Search Results
. 2001 , Weiss et al. 2004 ). Various studies have dealt with these interactions, including the development of land–atmosphere models ( Noilhan and Planton 1989 ; Pitman 1991 ; Xue et al. 1991 ) and the relation of vegetation dynamics to other land and climate variables ( Betts et al. 1997 ; Bounoua et al. 1999 ; Weiss et al. 2004 ). Meteorological and climatological conditions both impact and are influenced by vegetation distribution and dynamics ( Sellers et al. 1996 ; Betts et al. 1997
. 2001 , Weiss et al. 2004 ). Various studies have dealt with these interactions, including the development of land–atmosphere models ( Noilhan and Planton 1989 ; Pitman 1991 ; Xue et al. 1991 ) and the relation of vegetation dynamics to other land and climate variables ( Betts et al. 1997 ; Bounoua et al. 1999 ; Weiss et al. 2004 ). Meteorological and climatological conditions both impact and are influenced by vegetation distribution and dynamics ( Sellers et al. 1996 ; Betts et al. 1997
-term variability in the climate system. This hypothesis was first studied in semiarid areas where vegetation is water limited, like the Sahel. Over the past few centuries, the climate of the Sahel has been characterized by a succession of multidecadal dry and wet periods. In the rest of the world, however, wet and dry spells do not usually exceed 2–5 yr ( Nicholson 2000 ). In a review of observational and modeling work, Nicholson (2000) showed how vegetation modulates land–atmosphere interactions by
-term variability in the climate system. This hypothesis was first studied in semiarid areas where vegetation is water limited, like the Sahel. Over the past few centuries, the climate of the Sahel has been characterized by a succession of multidecadal dry and wet periods. In the rest of the world, however, wet and dry spells do not usually exceed 2–5 yr ( Nicholson 2000 ). In a review of observational and modeling work, Nicholson (2000) showed how vegetation modulates land–atmosphere interactions by
limited process understanding and hence predictive ability. Vegetation and soil are the interface between the atmosphere and terrestrial ecosystems through emissions and wet and dry deposition of air pollutants and related compounds. F ig . 1. Demonstration of key elements and processes in air pollution–terrestrial ecosystem interactions, including vegetation and soil uptake and emissions of air pollutants and precursors, in-canopy turbulence, and effects of human activities, fires, and meteorology
limited process understanding and hence predictive ability. Vegetation and soil are the interface between the atmosphere and terrestrial ecosystems through emissions and wet and dry deposition of air pollutants and related compounds. F ig . 1. Demonstration of key elements and processes in air pollution–terrestrial ecosystem interactions, including vegetation and soil uptake and emissions of air pollutants and precursors, in-canopy turbulence, and effects of human activities, fires, and meteorology
et al. (2009 , 2010) , and Saatchi et al. (2013) . Land–atmosphere feedbacks enhance the importance of drought impacts on vegetation. Climate-forced changes in vegetation produce feedbacks to the atmospheric system because of modifications in biogeophysical properties. When vegetation is water stressed, albedo increases and latent energy flux decreases, which may decrease atmospheric instability, convection, and cloud development. Human-forced LULC changes further complicate these land–atmosphere
et al. (2009 , 2010) , and Saatchi et al. (2013) . Land–atmosphere feedbacks enhance the importance of drought impacts on vegetation. Climate-forced changes in vegetation produce feedbacks to the atmospheric system because of modifications in biogeophysical properties. When vegetation is water stressed, albedo increases and latent energy flux decreases, which may decrease atmospheric instability, convection, and cloud development. Human-forced LULC changes further complicate these land–atmosphere
-scale field studies of VBP–atmosphere relationships, and even fewer have intended to deduct relevant information from observational data to validate simulated vegetation–climate interactions. However, application of limited observation has been found to effectively highlight VBP effects in a few regional climate simulation studies ( Beljaars et al. 1996 ; Zeng et al. 1999 ; Hong and Kalnay 2000 ; Douville et al. 2001 ; Xue et al. 2001 , 2004b , 2006 ; Wang et al. 2004 ). In the present study we
-scale field studies of VBP–atmosphere relationships, and even fewer have intended to deduct relevant information from observational data to validate simulated vegetation–climate interactions. However, application of limited observation has been found to effectively highlight VBP effects in a few regional climate simulation studies ( Beljaars et al. 1996 ; Zeng et al. 1999 ; Hong and Kalnay 2000 ; Douville et al. 2001 ; Xue et al. 2001 , 2004b , 2006 ; Wang et al. 2004 ). In the present study we
