1 Introduction

Nowadays, whenever an extreme weather or climate event occurs, the question inevitably arises whether it was caused by climate change or, more precisely, by the influence of human activities on climate. The interpretation of this question hinges on the meaning of the word ‘caused’: just as in the connection between smoking and lung cancer, the influence of anthropogenic climate change on extreme events is inherently probabilistic. Allen (2003) proposed that a related question could be answered: how has climate change affected the probability of this extreme event occurring? The process to answer these questions is referred to as ‘extreme event attribution’ and also includes attributing the change due to anthropogenic climate change in other characteristics, such as the intensity of the event.

Since its initiation in 2015, the World Weather Attribution (WWA), an international collaboration of climate scientists, has sought to provide a rapid response to this question in a scientifically rigorous way for many extreme events with large impacts around the world. The motivation is that, by providing scientifically sound answers when the information is most in demand, within a few weeks after the event. It has three uses: answering the questions from the public, informing the adaptation after the extreme event (e.g. Sippel et al. 2015; Singh et al. 2019) and increasing the ‘immediacy’ of climate change, thereby increasing support for mitigation (e.g. Wallace, 2012 ). Because rapid studies are necessarily restricted to types of events for which we have experience, WWA has also conducted slow attribution studies to build up experience for other extremes. The methodology developed by WWA for both types is documented in a companion paper (Philip et al. 2020a), whilst here we highlight the lessons learned along the way. These are more general and also apply to other extreme event attribution methodologies and other fields in climate projections that seek to predict or project properties of extreme weather and climate events.

Despite the extreme event attribution branch of climate science only originating in the twenty-first century, there is already a wealth of literature on it. An early overview, reporting experiences similar to ours is Stott et al. (2013). The assessment of the National Academies of Sciences (2016) gives a thorough evaluation of the methods of extreme event attribution, which we have applied and expanded in the WWA methodology. Recent reviews of emerging methods and differences in framing (the scientific interpretation of the question that will be answered) and event definition (the scientific definition of the extreme event, specifying its spatial-temporal scales, magnitude and/or other characteristics) were given by Stott et al. (2016), Easterling et al. (2016), and Otto (2017). While the term ‘event attribution’ is also used in studies focussing mainly on the evolution and predictability of an event due to all external factors (e.g. Hoerling et al. 2013a), we here apply it to the probabilistic approach suggested by Allen (2003) that only focuses on anthropogenic climate change, which is also the methodology applied in recent rapid attribution studies. The basic building blocks have been refined since the first study of the 2003 European heat wave (Stott et al. 2004). Originally, event attribution was based on the frequency of the class of events in a model of the factual world in which we live, compared to the frequency in a counterfactual world without anthropogenic emissions of greenhouse gases and aerosols. Later, it was realised that climate change is now so strong that this attribution result from model data can often be compared to the observed trend over a long time interval (van Oldenborgh 2007). Note that the framing of the attribution question for observations is not exactly comparable to that for models: even when human-induced climate change is the strongest observed driver of change, it is not the only one as it often is in model experiments. This is similar to the difference between Bindoff et al. (2013), who consider only anthropogenic climate change, and Cramer et al. (2014), who consider all changes in the climate.

We, the authors, collectively and individually have conducted many event attribution analyses over recent years, often in the World Weather Attribution (WWA) collaboration that aims to do scientifically defensible attribution on short time scales after events with large impacts (www.worldweatherattribution.org). This paper follows the eight-step procedure has emerged from our experience:

  1. 1.

    analysis trigger,

  2. 2.

    event definition,

  3. 3.

    observational trend analysis,

  4. 4.

    climate model evaluation,

  5. 5.

    climate model analysis,

  6. 6.

    hazard synthesis,

  7. 7.

    analysis of trends in vulnerability and exposure, and

  8. 8.


The final result usually takes the form of a probability ratio (PR): ‘the event has become X times more/less likely due to climate change with a 95% confidence interval of Y to Z’, often combined with ‘other factors such as … have also influenced the impact of this extreme event’. Similar statements can be made about other properties of the extreme event, such as the intensity: ‘the heat wave has become T (T1 to T2) degrees warmer due to climate change.’ We discuss the pathways through the steps leading to such statements in turn, emphasising the pitfalls we have encountered.

2 Analysis trigger

First, a choice has to be made which events to analyse. The Earth is so large that extreme weather events happen somewhere almost every day. Given finite resources, a method is needed to choose which ones to attribute. In WWA, we choose to focus on extremes that impact people, a threshold could for example be the number of casualties or people affected. This implies considering more events in less developed regions where extreme weather has large impacts, and less in developed regions with numerically smaller impacts but more news coverage (Otto et al. 2020). Alternatively, meteorological (near-)records without notable impacts on humans also sometimes lead to public debate of the role of climate change, warranting scientific evidence. Stakeholders such as funding organisations often prioritise local events, so that small events locally can often be considered as important as bigger events elsewhere. This implies that at this stage, the vulnerability and exposure context as well as the communication plans play a large role.

Impact-based thresholds need careful consideration. An economic impact–based threshold will focus event analysis on higher-income countries, and a population-based threshold will focus event analysis on densely populated regions such as in India or China, or affecting major cities. In line with our goal of attributing impactful events, the WWA team has developed a series of thresholds which combine meteorological extremes with extreme economic loss or extreme loss of life, the latter two both in absolute terms as well as percent loss within a national boundary. The current criteria are ≥ 100 deaths or ≥ 1,000,000 people affected or ≥ 50% of total population affected. For heat and cold waves, the true impact is very hard to estimate in real time so we use more indirect measures. In practice, these are typically the events that the International Federation of Red Cross and Red Crescent Societies do international appeals for.

Even if the selection criteria would be totally objective, one cannot interpret the set of extreme event attribution statements as an indicator of climate change because events that become less extreme will be underrepresented in the analyses (Stott et al. 2013). As an example, a spring flood due to snowmelt has not happened recently in England, but did occur occasionally in the nineteenth and early twentieth centuries (e.g. 1947). Hence, there are no attribution studies that show that these events have become much less common (Bindoff et al. 2013) and an aggregate of individual attribution studies will leave out this negative trend. To study global changes in extremes systematically, other methods are needed.

In practice, the situation is worse, because the question on the influence of climate change is posed most often when a positive connection is suspected. The result of this bias and the selection bias discussed above is that even while each event attribution study strives to give an unbiased estimate of the influence of climate change on the extreme event, a set of event attribution studies does not give an unbiased estimate of the influence on extreme events in general. Therefore, while cataloguing extreme event analyses is useful, synthesising results across event attribution studies is problematic (Stott et al. 2013).

In addition to these issues encountered when considering events that we would like to analyse, we acknowledge there are additional practical issues that place further restrictions on which events can currently be readily analysed. It must be possible for models to represent the event and if impact (hydrological) modelling is required, the time available must allow for it. For example, long runs from convection-permitting models need to become available before it is possible to conduct a rapid analysis on convective rainfall extremes. Further examples of event types that currently pose a challenge are listed in the next section.

3 Event definition

Once an extreme event has been selected for analysis, our next step is to define it quantitatively. This step has turned out to be one of the most problematic ones in event attribution, both on theoretical and practical grounds. The results of the attribution study can depend strongly on this definition, so the choice needs to be communicated clearly. It is also influenced by pragmatic factors like observational and model data availability and reliability.

There are three possible goals for an event attribution analysis that lead to different requirements on the event definition. The first is the traditional Detection and Attribution goal of identifying the index that maximises the anthropogenic contribution to test for an external forcing signal (Bindoff et al. 2013), which calls for averages over large areas and time scales. However, these scales are often not relevant for the impacts that really affect people. In those cases, this event definition does not meet our goal to be relevant for adaptation and only addresses the need for mitigation in an abstract way. These goals usually require a more local event definition, so we do not pursue this here. The second goal is the meteorological extreme event definition, choosing an index that maximises the return time of the event (Cattiaux and Ribes 2018). This often also increases the signal-to-noise ratio. The third is to consider how the event impacts on human society and ecosystems and identify an index that is best linked to these impacts. As these systems have often developed to cope with relatively common events but are vulnerable to very rare events, all definitions may overlap. As in the trigger phase, the communication plan also gives input to the event definition, noting carefully what questions are being asked and how we can map these on physical variables that we can attempt to attribute. These can be meteorological variables, but we also include other variables such as hydrological variables and other physical impact models.

The answer also depends on the ‘framing’: how the attribution question to be answered will be posed scientifically. We choose to use a ‘class-based’ definition, considering all events of a similar magnitude or more extreme on some linear scale. This class-based definition is well-defined as the probability of these events can be determined in principle from counting occurrences in the present and past climates, or model representations of these. Within the class-based framing, one can choose which factors to vary and which to keep constant. We generally choose the most general framing, keeping only non-climate factors except land use constant.

Alternative framings restrict the class of events by prescribing boundary conditions that were present, from the phase of El Niño to the particular event in all its details (Otto et al. 2015a; Hannart et al. 2016; Otto et al. 2016; Stott et al. 2017). Because each event is unique (van den Dool 1994), the latter ‘individual event’ interpretation is harder to define: it has a probability very close to zero and the results depend on how the extreme event is separated from the large-scale circulation that is prescribed. Using this definition also risks giving a non-robust and hence unusable result due to selection bias and the chaotic nature of the atmosphere: the conditions were optimal to produce the extreme event in the current climate and any perturbation, including the shift to a colder climate that is done in the attribution exercise, can decrease the probability of such an event, sometimes dramatically (Cipullo 2013; Shepherd 2016). Such a decrease then does not describe the effect of climate change on the event but the effect of changing boundary conditions away from the optimal conditions. We therefore use event definitions at the inclusive end of the spectrum, either including all climate variables or prescribing only SST. The event being attributed only sets the threshold, so the procedure can also be followed for past or fictitious events.

It should also be noted that many hazards result in impacts through a variety of factors. One example is a flood, triggered by extreme rainfall in a river basin. In addition, however, the impact may also be affected by prior rainfall, snowmelt, dams or storage basins. If hydrological data are available (observations and model output), we can include these factors. In addition, the vulnerability and exposure of the population or infrastructure to the hazard also determines the total impact and thus are also taken into consideration for the event definition. At the moment, quantitative attribution of impacts of anthropogenic climate change is work in progress, although a number of such studies have been published, e.g. Schaller et al. (2016) and Mitchell et al. (2016). They are significantly more challenging as the vulnerability and exposure vary with time, behaviour and policies. It is discussed further in Section 8.

As noted above, we use a class-based definition of extreme events attribution, i.e. we consider the change in probability of an event that is as extreme or worse than the observed current one, p(Xx0) in two climates. This is described by the probability ratio PR = p1/p0, with p1 the probability in the current climate and p0 the probability in the counterfactual climate without climate change or the pre-industrial climate (often approximated by late nineteenth century climate). The univariate measurable, physical variable X measures the magnitude of the event. Compound events in meteorological variables can be analysed the same way using a physical impact model to compute a one-dimensional impact variable. As an example, moderate rainfall and moderate wind causing extreme water levels in the North of the Netherlands (van den Hurk et al. 2015) can be analysed using a model of water level. Similarly, drought and heat can be combined onto a forest fire risk index, which lends itself to the class-based event event definition (Kirchmeier-Young et al. 2017; Krikken et al. 2019; van Oldenborgh et al. 2020)

The definition next includes the spatial and temporal properties of the variable: the average or maximum over a region and timescale, possibly restricted to a season. As an example, an event definition would be the April–June maximum of 3-day averaged rainfall over the Seine basin, with the threshold given by the 2016 flooding event (Philip et al. 2018a). The temporal/spatial averaging has a large influence on the outcomes, as large scales often reduce the natural variability without affecting the trend much, thus increasing the Probability Ratio (Fischer et al. 2013; Angélil et al. 2014; Uhe et al. 2016; Angélil et al. 2018; Leach et al. 2020). Describing the 2003 European heat wave by the temperature averaged over Europe and June–August (Stott et al. 2004) gives higher probability ratios than concentrating on the hottest 10 days in a few specific locations, which gave the largest impacts (Mitchell et al. 2016). We reduce the range of possible outcomes by demanding that the event definition should be as close as possible to the question asked by or on behalf of people affected by the extreme, while being representable by the models. Additional information from local experts is essential. For instance, for the event definition used in the study of the Ethiopian drought in 2015 (Philip et al. 2018b), the northern boundary of the spatial event definition, a rectangular box, was originally taken to be at 14 N, but after discussions with local experts, who informed us about the influence of the Red Sea in the North, we changed the northern boundary to 13 N.

