No pause in the increase of hot temperature extremes

SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE2145 No pause in the increase of hot temperature extremes Sonia I. Seneviratne 1, Markus G. Donat 2,3, Brigitte Mueller 4,1, and Lisa V. Alexander 2,3 1 Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland 2 Climate Change Research Centre, University of New South Wales, Sydney, Australia 3 ARC Centre of Excellence in Climate System Science, University of New South Wales, Sydney, Australia 4 Environment Canada, Climate Research Division, Toronto, Canada S1. Calculation of extreme warm day exceedances (ExD10, ExD30, ExD50) for ERA-Interim and HadEX2 datasets We calculated indices representing exceedances of warm days at each grid box for Figure 1. These are based on the 90 th percentile warm day frequency (TX90p) as defined by the Expert Team for Climate Change Detection and Indices 1,2, which counts the annual number of days with daily maximum temperature above the 90 th percentile. The percentile threshold for each day is calculated using a five-day window (centered on each calendar day), using a bootstrapping method 3 to avoid inhomogeneities at the beginning and end of the percentile base period. The reference number of extreme warm days (ExDref) is calculated at each location as the TX90p average over the 1979-2010 time period. The yearly timeseries of extreme warm day exceedances (ExD10, ExD30, ExD50) are computed with respect to ExDref. They indicate the land area ratio in each year (using a weighted area sum according to the size of the respective grid cells), affected by an exceedance of 10 (ExD10), 30 (ExD30), and 50 (ExD50) extreme warm days, respectively. A ratio of 1 indicates that the land area fraction affected by a given exceedance corresponds to the 1979-2010 reference value, a ratio of 2 indicates a doubling (see Fig. 1). HadEX2 is a gridded land-based dataset of temperature and precipitation extremes 4. Several climate indices (including TX90p) are first calculated for each station and then interpolated onto a global 3.75 x2.5 longitude-latitude grid. The station-based indices are calculated relative to the percentile threshold over the 1961-1990 base period. We also calculated the climate indices for the ECMWF reanalysis ERA-Interim 5, which is produced on a regular 1.5 x1.5 grid and covers the period 1979 to present. The daily maximum temperature was derived as the daily maximum of instantaneous 6-hourly 2mair temperature values. TX90p was calculated relative to the percentile values during the first 30 years of this data set (1979-2008). Note that the 30-year base periods for calculating the 90 th percentile are different for HadEX2 (1961-1990) and for ERA-Interim (1979-2008). It was not possible to calculate indices with consistent base periods for both datasets due to limitations with data availability (the station-based indices for HadEX2 were calculated by local contributors using the predefined 1961-1990 base period, whereas ERA-Interim reanalysis is available only from 1979 to present). As a consequence, the percentiles for ERA-Interim are representative of a warmer climate than the percentiles used for HadEX2, which would be expected to result in fewer exceedances of the threshold. Nonetheless, sensitivity tests changing the ERA-Interim base period (e.g. to 1979-1990) revealed no strong sensitivity of the results to this choice. Another difference is the fact that HadEX2 does not have a complete global coverage, unlike ERA-Interim. However, computations NATURE CLIMATE CHANGE www.nature.com/natureclimatechange 1

for ERA-Interim restricted to the land area covered by HadEX2 also provide qualitatively similar results, Supplementary Fig. S1a,b). Supplementary Fig. S1. Sensitivity of Fig 1b results to geographical coverage. (a, left) Computation of ERA- Interim timeseries of Fig. 1b using ERA-Interim data only from grid boxes where HadEX2 provides data (with total coverage changing in each year). Note that the time series ends in 2010 because there is no HadEX2 data in 2011 and 2012 (i.e., no valid data remain after masking ERA-Interim). (b, right) Overview of coverage of HadEX2 dataset (shaded grid points have yearly TX90p statistics in at least 30 out of 32 years over the 1979-2010 time period). S2. Calculation of timeseries of yearly hot and cold temperature percentiles and of yearly global and land mean temperature The yearly land-based temperature percentiles timeseries for daily maximum (Txp95_Land, Txp5_Land) and minimum (Tnp95_Land) temperatures displayed in Figure 2 and Supplementary Figs. S2-S8 were calculated from the daily maximum (respectively minimum) of 6-hourly instantaneous 2m-air temperature values of the ERA- Interim 5 dataset. The yearly global and land mean temperature timeseries (Tm_Glob, Tm_Land) displayed in Fig. 2, and Supplementary Figs. S3, S4 (top two panels), and S5, were calculated from daily averages of the instantaneous 6-hourly data from the ERA-Interim 5 dataset and from monthly gridded temperature observations from the HadCRUT4 6 dataset. HadCRUT4 provides an ensemble of 100 realizations to account for uncertainties in the observational data. Here we used the ensemble median for the global-mean time series of annual mean temperatures (calculated as the average of the 12 monthly values). In addition, we also provide analyses of Tm_Glob and Tm_Land timeseries for the recently derived hybrid and krigging datasets of Cowtan and Way (2014) 7 in Suppl. Fig. S4 (bottom two panels) The derived land-based and global mean timeseries were computed using weighted area averages (taking into account the area of the respective grid cells).

