The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

Abstract:

The representation of the Madden-Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) due to the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations.

One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth-death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation.

In this work, we use for the first time the SMCM in a GCM. We build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR-GCM to a simple multicloud model. We tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJO's that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.