Work

Investigating western North America atmospheric rivers with machine learning-based detection

Year: 2026

Type: article

Abstract: Atmospheric rivers (ARs) are long, narrow conduits transporting water vapor poleward. ARs are often associated with extreme precipitation and can have significant impacts on humans and the environment when they make landfall. Here we use a previously developed deep learning model, CGNet, to detect ARs in high resolution Community Earth System Model (CESM) historical and future simulations and evaluate the results using multiple reanalysis products. CGNet is a convolutional neural network trained on expert-labeled AR and tropical cyclone masks from the ClimateNet project. We probe the sensitivities of CGNet by testing how different characteristics of the input (variables, normalization values, spatial and temporal resolution) impact the resulting AR detection. Global and regional AR frequency magnitudes are strongly dependent on source data. To understand future changes, we compare features of the detected ARs across CESM simulations using historical and Representative Concentration Pathway (RCP) 8.5 forcing. Focusing on western North America, we find that ARs largely increase over California and decrease over the Pacific Northwest and western Canada in response to projected warming. We leverage built-in spatiotemporal tracking with CGNet to connect regional changes in ARs with shifts in lifecycle characteristics. We further investigate precipitation associated with ARs and find that AR precipitation increases in California are largely driven by changes in AR precipitation intensity, whereas AR frequency provides an important contribution to AR precipitation changes at more northerly latitudes. AR-associated extreme precipitation is projected to increase over much of the region, with important implications for impacts.

Source: Machine Learning Earth

DOI: 10.1088/3049-4753/aeaae7