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1. Introduction The Tibetan Plateau (TP; also Qinghai-Xizang Plateau in China) extends over the area of 27°–45°N, 70°–105°E, covering a region about a quarter of the size of the Chinese territory. Its mean elevation is more than 4000 m above sea level with the 8844-m (near 300 hPa) peak of Mount Everest standing on its southern fringe. The mean TP altitude lies above 40% of the atmosphere. Because of the lower air pressure, various radiation processes over the plateau, particularly in the
1. Introduction The Tibetan Plateau (TP; also Qinghai-Xizang Plateau in China) extends over the area of 27°–45°N, 70°–105°E, covering a region about a quarter of the size of the Chinese territory. Its mean elevation is more than 4000 m above sea level with the 8844-m (near 300 hPa) peak of Mount Everest standing on its southern fringe. The mean TP altitude lies above 40% of the atmosphere. Because of the lower air pressure, various radiation processes over the plateau, particularly in the
forcings of the hydrological cycle, such as solar radiation ( Qian et al. 2006 ). It is well established that latent heating in the atmosphere dominates the structural patterns of total diabatic heating ( Trenberth and Stepaniak 2003a , b ) and thus there is a close relationship between the water and energy cycles in the atmosphere. Water vapor is the dominant greenhouse gas ( Kiehl and Trenberth 1997 ) and is responsible for the dominant feedback in the climate system ( Karl and Trenberth 2003
forcings of the hydrological cycle, such as solar radiation ( Qian et al. 2006 ). It is well established that latent heating in the atmosphere dominates the structural patterns of total diabatic heating ( Trenberth and Stepaniak 2003a , b ) and thus there is a close relationship between the water and energy cycles in the atmosphere. Water vapor is the dominant greenhouse gas ( Kiehl and Trenberth 1997 ) and is responsible for the dominant feedback in the climate system ( Karl and Trenberth 2003
1. Introduction Investigating river basin water cycles in the western U.S. mountainous region poses great challenges for the hydrology community because the region’s multiscale terrain leads to complex atmosphere–land surface interactions and makes it difficult to produce accurate observations. However, over the western United States, especially in the southwest semiarid region, the limited supply and increasing demand for water resources require accurate estimates of regional and local
1. Introduction Investigating river basin water cycles in the western U.S. mountainous region poses great challenges for the hydrology community because the region’s multiscale terrain leads to complex atmosphere–land surface interactions and makes it difficult to produce accurate observations. However, over the western United States, especially in the southwest semiarid region, the limited supply and increasing demand for water resources require accurate estimates of regional and local
convergence is reached even when drastic phase change occurs. b. Boundary conditions The upper boundary condition for Eq. (1) on a bare soil surface is given by the vertical moisture flux q s θ (m s −1 ) and is defined as where E is evaporation rate (m s −1 ), U p is rainfall rate (m s −1 ) on the soil surface, and R s is surface runoff. If there is snow falling, the snow will create a snow cover over bare soil and a snow cover model, such as the snow–atmosphere–soil transfer model (SAST
convergence is reached even when drastic phase change occurs. b. Boundary conditions The upper boundary condition for Eq. (1) on a bare soil surface is given by the vertical moisture flux q s θ (m s −1 ) and is defined as where E is evaporation rate (m s −1 ), U p is rainfall rate (m s −1 ) on the soil surface, and R s is surface runoff. If there is snow falling, the snow will create a snow cover over bare soil and a snow cover model, such as the snow–atmosphere–soil transfer model (SAST
1995 at the NASA Langley Web page. (More information is available online at http://eosweb.larc.nasa.gov .) The SW and LW fluxes are derived using Pinker and Laszlo (1992) and Fu et al. (1997) algorithms, respectively. The SW models used clear-sky top-of-atmosphere albedo from the Earth Radiation Budget Experiment (ERBE). The cloud properties were taken from the International Satellite Cloud Climatology Project (ISCCP) DX data. The Goddard Earth Observing System-1 (GEOS-1) data assimilation
1995 at the NASA Langley Web page. (More information is available online at http://eosweb.larc.nasa.gov .) The SW and LW fluxes are derived using Pinker and Laszlo (1992) and Fu et al. (1997) algorithms, respectively. The SW models used clear-sky top-of-atmosphere albedo from the Earth Radiation Budget Experiment (ERBE). The cloud properties were taken from the International Satellite Cloud Climatology Project (ISCCP) DX data. The Goddard Earth Observing System-1 (GEOS-1) data assimilation
.4), and neutral months (middle half of Niño-3.4). It is clear that the histogram shifts to greater fractional coverage values ( Fig. 2c ) during both phases of ENSO. For ocean-only ( Figs. 2d–f ), as compared to the entire tropical domain, the relationship between positive Niño-3.4 values and precipitation extremes improves for wet months (from r El = 0.41 to r El = 0.54) and degrades slightly for dry months (from r El = 0.67 to r El = 0.62). By restricting to ocean-only the relationship
.4), and neutral months (middle half of Niño-3.4). It is clear that the histogram shifts to greater fractional coverage values ( Fig. 2c ) during both phases of ENSO. For ocean-only ( Figs. 2d–f ), as compared to the entire tropical domain, the relationship between positive Niño-3.4 values and precipitation extremes improves for wet months (from r El = 0.41 to r El = 0.54) and degrades slightly for dry months (from r El = 0.67 to r El = 0.62). By restricting to ocean-only the relationship
