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| Titel |
Stochastic rainfall downscaling of the PROTHEUS regional climate model |
| VerfasserIn |
D. D'Onofrio, V. Artale, S. Calmanti, J. von Hardenberg, E. Palazzi, A. Provenzale |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250069312
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| Zusammenfassung |
| Regional climate models have a temporal resolution which is often adequate for the application in climate change impact studies, but a spatial resolution which can be insufficient to resolve precipitation extremes and small-scale variability, particularly in the presence of complex terrain and heterogeneous orography. In the absence of fully deterministic models of small-scale rainfall, this scale gap can be bridged using stochastic downscaling techniques to generate ensembles of high-resolution scenarios of rainfall patterns.
The aim of this work is to investigate whether precipitation produced by a regional climate model, and downscaled stochastically, is able to reproduce the main properties of precipitation observed by a network of rain gauges. We use a version the stochastic downscaling procedure RainFarm (Rainfall Filtered Auto Regressive Model), optimized for climatic applications, to downscale the rainfall field produced by the atmospheric-ocean regional climate model PROTHEUS. The statistics of the downscaled rainfall fields are compared with rainfall data from a network of 122 rain gauges located in the Piemonte region, North-West of Italy, for the time period from 1958 to 2001.
We find that the high-resolution precipitation fields obtained downscaling the PROTHEUS model outputs reproduce well the seasonality and the amplitude distributions of observed rain gauge precipitation during most of the year. Of course, a stochastic downscaling procedure cannot correct the model outputs at large-scales, as evidenced by a the presence of a bias in average precipitation and a disagreement in the frequency of precipitation events, particularly during the winter season. |
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