In:
Scientific Reports, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2023-01-31)
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.
Type of Medium:
Online Resource
ISSN:
2045-2322
DOI:
10.1038/s41598-023-28827-2
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2023
detail.hit.zdb_id:
2615211-3
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