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Anthropogenic fingerprints in daily precipitation revealed by deep learning

Cited 6 time in Web of Science Cited 8 time in Scopus

Ham, Yoo-Geun; Kim, Jeong-Hwan; Min, Seung-Ki; Kim, Daehyun; Li, Tim; Timmermann, Axel; Stuecker, Malte F.

Issue Date
Nature Research
Nature, Vol.622 No.7982, pp.301-307
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 simulations 6. 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.
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  • College of Natural Sciences
  • Department of Earth and Environmental Sciences
Research Area Climate Change, Earth & Environmental Data, Severe Weather, 기후과학, 위험기상, 지구환경 데이터과학


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