부산대학교 도서관

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 weather/climate models using bulk formulae based on the Monin‐Obukhov Similarity Theory, 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 models. Plain Language Summary: Heat can make its way into or out of sea ice via unpredictable air movements, known as turbulence, near the sea surface. In order to predict how quickly Arctic sea ice will melt in the future, we need to know how much heat the turbulence can transport in different weather conditions. Traditionally, turbulence calculations have been performed using sophisticated mathematical formulae from physics. In this study, we test an alternative method for predicting turbulent heat exchange: a computer algorithm known as an artificial neural network. By showing turbulence data, measured in the Arctic during previous scientific expeditions, to the network, it can be "trained" to make predictions in a process known as machine learning. We compare turbulence measurements, taken above sea ice in the recent MOSAiC expedition, with predictions from trained neural networks. We find that the neural networks are better than the traditional physics at predicting what the scientists at MOSAiC observed. The trained neural networks have been made publicly available so that they can be used by scientists for predicting climate change. Key Points: Neural networks trained on previous Arctic campaigns predict surface turbulent fluxes from MOSAiC more accurately than bulk methodsUpdated parametrizations using the MOSAiC data have been developed and implemented in Fortran for deployment in weather/climate modelsModest performance gains (up to +7% R2) from recalibration on MOSAiC indicate good generalizability to the pan‐Arctic sea ice domain [ABSTRACT FROM AUTHOR]