Environ. Res. Lett. 15 (2020) 094077 https://doi.org/10.1088/1748-9326/aba3d4 Environmental Research Letters

LETTER Human contribution to the record-breaking June and July 2019 OPENACCESS heatwaves in Western RECEIVED 31 December 2019 Robert Vautard1, Maarten van Aalst2, Olivier Boucher1, Agathe Drouin3, Karsten Haustein4, 5 6 4 3 3 REVISED Frank Kreienkamp , Geert Jan van Oldenborgh , Friederike E L Otto , Aur´elien Ribes , Yoann Robin , 18 June 2020 Michel Schneider3, Jean-Michel Soubeyroux3, Peter Stott7, Sonia I Seneviratne8, Martha M Vogel8 ACCEPTEDFORPUBLICATION 9 8 July 2020 and Michael Wehner 1 PUBLISHED Institut Pierre-Simon Laplace, , 28 August 2020 2 University of Twente and Red Cross Red Crescent Climate Centre, The 3 M´et´eo-France, Toulouse, France 4 Original content from Environmental Change Institute, University of Oxford, Oxford, 5 this work may be used Deutsche Wetterdienst, Offenbach, under the terms of the 6 KNMI, de Bilt, The Netherlands Creative Commons 7 U.K. Met. Office, Exeter, United Kingdom Attribution 4.0 licence. 8 ETH Zürich, Zürich, Any further distribution 9 Lawrence Berkeley National Laboratory, Berkeley, United States of America of this work must maintain attribution to E-mail: [email protected] the author(s) and the title of the work, journal Keywords: , , extreme event attribution citation and DOI. Supplementary material for this article is available online

Abstract Two extreme heatwaves hit Western Europe in the summer of 2019, with historical records broken by more than a degree in many locations, and significant societal impacts, including excess mortality of several thousand people. The extent to which human influence has played a role in the occurrence of these events has been of large interest to scientists, media and decision makers. However, the outstanding nature of these events poses challenges for physical and statistical modeling. Using an unprecedented number of climate model ensembles and statistical extreme value modeling, we demonstrate that these short and intense events would have had extremely small odds in the absence of human-induced climate change, and equivalently frequent events would have been 1.5 ◦C to 3 ◦C colder. For instance, in France and in The Netherlands, the July 3-day heatwave has a 50–150-year return period in the current climate and a return period of more than 1000 years without human forcing. The increase in the intensities is larger than the global warming by a factor 2 to 3. Finally, we note that the observed trends are much larger than those in current climate models.

1. Introduction more than 40 stations experienced record daily maximum temperatures for June. In Austria, The Two record-breaking heatwaves struck Western Netherlands, Germany and even Europe the whole Europe in June and July 2019. These heatwaves were month of June 2019 was the warmest ever recor- recognized as the deadliest disaster of 2019 in the ded (https://climate.copernicus.eu/record-breaking- world (CRED 2020). A first event took place in the last temperatures-june). Such extreme heatwaves usually week of June 2019. The event broke several historical occur in mid-summer, when they have less impact on records at single locations, including France, Switzer- school days and professional activities than in June or land, Austria, Germany, the , Italy September. In France, due to the heat in June 2019, and Spain. In particular, the all-time temperature the government decided to postpone one national record for any single station in metropolitan France school exam, inducing organizational challenges at (old record 44.1 ◦C, ) was broken on large scale. In the hottest areas of Europe, a number June 28 by almost 2 ◦C with a new record of 46.0 ◦C, of took place, and train tracks were damaged established near the city of Nˆımes. In Switzerland, in Switzerland.

© 2020 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 15 (2020) 094077 R Vautard et al

Figure 1. Rank of annual maximum temperatures observed in Europe in 2019 compared to 1950–2018, based on the E-OBS data set (Haylock et al 2008, version 20.0). Figure 2. ERA5 500 hPa (upper panels), for 27 June 2019 (left) and 25 July 2019 (right), together with back-trajectories calculated using the NOAA HYSPLIT online trajectory tool (https://www.ready.noaa.gov/hypub-bin/trajtype.pl); back-trajectories are calculated to arrive at 3 altitudes A second short (3–4 d) record-breaking heatwave (1000 m, 2000 m and 3000 m) to sample the heated low struck Western Europe and at the end atmosphere. of July 2019. Records were broken again, albeit in different areas. In France, the highest amplitudes of the heatwave were found in Northern and Central parts of the country, with records of either 1947 or HYSPLIT back-trajectories (figures 2(b) and (d)). 2003 broken by a large departure on July 25. For Soil conditions across Europe were not anomalously instance, the historical record of Paris (Station Paris- dry prior to the June event, which rules out a large Montsouris) of 40.4 ◦C became 42.6 ◦C and a tem- warming amplification by soil-atmosphere feedbacks. perature of 43.6 ◦C was measured in the Paris sub- By contrast, the July heatwave was accompanied by urbs. In and the Netherlands for the first severe conditions in areas such as France, time ever temperatures above 40 ◦C were observed. parts of the Netherlands and Germany, which prob- In Germany, the historical record of 40.3 ◦C was ably contributed to heat development given that dry surpassed at 14 stations, with one station reaching soils have been shown to cause an additional tem- 42.6 ◦C (Lingen). In the UK, a new highest ever max- perature increase at regional scales due to land- imum temperature of 38.7 ◦C was measured in Cam- atmosphere feedbacks (e.g. Seneviratne et al 2010). bridge. Further west, where the heatwave was slightly Interestingly, this may also point to a link between the less intense, the record from 1932 (35.1 ◦C) at the his- two events, since the dry soils prior to the July event toric Oxford Radcliffe Meteorological Station (con- were largely the result of the June event. A similar tinuous measurements for more than 200 years; Burt mechanism played a role in the twin 2003 June and and Burt 2019) was broken by more than one degree, August heatwaves in Europe, with the June heatwave with a new record maximum temperature of 36.5 ◦C. likely enhancing the intensity of the August heat- These high temperatures caused hundreds of extra wave because of its effect on soil drying (Seneviratne deaths in Europe (see section 5). et al 2012). Taking into account both episodes, the spa- We present here the results of an attribution ana- tial extent of broken historical records is large and lysis following the same methodology used in previ- includes most areas of France, the Benelux, Switzer- ous analyses (e.g. Otto et al 2018, Philip et al 2018, land, Germany, the Eastern U.K. and Northern Italy Kew et al 2019). We also refer to these studies and van (figure 1). A few days after each of the events, reports Oldenborgh et al (2019) and Vautard et al (2019a) for of attribution to human influences were made (van a detailed explanation of methods and models. Oldenborgh et al 2019, Vautard et al 2019a). In this article, we collect these results in a single study to draw 2. Event definition, observations and common conclusions. trends Both heatwaves occurred due to a ridge across western Europe, together with a low-pressure sys- In both cases, we use an event definition that illus- tem developing offshore the Iberian peninsula, as trates potential impacts on human health, by com- shown in figures 2(a) and (c). These weather pat- bining both daytime and nighttime heat and the per- terns induced intense advection of hot air from North sistence of the episode as multi-day events have been Africa across Spain to France as shown by the NOAA shown to have disproportionately larger health risks

