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The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability |
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| Authors: | M Déqué S Somot E Sanchez-Gomez C M Goodess D Jacob G Lenderink O B Christensen |
| Institution: | 1. Météo-France, Centre National de Recherches Météorologiques, CNRS/GAME, 42 Avenue Coriolis, 31057, Toulouse Cedex 01, France 2. Cerfacs/CNRS, SUC URA1875, 42 Avenue Coriolis, 31057, Toulouse Cedex 01, France 3. Climatic Research Unit, University of East Anglia, Norwich, NR4 7TJ, UK 4. Max Planck Institute for Meteorology, Bundesstr. 53, 20146, Hamburg, Germany 5. KNMI, Postbus 201, 3730 AE, De Bilt, The Netherlands 6. Danish Meteorological Institute, Lyngbyvej 100, 2100, Copenhagen ?, Denmark |
| Abstract: | Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021–2050 and the 1961–1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM?×?GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021–2050 response which shows a similar pattern to the one obtained for 2071–2100 in PRUDENCE. The uncertainty about precipitation prevents any quantitative assessment on the response at grid point level for the 2021–2050 period. One can however see, as in PRUDENCE, a positive response in winter (more rain in the scenario than in the reference) in northern Europe and a negative summer response in southern Europe. |
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