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Approaching the potential of model-data comparisons of global land carbon storage

Wu, Zhendong LU ; Hugelius, Gustaf ; Luo, Yiqi ; Smith, Benjamin LU ; Xia, Jianyang ; Fensholt, Rasmus ; Lehsten, Veiko LU and Ahlström, Anders LU orcid (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
; ; ; ; ; ; and
organization
publishing date
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}},
}