www.sciencemag.org/cgi/content/full/science.1201224/DC1

Supporting Online Material for

The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe

David Barriopedro,* Erich M. Fischer, Jürg Luterbacher, Ricardo M. Trigo, Ricardo García-Herrera

*To whom correspondence should be addressed. E-mail: dbarriopedro@fc.ul.pt

Published 17 March 2011 on Science Express

DOI: 10.1126/science.1201224

This PDF file includes:

Materials and Methods

SOM text

Figs. S1 to S18

Table S1

References and Notes

1

Supporting Online Material for

The hot summer of 2010: redrawing the temperature record map of Europe

David Barriopedro*, Erich M. Fischer, Jürg Luterbacher, Ricardo M. Trigo,

Ricardo García-Herrera

*To whom correspondence should be addressed. E-mail: dbarriopedro@fc.ul.pt This PDF file includes:

Materials and Methods SOM Text Figs. S1 to S18 Table S1 Notes and References

2

Materials and Methods

References for the impacts of the 2010 event in Russia

The information regarding the impacts of the 2010 summer event was obtained official declarations published in the media and press reports from the Moscow health department, the Moscow registry office, the Russian Ministry of Emergency Control, the Russian Ministry of Economic Development and the Russian Ministry of Health and Social Care. According to these sources, the death rates already increased by July 2010 in many Russian regions (S1). The number of deaths in Moscow during July 2010 exceeded those of July 2009 by at least ~4,800 people (S2) and it further increased during the first days of August, doubling the normal rates and causing the city morgues close to their full capacity (S3). The city experienced an increase of ~6,100 deaths in August 2010 compared with the same period of 2009, which added to the July rates, gives ~10,900 deaths July-August 2010 (a 60% rise in the mortality rate, S4). As a whole, mortality in Russia might have increased by ~14,500 people in July 2010 and ~41,300 people in August 2010 (S5,S6). However, these values are still provisional and they should not be overstated (e.g. the death toll does not only include heat-related causes), thus requiring confirmation by national and international (e.g. WHO) institutions. Taking into account the magnitude, duration and spatial extent of this heatwave it is expectable that other countries suffered excessive mortality, particularly Ukraine, Kazakhstan, Baltic countries and Belarus. On the other hand, extensive fires (that by the end of September had affected the total area of ~1,500,000 ha since the beginning of the fire season 2010) killed ~50 people and left about 3,000 people homeless, threatening the main nuclear research center of the country and an area bordering Ukraine that was contaminated with radioactive material in the soils following the 1986 Chernobyl disaster (S3,S7-S9). The 'mega-heatwave' and the associated drought conditions and fires also caused a ~25-30% drop in the forecast of annual countrywide grain crop production, as compared to that of 2009 (S9,S10). Due to the loss of crop and the shortage for domestic markets, a ban on grain export was imposed by Russia, one of the world’s largest wheat exporters, from 15 August to 31 December 2010 (S3,S10). This ban contributed to about a 50% rise in the wheat prices on the market compared to June 2010 (S11). According to preliminary estimates based on economists’ analyses, the financial loss of the 2010 summer may amount to ~$15 billion to the Russian government and the economic growth may slow down in 2010 by at least 0.5-1% (S10).

Reanalysis datasets

The historical record consists of daily mean surface temperature data and geopotential height fields at 500 hPa (Z500) provided by the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) reanalysis (1948-2010) (S12) and the recently released 20th century reanalysis (20CR) v2 (1871-1947) (S13).

3

The 20CR (1871-2008) represents an international project to produce a reanalysis dataset assimilating only surface observations. The reanalysis provides “first guess” fields by using a global numerical weather prediction model developed at the NCEP with observed monthly sea surface temperature and sea ice distributions as boundary conditions. An ensemble filter data assimilation method yields the most likely state of the global atmosphere for the 1871-2008 period, each 6-h and with 2ºx2º longitude-latitude resolution. Intercomparisons with independent radiosonde data and other reanalyses indicate high agreement, particularly in the Northern Hemisphere and, more specifically, in Europe (S13).

To account for the different spatial resolution of both reanalyses, the 20CR dataset has been linearly interpolated to the 2.5ºx2.5º NCEP/NCAR grid. Daily anomalies are computed for the analyzed period of each reanalysis with reference to its 1970-1999 climatology by subtracting the corresponding 1970-1999 mean from each calendar day. Figure S2 shows a comparison of summer (1 June to 31 August) surface temperature daily anomalies between the NCEP/NCAR reanalysis and the 20CR for the common overlapping period (1948-2008). The correlation coefficient is above 0.8-0.85 over most of Europe, which is highly significant (p<0.01) taking into account the sampling size (92x61). However, the mean bias can reach up to 0.3ºC (a root mean square error of ~2ºC) over some of the regions analyzed herein (e.g. southwestern Russia), thus calling for caution. To further check the reliability of the 20CR, an additional test was performed. We recomputed Figure 1 by confining the analysis to the most recent 1948-2010 period of the NCEP/NCAR reanalysis (Fig.S3) and compared it with the results obtained by using 20CR data for the 1948-2008 period (Fig.S4). The 2010 record-breaking pattern in the 20CR shows high resemblance with that of the NCEP/NCAR reanalysis. The spatio-temporal evolution of record-breaking areas in 2003 and 2010 is also similar, although with some underestimation of the spatial extent in the 20CR. Since many record-breaking temperatures occurred over regions that are not affected by strong biases and the amplitude of the 2010 event was in most of the cases much larger than the local bias, the results of Figure 1 are expected to be quite reliable. Therefore, we can state that the reported record-breaking anomalies were, at least, the warmest of the last 60 years and very likely the warmest of the last 140 years.

