W16. Model-data fusion: applications to carbon-climate-human and other systems

Due to the start of European Emissions Trading Scheme, United Nations' Clean Development Mechanism and other market instruments for controlling greenhouse gases there will be an incentive to under-report emissions and exaggerate carbon sequestering (Nature, 433: 683). An effective multinational program for atmospheric verification of the efforts on controlling atmospheric CO2 concentration would not cost much, if the models of global carbon cycle will be improved to the certain level of credibility. Developing efficient model-data fusion techniques can do this.

Model-data fusion embraces a number of approaches for introducing observations into a modeling framework. They include inverse methods, data assimilation, parameter estimation, and constrained optimization. This workshop is to discuss

• 'Upscaling' methods (that is, methods for integrating local observations into a global scale model)
• The use of complex datasets for parameter estimation
• The use of information coming from wide range of observations for evaluating model consistency
• Evaluation of current and planned observing systems with respect to model-data fusion

NB. Numerical methods of model-data fusion are not specific to the carbon-climate-human system, and therefore those who are developing such methods with respect to other environmental problems are welcome to participate and share their experience.

Organizers:

Georgii Alexandrov -- Center for Global Environmental Research, National Institute for Environmental Studies, Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan; E-mail: g.alexandrov@nies.go.jp

Go To Workshop Blog

Position Paper
G. A. Alexandrov, D. Chan, M. Chen, K. Gurney, K. Higuchi, A. Ito, C. D. Jones, A. Komarov, K. Mabuchi, D. M. Matross, F. Veroustraete, W. W. Verstraeten	Model-data fusion in the studies of terrestrial carbon sink
Participants
Kazuo Mabuchi, Hideji Kida	On-Line Climate Model Simulation of The Global Carbon Cycle And Verification Using the in Situ Observation Data
Mingshi Chen	State-Parameter Estimation of Ecosystem Models Using a Smoothed Ensemble Kalman Filter
Georgii Alexandrov	Getting global pattern of plant productivity through combining observations and a process model
Daniel Matross, Arlyn Andrews, Christoph Gerbig, Steven Wofsy, Pathmathevan Mahadevan	A receptor-oriented modeling approach to estimate regional carbon exchange in New England and Quebec by combining atmospheric, ground-based, and satellite data
Akihiko Ito	Development of an ecosystem model using observational data for making semi-real-time prediction of forest fires
Frank Veroustraete, Willem Verstraeten	The ratio of anthropogenic carbon emissions to net ecosystem carbon uptake: A hot issue for emission traders?
Mingshi Chen, Shuguang Liu, Larry Tieszen	State-Parameter Estimation of Ecosystem Models Using a Smoothed Ensemble Kalman Filter
Douglas Chan, Misa Ishizawa, Kaz Higuchi	Using Regional Biospheric Model to Constrain CO2 Inversion
Alexander Komarov	Nonlinear Effects In Ecosystem Models Of Elements Dynamics At Local Level
Alexey Mikhaylov, Andrey Martynkin, Alexander Komarov	Forest and soil dynamics at different silvicultural regimes and forest fires: simulation modelling