Model-data fusion in the studies of terrestrial carbon sink

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current uncertainty in quantifying the global carbon budget remains a major contributing source of uncertainty in reliably projecting future climate change. Furthermore, quantifying the global carbon budget and characterizing uncertainties have emerged as critical to a successful implementation of United National Framework Convention on Climate Change and its Kyoto Protocol. Beyond fundamental quantification, attribution of the processes responsible for the so-called 'residual terrestrial uptake' is important to the carbon cycle communities' ability to simulated the future response of the terrestrial biosphere to climate change and intentional sequestration activities. This paper's objective is to describe the efforts of the workshop participants and their approaches to model-data fusion enabling continued advances in the solution of quantifying carbon cycling and the terrestrial mechanisms at work.

Original languageEnglish (US)
Title of host publicationProceedings of the iEMSs 3rd Biennial Meeting,Summit on Environmental Modelling and Software
StatePublished - Dec 1 2006
Event3rd Biennial Meeting of the International Environmental Modelling and Software Society: Summit on Environmental Modelling and Software, iEMSs 2006 - Burlington, VT, United States
Duration: Jul 9 2006Jul 13 2006

Publication series

NameProceedings of the iEMSs 3rd Biennial Meeting," Summit on Environmental Modelling and Software"

Other

Other3rd Biennial Meeting of the International Environmental Modelling and Software Society: Summit on Environmental Modelling and Software, iEMSs 2006
CountryUnited States
CityBurlington, VT
Period7/9/067/13/06

Keywords

  • Carbon cycle
  • Model-data fusion
  • Modelling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Environmental Engineering
  • Modeling and Simulation

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