Time series analysis within Ocean Colour Climate Change Initiative project
Belo do Couto, Andre1; Grant, Mike2; Groom, Steve2; Melin, Frederic3; Krasemann, Hajo4; Muller, Dagmar4; von Storch, Hans4; Valente, Andre1; Brotas, Vanda1
1University of Lisbon, Centre of Oceanography, PORTUGAL; 2PML, UNITED KINGDOM; 3JRC, FRANCE; 4HZG, GERMANY
ESA's Climate Change Initiative project aims to develop and validate algorithms to meet the Ocean Colour GCOS ECV requirements for consistent, stable, error-characterized global satellite data products from multi-sensor data archives and produce and validate, within an R&D context, the most complete and consistent possible time series of multi-sensor global satellite data products for climate research and modelling. The Ocean Colour CCI (OC-CCI) focuses on Ocean Colour and uses remote sensing reflectances to derive inherent optical properties and chlorophyll a concentration from ESA's MERIS (2002-2012) and NASA's SeaWiFS (1997 - 2010) and MODIS (2002-2012) sensor archives. Merging takes account of inter-sensor biases and accounts for the different wavelengths of the individual sensors. The product is available at 4-km resolution with daily, weekly and monthly composites with pixel by pixel uncertainty characterization. In this presentation we present the OC CCI Version 1 global time series and the analysis performed to consider possible breaks and creeping inhomogeneities, spatial differences for different oceanic provinces (e.g. tropics, sub-tropics, upwelling areas, higher latitudes). We also make comparisons in terms of temporal and spatial characteristics with other existing ocean colour datasets e.g. MyOcean2/GlobColour, NASA and NOAA data products. We also present preliminary results on the use of the NOAA VIIRS and ISRO OCM-2 sensors to fill the gaps caused by the loss of MERIS in 2012 and SeaWiFS in 2010 prior to the Sentinel 3 OLCI era. Finally, we present time series analysis of phytoplankton phenology (such as the timing, amplitude and duration of the spring bloom), key diagnostic properties of the marine ecosystem that can be derived from ocean-colour time series.