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GP shown in Figs. 1 and 2 . This discrepancy suggests changes in the future summer circulation regime that would produce less precipitation in a moister environment with possibly a different intensity distribution of precipitation events in the GP, similar to what is observed in the northeast United States presented in section 4b . f. Arctic sea ice Significant reductions in the extent and thickness of the Arctic sea ice cover have occurred during the past several decades (e.g., Stroeve et
GP shown in Figs. 1 and 2 . This discrepancy suggests changes in the future summer circulation regime that would produce less precipitation in a moister environment with possibly a different intensity distribution of precipitation events in the GP, similar to what is observed in the northeast United States presented in section 4b . f. Arctic sea ice Significant reductions in the extent and thickness of the Arctic sea ice cover have occurred during the past several decades (e.g., Stroeve et
surface temperatures, and atmospheric and surface water budgets. Section 4 evaluates the model simulations of extremes of temperature and surface hydrology and temperature-based biophysical indicators such as growing season length. Section 5 focuses on regional climate features such as North Atlantic winter storms, the Great Plains low-level jet, and Arctic sea ice. The results are synthesized in section 6 and compared to results from CMIP3 models for selected variables. 2. CMIP5 models and
surface temperatures, and atmospheric and surface water budgets. Section 4 evaluates the model simulations of extremes of temperature and surface hydrology and temperature-based biophysical indicators such as growing season length. Section 5 focuses on regional climate features such as North Atlantic winter storms, the Great Plains low-level jet, and Arctic sea ice. The results are synthesized in section 6 and compared to results from CMIP3 models for selected variables. 2. CMIP5 models and
Precipitation Climatology Project (GPCP; Huffman et al. 2009 ) data are used in this study. In addition, the datasets including 1) Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003 ) and 2) the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) Global Reanalysis 2 data (NCEP2; Kanamitsu et al. 2002 ) are also employed. As the surface heat fluxes of NCEP2 are subject to larger errors in the eastern Pacific ( Cronin et al. 2006 ), to
Precipitation Climatology Project (GPCP; Huffman et al. 2009 ) data are used in this study. In addition, the datasets including 1) Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003 ) and 2) the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) Global Reanalysis 2 data (NCEP2; Kanamitsu et al. 2002 ) are also employed. As the surface heat fluxes of NCEP2 are subject to larger errors in the eastern Pacific ( Cronin et al. 2006 ), to
attribution has been applied to zonal mean precipitation patterns (e.g., Lambert et al. 2005 ), surface temperature extremes (e.g., Tebaldi et al. 2006 ; Stott et al. 2011 ), ocean heat content ( Barnett et al. 2005 ), Arctic sea ice ( Min et al. 2008 ), western U.S. hydroclimate ( Barnett et al. 2008 ; Pierce et al. 2008 ), northern and southern annular modes ( Gillett et al. 2005 ), and more. A related approach, fractional attributable risk, has been applied to specific extreme events like the 2003
attribution has been applied to zonal mean precipitation patterns (e.g., Lambert et al. 2005 ), surface temperature extremes (e.g., Tebaldi et al. 2006 ; Stott et al. 2011 ), ocean heat content ( Barnett et al. 2005 ), Arctic sea ice ( Min et al. 2008 ), western U.S. hydroclimate ( Barnett et al. 2008 ; Pierce et al. 2008 ), northern and southern annular modes ( Gillett et al. 2005 ), and more. A related approach, fractional attributable risk, has been applied to specific extreme events like the 2003
–2008), assimilating only surface observations of synoptic pressure, monthly SST, and sea ice distribution. [More information about this dataset is provided at http://www.esrl.noaa.gov/psd/data/20thC_Rean/ . Latent heat flux and surface winds from L'Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) are provided from ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/flux-merged ( Bentamy et al. 2008 ).] This entire 16-yr (1992–2007) surface turbulent flux dataset estimated from satellite
–2008), assimilating only surface observations of synoptic pressure, monthly SST, and sea ice distribution. [More information about this dataset is provided at http://www.esrl.noaa.gov/psd/data/20thC_Rean/ . Latent heat flux and surface winds from L'Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) are provided from ftp://ftp.ifremer.fr/ifremer/cersat/products/gridded/flux-merged ( Bentamy et al. 2008 ).] This entire 16-yr (1992–2007) surface turbulent flux dataset estimated from satellite
