Text S1.
Calculation of diagnostics
Changes in the main article are shown for two arbitrary but indicative types of
northern hemisphere summer heat events, both using daily maximum temperature
data. For each ensemble member and each model grid point, the chosen indicators of
heat extremes were calculated from 1800 values of daily maximum temperature
obtained during June to August during 20 year periods of the doubled CO2 and
present-day control simulations, the difference giving the simulated response. The
first indicator (TX99) was an estimate of the 99th percentile of daily maximum
temperature, obtained by ranking the 1800 daily values, and then taking the value
ranked 18 places below the hottest individual temperature (noting that similar changes
were obtained if the value 30, 40 or 50 places below the maximum was used instead).
The second indicator shown characterised severe heatwaves, defined as the
temperature threshold for which there was only one period of seven consecutive days
in the relevant simulation for which that temperature was exceeded or equalled on
each day. This threshold was taken to represent a heatwave with a return period of 20
years.
Regional mean and extreme changes
In large parts of North America, Europe and Asia, the greatest changes in heat
extremes shown in Figure 1 in the article are substantially different from the
associated globally averaged increases in mean surface temperature. This arises from
three factors. The first is spatial variations in mean temperature, which influence
changes in extremes in the absence of changes in the distribution of daily temperature
anomalies relative to the mean; see for example the 2nd panel in the top row of Figure
S1, showing the median estimate of changes in the mean daily maximum temperature.
Increases, for example, are greater over land compared to over sea. The second factor
arises from spatial variability in the ensemble uncertainty of the changes in the mean
temperature (see 4th panel of the top row). The uncertainty is greater over Siberia for
example. The third factor is changes in higher moments of the daily temperature
distribution. The impact of these can be seen by comparing the changes in the mean
temperatures (top row) and the changes in the 99th percentile temperatures (bottom
row). Whilst the lower estimates are similar, the upper estimates (see the 3rd panels on
both rows) for changes in the 99th percentile temperatures are much larger than
changes in the mean, especially over north-eastern North America, western Europe
and Siberia. The third row of Figure S2 also highlights this result.
Assessing possible underestimation of uncertainties arising from sub-selecting
simulation pairs based on their globally averaged response
Figure 1 in the article shows the lower and upper estimates of increases in the 99th
percentile of daily maximum temperature using the 44 pairs of simulations which give
climate sensitivities in the range 1.5 to 2.5 deg.C. By excluding the other 180
simulation pairs, the possibility arises of an underestimation of the uncertainties in the
99th percentile changes. This could arise because our simulations are idealised studies
of the long term equilibrium response to doubled CO2, whereas in the real world a 2
Deg C warming would first be reached during some specific future period, as part of a
time-varying evolution of climate. The timing of a 2 Deg C warming, and the
magnitude of the associated regional changes, would depend on the future pathway of
anthropogenic emissions, the efficiency of ocean heat uptake and on climate
sensitivity, all of which are uncertain. While simulated patterns of equilibrium climate
change over land are known to be strongly related to those of time-dependent changes
under a plausible emissions scenario (e.g. Harris et al., 2006), there is a possibility
that our use of equilibrium simulations within a relatively narrow climate sensitivity
range may underestimate the full uncertainty associated with transient projections of a
2 Deg C warming. To investigate this, the following scaling exercise was carried out.
Changes in the 99th percentile for each pair of simulations were scaled to be
representative of a 2.0 deg.C globally averaged warmed world by multiplying the 99th
percentile changes by 2.0 and dividing by the climate sensitivity of the simulation
pair. This was done for all 224 pairs. Results of this scaling are shown in Figure S2.
The top panels show the lower and upper estimates, from the 44 simulation pairs
analogous to those in Figure 1 in the main article, which give a 1.5 to 2.5deg.C global
response, but with the 99th percentile changes scaled. The bottom panels show the
estimates when using the scaled changes from all 224 pairs. The top and bottom rows
show similar results, suggesting that neglecting higher climate sensitivities through
sub-selection of models with global responses of 2.0±0.5 deg C, is unlikely to lead to
significant underestimation in uncertainties in estimates of 99th percentile increases
for a 2.0deg.C warmed world. Indeed, our estimates of the upper end of the
uncertainty range per unit of global temperature increase are actually somewhat larger
over parts of North America, south western Europe and Siberia when only the low
sensitivity models are used (compare the right hand panels).
