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24
High-resolution dynamical downscaling error components over complex
terrain
A. Gobiet 1 , M. Suklitsch 1 , A. Prein 1 , H. Truhetz 1 , N.K. Awan 1 , H. Goettel 2 and D. Jacob 2
1 Wegener Center for Climate and Global Change and Institute for Geophysics, Astrophysics and Meteorology, University of
Graz, Austria. 2 Max Planck Institute for Meteorology, Hamburg, Germany
(andreas.gobiet@uni-graz.at)
1. Introduction
Recent regional climate scenarios for are often based on
regional climate models (RCMs) operated on 50 km to
25 km spaced grids. The emerging generation of regional
climate scenarios is often given on 10 km grids, which is
particularly useful in mountainous and climatologically
complex areas like the European Alpine region. This
resolution enables to resolve climate characteristics of
relatively small sub-regions (about 50 x 50 km). However,
the typical error characteristics of such high resolution
climate simulations and the impact of model setup on model
errors are not properly characterized yet.
2. Methods
In this study we quantify the relative importance of major
downscaling error components (errors due to spatial setup,
model structure, and physical parameterization) of high
resolution RCMs (10 km grid spacing) in the European
Greater Alpine Region (GAR) on a sub-regional basis (see
Fig. 1 for the sub-regions). The study is based on a large
ensemble of about 60 one-year simulations performed with
four different RCMs (COSMO-CLM, MM5, WRF, REMO)
driven by lateral boundary conditions of the ERA-40
reanalysis. We investigate seasonal precipitation and
temperature errors (see Suklitsch et al. [2008] for such an
evaluation for parts of the ensemble) and the relative impact
of details in the downscaling strategy on error variability
using a similar method as Déqué et al. [2007]
could not yet be reliably quantified due to a too small
ensemble size (only 2 RCMs). The full ensemble
consisting of 4 RCMs will enable a rough estimation of
this error component.
0% 100%
8 7 9 2 6 1 3 10 4 GAR
Region
Figure 2. Relative contribution of details in
downscaling strategy on the temperature error
variability [%] (annual mean): Selection of RCM
(blue), physical parameterization (green), domain
location and size (orange), further error components
(grey).
In addition to the analysis of error components, we’ll
quantitatively characterize temperature and precipitation
errors of high resolution RCMs operated in mountainous
areas.
Acknowledgements
Figure 1. Climatological sub-regions in the Greater
Alpine Region (GAR) based on clustering daily
precipitation time series (Suklitsch et al. [2008]).
3. First Results
First results for a subset of the entire ensemble consisting of
29 COSMO-CLM and MM5 simulations indicate that the
selection of the model domain (orange in Fig. 2) plays a
major role in the error budget in winter, in higher elevated
regions, and near the inflow boundary of the model domain.
Error variability due to physical parameterization (green in
Fig. 2) is more important in summer and in regions that are
orographically shaded from the synoptic flow. Error
variability contributed by the type of RCM (blue in Fig. 2)
The authors thank the University Information Service of
the Univ. Graz for the provision of computing
infrastructure, the Swiss Federal Institute of Technology
Zurich (C. Frei) and Central Institute for Meteorology and
Geodynamics and the for the provision of observational
data. This study is part of the project NHCM-1 (P19619-
N10) funded by the Austrian Science Fund (FWF).
References
Déqué, M., D. P. Rowell, D. Lüthi, F. Giorgi, J. H.
Christensen, B. Rockel, D. Jacob, E. Kjellström, M. de
Castro, B. van den Hurk, An intercomparison of
regional climate simulations for Europe: assessing
uncertainties in model projections, Clim. Change, 81,
DOI 10.1007/s10584-006-9228-x, 2007.
Suklitsch, M., A. Gobiet, A. Leuprecht and C. Frei, “High
Resolution Sensitivity Studies with the Regional
Climate Model CCLM in the Alpine Region”,
Meteorol. Z., 17, 4, pp. 467-476, 2008.