Anthropogenic fingerprints in daily precipitation revealed by deep learning

Details

• Journal Title:
  Nature
• Personal Author:
  Ham, Yoo-Geun ; Kim, Jeong-Hwan ; Min, Seung-Ki ; Kim, Daehyun ; Li, Tim ; Timmermann, Axel ; Stuecker, Malte F.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research)
• 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:
  https://doi.org/10.1038/s41586-023-06474-x
• ISSN:
  0028-0836 ; 1476-4687
• Format:
  PDF
• Publisher:
  Springer Science and Business Media LLC
• Document Type:
  Journal Article
• Funding:
  Grant no. NA18OAR4310298 ; Grant no. NA20OAR4310445 ; Grant no. NA21OAR4310343 ; Grant no. NA22OAR4310608
• License:
  https://creativecommons.org/licenses/by/4.0
• Rights Information:
  CC BY
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:07c18e86a94b5c4144db0177f0b430b0122e00eb8bb2516e8f85ada2590c78ca3488608e97667e59ca048f38b88cfb66080350b79955228508e31453147af5d8
• Download URL:
  https://repository.library.noaa.gov/view/noaa/64818/noaa_64818_DS1.pdf
• File Type:
  [PDF - 9.65 MB ]