Effects of persistent wind speeds on turbulent fluxes in the water-atmosphere interface
- 15 Downloads
Understanding air-water interactions is critical to establishing the role of inland water bodies in regulating local and regional weather so that more accurate parameterizations of flux exchange in numerical weather models can be achieved. Wind-induced mixing actively alters environmental variables, leading to changes in turbulent exchanges of latent heat (LE) and sensible heat (H) fluxes above water surfaces. It remains extensively unexplored as to how winds in different wind speed ranges modulate coupling of different variables, which in turn regulates LE and H. Here, we analyze 28-month eddy covariance data collected over a large reservoir. We categorize the dataset into four wind classes with different wind speed ranges: I (< 2.32 m s−1), II (2.32–3.69 m s−1), III (3.69–5.13 m s−1), and IV (> 5.13 m s−1). The enhanced mechanical mixing promotes LE and H with the increased wind classes due to the increased sensitivity to Δe and ΔT despite the reduced role of atmospheric stability. Hence, the highest LE and H occur in IV, under moderately unstable and stable conditions. Overall, the bulk transfer coefficients behave similarly under a certain stability condition across all wind classes while the similarity theory systematically underestimates their magnitudes. These results have important applications in improving parameterization schemes to estimate fluxes over water surfaces in numerical models.
KeywordsWater-atmosphere interaction Bulk transfer relations Eddy covariance fluxes Lake evaporation Atmospheric stability
We wish to thank the two anonymous reviewers for their constructive comments. We are grateful for Dan Gaillet, Billy Lester, Jason Temple, and many other people in Pearl River Valley Water Supply District in Ridgeland, Mississippi, as well as Yu Zhang, Haimei Jiang, Li Sheng, Rongping Li, Yu Wang, and Guo Zhang who contributed to the fieldwork. We thank Qianyu Zhang for her initial analyses of the data used in this work. According to the AGU Publications Data Policy, the data used in this paper are deposited in a public domain repository ( https://doi.org/10.6084/m9.figshare.5576371.v2).
The National Science Foundation AGS provided support under grant 1112938. Y.Y. received support from Universiti Sains Malaysia (USM) that awarded the Research University (RU) grant 1001/PTEKIND/811316 and Universiti Sains Malaysia (USM) Bridging Grant 2018 304/PTEKIND/6316289 to prepare this paper.
- Garratt JR (1992) The atmospheric boundary layer. Cambridge University Press, CambridgeGoogle Scholar
- Gutiérrez-Loza L, Wallin MB, Sahlée E, Nilsson E, Bange HW, Kock A, Rutgersson A (2019) Measurement of air-sea methane fluxes in the Baltic Sea using the eddy covariance method. Front Earth Sci 7(93):2296–6463Google Scholar
- Liu H, Zhang Y, Liu S, Jiang H, Sheng L, Williams QL (2009) Eddy covariance measurements of surface energy budget and evaporation in a cool season over southern open water in Mississippi. J Geophys Res Atmos 114(D4):D04110Google Scholar
- Mauder M, Oncley SP, Vogt R, Weidinger T, Ribeiro L, Bernhofer C, Foken T, Kohsiek W, De Bruin HAR, Liu H (2007) The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods. Bound-Layer Meteorol 123(1):29–54CrossRefGoogle Scholar
- Subin ZM, Riley WJ, Mironov D (2012) An improved lake model for climate simulations: model structure, evaluation, and sensitivity analyses in CESM1. J Adv Model Earth Syst 4(1):M02001Google Scholar