Historical record and record-breaking temperature definition

In order to identify record-breaking temperature anomalies in a specific summer, we define the respective historical period as all summer days (1 June to 31 August) from 1871 up to the year of the given event. Record-breaking values are obtained as follows: 1) we compute running means of daily anomalies for different time scales (between 1-day to 91-day) centered on each summer day of the historical period, allowing superposition (e.g., for the 5-day time-scale, we start at 30 May-3 June, then 31 May-4 of June and so on until 29 August-2 September); 2) for each grid-point and time-scale, the maximum value of the

4

nx92 sample is retained as historical maximum, with n being the number of years from 1871 to the year before the extreme summer; 3) a record-breaking is identified if the mean anomaly for a given summer period surpasses the corresponding temporal-scale historical maximum.

Taking into account the occurrence of frequent heatwaves during the first decade of the 21st century we would like to know their potential impact on altering the historical maximum threshold used for the 2010 summer. However, results are virtually identical to those of Figure 1 if the historical record (used to compute the maxima) is confined to a fixed period (e.g. 1871-2000), i.e., with the same historical maxima being used for the 2003 and 2010 heatwaves, instead of updating automatically the historical record (not shown). This appear to result from the fact that regions affected by these heatwaves present very little spatial superposition, i.e. record-breaking temperatures obtained in 2003 or 2007 do not affect the analysis for 2010.

Multi-proxy reconstructions of seasonal temperatures and European mean summer temperature

The temperature reconstruction is based on a comprehensive dataset that includes a large number of homogenized and quality-checked long instrumental data series and different proxies, including temperature indices from documentary records, Greenland ice cores and tree rings (S14). The reconstruction is provided on a 0.5ºx0.5º regular grid over the region defined by [35ºN, 70ºN] and [25ºW, 40ºE]. For further details on the reconstruction, the reader is referred to S14. Data for the 2003-2010 period are monthly anomalies based on surface temperature analyses of the Goddard Institute for Space Studies (GISS) at 2ºx2º spatial resolution (e.g. S15). It is important to note that the multi-proxy reconstruction also uses GISS data from 1998 to 2002. For coherence throughout this manuscript and a better visualization of Figure 3, both datasets have been linearly interpolated into a common 2.5ºx2.5º resolution grid. Only those 2.5º cells containing more than half of their embedded 0.5º gridpoints over land are considered.

To obtain the map of the hottest summers across Europe (Figure 3), we followed the same procedure to that employed with the 20CR and NCEP/NCAR reanalysis in Figure 1. Thus, for each 2.5º gridpoint, we first computed seasonal temperature anomalies for the periods 1500-2002 of (S14) and 2003-2010 of (S15) with reference to their respective 1970-1999 climatologies. The hottest summer (JJA) at each gridpoint is chosen as the maximum anomaly of the full 1500-2010 period. Figure S8 compares the summer mean temperature anomalies of both datasets at the local scales for the instrumental period of (S14). The correlations are above 0.9 (p<0.01) over most of Europe (expect specific regions of the Mediterranean). The regions strongly affected by the 2010 heatwave (east of ~30ºE) are characterized by local absolute biases below ~0.2ºC (i.e. much lower than the exceedance

5

of the 2010 seasonal anomalies over their previous maxima), whereas in western and central Europe the reconstructions tend to overestimate GISS data. Therefore, there is relatively high confidence that at least 2003 and 2010 were record-breaking summers over many gridpoints.

To compute a European mean summer temperature anomaly, we used seasonal mean anomaly data for the 1500-2010 period and the 2.5ºx2.5º resolution grid of Figure 3, as described above. The European mean temperature anomaly of each summer is obtained as the average of the seasonal values over all those land 2.5º gridpoints, weighting the mean by the area of each cell. Obviously, the European mean temperature depends on the spatial domain and the specific grid used. There are also slight changes after weighting by the area of each cell, but they do not affect the main results reported herein, namely that 2010 is by far the warmest European summer since 1500 and that the 2001-2010 decade embraces more extreme European summers than any other decade back to 1500. To check the consistency between the seasonal European temperature series of (S14) and (S15), their common overlapping period (1880-2002) has been considered. Temperature anomalies correlate at 0.97 (significant at p<0.01) with a mean bias of 0.13ºC. The root mean square error is 0.19ºC.

