Evaluating state-of-the-art process-based and data-driven models in simulating CO<sub>2</sub> fluxes and their relationship with climate in western European temperate forests

Michel, Gaïa; Crétat, Julien; Mathieu, Olivier; Thévenot, Mathieu; Dara, Andrey; Granat, Robert; Wu, Zhendong; Bonnefoy-Claudet, Clément; Capelle, Julianne; Cacot, Jean; Kimball, John S.

doi:https://doi.org/10.5194/egusphere-2024-1758

Preprints

https://doi.org/10.5194/egusphere-2024-1758

Preprints

20 Jun 2024

| 20 Jun 2024

Evaluating state-of-the-art process-based and data-driven models in simulating CO₂ fluxes and their relationship with climate in western European temperate forests

Gaïa Michel, Julien Crétat, Olivier Mathieu, Mathieu Thévenot, Andrey Dara, Robert Granat, Zhendong Wu, Clément Bonnefoy-Claudet, Julianne Capelle, Jean Cacot, and John S. Kimball

Abstract. This study evaluates two process-based (LPJ-GUESS and SMAP-L4C) and two data-driven (CarbonSpace and FLUXCOM) models to capture the temporal variability of CO₂flux exchanges (GPP, RECO and NEE) of evergreen needleleaf and deciduous broadleaf forests (ENFs and DBFs) in temperate western Europe and its relationship with climate. Three sites from the FLUXNET network are considered together with two non-instrumented sites located in Burgundy (North-East France). The focus is put on the representation of the annual cycle, annual budget, interannual variability and “long-term” trend. The data-driven models are the best models for representing the mean annual cycle and mean annual budget in CO₂fluxes despite magnitude uncertainties. In particular, the models accounting for plant functional types in their outputs tend to simulate more marked annual cycle and lower annual CO₂ sequestration for DBFs than ENFs in Burgundy. At the interannual timescale, the CO₂ flux – climate relationshipis stronger for GPP and RECO than NEE, with increased CO₂ fluxes when 2 m temperature, vapor pressure deficit and evapotranspiration increase and when precipitation and soil moisture decrease. The models forced by dynamic climate conditions clearly outperform those driven by static climate conditions. The “long-term” trend is not obvious for NEE neither in the observations nor in the simulations, partly because both GPP and RECO tend to increase in western Europe. Our results suggest that the spatial resolution of the climate drivers is likely very important for capturing spatial and temporal patterns in CO₂ exchanges and point towards the need to choose the appropriate model and spatial resolution according to the scientific question to deal with.

Received: 11 Jun 2024 – Discussion started: 20 Jun 2024

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Journal article(s) based on this preprint

26 Aug 2025

Climate impact on mean annual cycle and interannual variability of CO₂ fluxes in European deciduous broadleaf and evergreen needleleaf forests: insights from observations and state-of-the-art data-driven and process-based models

Asmat Ullah, Julien Crétat, Gaïa Michel, Olivier Mathieu, Mathieu Thevenot, Andrey Dara, Robert Granat, Zhendong Wu, Clément Bonnefoy-Claudet, Julianne Capelle, Jean Cacot, and John S. Kimball

Biogeosciences, 22, 4135–4162, https://doi.org/10.5194/bg-22-4135-2025,https://doi.org/10.5194/bg-22-4135-2025, 2025

Short summary

Gaïa Michel, Julien Crétat, Olivier Mathieu, Mathieu Thévenot, Andrey Dara, Robert Granat, Zhendong Wu, Clément Bonnefoy-Claudet, Julianne Capelle, Jean Cacot, and John S. Kimball

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2024-1758', S E Quick, 21 Jun 2024

Section 1 Introduction:
Many of your references seem to be missing from this preprint or are cited incorrectly, e.g. Smith et al., 2020; Thompson et al., 2020; Yuan et al., 2009
Section 2.1 Site description:
It would be useful to know when each forest was established ( e.g. 10s,100s or 1000s of year ago.)

Citation: https://doi.org/10.5194/egusphere-2024-1758-CC1
- AC4: 'Reply on CC1', Julien Crétat, 26 Sep 2024
  
  Section 1 Introduction:
  Many of your references seem to be missing from this preprint or are cited incorrectly, e.g. Smith et al., 2020; Thompson et al., 2020; Yuan et al., 2009
  Thank you for these remarks. We will carefully check the reference list in the revised manuscript and cite the references following the journal guideline.
  
