Articles | Volume 15, issue 1
https://doi.org/10.5194/bg-15-187-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-15-187-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates
Hanna Post
Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Institute of Geography, University of Cologne, Cologne, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems:
HPSC TerrSys, Geoverbund ABC/J, Leo-Brandt-Strasse, 52425 Jülich,
Germany
Harrie-Jan Hendricks Franssen
CORRESPONDING AUTHOR
Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems:
HPSC TerrSys, Geoverbund ABC/J, Leo-Brandt-Strasse, 52425 Jülich,
Germany
Xujun Han
Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems:
HPSC TerrSys, Geoverbund ABC/J, Leo-Brandt-Strasse, 52425 Jülich,
Germany
Roland Baatz
Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems:
HPSC TerrSys, Geoverbund ABC/J, Leo-Brandt-Strasse, 52425 Jülich,
Germany
Carsten Montzka
Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Marius Schmidt
Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Harry Vereecken
Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems:
HPSC TerrSys, Geoverbund ABC/J, Leo-Brandt-Strasse, 52425 Jülich,
Germany
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- Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation J. Baumert et al. https://doi.org/10.1016/j.ecoinf.2024.102836
- Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning O. Reitz et al. https://doi.org/10.1029/2020JG005814
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19 citations as recorded by crossref.
- Soil moisture dominates the severe decline in gross primary productivity during a 2023–2024 compound heatwave-drought event over the Amazon T. Yang et al. https://doi.org/10.1088/1748-9326/adb0e1
- Process refinement contributed more than parameter optimization to improve the CoLM's performance in simulating the carbon and water fluxes in a grassland Y. Li et al. https://doi.org/10.1016/j.agrformet.2020.108067
- Influence of Vertical Heterogeneities in the Canopy Microenvironment on Interannual Variability of Carbon Uptake in Temperate Deciduous Forests M. Wozniak et al. https://doi.org/10.1029/2020JG005658
- Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China Y. Zhao et al. https://doi.org/10.3390/rs12193122
- Component-specific shifts in soil respiration and its temperature sensitivity following natural forest conversion R. Fan et al. https://doi.org/10.1038/s43247-026-03449-4
- Effects of Litter Input on Temperature Sensitivity of Soil Organic Carbon Mineralization along a Forest Elevation Gradient J. Wei et al. https://doi.org/10.3390/f13081250
- Thermodynamic mathematical model of the Kastanozem complex and new principles of sustainable semiarid protective silviculture management V. Kalinitchenko et al. https://doi.org/10.1016/j.envres.2020.110605
- Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information C. Brogi et al. https://doi.org/10.1002/vzj2.20009
- Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product Z. Duan et al. https://doi.org/10.3390/rs13214229
- Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation J. Baumert et al. https://doi.org/10.1016/j.ecoinf.2024.102836
- Upscaling Net Ecosystem Exchange Over Heterogeneous Landscapes With Machine Learning O. Reitz et al. https://doi.org/10.1029/2020JG005814
- Rhizosphere influence on microbial functions: consequence for temperature sensitivity of soil organic matter decomposition at early stage of plant growth J. Zhou et al. https://doi.org/10.1007/s11104-023-06258-2
- Evapotranspiration prediction for European forest sites does not improve with assimilation of in situ soil water content data L. Strebel et al. https://doi.org/10.5194/hess-28-1001-2024
- Spatial heterogeneity of temperature sensitivity of soil respiration: A global analysis of field observations J. Li et al. https://doi.org/10.1016/j.soilbio.2019.107675
- Impact of Environmental Conditions on Grass Phenology in the Regional Climate Model COSMO-CLM E. Hartmann et al. https://doi.org/10.3390/atmos11121364
- The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. https://doi.org/10.5194/gmd-13-55-2020
- Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions Y. Li et al. https://doi.org/10.1016/j.ecoinf.2022.101591
- Projections of wildfire risk and activities under 1.5 °C and 2.0 °C global warming scenarios X. Peng et al. https://doi.org/10.1088/2515-7620/acbf13
- Implementing a New Rubber Plant Functional Type in the Community Land Model (CLM5) Improves Accuracy of Carbon and Water Flux Estimation A. Ali et al. https://doi.org/10.3390/land11020183
Saved (final revised paper)
Latest update: 20 Sep 2026
Short summary
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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