Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

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 weather/climate m...

Full description

Bibliographic Details
Published in:Geophysical Research Letters
Main Authors: Donald P. Cummins, Virginie Guemas, Christopher J. Cox, Michael R. Gallagher, Matthew D. Shupe
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2023
Subjects:
artificial neural networks
machine learning
Monin‐Obukhov similarity theory
surface layer
sea ice
Arctic
Geophysics. Cosmic physics
QC801-809
Sea ice
Online Access:https://doi.org/10.1029/2023GL105698
https://doaj.org/article/9874f77e6f2648f6bd4c52cd545f5241
Description
Summary: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 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.

Similar Items