The GlobalMass team has had a new paper published in Environmetrics: ‘Bayesian model–data synthesis with an application to global glacio‐isostatic adjustment‘.
The paper describes the key methodological developments that we have made to the Bayesian Hierarchical Modelling (BHM) framework to apply it to the investigation of global sea level rise. Specifically, it addresses the challenges associated with (i) the large (global) scale of the problem and (ii) the need to accommodate geospatial processes that are non-stationary globally (i.e. that do not tend towards a constant long-term average or spread through time).
The developments are tested and illustrated by showing how the BHM can combine a computer model simulation and GPS measurements to produce an improved global prediction of glacio‐isostatic adjustment (GIA).
A plain language summary is available here.
Sha Z, Rougier JC, Schumacher M and Bamber JL. (2018) Bayesian model–data synthesis with an application to global glacio-isostatic adjustment. Environmetrics. 2018; e2530. (DOI: 10.1002/env.2530)