Learning Why: Data-Driven Causal Evaluations of Climate Models (Proposals Track)

Jeffrey J Nichol (University of New Mexico); Matthew Peterson (Sandia National Laboratories); George M Fricke (UNM); Kara Peterson (Sandia National Laboratories)

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Climate Science & Modeling Causal & Bayesian Methods


We plan to use nascent data-driven causal discovery methods to find and compare causal relationships in observed data and climate model output. We will look at ten different features in the Arctic climate collected from public databases and from the Energy Exascale Earth System Model (E3SM). In identifying and comparing the resulting causal networks, we hope to find important differences between observed causal relationships and those in climate models. With these, climate modeling experts will be able to improve the coupling and parameterization of E3SM and other climate models.