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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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Cenlin He, Olivia Clifton, Emmi Felker-Quinn, S. Ryan Fulgham, Julieta F. Juncosa Calahorrano, Danica Lombardozzi, Gemma Purser, Mj Riches, Rebecca Schwantes, Wenfu Tang, Benjamin Poulter, and Allison L. Steiner

–terrestrial ecosystems interactions? How does climate internal variability influence these interactions and feedbacks? How do human-induced changes in emissions of greenhouse gases and air pollutants, land use and land cover, fires, and meteorology alter these interactions and feedbacks? The recent Integrated Land Ecosystem–Atmosphere Processes Study (iLEAPS) early-career workshop held in Boulder, Colorado, during 16–17 October 2019 discussed current challenges and future directions for advancing process

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

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

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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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