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

extremely dry conditions within a 3-month period from May through July. As seen in Fig. 1 , the event was fueled, to first order, by persistent monthly precipitation deficits and extremely warm temperatures, both of which led to a rapid drying of the soil. The drought had significant and widespread impacts on agriculture, vegetation, and the U.S. economy ( Hoerling et al. 2014 ; Rippey 2015 ; Otkin et al. 2016 ), with agricultural losses amounting to more than $30 billion ( NCDC 2019 ). Fig . 1

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Richard Seager, Jennifer Nakamura, and Mingfang Ting

seasonal to interannual time scale, their onsets and terminations are likely controlled by internal atmosphere variability and fall between the initial value and ocean boundary condition sources of predictability. However, SNT was a purely observational study. Here we report on how well DO&Ts are forecast in the operational seasonal predictions systems of the National Multimodel Ensemble (NMME; Kirtman et al. 2014 ). These systems forecast SST from imposed initial conditions. Several of the models

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

, land surface and hydrologic properties can be simulated and predicted by land surface models which provide a simplified representation of physical processes. However, an accurate prediction of these components, such as SM, ET, and streamflow, is highly dependent on the quality of model forcing data, the model parameters (measured or estimated through calibration), initial and boundary conditions, and model structure. For land surface and hydrologic models, the integration of data assimilation (DA

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

initialized with NCEP CFSR land states for the common period of 1979–2010 as the CFSR reforecasts. The GLDAS and CFSR reforecasts form the identical-twin sets of 32-yr (1979–2010) CFSv2 reforecasts, which have exactly the same initial conditions other than land states. Therefore, they enable us to assess the effect of the uncertainty of land initial states on the subseasonal prediction of global surface air temperature ( Shin et al. 2020 ), and particularly, the U.S. drought predictions in this study. The

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

. 2016 ; Dirmeyer and Halder 2016 , 2017 ; Dirmeyer et al. 2018b ; Halder et al. 2018 ). Model fidelity in representing coupled land–atmosphere processes is also necessary, including proper simulation of variability, covariability, sensitivity, and critical transitions in the chain of processes linking land surface states to surface fluxes, near-surface atmospheric states, boundary layer characteristics, cloud formation, and precipitation ( Dirmeyer and Halder 2017 ; Santanello et al. 2018

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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

water demand, and thus SM could also be a crucial factor affecting socioeconomic conditions. Despite the criticality of SM in the Earth system, accurate estimation of large-scale soil moisture is still a challenge, mainly due to its rapid fluctuations and the lack of sufficient ground truth observations. Currently, most large-scale SM products are either retrieved from satellite data or produced from land surface models (LSMs). As an example of product derived from satellites, the European Space

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