Relating Convection to GCM Grid-Scale Fields Using Cloud-Resolving Model Simulation of a Squall Line Observed during MC3E Field Experiment

Abstract

In this study, a WRF (Weather Research and Forecasting) model is used as a cloud-resolving model to simulate a squall line observed on 20 May 2011 in the Southern Great Plains (SGP) of the United States. The model output is then used to examine the relationships between convective precipitation and coarse-grained variables averaged over a range of subdomain sizes equivalent to various global climate model horizontal resolutions. The objective is to determine to what extent convection within the subdomains can be related to these “large-scale” variables, thus that they can potentially serve as closure in convective parameterization. Results show that convective precipitation is well correlated with the vertical velocity at 500 hPa, column integrated moisture convergence and CAPE change due to large-scale advective forcing (dCAPE) for various subdomain sizes, but the correlation decreases with decreasing subdomain size. dCAPE leads convective precipitation for all subdomain sizes examined; however, the lead time decreases with decreasing subdomain size. Moisture convergence leads convective precipitation for subdomain sizes greater than 32 km but has no lead time for smaller subdomain sizes. Mid-tropospheric vertical velocity has no lead time or slightly lags convective precipitation. The lead/lag composite analysis with respect to maximum precipitation time indicates that peaks of large-scale variables increase with decreasing subdomain size. The peaks of 500 hPa vertical velocity and column integrated moisture convergence occur at the same time as maximum precipitation, but maximum dCAPE leads maximum precipitation by twelve minutes.

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

“…We evaluate the representation of precipitation structure in cloud system resolving simulations of the 20 May 2011 squallline event during the Mid-Latitude Continental Convective Clouds Experiment (MC3E; Jensen et al 2016) in Oklahoma using radar observations. This case has been previously simulated in several previous studies (Tao et al 2013;Fan et al 2015;Marinescu et al 2016;Saleeby et al 2016;Tao et al 2016;Fan et al 2017;Fridlind et al 2017;Xue et al 2017;Cheng and Zhang 2019;Han et al 2019;Stanford et al 2019). Biases in simulated precipitation structure are then connected to differences in observed and simulated mesoscale circulations.…”

Characteristics of convective precipitation over tropical Africa in storm‐resolving global simulations

“…In the mature stage, CAPE is often low, even though deep convection as well as moisture convergence can be intense (e.g., Fuchs‐Stone et al ., 2020). Moreover, adding to the instability closure allows for a better coupling of the convective parametrization to the dynamics, which is particularly important at high model resolutions (e.g., Cheng and Zhang, 2019).…”

“…In the mature stage, CAPE is often low, even though deep convection as well as moisture convergence can be intense (e.g., Fuchs-Stone et al, 2020). Moreover, adding Q adv to the instability closure allows for a better coupling of the convective parametrization to the dynamics, which is particularly important at high model resolutions (e.g., Cheng and Zhang, 2019). We found that, over tropical Africa, the characteristics of convective precipitation agree best with GPM IMERG when Q adv is added to the instability closure, at both 9 and 4 km resolution.…”

“…Meanwhile, CAPE is a good measure of deep convection during its initiation, and the memory of CAPE helps coupling the convective activity to the diurnal cycle. However, both CAPE and the convergence of moisture are less strongly correlated with convective precipitation at storm‐resolving scales than at coarser resolutions (Cheng and Zhang, 2019). This is because stochasticity increases relative to the large‐scale forcing of convection and because the quasi‐equilibrium assumption (Arakawa and Schubert, 1974), which requires that the parametrized convection adapts much faster to an external forcing than that forcing evolves, is not fulfilled at kilometre‐scale resolutions.…”