1. Introduction The atmosphere and vegetation interact in a complex way. The atmosphere exerts a dominant control on vegetation through variations in air temperature, precipitation, solar radiation, wind, and CO 2 concentration ( Budyko 1974 ; Woodward 1987 ; Nemani et al. 2003 ; Woodward et al. 2004 ). As a result, the spatial distribution of major vegetation types is consistent with climate zones on a global scale ( Bryson 1966 ). Although this two-way interaction is dominated by the
1. Introduction The atmosphere and vegetation interact in a complex way. The atmosphere exerts a dominant control on vegetation through variations in air temperature, precipitation, solar radiation, wind, and CO 2 concentration ( Budyko 1974 ; Woodward 1987 ; Nemani et al. 2003 ; Woodward et al. 2004 ). As a result, the spatial distribution of major vegetation types is consistent with climate zones on a global scale ( Bryson 1966 ). Although this two-way interaction is dominated by the
challenging and complex (e.g., Santanello et al. 2018 ). In an early study, Charney (1975) first presented a theory of how a reduction in vegetation might feedback to produce a decrease in rainfall through land–atmosphere interactions. Changes in land cover can modify surface roughness and impact heat and moisture exchange between the land surface and atmosphere and eventually can modulate temperature and precipitation ( Boyaj et al. 2020 ; Chen et al. 2020 ; Pielke et al. 2016 ; Pielke and Niyogi
challenging and complex (e.g., Santanello et al. 2018 ). In an early study, Charney (1975) first presented a theory of how a reduction in vegetation might feedback to produce a decrease in rainfall through land–atmosphere interactions. Changes in land cover can modify surface roughness and impact heat and moisture exchange between the land surface and atmosphere and eventually can modulate temperature and precipitation ( Boyaj et al. 2020 ; Chen et al. 2020 ; Pielke et al. 2016 ; Pielke and Niyogi
, studying the relationship between vegetation and SM is particularly important for understanding the land–atmosphere system and its influence. According to the existing literature, the relationship between vegetation and SM is complex. More vegetation may correspond to either increased ( Bounoua et al. 2000 ; Buermann et al. 2001 ) or reduced SM ( Pielke et al. 1998 ; Wang et al. 2006 ). This study investigates the relationship between LAI and SM in different types of soil using observational data
, studying the relationship between vegetation and SM is particularly important for understanding the land–atmosphere system and its influence. According to the existing literature, the relationship between vegetation and SM is complex. More vegetation may correspond to either increased ( Bounoua et al. 2000 ; Buermann et al. 2001 ) or reduced SM ( Pielke et al. 1998 ; Wang et al. 2006 ). This study investigates the relationship between LAI and SM in different types of soil using observational data
1. Introduction It is a standard practice in modeling land surface–atmosphere interaction that momentum or scalar fluxes can be parameterized by relating them to the mean velocity gradient or scalar concentration gradient by means of a turbulent diffusion coefficient, called gradient-diffusion parameterization or K theory ( Raupach and Thom 1981 ; Katul et al. 2013 ). The K theory has enjoyed a high degree of popularity over the years, especially because of the ease of usage. It has been
1. Introduction It is a standard practice in modeling land surface–atmosphere interaction that momentum or scalar fluxes can be parameterized by relating them to the mean velocity gradient or scalar concentration gradient by means of a turbulent diffusion coefficient, called gradient-diffusion parameterization or K theory ( Raupach and Thom 1981 ; Katul et al. 2013 ). The K theory has enjoyed a high degree of popularity over the years, especially because of the ease of usage. It has been
deposition case presently considered. They also question the applicability of the concept of a roughness length, on which much of the existing suite of resistance formations is based. Katul et al. (2006) extend the theoretical analysis for CO 2 to a uniformly forested hill and confirm that advection is a dominating factor in the overall air surface exchange. Ross and Vosper (2005) conclude that “dynamic interaction of forest canopies with the atmosphere over complex terrain . . . can lead to
deposition case presently considered. They also question the applicability of the concept of a roughness length, on which much of the existing suite of resistance formations is based. Katul et al. (2006) extend the theoretical analysis for CO 2 to a uniformly forested hill and confirm that advection is a dominating factor in the overall air surface exchange. Ross and Vosper (2005) conclude that “dynamic interaction of forest canopies with the atmosphere over complex terrain . . . can lead to
