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radiation effects and the feedback of land–atmosphere interaction on the warm amplification over arid/semiarid regions. In addition, various studies have explored the possible reasons for the abnormal warming of the Eurasian continent from aspects of changes in cloud amount ( Dai et al. 1997 , 1999 ; Tang and Leng 2012 ; Tang et al. 2012 ) and precipitation ( Dai et al. 1997 , 1999 ; Trenberth and Shea 2005 ). Dai et al. (1997 , 1999) pointed out that increased cloud amount can reduce solar
radiation effects and the feedback of land–atmosphere interaction on the warm amplification over arid/semiarid regions. In addition, various studies have explored the possible reasons for the abnormal warming of the Eurasian continent from aspects of changes in cloud amount ( Dai et al. 1997 , 1999 ; Tang and Leng 2012 ; Tang et al. 2012 ) and precipitation ( Dai et al. 1997 , 1999 ; Trenberth and Shea 2005 ). Dai et al. (1997 , 1999) pointed out that increased cloud amount can reduce solar
). Evaporation is a crucial water cycle component related to the energy and carbon exchanges on Earth ( Trenberth et al. 2009 ; Jung et al. 2010 ; Friedlingstein et al. 2014 ; Green et al. 2019 ; Ma et al. 2021 ; Liu et al. 2022 ). The increase in latent heat flux (proportional to evaporation) is accompanied by a decrease in sensible heat flux, which is determined by the surface energy balance. Since the TP is one of the regions with the strongest land–atmosphere interactions ( Xue et al. 2010 , 2021
). Evaporation is a crucial water cycle component related to the energy and carbon exchanges on Earth ( Trenberth et al. 2009 ; Jung et al. 2010 ; Friedlingstein et al. 2014 ; Green et al. 2019 ; Ma et al. 2021 ; Liu et al. 2022 ). The increase in latent heat flux (proportional to evaporation) is accompanied by a decrease in sensible heat flux, which is determined by the surface energy balance. Since the TP is one of the regions with the strongest land–atmosphere interactions ( Xue et al. 2010 , 2021
1. Introduction The inherent coupled nature of earth’s energy and water cycles places significant importance on the proper representation and diagnosis of land–atmosphere (LA) interactions in hydrometeorological prediction models ( Entekhabi et al. 1999 ; Betts and Silva Dias 2010 ). Unfortunately, the disparate resolutions and complexities of the governing processes have made it difficult to quantify these interactions in models or observations ( Angevine 1999 ; Betts 2000 ; Cheng and
1. Introduction The inherent coupled nature of earth’s energy and water cycles places significant importance on the proper representation and diagnosis of land–atmosphere (LA) interactions in hydrometeorological prediction models ( Entekhabi et al. 1999 ; Betts and Silva Dias 2010 ). Unfortunately, the disparate resolutions and complexities of the governing processes have made it difficult to quantify these interactions in models or observations ( Angevine 1999 ; Betts 2000 ; Cheng and
evaluation approaches over the past decade helping to drive improvements in model development, and to assess the credibility of future climate projections ( Eyring et al. 2016a ; Duveiller et al. 2018 ; Eyring et al. 2019 , 2020 ; Fasullo 2020 ). In South America, interactions between the land surface and the atmosphere are particularly important for climate, and thus need to be accurately represented in climate models. Studies integrating remote sensing and reanalysis datasets have highlighted the
evaluation approaches over the past decade helping to drive improvements in model development, and to assess the credibility of future climate projections ( Eyring et al. 2016a ; Duveiller et al. 2018 ; Eyring et al. 2019 , 2020 ; Fasullo 2020 ). In South America, interactions between the land surface and the atmosphere are particularly important for climate, and thus need to be accurately represented in climate models. Studies integrating remote sensing and reanalysis datasets have highlighted the
surface states on global climate have relied on parameterizations of the land surface coupled to weather and climate models. This modeling approach is used to understand large-scale patterns and long-term statistics (cf. Seneviratne et al. 2010 ). The Coupled Model Intercomparison Project phase 5 (CMIP5; Taylor et al. 2012 ) provides an opportunity for multimodel assessment of the evolving nature of land–atmosphere interactions from past to present to future. A much broader evaluation is possible
surface states on global climate have relied on parameterizations of the land surface coupled to weather and climate models. This modeling approach is used to understand large-scale patterns and long-term statistics (cf. Seneviratne et al. 2010 ). The Coupled Model Intercomparison Project phase 5 (CMIP5; Taylor et al. 2012 ) provides an opportunity for multimodel assessment of the evolving nature of land–atmosphere interactions from past to present to future. A much broader evaluation is possible
