The GlobalMass project started in August 2016 and much of our work to date has been focussed on developing and testing the Bayesian Hierarchical Model (BHM) – the statistical framework that is fundamental to the project aim and objectives. In particular, we have translated the BHM approach to a global (and therefore spherical) grid and successfully implemented it via modifications to the functionality of a statistical computing package (read more).
We have also achieved some other notable outputs:
• Creation of a global GPS dataset to provide a ‘clean’ signal of glacial isostatic adjustment (GIA) – We developed an automated method for processing GPS time series to isolate the GIA signal and hence provide an observational estimate of global GIA uplift rates (read more)
• Extension of the BHM approach to investigate ice mass trends for Antarctica – We extended mass balance trends calculated for the Antarctic Ice Sheet using a BHM approach up to 2015, and contributed these results to the first World Climate Research Programme (WCRP) global sea level budget (GSLB) assessment (read more).
• New estimate of land ice contribution to sea level rise – We produced a new synthesis of land ice mass trends during the satellite era (1992 to 2016) focusing on its contribution to sea level rise (read more)
• New estimate of ice sheet contribution to future sea level rise – We used an approach called Structured Expert Judgement, which combines multiple experts’ individual predictions to estimate future ice sheet contributions to sea level rise under different temperature scenarios (read more)