Download Title: Influence of tropical volcanic eruptions on regional precipitation

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
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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
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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
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(Manuscript received 29 August 2013,
in final form 15 January 2014)
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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).
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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
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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).
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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
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winters after each eruption were considered volcanically perturbed.
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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 =
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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
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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.
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Fig. S4. Boxplot of the summer SEU precipitation VOLC-NOVOLC anomaly in our model run
for each ensemble member (EM) and all volcanic eruptions.
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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).
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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.
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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
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eruptions (ALL-REF). Dotted areas in (a) and (b) represent significant differences (one-sample ttest, 95% confidence level).
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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)
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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
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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
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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
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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.
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