Significance test of the decadal frequency of extreme European summers

We explore if the decadal frequency of extreme European summers (F) is significantly larger than that expected by random chance (P). The statistical significance of F is determined with a bootstrap test wherein the 1500-2010 time series of European summer temperature anomaly is randomly resampled 1000 times. For each i member of this ensemble, we compute the corresponding running decadal frequency and pick the maximum value, mi. P is defined from the 95th percentile of the distribution constructed from the 1000 randomized m values. Therefore, P is the (one sided) 95% confidence limit that identifies those decadal frequencies whose likelihood of occurring by chance is <5%.

Influence of the summer temperature reconstruction uncertainties

We also test the robustness of the results presented in Figure 2 by taking into account the uncertainties in the reconstruction. We adopt the most conservative scenario, which assumes that the European mean summer temperatures of 1750-1850 (the second period with more extreme summers) were underestimated by 0.3ºC, which approximately corresponds to the 2 standard error (SE) of the European summer temperature reconstructions for that period (S14). The resulting distribution is shown in Figure S9 and provides further evidence that the frequency of extreme European summers in the 2001-2010 decade most likely stands above any other decade back to 1500. Under this skeptic scenario, the 2002, 2006 and 2007 summers compete in amplitude with those of the 1750-1850 period. However, it should also be stated that summer temperature observations from

6

Sweden could have been positively biased by as much as 0.7ºC before ~1860 (S14), potentially causing an artificial warming in the 1750-1850 period.

RCMs

We analyze daily 2-m temperatures of transient experiments performed with 11 high-resolution RCMs within the European project ENSEMBLES (S16). The evaluation of a suite of RCMs, all of which are viewed as equally probable estimates of the future climate, allows assessing model-dependent uncertainties. The RCMs are driven by different General Circulation Model (GCM) runs. The simulations were run over the entire European continent at a horizontal resolution of about 25 km and they were forced with the SRES A1B emission scenario for the full period 1951-2099. Table S1 lists the RCMs and GCMs. Further details on these models can be found in (S16).

To obtain the regional temperature series used in Figure 4 and Figures S12-S17, the following procedure is applied: i) we first calculate daily regional means of 2-m temperature over the western (WE) and eastern (EE) European regions defined in Figure S11, and express them as anomalies relative to the 1970-1999 period (by subtracting the corresponding 1970-1999 mean from each calendar day); ii) we then compute running means of the regional daily anomalies for different time scales (from 1-day to 91-day) centered on each summer day (1 June to 31 August or 1 June to 30 August, depending on the RCM); iii) Next, these time series are normalized with the respective (time-scale) mean and standard deviation of all summer days of the 1970-1999 period; Finally, for each model and temporal scale, the regional temperature series are obtained by choosing the summer maximum (in SDs) among all summer days. The same procedure and regions are employed to derive the corresponding 2-m regional summer temperature values for the 2003 and 2010 events from the NCEP/NCAR reanalysis (S12).

Generalized Extreme Value analysis

In order to estimate the return periods (RPs) of 'mega-heatwaves', we first construct the time series of 7-day mean regional summer temperature (in SDs) for three time slices (1970-1999, 2020-2049 and 2070-2099) and choose the maximum 7-day regional temperature for each summer (see the previous section for further details on the computation of the regional temperature series). The 30-yr length of the time slices is chosen as a compromise between long periods (that provide robust estimates) and short periods (that avoid the expected non-stationarity of the RPs). The RPs and their corresponding 95% confidence intervals are obtained by fitting these summer block maxima to a Generalized Extreme Value (GEV) distribution using the L-moments method (S17), and obtaining estimates of the location, shape and scale parameters. The 95% confidence bounds for the return periods are derived by parametric resampling (1000 times).

7

Supporting Text

Record-breaking temperatures in eastern Europe and Russia

The exceptional warmth of the 2010 summer as reported herein from reanalyses and multi-proxy reconstructions of seasonal temperature in Europe is also supported by other datasets. Figure S1 shows the 2001 summer (1 June to 31 August) maximum of daily mean (TG), daily maximum (TX) and daily minimum (TN) temperature, as obtained from the E-OBS v3 gridded dataset at 0.25ºx0.25º spatial resolution (S18). This dataset is obtained through interpolation of the ECA&D station-blended data (S19) and hence it may display biases in areas where relatively few stations are available (S20). A subset of the underlying weather stations (going back to 1970, at least) is also shown in Figure S1, along with the 2010 summer maximum temperature recorded in some cities that broke their all-time records. The length of the temperature series varies from station to station (some of them go back to the late 19th century). Daily mean summer temperatures exceeded 30ºC over large parts of eastern Europe and western Russia (e.g. Moscow: 32.1ºC) and 25ºC further north (e.g. Helsinki: 26.1ºC; Tallinn 27.0ºC; Saint Petersburg: 29.3ºC). These exceptional daily mean temperatures were also accompanied by corresponding maxima of daily maximum and daily minimum temperatures, which exceeded 35ºC and 25ºC, respectively, in many places.

