Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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Statistical Precipitation Downscaling in Central Sweden. Intercomparison
of Different Approaches
Fredrik Wetterhall, Sven Halldin and Chong-yu Xu
Air and Water Science, Department of Earth Sciences, Uppsala University, Villavägen 16, SE-752 36 UPPSALA, Sweden
Corresponging author: Sven.Halldin@hyd.uu.se
1. Introduction
Most climate predictions show significant consequences
globally and regionally, but many of its critical impacts will
occur at sub-regional and local scales. Downscaling methods
are, thus, needed to assess effects of large-scale atmospheric
circulation on local parameters such as precipitation and
runoff. This study aims at evaluating the analogue method
(AM; Zorita & von Storch, 1999) as a benchmark method
for precipitation downscaling in northern Europe in
comparison with more sophisticated methods. Both summer
and winter precipitation were studied.
2. Downscaling Methods
AM is not suitable to downscale future climate but is good
as a benchmark since it retains the statistical properties of
the precipitation and contains few subjective factors. The
predictor in this study was a daily NCEP/NCAR-reanalysis
(Kalnay et al., 1996) sea-level-pressure grid with a 2.5° ×
2.5° resolution from 1961-97 in an area 45°-75° N and 30°
W-40° E (Figure 1). Analogues for daily precipitation at 7
stations in south-central Sweden were established with two
AM techniques, principle-component analysis (PCA) and
the Teweles-Wobus score (TWS). The other two methods
were a multi-regression technique with a weather generator
producing precipitation (SDSM; Wilby et al., 2002) and a
Fuzzy-rule-based weather-pattern-classification method
(MOFRBC; Stehlik & Bardossy, 2002). The training period
for the methods was 1960-1980, the calibration period 1981-
1990 whereas 1991-1997 was used as validation period.
3. Results
The analogue method is a downscaling but not a modelling
method. AM basically reshuffles measured time-series data
so the statistical properties of the underlying data are
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preserved as long as the downscaling and training periods
have the same climate. This is the reason for expecting
AM to work as a benchmark for downscaling methods
with predictive capacity. The training period in this study,
20 years, was large enough to incorporate sufficient
precipitation variability. PCA has been widely used
whereas TWS is an old, less recognised technique. AM
downscaling on both daily and monthly basis was
generally much better than a random baseline but
depended on the objective function used for assessment;
PCA and TWS produced similar results in most cases but
TWS was superior in simulating precipitation duration and
intensity (Figure 2). The conceptual simplicity of TWS
and its lower degree of subjectivity could be taken as other
arguments to favour TWS over PCA in future AM
downscaling studies.
All downscaling methods generally gave much better
statistical properties than a random baseline. The success
of a given method can, however, only be fully evaluated if
the goal is clearly stated. Several goals, in the form of
different objective function were used in this study since
the purpose was to carry out a general assessment of the
methods in northern Europe.
The study focused on areal and temporal variations in the
downscaling techniques. Downscaling was improved
when the SLP field was confined to those geographical
areas that contributed most to precipitation in southcentral
Sweden. Both of the AM techniques, the PCA and
TWS identified an area south of Iceland as important for
the precipitation in central Sweden, indicating influence of
the NAO index. The MOFRBC used a number of typical
circulation patterns to condition the predictand, and some
these patterns also showed an influence of the NAO index.
Figure 1. Selected predictor grid area in the study. The location of the 7 precipitation stations in the NOPEX region
are marked with black dots.