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Regional climate change impact studies in the upper Danube and upper
Brahmaputra river basin using CLM projections
B.Ahrens and A.Dobler
Institute for Atmosphere and Environment, Goethe-University, Frankfurt/M., Germany (Bodo.Ahrens@iau.uni-frankfurt.de)
1. Introduction
This contribution focuses on regional impact studies
based on climate change projections from the regional
climate model CLM (the climate version of the COSMOmodel,
see www.clm-community.eu) in two alpine
regions. To this end, CLM simulations have been carried
out in two domains, one containing the upper Danube
river basin (UDRB, see Fig. 1) in the European Alps, the
other containing the upper Brahmaputra river basin
(UBRB, see Fig. 2) in the Himalayas. The model
configurations are detailed in Dobler and Ahrens (2008).
Inside the two major river basins (RBs), five sub-basins
of interest are considered: Lech RB, Salzach RB, Assam,
Lhasa RB, and Wang-Chu RB.
Figure 2: As for Fig. 1, but for the South Asian
computational domain with the Assam region
(bottom right) and the Upper Brahmaputra (red),
the Lhasa (top) and the Wang-Chu (bottom left)
river basins.
To remove constant model biases, a normalization via
division by the mean value of the reference period 1971-
2000 has been carried out. This bias has to be corrected
for application of the downscaled projections in inmpact
research. This assumes that the bias is constant – an
assumption that is debated.
Figure 1: Orography (m) used for the regional
climate simulations with the CLM. The colored
areas denote the Upper Danube (red), the Lech
(left), and the Salzach (right) river basins.
2. Methods
Coarse-scale projections from the ECHAM5/MPI-OM
AR4 projections (Roeckner et al. 2003) have been
dynamically downscaled from about 2° grid resolution to
0.44° for the years 1960-2100 using the CLM. The
simulations cover the four SRES scenarios A1B, A2, B1,
and the commitment scenario.
Besides annual and seasonal temperature and
precipitation amounts, daily precipitation indices are
calculated for four seasons on a yearly basis. For the
European regions the four seasons used are: spring
(MAM), summer (JJA), autumn (SON) and winter (DJF).
For the South Asian regions these are: summer (MAM),
monsoon (JJAS), post-monsoon (ON) and winter (DJF).
An overview on the applied precipitation indices is
provided in Table 1. The linear trends of these indices are
tested for significance at 5% significance level using the
Mann-Kendall trend test.
Acronym Description Unit
PFRE Fraction of wet days 1
PINT Mean precipitation amount on mm/d
wet days
PQ90 90% quantile of wet days mm/d
precipitation
PX5D Max. 5-day precipitation mm
PCDD Longest period of
consecutive dry days
d
Table 1: List of daily precipitation statistics.
3. Results and conclusions
An overview on the projected precipitation statistics for
the A1B scenario and the years 1960-2080 is given in
Ahrens and Dobler (2008). While the focus therein is on
one single scenario, we here present the results from all
four scenario runs. With this we are able to examine the
impact uncertainties coming from the different scenarios