Search Results
evaporation need an accurate assessment of the external drivers of the evaporation process. However, because of nonlinearity in the relationships between the drivers of evaporation (particularly temperature) it is not possible to make such an assessment using daily average meteorological data. Instead, accurate assessment requires data that resolve the full diurnal cycle. This paper describes the creation of the Water and Global Change (WATCH) Forcing Data (WFD), a dataset that is available for the whole
evaporation need an accurate assessment of the external drivers of the evaporation process. However, because of nonlinearity in the relationships between the drivers of evaporation (particularly temperature) it is not possible to make such an assessment using daily average meteorological data. Instead, accurate assessment requires data that resolve the full diurnal cycle. This paper describes the creation of the Water and Global Change (WATCH) Forcing Data (WFD), a dataset that is available for the whole
) ( Henderson-Sellers et al. 1995 ), the Global Soil Wetness Project (GSWP) ( Oki et al. 1999 ; Dirmeyer et al. 2006 ; Dirmeyer 2011 ), and the Water Model Intercomparison Project (WaterMIP) ( Haddeland et al. 2011 ). In general, these studies conclude that there are large differences between the models, which may be caused by incomplete process understanding, different parameter estimates, and imperfect atmospheric forcing data. Several multimodel evaluation studies not only compare individual models to
) ( Henderson-Sellers et al. 1995 ), the Global Soil Wetness Project (GSWP) ( Oki et al. 1999 ; Dirmeyer et al. 2006 ; Dirmeyer 2011 ), and the Water Model Intercomparison Project (WaterMIP) ( Haddeland et al. 2011 ). In general, these studies conclude that there are large differences between the models, which may be caused by incomplete process understanding, different parameter estimates, and imperfect atmospheric forcing data. Several multimodel evaluation studies not only compare individual models to
forcing dataset named the Water and Global Change (WATCH) Forcing Data (WFD; WATCH is a European Union–funded research project — for details on WFD see Weedon et al. 2010 ) was used. The data were derived from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) product as described by Uppala et al. (2005) via sequential interpolation to 0.5° resolution, elevation correction, and monthly-scale adjustments (corrected temperature, diurnal temperature range) based
forcing dataset named the Water and Global Change (WATCH) Forcing Data (WFD; WATCH is a European Union–funded research project — for details on WFD see Weedon et al. 2010 ) was used. The data were derived from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) product as described by Uppala et al. (2005) via sequential interpolation to 0.5° resolution, elevation correction, and monthly-scale adjustments (corrected temperature, diurnal temperature range) based
, the north Bay of Bengal, and northeast India are poorly simulated by most GCMs ( Christensen et al. 2007 ; Kripalani et al. 2007 ). This is likely caused by the coarse resolutions of the GCMs, which are not able to correctly represent the regional forcings such as the steep topography of the Himalayas and the Western Ghats ( Rupa Kumar et al. 2006 ). The computer power currently available constrains GCMs to perform long global climate simulations on a regular grid at a horizontal resolution of
, the north Bay of Bengal, and northeast India are poorly simulated by most GCMs ( Christensen et al. 2007 ; Kripalani et al. 2007 ). This is likely caused by the coarse resolutions of the GCMs, which are not able to correctly represent the regional forcings such as the steep topography of the Himalayas and the Western Ghats ( Rupa Kumar et al. 2006 ). The computer power currently available constrains GCMs to perform long global climate simulations on a regular grid at a horizontal resolution of
more than any other changes (e.g., with regard to flood risks and changes in water availability and water quality). Consequently, the quantification of these implications is also a major objective of the EU project Water and Global Change (WATCH; http://www.eu-watch.org ). Simulations of projected components of the hydrological cycle, under a range of GHG forcing scenarios ( Gutowski et al. 2007 ; Boberg et al. 2007 ), are essential tools for strategic freshwater resource management, particularly
more than any other changes (e.g., with regard to flood risks and changes in water availability and water quality). Consequently, the quantification of these implications is also a major objective of the EU project Water and Global Change (WATCH; http://www.eu-watch.org ). Simulations of projected components of the hydrological cycle, under a range of GHG forcing scenarios ( Gutowski et al. 2007 ; Boberg et al. 2007 ), are essential tools for strategic freshwater resource management, particularly
