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A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates.
- Source :
-
Scientific Reports . 1/31/2023, Vol. 13 Issue 1, p1-9. 9p. - Publication Year :
- 2023
-
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 ∘ ) sites. MDS systematically overestimates the carbon dioxide (CO 2 ) emissions of carbon sources and underestimates the CO 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 13
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 161625688
- Full Text :
- https://doi.org/10.1038/s41598-023-28827-2