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Shanshui Yuan, Steven M. Quiring, and Chen Zhao

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

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Anthony M. DeAngelis, Hailan Wang, Randal D. Koster, Siegfried D. Schubert, Yehui Chang, and Jelena Marshak

droughts in general. One reason is that strong land–atmosphere interaction is intrinsic to the central United States during summer (e.g., Koster et al. 2004a , 2009a ); our results based on flash drought events in three different years support this ( Fig. 6 ). Another reason is that stationary Rossby waves, which are known to induce short-term warm-season droughts in the United States, are often a manifestation of recurring modes of the subseasonal atmospheric circulation (e.g., Schubert et al. 2011

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Chul-Su Shin, Paul A. Dirmeyer, Bohua Huang, Subhadeep Halder, and Arun Kumar

, https://doi.org/10.1175/JHM-D-16-0064.1 . 10.1175/JHM-D-16-0064.1 Dirmeyer , P. A. , G. Balsamo , and C. D. Peters-Lidard , 2015 : Land-atmosphere interactions and the water cycle. Seamless Prediction of the Earth System: From Minutes to Months , G. Brunet, S. Jones, and P. M. Ruti, Eds., World Meteorological Organization, 145–154 . Dirmeyer , P. A. , and Coauthors , 2016 : Confronting weather and climate models with observational data from soil moisture networks over the United States

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Chul-Su Shin, Bohua Huang, Paul A. Dirmeyer, Subhadeep Halder, and Arun Kumar

for the GLDAS reforecasts. We argue that different land–atmosphere interactions triggered by the differences in land initial state account for a part of the different drought predictions between the CFSR and GLDAS reforecasts. Land surface memory relevant to subseasonal to seasonal prediction is typically defined based on anomalies of soil moisture. Figure 9 displays anomalies of volumetric soil moisture within the top 40-cm layer in the two land initial states as well as in observations (SMERGE

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Keyhan Gavahi, Peyman Abbaszadeh, Hamid Moradkhani, Xiwu Zhan, and Christopher Hain

interaction processes ( Walker et al. 2019 ). The literature shows that ET variation is highly dependent on SM ( Berg and Sheffield 2018 ; Jung et al. 2010 ; Purdy et al. 2018 ; Walker et al. 2019 ). Soil moisture controls latent and sensible heat exchange between land and atmosphere which causes feedback mechanisms in land–atmosphere interactions ( Brutsaert and Stricker 1979 ). Several studies have shown that ET significantly contributes to the improvements of SM estimations ( Berg and Sheffield 2018

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Richard Seager, Jennifer Nakamura, and Mingfang Ting

roles of ocean driving by sea surface temperature (SST) anomalies and internal atmosphere variability has advanced considerably over the last two decades [see recent review by Seager and Hoerling (2014) ]. The role of land surface–vegetation–atmosphere interactions in drought evolution is also receiving increased attention (e.g., Sun et al. 2015 ; Mo and Lettenmaier 2016 ; Otkin et al. 2016 ; Basara and Christian 2018 ; Ford et al. 2017 ; Basara et al. 2019 ). However, when a drought is

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Yizhou Zhuang, Amir Erfanian, and Rong Fu

2012 over much of the Great Plains. The delayed response of a regional climate to slowly varying oceanic forcing and land–atmosphere interaction provides the foundation for seasonal prediction over many regions around the world. State-of-the-art seasonal prediction models provide relatively skillful predictions of winter hydroclimate over the United States, but show virtually no skill in prediction of summer rainfall anomalies over much of the North American continent ( Quan et al. 2012 ). Seasonal

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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

research topics such as water and carbon fluxes in agroecosystems, and land–atmosphere interactions. Second, we shed light on the effects of different cropping patterns on SM and seek alternative cropping patterns that may alleviate SM decline. This exploration will prompt decision making in ameliorating soil water stress and inform adaption plans in an increasingly water-scarce future. 2. Methods a. Neural network The neural network used in this work is a deep feed forward NN, which consists of

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Kingtse C. Mo and Dennis P Lettenmaier

P -deficit flash droughts following Mo and Lettenmaier (2015 , 2016) . 2. Flash drought forecasts As indicated by Pendergrass et al. (2020) , prediction of flash drought is a challenge because of their rapid onset, and the fact that most land–atmosphere coupled models do not predict land atmosphere interactions well. An alternative is to use statistical methods; for instance, Otkin et al. (2015) predicted flash drought intensification probabilities derived from their RCI. Here, we prefer to

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