31 May 2022
31 May 2022
Status: this preprint is currently under review for the journal BG.

Assimilation of multiple different datasets results in large differences in regional to global-scale NEE and GPP budgets simulated by a terrestrial biosphere model

Cédric Bacour1,a, Natasha MacBean2, Frédéric Chevallier1, Sébastien Léonard1,b, Ernest N. Koffi1,c, and Philippe Peylin1 Cédric Bacour et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, F-91191, France
  • 2Department of Geography, Indiana University, Bloomington, IN, 47405, USA
  • aformerly at: NOVELTIS, Labège, France
  • bnow at: Air Liquide R&D, Innovation Campus Paris - Les-Loges-en-Josas, France
  • cnow at: European Centre for Medium-Range Weather Forecasts, Robert-Schuman-Platz 3, 53175 Bonn, Germany

Abstract. In spite of the importance of land ecosystems in offsetting carbon dioxide emissions released by anthropogenic activities into the atmosphere, the spatio-temporal dynamics of the carbon fluxes remain largely uncertain at regional to global scales. Over the past decade, Data Assimilation (DA) techniques have grown in importance for improving these fluxes simulated by Terrestrial Biosphere Models (TBMs), by optimizing model parameter values while also pinpointing possible parameterization deficiencies. Although the joint assimilation of multiple data streams is expected to constrain a wider range of model processes, their actual benefits in terms of reduction in model uncertainty are still under-researched, also given the technical challenges. In this study, we investigated with a consistent DA framework and the ORCHIDEE-LMDz TBM-atmosphere model how the assimilation of different combinations of data streams may result in different regional to global carbon budgets. To do so, we performed comprehensive DA experiments where three datasets (in situ measurements of net carbon exchange and latent heat fluxes, space-borne estimates of the Normalized Difference Vegetation Index, and atmospheric CO2 concentration data at stations) are assimilated alone or simultaneously. We thus evaluated their complementarity and usefulness to constrain net and gross C fluxes. We found that a major challenge in improving the spatial distribution of the land sinks/sources with atmospheric CO2 data relates to the correction of the initial carbon stocks.

Cédric Bacour et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on bg-2022-109', Cédric Bacour, 10 Jun 2022
  • RC1: 'Comment on bg-2022-109', Anonymous Referee #1, 31 Aug 2022
    • AC2: 'Reply on RC1', Cédric Bacour, 25 Nov 2022
  • RC2: 'Comment on bg-2022-109', Anonymous Referee #2, 25 Sep 2022
    • AC3: 'Reply on RC2', Cédric Bacour, 25 Nov 2022

Cédric Bacour et al.

Cédric Bacour et al.


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Short summary
1. The impact of assimilating different dataset combinations on regional to global scale C budgets is explored with the ORCHIDEE model, 2. Assimilating simultaneously multiple datasets is preferable to optimize the values of the model parameters and avoid model overfitting, 3. The challenges in optimizing soil C pools using atmospheric CO2 data are highlighted for an accurate prediction of the land sink distribution