  Section 2.1 Site description:
  It would be useful to know when each forest was established ( e.g. 10s,100s or 1000s of year ago.)
  We will detail, as much as possible, the age of the forests at the ICOS sites in the revised version
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC4
RC1:
'Comment on egusphere-2024-1758', Anonymous Referee #1, 15 Jul 2024
The study evaluated CO₂ flux outputs from models at six sites and their relationship with climate in temperate forests of Western Europe. Overall, this manuscript addresses an important topic and presents some interesting findings. However, I am not entirely convinced that the results significantly advance our understanding of CO₂ fluxes at forest sites across different temporal resolutions (such as monthly and annual, as analyzed in this study).
General comments
The temporal variability in this study is not adequately addressed and may need reorganization. For example, there are ten figures illustrating monthly timescale results. However, these findings are neither included nor highlighted in the abstract or conclusion. Although there are similar patterns between the monthly and annual results, as shown in Figures 6 and 8, the monthly findings should be incorporated. Alternatively, I suggest removing or reducing the figures or text about the monthly results and focusing on annual and interannual scales.

While I am not an expert in statistics, the R and R² results in this study (such as those in Figures 6 and 7) seem very close. Is it necessary to present Figure 7 in the main text? Regarding the long-term evolution, which method was used? Why do the authors state that it 'does not depict any trend'? Please provide this information in the Methods section.

The introduction of this study is not well-articulated. The discussion of the knowledge gap lacks a solid basis. Why is a finer spatial scale important? Why did the authors focus on monthly and annual timescales? The second objective, which involves the climate relationship, lacks motivation regarding the choice of variables in this study. Why were radiation or PPFD not considered?

It is not appropriate to assess flux trends using the FLUXCOM dataset, as FLUXCOM does not account for CO₂ fertilization effects. Please refer to the following paper: Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., ... & Reichstein, M. (2020). Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5), 1343-1365.

Line-by-line comments:
Line 42: “..the best models…” sounds inaccurate. Perhaps "better models" would be more appropriate, as the comparison is only made with process-based models.
LL 45-48: could you please explain why?
LL 45-47: The sentence contains a lot of information and lacks clarity. I suggest revising it for better readability.
Line 49: How long is the ‘long-term’?
LL 50-53: The statement is too general and does not seem related to the main point of the study.
Line 61: ‘At the global scale, forest ecosystems cover about 30% of landmasses’ suggest adding a reference.
LL 71-72: What is the meaning of this number here?
Line 101: Suggest deleting ‘when available’
LL 101-113: Suggest moving these sentences to another paragraph or placing them above the objectives.
LL127-128: Redundace, and which varaible? all fluxes?
Line 132: "Extreme" can refer to both high and low conditions. To specify, consider using "extreme high" or "extreme hot" for clarity.
LL 132-133: Suggest revising the sentence.
LL147-149: Suggest deleting these sentences.
Figure 1: If the reader is not familiar with Europe, it may not be clear. Consider adding specific locations, such as "France".
Table 1: The table format needs adjustment. Please refer to the journal's guidelines, which typically suggest using horizontal lines only above and below the table, and as a separator between the table header and the main body.
Figure 9-11: Perhaps move to supplement.
LL555-556: Suggest deleting the sentence.
Line 603: “qualitatively similar” might sound speculative?
Line 724: Replace ‘best’ to ‘better’.
Line 772: Should be ‘Fig. 7’ not ‘Fig, 5’
Line 776: ‘Fig. 7’
Citation: https://doi.org/10.5194/egusphere-2024-1758-RC1
- AC1: 'Reply on RC1', Julien Crétat, 26 Sep 2024
  
  Please, see our responses in the pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC1
RC2:
'Comment on egusphere-2024-1758', Anonymous Referee #2, 05 Sep 2024