At seasonal scales, station-based measurements (S21) indicate that the average 2010 June-August temperatures in Moscow were by far the hottest (+5.1°C with reference to the 1961-1990 reference period) since instrumental data are available (1777). Temperature anomalies were even higher (+6.0°C) in some areas south-east of Moscow (S21), in good agreement with Figure 1. On the other hand, proxy data suggests that the summer of 2010 in central Russia was likely the warmest since the early 1360s, maybe even further back to the 10th and 11th centuries, when similar magnitudes may have been experienced (S22-S24).

To stress the rareness of a European summer like 2010, we have computed its RP (Fig.S10), taking into account the varying trend over time of the temperature series (S14). The estimation of the RP is obtained by fitting the time-dependent trend of the seasonal European surface temperature to a spline function and assuming independent and identical Gaussian distributed residuals (see S14 for further details). The RP changes with time according to the mean summer conditions, thus oscillating from millions of years at the beginning of the 20th century to ~50-100 years for the mean summer conditions observed during the first decade of the 21st century.

RCM projections of extreme regional events

Figure S14 displays the occurrence of 2003 and 2010 summer analogues at different temporal scales for the 21st century, as simulated by a suite of 11 RCMs, taking the regional anomalies of 2003 and 2010 as a reference for the WE and EE regions, respectively. Since

8

that anomaly (here used as threshold) varies between the two cases, Fig. S14 does not provide a fair regional comparison.

Herein, we further explore the RCM summer temperature series in order to evaluate region- and time-dependent changes in heat anomalies. Figure S15 provides a simple estimate of the expected decadal frequency of summers experiencing regional anomalies exceeding a given threshold (in SDs of the 1970-1999 reference period). The use of SDs allows for fair comparisons across regions and time scales. By the mid-century, about 5-10 times more summers with extreme anomalies (above 3 SDs) are expected over both regions and all temporal scales. In the second half of the 21st century, the trend to more extreme summers accelerates particularly in WE, at a considerably higher rate. The results also indicate a larger increase for seasonal than weekly episodes, particularly during the second half of the 21st century. This may reflect the contribution of changes at shorter time scales plus mean warming and/or the occurrence of several short-term spells of moderate amplitude within the same summer (note that we do not address the absolute frequency of extreme regional episodes but the number of summers with at least one extreme event). As a consequence, by the end of the 21st century, almost all summers in WE may experience seasonal temperatures above 3 SDs, whereas the same would occur in EE every second summer. Note that the above projections involve substantial model uncertainties. The range across different models provides an indication for the model disagreement but does not cover the full range of uncertainties.

Discussion on mechanisms associated with future 'mega-heatwaves'

Figure S5 shows that the extreme summer temperatures during the 2010 event collocate with the anomalous anticyclonic circulation aloft at any temporal scale. A similar correspondence was also observed during the 2003 summer over WE (S25). Herein, we explore if the patterns observed during the two 'mega-heatwaves' are still found during future events in WE and EE. To address this question, we have used the warmest 7-day period of each summer and RCM for the following time slices: 1970-1999, 2020-2049 and 2070-2099. Daily Z500 data have been obtained from the GCM experiment driving the respective RCM. The analysis includes 4 out of the 5 GCMs listed in Table S1, which cover 10 out of the 11 RCMs (excluding the ARPEGE GCM experiment).

For each heat spell, we compute the corresponding 7-day mean Z500 anomaly (with reference to 1970-1999), giving a total of 30 cases for each period and RCM. The mean Z500 pattern associated with regional heat episodes in each RCM and period is obtained by averaging across those 30 cases. Figure S16 shows the multi-model ensemble mean pattern for each time slice and region. The atmospheric circulation anomalies accompanying the occurrence of WE and EE heat spells in the reference period (1970-1999) reveal semi-stationary Z500 positive height anomalies over the 'mega-heatwave' areas. The WE and EE

9

patterns resemble the ones observed during the 2003 and 2010 summers, respectively, and they are in good agreement with the two main Canonical Correlation Analysis (CCA) patterns of sea level pressure associated with European heatwaves (S26). A comparison between time slices reveals that the same spatial patterns that are responsible for weekly heat spells in the 20th century are also associated with 'mega-heatwaves' in the future. Nonetheless, in agreement with (S27) there is an amplification of the positive height anomalies towards the end of the 21st century.

To estimate changes in the future frequency of patterns associated with 'mega-heatwaves' over WE and EE, we have computed a daily index (ZI) that provides a measure of the resemblance between 7-day mean atmospheric circulation anomalies and the Z500 patterns related with regional heat spells in the reference period 1970-1999. Following a methodology similar to that employed by (S28), the ZI is computed by projecting the 7-day mean Z500 anomalies (relative to 1970-1999) of each summer day onto the WE and EE spatial patterns shown in the upper panels of Figure S16. Changes in intensity and recurrence of patterns prone to regional warm episodes are addressed by the maximum ZI value of each summer and by the number of summer days with ZI values above unity, respectively. The corresponding mean numbers for each 30-yr period and region are shown in Figure S16 (upper left corner of each panel) and they reveal a tendency for 'mega-heatwave'-like patterns in the future.

