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1. Introduction Retrospective analysis (reanalysis) data products provide global, subdaily estimates of atmospheric and land surface conditions across several decades. Such products are based on the assimilation of a large amount of in situ and remote sensing observations into an atmospheric general circulation model (AGCM) and are among the most widely used datasets in Earth science. The recent Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al
1. Introduction Retrospective analysis (reanalysis) data products provide global, subdaily estimates of atmospheric and land surface conditions across several decades. Such products are based on the assimilation of a large amount of in situ and remote sensing observations into an atmospheric general circulation model (AGCM) and are among the most widely used datasets in Earth science. The recent Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al
1. Introduction Hydrological cycling between the atmosphere, vegetation, and soil plays an important role in land surface processes. Understanding its variability and relationship to atmospheric processes on various time scales will help better understand the impact of soil moisture on weather and climate and better manage water resources. One of the important aspects of the surface hydrological cycle is how the land surface components (soil moisture, evapotranspiration, and runoff) respond to
1. Introduction Hydrological cycling between the atmosphere, vegetation, and soil plays an important role in land surface processes. Understanding its variability and relationship to atmospheric processes on various time scales will help better understand the impact of soil moisture on weather and climate and better manage water resources. One of the important aspects of the surface hydrological cycle is how the land surface components (soil moisture, evapotranspiration, and runoff) respond to
1. Introduction As one of the principal physical processes in the land–atmosphere interaction system ( Zhang et al. 2003 ), land surface energy exchange is restricted by the climate system, but it also imposes strong feedbacks on the climate system ( Claussen et al. 2001 ). Energy exchange between land and the atmosphere is mainly determined by sensible heat flux ( H ), latent heat flux (LE), and net radiation (NR) ( Falge et al. 2005 ). Investigating the spatiotemporal changes in individual
1. Introduction As one of the principal physical processes in the land–atmosphere interaction system ( Zhang et al. 2003 ), land surface energy exchange is restricted by the climate system, but it also imposes strong feedbacks on the climate system ( Claussen et al. 2001 ). Energy exchange between land and the atmosphere is mainly determined by sensible heat flux ( H ), latent heat flux (LE), and net radiation (NR) ( Falge et al. 2005 ). Investigating the spatiotemporal changes in individual
1. Introduction Land surface temperature (LST) is a key variable in determination of the land surface energy budget and is thus often assimilated into land surface models ( Rodell et al. 2004 ). LST (as soil or vegetation canopy temperature) is also used in models of vegetation stress (e.g., Jackson et al. 1981 ; Moran et al. 1994 ; Anderson et al. 2007 ). When observed over multiple years, LST can also be assessed for climatic trends (e.g., Jin 2004 ). Because of the relatively small
1. Introduction Land surface temperature (LST) is a key variable in determination of the land surface energy budget and is thus often assimilated into land surface models ( Rodell et al. 2004 ). LST (as soil or vegetation canopy temperature) is also used in models of vegetation stress (e.g., Jackson et al. 1981 ; Moran et al. 1994 ; Anderson et al. 2007 ). When observed over multiple years, LST can also be assessed for climatic trends (e.g., Jin 2004 ). Because of the relatively small
1. Introduction With the resolution of numerical weather prediction models constrained by computational cost, a single grid box may span a wide variety of surface types. The subgrid-scale land surface heterogeneity must be parameterized in the surface scheme so that the land characteristics are accounted for in the model. Two alternative approaches are normally used to represent different surface types within a grid box. The first one combines the surface types to calculate effective parameters
1. Introduction With the resolution of numerical weather prediction models constrained by computational cost, a single grid box may span a wide variety of surface types. The subgrid-scale land surface heterogeneity must be parameterized in the surface scheme so that the land characteristics are accounted for in the model. Two alternative approaches are normally used to represent different surface types within a grid box. The first one combines the surface types to calculate effective parameters
and improve the parameterizations of the solar zenith angle (SZA)–albedo relationship for climate and weather forecast models. Land surface albedos over both bare soil and plant canopies have a strong dependence on solar zenith angle and the surface characteristics. Since the surface types change considerably from place to place and throughout a growing season, it is a formidable problem to develop different schemes to model the dependence of surface albedo on SZA for different surface types
and improve the parameterizations of the solar zenith angle (SZA)–albedo relationship for climate and weather forecast models. Land surface albedos over both bare soil and plant canopies have a strong dependence on solar zenith angle and the surface characteristics. Since the surface types change considerably from place to place and throughout a growing season, it is a formidable problem to develop different schemes to model the dependence of surface albedo on SZA for different surface types
1. Introduction Evapotranspiration (ET) is a major component of the water and energy exchanges between the atmosphere and the land surface. Although in situ measurements and remote sensing can provide ET estimates for limited spatial locations and time periods, mathematical models are the most efficient approach for the continuous monitoring of ET dynamics. Because of the incomplete model physics and/or input data uncertainties, model estimates may contain significant errors, and merging model
1. Introduction Evapotranspiration (ET) is a major component of the water and energy exchanges between the atmosphere and the land surface. Although in situ measurements and remote sensing can provide ET estimates for limited spatial locations and time periods, mathematical models are the most efficient approach for the continuous monitoring of ET dynamics. Because of the incomplete model physics and/or input data uncertainties, model estimates may contain significant errors, and merging model
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
1. Introduction In recent years, the important role played by the land surface in the global climate system has been recognized ( Koster et al. 2006 ), and increasingly sophisticated land surface schemes (LSSs) have been developed for general circulation models (GCMs; e.g., Bonan et al. 2002 ; Alessandri et al. 2007 ). Not only is an accurate simulation of the land surface state crucial for the skill of seasonal and weather forecasts ( Ferranti and Viterbo 2006 ; Fischer et al. 2007
1. Introduction In recent years, the important role played by the land surface in the global climate system has been recognized ( Koster et al. 2006 ), and increasingly sophisticated land surface schemes (LSSs) have been developed for general circulation models (GCMs; e.g., Bonan et al. 2002 ; Alessandri et al. 2007 ). Not only is an accurate simulation of the land surface state crucial for the skill of seasonal and weather forecasts ( Ferranti and Viterbo 2006 ; Fischer et al. 2007
1. Introduction The Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011 ) is a recent addition to the suite of global, long-term reanalysis products that are based on the assimilation of in situ and remote sensing observations into numerical models of the global atmosphere and land surface ( Kalnay et al. 1996 ; Kanamitsu et al. 2002 ; Uppala et al. 2005 ; Onogi et al. 2007 ; Dee et al. 2011; Saha et al. 2010 ). Besides estimates of atmospheric
1. Introduction The Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011 ) is a recent addition to the suite of global, long-term reanalysis products that are based on the assimilation of in situ and remote sensing observations into numerical models of the global atmosphere and land surface ( Kalnay et al. 1996 ; Kanamitsu et al. 2002 ; Uppala et al. 2005 ; Onogi et al. 2007 ; Dee et al. 2011; Saha et al. 2010 ). Besides estimates of atmospheric
