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

sort of “upper bound” for radiative forcing on coupled land–atmosphere responses in the CMIP5 dataset. RCP85 is characterized by high ongoing anthropogenic CO 2 emissions, a severe curtailment of aerosols, and ongoing land use change, particularly in low latitudes and the Southern Hemisphere ( Riahi et al. 2011 ). The variables used in this study are the soil wetness in the top 10 cm of the soil column, surface sensible and latent heat fluxes, near-surface temperature, and relative humidity

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

project that features a continuous rise in radiative forcing during the twenty-first century, which leads to a value of about 8.5 W m −2 in 2100 ( Riahi et al. 2011 ). The main variables analyzed in this study include precipitation, surface latent heat flux, and 850-hPa winds. Table 1. CMIP5 models analyzed in this study. b. Observational datasets Rainfall observations are based on the Tropical Rainfall Measuring Mission (TRMM product 3B42, version V6) ( Huffman et al. 1995 ) precipitation dataset

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

-area model, and Berbery (2001) showed that NCEP's Eta limited-area model could additionally reproduce the diurnal cycle of moisture flux. Gao et al. (2007) used the MM5 limited-area model to demonstrate improvement over global models in representing spatial and temporal precipitation patterns but found model deficiencies in representing the evolution of the diurnal cycle. Castro et al. (2007a) used the Regional Atmospheric Modeling System (RAMS) limited-area model driven with global reanalysis data

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

Lee (2005) showed that the AWP variation is largely forced by shortwave radiation while latent heat flux plays a secondary role, particularly during the AWP decay phase. This seasonal cycle in many of the CMIP3 models has a significant cold bias in the AWP region ( Chang et al. 2007 , 2008 ; Richter and Xie 2008 ; Misra et al. 2009 ; Richter et al. 2012 ; LWLE12 ). Large and Danabasoglu (2006) and Chang et al. (2007) both pointed out that the North Atlantic subtropical high and

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

downwelling longwave radiation causes increased evaporation, which leads to near-surface increases in moist static energy, instability, and precipitation. The increase in precipitation is then reinforced by enhanced moisture convergence. Where moisture is insufficient, increased terrestrial radiation is balanced by increased sensible heat flux. In our CMIP3 analysis, the remote mechanism dominates during the dry season and the local mechanism dominates during the rainy season. During the transition from

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Lin Chen, Yongqiang Yu, and De-Zheng Sun

that they are the models whose outputs including both historical runs and AMIP runs had been submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI) at the time of our analysis. The historical (AMIP) runs of these models cover the period 1850–2005 (1979–2005). Table 1. List of CMIP5 models analyzed in this study. The radiative fluxes supplied by the International Satellite Cloud Climatology Project (ISCCP; Zhang et al. 2004 ) and the precipitation derived from the Global

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

in the CMIP3 version. Some of the CMIP5 models have also evolved from “climate system models” to “Earth system models” and include biogeochemical components and time-varying carbon fluxes among the ocean, atmosphere, and terrestrial biosphere. The evaluation of the performance of the CMIP5 models in terms of the tropical intraseasonal variability and the comparison of the simulation fidelity against that of the former generation of models is the goal of this study. The evaluation of the tropical

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Jeanne M. Thibeault and Anji Seth

changes in CanESM2 are small, but drier conditions are projected for portions of the northeast region, including coastal areas, southern Quebec, and southeastern Ontario. This drying is inconsistent with its positive trend in northeast-region υ850 anomalies and increase in northward VIMT. This inconsistency is explained by the weakening of the relationship between northeast-region precipitation and υ850, as well as the projected increase in mean moisture flux divergence for CanESM2 over portions of

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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

easterly summer-mean flow (>4 m s −1 ). This suggests that realistic representation of the mean state could be crucial for improved simulations of the EP ISV, which is in agreement with a recent study by Rydbeck et al. (2013) , and has also been discussed for Madden–Julian oscillation (MJO) simulations over the western Pacific and Indian Ocean (e.g., Kim et al. 2009 ). One hypothesis is that a realistic mean state produces the correct sign of surface flux anomalies relative to intraseasonal

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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

–2004. d. Seasonal atmospheric and land water budgets We next evaluate the climatologies of the atmospheric and land water budgets. Seasonal changes in atmospheric water content are relatively small compared to the moisture fluxes and so we focus on the latter. Variations in moisture divergence are generally correlated with seasonal precipitation and so may help explain biases in model precipitation. The vertically integrated moisture transport (vectors) and its divergence (contours) are shown in Fig

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