Further analyses indicate that the amplification and rising frequency of the 'mega-heatwave'-related patterns obtained in Figure S16 for the 30-yr periods of the 21st century are no longer evident if the composites are computed with reference to its corresponding 30-yr climatological mean (Fig.S17). In other words, the intensification and regularity of 'mega-heatwave' patterns towards the end of the 21st century is largely determined by a mean Z500 rise in the base-state superimposed to an almost constant recurrence of anticyclonic circulation departures. Thereby the geopotential height gradients on the Z500 pressure level are about the same in future and present regional heat spells. The underlying mean Z500 rise is expectable from the troposphere expansion induced by the mean warming under increasing atmospheric greenhouse-gas concentrations, and it might be enough to enhance atmospheric circulation patterns favorable to local heatwaves (S27).

To further investigate the role of mean warming for the increase in regional 'mega-heatwaves', we have reproduced the GEV fits of Figure 4 for a ‘warming-only’ case. To this end the mean regional summer (JJA) warming over WE and EE was added to the respective daily climatology for 1970-1999 period (Fig.S18). For small to moderate weekly heat anomalies, the RPs derived from the ‘warming-only’ GEV fits compare relatively well with the original ones of Figure 4. This suggests that the mean warming accounts for a large fraction of the increase in heat spells. However, for 'mega-heatwaves' with the longest RPs there are still considerable discrepancies between the original (Fig.4) and the

10

‘warming-only’ scenario (Fig.S18). The increase in the frequency of 'mega-heatwaves' that cannot be explained by the mean warming is induced by enhanced variability of regional summer temperatures. Recent modeling studies have demonstrated the decisive contribution of changes in interannual to intraseasonal variability of summer temperatures to the enhancement of future heatwave intensity (S29). The physical processes driving changes in variability vary across RCMs, but they are generally related to enhanced land-atmospheric coupling resulting from the projected decrease in soil moisture availability and/or enhanced variability of surface net radiation as well as reduced relative humidity (S30-S33).

11

Supporting Figures

Figure S1. Best estimates for the 2010 summer (1 June to 31August) maximum of: a) mean daily temperature; b) maximum daily temperature; c) minimum daily temperature (ºC), as obtained from (S18). Grey dots show the location of the underlying stations (S19) for which daily records are available since at least 1970. Some cities that exceeded their all-time record temperatures during the 2010 summer are also highlighted, along with the record-breaking temperature and the corresponding anomaly (in parentheses) with reference to 1970-1999. The daily mean temperature evolution in: d) Tallinn; e) Moscow is also shown. Red (blue) shading areas in d) and e) indentify summer periods with positive (negative) daily anomalies relative to the 1970-1999 climatological daily mean (grey baseline).

12

Figure S2. Performance of the 20CR as compared to the NCEP/NCAR reanalysis for the 1948-2008 period. a) mean bias (ºC) defined as the averaged 20CR minus NCEP/NCAR daily difference of surface temperature anomalies for all summer days of the 1948-2008 period; b) Pearson correlation coefficient (R) between the daily time series of summer surface temperature anomalies of the 20CR and the NCEP/NCAR reanalyses for the 1948-2008 period.

13

Figure S3. As Figure 1 but for the NCEP/NCAR period (1948-2010).

14

Figure S4. As Figure 1 but for the 1948-2010 period of the 20CR (1948-2008) and the NCEP/NCAR reanalysis (2009-2010).

15

Figure S5. Synoptic conditions for the 2010 summer periods when a maximum number of places were simultaneously experiencing record-breaking temperatures at different temporal scales. Shading indicates surface temperatures (ºC) and contours depict Z500 (gpm) anomalies (with reference to the 1970-1999 period), respectively, averaged for: a) 7-day; b) 15-day; c) 31-day; d) 81-day periods centered on the day indicated at the top of each panel. Gridpoints with record-breaking temperatures are marked with a black point, the size being proportional to the exceedance of the temperature anomaly over the previous maximum (since 1871). Data sources are (S12) for 1948-2010 and (S13) for 1871-1947.

16

Figure S6. a) Precipitation departures (expressed in percentage relative to the 1970-1999 normals) for the January-July period of 2010. To avoid misinterpretations over very dry regions, only gridpoints with climatological mean precipitation above 20 mm month-1 are shown. Monthly data are obtained at 1ºx1º resolution from the Full (1901-2007) and the Monitoring Product (2008-2010) of the Global Precipitation Climatology Centre (GPCC) (S34); b) Snow cover anomalies during April 2010 (expressed as the difference between the percentage of snow covered areas in April 2010 and the corresponding climatological mean for the 1970-1999 period). The dashed (dotted) white line indicates the April 2010 (climatological mean) location of the snow cover line, defined as the boundary line delimiting those cells being more than 50% snow covered. April represents the month with largest climatological snow disappearance in western and central Russia. The results are similar if the monthly snow cover series are detrended. Monthly snow data are obtained from NOAA visible charts and they were provided by the Rutgers University Climate Lab (RUCL) in an 89x89 matrix for the 1966-2010 period (S35).

