Towards Better Understanding and Simulating Changes of Mesoscale Convective Systems Under Climate Change

Dr. Zhe Feng is an Atmospheric Measurement & Data Sciences Scientist from Pacific Northwest National Laboratory

Abstract for Weekly Colloquium on Thursday, February 15, 2018 at 4 pm in Harvill 318 

Mesoscale convective systems (MCSs) are the largest type of intense thunderstorms on Earth. They range in size between one hundred to several hundred kilometers. These storms are responsible for producing well over half of the spring and summer rainfall in the central U.S., and account for the majority of the 100-year extreme rainfall events.  Our recent study found that MCSs have become more common and more intense during spring in central U.S. over the past 35 years. Unfortunately, global climate models  generally fail to simulate MCSs, undermining our confidence on their projected changes in flood-producing storms in the future climate. Recent advancements in computational power have begun to allow convection permitting simulations in regional models or regional refinement in global models. Explicit representation of convection shows promising improvements in simulating “MCS-like” features in climate models.

We examine two sets of regional climate simulations over the continental U.S. during the boreal warm season of 2011 using WRF model in convection-permitting resolution (4 km) with two commonly used microphysics parameterizations. Sensitivities of the simulated MCS properties and feedbacks to large-scale environments in the central U.S. are systematically examined against high-resolution geostationary satellite and 3-D mosaic radar observations. While MCS precipitation mean and variability are reasonably captured by the model, significant differences in MCS properties exist between the two simulations. We found that microphysics that simulates better agreement of MCS convective and stratiform precipitation amount with observations produces more topheavy diabatic heating profiles and a stronger dynamical feedback to the large-scale environments. The positive dynamical feedback may help further prolong the robust long-lived MCSs, which dominates precipitation amount compared to short-lived ones. Our results show that cloud microphysical processes have profound large-scale impact on simulating the hydrological cycle and extremes of our climate system. This study provides a framework for understanding and modeling the potential changes in MCSs and associated hydrometeorological extremes in future climate.