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Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, and Stephen Wright

; McCabe and Dettinger 1999 ; Dai 2013 ], also exhibit limited predictive skill. The main reason is that the complex and nonstationary interactions between large-scale dynamics and regional hydroclimate cannot be captured sufficiently well with a limited number of prespecified climate indices (regions used for computing sea surface temperature anomalies) as predictors, even when sophisticated statistical schemes are used (nonlinear statistical schemes, Bayesian techniques, etc.). Recognizing the

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Clément Guilloteau, Antonios Mamalakis, Lawrence Vulis, Phong V. V. Le, Tryphon T. Georgiou, and Efi Foufoula-Georgiou

interest, exhibiting a plethora of modes caused by different physical processes (e.g., solar forcing, oceanic/atmospheric circulations, land–atmosphere interactions, etc.), and imprinting themselves at various spatial and temporal scales. The accurate identification and modeling of the modes of the climate system is necessary for many key problems in geosciences, such as weather/climate prediction, attribution of extreme events and hazards, and assessment of climate change impacts. The comprehensive

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Stephen E. Lang and Wei-Kuo Tao

pairs of rain-normalized convective and stratiform diabatic heating profiles [i.e., Q 1 or the apparent heat source; Yanai et al. (1973) ], one pair for land and one for ocean, obtained from composites of both GCE model ( Tao and Simpson 1993 ) simulations and sounding budget calculations; a single additional pair was later added for shallow heating. Using surface rainfall rates and the proportion of stratiform rain, cloud heating profiles could then be retrieved remotely from satellite or other

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