Approaching the potential of model-data comparisons of global land carbon storage
(2019) In Scientific Reports 9(1).- Abstract
Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing... (More)
Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.
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- author
- Wu, Zhendong LU ; Hugelius, Gustaf ; Luo, Yiqi ; Smith, Benjamin LU ; Xia, Jianyang ; Fensholt, Rasmus ; Lehsten, Veiko LU and Ahlström, Anders LU
- organization
-
- Dept of Physical Geography and Ecosystem Science
- BECC: Biodiversity and Ecosystem services in a Changing Climate
- eSSENCE: The e-Science Collaboration
- MERGE: ModElling the Regional and Global Earth system
- Centre for Advanced Middle Eastern Studies (CMES)
- MECW: The Middle East in the Contemporary World
- publishing date
- 2019-03-04
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Scientific Reports
- volume
- 9
- issue
- 1
- article number
- 3367
- publisher
- Nature Publishing Group
- external identifiers
-
- pmid:30833586
- scopus:85062388126
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-019-38976-y
- language
- English
- LU publication?
- yes
- id
- 8c1f17ef-fe23-4fc1-9130-c08b0a10f75f
- date added to LUP
- 2019-03-12 08:26:41
- date last changed
- 2024-02-14 19:32:54
@article{8c1f17ef-fe23-4fc1-9130-c08b0a10f75f, abstract = {{<p>Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.</p>}}, author = {{Wu, Zhendong and Hugelius, Gustaf and Luo, Yiqi and Smith, Benjamin and Xia, Jianyang and Fensholt, Rasmus and Lehsten, Veiko and Ahlström, Anders}}, issn = {{2045-2322}}, language = {{eng}}, month = {{03}}, number = {{1}}, publisher = {{Nature Publishing Group}}, series = {{Scientific Reports}}, title = {{Approaching the potential of model-data comparisons of global land carbon storage}}, url = {{http://dx.doi.org/10.1038/s41598-019-38976-y}}, doi = {{10.1038/s41598-019-38976-y}}, volume = {{9}}, year = {{2019}}, }