 |
| Titel |
Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? |
| VerfasserIn |
C. Teutschbein, J. Seibert |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 12 ; Nr. 17, no. 12 (2013-12-13), S.5061-5077 |
| Datensatznummer |
250086030
|
| Publikation (Nr.) |
 copernicus.org/hess-17-5061-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
In hydrological climate-change impact studies, regional climate models
(RCMs) are commonly used to transfer large-scale global climate model (GCM)
data to smaller scales and to provide more detailed regional information.
Due to systematic and random model errors, however, RCM simulations often
show considerable deviations from observations. This has led to the
development of a number of correction approaches that rely on the assumption
that RCM errors do not change over time. It is in principle not possible to
test whether this underlying assumption of error stationarity is actually
fulfilled for future climate conditions. In this study, however, we
demonstrate that it is possible to evaluate how well correction methods
perform for conditions different from those used for calibration with the
relatively simple differential split-sample test.
For five Swedish catchments, precipitation and temperature simulations from
15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for
Medium-Range Weather Forecasts (ECMWF)) were corrected with different
commonly used bias correction methods. We then performed differential
split-sample tests by dividing the data series into cold and warm respective
dry and wet years. This enabled us to cross-evaluate the performance of
different correction procedures under systematically varying climate
conditions. The differential split-sample test identified major differences
in the ability of the applied correction methods to reduce model errors and
to cope with non-stationary biases. More advanced correction methods
performed better, whereas large deviations remained for climate model
simulations corrected with simpler approaches. Therefore, we question the
use of simple correction methods such as the widely used delta-change
approach and linear transformation for RCM-based climate-change impact
studies. Instead, we recommend using higher-skill correction methods such as
distribution mapping. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|