Bourras, D., Cambra, R., Marié, L., Bouin, M. N., Baggio, L., Branger, H., Beghoura, H., Reverdin, G., Dewitte, B., Paulmier, A., Maes, C., Ardhuin, F., Pairaud, I., Fraunié, P., Luneau, C., and Hauser, D.: Air–Sea Turbulent Fluxes From a Wave – Following Platform During Six Experiments at Sea, J. Geophys. Res.-Oceans, 124, 4290–4321, https://doi.org/10.1029/2018jc014803, 2019.
Brodeau, L., Barnier, B., Gulev, S. K., and Woods, C.: Climatologically Significant Effects of Some Approximations in the Bulk Parameterizations of Turbulent Air–Sea Fluxes, J. Phys. Oceanogr., 47, 5–28, https://doi.org/10.1175/jpo-d-16-0169.1, 2017.
Brunke, M. A.: Uncertainties in sea surface turbulent flux algorithms and data sets, J. Geophys. Res., 107, https://doi.org/10.1029/2001jc000992, 2002.
Cai, L., Wang, B., Wang, W., and Feng, X.: The Impact of Air–Sea Flux Parameterization Methods on Simulating Storm Surges and Ocean Surface Currents, Journal of Marine Science and Engineering, 13, https://doi.org/10.3390/jmse13030541, 2025.
Cai, M. and Lu, J.: Stabilization of the Atmospheric Boundary Layer and the Muted Global Hydrological Cycle Response to Global Warming, J. Hydrometeorol., 10, 347–352, https://doi.org/10.1175/2008jhm1058.1, 2009.
Chen, X., Yao, Y., Zhao, S., Li, Y., Jia, K., Zhang, X., Shang, K., Xu, J., and Bei, X.: Estimation of High-Resolution Global Monthly Ocean Latent Heat Flux from MODIS SST Product and AMSR-E Data, Adv. Meteorol., 2020, 1–19, https://doi.org/10.1155/2020/8857618, 2020a.
Chen, X., Yao, Y., Li, Y., Zhang, Y., Jia, K., Zhang, X., Shang, K., Yang, J., Bei, X., and Guo, X.: ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products, Sensors, 20, https://doi.org/10.3390/s20174773, 2020b.
Clayson, C. and Brown, J.: NOAA Climate Data Record Ocean Surface Bundle (OSB) Climate Data Record (CDR) of Ocean Heat Fluxes, Version 2, Clim. Algorithm Theor. Basis Doc. C-ATBD Asheville NC NOAA Natl. Cent. Environ. Inf. Doi, 10, V59K4885, https://doi.org/10.7289/V59K4885, 2016.
Cummins, D. P., Guemas, V., Cox, C. J., Gallagher, M. R., and Shupe, M. D.: Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning, Geophys. Res. Lett., 50, https://doi.org/10.1029/2023gl105698, 2023.
Cummins, D. P., Guemas, V., Blein, S., Brooks, I. M., Renfrew, I. A., Elvidge, A. D., and Prytherch, J.: Reducing Parametrization Errors for Polar Surface Turbulent Fluxes Using Machine Learning, Bound.-Lay. Meteorol., 190, https://doi.org/10.1007/s10546-023-00852-8, 2024.
Edson, J. B., Venkata, K., Weller, R. A., Bigorre, S. P., Plueddemann, A. J., Fairall, C. W., Miller, S. D., Mahrt, L., Vickers, D., and Hersbach, H.: On the Exchange of Momentum over the Open Ocean, J. Phys. Oceanogr., 43, 1589–1610, https://doi.org/10.1175/jpo-d-12-0173.1, 2013.
Fasullo, J. T., Trenberth, K. E., and Balmaseda, M. A.: Earth's Energy Imbalance, J. Climate, 27, 3129–3144, https://doi.org/10.1175/jcli-d-13-00294.1, 2014.
