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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
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
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
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
