Articles | Volume 9, issue 10
Geosci. Model Dev., 9, 3569–3588, 2016
https://doi.org/10.5194/gmd-9-3569-2016

Special issue: Data assimilation in carbon/biogeochemical cycles: consistent...

Geosci. Model Dev., 9, 3569–3588, 2016
https://doi.org/10.5194/gmd-9-3569-2016

Methods for assessment of models 04 Oct 2016

Methods for assessment of models | 04 Oct 2016

Consistent assimilation of multiple data streams in a carbon cycle data assimilation system

Natasha MacBean et al.

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Cited articles

Alton, P. B.: From site-level to global simulation: Reconciling carbon, water and energy fluxes over different spatial scales using a process-based ecophysiological land-surface model, Agr. Forest Meteorol., 176, 111–124, https://doi.org/10.1016/j.agrformet.2013.03.010, 2013.
Anav, A., Friedlingstein, P., Kidston, M., Bopp, L., Ciais, P., Cox, P., Jones, C., Jung, M., Myneni, R., and Zhu, Z.: Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models, J. Climate, 26, 6801–6843, https://doi.org/10.1175/JCLI-D-12-00417.1, 2013.
Bacour, C., Peylin, P., MacBean, N., Rayner, P. J., Delage, F., Chevallier, F., Weiss, M., Demarty, J., Santaren, D., Baret, F., Berveiller, D., Dufrêne, E., and Prunet, P.: Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process-oriented biosphere model, J. Geophys. Res.-Biogeo., 120, 1839–1857, https://doi.org/10.1002/2015JG002966, 2015.
Barrett, D. J., Hill, M. J., Hutley, L. B., Beringer, J., Xu, J. H., Cook, G. D., and Williams, R. J.: Prospects for improving savanna biophysical models by using multiple-constraints model-data assimilation methods, Aust. J. Botany, 53, 689–714, 2005.
Bloom, A. A. and Williams, M.: Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological “common sense” in a model-data fusion framework, Biogeosciences, 12, 1299–1315, https://doi.org/10.5194/bg-12-1299-2015, 2015.
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Short summary
Model projections of the response of the terrestrial biosphere to anthropogenic emissions are uncertain, in part due to unknown fixed parameters in a model. Data assimilation can address this by using observations to optimise these parameter values. Using multiple types of data is beneficial for constraining different model processes, but it can also pose challenges in a DA context. This paper demonstrates and discusses the issues involved using toy models and examples from existing literature.