This study evaluates the performance of various carbon flux products against eddy covariance measurements at three forest sites in France. The authors investigate the monthly, seasonal, and inter-annual variability of NEE, GPP, and RECO to assess different global products and explore their relationships with meteorological variables. While the manuscript is generally written clearly, the analysis lacks sufficient depth and significance for the scientific community. This isn't to say that evaluating existing products isn't valuable, but the limited number of eddy covariance sites and the selection of only four global products raise concerns about the comprehensiveness of the study.
1.Choice of Models (LPJ-GUESS and FLUXCOM v1): Why were LPJ-GUESS and FLUXCOM v1 selected when many other land surface models or upscaled products are available for evaluation? Including more models and products could improve relevance, especially since ensemble models are commonly used in land carbon sink studies.
2.Spatial Resolution Mismatch: The 50 km spatial resolution of LPJ-GUESS and FLUXCOM products may not align with the footprint of the eddy covariance sites, and this mismatch is not addressed in the manuscript.
3.Insufficient Number of Sites: Only three forest sites are used in the analysis, despite the availability of hundreds of FLUXNET sites globally. Using only these sites may not provide a robust basis for summarizing product performance.
4.Correlation Analysis: The correlation analysis in Figure 6 lacks a logical basis, as some variables (e.g., VPD and RECO) do not have clear biogeochemical or biophysical relationships. Also, the analysis does not account for multicollinearity among variables, which affects the validity of the results.
5.Focus on Temperature: The authors only consider temperature when analyzing carbon fluxes in Figure 6 and do not include other important variables, such as soil moisture, which were emphasized in the introduction. Given this, the use of polynomial regressions without considering other factors raises questions about interactive effects of multivariate factors of the carbon fluxes.
Specific Points:
Inconsistent Visualization (Figures 5 and 13): Figures 5 and 13 present similar data for annual and monthly scales, but the visualizations need to be consistent to enable direct comparison.
Climate Anomalies Definition: The authors should clarify how climate anomalies are defined, as the methods section only explains CO2 flux anomalies. Also, the choice of the -0.5/+0.5 thresholds for carbon flux anomalies seems arbitrary and needs further justification.

Citation: https://doi.org/10.5194/egusphere-2024-1758-RC2
- AC2: 'Reply on RC2', Julien Crétat, 26 Sep 2024
  
  Please, see our responses in the pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC2
- AC3: 'Reply on RC2', Julien Crétat, 26 Sep 2024
  
  Please, see our responses in the pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC3

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2024-1758', S E Quick, 21 Jun 2024

Section 1 Introduction:
Many of your references seem to be missing from this preprint or are cited incorrectly, e.g. Smith et al., 2020; Thompson et al., 2020; Yuan et al., 2009
Section 2.1 Site description:
It would be useful to know when each forest was established ( e.g. 10s,100s or 1000s of year ago.)

Citation: https://doi.org/10.5194/egusphere-2024-1758-CC1
- AC4: 'Reply on CC1', Julien Crétat, 26 Sep 2024
  
  Section 1 Introduction:
  Many of your references seem to be missing from this preprint or are cited incorrectly, e.g. Smith et al., 2020; Thompson et al., 2020; Yuan et al., 2009
  Thank you for these remarks. We will carefully check the reference list in the revised manuscript and cite the references following the journal guideline.
  
  Section 2.1 Site description:
  It would be useful to know when each forest was established ( e.g. 10s,100s or 1000s of year ago.)
  We will detail, as much as possible, the age of the forests at the ICOS sites in the revised version
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC4
RC1:
'Comment on egusphere-2024-1758', Anonymous Referee #1, 15 Jul 2024
The study evaluated CO₂ flux outputs from models at six sites and their relationship with climate in temperate forests of Western Europe. Overall, this manuscript addresses an important topic and presents some interesting findings. However, I am not entirely convinced that the results significantly advance our understanding of CO₂ fluxes at forest sites across different temporal resolutions (such as monthly and annual, as analyzed in this study).
General comments
The temporal variability in this study is not adequately addressed and may need reorganization. For example, there are ten figures illustrating monthly timescale results. However, these findings are neither included nor highlighted in the abstract or conclusion. Although there are similar patterns between the monthly and annual results, as shown in Figures 6 and 8, the monthly findings should be incorporated. Alternatively, I suggest removing or reducing the figures or text about the monthly results and focusing on annual and interannual scales.

While I am not an expert in statistics, the R and R² results in this study (such as those in Figures 6 and 7) seem very close. Is it necessary to present Figure 7 in the main text? Regarding the long-term evolution, which method was used? Why do the authors state that it 'does not depict any trend'? Please provide this information in the Methods section.

The introduction of this study is not well-articulated. The discussion of the knowledge gap lacks a solid basis. Why is a finer spatial scale important? Why did the authors focus on monthly and annual timescales? The second objective, which involves the climate relationship, lacks motivation regarding the choice of variables in this study. Why were radiation or PPFD not considered?

