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Why we need lower-performance climate models

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  • Ryan O’Loughlin

    (Queens College, CUNY)

Abstract

All models are wrong, but models are not all equally wrong. Indeed, they can be wrong to different degrees and in entirely different ways. Here, we show that GCMs which are lower-performance (for particular tasks and applications) play a crucial role in climate science research. That is, lower-performance models help scientists gain knowledge they would otherwise lack, a point that is often underappreciated and has been under-theorized. More specifically, in the climate science literature, we see that lower-performance models help constrain the estimates of climate variables, lower-performance models provide data to test model weighting schemes, and lower-performance models serve as evidence to help resolve model-data discrepancies. This implies that (i) lower-performance models ought not be eliminated from analysis too hastily and (ii) the value of multi-model ensembles goes beyond exploring structural uncertainty and includes the counterintuitive generation of new knowledge via, in part, lower-performance models. As a result of (ii), model intercomparison efforts require reappraisal, particularly when deciding how to allocate modeling resources.

Suggested Citation

  • Ryan O’Loughlin, 2024. "Why we need lower-performance climate models," Climatic Change, Springer, vol. 177(2), pages 1-20, February.
    • Handle: RePEc:spr:climat:v:177:y:2024:i:2:d:10.1007_s10584-023-03661-7
    DOI: 10.1007/s10584-023-03661-7
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    References listed on IDEAS

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