Articles | Volume 9, issue 9
Geosci. Model Dev., 9, 3321–3346, 2016
https://doi.org/10.5194/gmd-9-3321-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 3321–3346, 2016
https://doi.org/10.5194/gmd-9-3321-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
20 Sep 2016
Methods for assessment of models
| 20 Sep 2016
A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle
Philippe Peylin et al.
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44 citations as recorded by crossref.
- Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model M. Peaucelle et al. 10.1111/geb.12937
- Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects J. Exbrayat et al. 10.1007/s10712-019-09506-2
- Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO 2 A. Walker et al. 10.1111/nph.16866
- A model-data fusion approach to analyse carbon dynamics in managed grasslands V. Myrgiotis et al. 10.1016/j.agsy.2020.102907
- Climate Sensitivities of Carbon Turnover Times in Soil and Vegetation: Understanding Their Effects on Forest Carbon Sequestration R. Ge et al. 10.1029/2020JG005880
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- Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations K. Castro-Morales et al. 10.5194/bg-16-3009-2019
- Data assimilation using an ensemble of models: a hierarchical approach P. Rayner 10.5194/acp-20-3725-2020
- The potential benefit of using forest biomass data in addition to carbon and water flux measurements to constrain ecosystem model parameters: Case studies at two temperate forest sites T. Thum et al. 10.1016/j.agrformet.2016.12.004
- Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH J. Mäkelä et al. 10.5194/gmd-12-4075-2019
- Reducing the uncertainty of parameters controlling seasonal carbon and water fluxes in Chinese forests and its implication for simulated climate sensitivities Y. Li et al. 10.1002/2017GB005714
- Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2) V. Bastrikov et al. 10.5194/gmd-11-4739-2018
- Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years J. Xiao et al. 10.1016/j.rse.2019.111383
- What can we learn from multi-data calibration of a process-based ecohydrological model? S. Kuppel et al. 10.1016/j.envsoft.2018.01.001
- The CO2 Human Emissions (CHE) Project: First Steps Towards a European Operational Capacity to Monitor Anthropogenic CO2 Emissions G. Balsamo et al. 10.3389/frsen.2021.707247
- Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
- Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models Y. Huang et al. 10.5194/gmd-12-1119-2019
- Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
- High resolution temporal profiles in the Emissions Database for Global Atmospheric Research M. Crippa et al. 10.1038/s41597-020-0462-2
- Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. 10.5194/bg-14-3401-2017
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- Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data V. Myrgiotis et al. 10.1016/j.agrformet.2021.108466
- The EU-FP7 ERA-CLIM2 Project Contribution to Advancing Science and Production of Earth System Climate Reanalyses R. Buizza et al. 10.1175/BAMS-D-17-0199.1
- Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO 2 Flux and its Inter‐Annual Variability K. Mahmud et al. 10.1029/2021JG006400
- 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. 10.1016/j.agrformet.2020.108067
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Discussed (final revised paper)
Latest update: 01 Dec 2022
Short summary
The study describes a carbon cycle data assimilation system that uses satellite observations of vegetation activity, net ecosystem exchange of carbon and water at many sites and atmospheric CO2 concentrations, in order to optimize the parameters of the ORCHIDEE land surface model. The optimized model is able to fit all three data streams leading to a land carbon uptake similar to independent estimates, which opens new perspectives for better prediction of the land carbon balance.
The study describes a carbon cycle data assimilation system that uses satellite observations of...
