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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2022-108', Anonymous Referee #1, 02 Jun 2022
    • AC1: 'Reply on RC1', Matti Kämäräinen, 18 Aug 2022
  • RC2: 'Comment on bg-2022-108', Anonymous Referee #2, 03 Jun 2022
    • AC2: 'Reply on RC2', Matti Kämäräinen, 18 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (24 Aug 2022) by Trevor Keenan
AR by Matti Kämäräinen on behalf of the Authors (18 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (17 Dec 2022) by Trevor Keenan
RR by Anonymous Referee #1 (06 Jan 2023)
ED: Publish subject to technical corrections (08 Feb 2023) by Trevor Keenan
AR by Matti Kämäräinen on behalf of the Authors (15 Feb 2023)  Manuscript 
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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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