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participating in the Second Global Soil Wetness Project (GSWP-2; Dirmeyer et al. 2006 ; see Table 1 ) are used as forcings for the Hydrology Discharge (HD) model ( Hagemann and Dümenil 1998a ). The response of river discharges produced by HD to the different surface and subsurface runoff values is evaluated. Moreover, the quality of meteorological forcing data has a strong influence on the simulation of land surface components of the hydrological cycle ( Guo et al. 2006 ). GSWP-2 included a number of
participating in the Second Global Soil Wetness Project (GSWP-2; Dirmeyer et al. 2006 ; see Table 1 ) are used as forcings for the Hydrology Discharge (HD) model ( Hagemann and Dümenil 1998a ). The response of river discharges produced by HD to the different surface and subsurface runoff values is evaluated. Moreover, the quality of meteorological forcing data has a strong influence on the simulation of land surface components of the hydrological cycle ( Guo et al. 2006 ). GSWP-2 included a number of
, a 4-km elevation map can capture much more detailed topography features over the URGB than can a 12-km map. So far, however, few attempts have been made to employ very fine spatial resolutions for (long term) hydroclimatology studies. One of the objectives of this research was to investigate whether, or to what extent, this modeling can improve the accuracy of hydroclimate studies. In addition to model spatial resolution, the choice of atmospheric forcing field exerts an important influence on
, a 4-km elevation map can capture much more detailed topography features over the URGB than can a 12-km map. So far, however, few attempts have been made to employ very fine spatial resolutions for (long term) hydroclimatology studies. One of the objectives of this research was to investigate whether, or to what extent, this modeling can improve the accuracy of hydroclimate studies. In addition to model spatial resolution, the choice of atmospheric forcing field exerts an important influence on
1. Introduction The impact of aerosol radiative forcing on the energy and water cycle is an important concern in understanding regional climate, but the details of its spatiotemporal variability remain uncertain. Aerosols influence the energy and water cycle primarily via scattering and absorption of solar radiation (direct effect) and via their impact on the characteristics of clouds and precipitation (indirect effect). In climate modeling and long-range forecasts, uncertainties in aerosol
1. Introduction The impact of aerosol radiative forcing on the energy and water cycle is an important concern in understanding regional climate, but the details of its spatiotemporal variability remain uncertain. Aerosols influence the energy and water cycle primarily via scattering and absorption of solar radiation (direct effect) and via their impact on the characteristics of clouds and precipitation (indirect effect). In climate modeling and long-range forecasts, uncertainties in aerosol
environment. The evaluation is conducted by comparing the effects on model performance of aggregated to distributed initial conditions and forcing data. 2. Study basin and observations The selected study area was Granger Basin (60°31′N, 135°07′W) which is part of Wolf Creek Research Basin situated 15 km south of Whitehorse, Yukon Territory, Canada ( Fig. 1 ). Granger Basin, drained by Granger Creek, is located in the mountainous headwaters of the Yukon River basin and compromises a drainage area about 8
environment. The evaluation is conducted by comparing the effects on model performance of aggregated to distributed initial conditions and forcing data. 2. Study basin and observations The selected study area was Granger Basin (60°31′N, 135°07′W) which is part of Wolf Creek Research Basin situated 15 km south of Whitehorse, Yukon Territory, Canada ( Fig. 1 ). Granger Basin, drained by Granger Creek, is located in the mountainous headwaters of the Yukon River basin and compromises a drainage area about 8
conditions in conjunction with warm SST anomalies in the Indian Ocean. Similarly, Schubert et al. (2004b) and Seager et al. (2005) showed that ensembles of AGCM simulations forced with observed twentieth-century SST reproduced much of the observed low-frequency variability in twentieth-century precipitation over the U.S. Great Plains, including the timing and duration of major droughts of the 1930s and 1950s. These studies suggest that ocean–atmosphere forcing by persistent SST anomalies is a primary
conditions in conjunction with warm SST anomalies in the Indian Ocean. Similarly, Schubert et al. (2004b) and Seager et al. (2005) showed that ensembles of AGCM simulations forced with observed twentieth-century SST reproduced much of the observed low-frequency variability in twentieth-century precipitation over the U.S. Great Plains, including the timing and duration of major droughts of the 1930s and 1950s. These studies suggest that ocean–atmosphere forcing by persistent SST anomalies is a primary
, only the studies of Brubaker and Entekhabi (1996) and Margulis and Entekhabi (2001) have provided methods to quantify the influence of individual forcings and feedbacks in the coupled land–atmosphere system on evapotranspiration. Our study focuses on evapotranspiration on the diurnal time scale and is therefore complementary to the work of Brubaker and Entekhabi . Their study aims at understanding the longer time scales involved in the heat and moisture budget, which can for instance be seen
, only the studies of Brubaker and Entekhabi (1996) and Margulis and Entekhabi (2001) have provided methods to quantify the influence of individual forcings and feedbacks in the coupled land–atmosphere system on evapotranspiration. Our study focuses on evapotranspiration on the diurnal time scale and is therefore complementary to the work of Brubaker and Entekhabi . Their study aims at understanding the longer time scales involved in the heat and moisture budget, which can for instance be seen
Amazon, French Guiana, Suriname, Guyana, and Venezuela. This region is hereafter referred as the EA and is outlined in Fig. 1a . Given the EA’s close proximity to the equator, the motivation for this paper is to understand if local processes, such as the diurnal variation, amplify the remote ENSO forcing. 2. Model description and data a. Model description The Center for Ocean–Land–Atmosphere Studies (COLA) coupled climate model ( Misra et al. 2007 ; Misra and Marx 2007 ) is used in this study. Its
Amazon, French Guiana, Suriname, Guyana, and Venezuela. This region is hereafter referred as the EA and is outlined in Fig. 1a . Given the EA’s close proximity to the equator, the motivation for this paper is to understand if local processes, such as the diurnal variation, amplify the remote ENSO forcing. 2. Model description and data a. Model description The Center for Ocean–Land–Atmosphere Studies (COLA) coupled climate model ( Misra et al. 2007 ; Misra and Marx 2007 ) is used in this study. Its
. 1996 ; Koster and Suarez 1999 ; Fennessey and Shukla 1999 ; Koster et al. 2004 ; de Goncalves et al. 2006a ), and that surface states such as soil moisture and temperature can affect atmospheric numerical model predictions. There are continuing efforts to increase the accuracy (and, as a result, complexity) of the representation within LSMs of the processes involved in the soil–vegetation–atmosphere system. However, realism can only ensue if LSMs are provided with realistic forcing data. Such
. 1996 ; Koster and Suarez 1999 ; Fennessey and Shukla 1999 ; Koster et al. 2004 ; de Goncalves et al. 2006a ), and that surface states such as soil moisture and temperature can affect atmospheric numerical model predictions. There are continuing efforts to increase the accuracy (and, as a result, complexity) of the representation within LSMs of the processes involved in the soil–vegetation–atmosphere system. However, realism can only ensue if LSMs are provided with realistic forcing data. Such
these studies, as well as water management decisions, understanding the uncertainty associated with various underlying modeling application choices is critical. In an assessment of climate change impacts on water resources, modeling application choices may include historical and projected future climate datasets, model structure, and model calibration metrics, objective function, and calibration scheme. With respect to choice of historical meteorological forcings, studies have shown that the dataset
these studies, as well as water management decisions, understanding the uncertainty associated with various underlying modeling application choices is critical. In an assessment of climate change impacts on water resources, modeling application choices may include historical and projected future climate datasets, model structure, and model calibration metrics, objective function, and calibration scheme. With respect to choice of historical meteorological forcings, studies have shown that the dataset
Laboratory (GFDL), and the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA GMAO) are investigated in this study. The AGCMs are evaluated at a typical climate model resolution (approximately 2° latitude–longitude), in order to address 1) how accurately current climate models resolve the observed characteristics of warm-season diurnal cycle of rainfall, 2) how faithfully they simulate the local and large-scale forcing mechanisms that drive the diurnal convection
Laboratory (GFDL), and the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA GMAO) are investigated in this study. The AGCMs are evaluated at a typical climate model resolution (approximately 2° latitude–longitude), in order to address 1) how accurately current climate models resolve the observed characteristics of warm-season diurnal cycle of rainfall, 2) how faithfully they simulate the local and large-scale forcing mechanisms that drive the diurnal convection
