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Zhe Feng, Fengfei Song, Koichi Sakaguchi, and L. Ruby Leung

across three different resolutions targeting next-generation GCMs (50, 25, 12 km) has been developed. The method jointly uses infrared brightness temperature (or outgoing longwave radiation) and surface precipitation, both commonly available in observations and model output, to track and identify MCSs. We show that this new tracking algorithm is able to track MCSs consistently across the three resolutions to reproduce the MCS frequency, seasonal, and diurnal cycle, impact on precipitation ( Figs. 4

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Allison A. Wing, Suzana J. Camargo, Adam H. Sobel, Daehyun Kim, Yumin Moon, Hiroyuki Murakami, Kevin A. Reed, Gabriel A. Vecchi, Michael F. Wehner, Colin Zarzycki, and Ming Zhao

://doi.org/10.1029/2011GL047629 . 10.1029/2011GL047629 Chen , J.-H. , and S.-J. Lin , 2013 : Seasonal predictions of tropical cyclones using a 25-km-resolution general circulation model . J. Climate , 26 , 380 – 398 , https://doi.org/10.1175/JCLI-D-12-00061.1 . 10.1175/JCLI-D-12-00061.1 Chou , M.-D. , and M. J. Suarez , 1994 : An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, Vol. 3, 93 pp. Chou , M.-D. , and M. J

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Catherine M. Naud, James F. Booth, Jeyavinoth Jeyaratnam, Leo J. Donner, Charles J. Seman, Ming Zhao, Huan Guo, and Yi Ming

in the amount of shortwave radiation reaching the ocean surface ( Trenberth and Fasullo 2010 ) and biases in atmospheric circulation change predictions (e.g., Ceppi and Hartmann 2016 ; Grise and Medeiros 2016 ) and ultimately affects climate sensitivity in models ( Frey and Kay 2018 ). Most specifically for ocean–atmosphere coupled models, the cloud bias can affect southern ocean ventilation and the location of the intertropical convergence zone (e.g., Xiang et al. 2018 ). One potential reason

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Eric D. Maloney, Andrew Gettelman, Yi Ming, J. David Neelin, Daniel Barrie, Annarita Mariotti, C.-C. Chen, Danielle R. B. Coleman, Yi-Hung Kuo, Bohar Singh, H. Annamalai, Alexis Berg, James F. Booth, Suzana J. Camargo, Aiguo Dai, Alex Gonzalez, Jan Hafner, Xianan Jiang, Xianwen Jing, Daehyun Kim, Arun Kumar, Yumin Moon, Catherine M. Naud, Adam H. Sobel, Kentaroh Suzuki, Fuchang Wang, Junhong Wang, Allison A. Wing, Xiaobiao Xu, and Ming Zhao

between summertime-mean values of surface (top 10 cm) soil moisture (SM) and incoming solar radiation (Rsds), respectively, with ET ( Berg and Sheffield 2018 ). Regions of positive SM–ET correlations in Fig. 9 indicate soil moisture–limited regions, where soil moisture variability controls ET variability—generally in drier summer midlatitude regions. The value of the correlation indicates how strongly SM controls ET. Conversely, negative values indicate that ET variations drive variations in soil

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