S3. Suitability of ERA-Interim for assessment of trends in temperature extremes As shown in Fig. 1a,b, the ExD10, ExD30, and ExD50 timeseries derived from ERA- Interim agree well with those derived from the HadEX2 dataset. This is the case despite the different base periods used for the definition of the 90 th percentile thresholds in these two datasets (see Supplementary Discussion S1). For further evaluation of the ERA-Interim-based analyses in Fig. 2, we additionally compare the trends in Txp95 in ERA-Interim with respective analyses based on the HadGHCND dataset 8 of daily gridded temperature fields interpolated from station data (Fig. S2a). HadGHCND has a more limited spatial coverage than e.g. HadEX2 (Supplementary Fig. S2b), but as it provides daily data unlike HadEX2 8,9, it allows calculation/comparison of annual percentile values. Also this comparison suggests that ERA-Interim correctly captures the overall behavior of station-based trends in extremes over the area covered by the HadGHCND dataset (Suppl. Fig. S2a,b; compare pink and green lines in Suppl. Fig. S2a). For the main analyses, the HadGHCND is not considered because of its too sparse coverage and lower level of quality checks. The ERA-Interim analyses suggest that the data coverage of HadGHCND would lead to an underestimation of the trends of warm extremes for the overall land area (Suppl. Fig. S2a; compare red and pink lines). The identified good agreement of ERA-Interim with station-based observational products of extreme indices (Fig. 1 and Supplementary Fig. S2) is consistent with results from previous global-scale observation-based analyses showing that observed trends in temperature extremes are well captured in the ERA-Interim reanalysis, compared to the performance of other reanalysis products 10. These results do also not suggest the presence of a discontinuity in the record after 2000 11. Supplementary Fig. S2. (a, left) Comparison of Txp95 time series of ERA-Interim and HadGHCND dataset over area covered by HadGHCND. (b, right) Coverage of HadGHCND dataset for computation of yearly 95 th percentile Tmax (at least 95% data over 1979-2011 time period).

S4. Trends in hot extremes over land vs trends in mean temperature over land The signal identified in the trends of hot temperature extremes over land is partly due to an overall larger warming of the mean temperature over land compared to the evolution of the mean global (land + oceans) temperature (Suppl. Figures S3 and S4). This result is consistent with the identified large heat uptake in the oceans 12 (and overall cooling tendencies in sea surface temperatures). Supplementary Fig. S4 shows that it is also identified in a range of datasets (ERA-Interim, HadCRUT4 / CRUTEM4, and the two recently compiled datasets of Cowtan and Way (2014) 7 ), and that the overall tendencies over land are similar for the different datasets. However, as seen in Suppl. Fig. S3, there is a distinct additional warming of extremes compared to the mean land temperatures. This feature is also seen in geographical maps of trends (Suppl. Fig. S5) with a particular strong signal in mid-latitude regions (excess warming in eastern Europe, part of North America, and South America; lack of cooling in continental Asia), as well as in Greenland. S5. Seasonal trends in hot extremes over land Supplementary Figure S6 provides maps of the ERA-Interim-based 1997-2012 trends in seasonal 95 th percentile of Tmax (for the December-January-February (DJF), March- April-May (MAM), June-July-August (JJA), and September-October-November (SON) seasons). These analyses reveal an overall strong correspondence between the trends in annual Txp95 and those in JJA Txp95, while the large trends in annual Txp95 in South America can be related to the respective trends in the DJF season in this region. Cooling trends in the seasonal Txp95 are also found, especially in mid- to high latitudes in boreal winter.

Supplementary Fig. S3. Same analysis as in Fig. 2, but also including the ERA-Interim-based trends in mean temperature over land (brown line). Supplementary Fig. S4. Same analysis as Supplementary Fig. S3, but only for trends in global (black) and land (brown) mean temperature, and for the following datasets: (top left) ERA-Interim; (top right) HadCRUT4 and CRUTEM4 (CRUTEM4 is the land component of the HadCRUT4 dataset); hybrid and krigging datasets of Cowtan and Way (2014) 7.

