The MJO exhibits peculiar behaviors near the Indo-Pacific Maritime Continent (MC) region. During boreal winter, the MJO detours the islands in the MC southward and propagates through the oceanic region between Indonesia and Australia. Also, about half of the MJO events that start off over the Indian Ocean cease around the MC region. In the papers of Kim et al. (2014b) and Kim et al. (2017), we studied the mechanisms through which the MC affects MJO propagation. In the former paper, we compared the observed MJO events that propagate through the MC with those that terminate in the MC and showed that the magnitude of meridional moisture advection to the east of enhanced convection is what distinguishes the propagating events from the non-propagating ones. The latter paper showed that the MJO detours the equatorial MC area southward because the mean horizontal moisture gradient is weaker over there than in the southern MC region. Both studies elucidated the role of horizontal moisture advection process on the eastward propagation of the MJO, especially over the MC region.

After several observational work showed that tropical convection is tightly coupled with column moisture, the view that considers the MJO as a type of moisture mode had gained much attention. Analyzing moisture or moist static energy budget of the MJO in observations and model simulations had become popular. What was lacking was a self-consistent theoretical model that is capable of predicting the fundamental characteristics of the MJO with realistic parameters. In Adames and Kim (2016), partly guided by my earlier observational work that emphasizes meridional moisture advection (Kim et al. 2014b), we developed a linear moisture mode model of the MJO that successfully explains many salient features of the MJO. In the same paper, we also presented observational evidence that strongly suggests that the MJO is a dispersive wave with a westward group velocity.

Considering the significant impacts that the MJO has on the global weather-climate-environment system, it is of great interest to understand the possible changes in the MJO characteristics in the future. In a series of papers, we examined the responses of MJO’s amplitude and phase speed to the greenhouse gas-induced global warming (Adames et al. 2017, 2018; Rushley et al. 2019). We showed that the MJO’s amplitude increases, likely due to increases in the background precipitation variability, and that the MJO accelerates with the warming. In Adames et al (2018) and Rushley et al. (2019), we also demonstrated that the linear moisture mode model of Adames and Kim is able to predict the rate at which the simulated MJO’s phase speed increases with surface temperature quite accurately. The theory-based analysis suggests that the MJO accelerates in a warmer climate as a result of the increases in the meridional humidity gradient, the gross dry stability, the convective moisture adjustment timescale, and the decrease in MJO’s zonal wavenumber.

Accurate simulations of the MJO have historically been a difficult test for many weather and climate models. A lack of diagnostics that could provide insights into process-level errors in the model representation of the MJO has slowed model improvement. As co-chair of the WMO Working Group on Numerical Experimentation (WGNE) MJO Task Force, I have been active in the development of the MJO process-oriented diagnostics, which are designed to identify model errors in the processes that are key to MJO simulation and thereby to guide model development. With the diagnostics developed within my group, we showed that MJO simulation fidelity in climate models is strongly affected by the degrees to which model convection is coupled to environmental moisture (Kim et al. 2014a) and to which longwave radiative fluxes are altered by convection (Kim et al. 2015). We also have applied these process-oriented diagnostics to many climate model simulations (Jiang et al. 2015; Ahn et al. 2017).

While previous studies have demonstrated that MJO simulations in global models can be improved by changing aspects of the cumulus parameterization, the same methods tend to degrade other aspects of the simulation, such as the mean state (Kim et al. 2011; Kim and Maloney 2017). We demonstrated in a recent modeling study that the MJO-mean state tradeoff is a result of a structural bias in the cumulus parameterizations: the lack of mesoscale convective organization (Ahn et al. 2018). We showed that the model that explicitly represents mesoscale convective organization can reasonably simulate both the mean state and the MJO.

Our examinations of the MJO have revealed that the horizontal and vertical gradient of the background moisture is the key aspects of the basic state for MJO’s propagation and maintenance. By building upon these findings, I am planning on addressing the following two questions: i) what are the factors that determine the basic state moisture distribution in the Indo-Pacific warm pool? and ii) to what degree the moisture mode framework can explain MJO’s behavior in a climate that is vastly different from the current one? Considering the first question, I am particularly curious to understand the role of convection in the MC islands on the mean state. Regarding the second question, we are currently analyzing a set of climate model simulations in which the orbital parameters are altered to those of select paleoclimates. We found the MJO responds to the orbital parameter changes and are testing the linear moisture mode theory with the simulations. Another important future work that we are planning to do is to continue developing the MJO process-oriented diagnostics and applying them to a wide set of model simulations. In particular, we plan on taking the opportunity of the new set of climate model simulations (CMIP6) that have become available recently. We expect to obtain new insights into the process-level model biases from our planned examination of the new model simulations.