Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning. (May 2021)

• Record Type: Journal Article
• Authors: Chandra, Rohitash; Cripps, Sally; Butterworth, Nathaniel; Muller, R. Dietmar
• Is Part Of: Environmental modelling & software. Volume 139(2021)
• Publication Date: 2021-05
• Subjects: Paleo-climate -- Gaussian process -- Bayesian methods -- Forecasting -- Precipitation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105002
• Languages: English
• ISSNs: 1364-8152

Abstract

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 supercontinent 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 model's 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. Highlights: .We present an approach that links sedimentary deposits to reconstructed paleo-elevation maps. .We use Bayesian machine learning to model the joint distribution of climate-sensitive sediments and annual precipitation through geological time. .Our approach provides a set of 13 data-driven global paleo-precipitation maps between 14 and 249 Ma. .We capture major changes in paleo-precipitation as a function of plate tectonics, paleo-elevation and climate change.