Uncertainties of Drag Coefficient Estimates Above Sea Ice from Field Data

International audience Surface turbulent exchanges play a key role on sea ice dynamics, on ocean and sea ice heat budgets and on the polar atmosphere. Uncertainties in parameterizations of surface turbulent fluxes are mostly held by the transfer coefficients and estimates of those transfer coefficie...

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Bibliographic Details
Published in:Boundary-Layer Meteorology
Main Authors: Blein, Sébastien, Guemas, Virginie, Brooks, Ian, M, Elvidge, Andrew, D, Renfrew, Ian, A
Other Authors: Groupe de Météorologie de Grande Échelle et Climat (GMGEC), 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)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), 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), University of Leeds, University of East Anglia Norwich (UEA), 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 2024
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Online Access:https://hal.science/hal-04554639
https://hal.science/hal-04554639/document
https://hal.science/hal-04554639/file/Blein_et_al_2024.pdf
https://doi.org/10.1007/s10546-023-00851-9
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Summary:International audience Surface turbulent exchanges play a key role on sea ice dynamics, on ocean and sea ice heat budgets and on the polar atmosphere. Uncertainties in parameterizations of surface turbulent fluxes are mostly held by the transfer coefficients and estimates of those transfer coefficients from field data are required for parameterization development. Measurement errors propagate through the computation of transfer coefficients and contribute to its total error together with the uncertainties in the empirical stability functions used to correct for stability effects. Here we propose a methodology to assess their contributions individually to each coefficient estimate as well as the total drag coefficient uncertainty and we apply this methodology on the example of the SHEBA campaign. We conclude that for most common drag coefficient values (between 1.0 × 10−3 and 2.5 × 10−3), the relative total uncertainty ranges from 25 and 50%. For stable or unstable conditions with a stability parameter |ζ| > 1 on average, the total uncertainty in the neutral drag coefficient exceeds the neutral drag coefficient value itself, while for |ζ| < 1 the total uncertainty is around 25% of the drag coefficient. For closer-to-neutral conditions, this uncertainty is dominated by measurement uncertainties in surface turbulent momentum fluxes which should therefore be the target of efforts in uncertainty reduction. We also propose an objective data-screening procedure for field data, which consists of retaining data for which the relative error on neutral drag coefficient does not exceed a given threshold. This method, in addition to the commonly used flux quality control procedure, allows for a reduction of the drag coefficient dispersion compared to other data-screening methods, which we take as an indication of better dataset quality.