Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

• Published in: Geophysical Research Letters
• Main Authors: Cummins, Donald, P, Guemas, Virginie, Cox, Christopher, J, Gallagher, Michael, R, Shupe, Matthew, D
• Other Authors: Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder -National Oceanic and Atmospheric Administration (NOAA), National Oceanic and Atmospheric Administration (NOAA), NOAA Physical Sciences Laboratory (PSL), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS), ANR-17-MPGA-0003,ASET,Atmosphere - Sea ice Exchanges and Teleconections(2017), European Project: 101003826,CRiceS
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology; Arctic; Sea ice
• Online Access: https://hal.science/hal-04554644; https://hal.science/hal-04554644v1/document; https://hal.science/hal-04554644v1/file/Cummins_et_al_2023.pdf; https://doi.org/10.1029/2023gl105698

International audience 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.