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Anthropogenic fingerprints in daily precipitation revealed by deep learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ham, Yoo-Geun | - |
dc.contributor.author | Kim, Jeong-Hwan | - |
dc.contributor.author | Min, Seung-Ki | - |
dc.contributor.author | Kim, Daehyun | - |
dc.contributor.author | Li, Tim | - |
dc.contributor.author | Timmermann, Axel | - |
dc.contributor.author | Stuecker, Malte F. | - |
dc.date.accessioned | 2024-05-07T01:29:56Z | - |
dc.date.available | 2024-05-07T01:29:56Z | - |
dc.date.created | 2024-04-17 | - |
dc.date.created | 2024-04-17 | - |
dc.date.issued | 2023-10 | - |
dc.identifier.citation | Nature, Vol.622 No.7982, pp.301-307 | - |
dc.identifier.issn | 0028-0836 | - |
dc.identifier.uri | https://hdl.handle.net/10371/200925 | - |
dc.description.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 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. | - |
dc.language | 영어 | - |
dc.publisher | Nature Research | - |
dc.title | Anthropogenic fingerprints in daily precipitation revealed by deep learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.1038/s41586-023-06474-x | - |
dc.citation.journaltitle | Nature | - |
dc.identifier.wosid | 001064862900010 | - |
dc.identifier.scopusid | 2-s2.0-85169167193 | - |
dc.citation.endpage | 307 | - |
dc.citation.number | 7982 | - |
dc.citation.startpage | 301 | - |
dc.citation.volume | 622 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Kim, Daehyun | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | CLIMATE-CHANGE | - |
dc.subject.keywordPlus | QUANTIFICATION | - |
dc.subject.keywordPlus | CONSTRAINTS | - |
dc.subject.keywordPlus | EXTREMES | - |
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- College of Natural Sciences
- Department of Earth and Environmental Sciences
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