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Data assimilation into land surface models: the implications for climate feedbacks

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journal contribution
posted on 25.05.2011, 14:09 by D. Ghent, Jörg Kaduk, J. Remedios, Heiko Balzter
Land surface models (LSMs) are integral components of general circulation models (GCMs), consisting of a complex framework of mathematical representations of coupled biophysical processes. Considerable variability exists between different models, with much uncertainty in their respective representations of processes and their sensitivity to changes in key variables. Data assimilation is a powerful tool that is increasingly being used to constrain LSM predictions with available observation data. The technique involves the adjustment of the model state at observation times with measurements of a predictable uncertainty, to minimize the uncertainties in the model simulations. By assimilating a single state variable into a sophisticated LSM, this article investigates the effect this has on terrestrial feedbacks to the climate system, thereby taking a wider view on the process of data assimilation and the implications for biogeochemical cycling, which is of considerable relevance to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report.

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Citation

International Journal of Remote Sensing, 2011, 32 (3), pp. 617-632.

Version

AM (Accepted Manuscript)

Published in

International Journal of Remote Sensing

Publisher

Taylor & Francis

issn

0143-1161

eissn

1366-5901

Copyright date

2011

Available date

25/05/2011

Publisher version

http://www.tandfonline.com/doi/abs/10.1080/01431161.2010.517794

Language

en

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