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10.1175/JCLI-D-13-00524.s1 Wegmann et al. Supplemental Material for Volcanic Influence on European Summer Precipitation through Monsoons: Possible Cause for “Years without Summer” Martin Wegmann and Stefan Brönnimann 5 Oeschger Centre for Climate Change Research and Institute of Geography, University of Bern, Bern, Switzerland Jonas Bhend Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland, and CSIRO Marine and Atmospheric Research, Aspendale, Victoria, Australia Jörg Franke 10 Oeschger Centre for Climate Change Research and Institute of Geography, University of Bern, Bern, Switzerland Doris Folini and Martin Wild Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland Jürg Luterbacher Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University of Giessen, Giessen, Germany 15 (Manuscript received 29 August 2013, in final form 15 January 2014) 1 Fig. S1. Boreal winter (December to February) temperature and precipitation anomalies averaged across 14 tropical volcanic eruptions in ECHAM5.4 relative a to reference period around each eruption (ALL-REF). Dotted areas represent significant differences (t-test, 95% confidence level). Both panels are field significant (95% confidence level). 2 10.1175/JCLI-D-13-00524.s1 Wegmann et al. Fig. S2. Averaged boreal summer response to volcanic eruptions in ECHAM5.4 climate model simulations. (a) surface air temperature, (b) evaporation (positive values denote an upward flux), (c) vertical velocity (colours) and geopotential height (contour spacing 2 gpm, dashed contours 5 are negative) at 500 hPa and (d) wind vectors and zonal wind at 850 hPa. The plotted differences are calculated for each eruption relative a to reference period around each eruption, then averaged across all 14 eruptions (ALL-REF; see Fig. 2 for corresponding ALL-NOVOLC differences). Dotted areas in (a) and (b) represent significant differences (one-sample t-test, 95% confidence level). All panels are field significant (95% confidence level). 10 3 Fig. S3. Coincidence of volcanically perturbed boreal winters and boreal fall-to-winter ENSO events. The figure is based on Table 1 in Brönnimann et al. (2007), but only using the period after 1600. The selection of volcanic eruptions is the same as in this study. Note that the first two 5 winters after each eruption were considered volcanically perturbed. 10 4 10.1175/JCLI-D-13-00524.s1 Wegmann et al. Table S1: Standard deviation of reconstructed and modeled precipitation timeseries in mm Region ZIM NWT EM EU JJA EU DJF SEU JJA SEU DJF Standard 76 (134 deviation Obs.) reconstructions 51.95 14.78 9.46 13.18 8.57 28.07 Standard deviation model (mean) 99.96 43.86 14.13 17.50 15.69 5.2 46.51 Standard deviation model (max) 109.55 51.19 15.02 18.38 16.94 5.61 49.22 Standard deviation model (min) 91.93 39.98 13.28 16.26 14.67 4.58 42.91 Listed are standard deviations of exemplary precipitation timeseries from reconstructions and from our model output. ZIM = Zimbabwe Summer Precipitation by Therrell et al. 2006. NWT = 5 Northwest Turkey Precipitation by Akkemik et al. 2005. EM = Eastern Mediterranean MayAugust Precipitation by Touchan et al. 2005. Time and space domains can be found in the cited publications. The EU and SEU values are taken from Pauling et al. 2006 with the respective coordinates 31°-70°N, 30°W-40°E and 36°-40°N, 10°W-40°E. The model output is calculated from the nearest model gridcells to the reconstruction domain. Time and season domain are the 10 same as the respective reconstruction. Be aware that the model output may be only one gridcell. The output was field averaged in any case. All model values are land only precipation. The listed values show the mean, maximum and minimum of all 30 ensemble members. 5 Fig. S4. Boxplot of the summer SEU precipitation VOLC-NOVOLC anomaly in our model run for each ensemble member (EM) and all volcanic eruptions. 6 10.1175/JCLI-D-13-00524.s1 Wegmann et al. Fig. S5. Histogram and density curve of VOLC-NOVOLC SEU composite precipitation signal for all of the 30 ensemble member (black) and density curve of VOLC-NOVOLC SEU composite precipitation of 500 randomly sampled members (red). 7 Fig. S6. Boreal summer temperature and precipitation anomalies for the Tambora eruption. (a,c) Difference from statistical climate reconstructions, (b,d) ensemble mean difference in ECHAM5.4 climate model simulations. 8 10.1175/JCLI-D-13-00524.s1 Wegmann et al. Fig. S7. Averaged boreal summer response to volcanic eruptions in ECHAM5.4 climate model simulations. (a) Soil wetness in East Asia and (b) global. The plotted differences are calculated for each eruption relative a to reference period around each eruption, then averaged across all 14 5 eruptions (ALL-REF). Dotted areas in (a) and (b) represent significant differences (one-sample ttest, 95% confidence level). 9 Fig. S8. Extended version of Figure 4c,d in the main text. a) 850 hPa zonal wind anomalies, b) 850 hPa meridional wind anomalies , c) 500 hPa omega anomalies and d) 500 hPa geopotential height anomalies. Dotted areas represent significant differences (t-test, 95% confidence level) 5 10 10.1175/JCLI-D-13-00524.s1 Wegmann et al. Possible model shortcomings Using the reconstructed SST as a boundary condition in GCM simulations introduces a slight inhomogeneity in 1850 when switching from proxy-based reconstructions to principal components filtered observations (Mann et al. 2009). The variability in reconstructed SST is 5 generally damped compared to the observations. This is of minor concern in the current study as summers after volcanic eruptions are compared to the same years in simulations without volcanic eruption (ALL-NOVOLC) and to adjacent years (ALL-REF). In the latter case, damped variability before 1850 leads to an underestimate in the climatic response to volcanic eruptions and our estimates of the response may be conservative. A brief assessment of local precipitation 10 variability in the model compared to reconstructions did not reveal a general underestimation of the model for the time period covered (see Supplemental Material). Moreover, differences between model and reconstruction variability may arise from several factors, e.g. from the reconstruction itself, specific local climate features at the reconstruction site and the spatial resolution of the model. Although it is known (Thomas et al. 2009) that the ECHAM5 model 15 overestimates stratospheric warming after volcanic eruption, it does not change the resemblance at the surface with the reconstructions neither does it affect the correlation between African Monsoon circulation and precipitation, since our proposed mechanism is forced by surface anomalies. 11