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.
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.
the Creative Commons Attribution 4.0 License.
Spatiotemporal lagging of predictors improves machine learning estimates of atmosphere–forest CO2 exchange
Weather and Climate Change Impact Research, Finnish Meteorological
Institute, Helsinki, Finland
Juha-Pekka Tuovinen
Climate System Research, Finnish Meteorological Institute, Helsinki,
Finland
Markku Kulmala
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Ivan Mammarella
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Juha Aalto
Weather and Climate Change Impact Research, Finnish Meteorological
Institute, Helsinki, Finland
Department of Geosciences and Geography, University of Helsinki,
Helsinki, Finland
Henriikka Vekuri
Climate System Research, Finnish Meteorological Institute, Helsinki,
Finland
Annalea Lohila
Climate System Research, Finnish Meteorological Institute, Helsinki,
Finland
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Anna Lintunen
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, Finland
Institute for Atmospheric and Earth System Research/Forest Sciences,
Faculty of Agriculture and Forestry, University of Helsinki, Helsinki,
Finland
Model code and software
Gradient boosting and random forest tools for modeling the NEE M. Kämäräinen, A. Lintunen, M. Kulmala, J. Tuovinen, I. Mammarella, J. Aalto, H. Vekuri, and A. Lohila https://doi.org/10.5281/zenodo.7333975
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.
In this study, we introduce a new method for modeling the exchange of carbon between the...
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