Potential for equation discovery with AI in the climate sciences

Huntingford, Chris ORCID: https://orcid.org/0000-0002-5941-7770; Nicoll, Andrew J.; Klein, Cornelia ORCID: https://orcid.org/0000-0001-6686-0458; Ahmad, Jawairia A.. 2024 Potential for equation discovery with AI in the climate sciences. Earth System Dynamics Discussions, Preprint esd-2024-30. 10.5194/esd-2024-30

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Abstract/Summary

Climate change and Artificial Intelligence (AI) are both attracting great interest across society. There is also substantial interest in merging the two sciences, with evidence already that AI can identify earlier precursors to extreme weather events. There are a range of AI algorithms, and selection of the most appropriate one maximizes the amount of additional understanding extractable for any dataset. However, most AI algorithms are statistically based and even with careful splitting between data for training and testing, they arguably remain as emulators. Emulators may make unreliable predictions when driven by out-of-sample forcing and climate change is an example of this, requiring understanding responses to atmospheric Greenhouse Gas (GHG) concentrations that may be substantially higher than present or the recent past. Notable, though, is the emerging AI technique of “equation discovery”. AI-derived equations from data also does not automatically guarantee good performance for new forcing regimes. However, access to equations rather than a statistical emulator guides system understanding, as their variables and parameters often have a better interpretation. Better process knowledge enables judgements as to whether equations are trusted under extrapolation. For many climate system attributes, descriptive equations are not yet fully available or may be unreliable. This uncertainty is hindering the development of Earth System Models (ESMs) which remain the main tool for projections of large-scale environmental change as GHGs rise. Here, we make the case for using AI-driven equation discovery in climate research, given that its outputs are more interpretable in terms of processes. As ESMs are based around the numerical discretisation of equations that describe climate components, equation discovery from new datasets provides a format amenable to direct inclusion into such models where representation of environmental systems is missing. We present three illustrative examples of how AI-led equation discovery may advance future climate science research. These are generating new equations related to atmospheric convection, parameter derivation for existing equations of the terrestrial carbon cycle, and (additional to ESM improvement) the creation of simplified models of large-scale oceanic features to assess Tipping Point (TP) risks.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.5194/esd-2024-30
UKCEH and CEH Sections/Science Areas:	Hydro-climate Risks (Science Area 2017-)
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Ecology and Environment Computer Science
Related URLs:	Other
Date made live:	13 Sep 2024 13:09 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538022

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