 |
| Titel |
Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods |
| VerfasserIn |
E. P. Maurer, H. G. Hidalgo |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 12, no. 2 ; Nr. 12, no. 2 (2008-03-13), S.551-563 |
| Datensatznummer |
250010576
|
| Publikation (Nr.) |
 copernicus.org/hess-12-551-2008.pdf |
|
|
|
|
|
| Zusammenfassung |
| Downscaling of climate model data is essential to local and regional impact
analysis. We compare two methods of statistical downscaling to produce
continuous, gridded time series of precipitation and surface air temperature
at a 1/8-degree (approximately 140 km2 per grid cell) resolution over
the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a
surrogate General Circulation Model (GCM). The two methods included are
constructed analogues (CA) and a bias correction and spatial downscaling
(BCSD), both of which have been shown to be skillful in different settings,
and BCSD has been used extensively in hydrologic impact analysis. Both
methods use the coarse scale Reanalysis fields of precipitation and
temperature as predictors of the corresponding fine scale fields. CA
downscales daily large-scale data directly and BCSD downscales monthly data,
with a random resampling technique to generate daily values. The methods
produce generally comparable skill in producing downscaled, gridded fields
of precipitation and temperatures at a monthly and seasonal level. For daily
precipitation, both methods exhibit limited skill in reproducing both
observed wet and dry extremes and the difference between the methods is not
significant, reflecting the general low skill in daily precipitation
variability in the reanalysis data. For low temperature extremes, the CA
method produces greater downscaling skill than BCSD for fall and winter
seasons. For high temperature extremes, CA demonstrates higher skill than
BCSD in summer. We find that the choice of most appropriate downscaling
technique depends on the variables, seasons, and regions of interest, on the
availability of daily data, and whether the day to day correspondence of
weather from the GCM needs to be reproduced for some applications. The
ability to produce skillful downscaled daily data depends primarily on the
ability of the climate model to show daily skill. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|