Cummins, D.P. orcid.org/0000-0003-3600-5367, Guemas, V., Cox, C.J. et al. (2 more authors) (2023) Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning. Geophysical Research Letters, 50 (23). e2023GL105698. ISSN 0094-8276
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | artificial neural networks; machine learning; Monin-Obukhov similarity theory; surface layer; sea ice; Arctic |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Jul 2024 10:45 |
Last Modified: | 12 Jul 2024 10:45 |
Status: | Published |
Publisher: | American Geophysical Union (AGU) |
Identification Number: | 10.1029/2023gl105698 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214215 |