{"title":"Reducing Parametrization Errors for Polar Surface Turbulent Fluxes Using Machine Learning","authors":"","doi":"10.1007/s10546-023-00852-8","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Turbulent exchanges between sea ice and the atmosphere are known to influence the melting rate of sea ice, the development of atmospheric circulation anomalies and, potentially, teleconnections between polar and non-polar regions. Large model errors remain in the parametrization of turbulent heat fluxes over sea ice in climate models, resulting in significant uncertainties in projections of future climate. Fluxes are typically calculated using bulk formulae, based on Monin-Obukhov similarity theory, which have shown particular limitations in polar regions. Parametrizations developed specifically for polar conditions (e.g. representing form drag from ridges or melt ponds on sea ice) rely on sparse observations and thus may not be universally applicable. In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural-network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of a state-of-the-art bulk formulation. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments. </p>","PeriodicalId":9153,"journal":{"name":"Boundary-Layer Meteorology","volume":"9 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Boundary-Layer Meteorology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s10546-023-00852-8","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Turbulent exchanges between sea ice and the atmosphere are known to influence the melting rate of sea ice, the development of atmospheric circulation anomalies and, potentially, teleconnections between polar and non-polar regions. Large model errors remain in the parametrization of turbulent heat fluxes over sea ice in climate models, resulting in significant uncertainties in projections of future climate. Fluxes are typically calculated using bulk formulae, based on Monin-Obukhov similarity theory, which have shown particular limitations in polar regions. Parametrizations developed specifically for polar conditions (e.g. representing form drag from ridges or melt ponds on sea ice) rely on sparse observations and thus may not be universally applicable. In this study, new data-driven parametrizations have been developed for surface turbulent fluxes of momentum, sensible heat and latent heat in the Arctic. Machine learning has already been used outside the polar regions to provide accurate and computationally inexpensive estimates of surface turbulent fluxes. To investigate the feasibility of this approach in the Arctic, we have fitted neural-network models to a reference dataset (SHEBA). Predictive performance has been tested using data from other observational campaigns. For momentum and sensible heat, performance of the neural networks is found to be comparable to, and in some cases substantially better than, that of a state-of-the-art bulk formulation. These results offer an efficient alternative to the traditional bulk approach in cases where the latter fails, and can serve to inform further physically based developments.
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Boundary-Layer Meteorology offers several publishing options: Research Letters, Research Articles, and Notes and Comments. The Research Letters section is designed to allow quick dissemination of new scientific findings, with an initial review period of no longer than one month. The Research Articles section offers traditional scientific papers that present results and interpretations based on substantial research studies or critical reviews of ongoing research. The Notes and Comments section comprises occasional notes and comments on specific topics with no requirement for rapid publication. Research Letters are limited in size to five journal pages, including no more than three figures, and cannot contain supplementary online material; Research Articles are generally fifteen to twenty pages in length with no more than fifteen figures; Notes and Comments are limited to ten journal pages and five figures. Authors submitting Research Letters should include within their cover letter an explanation of the need for rapid publication. More information regarding all publication formats can be found in the recent Editorial ‘Introducing Research Letters to Boundary-Layer Meteorology’.
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