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| Titel |
Regional climate models downscaling in the Alpine area with multimodel superensemble |
| VerfasserIn |
D. Cane, S. Barbarino, L. A. Renier, C. Ronchi |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 5 ; Nr. 17, no. 5 (2013-05-29), S.2017-2028 |
| Datensatznummer |
250018886
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| Publikation (Nr.) |
 copernicus.org/hess-17-2017-2013.pdf |
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| Zusammenfassung |
The climatic scenarios show a strong signal of warming in the Alpine area
already for the mid-XXI century. The climate simulations, however, even when
obtained with regional climate models (RCMs), are affected by strong errors
when compared with observations, due both to their difficulties in
representing the complex orography of the Alps and to limitations in their
physical parametrization.
Therefore, the aim of this work is to reduce these model biases by using a
specific post processing statistic technique, in order to obtain a more
suitable projection of climate change scenarios in the Alpine area.
For our purposes we used a selection of regional climate models (RCMs) runs
which were developed in the framework of the ENSEMBLES project. They were
carefully chosen with the aim to maximise the variety of leading global
climate models and of the RCMs themselves, calculated on the SRES scenario
A1B. The reference observations for the greater Alpine area were extracted
from the European dataset E-OBS (produced by the ENSEMBLES project), which
have an available resolution of 25 km. For the study area of Piedmont daily
temperature and precipitation observations (covering the period from 1957 to
the present) were carefully gridded on a 14 km grid over Piedmont region
through the use of an optimal interpolation technique.
Hence, we applied the multimodel superensemble technique to temperature
fields, reducing the high biases of RCMs temperature field compared to
observations in the control period.
We also proposed the application of a brand new probabilistic multimodel
superensemble dressing technique, already applied to weather forecast models
successfully, to RCMS: the aim was to estimate precipitation fields, with
careful description of precipitation probability density functions
conditioned to the model outputs. This technique allowed for reducing the
strong precipitation overestimation, arising from the use of RCMs, over the
Alpine chain and to reproduce well the monthly behaviour of precipitation in
the control period. |
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