Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications

Raupach, M. R., Rayner, P. J., Barrett, D. J., DeFries, R. S., Heimann, M., Ojima, D. S., Quegan, S. and Schmullius, C. C. (2005) Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications. Global Change Biology, 11 3: 378-397. doi:10.1111/j.1365-2486.2005.00917.x

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Author Raupach, M. R.
Rayner, P. J.
Barrett, D. J.
DeFries, R. S.
Heimann, M.
Ojima, D. S.
Quegan, S.
Schmullius, C. C.
Title Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications
Journal name Global Change Biology   Check publisher's open access policy
ISSN 1354-1013
1365-2486
Publication date 2005-03
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1111/j.1365-2486.2005.00917.x
Volume 11
Issue 3
Start page 378
End page 397
Total pages 20
Place of publication Oxford, United Kingdom
Publisher Blackwell
Language eng
Subject 270708 Conservation and Biodiversity
05 Environmental Sciences
Abstract Systematic, operational, long-term observations of the terrestrial carbon cycle (including its interactions with water, energy and nutrient cycles and ecosystem dynamics) are important for the prediction and management of climate, water resources, food resources, biodiversity and desertification. To contribute to these goals, a terrestrial carbon observing system requires the synthesis of several kinds of observation into terrestrial biosphere models encompassing the coupled cycles of carbon, water, energy and nutrients. Relevant observations include atmospheric composition (concentrations of CO2 and other gases); remote sensing; flux and process measurements from intensive study sites; in situ vegetation and soil monitoring; weather, climate and hydrological data; and contemporary and historical data on land use, land use change and disturbance (grazing, harvest, clearing, fire). A review of model-data synthesis tools for terrestrial carbon observation identifies 'nonsequential' and 'sequential' approaches as major categories, differing according to whether data are treated all at once or sequentially. The structure underlying both approaches is reviewed, highlighting several basic commonalities in formalism and data requirements. An essential commonality is that for all model-data synthesis problems, both nonsequential and sequential, data uncertainties are as important as data values themselves and have a comparable role in determining the outcome. Given the importance of data uncertainties, there is an urgent need for soundly based uncertainty characterizations for the main kinds of data used in terrestrial carbon observation. The first requirement is a specification of the main properties of the error covariance matrix. As a step towards this goal, semi-quantitative estimates are made of the main properties of the error covariance matrix for four kinds of data essential for terrestrial carbon observation: remote sensing of land surface properties, atmospheric composition measurements, direct flux measurements, and measurements of carbon stores.
Keyword Atmospheric Transport
Data Assimilation
Theoretical Aspects
CO2 Iinversions
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collection: Sustainable Minerals Institute Publications
 
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Created: Wed, 11 Nov 2009, 12:45:49 EST