Guidance for viewing Figure 2 in main text
We provide here a fuller description of information shown in panels b,c and d of
Figure 2 which appears in the main article. The objective of the plots is to
demonstrate how soil moisture changes and control climatology relate to temperature.
The x axis represents daily ‘soil moisture percentiles’ which range from the driest
days (low percentiles) to the wettest days (high percentiles). The left vertical axis
represents normalised soil moisture with the green lines showing the values of the
control simulations and the light blue lines the 2xCO2 simulations. Focussing on panel
2c for example, we can see that the wettest 30% of control days have a normalised
soil moisture greater than 1.0 (soil moisture percentiles 70 to 100) and the driest 20%
of days a normalised soil moisture of between 0.0 and 0.2. Fewer than 15% of the
2xCO2 days have a soil moisture greater than 1.0. The dark blue line shows the
difference in soil moisture between the control and 2xCO2 runs and uses the same left
axis scale. For this particular ensemble member, the dark blue line is consistently
showing negative values for the whole soil moisture spectrum indicating general
reductions in soil moisture on CO2 doubling. The magnitudes of change do not vary
uniformly with soil moisture percentile and can also vary considerably between
ensemble members. By noting the actual dates corresponding to each soil moisture
percentile, the average daily maximum temperature found in the control and 2xCO2
simulations for each percentile can be calculated. The increase in temperature in
response to CO2 doubling is shown by the red lines, whose values in deg.C
correspond to the right vertical axis. Focussing on panel 2c again, we see temperature
increases of between approximately 3 and 5deg.C on the wettest 20% of days (soil
moisture percentiles 80 to 100). Moderate reductions in soil moisture occurred on
these days. If we examine soil moisture percentiles where soil moisture reductions are
larger (eg percentiles 50 to 80), we clearly see that temperature changes are also
larger (increases of between 6 and 10deg.C). However, on the driest 20% of days,
where soil moisture reductions are very small, due principally to lack of soil moisture
in the control simulations, temperature increases are small. By considering panel 2c as
a whole, the temperature increases appear to follow a clear relationship with soil
moisture. This relationship is also present in panels 2b and d but with large changes
confined to the wet and dry parts of the soil moisture spectrum respectively.
A point made in the main article concerns the relevance of soil moisture reduction on
doubling CO2 to changes in temperature associated with extreme hot days. By noting
the actual dates of extreme hot days, we can identify the parts of the soil moisture
spectrum in which they occur on average. These are shown by the blue and red
diamonds for the control and doubled CO2 simulations respectively. From panels 2b
and c, extreme hot days occur in parts of the soil moisture spectrum where drying on
CO2 doubling is small and so enhanced warming is small. However, for panel 2d, the
hot days occur in the part of the moisture spectrum with the greatest soil moisture
reductions and where temperature increases on CO2 doubling are larger.
Validation of model simulations
Model errors were calculated using 20 year averages of annual spatial fields and
verifying observations of recent climate, for a set of 15 variables typically used to
assess climate model simulations (Taylor, 2001; Murphy et al., 2004; Reichler and
Kim, 2008). For each perturbed physics ensemble member, we calculated a simple
measure of model error, normalised by the average model error calculated from an
independent multi-model ensemble dataset derived from coupled atmosphere/mixed
layer ocean models submitted to the latest IPCC assessment (see Table 8.1 of IPCC
(2007)). We then averaged these normalised relative errors over our set of verification
variables, to provide a single metric Mi of overall model performance for each
perturbed physics member i. For the regions listed in Figure 1 (in main article), and
for three separate globally averaged warming targets, Table S1 compares the average
scores for those members giving increases in the 99th percentile of summer daily
maximum temperature above and below (columns M_HIGH and M_LOW
respectively) the upper 90% estimate of the ensemble distribution of changes.
References
Harris, G.R., D.M.H. Sexton, B.B.B. Booth, M. Collins, J.M. Murphy and M.J. Webb
(2006) Frequency distributions of transient regional climate change from perturbed
physics ensembles of general circulation model simulations. Climate Dynamics, 27,
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IPCC, (2007): Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change [Solomon, S. et al (eds.)]. Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 996 pp.
Reichler, T. and J. Kim (2008) How well do coupled models simulate today's climate?
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