Roles of Deep and Shallow Convection and Microphysics in MJO Simulations

The November event of the Madden-Julian Oscillation (MJO) during the DYNAMO field campaign was simulated using the global compressible nonhydrostatic Model for Prediction Across Scales (MPAS) with global coarse (60 and 15 km) and regional (the Indian Ocean) cloud-permitting (3 km) meshes. The results reveal that microphysics alone (without cumulus parameterization) is able to produce strong signals of the MJO in precipitation with 3 km mesh and weak MJO signals with 15 km mesh. Shallow convection enhances the MJO signals produced by microphysics but makes them less well organized on large scales. A deep cumulus scheme can either improve the large-scale organization of MJO precipitation produced by microphysics and shallow convection or destroy them. The deep scheme cannot reproduce the MJO without its shallow counterpart. The main role of shallow convection in the model is to moisten the low-mid troposphere. By doing so, it removes dry biases in the low-mid troposphere, a distinct feature in simulations without MJO signals, and enhances total precipitation and amplitudes of vertical velocities, diabatic heating and drying produced by microphysics and deep cumulus schemes. Results from this study suggest that we should stop treating problems of MJO simulations as one of deep cumulus parameterization. Instead, a holistic approach should be taken that consider parameterization of shallow cumulus, microphysics, boundary layer, as well as deep cumulus as a whole for improvement.