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Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science

Published online by Cambridge University Press:  01 January 2022

Suzanne Kawamleh
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Abstract

Scientists and decision makers rely on climate models for predictions concerning future climate change. Traditionally, physical processes that are key to predicting extreme events are either directly represented (resolved) or indirectly represented (parameterized). Scientists are now replacing physically based parameterizations with neural networks that do not represent physical processes directly or indirectly. I analyze the epistemic implications of this method and argue that it undermines the reliability of model predictions. I attribute the widespread failure in neural network generalizability to the lack of process representation. The representation of climate processes adds significant and irreducible value to the reliability of climate model predictions.

Type
Computer Simulation and Computer Science
Information
Philosophy of Science , Volume 88 , Issue 5 , December 2021 , pp. 1008 - 1020
Copyright
Copyright 2021 by the Philosophy of Science Association. All rights reserved.

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Footnotes

†

I would like to thank Gary Ebbs, Elisabeth Lloyd, and Greg Lusk for their detailed comments on this article. This article also benefited greatly from feedback from participants of the Data Science in Climate and Climate Impact Research Workshop at ETH Zurich, particularly from Julie Jebeile, Tim Raz, and Vincent Lam.

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