The November event of the Madden-Julian oscillation (MJO) during the Dynamics of North
Atlantic Models (DYNAMO) field campaign was simulated using the global compressible nonhydrostatic
Model for Prediction Across Scales with global coarse (60 and 15 km) and regional (the Indian Ocean)
cloud-permitting (3 km) meshes. The purpose of this study is to compare roles of parameterized deep and
shallow cumulus and microphysics in MJO simulations. Two cumulus schemes were used: Tiedtke and
Grell-Freitas. The deep and shallow components of Tiedtke scheme can be turned on and off individually. 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. A shallow scheme
(Tiedtke) along with microphysics strengthens the MJO signals 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 (Tiedtke) or impair them (Grell-Freitas). The deep scheme
of Tiedtke cannot reproduce the MJO well without its shallow counterpart. The main role of shallow
convection in the model is to transport moisture upward to the lower to middle troposphere. By doing so, it
removes dry biases in the lower to middle troposphere, a distinct feature in simulations with weak or no MJO
signals, and enhances total precipitation and diabatic heating produced by microphysics and deep cumulus
schemes. Changing model grid spacing from 60 to 15 km makes a little difference in the model fidelity of
reproducing the MJO. All roles of shallow convection in 15 km simulations with parameterized deep
convection cannot be reproduced in 3 km simulations without parameterized deep convection. Results from
this study suggest that we should pay more attention to the treatment of shallow convection and its
connection to other parameterized processes for improving MJO simulations. In other words, a holistic
approach should be taken that consider parameterization of shallow cumulus, microphysics, boundary layer,
and deep cumulus as a whole for model improvement.