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Jason A. Otkin, Martha C. Anderson, Christopher Hain, Iliana E. Mladenova, Jeffrey B. Basara, and Mark Svoboda

becomes strong after significant damage has already occurred to the vegetation ( Moran 2003 ). A faster response signal of incipient drought stress may be conveyed through remotely sensed maps of land surface temperature (LST), retrieved using satellite-based thermal infrared (TIR) observations ( Anderson et al. 2013 ). As the amount of root zone moisture decreases, less energy is used to evaporate and transpire water, thereby causing canopy temperatures to elevate in comparison with unstressed

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Richard Seager, Lisa Goddard, Jennifer Nakamura, Naomi Henderson, and Dong Eun Lee

variability or much stronger local land–atmosphere interactions than in the models. Despite the model support for tropical Pacific SSTs as the cause of the onset and continuation of the drought, detailed analysis of precipitation and mean and transient atmospheric circulation fields provides evidence that the actual drought was also strongly influenced by internal atmospheric variability that caused departures of these patterns from those typically associated with La Niña conditions. For example, during

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Hailan Wang, Siegfried Schubert, Randal Koster, Yoo-Geun Ham, and Max Suarez

drought and determining the timing of the drought, sustaining and/or amplifying droughts over the United States involves other factors such as local soil moisture feedback and random atmospheric internal variability (e.g., Koster et al. 2003 ; Ferguson et al. 2010 ). For example, a month with low precipitation leads to a drier-than-average soil, which in turn can lead to lower-than-average evaporation, which may lead to continued low precipitation. Such feedback between the land and atmosphere plays

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Xing Yuan, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan

(CGCMs), developed based on the Navier–Stokes equations and parameterizations that characterize the water and energy exchanges among the land, atmosphere, and ocean, are beginning to outperform statistical models for ENSO prediction skill ( Barnston et al. 2012 ). This is mainly because of gradual improvements in observations and assimilation systems, physical parameterizations, spatial resolution, and understanding of ENSO-related ocean–atmosphere interactions ( Barnston et al. 2012 ). Thus, we

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Eric F. Wood, Siegfried D. Schubert, Andrew W. Wood, Christa D. Peters-Lidard, Kingtse C. Mo, Annarita Mariotti, and Roger S. Pulwarty

new strategies for using satellite data to monitor drought (and floods), which can provide an assessment of drought characteristics independent of land surface model analyses. Anderson et al. (2013) compare the ESI with NLDAS model-based estimates of SM, evapotranspiration ET, and runoff anomalies, and with other empirical indices such as the vegetation health index (VHI) and SPI, using the USDM classifications as a reference. The ESI uses the thermal infrared (TIR) satellite-based Atmosphere–Land

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