2 Environ. Res. Lett. 15 (2020) 094077 R Vautard et al in Europe (D’Ippoliti, 2010). For the June case we yearly maximum. The trend in observed series is then defined the event as the highest 3-day averaged daily quantified using the properties of the fit of a gen- mean temperature for the month of June each year eralized extreme value (GEV) analysis with a cov- (TG3x-Jun) and for the July case we used the all-year ariate (smoothed Global Mean Surface Temperature, 3-day maximum (TG3x). The time span of the indic- GMST) representing an indicator of climate change ator almost corresponds or exceeds the length of the (from anthropogenic and natural factors) on the pos- heatwave period. While the 3-day average maximum ition parameter, keeping the scale and shape paramet- is slightly lower than the single day maximum, we are ers constant. expecting it to be more sensitive to global warming For extreme heat, the GEV has a negative shape (Tebaldi and Wehner 2018). parameter, which describes an upper bound to the For the June case, the analysis is limited to distribution. This bound is however increased by France where it was most intense while for the July global warming. If the temperature in 2019 is above case it is extended to several European countries: the bound in 1900, the probability of the event occur- France, Germany, The Netherlands and the U.K. ring without the warming trend is zero and the prob- These are countries in which a number of temperat- ability ratio (PR) formally infinite, subject to the ure records were broken and data were readily avail- assumptions made and sampling uncertainties. Res- ability through study participants or public websites. ults for each station are shown in table 1. The locations considered are single weather stations In June, as observed in France at the country scale, shown in supplementary table 1 (available online the exceedance of observed TG3x-Jun has a current- at stacks.iop.org/ERL/15/094077/mmedia). In both climate return period of 30 year (15 year to 200 year) cases we also used the average over metropolitan (table 1). This is roughly 180 times more than it would France as obtained from the E-OBS data base (Hay- have been around 1901 (at least 12 times more). The lock et al 2008). It is close to the value of the offi- increase in TG3x-Jun since 1901 is estimated to be cial French thermal index (also used), which aver- 4.0 ◦C (3.0 ◦C to 5.2 ◦C). This implies a much higher ages temperature over 30 sites well distributed over warming trend in France in June hot extremes com- the metropolitan area and is used to characterize heat- pared to that of the average European land sum- waves and cold spells at the scale of the country. mer temperature, which has warmed by about two The rest of the analysis is based on a set of 6 indi- degrees. For the station of Toulouse,similar results are vidual weather stations, with the purpose to make the found. analysis more concrete for effects at local scale, which In July, the change in intensity for similarly likely is the scale relevant for impacts, and also some of heatwaves varies between 2 ◦C and 3.5 ◦C depending the selecte stations had records with a long history. on the location. The return periods range from about We selected the stations based on the availability of 8 years in Oxford to 80 years in . For the met- data, the relevance to the heatwave, their series length ropolitan France average, best estimates of the return (at least starting in 1951) and avoidance of urban periods are of the order of 130 years, even taking the heat island and irrigation cooling effects, which res- trend into account. In France, Benelux and Germany ult in non-climatic trends. The locations considered the return periods for individual stations are relatively (Toulouse for June, and Lille-Lesquin, de Bilt, Cam- similar (60–80 years). In Germany for the selected sta- bridge, Oxford, Weilerswist-Lommersum for July) all tion we find a return period of 12 years. This relatively witnessed a historical record both in daily maximum low return period could be due to the fact that the and in 3-day mean temperature (apart from Oxford station is located slightly on the eastern edge of the and Weilerswist-Lommersum where only daily max- affected region and the core event was shorter than imum temperatures set a record). Further, the selec- 3 d. In the U.K., return periods are shorter because ted stations are either the nearest station with a long the event was in fact shorter than 3 d and 3-day aver- enough record to where the study authors reside, or ages there mix hot temperatures with cooler ones. As representing a national record. Most of these daily seen in table 1, uncertainties on the return period are temperature time-series have been quality controlled, very large which leads to similarly large uncertainties and do not exhibit major homogeneity breaks at for the PRs with many cases where an upper bound the monthly time-scale. However, no homogeniza- is infinite. In a few cases, the best fit also gives zero tion procedure is applied, as homogenization of daily probability in 1900 thus only a lower bound can be time-series remains a challenging task (Mestre et al given. 2011). As a consequence, breaks related to changes in the measurement procedure can still affect these data, 3. Models and their evaluation and in particular observed trends. There is a clear trend in observed annual values The observations give a trend, but do not allow to of the event indicators in each case (see e.g. supple- attribute the trend to a cause in the traditional Pearl mentary figure 1 for the July case stations with TG3x), interpretation (Pearl 1988, Hannart et al 2016). For and the 2019 values represent a large excursion away the attribution analysis we used a large set of 8 cli- from the average indicator value which is already a mate model ensembles including the multi-model

3 Environ. Res. Lett. 15 (2020) 094077 R Vautard et al

Table 1. Statistical quantities linked to the trend in the observed values of the indicator. Location Value 2019 (◦C) Return Period 2019 (Year) Proba-bility Ratio Change in intensity (◦C)

