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| Titel |
Statistical downscaling of the French Mediterranean climate: assessment for present and projection in an anthropogenic scenario |
| VerfasserIn |
C. Lavaysse, M. Vrac, P. Drobinski, M. Lengaigne, T. Vischel |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 3 ; Nr. 12, no. 3 (2012-03-19), S.651-670 |
| Datensatznummer |
250010609
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| Publikation (Nr.) |
 copernicus.org/nhess-12-651-2012.pdf |
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| Zusammenfassung |
| The Mediterranean basin is a particularly vulnerable region to climate
change, featuring a sharply contrasted climate between the North and South
and governed by a semi-enclosed sea with pronounced surrounding topography
covering parts of the Europe, Africa and Asia regions. The physiographic
specificities contribute to produce mesoscale atmospheric features that can
evolve to high-impact weather systems such as heavy precipitation, wind
storms, heat waves and droughts. The evolution of these meteorological
extremes in the context of global warming is still an open question, partly
because of the large uncertainty associated with existing estimates produced
by global climate models (GCM) with coarse horizontal resolution
(~200 km). Downscaling climatic information at a local scale is, thus,
needed to improve the climate extreme prediction and to provide relevant
information for vulnerability and adaptation studies. In this study, we
investigate wind, temperature and precipitation distributions for past recent
climate and future scenarios at eight meteorological stations in the French
Mediterranean region using one statistical downscaling model, referred as the
"Cumulative Distribution Function transform" (CDF-t) approach. A thorough
analysis of the uncertainty associated with statistical downscaling and
bi-linear interpolation of large-scale wind speed, temperature and rainfall
from reanalyses (ERA-40) and three GCM historical simulations, has been
conducted and quantified in terms of Kolmogorov-Smirnov scores. CDF-t
produces a more accurate and reliable local wind speed, temperature and
rainfall. Generally, wind speed, temperature and rainfall CDF obtained with
CDF-t are significantly similar with the observed CDF, even though CDF-t
performance may vary from one station to another due to the sensitivity of
the driving large-scale fields or local impact. CDF-t has then been applied
to climate simulations of the 21st century under B1 and A2 scenarios for the
three GCMs. As expected, the most striking trend is obtained for temperature
(median and extremes), whereas for wind speed and rainfall, the evolution of
the distributions is weaker. Mean surface wind speed and wind extremes seem
to decrease in most locations, whereas the mean rainfall value decreases
while the extremes seem to slightly increase. This is consistent with
previous studies, but if this trend is clear with wind speed and rainfall data
interpolated from GCM simulations at station locations, conversely CDF-t
produces a more uncertain trend. |
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