Abstract
Reliable boundary-layer turbulence parametrizations for polar conditions
are needed to reduce uncertainty in projections of Arctic sea ice
melting rate and its potential global repercussions. Surface turbulent
fluxes of sensible and latent heat are typically represented in climate
models using bulk formulae based on the Monin-Obukhov Similarity Theory
(MOST), sometimes finely tuned to high stability conditions and the
potential presence of sea ice. In this study, we test the performance of
new, machine-learning (ML) flux parametrizations, using an advanced
polar-specific bulk algorithm as a baseline. Neural networks, trained on
observations from previous Arctic campaigns, are used to predict surface
turbulent fluxes measured over sea ice as part of the recent MOSAiC
expedition. The ML parametrizations outperform the bulk at the MOSAiC
sites, with RMSE reductions of up to 70 percent. We provide a plug-in
Fortran implementation of the neural networks for use in climate models.