Predicting carbon dioxide and energy fluxes across global FLUXNET sites with
regression algorithms

Tramontana, Gianluca; Jung, Martin; Schwalm, Christopher R.; Ichii, Kazuhito; Camps-Valls, Gustau; Ráduly, Botond; Reichstein, Markus; Arain, M. Altaf; Cescatti, Alessandro; Kiely, Gerard; Merbold, Lutz; Serrano-Ortiz, Penelope; Sickert, Sven; Wolf, Sebastian; Papale, Dario

doi:https://doi.org/10.5194/bg-13-4291-2016

Articles | Volume 13, issue 14

Biogeosciences, 13, 4291–4313, 2016

https://doi.org/10.5194/bg-13-4291-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Biogeosciences, 13, 4291–4313, 2016

https://doi.org/10.5194/bg-13-4291-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 13, issue 14

Research article 29 Jul 2016

Research article | 29 Jul 2016

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana et al.

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Final revised paper (published on 29 Jul 2016)
Supplement to the final revised paper
Preprint (discussion started on 07 Mar 2016)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms', Anonymous Referee #2, 28 Mar 2016
- AC1: 'Answers by authors to comments by Referee #2', Gianluca Tramontana, 31 May 2016
RC2: 'Review of Tramontana et al.', Anonymous Referee #3, 01 Apr 2016
- AC2: 'Answers by authors to comments by Referee #3', Gianluca Tramontana, 31 May 2016
RC3: 'Review Report of Referee #3', Anonymous Referee #4, 11 Apr 2016
- AC3: 'Answers by authors to comments by Referee #4', Gianluca Tramontana, 31 May 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (Editor review) (31 May 2016) by Georg Wohlfahrt

AR by Gianluca Tramontana on behalf of the Authors (24 Jun 2016) Author's response Manuscript

ED: Publish as is (29 Jun 2016) by Georg Wohlfahrt

Short summary

We have evaluated 11 machine learning (ML) methods and two complementary drivers' setup to estimate the carbon dioxide (CO₂) and energy exchanges between land ecosystems and atmosphere. Obtained results have shown high consistency among ML and high capability to estimate the spatial and seasonal variability of the target fluxes. The results were good for all the ecosystems, with limitations to the ones in the extreme environments (cold, hot) or less represented in the training data (tropics).