Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates
Year: 2022
Type: article
Abstract: Extreme precipitation events, including those associated with weather fronts, have wide‐ranging impacts across the world. Here we use a deep learning algorithm to identify weather fronts in high resolution Community Earth System Model (CESM) simulations over the contiguous United States (CONUS), and evaluate the results using observational and reanalysis products. We further compare results between CESM simulations using present‐day and future climate forcing, to study how these features might change with climate change. We find that detected front frequencies in CESM have seasonally varying spatial patterns and responses to climate change and are found to be associated with modeled changes in large scale circulation such as the jet stream. We also associate the detected fronts with precipitation and find that total and extreme frontal precipitation mostly decreases with climate change, with some seasonal and regional differences. Decreases in Northern Hemisphere summer frontal precipitation are largely driven by changes in the frequency of different front types, especially cold and stationary fronts. On the other hand, Northern Hemisphere winter exhibits some regional increases in frontal precipitation that are largely driven by changes in frontal precipitation intensity. While CONUS mean and extreme precipitation generally increase during all seasons in these climate change simulations, the likelihood of frontal extreme precipitation decreases, demonstrating that extreme precipitation has seasonally varying sources and mechanisms that will continue to evolve with climate change.
DOI: 10.1029/2022jd037038
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: bronze
Funders: National Science Foundation, U.S. Department of Energy, National Center for Atmospheric Research, National Energy Research Scientific Computing Center, National Oceanic and Atmospheric Administration, University of Illinois at Urbana-Champaign, Office of Science, Biological and Environmental Research, Argonne National Laboratory
Awards: G102163711, Department of Energy (DoE) Support of the National Center for Atmospheric Research Activities, G107409864, G107546177, G1313983767, Leadership Class Scientific and Engineering Computing: Breaking Through the Limits, G1664757510, High Resolution Earth System Modeling for International Climate Assessment Using Blue Waters Capabilities, G2287611682, The Management and Operation of the National Center for Atmoshperic Research (NCAR), G2415155667, G262962599, G2784524989, G2952167421, G2982274767, G3075337988, G3137522415, G3477018482, COOPERATIVE AGREEMENT TO ANALYZE VARIABILITY, CHANGE AND PREDICTABILITY IN THE EARTH SYSTEM (CATALYST), G4326986265, G4736197316, G4810716312, Sustained-Petascale In Action: Blue Waters Enabling Transformative Science And Engineering, G498139845, COOPERATIVE AGREEMENT TO ANALYZE VARIABILITY, CHANGE AND PREDICTABILITY IN THE EARTH SYSTEM (CATALYST), G5127309730, G513797252, G5330321839, G5367696895, G5477578438, G5606180237, G6457648290, G6848031779, G6918803902, G8324313409, G969889393, G993149817
OpenAlex ID: W4306919374