Mass evolution of the Antarctic Peninsula over the last 2 decades from a joint Bayesian inversion

• Published in: The Cryosphere
• Main Authors: Chuter, Stephen J, Zammit-Mangion, Andrew, Rougier, Jonty, Dawson, Geoffrey J, Bamber, Jonathan L
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Zenodo 2022
• Subjects: Antarc*; Antarctic; Antarctic Peninsula; Ice Sheet; Palmer Land
• Online Access: https://doi.org/10.5194/tc-16-1349-2022

The Antarctic Peninsula has become an increasingly important component of the Antarctic Ice Sheet mass budget over the last 2 decades, with mass losses generally increasing. However, due to the challenges presented by the topography and geometry of the region, there remain large variations in mass balance estimates from conventional approaches and in assessing the relative contribution of individual ice sheet processes. Here, we use a regionally optimized Bayesian hierarchical model joint inversion approach that combines data from multiple altimetry studies (ENVISAT, ICESat, CryoSat-2 swath), gravimetry (GRACE and GRACE-FO), and localized DEM differencing observations to solve for annual mass trends and their attribution to individual driving processes for the period 2003–2019. This is first time that such localized observations have been assimilated directly to estimate mass balance as part of a wider-scale regional assessment. The region experienced a mass imbalance rate of -19 ± 1.1. Gt yr −1 between 2003 and 2019, predominantly driven by accelerations in ice dynamic mass losses in the first decade and sustained thereafter. Inter-annual variability is driven by surface processes, particularly in 2016 due to increased precipitation driven by an extreme El Niño, which temporarily returned the sector back to a state of positive mass balance. In the West Palmer Land and the English Coast regions, surface processes are a greater contributor to mass loss than ice dynamics in the early part of the 2010s. Our results show good agreement with conventional and other combination approaches, improving confidence in the robustness of mass trend estimates, and in turn, understanding of the region's response to changes in external forcing.