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Workshop: Tackling Climate Change with Machine Learning

Identifying latent climate signals using sparse hierarchical Gaussian processes

Matt Amos · Thomas Pinder · Paul Young


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.

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