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Temporal and meridional variability of Satellite-estimates of surface chlorophyll concentration239sent work, data from SeaWiFS refer to reprocessing2009.1 (December 2009), data from MODIS/Aquarefer to reprocessing 1.1 (August 2005), and datafrom both CZCS and OCTS were last reprocessed inOctober 2006. MODIS/Aqua has been fullyoperational since its launch while a number of gapsfor SeaWiFs data, due to problems with theinstrument, occurred in 2008 (January 3 to April 3;July 2 to August 18) and 2009 (April 24 to June 15;July 3 to July 17; August 31 to November 5; andNovember 14 to 30). Further details on each sensorand respective data sets can be found in the officialdistribution site (http://oceancolor.gsfc.nasa.gov).Products distributed as Level 3 used in thiswork included chlorophyll concentration (allsensors), sea surface temperature (MODIS/Aqua),fluorescence line height (MODIS/Aqua) and theCDOM index (SeaWiFS), computed by therespective standard global algorithms and masks.Note that spatial resolutions for L3 images are 4 kmfor CZCS and MODIS/Aqua, and 9 km for OCTSand SeaWiFS, and were preserved as such.Images were processed using SeaDAS(v5.4) - a multi-platform software freely distributedby NASA (http://oceancolor.gsfc.nasa.gov/seadas).The bathymetry dataset from SEADAS was used toset apart image pixels occurring over the continentalshelf, assumed here as those where local depthsranged from 20 to 200 m. The lower limit of 20 mwas set to exclude the effects of both localgeography and possible bottom effects. For eachsatellite product and sensor, the proportion of datacoverage was computed as the number of pixels withvalid data (i.e., those not masked by land, clouds oralgorithm’s failures) over the total number of pixelsexpected between 20 and 200 meters. We used 8-Day spatial resolution of all products and sensorsthat yielded 46 observations per year. We alsogrouped SeaWiFS and MODIS/Aqua chlorophyllconcentration and data coverage by seasons, so thatFall refers to images starting on days of the year 80to 162, Winter images from days 168 to 258, Springimages from days 264 to 346 and Summer imagesfrom days 352 to 361 and days 1 to 74.Meridional Division and Basic StatisticsThe BCS was divided into 7 (seven) largeareas, which are a slight modification of the divisioninto six compartments proposed by Castro andMiranda (1998). The original 6 (six) areas weredivided according with physical processes andpresence of distinct water masses. Our modificationscheme was basically shifting latitudinal boundariesnecessary to i) keep the areas more comparable insize; and ii) to accommodate, within each area,similar values for mean and standard deviations ofsatellite surface chlorophyll (Fig. 1). For each 8-Daysatellite product and area, basic statistics (mean,median, standard deviation) were computed, butrespective time series represent the median values,rather the mean, in order to focus our discussion oncentral values and also to avoid extreme or toolocalized values. Nonetheless, we present the entirestatistical distributions by area and ocean colorsensor (see Fig. 2).Annual Cycle and Long-Term Variability ofChlorophyllChlorophyll data for SeaWifs were averagedover the 46 8-Day composites to produce a pseudoclimatology,or general mean, for the 12 years (up to31 December 2009). The 8-Day general means werein turn used to compute 8-Day “anomaly” values forall the composites. The “chlorophyll anomalies”were then log-transformed and used to model theamplitude and phase of the mean annual cycleobserved over each area throughout the 12-yearseries. The annual mean model is based on anonlinear least square procedure (see Garcia et al.2004 for details), that assumes a sinusoidal formwith a single amplitude over the 46 compositesregistered in a year. The ratio of the varianceexplained by the annual model to the total variance -correlation of determination (r 2 ) - for each timeseries per area was also calculated to verify thegoodness of the model, and here describe theconsistence of a seasonal variability in thechlorophyll concentration. It is important to note thatlow correlation of determination observed per areareflects both the inadequacy of the single amplitude(e.g., seasonal cycles can have more than anoscillation per year) and also interannual variability,but in this case we expect the latter to be moreimportant as the majority of BCS is located intropical and subtropical areas.To observe long-term variability inSeaWiFS Chl anomaly per area, we first applied arunning mean filter (equivalent to 46, or a year of 8-Day mean observations), to create smoothed series,each being normalized by their respective standarddeviation. The filtered data were then subjected tospectral analysis, which used the variancepreservingform of the energy spectrum (Emery &Thomson 2004) so to make the total energy of thesignals observed among areas comparable. These aresimple techniques, that were chosen to accommodateour present goals, but future work should include anumber of more sophisticated time-series techniques(e.g., EOFs; wavelets) to better understand the longtermvariability of chlorophyll fields in the differentPan-American Journal of Aquatic Sciences (2010), 5(2): 236-253