Attributing human mortality during extreme heat waves to anthropogenic climate change: SI

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1 Attributing human mortality during extreme heat waves to anthropogenic climate change: SI Daniel Mitchell 1, Clare Heaviside 2, Sotiris Vardoulakis 2, Chris Huntingford 3, Giacomo Masato 4, Benoit Guillod 1, Peter Frumhoff 5, Andy Bowery 6, David Wallom 6 & Myles Allen 1 1 Environmental Change Institute, University of Oxford, Oxford, UK mitchell@atm.ox.ac.uk 2 Centre for Radiation, Chemical and Environmental Hazards, Public Health England, UK. 3 Centre for Ecology and Hydrology (CEH), Wallingford, UK. 4 Dept. of Meteorology, Reading University, Reading, UK. 5 Union of Concerned Scientists (UCS), Cambridge, USA. 6 Oxford e-research Centre, University of Oxford, Oxford, UK. February Model representation of the 2003 dynamics The direct thermodynamical response to increased GHGs is well understood[1, 2], whilst changes to the secondary dynamical mechanisms which could additionally lead to a non-linear climate response, are currently more debated in the literature[3, 4, 5]. One of the most prevalent theories that specifically relates to the 2003 heat wave is the amplification of quasi-stationary mid-latitude waves with zonal wave numbers 6-8 [3]. Amplitude increases, such as that observed in 2003, occur due to trapping of waves in mid-latitude wave-guides. There are numerous examples of these high-amplitude wave patterns reproduced in our model simulations, e.g. Fig. S1a, which can be compared with examples in Petoukhov et al, 2012 (see their Figure 2). Screen and Simmons, 2013b argue that such dynamical changes seen in the currently observed changes in frequency of extreme events, cannot be attributed to changing atmospheric gas composition. This is because trends in the daily amplitude of waves with wavenumber 6-8 were either absent, or masked by the low signal to noise. However the use of large ensembles, as performed for this analysis, does uniquely enable assessment of changing probabilities. Based on this, when wave amplitudes and phase angles of mid-latitude wave numbers 6, 7 and 8 (Fig S2) are calculated for the two scenarios used here there is clearly no difference between the Actual (red) and Nature (blue) scenarios in any of the summer months. The model does capture the magnitudes of the wave features well, with the green circles marking the 2003 ERA- Interim reanalysis calculated wave amplitudes for each of the summer months, generally

2 End-to-end attribution of heat related mortality falling within the spread of ensemble runs. There is clear orographic locking of waves in the model, but most apparently with wave numbers 6 and 7 (top and middle panels, Fig S2), where wave phases are clustered at phase = 2π and phase = 3π/2, respectively. The hottest temperatures in 2003 occurred in August, and for that month, for amplitudes of the wavenumber 7, and for phase magnitudes of k = 7 and 8 components diagnosed from the ECMWF re-analysis, then these are on the edge of the distributions for their respective phase magnitudes. This is especially true for the bottom right panel (August, k=8), which shows that the observed wave had a particularly uncommon phase angle, captured only in the tails of the modelled distribution. If the wave amplitudes grow too large, a natural damping mechanism may be the breaking of the waves into summer time blocks or ridge events, and these were known to be important during the 2003 heat wave[6] (Masato, In Review). Our model captures both of these features well compared to ECMWF re-analysis, both in general climatological terms, and in specific similarity to the 2003 heat wave, where numerous similar examples are found in our ensemble (Fig. S1b). We find no difference in either Atlantic blocking of ridging events between our natural and actual scenarios (Fig 3 in the main paper), which corresponds well to the also unchanging wave amplitudes (Fig. S2) Model temperature evolution 2.1. London and Paris Confirming that the model used in our ensemble is capable of representing well the dynamical features known to have occurred during the 2003 heat wave allows us to then focus on projections at the more local scales. Specifically, our study considers the two major European cities, Paris and London. The daily station near-surface apparent temperature records over London and Paris show that the largest temperatures were observed in the first two weeks of August (Fig S3, black lines). The most extreme members (top 3%) of the models simulations capture this for Paris (light red regions give the range over all ensemble members) Europe. Actual verses Natural ensemble warming features Calculating the geographical differences during the summer period (June, July, August; JJA) between near-surface temperatures, from our two ensembles of experiments (see Methods) shows a clear warming throughout the domain. This warming, between the local means of the ensembles, ranges between 0.4 K to 1.6 K (Fig. S4a). The black box shows the predefined region (10 o W - 40 o E and o N) used in Stott et al, 2004, which we use here so as not to bias our selection of the region to be considered. Fig. S5b (black line) shows the area-weighted near surface land temperature from the CRUTEM4 observations[7] averaged over this region for the summer period (JJA). The orange arrow marks the 2003 event, which is more extreme than any prior event in

