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Wenju Cai, Ariaan Purich, Tim Cowan, Peter van Rensch, and Evan Weller

Prediction–National Center for Atmospheric Research reanalysis (NNR; Kalnay et al. 1996 ) and also bilinearly interpolated to a 1° × 1° grid. However, it is known that spurious trends exist in the NNR at southern high latitudes ( Marshall 2003 ); to correct for this, MSLP is regressed onto an observational station-based SAM index before EOF analysis ( Marshall 2003 ; Purich et al. 2013 ). For consistency, the observed HCE is calculated from NNR meridional winds. We utilize outputs of precipitation (42

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Bradfield Lyon

between Greater Horn rainfall and the Indian Ocean zonal mode, but only consider the OND short rains. Motivated by earlier findings, Lyon et al. (2014) recently examined SST variations in the global oceans over roughly the last 110 years, emphasizing the MAM season. In the study the global average SST anomaly and (simultaneous) ENSO signal were first removed from the SST data using linear regression before applying an empirical orthogonal function (EOF) analysis on the residual field. Results for

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Lixia Zhang and Tianjun Zhou

. (a) Normalized time series of the EASMI (blue line) for 1979–2012 and the Niño-3.4 index (red line). The EASMI is defined as the normalized zonal wind shear between 850 and 200 hPa averaged over 20°–40°N, 110°–140°E. (b) The horizontal distribution of precipitation (shaded; mm day −1 ) and 850-hPa wind (vectors; m s −1 ) regressed on the EASMI. (c) As in (b), but for the total tropospheric (850–200 hPa) temperature (shaded; K) and 200-hPa wind (vectors; m s −1 ). (d) As in (b), but for the SST

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Richard Seager and Martin Hoerling

of soil moisture variability using an EOF analysis. The principal component time series associated with the spatial structures are then regressed with SSTs to identify connections to ocean variability. Figures 8 , 9 , and 10 show the first three EOFs of monthly soil moisture variability, which together explain about 46% of the total monthly contiguous U.S. soil moisture variability. (This percent of variance explained is higher than that found for the precipitation analysis in Fig. 2 . This

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Siegfried D. Schubert, Hailan Wang, Randal D. Koster, Max J. Suarez, and Pavel Ya. Groisman

discussing the morphology and metrics of droughts and heat waves. Section 3 examines the physical mechanisms responsible for their occurrence. Section 4 contains a review and analysis of interannual variability and trends, and section 5 discusses projections and predictability. A summary is provided in section 6 . In addition, two appendices are provided: Appendix A describes the datasets and model simulations used in this study, and appendix B provides a compilation (based on various sources

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