Improving the Representation of Mesoscale Convective Systems in Weather and Climate

PI Andreas Prein, National Center for Atmospheric Research
Prein Graphic

Hourly precipitation accumulation from one idealized MCS simulation in a central U.S. environment at Δx=12 km, 4 km, 1 km, and 250 m (left to right). The precipitation volume (PVol) and peak precipitation (Pmax) are shown in each panel(adapted from [16]).

Project Summary

This project is tackling this challenge by using unique MCS observations from the Department of Energy’s (DOE’s) Atmospheric Radiation Measurement (ARM) sites in the U.S. central Great Plains and the Amazon rain forest to evaluate the simulation of MCSs in state-of-the-art atmospheric models.

Project Description

Large complexes of thunderstorms known as Mesoscale Convective Systems (MCSs) are dominating the weather and climate of equatorial regions and parts of the mid-latitudes during summer. The central U.S. is a hotspot of MCS activity during the warm season with up to 70 % of the total summertime rainfall produced by those systems. Furthermore, most flash flooding events are caused by MCSs such as the West Virginia flooding or the Baton Rouge flooding of 2016. Most weather and climate models have deficiencies in simulating MCSs, limiting their predictive skill on time scales from days to decades. Improving the simulation of MCSs in these models is of uttermost importance to realistically simulate the earth’s energy and water cycle including the prediction of droughts and floods.

 Turbulence (large-eddy) resolving simulations of well-observed MCSs at a leadership computing facility allow unprecedented insights into scale interactions of convective processes across multiple orders of magnitude and can answer fundamental questions about large-scale environmental impacts on MCS development and systematic model biases in current weather and climate predictions. Furthermore, this project will identify model configurations that allow the realistic simulation of MCS processes at affordable computational costs and develop novel cloud parameterizations that will improve coarse resolution global earth system models that are used for climate change assessments. The research supports DOE’s Earth and Environmental Systems Sciences Division’s mission to enhance the seasonal to multidecadal predictability of the Earth system and resilience of infrastructure against extreme weather and hydrological changes.

Project Type