Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

Cummins, Donald P.; Guemas, Virginie; Cox, Christopher J.; Gallagher, Michael R.; Shupe, Matthew D.

doi:10.1029/2023GL105698

i

Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

2023
By Cummins, Donald P. ; Guemas, Virginie ; Cox, Christopher J. ; ...

Details

Journal Title:

Geophysical Research Letters
Personal Author:

Cummins, Donald P. ; Guemas, Virginie ; Cox, Christopher J. ; Gallagher, Michael R. ; Shupe, Matthew D.
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; PSL (Physical Sciences Laboratory)
Description:

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.
Keywords:

General Earth And Planetary Sciences Geophysics General Earth And Planetary Sciences General Earth And Planetary Sciences
Source:

Geophysical Research Letters, 50(23)
DOI:

https://doi.org/10.1029/2023GL105698
ISSN:

0094-8276 ; 1944-8007
Format:

PDF
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha256:474fc0c63a8be98d38fb8ee753c7eb7c29a41b574981fffb89ae82e0b1e73fcd
Download URL:

https://repository.library.noaa.gov/view/noaa/56648/noaa_56648_DS1.pdf
File Type:

[PDF - 607.61 KB ]

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