Despite recent improvements, many global climate models still show strong biases in the representation of TC activity, which limit the reliability of TC sub-seasonal and seasonal predictions and future projections. Inspired by the success of the MJO process-oriented diagnostics, my group has recently been developing process-oriented diagnostics for TC simulation (Kim et al. 2018). The diagnostics focus on how convection, moisture, clouds, and related processes are coupled at individual grid points and yield information about how the convective parameterizations interact with resolved model dynamics around simulated TC centers. In a handful of high-resolution GCM simulations, we found that models with stronger TCs exhibited tendency to produce a greater amount of precipitation, thus heating, near the TC center, and a greater contrast in relative humidity and surface latent heat flux between the inner and outer regions of TCs (Kim et al. 2018; Moon et al. 2019). These results emphasize that aspects of parameterization schemes that affect the moisture-convection coupling and the surface flux feedback are critical to the intensity of TCs in GCM simulations.

As part of our ongoing NOAA grant, we are currently examining moist thermodynamics of the observed TCs in long term satellite observations and reanalysis products. This will provide a “reference” version of our TC process diagnostics. We plan on taking advantage of the upcoming model intercomparison projects (CMIP6/HighResMIP) to evaluate and identify biases in the model simulations. We also plan on performing targeted global climate model experiments guided by the model evaluation results to better understand the processes that are responsible for inter-model spread in TC activity.