Mass evolution of the Antarctic Peninsula over the last two decades from a joint Bayesian inversion
The Antarctic Peninsula has been an increasingly significant contributor to Antarctic Ice Sheet mass losses over the last two decades. However, due to the challenges presented by the topography and geometry of the region, there remain large variations in mass balance estimates from conventional appr...
| Main Authors: | , , , , |
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| Format: | Text |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/tc-2021-178 https://tc.copernicus.org/preprints/tc-2021-178/ |
| Summary: | The Antarctic Peninsula has been an increasingly significant contributor to Antarctic Ice Sheet mass losses over the last two decades. 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 optimised Bayesian Hierarchical Model joint inversion approach, that combines data from multiple altimetry studies (ENVISAT, ICESat-1, CryoSat-2 swath), gravimetry (GRACE and GRACE-FO) and localised DEM differencing observations, to solve for annual mass trends and their attribution to individual driving processes for the period 2003–2019. 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, although both processes are acting simultaneously. 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. |
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