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Jeanne M. Thibeault and Anji Seth

Resour. Assoc. , 38 , 1287 – 1299 , doi:10.1111/j.1752-1688.2002.tb04348.x . Bradbury , J. A. , B. D. Keim , and C. P. Wake , 2002b : U.S. East Coast trough indices at 500 hPa and New England winter climate variability. J. Climate, 15, 3509–3517. Brekke , L. D. , M. D. Dettinger , E. P. Maurer , and M. Anderson , 2008 : Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments . Climatic Change , 89

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

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

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Suzana J. Camargo

members. There is a clear similarity of the global tracks and NTC characteristics among the ensemble members of MRI-CGCM3. This result is in agreement with the assessment of ensemble member uncertainty in TC activity of Reed and Jablonowski (2011b) , which concluded that the dominant differences were due to different model versions and resolutions and not due to internal variability. Fig . 6. Global tracks of the MPI-ESM-LR TCs for (a) ensemble member 1 (ENS1) and (b) ENS2. (c) Mean MPI-ESM-LR global

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Eric D. Maloney, Suzana J. Camargo, Edmund Chang, Brian Colle, Rong Fu, Kerrie L. Geil, Qi Hu, Xianan Jiang, Nathaniel Johnson, Kristopher B. Karnauskas, James Kinter, Benjamin Kirtman, Sanjiv Kumar, Baird Langenbrunner, Kelly Lombardo, Lindsey N. Long, Annarita Mariotti, Joyce E. Meyerson, Kingtse C. Mo, J. David Neelin, Zaitao Pan, Richard Seager, Yolande Serra, Anji Seth, Justin Sheffield, Julienne Stroeve, Jeanne Thibeault, Shang-Ping Xie, Chunzai Wang, Bruce Wyman, and Ming Zhao

changes are considered in the context of the ability of models to accurately simulate current climate, discussed in the two companion papers ( Part I and Part II ), which is generally comparable to that of CMIP3 models, with some improvement noted for individual models. Previous projections of NA climate change (e.g., CMIP3) have been evaluated as part of earlier climate assessments ( Solomon et al. 2007 ). The CMIP3 consensus projection indicated that, by 2080–99, annual mean temperature increases

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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

Atlantic climate variability . Nature , 484 , 228 – 232 . Braganza , K. , D. J. Karoly , and J. M. Arblaster , 2004 : Diurnal temperature range as an index of global climate change during the twentieth century. Geophys. Res. Lett., 31, L13217, doi:10.1029/2004GL019998 . Brekke , L. D , M. D. Dettinger , E. P. Maurer , and M. Anderson , 2008 : Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments

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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

fluctuations (e.g., Rotstayn and Lohmann 2002 ; Hawkins and Sutton 2009 ; C. Chang et al. 2011 ; Villarini et al. 2011b ; Booth et al. 2012 ; Zhang et al. 2013 ; Villarini and Vecchi 2012b ), one has to consider skill arising from both external factors and internal variability on multiyear time scales. A number of modeling groups are now following the same framework for phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012 ) to be assessed as part of the Fifth Assessment

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