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

directly from station data. Results using the CPC data are not explicitly shown but are discussed throughout the paper where appropriate. To evaluate soil moisture initialization accuracy in the SubX ensemble, we utilize data from phase 2 of the North American Land Data Assimilation System (NLDAS-2) ( Xia et al. 2012 ). NLDAS-2 is a collection of LSMs that were run offline and driven with common atmospheric forcing data to yield various surface fields over North America over the period from 1979 to

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

–south dipole structure over CONUS largely induced by the remote ENSO forcing (e.g., Huang et al. 2019 ; among many others). Fig . 1. Anomaly correlation coefficient maps of 3-month SPI (SPI3) for 1979–2010 at (a) 1-month lead (October), (b) 3-month lead (December), and (c) 5-month lead (February) in (left) the CFSR reforecasts and (center) the GLDAS reforecasts with October initial conditions (ICs), and (right) difference between GLDAS and CFSR reforecasts. Dashed curves in the left and center panels

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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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Richard Seager, Jennifer Nakamura, and Mingfang Ting

-forcing-data . To examine the large-scale context of the DO&Ts, we use geopotential heights and SSTs from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis ( Kistler et al. 2001 ; obtained from https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/?Set-Language=en ) and precipitation over land and sea from the Global Precipitation Climatology Project (GPCP) version 2.3 ( Huffman et al. 1997 ; obtained from https

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Kingtse C. Mo and Dennis P Lettenmaier

soil moisture (SM), and/or runoff deficits, usually for 6 months or longer ( Svoboda et al. 2002 ). Flash droughts have much shorter durations—typically a few weeks. Furthermore, while conventional droughts develop slowly, a key feature of flash droughts is their rapid onset and intensification ( Pendergrass et al. 2020 ). Mo and Lettenmaier (2015 , 2016) studied flash droughts over the United States, and classified them into two categories based on their forcings: heat wave flash drought ( Mo

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

atmospheric data such as precipitation and relative humidity, or land surface data acquisition such as SM and ET. The latter can be indirectly assimilated into the land surface models to achieve more accurate and reliable predictions of hydrologic fluxes as well as for monitoring purposes ( Kumar et al. 2014 ; Pan and Wood 2006 ; Pipunic et al. 2008 ; Reichle et al. 2014 ; Sawada et al. 2015 ; Xu et al. 2020 ). SM prediction using land surface models driven by meteorological forcing carries

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Shanshui Yuan, Steven M. Quiring, and Chen Zhao

varies over time and that it is modulated by remote sea surface temperature forcing. However, neither of these studies evaluated the suitability of using the SPI as a proxy for soil moisture. Therefore, it is not clear how sensitive their results are to using an indirect estimate of soil moisture. To date, there have only been a few studies that have examined the performance of drought indices using in situ soil moisture measurements because in situ soil moisture stations are too sparse or unevenly

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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

interference of dense plant canopy and limitations in the depth of detection within the soil column ( Entekhabi et al. 2014 ), as well as sensor limitations (especially before the use of L-band radiometers). Likewise, the fidelity of LSM simulations may be undermined by model errors (e.g., model structure error, model parameter error), forcing data uncertainties and limited usage of ground-based observations for model calibration and validation ( Xia et al. 2014 ). Thus, efforts that aim to improve large

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

Fig. 6 ). In general, relatively larger year-by-year variations of soil moisture inconsistencies between the two land initial states seem to be related to the melting of snowpack in the extratropics of the winter Hemisphere and in mountainous areas such as the Himalayas and Andes (right panels of Fig. 6 ). Inconsistencies in soil moisture and snow cover are primarily driven by the precipitation forcing in the two land surface analyses, but the other differences in soil and vegetation classes and

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