i
Anthropogenic fingerprints in daily precipitation revealed by deep learning
-
2023
-
-
Source: Nature, 622(7982), 301-307
Details:
-
Journal Title:Nature
-
Personal Author:
-
NOAA Program & Office:
-
Description:According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1–4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. 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 precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.
-
Source:Nature, 622(7982), 301-307
-
DOI:
-
ISSN:0028-0836;1476-4687;
-
Format:
-
Publisher:
-
Document Type:
-
Funding:
-
License:
-
Rights Information:CC BY
-
Compliance:Library
-
Main Document Checksum:
-
Download URL:
-
File Type: