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Motoki Nagura, J. P. McCreary, and H. Annamalai

sought to relate them to biases in the atmospheric component of the coupled models (e.g., Martin et al. 2010 ; Ma et al. 2014 ). Others have focused on the ocean component, considering the causes and impacts of surface (e.g., Han et al. 2012 ; Levine et al. 2013 ; Sandeep and Ajayamohan 2014 ) and subsurface (e.g., Chowdary et al. 2016 ) temperature biases and of Bay of Bengal salinity biases (e.g., Seo et al. 2009 ). Recently, Annamalai et al. (2017) argued that the cause of poor monsoon

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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

and MJO amplitude across multimodel simulations in Jiang et al. (2016 , Fig. 3c ). Parameter τ depicts how rapidly precipitation must occur to remove excess column water vapor, or alternately the efficiency of surface precipitation generation per unit column water vapor anomaly, and is highly relevant to the convective onset diagnostics described above. AMOC structure diagnostic. The AMOC, with large temperature ( T ) and salinity ( S ) differences between the northward-flowing upper limb and

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