Uncertainties of drag coefficient estimates above sea ice from field data

• Published in: Boundary-Layer Meteorology
• Main Authors: Blein, Sébastien, Guemas, Virginie, Brooks, Ian M., Elvidge, Andrew D., Renfrew, Ian A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2024
• Subjects: Sea ice
• Online Access: https://ueaeprints.uea.ac.uk/id/eprint/94754/; https://ueaeprints.uea.ac.uk/id/eprint/94754/1/s10546_023_00851_9.pdf; https://doi.org/10.1007/s10546-023-00851-9

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.