It is not appropriate to assess flux trends using the FLUXCOM dataset, as FLUXCOM does not account for CO₂ fertilization effects. Please refer to the following paper: Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., ... & Reichstein, M. (2020). Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5), 1343-1365.

Line-by-line comments:
Line 42: “..the best models…” sounds inaccurate. Perhaps "better models" would be more appropriate, as the comparison is only made with process-based models.
LL 45-48: could you please explain why?
LL 45-47: The sentence contains a lot of information and lacks clarity. I suggest revising it for better readability.
Line 49: How long is the ‘long-term’?
LL 50-53: The statement is too general and does not seem related to the main point of the study.
Line 61: ‘At the global scale, forest ecosystems cover about 30% of landmasses’ suggest adding a reference.
LL 71-72: What is the meaning of this number here?
Line 101: Suggest deleting ‘when available’
LL 101-113: Suggest moving these sentences to another paragraph or placing them above the objectives.
LL127-128: Redundace, and which varaible? all fluxes?
Line 132: "Extreme" can refer to both high and low conditions. To specify, consider using "extreme high" or "extreme hot" for clarity.
LL 132-133: Suggest revising the sentence.
LL147-149: Suggest deleting these sentences.
Figure 1: If the reader is not familiar with Europe, it may not be clear. Consider adding specific locations, such as "France".
Table 1: The table format needs adjustment. Please refer to the journal's guidelines, which typically suggest using horizontal lines only above and below the table, and as a separator between the table header and the main body.
Figure 9-11: Perhaps move to supplement.
LL555-556: Suggest deleting the sentence.
Line 603: “qualitatively similar” might sound speculative?
Line 724: Replace ‘best’ to ‘better’.
Line 772: Should be ‘Fig. 7’ not ‘Fig, 5’
Line 776: ‘Fig. 7’
Citation: https://doi.org/10.5194/egusphere-2024-1758-RC1
- AC1: 'Reply on RC1', Julien Crétat, 26 Sep 2024
  
  Please, see our responses in the pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC1
RC2:
'Comment on egusphere-2024-1758', Anonymous Referee #2, 05 Sep 2024

This study evaluates the performance of various carbon flux products against eddy covariance measurements at three forest sites in France. The authors investigate the monthly, seasonal, and inter-annual variability of NEE, GPP, and RECO to assess different global products and explore their relationships with meteorological variables. While the manuscript is generally written clearly, the analysis lacks sufficient depth and significance for the scientific community. This isn't to say that evaluating existing products isn't valuable, but the limited number of eddy covariance sites and the selection of only four global products raise concerns about the comprehensiveness of the study.
1.Choice of Models (LPJ-GUESS and FLUXCOM v1): Why were LPJ-GUESS and FLUXCOM v1 selected when many other land surface models or upscaled products are available for evaluation? Including more models and products could improve relevance, especially since ensemble models are commonly used in land carbon sink studies.
2.Spatial Resolution Mismatch: The 50 km spatial resolution of LPJ-GUESS and FLUXCOM products may not align with the footprint of the eddy covariance sites, and this mismatch is not addressed in the manuscript.
3.Insufficient Number of Sites: Only three forest sites are used in the analysis, despite the availability of hundreds of FLUXNET sites globally. Using only these sites may not provide a robust basis for summarizing product performance.
4.Correlation Analysis: The correlation analysis in Figure 6 lacks a logical basis, as some variables (e.g., VPD and RECO) do not have clear biogeochemical or biophysical relationships. Also, the analysis does not account for multicollinearity among variables, which affects the validity of the results.
5.Focus on Temperature: The authors only consider temperature when analyzing carbon fluxes in Figure 6 and do not include other important variables, such as soil moisture, which were emphasized in the introduction. Given this, the use of polynomial regressions without considering other factors raises questions about interactive effects of multivariate factors of the carbon fluxes.
Specific Points:
Inconsistent Visualization (Figures 5 and 13): Figures 5 and 13 present similar data for annual and monthly scales, but the visualizations need to be consistent to enable direct comparison.
Climate Anomalies Definition: The authors should clarify how climate anomalies are defined, as the methods section only explains CO2 flux anomalies. Also, the choice of the -0.5/+0.5 thresholds for carbon flux anomalies seems arbitrary and needs further justification.

Citation: https://doi.org/10.5194/egusphere-2024-1758-RC2
- AC2: 'Reply on RC2', Julien Crétat, 26 Sep 2024
  
  Please, see our responses in the pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC2
- AC3: 'Reply on RC2', Julien Crétat, 26 Sep 2024
  
  Please, see our responses in the pdf file.
  
  Citation: https://doi.org/10.5194/egusphere-2024-1758-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (30 Sep 2024) by Andrew Feldman

We’ve received two reviews of the manuscript. One suggests major revisions and the other recommends rejection. Upon reviewing the manuscript myself, I find the objectives to be interesting and the analysis to be expansive for validating and intercomparing several model types. Nevertheless, the reviewers raised some constructive points about the novelty and presentation of results that I agreed with nearly all. Therefore, we request substantial revisions addressing all comments. I’ve added a few overarching points to address giving context to the referee points.

1) The site selection needs more motivation than lines 154-156. The introduction motivates the number of FLUXNET and ICOS sites around line 80, but then only uses a few specific sites.

2) Both referees found the presentation of results to be fair or poor and I agree there are likely better methods to plot the data. For example, it would be nice to call out more clearly the FLUXNET data in the results figures. The color is similar to the others and not easy to quickly find. Please prioritize means to improve the readability of the figures such as increasing font size, or reducing panels if possible.

3) I agree with referee 1 that the introduction could go a bit further to be more specific about the knowledge gaps being addressed.

4) There are some limitation points that do need mention here. First, the spatial scale difference between the flux tower and model resolution as mentioned by a referee. Second, we need to acknowledge the data driven approaches may be trained partly on these flux towers and there may then be concern about out-of-sample testing (or performance on a new site it wasn’t trained on).

5) I think the use of monthly data to look at the “annual cycle” is acceptable. However, since the reviewer questioned this point, it might be good to reiterate in the results/discussion that the monthly results are focused on the annual cycle.

Sincerely,
Andrew Feldman

Hide

AR by Julien Crétat on behalf of the Authors (17 Feb 2025)

EF by Mario Ebel (18 Feb 2025) Manuscript Author's response

EF by Mario Ebel (18 Feb 2025) Author's tracked changes

ED: Referee Nomination & Report Request started (23 Feb 2025) by Andrew Feldman

RR by Anonymous Referee #3 (28 Mar 2025)

Suggestions for revision or reasons for rejection

In this revised manuscript, the authors have made substantial efforts to improve the original manuscript by incorporating most of the comments from previous reviewers. The key improvements are 1) shifting the research objective from evaluating models' performance to examining the climate impact on the mean annual cycle and interannual variability; 2) increasing the number of eddy covariance sites from three to 19; and 3) including more process-based and data-driven models. However, there is still one major issue regarding the interannual variability that I would like to see the authors address.

The major comment:
The authors have clearly shown that all process-based and data-driven products struggle to capture the magnitude and variance of the interannual variability of NEE (Figures 6 and 7). This is a long-standing and known issue at least for data-driven products (Jung et al., 2020; Nelson et al., 2024). But they still claimed that these products can be used to analyze the climate impacts on the interannual variability of NEE. It is hard for me to follow this logic. I agree with the authors that the length of some eddy covariance sites is probably not long enough to allow examining the interannual variability of NEE, but I do not think using long-term but problematic model products could help here. Highly inconsistent patterns among products in Figure 9 acknowledge this point. Nevertheless, examining the climate impact on the interannual variability of CO2 fluxes at the monthly scale is an interesting question and could improve our understanding of the ecosystem carbon cycling. Therefore, I would suggest that authors focus on eddy covariance data and completely give up using models, at least for the analysis of the interannual variability, which, of course, deviates a lot from the authors’ initial idea, but would gain more scientific insights. In fact, the required length to represent the interannual variability is site-specific (Chu et al., 2017), therefore, authors could use the methods in Chu et al., 2017 to identify the sites with adequate temporal representativeness. If not enough sites are available, authors could extend the vegetation types beyond DBF and ENF, and/or extend the research area beyond Europe to increase the number of long sites.