Choosing an average over the area impacted by the event has one unfortunate side effect: it automatically makes the event very rare, as other similar events will in general be offset in space and only partially reflected in the average. A more realistic estimate of the rarity of the event can be obtained by considering a field analysis: the probability of a similar event anywhere in a homogeneous area. For instance, we found that 3-day averaged station precipitation as high as observed in Houston during Hurricane Harvey had a return time of more than 9000 years, but the probability of such an event occurring anywhere on the US Gulf Coast is roughly one in 800 years (van Oldenborgh et al. 2017).

For heat, a common index is the highest maximum temperature of the year (TXx) recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). A major impact of heat is on health, mortality increases strongly during heat waves (Field et al. 2012). According to the impact literature, TXx is a useful measure when the vulnerable population is outdoors, e.g. in India (e.g. Nag et al., 2009 ) and accordingly, this is the measure we used in our Indian heat wave study (van Oldenborgh et al. 2018). In Europe, there is evidence that longer heat waves are more dangerous (D’Ippoliti et al. 2010), so that the maximum of 3-day average maximum temperatures, TX3x, may be a better indicator. We have also used the 3-day average daily mean temperature, TG3x, which correlates as well with impacts as the daily maximum and is less sensitive to small-scale factors affecting the observations.

There are many ways to define a heat index that is better correlated to health impacts than temperature. The Universal Thermal Comfort Index (UTCI) consists of a physical model of how humans dissipate heat (McGregor 2012). However, this necessitates the specification of the clothing style and skin colour, which makes the results less than universally applicable. From a practical point of view, it requires wind speed and radiation observations, which are often lacking or unreliable. A useful compromise is the wet bulb temperature (van Oldenborgh et al. 2018; Li et al. 2020), which is a physical variable based on temperature and humidity that is closely related to the possibility of cooling through perspiration..

Cold extremes can be described similarly to heat extremes. The annual minima of the daily minimum, maximum and mean temperature are often useful ETCCDI measures. For multiple-day events, such as the Eleven City Tour in the Netherlands, an averaging period or, in this case an ice model, can represent the event best (Visser and Petersen 2009; de Vries et al. 2012). Windchill factors giving a ‘feels like’ temperature have not yet been included in our attribution studies, due to the aforementioned problems of wind observations. Snowfall and snow depth also seem amenable to attribution studies, but only a handful of such studies have been done up to now (e.g. Rupp et al. 2013; van Oldenborgh and de Vries2018).

For high precipitation extremes, the main impact usually is flooding. This should be described by a hydrological model (e.g. Schaller et al., 2016 ), but precipitation averaged over the relevant river basin with a suitable time-averaging can often be used instead (Philip et al. 2019). For in situ flooding, we can choose point observations as the main indicator, as the time-averaging already implies larger spatial scales (e.g. van der Wiel et al. 2017; van Oldenborgh et al. 2017). A complicating factor is that human influence on flood risk is not only through changes in atmospheric composition and land use but also via river basin management, which often makes the attribution of floods rather than extreme precipitation more difficult.

Droughts add additional degrees of complication. Meteorological drought, lack of rain, is easy to define as the low tail of a long-time scale averaged precipitation, from a season to multiple years. Agricultural drought, lack of soil moisture, requires good soil modules in the climate model or a separate hydrological model that includes effects of irrigation. A water shortage also requires including other human interventions in the water cycle. In practice, lack of rain is often chosen, with qualitative observations on other factors (e.g. Hoerling et al. 2014; Otto et al. 2015b; Hauser et al. 2017; Otto et al. 2018a), although some studies have attributed drought indices like the self-calibrating Palmer Drought Severity Index (e.g. Williams et al.2015; Li et al. 2017) or soil moisture and potential evaporation (e.g. Kew et al. 2021).

There are many other types of events for which attribution studies are requested, for instance wind storms, tornadoes, hurricanes, hail and freezing rain. Standard event definitions for these have as far as we know not yet been developed and long homogeneous time series and model output for these variables are often not readily available.

4 Observed probability and trend

The next step is to analyse the observations to establish the return time of the event and how this has changed. This information is also needed to evaluate and bias-correct the climate models later on, so in our methodology the availability of sufficient observations is a requirement to be able to do an attribution study. This causes problems in some parts of the world, where these are not (freely) available (Otto et al. 2020). Early attribution studies relied solely on models. However, including observations is in our experience needed to investigate whether the climate models represent the real world well enough to be used in this way, making the results more convincing when this is the case.

A long time series is needed that includes the event but goes back at least 50 years and preferably more than 100 years. Observations are often less than perfect and care has to be taken to choose the most reliable dataset. As an example, Fig. 1 shows four estimates of the 3-day averaged precipitation from Hurricane Harvey around Houston. The radar analysis is probably the most reliable one, but is only available since 2005. The CPC gridded analysis only uses stations that report in real time and therefore underestimates the event. The satellite analysis diverges strongly from the others. Here, we chose the station data as the most reliable dataset with a long time series, more than 100 years for many stations.

Fig. 1
figure 1

Observed maximum 3-day averaged rainfall over coastal Texas January–September 2017 (mm/dy). a GHCN-D v2 rain gauges, b CPC 25 km analysis, c NOAA calibrated radar (August 25–30 only) and d NASA GPM/IMERG satellite analysis. From van Oldenborgh et al. (2017)

Due to the relatively small number of observed data points, some assumptions have to be made to extract the maximum information from the time series. These assumptions can later be checked in model data. They commonly are:

  • the statistics are described by an extreme value distribution, for instance generalised extreme value (GEV) function for block maxima or generalised Pareto distribution (GPD) for threshold exceedances; these depend on a position parameter μ or threshold u, a scale parameter σ and a shape parameter ξ (sometimes other distributions, such as the normal distribution, can be used),

  • the distribution only shifts or scales with changes in smoothed global mean surface temperature (GMST) and does not change shape,

  • global warming is the main factor affecting the extremes beyond year-to-year natural variability.

