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Anthropogenic fingerprints in daily precipitation revealed by deep learning

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dc.contributor.authorHam, Yoo-Geun-
dc.contributor.authorKim, Jeong-Hwan-
dc.contributor.authorMin, Seung-Ki-
dc.contributor.authorKim, Daehyun-
dc.contributor.authorLi, Tim-
dc.contributor.authorAxel Timmermann-
dc.contributor.authorStuecker, Malte F.-
dc.date.accessioned2023-11-22T22:00:23Z-
dc.date.available2023-11-22T22:00:23Z-
dc.date.created2023-10-24-
dc.date.issued2023-08-
dc.identifier.issn0028-0836-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/14192-
dc.description.abstractAccording 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.-
dc.language영어-
dc.publisherNATURE PORTFOLIO-
dc.titleAnthropogenic fingerprints in daily precipitation revealed by deep learning-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid001064862900010-
dc.identifier.scopusid2-s2.0-85169167193-
dc.identifier.rimsid81998-
dc.contributor.affiliatedAuthorAxel Timmermann-
dc.identifier.doi10.1038/s41586-023-06474-x-
dc.identifier.bibliographicCitationNATURE, v.622, no.7982, pp.301 - 307-
dc.relation.isPartOfNATURE-
dc.citation.titleNATURE-
dc.citation.volume622-
dc.citation.number7982-
dc.citation.startPage301-
dc.citation.endPage307-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryMultidisciplinary Sciences-
dc.subject.keywordPlusCLIMATE-CHANGE-
dc.subject.keywordPlusQUANTIFICATION-
dc.subject.keywordPlusCONSTRAINTS-
dc.subject.keywordPlusEXTREMES-
Appears in Collections:
Center for Climate Physics(기후물리 연구단) > 1. Journal Papers (저널논문)
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