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Accurate simulation of sea ice is critical for predictions of future Arcticsea ice loss, looming climate change impacts, and more. A key feature in Arcticsea ice is the formation of melt ponds. Each year melt ponds develop on thesurface of the ice and primarily via affecting the albedo, they have anenormous effect on the energy budget and climate of the Arctic. As melt pondsare 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 sensitivityanalysis is performed on an advanced melt pond parametrisation (MPP), inIcepack, a state-of-the-art thermodynamic column sea ice model. Results showthat the model is very sensitive to changing its uncertain MPP parametervalues, and that these have varying influences over model predictions bothspatially and temporally. Such extreme sensitivity to parameters makes MPPs apotential source of prediction error in sea-ice model, given that the (oftenmany) parameters in MPPs are usually poorly known.  Machine learning (ML) techniques have shown great potential in learning andreplacing subgrid scale processes in models. Given the complexity of melt pondphysics and the need for accurate parameter values in MPPs, we propose analternative data-driven MPPs that would prioritise the accuracy of albedopredictions. In particular, we constructed MPPs based either on linearregression or on nonlinear neural networks, and investigate if they couldsubstitute the original physics-based MPP in Icepack.  Our results shown that linear regression are insufficient as emulators,whilst neural networks can learn and emulate the MPP in Icepack very reliably.Icepack with the MPPs based on neural networks only slightly deviates from theoriginal Icepack and overall offers the same long term model behaviour.

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