Fu, S., Huang, W., Luo, J., Yang, Z., Fu, H., Luo, Y., and Wang, B.: Deep Learning – Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast, Geophys. Res. Lett., 50, https://doi.org/10.1029/2023gl106580, 2023.
Gentemann, C. L., Clayson, C. A., Brown, S., Lee, T., Parfitt, R., Farrar, J. T., Bourassa, M., Minnett, P. J., Seo, H., Gille, S. T., and Zlotnicki, V.: FluxSat: Measuring the Ocean–Atmosphere Turbulent Exchange of Heat and Moisture from Space, Remote Sens., 12, https://doi.org/10.3390/rs12111796, 2020.
Grist, J. P., Josey, S. A., Zika, J. D., Evans, D. G., and Skliris, N.: Assessing recent air–sea freshwater flux changes using a surface temperature-salinity space framework, J. Geophys. Res.-Oceans, 121, 8787–8806, https://doi.org/10.1002/2016jc012091, 2016.
Jiang, Y., Li, Y., Lu, Y., Wu, T., and Gao, Z.: Evaluating modifications to air–sea momentum flux parameterizations under light wind conditions in CAM6, Clim. Dynam., 62, 9687–9701, https://doi.org/10.1007/s00382-024-07415-8, 2024a.
Jiang, Y., Li, Y., Lu, Y., Wu, T., Zhang, J., and Gao, Z.: Evaluating nine different air–sea flux algorithms coupled with CAM6, Atmos. Res., https://doi.org/10.1016/j.atmosres.2024.107486, 2024b.
Jo, Y.-H.: Calculation of the Bowen ratio in the tropical Pacific using sea surface temperature data, J. Geophys. Res., 107, https://doi.org/10.1029/2001jc001150, 2002.
Jo, Y.-H., Yan, X.-H., Pan, J., Liu, W. T., and He, M.-X.: Sensible and latent heat flux in the tropical Pacific from satellite multi-sensor data, Remote Sens. Environ., 90, 166–177, https://doi.org/10.1016/j.rse.2003.12.003, 2004.
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., and Yang, L.: Physics-informed machine learning, Nature Reviews Physics, 3, 422–440, 2021.
Kashinath, K., Mustafa, M., Albert, A., Wu, J., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., and Singh, A.: Physics-informed machine learning: case studies for weather and climate modelling, Philos. T. R. Soc. A, 379, 20200093, https://doi.org/10.1098/rsta.2020.0093, 2021.
Kudryavtsev, V., Chapron, B., and Makin, V.: Impact of wind waves on the air–sea fluxes: A coupled model, J. Geophys. Res.-Oceans, 119, 1217–1236, https://doi.org/10.1002/2013jc009412, 2014.
Liman, J., Schröder, M., Fennig, K., Andersson, A., and Hollmann, R.: Uncertainty characterization of HOAPS 3.3 latent heat-flux-related parameters, Atmos. Meas. Tech., 11, 1793–1815, https://doi.org/10.5194/amt-11-1793-2018, 2018.
Loeb, N. G., Johnson, G. C., Thorsen, T. J., Lyman, J. M., Rose, F. G., and Kato, S.: Satellite and Ocean Data Reveal Marked Increase in Earths Heating Rate, Geophys. Res. Lett., 48, https://doi.org/10.1029/2021GL093047, 2021.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163–187, 1954.
Myslenkov, S., Shestakova, A., and Chechin, D.: The impact of sea waves on turbulent heat fluxes in the Barents Sea according to numerical modeling, Atmos. Chem. Phys., 21, 5575–5595, https://doi.org/10.5194/acp-21-5575-2021, 2021.
O, S. and Orth, R.: Global soil moisture data derived through machine learning trained with in-situ measurements, Sci. Data, 8, 170, https://doi.org/10.1038/s41597-021-00964-1, 2021.
