<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/8AFEFC3A-355D-47E5-B5B7-168942BC8F01"><efrbr-work:titleOfTheWork>Predicting global patterns of long-term climate change from short-term simulations using machine learning</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/8AFEFC3A-355D-47E5-B5B7-168942BC8F01"><efrbr-expression:titleOfTheExpression>Predicting global patterns of long-term climate change from short-term simulations using machine learning</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Peer-Reviewed Journal Publication
            Δημοσίευση σε Περιοδικό με Κριτές
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2021-04-20</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2020</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:otherDistinguishingCharacteristic>Supplementary information:
https://static-content.springer.com/esm/art%3A10.1038%2Fs41612-020-00148-5/MediaObjects/41612_2020_148_MOESM1_ESM.pdf</efrbr-expression:otherDistinguishingCharacteristic><efrbr-expression:summarizationOfContent>Understanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art climate models remains a central bottleneck in this endeavour. Here we introduce a machine learning approach, which utilises a unique dataset of existing climate model simulations to learn relationships between short-term and long-term temperature responses to different climate forcing scenarios. This approach not only has the potential to accelerate climate change projections by reducing the costs of scenario computations, but also helps uncover early indicators of modelled long-term climate responses, which is of relevance to climate change detection, predictability, and attribution. Our results highlight challenges and opportunities for data-driven climate modelling, especially concerning the incorporation of even larger model datasets in the future. We therefore encourage extensive data sharing among research institutes to build ever more powerful climate response emulators, and thus to enable faster climate change projections.</efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="journal name">npj Climate and Atmospheric Science</efrbr-expression:note><efrbr-expression:note type="journal volume">3</efrbr-expression:note><efrbr-expression:note type="journal number">1</efrbr-expression:note></efrbr-expression:expression><efrbr-manifestation:manifestation identifier="https://dias.library.tuc.gr/view/88780 https://dias.library.tuc.gr/view/88781"><efrbr-manifestation:titleOfTheManifestation>Mansfield_et_al._supplementary information.pdf</efrbr-manifestation:titleOfTheManifestation><efrbr-manifestation:titleOfTheManifestation>Mansfield_et_al._npj Clim. 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Atmos. Sci._3(1)_2020.pdf</efrbr-manifestation:titleOfTheManifestation><efrbr-manifestation:publicationDistribution><efrbr-manifestation:placeOfPublicationDistribution type="distribution">Chania [Greece]</efrbr-manifestation:placeOfPublicationDistribution><efrbr-manifestation:publisherDistributor type="distributor">Library of TUC</efrbr-manifestation:publisherDistributor><efrbr-manifestation:dateOfPublicationDistribution>2021-04-20</efrbr-manifestation:dateOfPublicationDistribution></efrbr-manifestation:publicationDistribution><efrbr-manifestation:publicationDistribution><efrbr-manifestation:placeOfPublicationDistribution type="distribution">Chania [Greece]</efrbr-manifestation:placeOfPublicationDistribution><efrbr-manifestation:publisherDistributor type="distributor">Library of TUC</efrbr-manifestation:publisherDistributor><efrbr-manifestation:dateOfPublicationDistribution>2021-04-20</efrbr-manifestation:dateOfPublicationDistribution></efrbr-manifestation:publicationDistribution><efrbr-manifestation:formOfCarrier>application/pdf</efrbr-manifestation:formOfCarrier><efrbr-manifestation:formOfCarrier>application/pdf</efrbr-manifestation:formOfCarrier><efrbr-manifestation:extentOfTheCarrier>8.4 MB</efrbr-manifestation:extentOfTheCarrier><efrbr-manifestation:extentOfTheCarrier>1.7 MB</efrbr-manifestation:extentOfTheCarrier><efrbr-manifestation:accessRestrictionsOnTheManifestation>free</efrbr-manifestation:accessRestrictionsOnTheManifestation><efrbr-manifestation:accessRestrictionsOnTheManifestation>free</efrbr-manifestation:accessRestrictionsOnTheManifestation></efrbr-manifestation:manifestation><efrbr-person:person identifier="708513E6-6313-4B11-AE14-DAC050D7E585"><efrbr-person:nameOfPerson vocabulary="">
            Mansfield Laura
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            Nowack Peer
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            Kasoar Matthew
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            Everitt Richard
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            Collins William
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            Voulgarakis Apostolos
            Βουλγαρακης Αποστολος
         </efrbr-person:nameOfPerson></efrbr-person:person><efrbr-corporateBody:corporateBody identifier="https://v2.sherpa.ac.uk/id/publisher/62037"><efrbr-corporateBody:nameOfTheCorporateBody vocabulary="S/R:PUBLISHERS">
            Springer Nature
         </efrbr-corporateBody:nameOfTheCorporateBody></efrbr-corporateBody:corporateBody><efrbr-concept:concept identifier="9B8FD5FC-7040-4F31-8C0B-1A82A1EE6EE7"><efrbr-concept:termForTheConcept>
            Atmospheric science
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="FAD1363F-B8D7-47C9-A747-347371BFB3E9"><efrbr-concept:termForTheConcept>
            Climate change
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="B1E1A605-8809-4475-9614-A7F8A028EAE0"><efrbr-concept:termForTheConcept>
            Climate-change impacts
         </efrbr-concept:termForTheConcept></efrbr-concept:concept><efrbr-concept:concept identifier="51CE2203-FB9F-4728-A476-54E8692F2AC6"><efrbr-concept:termForTheConcept>
            Climate-change mitigation
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            Projection and prediction
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