement.’ Human influence may be per-ceived as a nested set of behaviors, some originatinglocally (e.g., land use plans), some nationally (e.g.,health policy), and some internationally (e.g., anthro-pogenic climate change, private financing of recon-struction efforts). The interest in extreme eventattribution is not solely a climatic enquiry and attri-bution assessments need to take this into account.Nevertheless an attribution methodology that identi-fies changes in the meteorologically related hazardcomponent of climate risk can be regarded as highlyuseful by stakeholders who may have extensiveunderstanding of exposure and vulnerability but littleinformation on changes in hazard.66

Experience of meetings between attributionscientists and stakeholders representing sectors facedwith decision making in the context of climate

variability and change have demonstrated a keeninterest from stakeholders in understanding howinformation gleaned from event attribution sciencecould be applied.66,67 But there is no simple recipefor user engagement. Each sector potentially has dif-ferent uses for such information and therefore hasdifferent requirements.66,67 Whatever challengesattribution science may pose to potential users, itappears clear that such science should not be ignoredor seen as a distraction, but rather scientists and sta-keholders should work together to ensure the sciencesupports stakeholder needs.72 The robust link of onlya small fraction of excessive deaths in a heat wave tohuman-made climate change could have widespreadimplications for such discussions. The recognition ofsuch losses in the broader context of climate justicehas an ethical dimension.73 A continuous dialoguebetween scientists and stakeholders is required tofacilitate the pull through of knowledge intoinformed decision making.66–68 This should includean ongoing discussion of the merits and risks ofapplication of such knowledge in particularcontexts.74,75

DISCUSSION AND CONCLUSIONS

This article has reviewed the current status ofresearch into the attribution of extreme weather and

FIGURE 10 | Representation of a grounded theory of attribution in terms of causal chain and the potential interest in attribution bystakeholders.

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climate-related events and discussed the variety ofdifferent methodological approaches that have beentaken. This diversity of approaches represents thestrength of this field of research. No one particularapproach provides the best answer for all purposes.The robustness of findings is enhanced when differentapproaches provide consistent results which are sup-ported by a firm basis in physical understanding. Butwhen assessments synthesize current evidence basedon a multiplicity of approaches, it is importantthat the framing of those approaches is clearly articu-lated. Otherwise, users of such information may beconfused by apparently contradictory conclusionsresulting from the different framing of attributionquestions.

In an emerging field of research, there remainmany challenges in communicating clearly findings ofevent attribution studies in a way that facilitateseffective decision making by stakeholders. Theannual reports in BAMS explaining extreme eventsof the previous year provide one means for dissemi-nating results of attribution studies.17–20 An advan-tage of initiatives like this is that they encourage thedevelopment of the underpinning science while alsoprompting developments in the translational scienceneeded to communicate findings to a wider audiencethan the specialist scientific community. Other suchinitiatives include the European research projectEUCLEIA (European Climate and Weather Events:Interpretation and Attribution; www.eucleia.eu)which is developing an operational attribution systemfor Europe, and the World Weather Attribution proj-ect (http://www.climatecentral.org/wwa) which aimsto provide early, science-based assessments of theextent to which global warming caused by green-house gas emissions played a role in a weather or cli-mate event’s probability. A continuous dialoguebetween stakeholders and scientists is required toenable effective decision making based on suchinformation.

Research to date has shown much clearer evi-dence for human influence on extreme temperatureevents than extreme precipitation events, droughts,and storms. High confidence in attribution ofextreme temperature events results from a robustobservational basis, the ability of climate models torepresent the relevant processes and confirmatorystudies replicating results. The FAR for many conti-nental and subcontinental scale temperature extremeevents exceeds 0.75 consistent with findings that themajority of daily hot extremes occurring around theworld can be attributed to anthropogenic climatechange.9,30,46 For extreme precipitation events,droughts, and storms, the evidence is much more

mixed. The observational basis is less secure, climatemodels can struggle to capture relevant features ofthe events, and different methods of framing attribu-tion questions can produce contrasting results. How-ever, both thermodynamic and dynamical changesneed to be considered in event attribution studiesbecause both can influence the probability and mag-nitude of extreme events. As climate modeling capa-bility improves and our understanding of thedynamical causes of extreme events develops,the potential for making holistic event attributionstatements that consider all facets of the event inquestion will improve. In turn, developing the scien-tific understanding of extreme events and testingthe ability of climate models to represent them, willhelp to improve predictions of future changes inextreme events and thereby inform adaptationplanning.

The annual BAMS reports explaining extremeevents provide an early example of how underpin-ning science is being applied to answer topical ques-tions about real-life events. But even though theproduction of peer-reviewed reports for publicationin September of the following year places considera-ble demands on authors, reviewers, and editors, thesereports appear too late to be relevant on the time-scales when the media are asking questions about thecausality of damaging weather and climate events.Also attribution assessments included in the reporthave so far been carried out on a largely ad hocbasis, motivated largely by scientific teams’ capacityand interest in analyzing particular impactful events.As a result, geographical coverage has been far fromuniform and the ad hoc selection of events limits theability to draw wider conclusions for the year inquestion.

For event attribution to fulfill its potential toinform a wider group of stakeholders throughout theworld, there needs to be the development of the capa-bility to carry out operational attribution. Thiswould provide regularly updated attribution assess-ments based on predefined and tested methodologiesand event selection criteria. It would include thecapability to carry out event attribution studies on arange of timescales including very quickly so as beingable to inform the public during the course ofextreme events. This requires the continuing develop-ment of methodologies to assess the reliability ofevent attribution results and further work to under-stand the potential utility of event attribution forstakeholder groups and decision makers. There needsto be the development of regional capacity to carryout such studies throughout the globe building onlocal knowledge. And there needs to be a greater

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capability to incorporate the impacts of extremeweather into event attribution studies so that therisks of such events can be better understood, byincluding the effects of exposure and vulnerability inaddition to meteorological hazard.

Event attribution science is still relativelyyoung. Many questions still remain as to current cap-abilities to robustly attribute the contribution ofanthropogenic climate change to the risk of manyextreme weather and climate events. Further progressneeds to be made in understanding how best to com-municate the findings of event attribution studies to awide range of possible users. But there has been rapidprogress of this science in the last few years. Forexample, the first annual BAMS reports into eventsof 2011 contained analyses of six events restricted to

heat waves, cold spells, flood, and droughts. Thefourth report 3 years later contained 32 contributionsand in addition to heat waves, floods, and droughtsin 2014 included tropical cyclones, snow storms, andunusual sea ice extent.

It is important that climate models continue tobe assessed and improved and, that, methods forassessing the reliability of attribution results continueto be developed. In particular, where attributionassessments are based on a solid foundation of physi-cal understanding, they are more likely to be robust.As the science continues to mature, event attributionshould be seen as an integral component of climateservices to inform adaptation and mitigation pro-grams around the world and to support climate riskmanagement.

ACKNOWLEDGMENTS

The research leading to these results has received funding under the EUCLEIA (EUropean Climate and weatherEvents: Interpretation and Attribution) project under the European Union’s Seventh Framework Programme[FP7/2007-2013] under grant agreement no 607085 (PAS, NC, J-V, HvS, GvO, RV, PW, PY) PAS was par-tially supported by the UK-China Research & Innovation Partnership Fund through the Met Office ClimateScience for Service Partnership (CCSP) China as part of the Newton fund. PAS and NC were partially sup-ported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). PY acknowl-edges support from ERC Grant No. 338965-A2C2. Y. Sun is supported by Chinese programs 2012CB417205and GYHY201406020

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