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Title: Data assimilation into land surface models: the implications for climate feedbacks
Authors: Ghent, D.
Kaduk, Jörg
Remedios, J.
Balzter, Heiko
First Published: Feb-2011
Publisher: Taylor & Francis
Citation: International Journal of Remote Sensing, 2011, 32 (3), pp. 617-632.
Abstract: 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.
DOI Link: 10.1080/01431161.2010.517794
ISSN: 0143-1161
eISSN: 1366-5901
Version: Post print
Status: Peer reviewed
Type: Article
Rights: Copyright © 2011 Taylor & Francis. Deposited with reference to the publisher's archiving policy available on the SHERPA/RoMEO website. This is an electronic version of an article published in International Journal of Remote Sensing, 2011, 32 (3), pp. 617-632. International Journal of Remote Sensing is available online at:
Appears in Collections:Published Articles, Dept. of Geography

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