Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 1
Biogeosciences, 15, 187-208, 2018
https://doi.org/10.5194/bg-15-187-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Biogeosciences, 15, 187-208, 2018
https://doi.org/10.5194/bg-15-187-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Jan 2018

Research article | 10 Jan 2018

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Hanna Post1,2,3, Harrie-Jan Hendricks Franssen1,3, Xujun Han1,3, Roland Baatz1,3, Carsten Montzka1, Marius Schmidt1, and Harry Vereecken1,3 Hanna Post et al.
  • 1Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
  • 2Institute of Geography, University of Cologne, Cologne, Germany
  • 3Centre for High-Performance Scientific Computing in Terrestrial Systems: HPSC TerrSys, Geoverbund ABC/J, Leo-Brandt-Strasse, 52425 Jülich, Germany

Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE) for the different ensemble members from  ∼ 2 to 3g C m−2 yr−1 (with uncertain parameters) to  ∼ 45g C m−2 yr−1 (C3 grass) and  ∼ 75g C m−2 yr−1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0–13.5g C m−2 yr−1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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Estimated values of selected key CLM4.5-BGC parameters obtained with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) strongly altered catchment-scale NEE predictions in comparison to global default parameter values. The effect of perturbed meteorological input data on the uncertainty of the predicted carbon fluxes was notably higher for C3-grass and C3-crop than for coniferous and deciduous forest. A future distinction of different crop types including management is considered essential.
Estimated values of selected key CLM4.5-BGC parameters obtained with the Markov chain Monte...
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