Supplementary Fig. S5. Maps of 1997-2012 trends in global mean temperature (a,b) vs hot extremes (c). The analyses are based on HadCRUT4 (a: mean temperature) and ERA-Interim (b: mean temperature; c: 95 th percentile of Tmax, Txp95). Unit: C / 10 years.

Supplementary Fig. S6. Maps of 1997-2012 trends in seasonal 95 th percentile of Tmax (DJF: December- January-February; MAM: March-April-May; JJA: June-July-August; SON: September-October-November). Unit: C / 10 years. S6. Trends in land-based hot vs cold percentiles of Tmax Supplementary Fig. S7 displays the trends in land-based 95 th percentile Tmax in ERA- Interim (red; same as red line in Fig. 2) vs respective trends in land-based 5 th percentile Tmax (blue). This analysis reveals that the cold extremes of daily maximum temperature have shown a slight tendency towards cooling, unlike the tendency towards strong warming shown by the hot daily extremes. Supplementary Fig. S7. Time series of temperature anomalies (with respect to 1979-2010 time period) of ERA-Interim annual 95 th (red) and 5 th (blue) percentile of daily Tmax over land (Txp95_Land, Txp5_Land).

S7. Trends in land-based warm extremes of minimum (nighttime) temperature Supplementary Fig. S8 displays the trends in land-based 95 th percentile Tmin (minimum, i.e. nighttime, temperature, Tnp95_Land) in ERA-Interim (red) vs respective trends in global (land+ocean) mean temperature in ERA-Interim (blue) and HadCRUT4 (black; these latter two lines are identical to those displayed in Fig. 2). This analysis shows that the trends in warm extremes of minimum (nighttime) temperature (Tnp95) also display a stronger warming over the hiatus period than the global mean temperature, similarly to the trends in the high percentiles of maximum temperature (Tmax; Txp95_Land, see Fig. 2), although the overall warming is higher for Txp95_Land than for Tnp95_Land. Supplementary Fig. S8. Same as Fig. 2, but displaying the timeseries of the anomalies of the 95 th percentile of Tmin over land (Tnp95_Land, red) in ERA-Interim vs the global (ocean + land) mean temperature (Tm_Glob) in ERA-Interim (blue) and HadCRUT4 (black).

References of supplementary material 1 Klein Tank, A.M.G., Zwiers, F.W. & Zhang, X., 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. Rep. WCDMP-No. 72, World Meteorological Organization. 2 Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Klein Tank, A., Peterson, T.C., Trewin, B. & Zwiers, F.W., 2011. Indices for monitoring changes in extremes based on daily temperature and precipitation data, WIREs Climate Change, 2, 851 870, doi:10.1002/wcc.147. 3 Zhang, X., Hegerl, G., Zwiers, F.W. & Kenyon, J., 2005. Avoiding inhomogeneity in percentile-based indices of temperature extremes, J. Clim.,18, 1641 1651. 4 Donat, M.G., et al., 2013a. Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset, J. Geophys. Res. Atmos., 118, 2098 2118, doi:10.1002/jgrd.50150. 5 Dee D.P, et al., 2011. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteorol. Soc., 137, 553-597. 6 Morice, C.P., Kennedy, J.J., Rayner, N.A. & Jones, P.D., 2012. J. Geophys. Res., 117, D08101. 7 Cowtan, K. & Way, G., 2014. Quart. J. Roy. Met. Soc., in press. 8 Caesar, J., Alexander, L., & Vose, R., 2006. Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. Journal of Geophysical Research, 111(D5), D05101. 9 Donat, M.G., Alexander, L.V., Yang, H., Durre, I., Vose, R. & Caesar, J., 2013b. Global land-based datasets for monitoring climatic extremes. Bull. Am. Meteor. Soc., 997-1006. 10 Donat, M.G., Sillmann, J., Wild, S., Alexander, L.V., Lippmann, T. & Zwiers, F.W., 2014. Consistency of temperature and precipitation extremes across various global gridded in situ and reanalysis data sets, Journal of Climate (in review after minor revisions). 11 Trenberth, K.E. & Fasullo, J.T. 2013. An apparent hiatus in global warming? Earth s Future, doi: 10.1002/2013EF000165. 12 IPCC, 2013, Stocker, T. et al. (eds.), (Cambridge University Press 2013).