Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks

作者：Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Marc Bocquet, Einar Ólason · 发表于：Journal of Computational Science · 年份：2024 · DOI：10.1016/j.jocs.2024.102231 · 被引用次数：13 · 研究领域：Arctic and Antarctic ice dynamics、Cryospheric studies and observations、Climate change and permafrost

Accurate simulation of sea ice is critical for predictions of future Arctic sea ice loss, looming climate change impacts, and more. A key feature in Arctic sea ice is the formation of melt ponds. Each year melt ponds develop on the surface of the ice and primarily via affecting the albedo, they have an enormous effect on the energy budget and climate of the Arctic. As melt ponds are subgrid scale and their evolution occurs due to a number of competing, poorly understood factors, their representation in models is parametrised. Sobol sensitivity analysis, a form of variance based global sensitivity analysis is performed on an advanced melt pond parametrisation (MPP), in Icepack, a state-of-the-art thermodynamic column sea ice model. Results show that the model is very sensitive to changing its uncertain MPP parameter values, and that these have varying influences over model predictions both spatially and temporally. Such extreme sensitivity to parameters makes MPPs a potential source of prediction error in sea-ice model, given that the (often many) parameters in MPPs are usually poorly known. Machine learning (ML) techniques have shown great potential in learning and replacing subgrid scale processes in models. Given the complexity of melt pond physics and the need for accurate parameter values in MPPs, we propose an alternative data-driven MPPs that would prioritise the accuracy of albedo predictions. In particular, we constructed MPPs based either on linear regression or on n...