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Extreme events over historical and future projected periods within a dynamically downscaled CMIP6 GCM (AMS 2024)
Citation:
Mallard, M., J. Willison, T. Spero, J. Bowden, C. Nolte, A. Jalowska, G. Gray, AND G. Tierney. Extreme events over historical and future projected periods within a dynamically downscaled CMIP6 GCM (AMS 2024). 104th Annual Meeting of the American Meteorological Society, Baltimore, MD, January 28 - February 01, 2024.
Impact/Purpose:
Dynamical downscaling is a methodology which takes input from a global climate model (GCM) and uses it to drive a finer scale regional climate model (RCM). Here, results from a dynamical downscaling application are presented where global climate projections are downscaled over the southeast U.S., providing finer temporal and spatial scale data that can better inform local communities and resilience decisions. This presentation shows evaluations of the RCM's error over historical periods and also analyzes future projected changes in extreme events.
Description:
Regional climate models can be leveraged to dynamically downscale global climate models (GCMs) to limited area domains to increase spatial and temporal resolution of future climate projections to better inform local stakeholders and community resilience decisions. Recently, data at sufficiently-fine temporal resolution became available with which to dynamically downscale GCMs from the Coupled Model Intercomparison Project, 6th Phase (CMIP6). Here, multi-decadal simulations are produced using the Weather Research and Forecasting model to downscale the Max Planck Institute Earth System Model 1.2 High Resolution (MPI-ESM) model to a 12-km domain over the southeast U.S. The downscaled MPI-ESM 30-year historical runs will be evaluated with a focus on extreme temperature and precipitation events and whether value is added beyond the original GCM solution. Preliminary results indicate that, over the historical period, the diurnal range of 2-m temperature is better represented in the downscaled results, with cool biases in average daily maximum temperatures and warm biases in average daily minimum temperatures both improved in the downscaled results relative to the original GCM. The potential causes for these improved results will be explored. Additionally, projected changes in 2-m temperature and precipitation downscaled from MPI-ESM under Shared Socioeconomic Pathway (SSP) 3-7.0 will be explored from mid- to end-of-century.
