Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets.

ABSTRACT: The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can acco

SUBMITTER: Madakumbura GD 

PROVIDER: S-EPMC8260601 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature