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Baird Langenbrunner and J. David Neelin

modeled teleconnections to the observations. Table 1. CMIP5 and CMIP3 modeling centers and models used, and the number of AMIP runs available at the time of our analysis. Data are available for download at . Linear regression and Spearman's rank correlation are used to calculate DJF precipitation teleconnections for the selected time period. Linear regression is widely used for assessing the relationship between global precipitation and tropical Pacific SSTs, where

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Jeanne M. Thibeault and Anji Seth

model data. All northeast-region area-averaged model precipitation data are masked to exclude grid points over the ocean. Regression analysis is used to identify spatial patterns of observed and simulated summertime (JJA) relationships between northeast-region precipitation anomalies (UDel) and large-scale anomalies of precipitation, 850-hPa winds, 500-hPa geopotential height and winds, sea level pressure, VIMT, and VIMT divergence. All variables are detrended prior to performing regression analyses

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Justin Sheffield, Suzana J. Camargo, Rong Fu, Qi Hu, Xianan Jiang, Nathaniel Johnson, Kristopher B. Karnauskas, Seon Tae Kim, Jim Kinter, Sanjiv Kumar, Baird Langenbrunner, Eric Maloney, Annarita Mariotti, Joyce E. Meyerson, J. David Neelin, Sumant Nigam, Zaitao Pan, Alfredo Ruiz-Barradas, Richard Seager, Yolande L. Serra, De-Zheng Sun, Chunzai Wang, Shang-Ping Xie, Jin-Yi Yu, Tao Zhang, and Ming Zhao

be different (e.g., Mo 2010 ; Yu et al. 2012 ; Yu and Zou 2013 ). Here the ENSO teleconnection over the United States simulated in the CMIP5 models are further examined according to the ENSO type. Following Kao and Yu (2009) and Yu and Kim (2010) , a regression-EOF analysis is used to identify the CP and EP types from monthly SSTs. The SST anomalies regressed with the Niño-1+2 SST index were removed before the EOF analysis was applied to obtain the spatial pattern of the CP ENSO. Similarly

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Gabriel A. Vecchi, Rym Msadek, Whit Anderson, You-Soon Chang, Thomas Delworth, Keith Dixon, Rich Gudgel, Anthony Rosati, Bill Stern, Gabriele Villarini, Andrew Wittenberg, Xiasong Yang, Fanrong Zeng, Rong Zhang, and Shaoqing Zhang

produced skillful multiyear retrospective forecasts of hurricane activity, apart from the effect of the persistence of the shift. Here we provide additional support for our assessment through the analysis of retrospective forecasts of hurricane activity from DePreSys ( Smith et al. 2010 ), in addition to the work originally presented in V13 . Output from the DePreSys retrospective forecasts was kindly provided by Doug Smith. The data used in the present reply and in S13 differ from those in the

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David E. Rupp, Philip W. Mote, Nathaniel L. Bindoff, Peter A. Stott, and David A. Robinson

“piControl.” We used the following criteria for selecting a subset of the available (as of 1 December 2012) CMIP5 datasets for this analysis: 1) preindustrial control had to have lengths of at least 500 continuous years and 2) a GCM had to have at least three ensemble members for a historicalNat or historical scenario (ensemble members for a given GCM vary by their initial conditions only). A total of 30 historicalNat runs from 7 GCMs and 67 historical runs from 15 GCMs were used for testing whether the

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Hailong Liu, Chunzai Wang, Sang-Ki Lee, and David Enfield

. (2006) studied the delayed influence of ENSO and the NAO on the tropical North Atlantic region. Here we performed the same analysis as LWLE12 on the AWP region to show how this remote influence acts on the AWP in CGCMs. We regress zonally averaged observed variables including surface wind stress, net surface heat flux, and SST in the AWP region on the Niño-3 SST index ( Fig. 14a1 ) and the negative NAO index ( Fig. 14a2 ) from January to December. Figure 14a1 shows that positive ENSO events

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Xianan Jiang, Eric D. Maloney, Jui-Lin F. Li, and Duane E. Waliser

results indicate that the variance of ENP ISV tends to be underestimated in most of the CMIP3 GCMs. Meanwhile, the eastward propagation associated with observed ENP ISV is also poorly represented in these CMIP3 models. By applying an extended empirical orthogonal function (EEOF) technique, a recent analysis including many CMIP3-era models illustrated that, among the total of nine models examined, only two GCMs were able to realistically simulate both of the two observed leading ISV modes over the ENP

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Suzana J. Camargo

-resolution climate models (e.g., VS07b ; Camargo et al. 2013 ), as local PI has a high correlation with actual TC intensities at various time scales ( Emanuel 2000 ; Wing et al. 2007 ). Similarly to the case of the GPI, the PI was calculated on a 2° × 2° uniform grid for all models. The cluster analysis was developed in Gaffney (2004) and is described in detail in Gaffney et al. (2007) . The cluster technique constructs a mixture of quadratic regression models, which are used to fit the geographical shape

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Kerrie L. Geil, Yolande L. Serra, and Xubin Zeng

and is described by Castro et al. (2012) . This dataset was created from station data and considers the dependence of precipitation on elevation, similar to the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) dataset that covers only the United States ( Daly et al. 1994 ). For our daily time resolution analysis, we use the Tropical Rainfall Measuring Mission (TRMM) 3B42v6 daily precipitation estimates ( Huffman et al. 2007 ), which are provided on a 0.25° × 0.25° spatial grid

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Lin Chen, Yongqiang Yu, and De-Zheng Sun

nature with that in Atmospheric Model Intercomparison Project (AMIP) simulations by some leading climate models, Sun et al. (2006) revealed two common biases in the AMIP runs of models: 1) an underestimate of the strength of the negative shortwave cloud radiative forcing (SWCRF) feedback and 2) an overestimate of the positive feedback from the greenhouse effect of water vapor. Extending the same analysis to the fully coupled simulations of these models as well as other coupled models in phase 3 of

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