Machine learning-based detection of weather fronts and associated
extreme precipitation in historical and future climates
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.