17

Figure S7. As Figures 1a-d but for the summer of 2003.

18

Figure S8. As Figure S2 but for the summer mean surface temperature anomaly time series of (S14) and (S15) over the instrumental period of (S14).

19

Figure S9. As Figure 2 but adding the ~2 SE (+0.3ºC) of the multi-proxy reconstructions to the European mean seasonal temperature anomaly of each summer of the 1750-1850 period.

20

Fig.S10. Temporal evolution of the return period (years) of a European summer event such as the one of 2010, estimated with the European summer surface temperature trend over the most reliable period of the reconstructions (1750-2009) of (S14). Dotted lines indicate the corresponding 95% confidence intervals for the estimated return period.

21

Figure S11. European (large box) and regional domains (small boxes) used in the text. Circles (squares) indicate those 2.5º land gridpoints that exceeded the previous summer temperature maxima for any temporal scale between 7-day and 91-day in 2010 (2003). The eastern (western) European regions defined in the text are identified by filled circles (squares), which comprise high-populated European regions of about 1,000,000 km2 that were strongly impacted by the 2010 (2003) heatwave. The size of the regions is approximately equal to the spatial extent of the 7-day record-breaking areas during the 2003 and 2010 events. Spatial limits are [42.5ºN, 50ºN]; [0ºE, 15ºE] for western Europe, [52.5ºN, 60ºN]; [27.5ºE, 40ºE] for eastern Europe and [35ºN, 70ºN], [25ºW, 40ºE] for the European mean.

22

Figure S12. Return periods (years) of regional maximum 7-day summer temperature over EE for three 30-yr time slices (color lines) and 11 RCMs (panels). Temperature anomalies (right ordinate axis) are standardized with reference to the 1970-1999 climatology (left ordinate axis). Dashed lines indicate the 95% confident intervals.

23

Figure S13. As Figure S12 but for WE.

24

Figure S14. RCMs 2011-2099 time series (y-axis) of maximum summer temperature for different temporal scales between 15-day (bottom) to 91-day (top) over EE (left panels) and WE (right panels). Temperatures (z-axis) are in SDs relative to the 1970-1999 period. The x-axis represents each of the 11 RCMs. For each model, only those years exceeding the maximum anomalies of 2010 (left panels) and 2003 (right panels) summers at the given temporal scale are shown, with the bin height and color being proportional to the level of exceedance. The color bar in the front of each panel indicates the number of models with at least one event like 2003 (left column) and 2010 (right column) up to that date. The black mark on the frontal panel represents the first year when more than half RCMs have identified at least one analogue at that temporal scale. The black thick line on the plane is the earliest date when all temporal scales have experienced (in more than half of the RCMs) regional temperatures above the 2010 (left panels) and 2003 (right panels) event. The dotted line on the plane is 2050.

25

Figure S15. Change in the decadal frequency of summers with extreme episodes over EE (top) and WE (bottom) showing regional mean temperatures above 3 SDs (light), 4 SDs (medium) and 5 SDs (dark shading) at different temporal scales (SDs relative to the 1970-1999 period). The decadal frequency is based on moving 30-yr periods. Lines indicate the mean of the RCMs frequencies with the thickness proportional to the number of models giving at least one event. Shading indicates the ±0.5 SD level of the frequencies projected by the 11 RCMs for the given 30-yr period. The 7-day, 31-day and 91-day WE temperatures for the 2003 event were 3.7, 3.9 and 4.2 SDs. The corresponding ones for the 2010 event in EE were 4.5, 5.4 and 4.1 SDs.

26

Figure S16. Multi-GCM ensemble mean of 7-day mean Z500 (gpm) anomalies (relative to 1970-1999) during the 7-day warmest spells of each summer of: 1970-1999 (top); 2020-2049 (middle); 2070-2099 (bottom). The numbers at the upper left corner of each panel represent the 30-yr mean of the summer maximum regional ZI and the number of summer days with regional ZI values above 1 over EE (left panels) and WE (right panels). The symbol ‘*’ after these numbers denotes significant differences (at p<0.05 level) with the corresponding ZI values of the 1970-1999 reference period, after a two-tailed t-test. The regional ZI for EE and WE is obtained by projecting the 7-day mean Z500 anomalies of each summer day onto the spatial patterns of the 1970-1999 reference period (panels a) and b), respectively). The GCMs are ECHAM5-r3, HadCM3Q0, HadCM3Q3 and HadCM3Q16 (see Table S1).

27

Figure S17. As Figure 16 but computing the Z500 field anomalies with reference to the respective 30-yr mean of each time-slice.