3 End-to-end attribution of heat related mortality the observational record, although a similar magnitude event (when considering purely this region) occurred subsequently, in 2012 (see also Christidis et al, 2014). For this region, the red and blue box-and-whisker plots show the range of responses from our model simulations forced with actual 2003 conditions, and natural 2003 conditions, respectively. Individual plus symbols show the data beyond 5-95%, and it is only this data that is able to reach the heat wave extremes observed in 2003, although far more of these occurring in the actual conditions scenario. In the original Stott et al, 2004 study, which was the first to look at the 2003 heat wave from an attribution framework, they made the plausible assumption that the variability of summer heat waves did not change between their actual and natural conditions ensembles. In this study, as we use multi-thousand member ensembles, we can test this hypothesis explicitly and show that it holds true (i.e. the widths of boxes in Fig. S4b are equal). The overall shift in the temperature response observed in Fig. S4 is a typical signature of the direct thermodynamical response of the climate to increased radiative forcing. However, within the shifted climate, it is still the large-scale dynamics[6, 3] that really led to the extreme temperatures observed in 2003 (i.e. from Figs S1-S2) Model soil moisture Soil moisture can influence the partitioning of the energy available at the land surface into sensible and latent heat fluxes[8], thereby influencing temperature[9] and in some cases precipitation[10]. Land-atmosphere interactions can play an important role for hot temperature extremes[11, 12], in particular in transitional climate regions between wet and dry climate where soil moisture is the factor limiting evapotranspiration[8]. In these regions, evaporative cooling is reduced in situations with dry soils, leading to increased temperature locally. Since soil moisture exhibits memory characteristics at the scale of a few months[13], antecedent soil moisture conditions (e.g., in spring) can impact the magnitude of a heat wave in summer. Thus, while suitable large-scale atmospheric modes such as blockings are a requirement to heat waves, dry soils are also required for the occurrence of the strongest events[14]. Note that, in the case of 2003, recent observational studies have shown that the soils weren t particularly dry in spring, but that a rapid dry out took place in early summer leading to very dry summer conditions[15, 16]. Nonetheless, the potential impact of soil moisture on the simulated temperature calls for a validation of the underlying relationships in the model. We decompose soil moisture-temperature coupling in two steps. First, soil moisture persistence, which determines whether a spring soil moisture anomaly is likely to persist until the summer. Second, the strength of land-atmosphere coupling (i.e., the direct impact of soil moisture on the partitioning between latent and sensible heat fluxes) determines whether, in summer, soil moisture can impact the atmosphere. Together, these two aspects determine whether antecedent (spring) soil moisture conditions impact summer temperature.

4 End-to-end attribution of heat related mortality We therefore investigate these three characteristics in our model: soil moisture persistence, summer land-atmosphere coupling, and the relationship between spring soil moisture and summer temperature. We compare our results to two other data sources: (i) ERA: Air temperature from ERA-Interim[17], and soil moisture and latent heat flux from ERA-Land[18]. ERA-Land is an improved land-surface dataset based on ERA-Interim. Although it cannot be considered an observation, the reanalysis provides us with a gridded product that should at least approximate real conditions. (ii) N14: A large ensemble of the weather@home model from Massey et al. (2014) spanning multiple years ( ). The simulations from M14 are used to make sure that our results are not driven by the 2003 SST patterns. They provide a similar setup to ours, albeit where the regional climate model is run at a 50km resolution. Soil moisture in the upper 1m of soil is used for the analysis, after subtracting the mean seasonal cycle. For ERA-Land, volumetric water content is used (m 3 /m 3 ), while for weather@home, a soil moisture index (SMI) is computed, with value 1 if above the critical point (soil moisture not limiting), value 0 if below wilting point, and a linear interpolation in between (see also e.g., Betts, 2004). Figure S5 (a-c) shows the 1-month soil moisture autocorrelation, which displays a relatively uniform pattern throughout Europe, albeit with lower values over mountainous regions. Overall, soil moisture is slightly more persistent in the weather@home model (in both ensembles) than in ERA-Land, but this could also be due to the use of different units. Summer land-atmosphere coupling is estimated using the correlation between mean summer temperature and latent heat flux. This metric has been shown to agree well with land-atmosphere coupling regions derived from modelling experiments[19]. Positive values indicate that evaporation is energy-limited (i.e., low atmosphere coupling), while negative values indicate that ET is soil moisture limited (i.e., dry soils induce lower ET and thus higher sensible heat flux for constant net radiation, which in turn increases temperature). As shown in Fig. S5 (d), ERA displays patterns of coupling that align well with expectations, with strong coupling in southern Europe. In weather@home (Fig. S5e-f), the coupling is overly strong and it extends further north. This result aligns well with the dry bias identified by M14 in Southern and Central Europe. It is well known that the coupling strength strongly varies between models[20], and it should be noted that the coupling from ERA is mostly model-based. Nonetheless, these results suggest that the modelled temperature might be overly sensitive to soil moisture. Finally, Fig. S6 displays the resulting relationship between spring soil moisture and summer temperature over France. Results from ERA are noisy and no clear relationship can be identified (apart from 2003, which stands out as a very hot and dry year, but, as mentioned above, recent observational studies have shown that spring soil moisture did not, in fact, exhibit particularly low values that year). In the model, temperature distributions are slightly shifted to lower values with increased soil moisture, but the

