Understanding climate change with statistical downscaling and machine learning

Jebeile, Julie; Lam, Vincent; Räz, Tim (2020). Understanding climate change with statistical downscaling and machine learning. Synthese Springer Netherlands 10.1007/s11229-020-02865-z

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

06 Faculty of Humanities > Department of Art and Cultural Studies > Institute of Philosophy
10 Strategic Research Centers > Oeschger Centre for Climate Change Research (OCCR)

UniBE Contributor:

Jebeile, Julie Alia Nina and Lam, Vincent Minh Duc

Subjects:

100 Philosophy
100 Philosophy > 120 Epistemology
500 Science

ISSN:

0039-7857

Publisher:

Springer Netherlands

Funders:

[UNSPECIFIED] Swiss National Science Foundation

Projects:

[UNSPECIFIED] The epistemology of climate change

Language:

English

Submitter:

Vincent Minh Duc Lam

Date Deposited:

12 Feb 2021 11:17

Last Modified:

12 Feb 2021 11:26

Publisher DOI:

10.1007/s11229-020-02865-z

BORIS DOI:

10.48350/152020

URI:

https://boris.unibe.ch/id/eprint/152020

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