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  • Global Drought Information System - Drought Characterization, Occurrence, Driving Mechanisms, and Predictability Worldwide (GDIS Worldwide) x
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Siegfried D. Schubert, Ronald E. Stewart, Hailan Wang, Mathew Barlow, Ernesto H. Berbery, Wenju Cai, Martin P. Hoerling, Krishna K. Kanikicharla, Randal D. Koster, Bradfield Lyon, Annarita Mariotti, Carlos R. Mechoso, Omar V. Müller, Belen Rodriguez-Fonseca, Richard Seager, Sonia I. Seneviratne, Lixia Zhang, and Tianjun Zhou

is described by Rienecker et al. (2008) , and an overview of the model’s performance is provided by Molod et al. (2012) . For these experiments, the model was run with 72 hybrid sigma vertical levels extending to 0.01 hPa and with a 1° horizontal resolution on a latitude–longitude grid. The simulations consist of 12 ensemble members, each forced with observed monthly SST, sea ice, and time-varying greenhouse gases for the period from 1871 to the present. See Schubert et al. (2014) for further

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Richard Seager and Martin Hoerling

T42 spectral resolution with 18 vertical levels. The only time-varying forcing was the SST, which was from Kaplan et al. (1998) within the tropical Pacific and the Hadley Centre data elsewhere [see Seager et al. (2005) for more details]. Trace gas concentration were held fixed (CO 2 = 355 ppm, CH 4 = 1.71 × 10 −6 ppm, and N 2 O = 0.31 × 10 −6 ppm, all corresponding to levels around 1990) and the sea ice cover has a repeating climatological seasonal cycle. The ensemble mean of these

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

) hypothesized that the SST anomalies in the Barents and the Arabian Seas combined to produce warming over Eurasia during 2010, thus contributing to the heat wave; they suggest that such a dynamic response to SST (in particular to the expected warming and reduction in sea ice over the Barents Sea) will contribute to more frequent heat waves over Eurasia in the future. Wu et al. (2012) examined the impact of the NAO on the relationship between the East Asian summer monsoon and ENSO and found, among other

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Bradfield Lyon

Center Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003 ). The ERSST data have a spatial resolution of 2.0° latitude × 2.0° longitude; HadISST is at 1.0° resolution. Unless otherwise noted, the climatological base period used for computing anomalies in all fields was 1971–2000. A t test was used to evaluate the statistical significance of temporal correlations and composite anomaly fields. b. Model data The use of climate model experiments was limited to the examination of

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

; Min and Son 2013 ; Purich et al. 2013 ). Rainfall observations at a resolution of 0.05° × 0.05° are utilized from the Australian Bureau of Meteorology ( Jones et al. 2009 ). To calculate the observational ENSO and IOD indices, SST is taken from the Met Office Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003 ) and bilinearly interpolated to a 1° × 1° grid. To calculate the observational SAM index, MSLP is taken from the National Centers for Environmental

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Lixia Zhang and Tianjun Zhou

developed first in north China and then expanded to the Yangtze River valley along 30°N and then on into south China. The drought of 1638–41 occurred under the background of the Little Ice Age, when the East Asian summer monsoon circulation was generally weak ( Man et al. 2012 ). Drought years and the associated impacts in China for the period 1900–2012 are summarized in Table 2 ( Zhang et al. 2009 ). The droughts that occurred in the 1920s and the 1940s caused huge damage to the society and have been

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