Minor comments:
-Lines 71-78: These findings are questionable given the highly inconsistent patterns among products in Figure 9.
-Figure 1: The labels of ‘CH-Dav’ and ‘IT-Ren’ overlap.
-Line3 165-171: The definition of Northern, central, and Southern Europe appeared here. But it is unclear to tell from Figures 3-9 themselves. Adding this information to these figures would improve the readability.
-Line 298: Incident shortwave radiation is an essential driver of NEE and GPP. I would suggest keeping it.
-Figure 2: If authors still want to keep the model data, this figure is not necessarily important and could go to the supplement.
-Figure 3: Add the abbreviation ‘IV’ into the caption.
-Line 371: Any significance test was done here?
-Lines 375-376: Why? Since it is not relevant, I would suggest removing it.
-Lines 422-423: High correlation coefficient values could only indicate that the variance is captured well, but maybe not be true for the magnitude. Please rephrase it.
-Lines 515-517: As mentioned above, this statement is questionable.
-Lines 713-722: Same as above.

References:
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020.

Nelson, J. A., Walther, S., Gans, F., Kraft, B., Weber, U., Novick, K., Buchmann, N., Migliavacca, M., Wohlfahrt, G., Šigut, L., Ibrom, A., Papale, D., Göckede, M., Duveiller, G., Knohl, A., Hörtnagl, L., Scott, R. L., Zhang, W., Hamdi, Z. M., Reichstein, M., Aranda-Barranco, S., Ardö, J., Op de Beeck, M., Billdesbach, D., Bowling, D., Bracho, R., Brümmer, C., Camps-Valls, G., Chen, S., Cleverly, J. R., Desai, A., Dong, G., El-Madany, T. S., Euskirchen, E. S., Feigenwinter, I., Galvagno, M., Gerosa, G., Gielen, B., Goded, I., Goslee, S., Gough, C. M., Heinesch, B., Ichii, K., Jackowicz-Korczynski, M. A., Klosterhalfen, A., Knox, S., Kobayashi, H., Kohonen, K.-M., Korkiakoski, M., Mammarella, I., Mana, G., Marzuoli, R., Matamala, R., Metzger, S., Montagnani, L., Nicolini, G., O'Halloran, T., Ourcival, J.-M., Peichl, M., Pendall, E., Ruiz Reverter, B., Roland, M., Sabbatini, S., Sachs, T., Schmidt, M., Schwalm, C. R., Shekhar, A., Silberstein, R., Silveira, M. L., Spano, D., Tagesson, T., Tramontana, G., Trotta, C., Turco, F., Vesala, T., Vincke, C., Vitale, D., Vivoni, E. R., Wang, Y., Woodgate, W., Yepez, E. A., Zhang, J., Zona, D., and Jung, M.: X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-165, 2024.

Chu, H., Baldocchi, D. D., John, R., Wolf, S., & Reichstein, M. (2017). Fluxes all of the time? A primer on the temporal representativeness of FLUXNET. Journal of Geophysical Research: Biogeosciences, 122(2), 289-307.

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RR by Anonymous Referee #2 (05 Apr 2025)

ED: Reconsider after major revisions (07 Apr 2025) by Andrew Feldman

AR by Julien Crétat on behalf of the Authors (12 May 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (13 May 2025) by Andrew Feldman

RR by Anonymous Referee #3 (23 May 2025)

ED: Publish as is (29 May 2025) by Andrew Feldman

AR by Julien Crétat on behalf of the Authors (02 Jun 2025)

Journal article(s) based on this preprint

26 Aug 2025

Asmat Ullah, Julien Crétat, Gaïa Michel, Olivier Mathieu, Mathieu Thevenot, Andrey Dara, Robert Granat, Zhendong Wu, Clément Bonnefoy-Claudet, Julianne Capelle, Jean Cacot, and John S. Kimball

Biogeosciences, 22, 4135–4162, https://doi.org/10.5194/bg-22-4135-2025,https://doi.org/10.5194/bg-22-4135-2025, 2025

Short summary

Gaïa Michel, Julien Crétat, Olivier Mathieu, Mathieu Thévenot, Andrey Dara, Robert Granat, Zhendong Wu, Clément Bonnefoy-Claudet, Julianne Capelle, Jean Cacot, and John S. Kimball

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This study questions the usefulness of state-ot-the-art models to characterize the temporal variability of atmosphere-ecosystem CO₂ exchanges in western European forests. Their mean annual cycle and annual budget are better captured by statistical than physical models, while their interannual variability and long-term trend are better captured by models forced by climate variability. Accounting for both forest stands and climate variability is thus key for properly assessing CO₂ fluxes.


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Evaluating state-of-the-art process-based and data-driven models in simulating CO2 fluxes and their relationship with climate in western European temperate forests

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Evaluating state-of-the-art process-based and data-driven models in simulating CO₂ fluxes and their relationship with climate in western European temperate forests