Search Results
1. Introduction A land surface model (LSM) is used in a climate model to represent the interaction between the atmosphere and land surface. It simulates radiation, water, heat, and carbon exchanges, with explicit representation of vegetation and soil types (see Pitman 2003 ). LSMs are commonly evaluated using observed values of three key model outputs: latent heat flux (Qle), sensible heat flux (Qh), and Net Ecosystem Exchange (NEE) of CO 2 from eddy covariance flux measurements (e
1. Introduction A land surface model (LSM) is used in a climate model to represent the interaction between the atmosphere and land surface. It simulates radiation, water, heat, and carbon exchanges, with explicit representation of vegetation and soil types (see Pitman 2003 ). LSMs are commonly evaluated using observed values of three key model outputs: latent heat flux (Qle), sensible heat flux (Qh), and Net Ecosystem Exchange (NEE) of CO 2 from eddy covariance flux measurements (e
; Case et al. 2011 ; Rodell et al. 2004 ; Hao et al. 2014 ). This limits the ability of these systems to assimilate different types of vegetation data products. If LDASs were instead to use land surface models (LSMs) that directly simulate plant carbon uptake and partitioning, then vegetation-related observations could be assimilated directly, and these LDAS frameworks would be able, at least in theory, to derive information from almost any vegetation remote sensing product. Recently, the Noah LSM
; Case et al. 2011 ; Rodell et al. 2004 ; Hao et al. 2014 ). This limits the ability of these systems to assimilate different types of vegetation data products. If LDASs were instead to use land surface models (LSMs) that directly simulate plant carbon uptake and partitioning, then vegetation-related observations could be assimilated directly, and these LDAS frameworks would be able, at least in theory, to derive information from almost any vegetation remote sensing product. Recently, the Noah LSM
1. Introduction Model evaluation has been an integral part of developing global land surface models (LSMs) for weather and climate studies. Since the early 1990s, the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS; Henderson-Sellers et al. 1996 ) has evaluated the parameterization of surface energy and water fluxes against field observations at a number of selected sites. They found the simulated annual mean latent heat flux varied from 30 to 56 W m −2 , as
1. Introduction Model evaluation has been an integral part of developing global land surface models (LSMs) for weather and climate studies. Since the early 1990s, the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS; Henderson-Sellers et al. 1996 ) has evaluated the parameterization of surface energy and water fluxes against field observations at a number of selected sites. They found the simulated annual mean latent heat flux varied from 30 to 56 W m −2 , as
1. Introduction Since the Project for the Intercomparison of Land-Surface Parameterizations Schemes (PILPS; Henderson-Sellers et al. 1993 , 1995b ) began to compare land surface models (LSMs) in 1993, the land modeling community has used a range of methods to examine how and why these models differ from each other and from observations. PILPS began with offline synthetic forcing (e.g., Pitman et al. 1999 ) but moved to using observational atmospheric forcing for multiple sites, including
1. Introduction Since the Project for the Intercomparison of Land-Surface Parameterizations Schemes (PILPS; Henderson-Sellers et al. 1993 , 1995b ) began to compare land surface models (LSMs) in 1993, the land modeling community has used a range of methods to examine how and why these models differ from each other and from observations. PILPS began with offline synthetic forcing (e.g., Pitman et al. 1999 ) but moved to using observational atmospheric forcing for multiple sites, including
land surface processes play an important role not only in large-scale atmospheric models (e.g., Chen and Avissar 1994a ; Polcher et al. 1998 ; Desborough 1999 ; Chen 2005 ) but also in regional and mesoscale atmospheric processes including precipitation (e.g., Avissar and Pielke 1989 ; Chen and Avissar 1994b ; Tilley and Lynch 1998 ; Chen and Dudhia 2001a , b ; Chen et al. 2001 ; Xiu and Pleim 2001 ; Ek et al. 2003 ; Trier et al. 2004 ; Mölders and Walsh 2004 ). During the development
land surface processes play an important role not only in large-scale atmospheric models (e.g., Chen and Avissar 1994a ; Polcher et al. 1998 ; Desborough 1999 ; Chen 2005 ) but also in regional and mesoscale atmospheric processes including precipitation (e.g., Avissar and Pielke 1989 ; Chen and Avissar 1994b ; Tilley and Lynch 1998 ; Chen and Dudhia 2001a , b ; Chen et al. 2001 ; Xiu and Pleim 2001 ; Ek et al. 2003 ; Trier et al. 2004 ; Mölders and Walsh 2004 ). During the development
