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Validation of IPCC AR4 models over the Iberian Peninsula
Authors:Inigo Errasti  Agust��n Ezcurra  Jon S��enz  Gabriel Ibarra-Berastegi
Institution:1. Department of Nuclear Engineering and Fluids Mechanics, University of the Basque Country, Vitoria-Gasteiz, Spain
5. University College of Engineering, University of the Basque Country (EHU/UPV), 12 Nieves Cano, 01006, Vitoria-Gasteiz, Spain
2. Department of Applied Physics II, University of the Basque Country, Vitoria-Gasteiz, Spain
3. Department of Applied Physics II, University of the Basque Country, Bilbao, Spain
4. Department of Nuclear Engineering and Fluids Mechanics, University of the Basque Country, Bilbao, Spain
Abstract:This paper reports analysis of the ability of 24 coupled global climate models that were used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change to simulate the current monthly seasonal cycle of sea level pressure, surface air temperature and precipitation over the Iberian Peninsula in the last two decades of the twentieth century. The period investigated runs from 1979 to 1998. In order to assess the performance of the models, averaged seasonal cycles and probability density functions (PDFs) calculated from model simulations are compared with the corresponding seasonal cycles, whilst PDFs are also obtained using the data from the ERA40 reanalysis and the Global Precipitation Climatology Project. We found that simulated PDFs generally provided a better fit to actual PDFs than seasonal cycles do. This conclusion indicates that when evaluating model performance, the climate variability as measured by means of PDFs is not the only climatic element that should be tested. Regarding the comparison based on the seasonal cycle, results also show that the root mean square skill score is more useful than the r skill score. To rank the AR4 models, sea level pressure, surface air temperature and precipitation variables were selected and a group of five AR4 models were identified as the models which best reproduce current climate in the area: MIROC3.2-HIRES, MPI-ECHAM5, GFDL-CM2.1, BCCR-BCM2.0 and UKMO-HADGEM1. The rank obtained should not be understood in a hierarchical manner because there is a certain degree of internal variability in the model ensembles. Finally, it should be noted that these results are in good agreement with other classifications found in the scientific literature.
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