The first assumption can be checked by comparing the fitted distribution to the observed (or modelled) distribution, discrepancies are usually obvious. For temperature extremes, keeping the scale and shape parameters σ, ξ constant is often a good approximation. For precipitation and wind, we keep the dispersion parameter σ/μ and ξ constant, the index flood assumption (Hanel et al. 2009). This assumption can usually also be checked in model output. For many extremes, global warming is the main low-frequency cause of changes, but decadal variability, local aerosol forcing, irrigation and land surface changes may also be important factors. This will be checked later by comparing the results to those of climate models that allow drivers to be separated. For instance, increasing irrigation strongly depresses heat waves (Lobell et al. 2008; Puma and Cook 2010). In Europe, the strong downward trend in observed wind storm intensity over land areas appears connected to the increase in roughness (Vautard et al. 2019).

Often, the tail of the distribution is close to an exponential fall-off. If the change in intensity is close to independent of the magnitude, this implies that the probability ratio does not depend strongly on the magnitude of the event. In those common cases, the uncertainty associated with a preliminary estimation of the magnitude in a rapid attribution study does not influence the outcome much (Stott et al. 2004; Pall et al. 2011; Otto et al. 2018a; Philip et al. 2018a).

One exception is the distribution of the hottest temperature of the year, which typically appears to have an upper limit (ξ < 0). This limit increases with global warming. An example is shown in Fig. 2a, c (daily mean temperature at De Bilt, the Netherlands). Under the assumption of constant shape of the distribution, this threshold in the climate of 1900 was far below the observed value for 2018, so that formally the event would not have been possible then. However, this presumes the assumptions listed above are perfectly valid. As we cannot unequivocally show that this is the case, we prefer to state that the event would have been extremely unlikely or virtually impossible.

Fig. 2
figure 2

a, c Highest daily mean temperature of the year at De Bilt, the Netherlands (homogenised), fitted to a GEV that shifts with the 4-year smoothed GMST. a As a function of GMST and c in the climates of 1900 and 2018. b, d The same for the highest 3-day averaged precipitation along the US Gulf Coast fitted to a GEV that scales with 4-year smoothed GMST. From climexp.knmi.nl, (b, d) also from van Oldenborgh et al. (2017)

There are a few pitfalls to avoid in this analysis. In very incomplete time series, the varying amount of missing data may produce spurious trends, because extremes will simply not be recorded part of the time. For example, the maximum temperature series of Bikaner (India) has about 30% missing data in the 1970s but is almost complete in recent years. This gives a spurious increase in the probability of observing an extreme of a factor 100%/(100% − 30%) ≈ 1.4 that can be mistaken for an increase in the incidence of heat waves (van Oldenborgh et al. 2018).

A related problem may be found in gridded analyses: if the distance between stations is greater than the decorrelation scale of the extreme, the interpolation will depress the occurrence of extremes, so that a lower station density will give fewer extremes. An extreme example for this is given in Fig. 3, where the trend in May maximum temperature in India 1975–2016 in the CRU TS 4.01 dataset is shown to be based on hardly any station data at all and therefore unreliable. In contrast, reanalyses can produce extremes between stations and are based on many other sources of information. These are in principle therefore less susceptible to this problem than statistical analyses of observations. However, the quality of reanalyses varies greatly by variable and region (Angélil et al. 2016). We found in this case that the trends from a reanalysis in Fig. 3c are more reliable and in better agreement with statistical analyses of the Indian Meteorological Department (van Oldenborgh et al. 2018). Similar problems in depressed extremes and spurious trends were found in an interpolated precipitation analysis of Australia (King et al. 2013).

Fig. 3
figure 3

a Trend of May maximum temperature in South Asia 1979–2016 in the CRU TS 4.01 dataset. b Number of stations used to estimate the temperature at each grid point in CRU TS 4.01. c Trend of May maximum temperatures in the ERA5 reanalysis

There has been discussion on whether to include the event under study in the fit or not. We used not to do this to be conservative, but now realise that the event can be included if the event definition does not depend on the extreme event itself. If you analyse the Central England Temperature every year, an extreme is drawn from the same distribution as the other years and can be included. However, if the event is defined to be as unlikely as possible, the extreme value is drawn from a different distribution and cannot be included.

We can sometimes increase the amount of data available by spatial pooling. Figure 2b, d show a fit for precipitation data. In this case, the spatial extent of the events is smaller than the region over which they are very similar, so that we can make the additional assumption that the distribution of precipitation is the same for all stations. In this example, we show the annual maximum of 3-day averaged precipitation (RX3day) at 13 GHCN-D stations with more than 80 years of data and 1 apart along the US Gulf Coast. We verified that the mean of RX3day is indeed similar throughout this region. Despite the applied 1 minimum separation, the stations are still not independent, this is taken into account in the bootstrap that is used to estimate the uncertainties by using a moving block technique (Efron and Tibshirani 1998), giving about 1000 degrees of freedom. In spite of the diverse mechanisms for extreme rainfall in this area, the observations are fitted well by one GEV. The 2018 observation in Houston still requires a large extrapolation, so that we could only report a lower bound of about 9000 years for the return time. The trend in this extreme, deduced from the fit to the less extreme events in more than a century of observations along the Gulf Coast, is an increase of 18% (95% CI: 11–25%) in intensity (van Oldenborgh et al. 2017).

Whether trends can be detected in the observations depends strongly on the variable. Anthropogenic climate change is now strong enough that trends in heat extremes (e.g. Perkins et al., 2012) and short-duration precipitation extremes are usually detectable, for small-scale precipitation using spatial pooling (Westra et al. 2013; Vautard et al. 2015; Eden et al. 2016; Eden et al. 2018). Winter extremes are more variable but now also show significant trends (Otto et al. 2018a; van Oldenborgh et al. 2019). We hardly ever find significant trends in drought as the time scales are much longer and hence the number of independent events in the observations lower. Drought is also usually only a problem when the anomaly is large relative to the mean, which usually implies that it is also large relative to the trend, so the signal-to-noise ratio is poor.

