Understanding surface melt in Antarctica and implications for future ice sheet evolution

Global mean sea level (GMSL) is projected to continue rising this century, potentially impacting up to 1 billion people by 2050 (Lee et al., 2023). Antarctica, as the Earth’s largest ice reservoir with a sea level equivalent volume of around 58 meters (Morlighem et al., 2020), could significantly impact the magnitude of future sea level rise. However, how much sea level rise will be caused by the Antarctic Ice Sheet (AIS) is highly uncertain (Rintoul et al., 2018), partly because of unclear future stability of Antarctic ice shelves. Surface melt has been identified as a crucial factor contributing to ice shelf collapse (Rott et al., 1996; van den Broeke, 2005; Trusel et al., 2015) through mechanisms of hydrofracturing (Lai et al., 2020). Projections have shown that the magnitude of surface melt will increase and the melt extent will be widespread (Trusel et al., 2015; Gilbert and Kittel, 2021). However, the distribution of future surface melt is not well known at high spatial resolutions. This is because climate models that employ comprehensive surface energy balance (SEB) schemes are too computationally expensive to run at fine resolutions (van den Broeke et al., 2023). By contrast, temperature-index models, such as the positive degree-day (PDD) model, are computationally efficient and have been utilized for snowmelt estimation for more than 90 years (Rango and Martinec, 1995), offering an alternative approach for future melt projections. However, the PDD parameters commonly used for AIS modelling are typically based on those derived for the Greenland Ice Sheet. An assessment of the viability of the PDD modelling approach for AIS surface melt projections has not yet been conducted, and the accuracy of the PDD model in estimating surface melting on the AIS remains unclear.

This thesis first comprehensively assesses the PDD model for estimating surface melt on the AIS. The results from the assessment show that a PDD model with spatially-uniform parameters, when compared to estimates of surface melt days from satellites and surface melt rates from regional climate models over the past four decades, lacks accuracy in reconstructing AIS surface melt. Therefore, in order to improve the accuracy of the PDD model for AIS surface melt projections, I develop a novel grid-cell-level spatially-distributed PDD model by minimizing the error with respect to satellite estimates and SEB model outputs on each individual computing cell (minimal RMSE approach) for the past four decades. Evaluations of this PDD model demonstrate the robustness of the minimal RMSE approach and the applicability of the PDD model to warmer climate scenarios. To calculate future melting, I incorporate 100-meter-resolution topographic variability to downscale forcing temperature fields derived from ERA5, CMIP5, and CMIP6. The resultant 100-meter-resolution AIS surface melt projections show that the Larsen-C, Shackleton, Thwaites, and Totten ice shelves will all be at high risk of collapse this century due to increased surface melt if emissions follow the SSP3-7.0 pathway. Trajectories of latitudinal melt migration calculated from these high resolution AIS surface melt projections suggest that SSP1-2.6 is likely the only emissions pathway under which future AIS surface melt can be stabilized at present levels.