cycle that are directly influenced by anthropogenic forcing (e.g., increased evaporation through higher temperatures and increased rainfall as a result of higher moisture holding capacity in a warmer atmosphere), one of the most important potential impacts of climate change is on hydrological extremes (i.e., drought and flooding). Extremes are likely to be sensitive to climate change, raising the possibility that changes in the extremes of hydrological parameters may be more detectable than changes
cycle that are directly influenced by anthropogenic forcing (e.g., increased evaporation through higher temperatures and increased rainfall as a result of higher moisture holding capacity in a warmer atmosphere), one of the most important potential impacts of climate change is on hydrological extremes (i.e., drought and flooding). Extremes are likely to be sensitive to climate change, raising the possibility that changes in the extremes of hydrological parameters may be more detectable than changes
Northern Hemisphere tropics (West and East Africa and southern Asia) with distinct upward trends at high latitudes. Zhang et al. (2007) conclude that anthropogenic forcing has contributed significantly to these observed zonal changes in precipitation. There is some evidence for the increased intensity of precipitation in Europe ( Klein Tank and Konnen 2003 ; Zolina et al. 2010 ) and worldwide ( Groisman et al. 2005 ). The increase in heavy rainfall is more than the percentage average and is
Northern Hemisphere tropics (West and East Africa and southern Asia) with distinct upward trends at high latitudes. Zhang et al. (2007) conclude that anthropogenic forcing has contributed significantly to these observed zonal changes in precipitation. There is some evidence for the increased intensity of precipitation in Europe ( Klein Tank and Konnen 2003 ; Zolina et al. 2010 ) and worldwide ( Groisman et al. 2005 ). The increase in heavy rainfall is more than the percentage average and is
of model simulations including human influences and the impacts of climate change on global water resources. 2. Simulation setup and model descriptions In this first stage of WaterMIP, we assess the components of the contemporary global terrestrial water balance under naturalized conditions: that is, human impacts such as storage in man-made reservoirs and agricultural water withdrawal are not included in the model runs. The spatial resolution of the forcing data and the model simulations is 0
of model simulations including human influences and the impacts of climate change on global water resources. 2. Simulation setup and model descriptions In this first stage of WaterMIP, we assess the components of the contemporary global terrestrial water balance under naturalized conditions: that is, human impacts such as storage in man-made reservoirs and agricultural water withdrawal are not included in the model runs. The spatial resolution of the forcing data and the model simulations is 0
climate dynamics, such as the length of wet and dry spells, and both approaches are restricted to gauged catchments. As the spatial refinement of regional climate models continues to improve, spatial patterns of the hydrological response (particularly of extremes) to climate forcing are used directly for regional planning and adaptation. This requires, however, that we have sufficient trust in the models—that is, the models need to be validated at the spatial and temporal scale of interest
climate dynamics, such as the length of wet and dry spells, and both approaches are restricted to gauged catchments. As the spatial refinement of regional climate models continues to improve, spatial patterns of the hydrological response (particularly of extremes) to climate forcing are used directly for regional planning and adaptation. This requires, however, that we have sufficient trust in the models—that is, the models need to be validated at the spatial and temporal scale of interest
( Falloon and Betts 2006 ). Comparison of annual and monthly TRIP river flow outputs with observed river flow-gauge data has shown good agreement both using an independent runoff dataset ( Oki 1997 ; Oki et al. 1999 ), and the land surface scheme used to produce runoff as input to TRIP in HadGEM1 has been shown to reproduce observed changes in continental-scale (but not basin scale) runoff during the twentieth century ( Gedney et al. 2006 ), driven by climate, CO 2 , aerosol, and land use forcings. In
( Falloon and Betts 2006 ). Comparison of annual and monthly TRIP river flow outputs with observed river flow-gauge data has shown good agreement both using an independent runoff dataset ( Oki 1997 ; Oki et al. 1999 ), and the land surface scheme used to produce runoff as input to TRIP in HadGEM1 has been shown to reproduce observed changes in continental-scale (but not basin scale) runoff during the twentieth century ( Gedney et al. 2006 ), driven by climate, CO 2 , aerosol, and land use forcings. In
