Ocean Colour Climate Change Initiative Initial Scientific Results
Sathyendranath, Shubha1; Brewin, Bob1; Mueller, Dagmar2; Krasemann, Hajo2; Mélin, Frédéric3; Brockmann, Carsten4; Fomferra, Norman4; Peters, Marco4; Grant, Mike1; Steinmetz, Francois5; Deschamps, Pierre-Yves5; Werdell, Jeremy6; Franz, Bryan6; Devred, Emmanuel7; Lee, ZhongPing8; Hu, Chuanmin9; Regner, Peter10; Smyth, Tim1; Platt, Trevor1; Maritorena, Stéphane11; Doerffer, Roland4; Groom, Steve1; White, George12; Swinton, John13
1Plymouth Marine Laboratory, UNITED KINGDOM; 2Helmholtz-Zentrum Geesthacht, GERMANY; 3European Commission - Joint Research Centre, ITALY; 4Brockmann Consult, GERMANY; 5HYGEOS, FRANCE; 6NASA Ocean Biology Processing Group, UNITED STATES; 7Université Laval, CANADA; 8University of Massachusetts Boston, UNITED STATES; 9University of South Florida, UNITED STATES; 10ESA, ITALY; 11University of California Santa Barbara, UNITED STATES; 12Bedford Institute of Oceanography, CANADA; 13Telespazio VEGA UK Ltd, UNITED KINGDOM

Ocean colour is recognised as an essential climate variable by the Global Climate Observing System (GCOS). Generation of time series of ocean-colour products for use in climate studies requires that we pay careful attention to a number of user requirements. In the Ocean-Colour Climate Change Initiative (OC-CCI) of the European Space Agency, a user consultation was undertaken, targeting both the climate modelling community and the Earth Observation community. Once the requirements are identified, the best algorithms for atmospheric correction and in-water product generation have to be selected from all available options, bearing in mind their suitability for climate-change studies. The data from multiple sensors have to be merged, to create as long a time series as possible, accounting for inter-sensor bias to avoid artificial trends in the time series. Appropriate metrics have to be chosen to characterise errors in products, which have to be mapped on a pixel-by-pixel basis, to facilitate appropriate interpretation and application of the data. In the OC-CCI Project, a set of criteria was developed, for selecting the best ocean-colour algorithms for climate research. Candidate atmospheric correction algorithms and in-water algorithms have been submitted to a round-robin comparison. The overall best performers are being used to generate test products, to be evaluated further. A method has been identified to correct the data for inter-sensor bias. A classification of oceanic waters into optical water types forms the basis for mapping errors in products. The results allow us to examine how close the results are to meeting GCOS requirements for accuracy. This paper will present the test results, and outline next steps to be undertaken within the OC-CCI Project.