Peng, Z., Tang, R., Jiang, Y., Liu, M., and Li, Z.-L.: Global estimates of 500 m daily aerodynamic roughness length from MODIS data, ISPRS J. Photogramm., 183, 336–351, https://doi.org/10.1016/j.isprsjprs.2021.11.015, 2022.
Robertson, F. R., Roberts, J. B., Bosilovich, M. G., Bentamy, A., Clayson, C. A., Fennig, K., Schröder, M., Tomita, H., Compo, G. P., Gutenstein, M., Hersbach, H., Kobayashi, C., Ricciardulli, L., Sardeshmukh, P., and Slivinski, L. C.: Uncertainties in Ocean Latent Heat Flux Variations over Recent Decades in Satellite-Based Estimates and Reduced Observation Reanalyses, J. Climate, 33, 8415–8437, https://doi.org/10.1175/jcli-d-19-0954.1, 2020.
Shang, K., Yao, Y., Di, Z., Jia, K., Zhang, X., Fisher, J. B., Chen, J., Guo, X., Yang, J., Yu, R., Xie, Z., Liu, L., Ning, J., and Zhang, L.: Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau, Remote Sens. Environ., 289, https://doi.org/10.1016/j.rse.2023.113519, 2023.
Shie, C.-L., Chiu, L. S., Adler, R., Nelkin, E., Lin, I. I., Xie, P., Wang, F.-C., Chokngamwong, R., Olson, W., and Chu, D. A.: A note on reviving the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) dataset, Adv. Atmos. Sci., 26, 1071–1080, https://doi.org/10.1007/s00376-009-8138-z, 2009.
Song, X.: The Importance of Relative Wind Speed in Estimating Air–Sea Turbulent Heat Fluxes in Bulk Formulas: Examples in the Bohai Sea, J. Atmos. Ocean. Tech., 37, 589–603, https://doi.org/10.1175/jtech-d-19-0091.1, 2020.
Song, X.: The Importance of Including Sea Surface Current when Estimating Air–Sea Turbulent Heat Fluxes and Wind Stress in the Gulf Stream Region, J. Atmos. Ocean. Tech., 38, 119–138, https://doi.org/10.1175/jtech-d-20-0094.1, 2021.
Song, X., Xie, X., Yan, Y., and Xie, S.-P.: Observed sub-daily variations in air–sea turbulent heat fluxes under different marine atmospheric boundary layer stability conditions in the Gulf Stream, Mon. Weather Rev., https://doi.org/10.1175/mwr-d-24-0003.1, 2024.
Tang, R. and Wang, Y.: Global dataset of air–sea turbulent heat fluxes (sensible heat flux and latent heat flux) (1993–2017), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.302578, 2025.
Tang, R., Wang, Y., Jiang, Y., Liu, M., Peng, Z., Hu, Y., Huang, L., and Li, Z.-L.: A review of global products of air–sea turbulent heat flux: accuracy, mean, variability, and trend, Earth-Sci. Rev., 249, https://doi.org/10.1016/j.earscirev.2023.104662, 2024.
Tomita, H., Hihara, T., Kako, S., Kubota, M., and Kutsuwada, K.: An introduction to J-OFURO3, a third-generation Japanese ocean flux data set using remote-sensing observations, J. Oceanogr., 75, 171–194, https://doi.org/10.1007/s10872-018-0493-x, 2018.
van der Westhuizen, S., Heuvelink, G. B., and Hofmeyr, D. P.: Multivariate random forest for digital soil mapping, Geoderma, 431, 116365, https://doi.org/10.1016/j.geoderma.2023.116365, 2023.
Wang, J., Tang, R., Jiang, Y., Liu, M., and Li, Z.-L.: A practical method for angular normalization of global MODIS land surface temperature over vegetated surfaces, ISPRS J. Photogramm., 199, 289–304, https://doi.org/10.1016/j.isprsjprs.2023.04.015, 2023.
