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ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on August 13, 2009
ICES Journal of Marine Science: Journal du Conseil 2009 66(10):2133-2140; doi:10.1093/icesjms/fsp208
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© 2009 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Average seasonal changes in chlorophyll a in Icelandic waters

Kristinn Guðmundsson1, Mike R. Heath2 and Elizabeth D. Clarke2

1 Marine Research Institute, PO Box 1390, 121 Reykjavík, Iceland
2 Marine Scotland, Marine Laboratory, PO Box 101, 375 Victoria Road, Aberdeen AB11 9DB, Scotland, UK

Correspondence to K. Guðmundsson: tel: +354 5752000; fax: +354 5752001; e-mail: kristinn{at}hafro.is

Guðmundsson, K., Heath, M. R., and Clarke, E. D. 2009. Average seasonal changes in chlorophyll a in Icelandic waters. – ICES Journal of Marine Science, 66: 2133–2140.

The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ~60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset.

Keywords: chlorophyll a, modelling, North Atlantic, remote sensing, seasonal, Subarctic

Received 23 September 2008; accepted 22 June 2009; advance access publication 13 August 2009.


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