A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates. Vekuri, H., Tuovinen, J., Kulmala, L., Papale, D., Kolari, P., Aurela, M., Laurila, T., Liski, J., & Lohila, A. Scientific Reports, 13(1):1720, January, 2023. ![A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude $$\textgreater60\textasciicircum\circ$$ \textgreater 60 ∘ ) sites. MDS systematically overestimates the carbon dioxide (CO $$_2$$ 2 ) emissions of carbon sources and underestimates the CO $$_2$$ 2 sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.
@article{vekuri_widely-used_2023,
title = {A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates},
volume = {13},
issn = {2045-2322},
url = {https://www.nature.com/articles/s41598-023-28827-2},
doi = {10.1038/s41598-023-28827-2},
abstract = {Abstract
Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude
\$\${\textgreater}60{\textasciicircum}{\textbackslash}circ\$\$
{\textgreater}
60
∘
) sites. MDS systematically overestimates the carbon dioxide (CO
\$\$\_2\$\$
2
) emissions of carbon sources and underestimates the CO
\$\$\_2\$\$
2
sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.},
language = {en},
number = {1},
urldate = {2024-11-15},
journal = {Scientific Reports},
author = {Vekuri, Henriikka and Tuovinen, Juha-Pekka and Kulmala, Liisa and Papale, Dario and Kolari, Pasi and Aurela, Mika and Laurila, Tuomas and Liski, Jari and Lohila, Annalea},
month = jan,
year = {2023},
pages = {1720},
}
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A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude $$\\textgreater60\\textasciicircum\\circ$$ \\textgreater 60 ∘ ) sites. MDS systematically overestimates the carbon dioxide (CO $$_2$$ 2 ) emissions of carbon sources and underestimates the CO $$_2$$ 2 sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.","language":"en","number":"1","urldate":"2024-11-15","journal":"Scientific Reports","author":[{"propositions":[],"lastnames":["Vekuri"],"firstnames":["Henriikka"],"suffixes":[]},{"propositions":[],"lastnames":["Tuovinen"],"firstnames":["Juha-Pekka"],"suffixes":[]},{"propositions":[],"lastnames":["Kulmala"],"firstnames":["Liisa"],"suffixes":[]},{"propositions":[],"lastnames":["Papale"],"firstnames":["Dario"],"suffixes":[]},{"propositions":[],"lastnames":["Kolari"],"firstnames":["Pasi"],"suffixes":[]},{"propositions":[],"lastnames":["Aurela"],"firstnames":["Mika"],"suffixes":[]},{"propositions":[],"lastnames":["Laurila"],"firstnames":["Tuomas"],"suffixes":[]},{"propositions":[],"lastnames":["Liski"],"firstnames":["Jari"],"suffixes":[]},{"propositions":[],"lastnames":["Lohila"],"firstnames":["Annalea"],"suffixes":[]}],"month":"January","year":"2023","pages":"1720","bibtex":"@article{vekuri_widely-used_2023,\n\ttitle = {A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates},\n\tvolume = {13},\n\tissn = {2045-2322},\n\turl = {https://www.nature.com/articles/s41598-023-28827-2},\n\tdoi = {10.1038/s41598-023-28827-2},\n\tabstract = {Abstract\n \n Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude\n \n \n \\$\\${\\textgreater}60{\\textasciicircum}{\\textbackslash}circ\\$\\$\n \n \n {\\textgreater}\n \n 60\n ∘\n \n \n \n \n \n ) sites. MDS systematically overestimates the carbon dioxide (CO\n \n \n \\$\\$\\_2\\$\\$\n \n \n \n 2\n \n \n \n \n ) emissions of carbon sources and underestimates the CO\n \n \n \\$\\$\\_2\\$\\$\n \n \n \n 2\n \n \n \n \n sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2024-11-15},\n\tjournal = {Scientific Reports},\n\tauthor = {Vekuri, Henriikka and Tuovinen, Juha-Pekka and Kulmala, Liisa and Papale, Dario and Kolari, Pasi and Aurela, Mika and Laurila, Tuomas and Liski, Jari and Lohila, Annalea},\n\tmonth = jan,\n\tyear = {2023},\n\tpages = {1720},\n}\n\n\n\n\n\n\n\n","author_short":["Vekuri, H.","Tuovinen, J.","Kulmala, L.","Papale, D.","Kolari, P.","Aurela, M.","Laurila, T.","Liski, J.","Lohila, A."],"key":"vekuri_widely-used_2023","id":"vekuri_widely-used_2023","bibbaseid":"vekuri-tuovinen-kulmala-papale-kolari-aurela-laurila-liski-etal-awidelyusededdycovariancegapfillingmethodcreatessystematicbiasincarbonbalanceestimates-2023","role":"author","urls":{"Paper":"https://www.nature.com/articles/s41598-023-28827-2"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/tereno","dataSources":["cq3J5xX6zmBvc2TQC"],"keywords":[],"search_terms":["widely","used","eddy","covariance","gap","filling","method","creates","systematic","bias","carbon","balance","estimates","vekuri","tuovinen","kulmala","papale","kolari","aurela","laurila","liski","lohila"],"title":"A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates","year":2023}