1. Introduction A major objective of the Global Energy and Water Experiment (GEWEX) has been to develop land surface models (LSMs) for research, application, and prediction. LSMs developed for these purposes have included the Variable Infiltration Capacity model (VIC; Liang et al. 1994 ; Peters-Lidard 1997 ), the National Center for Atmospheric Research’s (NCAR’s) Common Land Model (CLM; Bonan et al. 2002 ), the National Aeronautic and Space Administration’s (NASA’s) Mosaic ( Koster and
1. Introduction A major objective of the Global Energy and Water Experiment (GEWEX) has been to develop land surface models (LSMs) for research, application, and prediction. LSMs developed for these purposes have included the Variable Infiltration Capacity model (VIC; Liang et al. 1994 ; Peters-Lidard 1997 ), the National Center for Atmospheric Research’s (NCAR’s) Common Land Model (CLM; Bonan et al. 2002 ), the National Aeronautic and Space Administration’s (NASA’s) Mosaic ( Koster and
of global warming (e.g., Randerson et al. 1999 ; Nemani et al. 2003 ). For example, Smith et al. (2004) , McDonald et al. (2004) , Kimball et al. (2006) , Kim et al. (2012) , and Wang et al. (2013) found consistency between these patterns and changes in seasonal F/T dynamics observed by satellite microwave remote sensing. Thus, for more accurate modeling and prediction of land surface hydrological and biospheric processes, a good representation of the landscape F/T state in land surface
of global warming (e.g., Randerson et al. 1999 ; Nemani et al. 2003 ). For example, Smith et al. (2004) , McDonald et al. (2004) , Kimball et al. (2006) , Kim et al. (2012) , and Wang et al. (2013) found consistency between these patterns and changes in seasonal F/T dynamics observed by satellite microwave remote sensing. Thus, for more accurate modeling and prediction of land surface hydrological and biospheric processes, a good representation of the landscape F/T state in land surface
–atmosphere interface play an important role in controlling the atmospheric heating and ground warming. It is, therefore, vital to be able to simulate the surface heat fluxes transfer accurately for quantifying and predicting the impact of global warming on the ecologically fragile high-altitude regions, such as the SRYR. Models of the surface heat fluxes transfer between the land surface and atmosphere usually employ the bulk formulations based on the Monin–Obukhov similarity theory (MOST; Garratt 1994
–atmosphere interface play an important role in controlling the atmospheric heating and ground warming. It is, therefore, vital to be able to simulate the surface heat fluxes transfer accurately for quantifying and predicting the impact of global warming on the ecologically fragile high-altitude regions, such as the SRYR. Models of the surface heat fluxes transfer between the land surface and atmosphere usually employ the bulk formulations based on the Monin–Obukhov similarity theory (MOST; Garratt 1994
MEP solution of the surface fluxes; and 3) solving the surface fluxes using only net radiation, surface temperature, and surface specific humidity. Although the surface fluxes are calculated from three input variables (temperature, humidity, and net radiation), it is important to emphasize that the effects of other atmospheric and land surface conditions are taken into account through the model parameters (i.e., thermal inertia). Even with fewer input variables compared to other physically based
MEP solution of the surface fluxes; and 3) solving the surface fluxes using only net radiation, surface temperature, and surface specific humidity. Although the surface fluxes are calculated from three input variables (temperature, humidity, and net radiation), it is important to emphasize that the effects of other atmospheric and land surface conditions are taken into account through the model parameters (i.e., thermal inertia). Even with fewer input variables compared to other physically based
1. Introduction The land surface components of meteorology modeling systems are responsible for the realistic representation of surface heat and moisture exchange processes and their dependence on vegetation and soil temperature and moisture. The surface fluxes of heat and moisture drive the near-surface air temperature and humidity and the evolution of the planetary boundary layer (PBL). The diurnal evolution of the PBL is of particular importance to air quality modeling applications. Thus
1. Introduction The land surface components of meteorology modeling systems are responsible for the realistic representation of surface heat and moisture exchange processes and their dependence on vegetation and soil temperature and moisture. The surface fluxes of heat and moisture drive the near-surface air temperature and humidity and the evolution of the planetary boundary layer (PBL). The diurnal evolution of the PBL is of particular importance to air quality modeling applications. Thus
