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

moisture and the flux ( Guo et al. 2006 ), and has the same units as the flux (W m −2 ). Variables relevant to the atmospheric boundary layer are also derived, including the height of the lifting condensation level: where the 2-m dewpoint T D 2m is estimated from near-surface relative humidity, temperature T 2m , and the mean surface pressure. Standard lapse rates Γ are used. Relative humidity is converted to specific humidity q 2m for several of our analyses as well. Finally, we estimate the

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

achievements have been made in modeling global ISV during past decades, significant challenges remain in current general circulation models (GCMs) (e.g., Slingo et al. 1996 ; Waliser et al. 2003 ; Lin et al. 2006 ; Kim et al. 2009 ). For ENP ISV, Lin et al. (2008) analyzed simulations of ISV and easterly waves over the ENP by the 22 phase 3 of the Coupled Model Intercomparison Project (CMIP3) models used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Their

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Lin Chen, Yongqiang Yu, and De-Zheng Sun

. However, the feedbacks from cloud and water vapor are considered as the largest source of uncertainty in climate predictions ( Bony and Dufresne 2005 ; Randall et al. 2007 ). Therefore, assessing the accuracy and narrowing the uncertainties of the cloud and water vapor feedbacks in current leading climate models is of obvious importance. Indeed, persistent efforts have been made in this regard ( Cess et al. 1990 ; Sun and Held 1996 ; Bony and Dufresne 2005 ; Stephens 2005 ; Bony et al. 2006

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

mesoscale downdrafts reducing the entropy of the lower troposphere, and diluted convective updrafts being sensitive to the change of boundary layer and lower troposphere entropy [see more detailed discussion in Lin et al. (2006) and references therein]. The current GCMs have not included all the above self-suppression processes in deep convection, especially the diluted convective updrafts, convective downdrafts, and mesoscale downdrafts. Better representation of these physical processes may help to

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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

Fig. 11 for a complete listing), and select models for eastern North America and the western and central North Atlantic. There is a maximum in cyclone density in the CFSR over the Great Lakes, the western Atlantic from east of the Carolinas northeastward to east of Canada, and just east of southern Greenland ( Fig. 10a ). The largest maximum over the western Atlantic (6–7 cyclones per cool season per 50 000 km 2 ) is located along the northern boundary of the Gulf Stream Current. The MME mean is

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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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Baird Langenbrunner and J. David Neelin

, implying that the differences between the atmospheric GCMs are “relatively insensitive” to the prescribed SST fields. Because challenges persist in correctly simulating a precipitation teleconnection response (e.g., Rowell 2013 ), analysis of the CMIP phase 5 (CMIP5) AMIP ensemble can provide a way to gauge the fidelity of the current generation of models in simulating large-scale atmospheric processes leading to rainfall. In particular, we evaluate December–February (DJF) ENSO precipitation

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

and found that the model's enhanced representation of the surface boundary produced an acceptable diurnal cycle of summer precipitation in the monsoon region that was not captured by the driving reanalysis. A recent study by the same group using the Weather Research and Forecasting Model (WRF; Castro et al. 2012 ) showed the potential for limited-area models to improve seasonal NAMS forecasts. The use of higher resolution limited-area models that are able to capture the diurnal cycle of

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Leila M. V. Carvalho and Charles Jones

by September. The onset of the rainy season over the Amazon is preceded by an increase in the frequency of the northerly cross-equatorial flow over South America that increases moisture in the boundary layer ( Marengo et al. 2001 , 2010 ; Wang and Fu 2002 ). The onset of the wet season in central and southeastern Brazil in the present climate typically occurs between September and November ( Silva and Carvalho 2007 ; Gan et al. 2004 ; Raia and Cavalcanti 2008 ). SAMS peaks from December

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Suzana J. Camargo

interesting to note that the MRI-CGCM3 SST is too warm in the Southern Hemisphere, where the model’s GPI is high and the model produces too many TCs. A few models have warm SST anomalies in the western boundaries of the American continent. Various studies showed that future TC projections are sensitive to the specific SST patterns in the models ( VS07b ; Sugi et al. 2009 ; Villarini et al. 2011 ). However, similar to GPI, the direct relationship of TC frequency and SST bias is not enough to explain the

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