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

. 2a–d ). The atmospheric ridge was part of a quasi-stationary Rossby wave train ( Hoskins and Ambrizzi 1993 ; Ambrizzi et al. 1995 ), that is, a series of atmospheric troughs and ridges that remained nearly stationary in geographic location but with energy slowly propagating from west to east along the zonal jet stream. The wave train of 2012 developed between Eurasia and the western North Pacific in early to mid-June and propagated eastward to North America over subsequent weeks ( Figs. 2g

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Richard Seager, Jennifer Nakamura, and Mingfang Ting

models, here we examine the prediction of the driving precipitation anomalies. Conclusions are as follows. Drought onset can be favored by La Niña conditions in the tropical Pacific Ocean that drive a wave train that places northerly flow above the southern Great Plains. This provides a source of predictability for drought onsets, but this will be limited by SST prediction skill and also, in the fall season, biases in the height teleconnection pattern. Ocean forcing alone may on occasions be

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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

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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