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Identifying latent climate signals using sparse hierarchical Gaussian processes (Papers Track)

Matt Amos (Lancaster University); Thomas Pinder (Lancaster University); Paul Young (Lancaster University)

Paper PDF Slides PDF Recorded Talk NeurIPS 2022 Poster Topia Link Cite

Climate Science & Modeling Causal & Bayesian Methods Uncertainty Quantification & Robustness

Abstract

Extracting latent climate signals from multiple climate model simulations is important to estimate future climate change. To tackle this we develop a sparse hierarchical Gaussian process (SHGP), which probabilistically learns a latent distribution from a set of vectors. We use this to predict the latent surface temperature change globally and for central England from an ensemble of climate models, in a scalable manner and with robust uncertainty propagation.

Recorded Talk (direct link)

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