Machine Learning Eliminates Reanalysis Warm Bias and Reveals Weaker Winter Surface Cooling Over Arctic Sea Ice
Akil Hossain, Paul Keil, Harsh Grover, Ian M. Brooks, Christopher J. Cox, Michael R. Gallagher, Mats A. Granskog, Heather Guy, Stephen R. Hudson, P. Ola G. Persson, Matthew D. Shupe, Michael Tjernström, Jutta Vüllers, Von P. Walden, Felix PithanAbstract
The surface energy budget governs Arctic sea‐ice growth/melt, yet observations are sparse, and reanalysis data sets suffer from systematic biases. Here, we train a neural network with observational data to bias‐correct hourly ERA5 fluxes over Arctic ice‐covered regions (≥70°N; sea‐ice concentration >80%) for 1994–2024. Training data cover two full seasonal cycles and different sea‐ice regimes. The neural network reduces RMSE for net shortwave radiation by ∼40%, downward longwave radiation by ∼16% and the total surface energy budget by ∼55%, eliminating the wintertime warm bias of ∼4 K in ERA5. Wintertime surface cooling is reduced by ∼50%, yielding thermodynamic ice‐growth estimates of ∼80–120 cm, consistent with SMOS–CryoSat satellite thickness increases and in contrast to the 150–200 cm growth implied by ERA5. Our bias‐corrected data capture the observed clear/cloudy states of the winter boundary layer and can be used to study Arctic climatology, evaluate climate models and drive sea‐ice‐ocean models.