Reducing uncertainties in projections of Antarctic ice mass loss

• Published in: The Cryosphere
• Main Authors: Durand, G., Pattyn, F.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2015
• Subjects: Antarctic; Pine Island Glacier; Antarc*; Ice Sheet
• Online Access: https://www.vliz.be/imisdocs/publications/291330.pdf

Climate model projections are often aggregated into multi-model averages of all models participating in an intercomparison project, such as the Coupled Model Inter-comparison Project (CMIP). The "multi-model" approach provides a sensitivity test to the models' structural choices and implicitly assumes that multiple models provide additional and more reliable information than a single model, with higher confidence being placed on results that are common to an ensemble. A first initiative of the ice sheet modeling community, SeaRISE, provided such multi-model average projections of polar ice sheets' contribution to sea-level rise. The SeaRISE Antarctic numerical experiments aggregated results from all models devoid of a priori selection, based on the capacity of such models to represent key ice-dynamical processes. Here, using the experimental setup proposed in SeaRISE, we demonstrate that correctly representing grounding line dynamics is essential to infer future Antarctic mass change. We further illustrate the significant impact on the ensemble mean and deviation of adding one model with a known bias in its ability of modeling grounding line dynamics. We show that this biased model can hardly be identified from the ensemble only based on its estimation of volume change, as ad hoc and untrustworthy parametrizations can force any modeled grounding line to retreat. However, tools are available to test parts of the response of marine ice sheet models to perturbations of climatic and/or oceanic origin (MISMIP, MISMIP3d). Based on recent projections of Pine Island Glacier mass loss, we further show that excluding ice sheet models that do not pass the MISMIP benchmarks decreases the mean contribution and standard deviation of the multi-model ensemble projection by an order of magnitude for that particular drainage basin.