significant retrospective correlations against a null of zero correlation, again largely because of the dominance of a trend. Fig . 4. Retrospective and future forecasts of the SST indices used for the hurricane emulator. (left) Time series of the 5-yr mean SSTAs averaged over the (top) global tropics and (bottom) Atlantic hurricane MDR, at lead 2–6. Black lines show observational estimates from the Hadley Centre Sea Ice and SST dataset, version 1 (HadISST.v1; Rayner et al. 2003 ; solid), and Extended
significant retrospective correlations against a null of zero correlation, again largely because of the dominance of a trend. Fig . 4. Retrospective and future forecasts of the SST indices used for the hurricane emulator. (left) Time series of the 5-yr mean SSTAs averaged over the (top) global tropics and (bottom) Atlantic hurricane MDR, at lead 2–6. Black lines show observational estimates from the Hadley Centre Sea Ice and SST dataset, version 1 (HadISST.v1; Rayner et al. 2003 ; solid), and Extended
et al. 2008 ; Knutson et al. 2008 ). Recent studies suggest that when forced by observed SSTs and sea ice concentration, a global atmospheric model with a resolution ranging from 50 to 20 km can simulate many aspects of tropical cyclone (TC)–hurricane frequency variability for the past few decades during which reliable observations are available (e.g., Oouchi et al. 2006 ; Bengtsson et al. 2007 ; Zhao et al. 2009 ). The success is not only a direct evaluation of model capability but also an
et al. 2008 ; Knutson et al. 2008 ). Recent studies suggest that when forced by observed SSTs and sea ice concentration, a global atmospheric model with a resolution ranging from 50 to 20 km can simulate many aspects of tropical cyclone (TC)–hurricane frequency variability for the past few decades during which reliable observations are available (e.g., Oouchi et al. 2006 ; Bengtsson et al. 2007 ; Zhao et al. 2009 ). The success is not only a direct evaluation of model capability but also an
's native grid for only ice-free land points based on each model's land–sea ice mask. The data from each model are then interpolated bilinearly onto the operational grid of the European Centre for Medium-Range Weather Forecasts operational forecast model, which is T1279 or a regular longitude–latitude grid of 2560 × 1280 grid boxes. This is a much higher resolution than any of the CMIP5 models, thus ensuring that information content is not lost in the process of interpolation through smoothing, either
's native grid for only ice-free land points based on each model's land–sea ice mask. The data from each model are then interpolated bilinearly onto the operational grid of the European Centre for Medium-Range Weather Forecasts operational forecast model, which is T1279 or a regular longitude–latitude grid of 2560 × 1280 grid boxes. This is a much higher resolution than any of the CMIP5 models, thus ensuring that information content is not lost in the process of interpolation through smoothing, either
Quebec and includes Pennsylvania, New York State, and most of New England. In particular, we examine the relationships between regional northeast-region precipitation anomalies and anomalies of large-scale precipitation and atmospheric circulation over central and eastern North America and the Atlantic Ocean: 850-hPa and 500-hPa winds (zonal and meridional components are designated as “ u ” and “υ,” respectively), 500-hPa geopotential height, sea level pressure, and vertically integrated moisture
Quebec and includes Pennsylvania, New York State, and most of New England. In particular, we examine the relationships between regional northeast-region precipitation anomalies and anomalies of large-scale precipitation and atmospheric circulation over central and eastern North America and the Atlantic Ocean: 850-hPa and 500-hPa winds (zonal and meridional components are designated as “ u ” and “υ,” respectively), 500-hPa geopotential height, sea level pressure, and vertically integrated moisture
the coast. Associated with these storms are heavy snow ( Novak et al. 2008 ), inland flooding ( Colle 2003 ), and storm surge ( Colle et al. 2008 ). Therefore, any change in the frequency and intensity of these midlatitude cyclones over the Northeast United States is of great interest given the potential catastrophic consequences. For example, coastal areas of the Northeast United States are extremely vulnerable to storm surge, with the problem likely to become worse as the sea level rises during
the coast. Associated with these storms are heavy snow ( Novak et al. 2008 ), inland flooding ( Colle 2003 ), and storm surge ( Colle et al. 2008 ). Therefore, any change in the frequency and intensity of these midlatitude cyclones over the Northeast United States is of great interest given the potential catastrophic consequences. For example, coastal areas of the Northeast United States are extremely vulnerable to storm surge, with the problem likely to become worse as the sea level rises during
