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1. Introduction Severe weather over the Mediterranean has only recently become the object of thorough analyses despite its prominent scientific interest and its strong effects on the heavily populated countries surrounding the basin. Preliminary studies have concentrated on the Western Mediterranean (e.g., Romero et al. 1999 ; Jansa et al. 2001 ) and the Eastern Mediterranean (e.g., Nicolaides et al. 2004 ; Ziv et al. 2009 ). A general classification of cloud systems associated with the
1. Introduction Severe weather over the Mediterranean has only recently become the object of thorough analyses despite its prominent scientific interest and its strong effects on the heavily populated countries surrounding the basin. Preliminary studies have concentrated on the Western Mediterranean (e.g., Romero et al. 1999 ; Jansa et al. 2001 ) and the Eastern Mediterranean (e.g., Nicolaides et al. 2004 ; Ziv et al. 2009 ). A general classification of cloud systems associated with the
GCMs—on a global grid—with consistent realistic meteorological forcing. This was in contrast to the early multimodel experiments of the Project to Intercompare Land Surface Parameterization Schemes (PILPS; Henderson-Sellers et al. 1993 ), which until that time were conducted at a series of individual sites. The second recommendation was to produce a global soil wetness climatology using one or more LSMs driven by internally consistent gridded near-surface atmospheric data, like that beginning to
GCMs—on a global grid—with consistent realistic meteorological forcing. This was in contrast to the early multimodel experiments of the Project to Intercompare Land Surface Parameterization Schemes (PILPS; Henderson-Sellers et al. 1993 ), which until that time were conducted at a series of individual sites. The second recommendation was to produce a global soil wetness climatology using one or more LSMs driven by internally consistent gridded near-surface atmospheric data, like that beginning to
performance in wet areas (with annual precipitation over 2000 mm) probably linked to hydrological processes that cannot be captured at monthly time steps over dry areas (i.e., rainstorms) and cloud water interception in cloud forests ( Bruijnzeel 2005 ) not being captured by current precipitation forcings. The bias in simulated annual runoff was tested across gradients of precipitation, altitude, forest cover, and catchment size and showed no trends except for small catchments (less than 10 pixels
performance in wet areas (with annual precipitation over 2000 mm) probably linked to hydrological processes that cannot be captured at monthly time steps over dry areas (i.e., rainstorms) and cloud water interception in cloud forests ( Bruijnzeel 2005 ) not being captured by current precipitation forcings. The bias in simulated annual runoff was tested across gradients of precipitation, altitude, forest cover, and catchment size and showed no trends except for small catchments (less than 10 pixels
underlying surface temperature, ω = 2 π /86 400 is the insolation, L ↓ is the downward longwave radiation, T a is the air temperature, q a is the specific humidity, U is the wind speed, and t is time. The prime sign denotes difference from the daily average. Matrices and include parameters that are listed above. In the derivation of Eq. (1) , the force-restore formulation ( Stull 1988 ) is implemented for the ground heat flux G g as in which where P g is the subsurface thermal
underlying surface temperature, ω = 2 π /86 400 is the insolation, L ↓ is the downward longwave radiation, T a is the air temperature, q a is the specific humidity, U is the wind speed, and t is time. The prime sign denotes difference from the daily average. Matrices and include parameters that are listed above. In the derivation of Eq. (1) , the force-restore formulation ( Stull 1988 ) is implemented for the ground heat flux G g as in which where P g is the subsurface thermal
potential uncertainty and limitations of this study). Historical changes in land use (fractional changes in cropland and pasture) were based on a dataset compiled by Hurtt et al. (2006) . The historical climate dataset Climatic Research Unit Time Series version 3.0 (CRU TS3.0) (air temperature, precipitation, cloud cover, and vapor pressure; Mitchell and Jones 2005 ) was used to drive the model. Historical changes in atmospheric CO 2 concentration were prescribed on the basis of observations
potential uncertainty and limitations of this study). Historical changes in land use (fractional changes in cropland and pasture) were based on a dataset compiled by Hurtt et al. (2006) . The historical climate dataset Climatic Research Unit Time Series version 3.0 (CRU TS3.0) (air temperature, precipitation, cloud cover, and vapor pressure; Mitchell and Jones 2005 ) was used to drive the model. Historical changes in atmospheric CO 2 concentration were prescribed on the basis of observations
