Biogeosciences
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Articles | Volume 20, issue 4
Articles | Volume 20, issue 4
https://doi.org/10.5194/bg-20-897-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-20-897-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 4
Research article
 | 
02 Mar 2023
Research article |  | 02 Mar 2023

Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange

Matti Kämäräinen, Juha-Pekka Tuovinen, Markku Kulmala, Ivan Mammarella, Juha Aalto, Henriikka Vekuri, Annalea Lohila, and Anna Lintunen

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Short summary
In this study, we introduce a new method for modeling the exchange of carbon between the atmosphere and a study site located in a boreal forest in southern Finland. Our method yields more accurate results than previous approaches in this context. Accurately estimating carbon exchange is crucial for gaining a better understanding of the role of forests in regulating atmospheric carbon and addressing climate change.
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