Articles | Volume 20, issue 4
https://doi.org/10.5194/bg-20-897-2023
https://doi.org/10.5194/bg-20-897-2023
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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Cited articles

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Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D., and Reichstein, M.: Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product, Earth Syst. Sci. Data, 10, 1327–1365, https://doi.org/10.5194/essd-10-1327-2018, 2018. 
Bradshaw, C. J. A. and Warkentin, I. G.: Global estimates of boreal forest carbon stocks and flux, Global Planet. Change, 128, 24–30, https://doi.org/10.1016/j.gloplacha.2015.02.004, 2015. 
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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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