P2C2: Leveraging Bayesian Approaches to Link Reconstructed, Observed, and Projected Meteorological Drought while Accounting for Inherent Data Biases

Ohio State University, The, Columbus OH

Investigators

Abstract

This project aims to investigate past, present and future meteorological droughts. Specifically, this project will address the issue of incompatible data biases e.g. (temporal resolution) from these three data sources by testing two fundamental questions: (1) do errors in proxy-based drought reconstructions significantly differ from those in Global Climate Model (GCM)-derived climate projections, particularly for higher order statistics, and (2) can a unified statistical modeling framework explicitly account for inherent data source differences to estimate drought severity from the pre-industrial past, through the instrumental period, and into the future. The researcher will apply novel statistical methods (Hierarchical Bayesian modelling) to merge drought reconstructions, observations, and projections, and generate a gridded drought time-series beginning 2000 years ago and extending to the end of this century (2100). The potential Broader Impacts include developing a visualization website that will allow the public to view 2100 years of drought index time series for any gridded cell, in North America which will also include model and climate uncertainty. A conference-based workshop with hands-on code demonstrations will be organized to train scientists on the statistical modelling framework developed in the project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.