5 End-to-end attribution of heat related mortality difference is rather small. Thus, in spite of more persistent soil moisture and stronger land-atmosphere coupling, the relationship between spring soil moisture and summer temperature remains relatively weak in the model. This relationship cannot be directly validated with ERA-interim and ERA-Land owing to short record length Methods 3.1. Modelling of large scale dynamics The process of defining ridges relies on the calculation of the meridional displacements of a daily geopotential contour against its zonal average around a latitude circle. To avoid the systematic bias caused by the dependence on longitude, the climatology of such meridional displacements is removed (see Masato 2015 for further details). The meridional overturnings that lead to blocking events are detected as a function of longitude whenever the daily geopotential contour intersects a meridian more than once. The associated amplitude is the difference in latitude between the northernmost and southernmost intersection. The procedure above is repeated at each grid-point within a latitude band o N, in order to step away from a sensitivity issue related to the choice of latitude. Once the displacements and overturnings are derived for the entire latitude band, their integral along the meridional direction is calculated so that a single value is returned as a function of longitude. The percentage of blocking and ridging is calculated as follows, for each day the number of points within the longitude band 10 o W-20 o E exceeding a given threshold in the meridional displacement is calculated. If the number of points is greater than half of the total size, the day is associated with the given event. The threshold for blocking and ridging is set to 5 and 0 lat degrees, respectively. It is noted here that such thresholds are quite low, however it should be borne in mind that an integral over a 10 degree latitude band was previously applied (see above paragraph). [1] Pall, P. et al. Anthropogenic greenhouse gas contribution to flood risk in england and wales in autumn Nature 470, (2011). [2] Palmer, T. Climate extremes and the role of dynamics. Proceedings of the National Academy of Sciences 110, (2013). [3] Petoukhov, V., Rahmstorf, S., Petri, S. & Schellnhuber, H. J. Quasiresonant amplification of planetary waves and recent northern hemisphere weather extremes. Proceedings of the National Academy of Sciences 110, (2013). [4] Francis, J. A. & Vavrus, S. J. Evidence for a wavier jet stream in response to rapid arctic warming. Environmental Research Letters 10, (2015). [5] Screen, J. A. & Simmonds, I. The central role of diminishing sea ice in recent arctic temperature amplification. Nature 464, (2010). [6] Black, E., Blackburn, M., Harrison, G., Hoskins, B. & Methven, J. Factors contributing to the summer 2003 european heatwave. Weather 59, (2004). [7] Jones, P. et al. Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to Journal of Geophysical Research: Atmospheres ( ) 117 (2012). [8] Seneviratne, S. I. et al. Investigating soil moisture climate interactions in a changing climate: A review. Earth-Science Reviews 99, (2010).

