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

• Main Authors: Driscoll, Simon, Carrassi, Alberto, Brajard, Julien, Bertino, Laurent, Bocquet, Marc, Olason, Einar
• Format: Report
• Language: unknown
• Published: arXiv 2023
• Subjects: Computational Physics physics.comp-ph; Atmospheric and Oceanic Physics physics.ao-ph; FOS Physical sciences; Arctic; albedo; Climate change; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2304.05407; https://arxiv.org/abs/2304.05407

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 ...