Understanding climate change with statistical downscaling and machine learning.
Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based 'black boxes' that may not provide any understanding. However, in many ways, understanding seems indispensable to...
| Publicado en: | Synthese Vol. 199; no. 1/2; pp. 1877 - 1898 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=153650856&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 153650856 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Dec2021 vid: 199 iid: 1/2 pid: 237 pub: Springer Nature artinfo: ui: 153650856 10.1007/s11229-020-02865-z ppf: 1877 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 308KB tig: atl: Understanding climate change with statistical downscaling and machine learning. aug: au: Jebeile, Julie Lam, Vincent Räz, Tim affil: Institute of Philosophy, University of Bern, Länggassstrasse 49a, 3012, Bern, Switzerland Oeschger Centre for Climate Change Research, University of Bern, Hochschulstrasse 4, 3012, Bern, Switzerland School of Historical and Philosophical Inquiry, The University of Queensland, 4072, St Lucia, QLD, Australia Institute of Biomedical Ethics and History of Medicine, University of Zürich, Winterthurerstrasse 30, 8006, Zürich, Switzerland su: Downscaling (Climatology) Machine learning Atmospheric models Statistical learning sug: subj: Downscaling (Climatology) Machine learning Atmospheric models Statistical learning keyword: Climate change Climate models Deep neural networks Dynamical and statistical downscaling Understanding ab: Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based 'black boxes' that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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