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Timmermann, Axel
기후물리 연구단
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Anthropogenic fingerprints in daily precipitation revealed by deep learning

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Title
Anthropogenic fingerprints in daily precipitation revealed by deep learning
Author(s)
Ham, Yoo-Geun; Kim, Jeong-Hwan; Min, Seung-Ki; Kim, Daehyun; Li, Tim; Axel Timmermann; Stuecker, Malte F.
Publication Date
2023-08
Journal
NATURE, v.622, no.7982, pp.301 - 307
Publisher
NATURE PORTFOLIO
Abstract
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. Deep learning using a convolutional neural network trained with daily precipitation fields and annual global mean surface air temperature data demonstrates that anthropogenically induced climate change has a detectable effect on daily hydrological fluctuations.
URI
https://pr.ibs.re.kr/handle/8788114/14192
DOI
10.1038/s41586-023-06474-x
ISSN
0028-0836
Appears in Collections:
Center for Climate Physics(기후물리 연구단) > 1. Journal Papers (저널논문)
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