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| Titel |
An artificial neural network technique for downscaling GCM outputs to RCM spatial scale |
| VerfasserIn |
R. Chadwick, E. Coppola, F. Giorgi |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 6 ; Nr. 18, no. 6 (2011-12-22), S.1013-1028 |
| Datensatznummer |
250014013
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| Publikation (Nr.) |
 copernicus.org/npg-18-1013-2011.pdf |
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| Zusammenfassung |
| An Artificial Neural Network (ANN) approach is used to downscale ECHAM5 GCM
temperature (T) and rainfall (R) fields to RegCM3 regional model scale
over Europe. The main inputs to the neural network were the ECHAM5 fields and
topography, and RegCM3 topography. An ANN trained for the period 1960–1980
was able to recreate the RegCM3 1981–2000 mean T and R fields with
reasonable accuracy. The ANN showed an improvement over a simple lapse-rate
correction method for T, although the ANN R field did not capture all the
fine-scale detail of the RCM field. An ANN trained over a smaller area of
Southern Europe was able to capture this detail with more precision. The ANN
was unable to accurately recreate the RCM climate change (CC) signal between
1981–2000 and 2081–2100, and it is suggested that this is because the
relationship between the GCM fields, RCM fields and topography is not
constant with time and changing climate. An ANN trained with three ten-year
"time-slices" was able to better reproduce the RCM CC signal, particularly
for the full European domain. This approach shows encouraging results but
will need further refinement before becoming a viable supplement to dynamical
regional climate modelling of temperature and rainfall. |
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