Low (web) Quality - BALTEX

268
Projected changes in daily temperature variability over Europe in an
ensemble of RCM simulations
Grigory Nikulin, Erik Kjellström and Lars Bärring
Rossby Centre, Swedish Meteorological and Hydrological Institute, Sweden (grigory.nikulin@smhi.se)
1. Introduction
Future climate scenarios show not only possible changes in
the mean state but also changes in variability. In addition to
shifts in the mean, enhanced or reduced variability also
influences weather extremes which may become more or
less frequent. Together with a pronounced warming over
Europe an increase in summer temperature variability
(interannual and intraseasonal) have been found from an
ensemble of regional climate model (RCM) simulations
driven by one global climate model (GCM) Vidale et al.
(2007) and Fischer and Schär (2009). Boundary conditions
only from one GCM substantially define the behaviour of
the entire ensemble of RCM simulations. In order to
supplement the above results we use an ensemble of
integrations with one RCM driven by different GCMs,
focusing on the question “How does a possible future
climate with increased greenhouse gas concentration
influence daily temperature variability over Europe in
summer and winter?”
2. Data and method
For downscaling of GCM scenarios over Europe we use the
Rossby Center Regional Climate Model (RCA3) Kjellström
et al. (2005) with a horizontal resolution of 0.44°
(approximately 50 km). The regional simulations are driven
by boundary conditions from five different GCMs:
ECHAM5 (MPI, Germany), CCSM3 (NCAR, USA),
HadCM3 (Hadley Center, UK), CNRM (CNRM, France),
BCM (NERSC, Norway) Meehl et al. 2007. All simulations
have employed the A1B scenario and two periods are chosen
to represent the recent (1961-1990, CTL) and future (2071-
2100, SCN) climates. As a measure of daily temperature
variability we use the variance (or standard deviation) of
daily temperature at the 2 meter level and separate the total
variability into four components, namely: seasonal-cycle,
interannual, intraseasonal and trend-induced variability,
accordingly to the methodology by Fischer and Schär
(2009). The simulated variability for the CTL period is
evaluated against the gridded ENSEMBLES observational
dataset (ENSOBS) Haylock et al. (2008).
3. Summer
In summer and for the CTL period (Fig. 1 top) the ensemble
mean total temperature variability has a band of large values
stretching from the Iberian Peninsula throughout central to
eastern Europe. Comparison to ENSOBS (not shown)
reveals that the ensemble mean variability is well captured
over central and eastern Europe while underestimated in
Scandinavia and the Alps (20-30%) and overestimated in the
in the Pyrenees (up to 50%). In the SCN period (Fig. 1
bottom) the simulated summer total temperature variability
is significantly enhanced over a substantial part of the
domain, approximately south of 50°N, with the maximum
increase up to 20-30% over southern and eastern Europe.
Detailed analysis of all four components of the total
variability shows that on average two main contributors to
the total variability increase are the seasonal-cycle (50%)
and intraseasonal (30%) variability while the interannual
component explains about 10 % of the total change.
o C
%
5
4
3
2
1
30
20
10
0
-10
-20
-30
SUMMER daily StdDev (CTL)
(SCN-CTL)/CTL
Figure 1. (top) The simulated summer
total daily standard deviation of the 2m
temperature for 1961-1990 and
(bottom) the relative change in the
standard deviation in 2071-2100 wrt
1961-1990. Only differences significant
at 5% level are shown.
4. Winter
The simulated winter total temperature variability (CTL)
shows a gradual increase from south to north with local
maxima over Iceland, northern Scandinavia and the
Barents Sea (Fig. 2 top). The winter variability is
generally underestimated in continental Europe (10-20%)
and overestimated in the Alps (50%), south part of the
Iberian Peninsula (40%) and northern Scandinavia (10-
20%) The overestimation in northern Scandinavia is
mainly due to the BCM and CNRM driven simulations
which heavily (up to 100%) overestimate the total
variability in this region that may reflect a problem with
the modeled sea ice in the Barents Sea in those driving