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).
We submitted our mid-project scientific report to the European Research Council in March 2019, and this is now available on the European Commission’s CORDIS website.