How Well Do Global Cloud-Resolving Models Simulate Mesoscale Storms?
A new class of high-resolution global atmosphere models is emerging for Earth system modeling. These state-of-the-art models can directly simulate convective storms and hold promise for improving modeling hydrological extremes and their potential changes in future climates. A new study assesses the fidelity of the convective storms simulated by these global models against high-resolution satellite observations. Researchers used a novel technique to track deep convective storms and consistently identify mesoscale convective systems (MCSs) in simulations and observations. They found that models produce a diverse range of deep convective storms, MCS frequencies, and their ratio in key climate regions.
Traditional Earth system models cannot simulate organized convection such as MCSs, which are responsible for much of the extreme precipitation over land. Next-generation global atmospheric cloud-resolving models may significantly improve simulations of convection and extreme precipitation. Facilitated by a global MCS database and MCS tracking algorithm, researchers provided the first assessment of the fidelity of six global cloud-resolving models in simulating the range of ordinary deep convective storms and MCSs. While the models simulate certain aspects of tropical MCSs reasonably well, challenges remain in faithfully representing the observed spectrum of convective storms.
Convective storms, particularly those that grow into MCSs, produce a large fraction of hazardous weather worldwide. How MCSs and their associated precipitation may change in a warming climate remains highly uncertain due to the inability of Earth system models to simulate the key processes responsible for MCS development. An international initiative called DYAMOND (Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) provides an intercomparison framework for a new class of atmospheric models known as either global cloud-resolving or convection-permitting models with sub-10 km horizontal resolutions. Researchers analyzed DYAMOND model simulations and high-resolution satellite observations. They found a surprisingly large inter-model spread in the simulated frequency of ordinary deep convection and MCSs. Most of the models captured important MCS characteristics such as lifetime, rainfall amount, and movement speeds, representing a notable improvement compared to typical global models with coarser resolutions. The DYAMOND models significantly overestimated convective rainfall intensity over both land and ocean, while consistently underestimating MCS rainfall area. This study highlights the need for more process-oriented model diagnostics to better understand the causes of the differences in simulated storm and precipitation characteristics as well as provide guidance for future model development.