28

Figure S18. As Figure 4 but after adding the mean regional warming to the 1970-1999 distribution of 7-day maximum regional temperature. Note that the GEV fits for the 1970-1999 reference period are the same than the ones in Figure 4.

29

Supporting Tables

Table S1. List of RCMs, driving GCMs, institutes and references for the RCMs used in this study. RCA: Rossby Centre regional Atmospheric climate model. HIRHAM: HIgh resolution limited area model + European centre HAmburg Model. CLM: Climate version of Local Model. HadRM: Hadley center Regional Model. RACMO: Regional Atmospheric Climate MOdel. REMO: REgional MOdel. HadCM: Hadley center Coupled Model. ARPEGE: Action de Recherche Petite Echelle Grande Echelle (research project on small and large scales). ECHAM: European Centre HAmburg Model. C4I: Community Climate Change Consortium for Ireland. DMI: Danish Meteorological Institute. ETHZ: Eidgenössische Technische Hochschule Zürich (Swiss Federal Institute of Technology Zurich). HC: Hadley Centre. KNMI: Koninklijk Nederlands Meteorologisch Instituut (Royal Netherlands Meteorological Institute). MPI: Max Planck Institute. SMHI: Rossby Center and Swedish Hydrological and Meteorological Institute.

Acronym RCM Driving GCM Center Reference

C4I ( HadCM3Q16) RCA3 HadCM3Q16 C4I S36

DMI (ARPEGE) HIRHAM ARPEGE DMI S37

DMI (ECHAM5) DMI-HIRHAM5 ECHAM5-r3 DMI S37

ETHZ (HadCM3Q0) CLM HadCM3Q0 ETHZ S38

HC (HadCM3Q0) HadRM3Q0 HadCM3Q0 HC S39

HC (HadCM3Q3) HadRM3Q3 HadCM3Q3 HC S39

HC (HadCM3Q16) HadRM3Q16 HadCM3Q16 HC S39

KNMI (ECHAM5) RACMO ECHAM5-r3 KNMI S40

MPI (ECHAM5) REMO ECHAM5-r3 MPI S41

SMHI (ECHAM5) RCA ECHAM5-r3 SMHI S36

SMHI (HadCM3Q3) RCA HadCM3Q3 SMHI S36

30

Supporting notes and references

S1. Materials from the Russian Ministry of Health and Social Care (Report release of 30 August 2010: http://www.minzdravsoc.ru/health/med-service/120 (in Russian))

S2. Materials based on declarations of the Moscow registry office (Press report release of 6 August 2010 by Telegraph Media Group: “Russian heatwave kills 5,000 as fires rage out of control“: http://www.telegraph.co.uk/news/worldnews/europe/russia/7931206/Russian-heatwave-kills-5000-as-fires-rage-out-of-control.html)

S3. Materials based on declarations of the Moscow's health chief (Press report release of 9 August 2010 by BBC News: “Death rate doubles in Moscow as heatwave continues“: http://www.bbc.co.uk/news/world-europe-10912658)

S4. Materials based on city official declarations (Press report release of 17 September 2010 by Agence France-Presse: "Russian heatwave caused 11,000 deaths in Moscow: official”: http://www.google.com/hostednews/afp/article/ALeqM5ih6EGeelXvvrhgivI66OKFJ1MFLA)

S5. Materials based on Rosstat (Federal State Statistics Service, http://www.gks.ru/bgd/regl/b10_01/Main.htm) estimates (Press report release of 29 October 2010 by the Agency of Federal Information Portal Regions.RU / News Federation: http://www.regions.ru/news/2321577/ (in Russian)) and Munich Reinsurance estimates (Press report of 3 January 2011 by Terra Daily: "Natural disasters killed 295,000 in 2010: reinsurer" http://www.terradaily.com/reports/Natural_disasters_killed_295000_in_2010_reinsurer_999.html)

S6. Materials from the Russian Economic Development Ministry (Report release of 25 October 2010: http://www.economy.gov.ru/minec/activity/sections/macro/monitoring/doc20101025_06 (in Russian))

S7. Materials from the Emergency Archives and daily Emergency reports of the Ministry of the Russian Federation for Civil Defense, Emergencies and Elimination of Consequences of Natural Disasters (e.g. http://www.mchs.gov.ru/digest/detail.php?ID=52140 (in Russian))

S8. N. Gilbert, Russia counts environmental cost of wildfires. Nature, doi:10.1038/news.2010.404 (2010).

S9. Materials based on declarations of the Ministry of Emergency Situations (Press report release of 5 August 2010 by Bloomberg: " Russian Wildfires' Death Toll Rises to 50; Drought May Force Export Ban”: http://www.bloomberg.com/news/2010-08-05/russian-wildfires-death-toll-rises-to-50-drought-may-force-export-ban.html)

S10. Materials based on declarations of economists at HSBC Holdings Plc (Press report release of 10 August 2010 by Bloomberg L.P. News Group: “Russia Heat Wave May Kill 15,000, Shave $15 Billion of GDP”: http://www.bloomberg.com/news/2010-08-10/russia-may-lose-15-000-lives-15-billion-of-economic-output-in-heat-wave.html)

S11. Materials based on FAO (Food and Agriculture Organization) estimates (Press report release of 9 September 2010 by the Economist: “Volatile wheat prices are as much a cause for alarm as are high prices”: http://www.economist.com/node/16994407)

S12. R. Kistler et al., Bull. Am. Meteorol. Soc. 82, 247–267 (2001).

