Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning

Abstract Although global circulation models (GCMs) have been used for the reconstruction of precipitation for selected geological time slices, there is a lack of a coherent set of precipitation models for the Mesozoic-Cenozoic period (the last 250 million years). There has been dramatic climate change during this time period capturing a super-continent hothouse climate, and continental breakup and dispersal associated with successive greenhouse and ice-house climate periods. We present an approach that links climate-sensitive sedimentary deposits such as coal, evaporites and glacial deposits to a global plate model, reconstructed paleo-elevation maps and high-resolution GCMs 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 precipitation to improve the models predictive accuracy. 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.