June case TG3x-Jun France Avg. M´et´eo-Fr: 27.5 30[15–200] >12 4.0[3.0–5.2] Toulouse 30.1 54[16–700] >10 4.3[2.9–6.0] July case TG3x France Avg. E-OBS: 28.2 134 [>30] >5 2.5 [1.5–3.4] M´et´eo-Fr: 28.7 Lille Lesquin 29.1 78 [>20] >20 3.5 [2.3–4.6] De Bilt 28.0 60[20–1400] >60 2.9 [2.0–3.7] Cambridge BG 26.0 28 [11–200] 250[9—∞] 2.3 [1.4–3.4] Oxford 25.0 7.7 [4.6–16] 12[5–290] 2.1 [1.3–2.9] Weilerswist- Lommersum 28.7 12 [6–60] 430 [18—∞] 3.4 [2.2–4.9] ensembles EURO-CORDEX and CMIP5, single- for area-averaged heatwaves in the eastern Mediter- model ensembles from the CMIP5 and CORDEX ranean (Kew et al 2019). We found no evidence of generation (EC-EARTH, RACMO), specific attribu- atmospheric dynamics biases, and the cause of the tion ensembles (HadGEM3-A, weather@home) as overestimation of variability is still unknown (see also well as two single-model ensembles from the CMIP6 Leach et al 2020). generation (IPSL-CM6-LR and CNRM-CM6.1) that Hence, we are formally left for the present analysis were available at the date of study. Supplementary with no real suitable ensemble to use for the attribu- table 2 summarizes the characteristics of the model tion (though we did not check the suitability of each ensembles and references. Note that one of the model single CMIP5 or EURO-CORDEX models and can- ensembles, CNRM-CM6.1, was not used in the June not exclude that some might be suitable for both para- case. Extraction of station points is done using a meters). Given this, we decided not to give a synthesis nearest neighbour method unless specified otherwise. result drawn from observations and models as in pre- As the grid spacing is smaller than the decorrelation vious studies but still proceed with analyzing all mod- scale for heatwaves the details do not make a differ- els, noting that the results are only indicative at best ence. when drawing conclusions. To evaluate these models we test whether the stat- In the July case (see supplementary table 2), the istics of extreme heat in these models are consistent same conclusions hold regarding models skill as in with the observed statistics. The test consists of fit- our analysis of the June heatwave. Models have a ting the models to the same GEV distribution as in the too high variability and hence overestimate the scale observations and comparing the scale (σ) and shape parameter, sometimes by a large amount (factor 1.5 (ξ) parameters of the fits to the model data with the to 2.5). This is particularly marked for the France parameters of the fits described in the section obser- average. However, HadGEM3-A, EC-EARTH, IPSL- vational analysis. We do not consider the position CM6-LR and CNRM-CM6.1 appear to have a reas- parameter (µ) as biases in this parameter can easily be onable departure from observations. For the other corrected without affecting the overall results. All res- models the 95% confidence intervals on the scale ults from this comparison are shown in supplement- parameter does not overlap with the confidence inter- ary figure 2. val on the scale parameter from the observations. For In the June case, The EURO-CORDEX and individual stations studied here, shape parameters CMIP5 multi-model ensembles and IPSL-CM6A-LR are simulated within observation uncertainties. The models overestimate the scale parameter by about discrepancy for the scale parameter is also reduced 50%, weather@home by a factor two. EC-Earth and except for weather@home where variability remains the dependent RACMO model would pass a test based too high. on this parameter for both the county scale and the Toulouse site. The shape parameter is generally neg- 4. Attribution ative for heatwaves, but in France the parameter is less negative than in most regions (Vautard et al The attribution was carried out using different meth- 2019b, see their figure 4). Although the uncertainty ods for each model ensemble, and also between the in shape parameter estimates can be large, models’s June and July cases, due to the nature of the sim- shape parameters are collectively more negative than ulations and the availability of methodologies and observations’. This induces a positive bias in thePR. production teams in real time. For transient sim- Taking the two tests together we find that barely ulations and the July case (EC-EARTH, RACMO, any ensemble passes the test that the fit parameters EURO-CORDEX, CMIP5, HadGEM3-A, IPSL-CM6- have to be compatible with the parameters describ- LR, CNRM-CM6.1), estimations are obtained from ing the observations, in line with issues encountered a GEV fit with the smoothed GMST covariate as an

