Improving MJO simulation by enhancing the interaction between boundary layer convergence and lower tropospheric heating

Abstract

It has been a great challenge for global weather and climate models to simulate realistic Madden-Julian Oscillation (MJO) while keeping global energy and water balance unaffected. This work demonstrates that, in the Nanjing University of Information Science and Technology Earth System Model, enhanced boundary layer (BL) convergence feedback to the lower tropospheric heating in both the modified Tidtke (TDK) and relaxed Arakawa-Schubert (RAS) convective schemes have significantly improved the quality of MJO simulation in terms of both the eastward propagation and three-dimensional dynamic and thermodynamic structures. The modifications to the TDK and RAS schemes include (a) a BL depth-dependent convective inhibition, and (b) a bottom-heavy diffusivity in the shallow convection scheme. To understand how these modifications improved the MJO simulation, we applied dynamics-oriented diagnostics to reveal the critical role of the interaction between the lower-tropospheric heating and the BL convergence. The modified schemes enhance the lower-tropospheric diabatic heating to the east of the MJO convective center, which leads to increased Kelvin wave easterly winds. The strengthened MJO easterly winds reinforce the BL moisture convergence to the east of the MJO center and therefore result in increased upward transports of moisture and heat from the BL to the free atmosphere, which further moisten and destabilize the lower troposphere and thereby increase the lower-tropospheric heating. The positive feedback between the BL convergence and lower tropospheric heating improves MJO eddy available potential energy generation to the east of major convection and promotes MJO eastward propagation. The results indicate that correct simulation of the heating induced by shallow and/ or congestus clouds and its interaction with BL dynamics is critical for realistic simulation of the MJO as suggested by the trio-interaction theory.

Mechanisms of Northward Propagation of Boreal Summer Intraseasonal Oscillation Revealed by Climate Model Experiments

“…The NESM3.0 consists of atmosphere, ocean, land, and sea ice component, and they are fully coupled with each other. The details of the model are described in Cao et al (), Yang and Wang (), and Yang et al (). The atmosphere model has T63 horizontally and 47 levels vertically.…”

“…The Nanjing University of Information Science and Technology Earth System Model (NESM3.0) has recently been developed for multidisciplinary numerical simulations (Cao et al, ). The NESM3.0 simulates not only reasonable climatology but also realistic MJO and BSISO (Yang & Wang, ). Signals of the northward propagation in boreal summer are strong and well organized over the IO.…”

Improving Seasonal Prediction of East Asian Summer Rainfall: Experiments with NESM3.0

“…The sea ice model resolution is about 1° latitude by longitude with four sea ice layers and one snow layer on the top of the ice surface. The details of the NESM3.0 is referred to Cao et al (2018) and Yang and Wang (2018).…”

“…For this purpose, we implemented four modified parameterizations to the NESM3.0. First, a boundary layer depthdependent convective inhibition (INH) was added to the convective scheme to suppress deep convection (Yang and Wang 2018). The INH suppresses deep clouds when the boundary layer depth is relatively shallow, which can reduce the precipitation over the SCS and Philippine Sea where the earlier NESM3.0 overestimates precipitation.…”

Can reanalysis products with only surface variables assimilated capture Madden–Julian oscillation characteristics?

“…Even a perfect atmospheric general circulation model (AGCM) could not reproduce the observed MJO events if it were forced by the observed daily sea surface temperature (SST) alone. This is because the MJO is in between synoptic time scale and seasonal time scale, and both the boundary layer condition and the initial condition are important to yield a real MJO (e.g., Ulate Medrano, ; Yang and Wang, ). To generate a long‐term dataset comparable to the observed MJO, a data assimilation system is needed which could assimilate atmospheric observations into an AGCM frequently.…”