Articles | Volume 21, issue 20
https://doi.org/10.5194/acp-21-15589-2021
https://doi.org/10.5194/acp-21-15589-2021
Technical note
 | 
18 Oct 2021
Technical note |  | 18 Oct 2021

Technical note: Uncertainties in eddy covariance CO2 fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches

Jingyu Yao, Zhongming Gao, Jianping Huang, Heping Liu, and Guoyin Wang

Related authors

Integrated dataset of atmospheric bioaerosols over east Asia
Zhongwei Huang, Wenjin Zhang, Qing Dong, Teruya Maki, Yongkai Wang, Yuanzong Ji, Fanli Xue, Xuefei Huo, Da Lu, Dongdong Wang, Jinsen Shi, Jianrong Bi, and Jianping Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-338,https://doi.org/10.5194/essd-2026-338, 2026
Preprint under review for ESSD
Short summary
Snow particle motion in process of cornice formation
Hongxiang Yu, Li Guang, Benjamin Walter, Jianping Huang, Ning Huang, and Michael Lehning
The Cryosphere, 19, 5389–5402, https://doi.org/10.5194/tc-19-5389-2025,https://doi.org/10.5194/tc-19-5389-2025, 2025
Short summary
Distinct structure, radiative effects, and precipitation characteristics of deep convection systems in the Tibetan Plateau compared to the tropical Indian Ocean
Yuxin Zhao, Jiming Li, Deyu Wen, Yarong Li, Yuan Wang, and Jianping Huang
Atmos. Chem. Phys., 24, 9435–9457, https://doi.org/10.5194/acp-24-9435-2024,https://doi.org/10.5194/acp-24-9435-2024, 2024
Short summary
Uncertainties in temperature statistics and fluxes determined by sonic anemometers due to wind-induced vibrations of mounting arms
Zhongming Gao, Heping Liu, Dan Li, Bai Yang, Von Walden, Lei Li, and Ivan Bogoev
Atmos. Meas. Tech., 17, 4109–4120, https://doi.org/10.5194/amt-17-4109-2024,https://doi.org/10.5194/amt-17-4109-2024, 2024
Short summary
The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020
Honglin Pan, Jianping Huang, Jiming Li, Zhongwei Huang, Minzhong Wang, Ali Mamtimin, Wen Huo, Fan Yang, Tian Zhou, and Kanike Raghavendra Kumar
Earth Syst. Sci. Data, 16, 1185–1207, https://doi.org/10.5194/essd-16-1185-2024,https://doi.org/10.5194/essd-16-1185-2024, 2024
Short summary

Cited articles

Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer, Dordrecht, the Netherlands, 438 pp., https://doi.org/10.1007/978-94-007-2351-1, 2012. 
Baldocchi, D. and Sturtevant, C.: Does day and night sampling reduce spurious correlation between canopy photosynthesis and ecosystem respiration?, Agr. Forest Meteorol., 207, 117–126, https://doi.org/10.1016/j.agrformet.2015.03.010, 2015. 
Baldocchi, D. D.: Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future, Glob. Change Biol., 9, 479–492, https://doi.org/10.1046/j.1365-2486.2003.00629.x, 2003. 
Berg, A. and McColl, K. A.: No projected global drylands expansion under greenhouse warming, Nat. Clim. Chang., 11, 331–337, https://doi.org/10.1038/s41558-021-01007-8, 2021. 
Download
Short summary
Gap-filling usually accounts for a large source of uncertainties in the annual CO2 fluxes, though gap-filling CO2 fluxes is challenging at dryland sites due to small fluxes. Using data collected from a semiarid site, four machine learning methods are evaluated with different lengths of artificial gaps. The artificial neural network and random forest methods outperform the other methods. With these methods, uncertainties in the annual CO2 flux at this site are estimated to be within 16 g C m−2.
Share
Altmetrics
Final-revised paper
Preprint