5 Model evaluation

In order to attribute the observed trend to different causes, notably global warming due to anthropogenic emissions of greenhouse gases (partially counteracted by emissions of aerosols), we have to use climate models. Observations can nowadays often give a trend, but cannot show what caused the trend. Approximations from simplified physics arguments, such as Clausius-Clapeyron scaling for extreme precipitation, have often been found to overestimate (e.g. Hoerling et al., 2013b) or underestimate (e.g. Lenderink and van Meijgaard, 2010) the modelled and observed trends, so we choose not to use these for the individual events that we try to attribute.

However, before we can use these models, we have to evaluate whether they are fit for purpose. We use the following three-step model evaluation.

  1. 1.

    Can the model in principle represent the extremes of interest?

  2. 2.

    Are the statistics of the modeled extreme events compatible with the statistics of the observed extremes?

  3. 3.

    Is the meteorology driving these extremes realistic?

If a model ensemble fails, any of these tests it is not used in the event attribution analysis. Of course a ‘yes’ answer to these three questions is a necessary but not a sufficient condition. These requirements do not prevent the model from being unable to represent the proper sensitivity to human influence. However, in practice, this condition is hard to verify; hence, we rely on several methods and models. In our experience, the results of a single model should never be accepted at face value, as climate models have not been designed to represent extremes well. Even when the extreme should be resolved numerically, the sub-grid scale parameterisations are often seen to fail under these extreme conditions. Trends can also differ widely between different climate models (Hauser et al. 2017). If the variable of interest is a hydrological or other physical impact variable, it is also better to use multiple impact models (Philip et al. 2020b; Kew et al. 2021).

The first step is rather trivial: it does not make sense to study extreme events in a model that does not have the resolution or does not include the physics to represent these. For instance, only models with a resolution of about 25 km can represent tropical cyclones somewhat realistically (Murakami et al. 2015), so to attribute precipitation extremes on the US Gulf Coast, we need these kinds of resolutions (van der Wiel et al. 2016; 2017). One-day summer precipitation extremes are usually from convective events, so one needs at least 12-km resolution and preferably non-hydrostatic models. Studying these events in 200-km resolution, CMIP5 models do not make much sense (Kendon et al. 2014; Luu et al. 2018). A work-around may be to use a two-step process in which coarser climate models represent the large-scale conditions conducive to extreme convection and a downscaling step translates these into actual extremes. However, this is contingent on a number of additional assumptions and we have not yet attempted it. In contrast, midlatitude winter extremes are represented well by relatively coarse-resolution models (e.g. Otto et al., 2018a).

In order to compare the model output to observations, care has to be taken that they are comparable. If the decorrelation scale of the event is larger than the model decorrelation scale (often grid size), point observations are comparable to grid values, because the extreme event will then cover multiple grid cells with similar values and in this area a point observation gives practically the same result as the nearest model grid point. In effect, this translates to our first model validation criterion. Another way to make observations and model output commensurate is to average both over a large area, such as precipitation over a river basin. We try not to interpolate data as this reduces extremes by definition.

A straightforward statistical evaluation entails fitting the tail of the distribution to an extreme value distribution and comparing the fit parameters to an equivalent fit to observed data, preferably at the same spatial scale. Allowing for a bias correction, additive for temperature and multiplicative for precipitation, this gives two or three checks:

  • the scale parameter σ or the dispersion parameter σ/μ corresponding to the variability of the extremes (relative to the mean),

  • the shape parameter ξ that indicates how fat or thin the tail is and

  • possibly the trend parameter.

The last check should only be used to invalidate a model if the discrepancy in trends is clearly due to a model deficiency, e.g. if other models reproduce the observed trends well or if there is other evidence for a model limitation. Otherwise, it is better to keep the model and evaluate the differences in the synthesis step (Section 7).

In our experience, for small-scale extremes, this often eliminates half the models as not realistic enough. This is often due to problems in the sub-gridcell parameterisations of the model. For instance, the MIROC-ESM family of models has unrealistic maximum temperatures in deserts well above 70 C. Another problem we found is that a large group of models have an apparent cut-off on precipitation of 200–300 mm/dy (van Oldenborgh et al. 2016), see Fig. 4. As climate models are developed further, there will likely be more emphasis on simulating extremes correctly, which will potentially allow more models to be included in the synthesis step.

Fig. 4
figure 4

Return time plots of extreme rainfall in Chennai, India, in two CMIP5 climate models with ten ensemble members (CSIRO-Mk3.6.0 and CNRM-CM5) and an attribution model (HadGEM3-A N216) showing an unphysical cut-off in precipitation extremes. The horizontal line represents the city-wide average in Chennai on 1 December 2015 (van Oldenborgh et al. 2016)

There are other cases where the discrepancies are not unphysical but the climate model results are still not compatible with observations. Heat waves provide good examples. In the Mediterranean, the scale parameter σ is about twice that in the observations in the models available to us with the required resolution, so that no model could be found that represents the average maximum temperature over the area of the 2017 heat wave realistically (Kew et al. 2019). In France, the HadGEM3-A model does not reproduce the much less negative shape parameter ξ that enables large heat waves (Vautard et al. 2018). The same holds for many other climate models (e.g. Vautard et al., 2020). As an example of wrong trends, heat waves over India of CMIP5 models are incompatible with the observed trends due to missing or misrepresented external forcings: an underestimation of aerosol cooling and the absence of the effects of increased irrigation in these models (van Oldenborgh et al. 2018). The power of these tests depends strongly on the quality and quantity of the observations. Bad quality observations can cause a good model to be rejected and natural variability in the observations can hide structural problems in a model.

Finally, ideally, we would like to check whether the extremes are caused by the same meteorological mechanisms as in reality. This can involve checking a large number of processes, making it difficult in practice. However, investigations increase confidence in the model’s ability to simulate extremes for the right reasons. King et al. (2016) demanded that ENSO teleconnections to Indonesia were reasonable. More generally, Ciavarella et al. (2018) verify all SST influences. For the US Gulf Coast, van der Wiel et al. (2017) showed that in both observations and models, about two-thirds of extreme precipitation events were due to landfalling tropical storms and hurricanes and one-third due to other phenomena, such as the cut-off low that caused flooding in Louisiana in 2016. There have also been attempts to trace back the water in extreme precipitation events (Trenberth et al. 2015; Eden et al. 2016), but these often found that the diversity of sources was too large to constrain models (Eden et al. 2018).

