Convection-Permitting Climate-Scale Simulations for Extreme Event Modeling

PI Rao Kotamarthi, Argonne National Laboratory
Co-PI Jiali Wang, Argonne National Laboratory
Dimitrios Fytanidis, Argonne National Laborator
Brandi Gamelin, Argonne NationalLaboratory
Chunyong Jung, Argonne National Laboratory
William Pringle, Argonne National Laboratory
Gokhan Sever, Argonne National Laboratory
Haochen Tan, Argonne National Laboratory
INCITE 2023 Kotamarthi Graphic

Cloud fraction from the convection permitting (4km horizontal grid resolution) decadal length simulations with the WRF model showing a single time slice at 00:00 UTC on September 3, 2003. The simulations are downscaled from the 30km spatial resolution ERA-5 reanalysis fields. The frame shows Hurricane Isabel to the east of the US. (Image: Chunyong Jung, Argonne National Laboratory)


Project Summary

With this INCITE project, researchers will use very high spatial-resolution regional-scale climate models to explore the physics underlying the formation and evolution of extremes in precipitation and temperature in the current and future climates under various greenhouse gas emission scenarios

Project Description

The ability to assess the risk from extreme climate events is critical for developing adaptation and mitigation strategies that are often made at local and regional scales of the impacted region. Therefore, improved capabilities for predicting the frequency, duration, and extent of such events and their potential impacts for various locations across the continental United States is becoming increasingly necessary.

However, identifying and evaluating the risk in a warming climate requires long timescales for the simulation covering multiple decades and/or a large ensemble that covers a selected time slice. In addition, simulating extreme events and their impacts requires very high spatial resolution in the models, often covering several orders of magnitude, from hundreds of kilometers to tens of meters. Both these factors make these calculations computationally intensive.

The team’s ultimate goal is to provide the research community with a large multi-petabyte dataset of climate simulations with well-characterized uncertainties and biases that can be used to realistically describe extreme events, understand the environmental drivers that contribute to these extremes, and estimate risk from these events at local scales. The results from this work will provide a unique database for performing further studies for developing AI-based emulators for extreme events and developing climate risk estimates at local scales from these events.