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Leila M. V. Carvalho and Charles Jones

-level temperatures in tropical areas. Regions characterized by monsoon systems experience their highest seasonal low-level temperatures during the premonsoon season when the solar radiation increases but convective activity is still modest (e.g., McGregory and Nieuwolt 1998 ). During the peak of the monsoon, the warmest temperatures in tropical regions are usually displaced poleward from areas of enhanced precipitation. In contrast, changes in precipitation and cloudiness in the tropics depend on low

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Suzana J. Camargo

. Also shown are global thresholds for the relaxed vorticity (10 −5 s −1 ) used to track the storms in the models, which are defined by the model resolution. Even imposing a warm core requirement on the storms, extratropical storms are not completely eliminated by our algorithm—a common problem in tracking algorithms ( Horn et al. 2013 ). Therefore we impose an additional constraint here that we only consider storms forming in the tropics (30°S–30°N) over the ocean. It should be noted that, in

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

weakening of the NASH and NPSH, the NASH is overextended toward the west and the NPSH is far too weak, resulting in an anomalously strong north–south gradient of geopotential height over the eastern North Pacific. The gradient in the tropics produces strong zonal winds within and to the north of the ITCZ, preventing the proper development of the ITCZ and cutting off ITCZ moisture to the monsoon region. Reduced convergence over Latin America results in extremely poor representation of the tropical wet

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Meng-Pai Hung, Jia-Lin Lin, Wanqiu Wang, Daehyun Kim, Toshiaki Shinoda, and Scott J. Weaver

1. Introduction Tropical convection is often organized into synoptic- to planetary-scale disturbances whose time scale is less than a season (~90 days) ( Wheeler and Kiladis 1999 , hereafter WK ; Wheeler and Weickmann 2001 ). This “subseasonal” variability plays an important role in the global climate system by modulating the location and timing of tropical deep convection and has been suggested as a key source of untapped predictability for the extended-range forecasts in both the tropics

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

evaporation in both energy and water budgets ( Neelin and Held 1987 ). Based on Giannini (2010) , two competing mechanisms were examined, involving the differing responses of simulated precipitation to greenhouse gas forcing: remote (or top down) and local (or bottom up). A schematic of these mechanisms is provided in Fig. 1 . In the remote mechanism, SST warming leads to large-scale tropospheric warming, enhances vertical stability in the global tropics ( Sobel et al. 2002 ; Chiang and Sobel 2002

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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

activity and SST and the sensitivity of dynamical models to SST perturbations. Observational analyses have highlighted correlations between SST changes in the tropical Atlantic and hurricane activity indices (e.g., Elsner and Jagger 2006 ; Emanuel 2005 ). However, observational correlations as high or higher have been found between hurricane activity and the weighted difference between Atlantic and tropical-mean SSTs (the SST changes in the Atlantic relative to the tropics, or “relative SST”) by

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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

individual models and the MME mean. All model output and observational data were regridded onto a common 0.5° grid. Table 3. Spatial correlation of the MSD between the CMIP5 models and the MME mean, calculated for 1850–2005. 4. East Pacific and Atlantic tropical storm track and cyclone activity a. Tropical storm track The density of traveling synoptic-scale disturbances across the tropics, referred to in the literature as the tropical storm track (e.g., Thorncroft and Hodges 2001 ; Serra et al. 2008

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

century at the seasonal, interannual, and multidecadal time scales, as well as for the remote connections with ENSO and the NAO. Both the AWPAI and AWPTI are defined to study the seasonal cycle. Seven models—NCAR CCSM4, CSIRO Mk 3.6.0, GISS-E2H, GISS-E2R, HadCM3, MPI-ESM-LR, and MPI-ESM-P—have the best performance in simulating the AWP seasonal cycle based on both indexes. The AWPAI is almost zero for some models in most years due to cold SST bias found in the northern tropics for most models. Thus

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Edmund K. M. Chang

the high latitudes and the deep tropics and enhanced divergence in the subtropics ( Wetherald and Manabe 2002 ; Kutzbach et al. 2005 ). The decrease in the subtropics can also be linked to the projected poleward expansion of the subtropical dry zone ( Scheff and Frierson 2012 ). Here we examine how storm-track changes may modulate these projections. The change in precipitation projected by CMIP5 models under RCP8.5, normalized to a Northern Hemisphere temperature increase of 5°C for each model

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Sanjiv Kumar, James Kinter III, Paul A. Dirmeyer, Zaitao Pan, and Jennifer Adams

1. Introduction As the earth warms, regional differences in warming rates can help us better understand the mechanisms of the warming. For example, we expect continents to warm faster than oceans based on thermodynamic arguments, and the polar latitudes to warm faster than the tropics or midlatitudes because of snow–surface albedo feedback. Departures from these expectations, such as the so-called “warming hole” in the eastern United States ( Pan et al. 2004 ), can provide clues to the nature

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