Wang, W., Chakraborty, T. C., Xiao, W., and Lee, X.: Ocean surface energy balance allows a constraint on the sensitivity of precipitation to global warming, Nat. Commun., 12, 2115, https://doi.org/10.1038/s41467-021-22406-7, 2021.
Wang, Y.: BrTHF v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.19595146, 2026.
Wang, Y., Tang, R., Huang, L., Liu, M., Jiang, Y., and Li, Z.-L.: A Bowen ratio-informed method for coordinating the estimates of air–sea turbulent heat fluxes, Environ. Res. Lett., 19, https://doi.org/10.1088/1748-9326/ad9341, 2024.
Weller, R. A., Lukas, R., Potemra, J., Plueddemann, A. J., Fairall, C., and Bigorre, S.: Ocean Reference Stations: Long-Term, Open-Ocean Observations of Surface Meteorology and Air–Sea Fluxes Are Essential Benchmarks, B. Am. Meteorol. Soc., 103, E1968–E1990, https://doi.org/10.1175/bams-d-21-0084.1, 2022.
Wild, M., Folini, D., Hakuba, M. Z., Schär, C., Seneviratne, S. I., Kato, S., Rutan, D., Ammann, C., Wood, E. F., and König-Langlo, G.: The energy balance over land and oceans: an assessment based on direct observations and CMIP5 climate models, Clim. Dynam., 44, 3393–3429, https://doi.org/10.1007/s00382-014-2430-z, 2014.
Yan, Y., Song, X., Wang, G., and Li, X.: Tropical Cool-Skin and Warm-Layer Effects and Their Impact on Surface Heat Fluxes, J. Phys. Oceanogr., 54, 45–62, https://doi.org/10.1175/jpo-d-23-0103.1, 2024.
Yang, Y. and Roderick, M. L.: Radiation, surface temperature and evaporation over wet surfaces, Q. J. Roy. Meteor. Soc., 145, 1118–1129, https://doi.org/10.1002/qj.3481, 2019.
Yang, Y., Sun, H., Wang, J., Zhang, W., Zhao, G., Wang, W., Cheng, L., Chen, L., Qin, H., and Cai, Z.: Global ocean surface heat fluxes derived from the maximum entropy production framework accounting for ocean heat storage and Bowen ratio adjustments, Earth Syst. Sci. Data, 17, 1191–1216, https://doi.org/10.5194/essd-17-1191-2025, 2025.
Yu, L.: Global Air–Sea Fluxes of Heat, Fresh Water, and Momentum: Energy Budget Closure and Unanswered Questions, Annu. Rev. Mar. Sci., 11, 227–248, https://doi.org/10.1146/annurev-marine-010816-060704, 2019.
Yu, L. and Weller, R. A.: Objectively Analyzed Air–Sea Heat Fluxes for the Global Ice-Free Oceans (1981–2005), B. Am. Meteorol. Soc., 88, 527–540, https://doi.org/10.1175/bams-88-4-527, 2007.
Zhang, R., Guo, W., Wang, X., and Wang, C.: Ambiguous Variations in Tropical Latent Heat Flux since the Years around 1998, J. Climate, 36, 3403–3415, 2023.
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., and Qiu, G. Y.: Physics-Constrained Machine Learning of Evapotranspiration, Geophys. Res. Lett., 46, 14496–14507, https://doi.org/10.1029/2019gl085291, 2019.
Zhou, S., Shi, R., Yu, H., Zhang, X., Dai, J., Huang, X., and Xu, F.: A Physical-Informed Neural Network for Improving Air–Sea Turbulent Heat Flux Parameterization, J. Geophys. Res.-Atmos., 129, https://doi.org/10.1029/2023jd040603, 2024.
Zhou, X., Ray, P., Barrett, B. S., and Hsu, P.-C.: Understanding the bias in surface latent and sensible heat fluxes in contemporary AGCMs over tropical oceans, Clim. Dynam., 55, 2957–2978, https://doi.org/10.1007/s00382-020-05431-y, 2020.
