A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates

Autor: Henriikka Vekuri, Juha-Pekka Tuovinen, Liisa Kulmala, Dario Papale, Pasi Kolari, Mika Aurela, Tuomas Laurila, Jari Liski, Annalea Lohila
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-28827-2
Popis: 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 $$>60^\circ$$ > 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.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje