Climatological representation of mesoscale convective systems in a dynamically downscaled climate simulation

Abstract

This research assesses the utility and validity of using simulated radar reflectivity to detect potential changes in linear and nonlinear mesoscale convective system (MCS) occurrence in the Midwest United States between the early and late 21st century using convection-permitting climate simulation output. These data include a control run and a pseudo-global warming (PGW) run that is based on RCP 8.5. First, using a novel segmentation, classification, and tracking procedure, MCS tracks are extracted from observed and simulated radar reflectivity. Next, a comparison between observed and the control run MCS statistics is performed, which finds a negative summertime bias that agrees with previous work. Using a convolutional neural network to perform probabilistic predictions, the MCS dataset is further stratified into highly organized, quasi-linear convective systems (QLCSs)-which can include bow echoes, squall lines, and line echo wave patterns-and generally less-organized, non-QLCS events. The morphologically stratified data reveal that the negative MCS bias in this region is largely driven by too few QLCSs. Although comparisons between the control run and a PGW run suggest that all MCS events are less common in the future (including QLCS and non-QLCS events), these changes are not spatially significant, whereas the biases between the control run and observations are spatially significant. A discussion on the importance and challenges of simulating QLCSs in convection-permitting climate model runs is provided. Finally, potential avenues of exploration are suggested related to the aforementioned issues. K E Y W O R D S climate modeling, mesoscale convective systems, convolutional neural network

Modelling Mediterranean heavy precipitation events at climate scale: an object-oriented evaluation of the CNRM-AROME convection-permitting regional climate model

“…Fitzpatrick et al (2020) apply a similar method to outgoing longwave radiation fields coming from a CPM of 4.4 km resolution in order to track MCSs in the West African Sahel. Studies on MCSs in the Midwest US were also carried out by Haberlie and Ashley (2019) using a tracking method based on observed and simulated radar reflectivities.…”

Effects of Under-Resolved Convective Dynamics on the Evolution of a Squall Line

“…Therefore, it is reasonable to suspect that biased deep convective kinematic and/or microphysical properties may bias overall system evolution. Several recent studies show differences between observed and simulated MCS life cycles and geographical distribution over the U.S. Great Plains for seasonal and multiyear simulations using horizontal grid spacing of 3-4 km (Prein et al 2017c;Feng et al 2018;Haberlie and Ashley 2019;Scaff et al 2019). However, the ways in which underresolved deep convective drafts within MCSs impact mesoscale kinematic and microphysical evolution remain unclear.…”

Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events

“…Weather radar data has been used for decades to identify CSM (e.g., Fujita, 1965). CSM identification can help assess the potential severity of an ongoing or imminent severe weather event (McNulty, 1995; Smith et al ., 2012), and can also be a useful tool for assessing operational (Snively and Gallus Jr., 2014) and climate model (Haberlie and Ashley, 2019) performance. The maturation of historical weather radar data archives has allowed the climatological exploration of radar‐derived event (e.g., thunderstorms, CSM ) frequency (Matyas, 2010; Fabry et al ., 2017).…”