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Jessica C. A. Baker, Dayana Castilho de Souza, Paulo Y. Kubota, Wolfgang Buermann, Caio A. S. Coelho, Martin B. Andrews, Manuel Gloor, Luis Garcia-Carreras, Silvio N. Figueroa, and Dominick V. Spracklen

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

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Joseph A. Santanello Jr., Paul A. Dirmeyer, Craig R. Ferguson, Kirsten L. Findell, Ahmed B. Tawfik, Alexis Berg, Michael Ek, Pierre Gentine, Benoit P. Guillod, Chiel van Heerwaarden, Joshua Roundy, and Volker Wulfmeyer

Metrics derived by the LoCo working group have matured and begun to enter the mainstream, signaling the success of the GEWEX approach to foster grassroots participation. The role of land–atmosphere (L-A) interactions in weather and climate prediction has emerged over the last two decades as important but inherently challenging and complex. One reason is that L-A interaction research has proceeded “in reverse” compared to most science. Typically in Earth system sciences, observations inform

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Heather Tollerud, Jesslyn Brown, Tom Loveland, Rezaul Mahmood, and Norman Bliss

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

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Anil Kumar, Fei Chen, Michael Barlage, Michael B. Ek, and Dev Niyogi

1. Introduction The interaction between the surface layer and lower atmospheric layers is important for weather and climate models. The role of land–atmosphere interactions becomes even more important over a warm, moist surface covered by different vegetation types ( Ek et al. 2003 ; Dirmeyer et al. 2010 ). The value of improving land surface models to enhance operational forecasts is recognized by the different numerical weather prediction (NWP) centers (e.g., Beljaars et al. 1996 ; Betts

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Paul A. Dirmeyer, Yan Jin, Bohar Singh, and Xiaoqin Yan

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

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Ning Zeng and J. David Neelin

study of land–atmosphere interaction. Eltahir and Bras (1993) developed a simple model to interpret some early Amazon deforestation GCM results, highlighting the competing feedback effects of a warmer surface and less precipitation, both of which can result from a reduction in evaporation. In an intermediate-level model and subsequent analysis, Zeng et al. (1996) and Zeng (1998) showed that the deforestation response is largely determined by a three-way balance among large-scale adiabatic

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E. M. Fischer, S. I. Seneviratne, P. L. Vidale, D. Lüthi, and C. Schär

attributed to past human influence on the climate system. Several model studies suggest that events such as the 2003 summer heat wave will become more frequent, more intense, and longer lasting in the future ( S04 ; Beniston 2004 ; Meehl and Tebaldi 2004 ; Vidale et al. 2007 ). Several studies have suggested that the projected changes in summer climate strongly rely on soil moisture–atmosphere interactions ( Seneviratne et al. 2006b ; Rowell 2005 ; Rowell and Jones 2006 ; Vidale et al. 2007 ). Heat

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Li Liu, Renhe Zhang, and Zhiyan Zuo

, Z. , Paredes P. , Liu Y. , Chi W. W. , and Pereira L. S. , 2015 : Modelling transpiration, soil evaporation and yield prediction of soybean in north China plain . Agric. Water Manage. , 147 , 43 – 53 , doi: 10.1016/j.agwat.2014.05.004 . Wu, W. , and Dickinson R. E. , 2004 : Time scales of layered soil moisture memory in the context of land–atmosphere interaction . J. Climate , 17 , 2752 – 2764 , doi: 10.1175/1520-0442(2004)017<2752:TSOLSM>2.0.CO;2 . Wu, W. , Geller M. A

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S. K. Yadav, E. Lee, and Y. He

as modeled data ( Pielke et al. 2011 ; Mahmood et al. 2014 ). Numerous studies have shown that even a small change in land surface conditions can affect local as well as regional climates (e.g., Cao et al. 2015 ; Chase et al. 1996 ; Foley et al. 2005 ; Pielke 2005 ). Understanding and modeling land and atmosphere coupling relating to land-cover change and regional climate continues to be a major research need in land–atmosphere interaction studies and studying these processes is inherently

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Haishan Chen, Bo Yu, Botao Zhou, Wanxin Zhang, and Jie Zhang

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

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