A Deep‐Learning Ensemble Method to Detect Atmospheric Rivers and Its Application to Projected Changes in Precipitation Regime
Year: 2023
Type: article
Abstract: This study aims to detect atmospheric rivers (ARs) around the world by developing a deep‐learning ensemble method using AR catalogs of the ClimateNet data set. The ensemble method, based on 20 semantic segmentation algorithms, notably reduces the bias of the testing data set, with its intersection over union score being 1.7%–10.1% higher than that of individual algorithms. This method is then applied to the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets to quantify AR frequency and its related precipitation in the historical period (1985–2014) and future period (2070–2099) under the Shared Socioeconomic Pathways 5–8.5 warming scenario. The six key regions, which are distributed in different continents of the globe and greatly influenced by ARs, are particularly highlighted. The results show that CMIP6 multi‐model mean with the deep‐learning ensemble method reasonably reproduces the observed AR frequency. In most key regions, both heavy precipitation (90–99 percentile) and extremely heavy precipitation (>99 percentile) are projected to increase in a warming climate mainly due to the increased AR‐related precipitation. The AR contributions to future heavy and extremely heavy precipitation increase range from 145.1% to 280.5% and from 36.2% to 213.5%, respectively, indicating that ARs should be taken into account to better understand the future extreme precipitation changes.
DOI: 10.1029/2022jd037041
Institutions: Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Seoul National University, Chinese Academy of Sciences, Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather, Nanjing University of Information Science and Technology
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
Estimated APC paid: $3,700
Funders: National Research Foundation, National Natural Science Foundation of China, Beijing Normal University, Korea Meteorological Administration, National Research Foundation of Korea, State Key Laboratory of Earth Surface Processes and Resource Ecology, National Key Research and Development Program of China
OpenAlex ID: W4380154462