Anthropogenic fingerprints in daily precipitation revealed by deep learning.

ABSTRACT: According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe^1-4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales^3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)⁵ with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations⁶. After applying the algorithm to the observational record, we found that the daily preci

SUBMITTER: Ham YG 

PROVIDER: S-EPMC10567562 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature