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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