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

and Katz 2013 ; Otkin et al. 2016 ; Choat et al. 2018 ; He et al. 2018 , 2019 ; Vogel et al. 2019 ). The central United States, extending from the southern Plains to the Midwest, has been identified as a region that is particularly prone to flash drought ( Christian et al. 2019 ; Koster et al. 2019 ; Chen et al. 2019 ). While precipitation deficits are a major factor contributing to flash drought onset in this region, abnormally high temperatures and evaporative demand are often associated

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Richard Seager, Jennifer Nakamura, and Mingfang Ting

. The clear exception was fall 2010 when La Niña conditions drove drying over the southern Plains and induced drought onset, a case that has been studied before (e.g., Seager et al. 2014 ). According to SNT , other onsets and terminations of drought were most likely driven by internal atmosphere variability. Their argument was essentially that while droughts, as phenomena that integrate surface moisture fluxes over time, can be driven by ocean variations and hence be potentially predictable on a

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

1. Introduction Useful predictability of deterministic weather forecasts is usually no more than 2 weeks, limited by the sensitivity to the atmospheric initial state, while longer memory from ocean heat content plays a dominant role in the climate predictability on seasonal and longer time scales (e.g., Lorenz 1963 , 1975 ; Shukla 1985 ; Lorenz 1993 ). There is a gap between the two time scales of weather and climate predictions, where inertia in the land surface, such as soil moisture

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Yizhou Zhuang, Amir Erfanian, and Rong Fu

1. Introduction As the world’s largest exporter of grains, the United States produces nearly 40% (35%) of the global maize (soybean). Extreme summer droughts and rainy periods over the United States can trigger large disruptions in the global crop yields, international grain market, and global food security ( Boyer et al. 2013 ; Lobell et al. 2014 ). Yet, we cannot predict these droughts, including the most recent extreme droughts that occurred in 2011 over the southern Great Plains and in

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Shanshui Yuan, Steven M. Quiring, and Chen Zhao

with soil moisture from 1994 to 1999 in North Carolina and found that short-term variations in soil wetness more closely match the SPI than the PDSI. Tian et al. (2018) demonstrated that soil moisture has good agreement with 1-month SPEI in the southern United States. Halwatura et al. (2017) also found that meteorological drought indices can accurately represent soil moisture drought in eastern Australia. Other studies have found that soil moisture is useful for representing agricultural

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

. In Fig. 9 , the week of 6–13 February shows the most severe drought status of the year 2018. The drought extends almost over the entire domain, however, except for ET-DA, all the other scenarios extend the categories to D4 showing that the conditions are more severe based on these scenarios. The ET-DA results show normal conditions in the southern and northwestern portion of the region while other areas exhibit D0–D1 drought categories. The OL follows a somewhat similar pattern but with more

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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

techniques in Earth science ( Reichstein et al. 2019 ). By predicting temporally varying target variables in land, ocean and atmosphere domains from temporally varying features, machine learning has been actively used to study Earth system dynamics. Particularly, compared to previous mechanistic or semiempirical modeling approaches, machine learning methods have been proven to be more powerful and flexible when inferring continental or global estimates from point observations, such as predicting carbon

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