Quantitative meteorological checks are still under development (Vautard et al. 2018). It is therefore still possible that the climate models we use generate the right extreme statistics for the wrong reasons.

The result of the model evaluation is a set of models that seem to represent the extreme event under study well enough. We discard the others and proceed with this set, assuming they all represent the real climate equally well. If there are no models that represent the extreme well enough, as in the case of the 2017 Sri Lanka floods, we either abandon the attribution study or publish only the observational results as a ‘quick-look’ analysis.

6 Modelled trends and differences with counterfactual climates

We now come to the extreme event attribution step originally proposed by Allen (2003): using climate models to compute the change in probability of the extreme event due to human influence on climate. There are two ways to do this. The most straightforward one, proposed by Allen (2003), is to run the climate model twice, once with the current climate and once with an estimate of what the climate would have been like without anthropogenic emissions, the counterfactual climate. With a large enough ensemble, we can just count the occurrences above a threshold corresponding to the event of interest to obtain the probabilities. Alternatively, we estimate the probabilities with a fit to an extreme value function. To account for model biases, we set the threshold at the same return time in the current climate as was estimated from the observed data rather than the same event magnitude. Bias correcting the position parameter was found to be problematic, as the event can end up above the upper limit that exists for some variables (notably heat).

The second way is to use transient climate experiments, such as the historical/RCP4.5 experiments for CMIP5, and analyse these the same way as the observations. This assumes that the major cause of the trends from the late nineteenth century until now in the models is due to anthropogenic emissions of greenhouse gases and aerosols, neglecting natural forcings: solar variability and volcanic eruptions. Volcanic eruptions have short impacts on the weather and therefore do not contribute much to long-term trends; the effects of changes in solar activity on the weather are negligible for most of the world in recent history (Pittock 2009).

Simulations starting at the end of the nineteenth century are not available in the case of regional climate simulations downscaling the CMIP simulations. Regional simulations usually start in 1950 or 1970. As this includes most of the anthropogenic heating, we can extrapolate the trend to a starting date of 1900. The interpretation is complicated by many regional simulations not including aerosol and land use changes.

For each of these two methods, there are different framings possible. We use two framings: models in which the sea surface temperature (SST) either is prescribed or is internally generated by a coupled ocean model. This makes a difference when the local weather is influenced by SSTs, for instance through an ENSO teleconnection. An example is the Texas drought and heat of 2011, where the drying due to La Niña exacerbated the temperature response to the anomalous circulation (Hoerling et al. 2013a). There are also regions where prescribing SST gives wrong teleconnection trends (e.g. Copsey et al., 2006), in other regions they have a more realistic climatology than coupled models. However, we find that often differences between these two framings are smaller than the noise due to natural variability and systematic differences between different climate models. As mentioned in Section 3, other framings in which more of the boundary conditions are fixed are also in use.

This step gives for every model experiment studied an estimate for the probability ratio (PR) and for the change in intensity ΔI due to anthropogenic global warming, plus uncertainty estimates due to limited sampling of the natural variability. These uncertainty estimates do not include the uncertainties due to model formulations and framing. The spread across multiple models and framings gives an indication of these and is used in the next step.

7 Hazard synthesis

Next, we combine all the information generated from the observations and different well-evaluated models into a single attribution statement for the physical extreme, the hazard in risk analysis terminology. This step follows from the inclusion of observational trends and from the experience that a multi-model ensemble is much more reliable than a single model (Hagedorn et al. 2005). The best way to do this is still an area of active research. Our procedure is first to plot the probability ratios and changes in intensity including uncertainties due to natural variability. This shows whether the model results are compatible with the estimates from observations and whether they are compatible with each other. As model experiments may have multiple ensemble members or long integrations, and the observations are based on a single earth and relatively short time series, the statistical uncertainties due to natural variability are generally smaller in the model results. The large uncertainties in the observed trend reflect the fact that changes in many extremes are only just emerging over the noise of natural variability. This explains some apparent discrepancies between models and observations, for instance the ‘East Africa paradox’ that models simulate a wetting trend whereas the observations show drying: even without taking decadal variability into account the uncertainties around the observed trend easily encompass the modelled trends (Uhe et al. 2018; Philip et al. 2018b).

If all model results are compatible with each other and with the observations (as shown by χ2/dof ≈ 1), the spread is mainly due to natural variability and a simple (weighted) mean can be taken over all results to give the final attribution result. An example is shown in Fig. 5a. Another possibility is that the models disagree, as in Fig. 5b: one model shows an 8% increase compatible with Clausius-Clapeyron scaling, but another model has a two times larger increase (the third model did not pass the model evaluation step). In this case, model spread is important and the uncertainty on the attribution statement should reflect this.

Fig. 5
figure 5

Synthesis plots of a the probability ratio of extreme 3-day averaged precipitation in April–June averaged over the Seine basin (Philip et al. 2018a), b the intensity of extreme 3-day precipitation on the Gulf Coast (van Oldenborgh et al. 2017) and c the probability ratio for changes in wind intensity over the region of storm Friederike on 18 January 2018 Vautard et al. (2019). Observations are shown in blue, models in red and the average in purple

These synthesis plots can also be used to illustrate that essential processes are missing or misrepresented in the models, as in Fig. 5c, showing the probability ratios for a wind storm like Friederike on 18 January 2018. It is clear that the observed strong negative trend is incompatible with the modelled weak positive trends (Vautard et al. 2019), so we cannot provide an attribution statement.

If possible, the synthesis can also include an outlook to the future, based on extrapolation of trends to the near future or model results. This is frequently requested by users of the attribution results.

