Process-Oriented Diagnosis of Tropical Cyclones in CMIP6 HighResMIP Experiments

This study examines tropical cyclones (TCs) that are simulated by CMIP6 HighResMIP global climate models (GCMs). In particular, this work performs an observation-based evaluation of GCM-simulated TCs by comparing against the satellite-based and reanalysis-based process-oriented diagnostics of TCs. The diagnostics used in this study examine TC structures/processes and also how convection, moisture, clouds and related processes are coupled at individual grid points, which provides useful information into how convective parameterizations interact with resolved model dynamics. Available long-term satellite observations of precipitation, column precipitable water, and outgoing longwave radiation, as well as multiple reanalysis products, are used to create a set of reference diagnostics that can be used to identify bias in the model representation of processes that are critical to TC simulations in GCMs. Preliminary results suggest that GCM-simulated TCs in the CMIP6 HighResMIP simulations tend to overestimate the inner-core rainfall in comparison to the satellite-derived rainfall at comparable intensity. This analysis also aims to contribute to the NOAA Model Diagnostics Task Force process-oriented diagnostics package.