4 Environ. Res. Lett. 15 (2020) 094077 R Vautard et al indicator of climate change and human activities. The one specific city, Toulouse). For both the average over training period for the fit is taken as the largest pos- France and the Toulousestation we find that the prob- sible period between 1900 and 2018 in order not to ability has increased by at least a factor five (excluding include the extreme event itself, as it would lead to the model with very strong bias in variability). How- a selection bias (see supplementary Material for the ever, observations indicate a much higher factor of a reference periods). For some model ensembles the fit few hundreds. Similarly, the observed trend in tem- was made over a shorter period as the data were not perature of the heat during an event with a similar available back to 1900 (such as for RACMO, EURO- frequency is around 4 ◦C, whereas the climate models CORDEX and HadGEM3-A). For weather@home, show a much lower trend (about 2 degrees). due to the large ensemble size, a non-parametric com- We note that while we are very confident about parison of the observed event in the simulation of the the positive trend and the fact that the probability present day climate with the same event in a counter- has increased by at least a factor five. It is impossible factual climate performed. In the June case, the same to assign one specific number (a ‘best guess’ based methods were used except for EURO-CORDEX and on all models and observations) on the extent of the IPSL-CM6 where a non-parametric comparison was increase, given the large uncertainties in the observed also made. Despite these methodological differences, trends (due to the relatively short time series from attribution is made comparable in all cases by com- 1947–2019) and systematic differences between the paring return periods or values for the exact same ref- representation of extreme heatwaves in the climate erence dates (1900 and 2019). models and in the observations. For ensembles where bias correction was applied prior to the analysis (the multi-model CMIP5, EURO-CORDEX), the estimation of PRs or intens- 4.2. July case ity changes is made based on events exceeding the For the France average, the heatwave was an event observed value of the index. For the non bias- with a return period estimated to be 134 years. As corrected ensembles, the estimation is made based on for the June case, except for HadGEM-3A, which has events with similar return period as in the observa- a hot and dry bias, the changes in intensity are sys- tions. tematically underestimated, as they range from 1.1 ◦C A synthesis is made based on observations and (CNRM-CM6.1) to 1.6 ◦C (EC-EARTH). By combin- the model ensembles that passed the evaluation by ing information from models and observations, we weighting the results for the July case, and based on conclude that the probability of such an event to occur all ensembles for June, but without synthesis between for France has increased by a factor of at least 10 (see observations and models due to model/observations the synthesis in figure 3). This factor is very uncertain inconsistency. For June, for each model ensemble, and could be two orders of magnitude higher. The uncertainty only considers sampling uncertainty change in intensity of an equally probable heatwave (representing natural variability). For the July case, is between 1.5 degrees and 3 degrees. We found sim- the same is represented, but we also added the ‘model ilar numerical results for Lille, with however an estim- uncertainty’ obtained as the model spread (inter- ate of change in intensity higher in the observations, model variability in the estimate) in addition to and models predict trend estimates that are consist- each model’s sampling uncertainty (open bars in ently lower than observation trends, a fact that needs figure 3). This allows a more realistic uncertainty for further investigation beyond the scope of this attribu- each model result. In the model/observation syn- tion study. We conclude for these cases that such an thesis (purple + open bars in figure 3), individual event would have had an extremely small probabil- model results are combined with the observed estim- ity to occur (less than about once every 1000 years) ate in two ways: a weighted average (by the inverse without . Climate change of the variances) denoted by the colored bar and an had therefore a major influence to explain such tem- unweighted average denoted by the open bar. Model peratures, making them about 100 times more likely spread is added to the model synthesis without reduc- (at least a factor of ten). tion due to the number of models. The unweighted For Germany, we analyzed Weilerswist- average thus puts more weight on observations. Lommersum. The changes in temperature are largely Wepresent all results for the PRs between the 2019 underestimated by the models compared to obser- and 1900 climates and for the change in intensity in vations by all but the HadGEM3-A model. Based on figure 3 for the two cases. observations and models, we find that the effect of climate change on heatwave intensity was to elevate 4.1. June case temperatures by 1.5 degrees to 3.5 degrees. Because Despite model/observations discrepancies, in June, the event was less rare, the PRs are also less extreme. the observations and almost all models show a large Again all models except HadGEM3-A multi-model increase in the probability of heatwaves like the one ensemble underestimate the trend up to now. This observed in June 2019 (as described by the 3-d mean leads to (much) lower PRs in these models than in temperature, both averaged over all of France and in the observations. The combination of models and