8 Vulnerability and exposure analysis

It is well documented in disasters literature that the impacts of any hazard, in this case hydrometeorological hazards, are due to a combination of three factors (United Nations 2009):

  1. 1.

    the hydrometeorological hazard: process or phenomenon of atmospheric, hydrological or oceanographic nature that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage,

  2. 2.

    the exposure: people, property, systems or other elements present in hazard zones that are thereby subject to potential losses, and

  3. 3.

    the vulnerability: the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard.

The interplay of these three components determines whether or not a disaster results (Field et al. 2012). As we usually choose to base the event trigger and definition on severe societal impacts of a natural hazard, rather than the purely physical nature of the extreme event, it is critical to also analyse trends in vulnerability and exposure in order to put the attribution results in context. Our analyses therefore include drivers that have changed exposure as well as vulnerability (which includes the capacity to manage the risk).

Due to the complexity of assessing changes in vulnerability and exposure as well as the persistent lack of standardised, granular (local or aggregated) data (Field et al. 2012), usually only a qualitative approach is feasible (cf. Cramer et al., 2014). This includes (1) a review and categorisation of the people, assets and systems that affected or were affected by the physical hazard extreme (done partly already in the event definition stage), (2) a review of existing peer-reviewed and select grey literature that describes changes to the vulnerability or exposure of the key categories of people, assets or systems over the past few decades and (3) key informant interviews with local experts. These results are then synthesised into an overview of key factors which may have enhanced or diminished the impacts of the extreme natural hazard. As the methodology associated with this analysis has advanced, the integration of this information into attribution studies has progressed from an introductory framing to incorporating findings in synthesis or conclusions sections to most recently including a stand-alone vulnerability and exposure section (Otto et al. 2015b; van Oldenborgh et al. 2017; Otto et al. 2018a; van Oldenborgh et al. 2020).

Analysis of vulnerability and exposure can significantly impact the overall narrative resulting from an attribution study as well as subsequent real-world actions which may result. For example an attribution study of the 2014–2015 drought in São Paulo, Brazil (Otto et al. 2015b) found that, contrary to widespread belief, anthropogenic climate change was not a major driver of the water scarcity in the city. Instead, the analysis showed that the increase of population of the city by roughly 20% in 20 years, and the even faster increase in per capita water usage, had not been addressed by commensurate updates in the storage and supply systems. Hence, in this case, the trends in vulnerability and exposure were the main driver of the significant water shortages in the city.

Similarly, a study of the 2015 drought in Ethiopia also found that while the drought was extremely rare, occurring with a lower bound estimate of once every 60 years, there was no detectable climate change influence. However in contrast to the São Paulo example, Ethiopia overall has seen drastic reduction in its population’s vulnerability, with the number of people living in extreme poverty dropping by 22 percentage points between 2000 and 2011 (World Bank 2018). This indicates that although roughly 1.7 million people were impacted in 2015 with moderate to acute malnutrition, the worst effects of the drought may have been avoided (Philip et al. 2018b). Just like trends in the hazard, trends in vulnerability and exposure can go both ways.

Vulnerability and exposure trends also play an important role in contextualising attribution results when a climate change influence is detected. For example, the 2016 study of flooding in France underscores the complexity and double jeopardy faced by water system managers in a changed climate, even when vulnerability and exposure trends are broadly managed well. In this case, a multi-reservoir system exists to reduce flood and drought risks throughout the year and reservoir managers follow daily retention targets to manage the system. In anticipation of entering the dry season, where reservoirs are needed to regulate shipping and drinking water to 20 million people, the system was 90% full and unable to absorb excess river runoff. In addition to the extreme rainfall and its increased likelihood due to climate change, the late occurrence of the event and the reservoir regime also played an important role in the sequence of events (Philip et al. 2018a).

The attribution study of the 2003 European heatwaves, combined with other studies on vulnerability and exposure during the same event, underscores the deadly impact of the combination of climate trends with an unprepared, vulnerable population, emphasising the need to establish early warning systems and adapt public health systems to prepare for increased risks (Stott et al. 2004; Kovats and Kristie 2006; Fouillet et al. 2008; de’ Donato et al. 2015). Similarly, the 2017 study of Hurricane Harvey underscores how rising risk emerges from a significant trend in extreme rainfall combined with vulnerability and exposure factors such as ageing infrastructure, urban growth policies and high population pressure (van Oldenborgh et al. 2017; Sebastian et al. 2019).

Despite the importance of the vulnerability and exposure analysis in ensuring a comprehensive overview of attribution results, we have encountered a few challenges in incorporating it into attribution studies. Firstly, it is usually at least partly qualitative and thus requires extensive consultation to ensure that the final summary is comprehensive, accurate and not biased by preconceptions or political agendas. There is a lack of standardised indicators that can be used to create a common, quantitative metric across studies. Finally, within attribution studies, vulnerability and exposure sections are often viewed as excess information, making it a prime location to reduce paper lengths in order to meet journal restrictions. This in turn limits the contributions and advancements in this area of analysis.

9 Communication

As mentioned in the introduction, we consider three groups of users of the results of our studies: people who ask the question, the adaptation community and participants in the mitigation debate. Tailored communication of the results of an attribution study is an important final step in ensuring the maximum uptake of the study’s findings. WWA from the beginning has contained communication experts and social scientists as recommended by, e.g. Fischoff (2007). We found that the key audiences that are interested in our attribution results should be stratified according to their level of expertise: scientists, policy-makers and emergency management agencies, media outlets and the general public. Over the years, we developed methods to present the results in tailored outputs intended for those audiences.

In presenting results to the scientific community, a focus on allowing full reproducibility is key. We always publish a scientific report that documents the attribution study in sufficient detail for another scientist to be able to reproduce or replicate the study’s results. If there are novel elements to the analysis, the paper should also undergo peer review. We have found that this documentation is also essential to ensure consistency within the team on numbers and conclusions. We recommend that all datasets used are also made publicly available (if not prohibited by the restrictive data policies of some national meteorological services).

We have found it very useful to summarise the main findings and graphs of the attribution study into a two-page scientific summary aimed at science-literate audiences who prefer a snapshot of the results. Such audiences include communication experts associated with the study, science journalists and other scientists seeking a brief summary.

