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Yizhou Zhuang, Amir Erfanian, and Rong Fu

prediction of the U.S. climate becomes especially difficult in summer as the jet stream and the storm track move farther north and the precipitation regime shifts from a dominantly frontal precipitation to a tropical-like convective regime with a more regional and local character and an enhanced influence of land surface conditions (e.g., Myoung and Nielsen-Gammon 2010 ). Over the Great Plains, summer holds the largest share (>40%) of annual precipitation and variability of its rainfall exerts a strong

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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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Shanshui Yuan, Steven M. Quiring, and Chen Zhao

. Atmos. , 120 , 11 519 – 11 535 , https://doi.org/10.1002/2015JD023975 . 10.1002/2015JD023975 Ford , T. W. , Q. Wang , and S. M. Quiring , 2016 : The observation record length necessary to generate robust soil moisture percentiles . J. Appl. Meteor. Climatol. , 55 , 2131 – 2149 , https://doi.org/10.1175/JAMC-D-16-0143.1 . 10.1175/JAMC-D-16-0143.1 Ford , T. W. , S. M. Quiring , and O. W. Frauenfeld , 2017 : Multi-decadal variability of soil moisture–temperature coupling over

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

, snow, and vegetation states can provide a source of predictability ( Dirmeyer et al. 2015 , 2018b ). Land surface memory relevant to subseasonal to seasonal prediction is typically defined based on anomalies of soil moisture. Since soil moisture anomalies in nature can persist from a week up to two months or more ( Vinnikov et al. 1996 ; Entin et al. 2000 ; Mahanama and Koster 2003 ; Seneviratne et al. 2006 ), the influence of soil moisture anomalies on atmospheric variability (viz., surface

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

prolong period of time) in the United States on seasonal time scales mainly result from sea surface temperature (SST) anomalies in the Pacific associated with El Niño–Southern Oscillation (ENSO) and/or the Pacific decadal oscillation (PDO), with lesser contribution from SST anomalies in the Atlantic and Indian Oceans (e.g., Hoerling and Kumar 2003 ; McCabe et al. 2004 ; Seager et al. 2005 ; Cook et al. 2007 ; Hoerling et al. 2009 ; Seager and Hoerling 2014 ; Schubert et al. 2016 ; Huang et al

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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

1. Introduction Soil moisture (SM) is an essential component of the Earth system. It affects the variability of the coupled energy (latent and sensible heat fluxes) and water fluxes (runoff and evapotranspiration) by modifying the partitioning of water and energy across the land–atmosphere interface ( Seneviratne et al. 2010 ). The effects of SM on evapotranspiration also impact temperature variability and may intrigue persistent heatwaves ( Fischer et al. 2007 ; Hirschi et al. 2011

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David M. Mocko, Sujay V. Kumar, Christa D. Peters-Lidard, and Shugong Wang

the years as more and finer-scale drought indicators have emerged over the decades. On the other hand, the drought categories from the model-simulated top 1-m soil moisture only represent agricultural drought, while the USDM will represent all droughts, including meteorological and hydrological as well. Comparisons to in situ soil moisture observations are presented to help provide confidence that using the USDM for assessing impacts on diagnosing agricultural drought is a reasonable approach. 3

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