interactions ( Koster et al. 2000 ). There has been a great deal of work over the last decade to quantify land–atmosphere interactions and feedbacks over a variety of scales that utilize observations and prediction models. Working groups as part of the Global Energy and Water Cycle Experiment (GEWEX) initiative have done much of this work. One such effort focuses on the local land–atmosphere coupling through diagnosing the interactions between the land surface and the planetary boundary layer for models
interactions ( Koster et al. 2000 ). There has been a great deal of work over the last decade to quantify land–atmosphere interactions and feedbacks over a variety of scales that utilize observations and prediction models. Working groups as part of the Global Energy and Water Cycle Experiment (GEWEX) initiative have done much of this work. One such effort focuses on the local land–atmosphere coupling through diagnosing the interactions between the land surface and the planetary boundary layer for models
land cover (LULC) plays an important role in land–atmosphere (L–A) interactions and hence impacts weather and climate ( Adegoke et al. 2007 ; Betts et al. 2007 ; Carleton et al. 2001 ; Mahmood et al. 2014 ; Gerken et al. 2019 ; LeMone et al. 2007 ; Pielke et al. 2007 ; Rabin et al. 1990 ; Segal et al. 1989 ; Zeng et al. 2016 ). These studies show LULC notably influences temperatures, moisture distribution, cloud development, and convective activities. Modifications of LULC [known as LULC
land cover (LULC) plays an important role in land–atmosphere (L–A) interactions and hence impacts weather and climate ( Adegoke et al. 2007 ; Betts et al. 2007 ; Carleton et al. 2001 ; Mahmood et al. 2014 ; Gerken et al. 2019 ; LeMone et al. 2007 ; Pielke et al. 2007 ; Rabin et al. 1990 ; Segal et al. 1989 ; Zeng et al. 2016 ). These studies show LULC notably influences temperatures, moisture distribution, cloud development, and convective activities. Modifications of LULC [known as LULC
inhibition; enhancing the probability of convective precipitation over drier soils ( Ford et al. 2015a , 2018 ; Tuttle and Salvucci 2016 ). Hence, soil moisture is a critical variable for both characterizing drought conditions and for investigating land–atmosphere interactions. Drought indices have been used to characterize near-surface moisture conditions in some land–atmosphere interaction studies because of the lack of available soil moisture measurements. For example, Hirschi et al. (2010) used
inhibition; enhancing the probability of convective precipitation over drier soils ( Ford et al. 2015a , 2018 ; Tuttle and Salvucci 2016 ). Hence, soil moisture is a critical variable for both characterizing drought conditions and for investigating land–atmosphere interactions. Drought indices have been used to characterize near-surface moisture conditions in some land–atmosphere interaction studies because of the lack of available soil moisture measurements. For example, Hirschi et al. (2010) used
describe the exchange of water, heat, and momentum across the land–atmosphere interface ( Brutsaert 1998 ; Albertson and Parlange 1999 ). Substantial progresses in representing the role of surface heterogeneity on land–atmosphere interaction has been achieved ( Henderson-Sellers and Pitman 1992 ; Lyons and Halldin 2004 ; Kanda et al. 2007 ; Ma et al. 2008 ; Brunsell et al. 2011 ). Numerous efforts have attempted to address the land surface parameters, such as roughness length, to ascertain area
describe the exchange of water, heat, and momentum across the land–atmosphere interface ( Brutsaert 1998 ; Albertson and Parlange 1999 ). Substantial progresses in representing the role of surface heterogeneity on land–atmosphere interaction has been achieved ( Henderson-Sellers and Pitman 1992 ; Lyons and Halldin 2004 ; Kanda et al. 2007 ; Ma et al. 2008 ; Brunsell et al. 2011 ). Numerous efforts have attempted to address the land surface parameters, such as roughness length, to ascertain area
vary from year to year ( Guo and Dirmeyer 2013 ) depending on the pattern of the climatology of soil moisture and the fluctuation of its anomalies. These results raise questions. Have the interactions between land and atmosphere on intraseasonal to interannual time scales changed since preindustrial times when atmospheric composition, aerosol loading, and global vegetation cover were different? More importantly, will land–atmosphere interactions change in the future? Phase 5 of the Coupled Model
vary from year to year ( Guo and Dirmeyer 2013 ) depending on the pattern of the climatology of soil moisture and the fluctuation of its anomalies. These results raise questions. Have the interactions between land and atmosphere on intraseasonal to interannual time scales changed since preindustrial times when atmospheric composition, aerosol loading, and global vegetation cover were different? More importantly, will land–atmosphere interactions change in the future? Phase 5 of the Coupled Model