For policy-makers, humanitarian aid workers and other non-scientific professional audiences, we found that the most effective way to communicate attribution findings in written form are briefing notes that summarise the most salient points from the physical science analysis (the event definition; past, present and possibly future return period; role of climate change), elaborate on the vulnerability and exposure context and then provide specific recommended next steps to increase resilience to this type of extreme event (Singh et al. 2019). These briefing notes can also be accompanied by a bilateral or roundtable dialogue process between these audiences and study experts in order to expound on any questions that may arise. The in-person follow-up is often critical to help non-experts interpret and apply the scientific findings to their contexts. For example, return periods, which are the standard for presenting probabilities of an extreme event to scientists and engineers, are often misunderstood and require further explanation and visualisation. This audience often requires the information to be available relatively quickly after the event (Holliday 2007; de Jong et al. 2019).

In order to reach the general public via the media, a press release and/or website news item can be prepared that communicates, in simplified language, the primary findings of the study. In addition to the physical science findings, these press releases typically provide a very brief, objective description of the non-physical science factors that contributed to the event. In developing this press piece, study authors need to be as unbiased as possible, for instance not emphasising lower bounds as conservative results (Lewandowsky et al. 2015). Consideration for how a press release will be translated into the press is also important. We learned to avoid the phrase ‘at least’ to indicate a lower boundary of a confidence interval, as it is almost always dropped after which the lower boundary is reported as the most likely result, as in the press release of Kew et al. (2019). Van der Bles et al. (2018) found that a numerical confidence interval (‘X (between Y and Z)’) does not decrease confidence in the findings, but verbal uncertainty communication (e.g. the phrase ‘with a large uncertainty’) does, both relative to not mentioning the uncertainties. Ensuring that study authors, especially local authors, are available for interviews can help to ensure that additional questions on study findings are clarified as accurately as possible.

It should also be noted that despite these general lessons learned on communication to general media in Western countries, effective communication of attribution findings is context-specific. This was shown by research in the context of attribution studies by the WWA consortium in East Africa and South Asia (Budimir and Brown 2017). This study also provides evidence-based advice, including basic phrases that are most likely to be understood for specific stakeholder groups, but also cautions about the context-specific nature of this advice, and the need to work with communications advisers and representatives of stakeholder groups for specific contexts to ensure effective communication.

When communicating with various audiences, we also found it useful to consider research on communication strategies for analogous types of climate messages. Padgham et al. (2013) specifically looked at vulnerable communities in developing countries and underscored the importance of using locally relevant terms and the need for knowledge co-generation and social learning between researchers and communities. WWA partner, the Red Cross Red Crescent Climate Centre relies on the global Red Cross Red Crescent network of national societies in over 190 countries to quickly get local input on the extreme event in question, including their input on any press releases that will go out sharing the findings of the studies. For example, in Kenya, when studying the impacts of 2016/17 drought, we sought inputs from the Kenya Red Cross on how best to share the findings of the attribution research. For this particular context, it was important to provide the attribution results (which did not indicate anthropogenic climate change played a role in the lack of rainfall) in the context of the seasonal forecast for the upcoming rainy season so that decision makers could compare and differentiate between the short-term rainfall variability and potential role of climate change.

Scienseed (2016) address issues relating to jargon, complexity and impersonality, as well as the need to be aware of mental models and biases through which information may be filtered. Martin et al. (2008) show lessons on communication of forecast uncertainty, also points to the need to understand users’ interpretations and invest in communication that is both clear and consistent from a scientific perspective, and tuned to how it will be interpreted.

In recent years attribution, experts are also exploring less traditional ways of communicating attribution findings. This includes the use of a ‘serious game’ to simulate the process a climate model undergoes to produce the statistics used in extreme event attribution (Parker et al. 2016). A modified version of this game has been used by WWA with stakeholders, including scientists, disaster managers and policymakers, in India and Ethiopia to explain the findings of studies conducted in both these countries.

10 Conclusions

In this paper, we have summarised the lessons we learned in doing rapid and slow attribution studies of extreme weather and climate events following guidance from the scientific literature. We have developed an eight-step procedure that worked well in practice, based on the following lessons we learned along the way.

  1. 1.

    We need a way to decide which extremes to investigate and which not, given finite resources. In our case, human impacts were the main driver.

  2. 2.

    As many others, we found that the results can depend strongly on the event definition, which should be carefully defined. An WWA event definition aims to characterise the hazard in a way that best matches the most relevant impact of the event, yet also enables effective scientific analysis.

  3. 3.

    A key step in our analysis is the analysis of changes in probability of this extreme in the observed record. This also allows the estimation of the probability in the current climate, which is often requested. It also provides benchmarks for quantitative model evaluation and trend estimates.

  4. 4.

    We need large ensembles or long experiments of multiple climate models and only use the models that represent the extreme under study in agreement with the observed record.

  5. 5.

    We need to pay attention that the key global and local forcings are taken into account in the different models to give realistic total trends. Local factors such as aerosol forcings, land use changes and irrigation can be just as important as greenhouse gases. SST-forced models can give different results from coupled models.

  6. 6.

    A careful synthesis of the observational and model results is needed, keeping the uncertainties due to natural variability and model uncertainties separate.

  7. 7.

    Results of the physical hazard analysis should be paired with a description of the vulnerability and exposure and trends in these factors, so trends in impacts are not attributed solely to trends in the hazard, even in the case of a strong attribution to climate change of the physical hazard.

  8. 8.

    We need to communicate in different ways to different audiences, but always should write up a full scientific text as a basis for communication, which also enables replicability and hence trust.

Taking these key points into account, we found that often, we could find a consistent message from the attribution study in the imperfect observations and model simulations. This we used to inform key audiences with a solid scientific result, in many cases quite quickly after the event when the interest is often highest. However, we also found many cases where the quality of the available observations or models was just not good enough to be able to make a statement on the influence of climate change on the event under study. This also points to scientific questions on the reliability of projections for these events and the need for model improvements.

Over the last 5 years, we found that when we can give answers, these are useful for informing risk reduction for future extremes after an event, and in the case of strong results also to raise awareness about the rising risks in a changing climate and thus the relevance of reducing greenhouse gas emissions. Most importantly, the results are relevant simply because the question is often asked—and if it is not answered scientifically, it will be answered unscientifically.