Modelling global precipitation since 250 million years ago

We have developed a novel data-driven approach to reconstruct precipitation patterns through geological time, since the supercontinent Pangea was in existence. Our approach involves linking climate-sensitive sedimentary deposits such as coal, evaporites and glacial deposits to a global plate model, reconstructed paleo-elevation maps and high-resolution General Circulation Models via Bayesian machine learning. We model the joint distribution of climate-sensitive sediments and annual precipitation through geological time, and use the dependency between sediments and modelled precipitation in the Miocene and Eocene to train the machine learning model. Our approach provides a set of 13 data-driven global paleo-precipitation maps between 14 and 249 Ma, capturing major changes in long-term annual rainfall patterns as a function of plate tectonics, paleo-elevation and climate change at a low computational cost.

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