5 Environ. Res. Lett. 15 (2020) 094077 R Vautard et al

France JUNE De Bilt JULY

Cambridge JULY Toulouse Blagnac JUNE

Oxford JULY France JULY

Lille Lesquin JULY

Weilerswist-Lommersum JULY

Figure 3. Changes in intensity (left panels) and probability ratios (right panels) obtained for all models and stations (one station per row). From top to bottom: June: France average; Toulouse; July: France average, Lille Lesquin, Weilerswist-Lommersum, De Bilt, Cambridge, Oxford; 95% confidence intervals are given with the red bar for models and blue bar for observations. The ‘CMIP5 M´et´eo France’ calculation presented here for comparison is based on a different method described in Ribes et al (2020). See text for uncertainty estimates.

observations leads to an increase of a factor of about 10. Combining models and observations gives a best 10 (at least 3). estimate of 300 with a lower bound of 25. In de Bilt, the change in temperature of the For U.K. stations, only four (Cambridge) and hottest 3 days of the year is 2.9 ◦C ± 1.0 ◦C in three (Oxford) model ensembles were kept in the the observations and around 1.5 ◦C in all models analysis based on our selection criteria. As for the except HadGEM3-A (which has a dry and warm bias) other locations, PRs cover a wide range. Combin- and EURO-CORDEX (which has no aerosol changes ing observations and models lead us to a PR of ~20 except for one of the models). The large deviation in Cambridge (at least a factor of 3). For Oxford of HadGEM3-A from the other models gives rise on the other hand, the heatwave was less extreme in to a large model spread term (white boxes, which TG3x and the PR numbers are lower. Interestingly, increases the uncertainty on the model estimate so the change in intensity is better simulated than for that it agrees with the observed trend). Without the other continental locations. Based on all information HadGEM3-A the models agree well with each other we find a rather similar range of temperature trends, but not with the observations. The overall synthesis from slightly less than 1.5 to ~2.5 degrees. The range provides, as for France, an intensity change in the is slightly higher for Cambridge than for Oxford. range of 1.5 degrees to 3 degrees. For the PR, we In all cases bias-corrected ensembles do not arbitrarily replaced the infinities by 10 000 year and appear to exhibit a different behaviour from non 100 000 year for the upper bound on the PR of the bias-corrected ensembles. This stems from the lack fit to the observations. As expected the models show of obvious relation between biases and response (much) lower PRs, due to the higher variability and to anthropogenic changes. Other methodological lower trends. The models with the lowest trends, EC- differences such as of the reference time peri- Earth and RACMO, also give the lowest PR, around ods selected and the method used (GEV fit vs.

