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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 http://pcmdi3.llnl.gov . 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
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 http://pcmdi3.llnl.gov . 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
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
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
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
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
“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
“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
. (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
. (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
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
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
-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
-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
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
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
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
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
that climate projections for the twenty-first century at the local and regional levels remain a substantial challenge. The present study provides a summary of projected twenty-first-century NA climate change in the updated state-of-the-art climate and Earth system models used in CMIP5. The results contained herein are contributed by members of the CMIP5 Task Force of the National Oceanographic and Atmospheric Administration (NOAA) Modeling, Analysis, Predictions and Projections Program (MAPP
that climate projections for the twenty-first century at the local and regional levels remain a substantial challenge. The present study provides a summary of projected twenty-first-century NA climate change in the updated state-of-the-art climate and Earth system models used in CMIP5. The results contained herein are contributed by members of the CMIP5 Task Force of the National Oceanographic and Atmospheric Administration (NOAA) Modeling, Analysis, Predictions and Projections Program (MAPP
