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

1. Introduction During boreal summer, convective activity over the eastern North Pacific Ocean (ENP) along the intertropical convergence zone (ITCZ) exhibits significant intraseasonal variability (ISV). Through its associated large-scale circulation and thermodynamical variations, the ISV exerts broad impacts on regional weather and climate systems, including the North American monsoon (NAM), midsummer drought over Central America, and Caribbean rainfall and low-level jet, as well as tropical

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Anji Seth, Sara A. Rauscher, Michela Biasutti, Alessandra Giannini, Suzana J. Camargo, and Maisa Rojas

shown in Figs. 4a,b , which present the annual cycle of zonal mean precipitation in the tropics (land and ocean) for the historical experiments (black contours, with thicker contours beginning at 5 mm day −1 ) and changes in the RCP8.5 scenario (color shading). The global precipitation annual cycle shows the tropical rainfall band migrating poleward in the summer hemisphere [December–February (DJF) in the Southern; JJA in the Northern]. The intensification of both wet and dry seasons is apparent in

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Suzana J. Camargo

. b. Data The observed TCs data used in this paper are based on best-track datasets from the National Hurricane Center (North Atlantic and eastern North Pacific) and the Joint Typhoon Warning Center (western North Pacific, north Indian Ocean, and Southern Hemisphere) and are available online ( Jarvinen et al. 1984 ; Neumann et al. 1999 ; Chu et al. 2002 ). The National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset was used in the

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J. David Neelin, Baird Langenbrunner, Joyce E. Meyerson, Alex Hall, and Neil Berg

base period storm-track precipitation. The CMIP5 MME mean contour agrees well with that of GPCP. The CMAP contour indicates lower rainfall estimates than either of these just off the coast and along the northern edge of the storm track, but is argued to be less reliable over oceans ( Yin et al. 2004 ). The key feature of the climatology for results here is the region off the California coast where the southern boundary of the storm track angles northeastward from about 25°N at 145°W to hit the

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Paul A. Dirmeyer, Yan Jin, Bohar Singh, and Xiaoqin Yan

Intercomparison Project (CMIP5) ( Taylor et al. 2012 ) provides an opportunity to address these questions in a multimodel framework. This study has been conducted under the aegis of the “CMIP5 Task Force” coordinated under the Modeling, Analysis, Prediction, and Projection (MAPP) program of the National Oceanic and Atmospheric Administration Climate Program Office. The overall goal of the task force is to evaluate CMIP5 simulations of the twentieth-century climate specifically over North America, as well as

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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

aspects of North American climate variability, organized by the time scale of the climate feature. Section 3 covers intraseasonal variability with focus on variability in the eastern Pacific Ocean and summer drought over the southern United States and Central America. Atlantic and east Pacific tropical cyclone activity is evaluated in section 4 . Interannual climate variability is assessed in section 5 . Decadal variability and multidecadal trends are assessed in sections 6 and 7 , respectively

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Jeanne M. Thibeault and Anji Seth

1. Introduction Warm-season precipitation [June–August (JJA)] is important to the economy and ecology of the midlatitude region of eastern North America, which encompasses the U.S. Northeast, southeastern Ontario, and southern Quebec [defined as 35°–50°N and 70°–80°W ( Fig. 1 ), which is referred to hereafter as the northeast region]. The northeast region is densely populated with large urban centers located preferentially along the coast, yet much of the region is rural, covered by forests or

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

across all basins, which is highly correlated with the tropical mean caused by biases in atmospheric simulations of cloud cover, and the other with large variability in the cold tongue regions caused by biases of oceanic thermocline depth. The AWP bias is more related to radiative flux errors due to local convection and clouds ( LWLE12 ). As the AWP is adjacent to the NTA and, in fact, includes the western NTA (the NTA is defined as the region of 5.5°–23.5°N, 57.5°–15°W), climate variability of the

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Justin Sheffield, Andrew P. Barrett, Brian Colle, D. Nelun Fernando, Rong Fu, Kerrie L. Geil, Qi Hu, Jim Kinter, Sanjiv Kumar, Baird Langenbrunner, Kelly Lombardo, Lindsey N. Long, Eric Maloney, Annarita Mariotti, Joyce E. Meyerson, Kingtse C. Mo, J. David Neelin, Sumant Nigam, Zaitao Pan, Tong Ren, Alfredo Ruiz-Barradas, Yolande L. Serra, Anji Seth, Jeanne M. Thibeault, Julienne C. Stroeve, Ze Yang, and Lei Yin

at daily to seasonal time scales, as well as selected climate features that have regional importance. Part II covers aspects of climate variability, such as intraseasonal variability in the tropical Pacific, the El Niño–Southern Oscillation (ENSO), and the Atlantic multidecadal oscillation, which play major roles in driving North American climate variability. This study draws from individual work by investigators within the CMIP5 Task Force of the National Oceanic and Atmospheric Administration

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Lin Chen, Yongqiang Yu, and De-Zheng Sun

warming. For one thing, the surface temperature is not likely to increase uniformly across the globe ( Xie et al. 2010 ). Another methodology used to examine the cloud and water vapor feedbacks in climate models involves examining the response of cloud and water vapor to SST changes on the time scales of El Niño–Southern Oscillation (ENSO) ( Sun et al. 2003 , 2006 ; S09 ; Lloyd et al. 2009 , 2011 , 2012 ; among others). By comparing the response of cloud and water vapor to the ENSO forcing in

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