6 Environ. Res. Lett. 15 (2020) 094077 R Vautard et al nonparametric method) do not seem to affect results However while these strong examples exist, on either, all model ensembles appearing to have similar a whole, Europe is still highly vulnerable to heat behavior after standardization to the reference dates extremes, with approximately 42% of its population (1900 and 2019). over 65 vulnerable to heat risks (Watts et al 2018), and as evidenced by the significant excess mortality during 5. Vulnerability, exposure and adaptation the heat episode discussed in this paper. In addition to life saving measures during a heatwave, it is also cru- Heatwaves are amongst the deadliest natural dis- cial to catalyze longer-term efforts to adapt to raising asters facing humanity today and their frequency heat risks in Europe (Bittner et al 2014). This includes and intensity is on the rise globally. Combined increasing urban green spaces, increasing concentra- with other risk factors such as age, certain non- tions of reflective roofs, upgrading building codes to communicable diseases, socio-economic disadvant- increase passive cooling strategies, and further bol- ages, and the urban heat island effect, extreme heat stering health systems to be prepared for excess case impacts become even more acute with climate change loads (Singh et al 2019). Adaptation measures are (Kovats and Hajat 2008). developing in European cities, such as for example by The most striking impacts of heatwaves, deaths, the Paris City, with measures such as: ensuring every- are not fully understood until weeks, months or even one is within a 7-min walk from a green space with years after the initial event. However first estimates drinking water; incorporating durable water cooling have shown that the two heatwaves led to a 50% systems into the urban landscape (fountains, reflect- extra death above normal during the alert periods ing pools, misting systems etc.); planting 20 000 trees; in France (about 1500 extra deaths) (Sant´e Pub- establishing 100 hectares of green roofs; integrating lique France 2019). Similar orders of magnitudes, but passive cooling measures into new and existing build- smaller numbers have been reported for July in The ings and updating building codes (de Paris 2015). Netherlands (400 extra deaths), Belgium (400 extra deaths), U.K. (200 extra deaths). 6. Synthesis and discussion Excess mortality is derived from statistical analysis comparing deaths during an extreme heat event to The heatwaves that struck western Europe were rather the typical projected number of deaths for the same short lived (3–4 d), yet very extreme as far as the time period based on historical record. (Mcgregor highest temperatures are concerned: many all-time et al 2015) Those at highest risk of death during a records were broken in most countries of Western heatwave are older people, people with respiratory ill- Europe, including historical records exceeded by 1–2 nesses, cardiovascular disease and other pre-existing degrees). The events were found to have, under cur- conditions, homeless, socially isolated, urban resid- rent climate conditions, return periods in the range of ents and others (Mcgregor et al 2015). Deaths among 10 years to 150 years depending on locations studied. these populations are not attributable to instances However, return periods can vary by large amounts of extreme heat in real time but become apparent from place to place. through a public health lens following the event. Eight model ensembles, including two of the new Compared to the 2003 heatwave in Europe CMIP6 models, were analyzed using the same event (estimates of 70 000 extra deaths), the numbers definition (3-d average of mean daily temperature) appear much reduced. While this would require a spe- and methodology, together with observations, for cific analysis for a good interpretation of these num- attributing the changes in both intensity and probab- bers, adaptation measures could have played a signi- ility of the event at six locations in France, Germany, ficant role (de Donato et al 2015). Following Europe’s the Netherlands and U.K. extreme heat event of 2003 many life saving measures At all locations analyzed, the combination of have been put in place. The Netherlands established observations and model results indicate that temper- a ‘National Heatwave Action Plan’,France established ature trends associated to this extreme event are in the ‘Plane Canicule’, in Germany a heatwave warning the approximate range of 1.5 degrees to 3 degrees, system has been established and The United Kingdom despite the fact that in June observations and model established ‘The Heatwave Plan for England’.Collect- values have a large discrepancy. This indicates that ively these plans include many proven good prac- without human-induced climate change heatwaves as tices such as: understanding local thresholds where exceptional as these one would have had temperatures excess heat becomes deadly, establishing early warn- about 1.5 to 3 degrees lower. Such temperature differ- ing systems, heat protocols for public health and eld- ences result in a substantial change in morbidity and erly care facilities, bolstering public communications mortality (Baccini et al 2008). about heat risks, ensuring people have access to cool At all locations analyzed, the change in prob- spaces for a few hours a day, such as cooling cen- ability of the event is large. In France and the ters, fountains and green spaces, and bolstering health Netherlands, we find changes of at least a factor systems to be prepared for a surge in demand (Ebi et al 10. Without climate change, this event would have 2004, Fouillet et al 2008, Public Health England 2019) been extremely improbable (return period larger than

