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Assimilating Globcolour Ocean Colour Data Into a Pre-operational Physical-biogeochemical Model : Volume 8, Issue 5 (05/09/2012)

By Ford, D. A.

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Book Id: WPLBN0004020608
Format Type: PDF Article :
File Size: Pages 21
Reproduction Date: 2015

Title: Assimilating Globcolour Ocean Colour Data Into a Pre-operational Physical-biogeochemical Model : Volume 8, Issue 5 (05/09/2012)  
Author: Ford, D. A.
Volume: Vol. 8, Issue 5
Language: English
Subject: Science, Ocean, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Barciela, R. M., Lea, D., Edwards, K. P., Martin, M. J., Ford, D. A., & Demaria, J. (2012). Assimilating Globcolour Ocean Colour Data Into a Pre-operational Physical-biogeochemical Model : Volume 8, Issue 5 (05/09/2012). Retrieved from

Description: Met Office, FitzRoy Road, Exeter, EX1 3PB, UK. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physical-biogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO2, but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

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