Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

Surface meltwater is widespread around the Antarctic Ice Sheet margin and has the potential to influence ice shelf stability, ice flow and ice-albedo feedbacks. Our understanding of the seasonal and multi-year evolution of Antarctic surface meltwater is limited. Attempts to generate robust meltwater cover time series have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we add a novel method for calculating visibility metrics to an existing meltwater detection method within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing time series of surface meltwater area to be automatically generated over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 to 3898 km(2) per melt season. By incorporating image visibility assessments, however, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values. We show that modelled melt predictions from a regional climate model provide a good indication of lake cover in the Amery region and that annual lake coverage is typically highest in years with a negative austral summer SAM index. Our results demonstrate that our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.

Automated summary

Research shows high interannual variability in surface meltwater cover in Antarctica, with the new method providing reliable spatiotemporal data. By incorporating image visibility assessments, more accurate estimates of surface meltwater area can be obtained, and predictions from regional climate models can effectively reflect actual conditions.