Title: Understanding the response of tropical ascent to warming using an energy balance framework



Abstract: Previous work has established that warming is associated with an increase in dry static stability, a weakening of the tropical circulation, and a decrease in the convective mass flux. Using a set of idealized simulations with specified surface warming and super-parameterized convection, we find support for these previous conclusions. We use an energy and mass balance framework to develop a simple diagnostic that links the fractional area covered by the region of upward motion to the strength of the mean circulation. We demonstrate that the diagnostic works well for our idealized simulations, and use it to understand how changes in tropical ascent area and the strength of the mean circulation relate to changes in heating in the ascending and descending regions. We show that the decrease in the strength of the mean circulation can be explained by the relatively slow rate at which atmospheric radiative cooling intensifies with warming. In our simulations, decreases in tropical ascent area are balanced by increases in non-radiative heating in convective regions. Consistent with previous work, we find a warming-induced decrease in the mean convective mass flux. However, when we condition by the sign of the mean vertical motion, the warming-induced changes in the convective mass flux are non-monontonic and opposite between the ascending and descending regions.



Data citation: Jenney, A., Randall, D. & Branson, M. (2020). Understanding the response of tropical ascent to warming using an energy balance framework. Colorado State University. Libraries. https://hdl.handle.net/10217/199724



For Community Earth System Model (CESM) simulations all run with super-parameterized (SP) cloud physics:



1) Monthly means

  A) Idealized aquaplanet simulations run for ~3 simulated years (SP-CAM4)

    A.1) constant sea surface temperature (SST) of 295K

    A.2) constant sea surface temperature (SST) of 300K

    A.3) constant sea surface temperature (SST) of 305K



  B) Real world simulations (SP-CESM1)

    B.1) Pre-industrial (PI) carbon dioxide levels

    B.2) 4xPI carbon dioxide levels



2) ~30 days of daily-mean data for all five simulations



3) 15 days of coincident daily-mean data (GCM-grid) and hourly-mean data (CRM-grid)



** simulation name prefixes **

A.1) QPC4_295k

A.2) QPC4_300k

A.3) QPC4_305k

B.1) spcesm_cam5_control

B.2) spcesm_cam5_4xCO2



GCM = global circulation model

CRM = cloud resolving model