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Alfredo Ruiz-Barradas and Sumant Nigam

links of summer precipitation variability in the Great Plains. Section 5 provides seasonal precipitation targets over North America for the idealized experiments. In section 6 one idealized experiment is compared with one of the regression targets. Finally, the paper ends with section 7 , which summarizes the main conclusions. 2. Datasets As mentioned in the introduction, the present analysis will focus on century-long simulations. Thus, only three of the models will be analyzed: CAM3.5, CCM3

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Yochanan Kushnir, Richard Seager, Mingfang Ting, Naomi Naik, and Jennifer Nakamura

5°S and 5°N and between 170° and 120°W, and the SST average over the tropical North Atlantic between the equator and 30°N (hereafter the TNA SST index). These indices were averaged over the cold and warm months of the hydrological year (October–March and April–September, respectively) and normalized (divided by their RMS value during the relevant period of analysis) when used in the multiple regression analysis calculations described below. b. Model experiments This study follows the methodology

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Renu Joseph and Ning Zeng

; Huffman et al. 1997 ) over land. The sea surface temperature (SST) reference dataset is the Hadley Center’s sea ice and SST analysis (HadISST data) ( Rayner et al. 2003 ). c. Methodology The timing of the three volcanic events (see Table 1 ) in this study is such that they all happened during periods when the El Niño also occurred. Therefore, while examining the associated climate response to volcanic events in observations, the natural variability associated with ENSO has to be removed. The method

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Scott J. Weaver, Siegfried Schubert, and Hailan Wang

—notwithstanding the potential NAM influence in reducing Great Plains precipitation in the mean seasonal cycle and interannual variability ( Higgins et al. 1997a , 1998 ). Furthermore, the monthly correlation of the Great Plains precipitation and LLJ indices during JAS (AMJ) is 0.62 (0.36). As such we will focus our attention on the JAS months in the remaining analysis. Figure 5 shows the regressions of the seasonal mean JAS GPLLJ index on 925-hPa meridional winds (contoured) and precipitation (shaded) in the

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Randal D. Koster, Hailan Wang, Siegfried D. Schubert, Max J. Suarez, and Sarith Mahanama

results from the DWG experiments, and in section 5 we evaluate our findings in the context of available observational data. 2. Overview of U.S. CLIVAR Working Group experiments Again, Schubert et al. (2009) describe the DWG experiments in detail. In essence, each AGCM simulation is run for 50 yr, with an idealized set of SST conditions. The imposed SSTs vary monthly but not interannually. The resulting simulation data across the globe are stored for comprehensive analysis; here, we focus on

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Bradfield Lyon

Africa were obtained from the South African Weather Service. To be used in the analysis here these stations had to have at least 95% complete observational records for the months of December–February (DJF) over the period 1961–2000. A total of 35 stations met this restriction and their locations are shown in Fig. 1 . The daily Tx station data were also used to compute monthly and seasonal values of Tx. Monthly, gridded analyses of precipitation based on station observations were obtained from the

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Rachel R. McCrary and David A. Randall

simulated drought periods, rather than out of phase, as found in the 1930s and 1950s observed droughts. Interestingly, in HadCM3, the droughts that correspond with warmer than normal conditions in the tropical North Atlantic tend to be the most severe droughts to occur in this model. This is similar to what has been found for the 1930s and 1950s droughts. 3) Regression analysis The relationship between Great Plains precipitation and global SST patterns can be further examined by using regression

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M. Biasutti, A. H. Sobel, and Suzana J. Camargo

variability to global SST ( Biasutti et al. 2008 ). The MIROC model was singled out by Cook and Vizy (2006) as producing one of the best simulations of the twentieth-century climate in West Africa; their analysis shows that the future increase in Sahel rainfall is associated with a westerly flow enhancement, as it is true for observed positive fluctuations on shorter time scales ( Grist and Nicholson 2001 ). 3. The Sahara low: Forced change We now explore how the CMIP3 models simulate the fundamental

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Kingtse C. Mo, Jae-Kyung E. Schemm, and Soo-Hyun Yoo

(available at http://www.hydro.washington.edu/forecast/monitor ). The P forcing for the VIC model is based on the cooperative observer station meteorological daily data with the Precipitation Regression on Independent Slopes Method (PRISM) correction ( Maurer et al. 2002 ). We use the P data from the VIC for this study to ensure that P , soil moisture, and runoff are consistent. The differences between the monthly mean P anomalies from this dataset and from the Climate Prediction Center (CPC

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Alfredo Ruiz-Barradas and Sumant Nigam

Centre Hamburg Model (ECHAM5)/Max Planck Institute Ocean Model (MPI-OM) ( Roeckner et al. 2003 ; Marsland et al. 2003 ). Apart from ECHAM5/MPI-OM climate model, the others were assessed in their capacity to simulate the observed interannual variability of Great Plains precipitation and its links to moisture fluxes in the twentieth century in a previous study ( Ruiz-Barradas and Nigam 2006 ). From this analysis it was found that while the HadCM3 best portrays the observed relationship between

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