6 End-to-end attribution of heat related mortality [9] Mueller, B. & Seneviratne, S. I. Hot days induced by precipitation deficits at the global scale. Proceedings of the national academy of sciences 109, (2012). [10] Guillod, B. P., Orlowsky, B., Miralles, D. G., Teuling, A. J. & Seneviratne, S. I. Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nature communications 6 (2015). [11] Vautard, R. et al. Summertime european heat and drought waves induced by wintertime mediterranean rainfall deficit. Geophysical Research Letters 34 (2007). [12] Hirschi, M. et al. Observational evidence for soil-moisture impact on hot extremes in southeastern europe. Nature Geoscience 4, (2011). [13] Koster, R. D. & Suarez, M. J. Soil moisture memory in climate models. Journal of hydrometeorology 2, (2001). [14] Quesada, B., Vautard, R., Yiou, P., Hirschi, M. & Seneviratne, S. I. Asymmetric european summer heat predictability from wet and dry southern winters and springs. Nature Climate Change 2, (2012). [15] Seneviratne, S. I. et al. Swiss prealpine rietholzbach research catchment and lysimeter: 32 year time series and 2003 drought event. Water Resources Research 48 (2012). [16] Whan, K. et al. Impact of soil moisture on extreme maximum temperatures in europe. Weather and Climate Extremes (2015). [17] Dee, D. et al. The era-interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137, (2011). [18] Balsamo, G. et al. Era-interim/land: a global land surface reanalysis data set. Hydrology and Earth System Sciences 19, (2015). [19] Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land atmosphere coupling and climate change in europe. Nature 443, (2006). [20] Koster, R. D. et al. Glace: the global land-atmosphere coupling experiment. part i: overview. Journal of Hydrometeorology 7, (2006).

End-to-end attribution of heat related mortality 7 a.)$ b.)$ Figure S1. Example wave and blocking cases.

) 2003-like blocking/ridging events in the model simulations (see main text for details).

) are ms 1, units of b.) are degrees latitude. Panels a.) and b.

7 End-to-end attribution of heat related mortality 7 a.)$ b.)$ Figure S1. Example wave and blocking cases. Examples of (a) 2003-like large amplitude wave events in our model simulations and in reanalysis, and (b.) 2003-like blocking/ridging events in the model simulations (see main text for details). (bottom) Red shows ridges, blue shows troughs, regions encircled with black show blocking. Units of a.) are ms 1, units of b.) are degrees latitude. Panels a.) and b.) can be compared with equivalent plots for reanalysis in Petoukhov et al, 2012, and Masato, 2015, respectively. Model cases were chosen based on their proximity in phase space to the observed event (i.e. the nearest dots to the green dot in Figure S2).

8 End-to-end attribution of heat related mortality 8 Figure S2. Fourier components of models and reanalysis. Amplitudes (ms 1 ) verses phase (radians) of monthly-mean Fourier components of meridional wind at 300 hpa averaged over the ( )o N latitude range. Left-to-right shows June-to-July. Topto-bottom shows waves numbers k=6,7,8. Red are for Actual conditions, blue are for Natural conditions. The green circle shows the ECMWF observed year-2003 values.

9 End-to-end attribution of heat related mortality 9 Figure S3. London and Paris apparent temperatures, summer Time series of apparent temperature over (left) London and (right) Paris. Black lines show city station data. Red regions show interquartile range and light red is the range of the Actual scenario simulations. Figure S4. Surface temperature differences between model ensemble. A.) Difference in mean 2003 summer (JJA) temperature over the 25 KM regional domain for the actual conditions minus natural conditions experiments. The cities of London and Paris are marked with black circles. The region used in Stott et al, 2004 is marked with a black box. B.) The black line shows the mean summer temperature anomaly (relative to ) in observations (CRUTEM4) averaged over the Stott et al region. The box and whisker plots show the median, interquartile range, 5-95% range and more extreme data as + symbols over the same region for the (red) Actual scenario and (blue) Natural scenario. Horizontal dashed lines show the 5-95% range of the modelled data. Year 2003 is marked with an orange arrow.

10 End-to-end attribution of heat related mortality 10 Figure S5. Soil moisture persistence and land-atmosphere coupling in (a,d) a reanalysis product, (b,e) our 2003 Actual simulations and (c,f) a large ensemble of weather@home spanning with the regional climate model at 50km resolution (M14). (a-c) Lag 1 autocorrelation in monthly averaged soil moisture anomalies. (d-f) Correlation between mean summer air temperature and latent heat flux (ρ LE,tas ), as a measure for land-atmosphere coupling. Positive ρ LE,tas indicates that evaporation is energy-limited, while negative ρ LE,tas indicates soil moisture-limited evaporation and hence strong land-atmosphere coupling. Figure S6. Relationship between spring soil moisture and summer temperature. Boxplots of mean summer (JJA) temperature for 5 bins of spring (MAM) soil moisture. Each soil moisture bin contains 20% of the data.

J1.7 SOIL MOISTURE ATMOSPHERE INTERACTIONS DURING THE 2003 EUROPEAN SUMMER HEATWAVE

J1.7 SOIL MOISTURE ATMOSPHERE INTERACTIONS DURING THE 2003 EUROPEAN SUMMER HEATWAVE E Fischer* (1), SI Seneviratne (1), D Lüthi (1), PL Vidale (2), and C Schär (1) 1 Institute for Atmospheric and Climate