7 Environ. Res. Lett. 15 (2020) 094077 R Vautard et al about 1000 years). For the other locations, changes and Hauser, submitted). Consistent with the CMIP5 in probabilities were smaller but still very large, at bias, preliminary investigations into the deficits of least a factor of 2–3 for the U.K. station, and 3 for weather@home have shown that cloud cover is often the German station. Differences found across coun- biased low in the model, which leads to unrealistic- tries are due to several factors, among which pro- ally high hot extremes due to excessive soil moisture cesses involved (e.g. soil moisture feedback), as well depletion during relatively short periods of simulated as the level of observed temperature obtained com- blocking. In contrast, low cold extremes in wet years bined with the sensitivity due to the negative shape of continue to be simulated in weather@home. Another the distribution. possible dynamical cause is that western Europe may One other finding is a significant difference in occasionally be influenced by advection of hot and the trend in heat extremes between the observations dry air from Spain and North Africa, leading to large and the models. While the models generally have too excursions of temperature which models might not large a variability compared to observations, in June capture well. the observations have a heavier tail than the mod- Regarding (ii), Robin and Ribes (2020) analysed els (which have too negative a shape parameter com- the same July event over France using a different stat- pared to the observations). Specifically for the June istical approach, and also provide a synthesis between 2019 case, the observations show a much stronger models and observations. They report a return period extreme temperature trend than the models, with a for that event of 30 year (16–125 year), which is sig- factor up to two in France. Further work is needed to nificantly less than in this study. However, the attri- understand this discrepancy, and determine whether bution diagnoses (risk ratio and change in intensity) the observed trends might be affected by measure- are well consistent with our results, suggesting some ment errors (e.g. homogenization), or if models just robustness in these findings. fail to capture a real emerging feature. Above all, the results of this study show that, This analysis triggers several key research ques- when considering extreme heatwaves, models exhibit tions, which are: (i) What are the physical mechan- a large spread of response to current climate change. isms involved in explaining the common model biases This calls for a systematic investigation using mul- in the extremes (i.e. too high variability, too small tiple models to answer the attribution question, and trends in Europe where trends are large)? (ii) Would a communication in qualitative terms can be con- one obtain similar results using different statistical sidered. This also calls for a more process based eval- methods (only two methods have been applied here), uation of models and selection. and other conditionings? (iii) Are models improv- Acknowledgments ing from CMIP5 to CMIP6 (Wehner et al 2020)? (iv) Has climate change induced more atmospheric flows The study was made possible thanks to a strong favorable to extreme heat, and, vice versa, for similar international collaboration between several institutes flows what are the changes in temperatures, and what and organizations in Europe (DWD, ETHZ, IPSL, design of numerical experiments could inform on this ITC/University of Twente, Red Cross/Red Crescent question? (v) How to attribute the co-occurrence of Climate Center, KNMI, M´et´eo-France, Univ. Oxford, the two extreme heat waves? U.K. Met Office), whose teams shared data and While more research is needed to address these yet methods, and thanks to the Climate Explorer tool unsolved questions which cannot be developed in this developed by KNMI. We would like to thank all the letter, we speculate that for (i) models may have diffi- volunteers who have donated their computing time culties to correctly simulate land-atmosphere interac- to climateprediction.net and weather@home. It was tions, resulting in a deficit of skill for the simulation of also supported by the ERA4CS ‘EUPHEME’ research heatwaves especially in regions where evapotranspira- project, grant #690462. tion regimes undergo transitions from energy-limited to soil-moisture limited regimes. This effect is known Data Availability to be strong in southern France and other regions with Mediterranean climate and is getting stronger The data that support the findings of this study are in central Europe with global warming (because available from the corresponding author upon reas- of decreased evaporational cooling if soil moisture onable request. levels become limiting for plants’ transpiration; e.g. Seneviratne et al (2010), Mueller and Seneviratne ORCID iDs (2012). Analyses of CMIP5 ESMs have shown that a subset of the CMIP5 models have a clear tend- Robert Vautard  https://orcid.org/0000-0001- ency to overestimate soil moisture-temperature coup- 5544-9903 ling, which leads to a bias of the overall ensemble Geert Jan van Oldenborgh  https://orcid.org/0000- (Sippel et al 2017, Vogel et al 2018). This bias is 0002-6898-9535 possibly reduced in the CMIP6 ensemble in Central Friederike E L Otto  https://orcid.org/0000-0001- Europe and Central North America (Seneviratne 8166-5917

8 Environ. Res. Lett. 15 (2020) 094077 R Vautard et al

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