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Tristan S. L’Ecuyer and Greg McGarragh

depicted in Fig. 3 also exhibit distinct time scales of variability reflecting the different modes of forcing that dominate variations in their local meteorology. Interannual variability in the vertical structure of radiative heating in each region is indicated by the thickness of the central black lines in Fig. 10 . Year-to-year variability in atmospheric radiative heating is generally small in the Amazon and southeast Pacific. Variations are larger in the west Pacific and Congo, where the

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Shoichi Shige, Yukari N. Takayabu, Satoshi Kida, Wei-Kuo Tao, Xiping Zeng, Chie Yokoyama, and Tristan L’Ecuyer

-based profiles of the apparent moisture sink for the SCSMEX NESA were larger than those for heating, key features of the vertical profiles agreed well. The two-dimensional (2D) CRM was used in Parts I , II , and III . Observed large-scale advective tendencies of temperature, moisture, and horizontal momentum were used as the main large-scale forcings that govern the CRM in a semiprognostic manner ( Soong and Ogura 1980 ). The availability of exponentially increasing computer capabilities has resulted in

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Wei-Kuo Tao, Stephen Lang, Xiping Zeng, Shoichi Shige, and Yukari Takayabu

others) wherein clouds are simulated under prescribed large-scale forcing. The default numerical experiment is two-dimensional (2D), using a 1-km horizontal resolution and vertical resolution that ranges from 42.5 m at the bottom to 1 km at the model top, which is at 22.5 km. The model uses a time step of 6 s and 512 × 41 grid points for integration. Please see Zeng et al. (2008 , 2009) for more details. b. Data 1) Oceanic convective systems (GATE, TOGA COARE, and SCSMEX) The South China Sea

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Mircea Grecu, William S. Olson, Chung-Lin Shie, Tristan S. L’Ecuyer, and Wei-Kuo Tao

the simulations and to study their impact on heating estimates. To this end, the simulations are performed at very high horizontal and vertical resolutions to produce more physically consistent precipitation and heating structures, and the environmental forcing of the simulations is varied to produce a diversity of structures. Also, the simulated heating structures incorporated into the PR training algorithm are adjusted to account for differences between the model-simulated convective

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Yukari N. Takayabu, Shoichi Shige, Wei-Kuo Tao, and Nagio Hirota

convergence zones in both hemispheres along the equator (double ITCZ). Also, Bony and Dufrence (2005) reported that the largest disagreements in sensitivity of cloud radiative forcing (CRF) among climate models and between models and observations are found in regions of large-scale subsidence. Their study indicates that at least some of the cumulus parameterizations utilized in AGCMs do not properly represent the suppression of deep convection under the large-scale subsidence. These results strongly

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Manuel D. Zuluaga, Carlos D. Hoyos, and Peter J. Webster

regions (see Fig. 5 in Hoyos and Webster 2007 ) than with the canonical response of the monsoon to ENSO. Rainfall anomalies in the 25–80-day band were positive over the EIO (BoB) region for 1998 (2002) and 2000 (2005) and negative for 1999 (1998) and 2001 (2000). These years correspond for the most part with the years of maximum and minimum LH in these regions, reinforcing the hypothesis that externally induced interannual variability (e.g., ENSO forcing) is not the only modulator of LH variability

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Xianan Jiang, Duane E. Waliser, William S. Olson, Wei-Kuo Tao, Tristan S. L’Ecuyer, Jui-Lin Li, Baijun Tian, Yuk L. Yung, Adrian M. Tompkins, Stephen E. Lang, and Mircea Grecu

(below 500 hPa). During convectively active phases of the MJO, the radiative cooling rate is about 0.5 K day −1 , while it is about 1.5 K day −1 during convectively inactive phases of the MJO. The large variations in tropospheric radiative cooling could be a result of the atmospheric longwave cloud radiative forcing due to high cloud variations associated with the MJO (e.g., Tian et al. 2001 ; Tian and Ramanathan 2002 ; Lin et al. 2004 ). To summarize, compared to the TRMM estimates, the EC

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Yasu-Masa Kodama, Masaki Katsumata, Shuichi Mori, Sinsuke Satoh, Yuki Hirose, and Hiroaki Ueda

Team 2005 ). The climatic distribution of warm rain and its relationship to the land–sea distribution, SST, low-level cloudiness, and low-level circulation are examined in this study. Another objective is to describe the large-scale distribution of LH profiles, with an emphasis on the contribution of warm rain. Latent heat is the major driving force of atmospheric circulation in the tropics and the subtropics. To evaluate LH profiles, the PR heating (PRH) algorithm is implemented in this study. The

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Shaocheng Xie, Timothy Hume, Christian Jakob, Stephen A. Klein, Renata B. McCoy, and Minghua Zhang

intertropical convergence zone of the eastern Atlantic. J. Atmos. Sci. , 36 , 53 – 72 . Vömel , H. , and Coauthors , 2007 : Radiation dry bias of the Vaisala RS 92 humidity sensor. J. Atmos. Oceanic Technol. , 24 , 953 – 963 . Xie , S. , S. A. Klein , M. Zhang , J. J. Yio , R. T. Cederwall , and R. McCoy , 2006 : Developing large-scale forcing data for single-column and cloud-resolving models from the Mixed-Phase Arctic Cloud Experiment. J. Geophys. Res. , 111 , D19104

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