S13. G. P. Compo et al., Q. J. R. Meteorol. Soc. 137, 1–28 (2011).

S14. J. Luterbacher, D. Dietrich, E. Xoplaki, M. Grosjean, H. Wanner, Science 303, 1499-1503 (2004).

S15. J. Hansen, R. Ruedy, J. Glascoe, M. Sato, J. Geophys. Res. 104, 30997-31022 (1999).

S16. P. van der Linden, J.F.B. Mitchell, "ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project" (Met. Office Hadley Centre, Fitzroy Road, Exeter EX1 3PB, UK. 2009), 160 pp.

S17. S. G. Coles, An Introduction to Statistical Modeling of Extreme Values (Springer, London, 2001).

31

S18. M. R. Haylock et al., J. Geophys. Res. 113, D20119, doi:10.1029/2008JD010201 (2008).

S19. E.J. Klok, A. M. G. Klein Tank, Int. J. Climatol. 29, 1182–1191, doi: 10.1002/joc.1779 (2008)

S20. J. Kyselý, E. Plavcová, J. Geophys. Res. 115, D23118, doi:10.1029/2010JD014123 (2010)

S21. Materials from mean monthly air temperature data set of Russian stations as provided by Scientific Research Institute of Hydrometeorological Information-World Data Center (RIHMI—WDC) (http://meteo.ru/english/climate/temp.php)

S22. MYe Lyakhov, Climatic extremes in the central part of the European territory of the USSR. Izvestiya of Russian Academy of Sciences Geographical Series 6, 68-74 (1984) (in Russian)

S23. A.M. Voronov, Estimate of the regional changes of hydroclimatic conditions at the European part of Soviet Union according to historical data. Vodnye resursy 4, 97-105 (1992) (in Russian)

S24. V. Klimenko, O. Solomina, in The Polish climate in the European context: an historical overview, R. Przybylak et al., Eds. (Springer, Berlin/Heidelberg/New York, doi 10.1007/978-90-481-3167-9_3, 2010), pp. 71-101.

S25. R.M. Trigo, R. García-Herrera, J. Díaz, I. F. Trigo, M. A. Valente, Geophys. Res. Lett. 32, L10701, doi:10.1029/2005GL022410 (2005).

S26. P. M. Della-Marta et al., Clim. Dyn. 29, 251–275 (2007)

S27. G. A. Meehl, C. Tebaldi, Science 305, 994–997 (2004).

S28. Q. Liu, Tellus 46A, 286–290 (1994).

S29. E. M. Fischer, C. Schär, Clim. Dyn. 33, 917-935 (2009).

S30. S. I. Seneviratne, D. Lüthi, M. Litschi, C. Schär, Nature 443, 205-209 (2006)

S31. G. Lenderink, A. van Ulden, B. van den Hurk, E. van Meijgaard, Clim. Change 81, 233–247 (2007)

S32. P.L. Vidale, D. Lüthi, R. Wegmann, C. Schär, Clim. Change 81, 209–232 (2007)

S33. E. M. Fischer, D.M. Lawrence, B.M. Sanderson, Clim. Dyn., doi:10.1007/s00382-010-0915-y (2010).

S34. U. Schneider, T. Fuchs, A. Meyer-Christoffer, B. Rudolf, “Global Precipitation Analysis Products of the GPCC” (Global Precipitation Climatology Centre, 2008) pp. 1-12.

S35. D. A. Robinson, K. F. Dewey, R. R. Heim, Bull. Am. Meteorol. Soc. 74, 1689– 1696 (1993)

S36. E. Kjellström et al., "A 140-year simulation of European climate with the new version of the Rossby Centre regional atmospheric climate model (RCA3)" (SMHI Reports Meteorology and Climatology, 108, SMHI, SE-60176 Norrköping, Sweden, 2005), 54 pp.

S37. J.E. Haugen, H. Haakenstad, "Validation of HIRHAM version 2 with 50 km and 25 km resolution" (RegClim General Technical Report No. 9., 2006), pp. 159-173.

S38. U. Böhm et al., "Clm - the climate version of lm: Brief description and long-term applications" (COSMO Newsletter, 6, 2006).

S39. Collins et al., Clim. Dyn. 27, 127-147 (2006)

S40. G. Lenderink, B. van den Hurk, E. van Meijgaard, A.van Ulden, J. Cuijpers, "Simulation of present-day climate in RACMO2: first results and model developments" (KNMI Technical Report 252, 2003), 24 pp.

S41. D. Jacob et al., Meteorol. Atmos. Phys. 77, 19-43 (2001).