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Mathematical algorithms relate satellite data of ocean color with the surface Chlorophyll-a concentration (Chl-a), a proxy of phytoplankton biomass. These mathematical tools work best when they are adapted to the unique bio-optical properties of a particular oceanic province. Ocean color algorithms should also consider that there are significant differences between datasets derived from different sensors. Common solutions are to provide different parameters for each sensor or use merged satellite data. In this paper, we use satellite data from the Copernicus merged product suite and in situ data from the southernmost part of the California Current System to test two widely used global algorithms, OCx and CI, and a regional algorithm, CalCOFI2. The OCx algorithm yielded the most favorable results. Consequently, we regionalized it and conducted further testing, leading to significant improvements, especially in eutrophic and oligotrophic waters. The database was then separated according to (a) dynamic boundaries in the area, (b) bio-optical properties, and (c) climatic conditions (El Niño/La Niña). Regional algorithms were obtained and tested for each partition. The Chl-a retrievals for each model were tested and compared. The best fit for the data was for the regional algorithms that considered the climatic conditions (El Niño/La Niña). These results will allow for the construction of consistent regionally adapted time series and, therefore, will demonstrate the importance of El Niño/La Niña events on the bio-optical properties of the area.

In the ocean, nitrogen availability is an important control of primary production and influences the amount of energy flowing through food webs. Mesoscale eddies play important roles in modulating the spatial distributions of physical and biogeochemical properties in the Gulf of Mexico (GM), including the availability of nitrate + nitrite (NN). In this study, we explore an oceanographic station classification based on the integrated NN stock that we have named the “nitracentric classification” and a classification based on hydrographic variables that we call the Best Fit Variables (BFVs), such as the depth of the 20°C isotherm and the depth of the 26 kg m-3 isopycnal, to identify stations under the influence of mesoscale eddies. We analyzed hydrographic profiles of CTD data and the NN concentrations in discrete samples collected in June 2016 during the oceanographic campaign XIXIMI-5, which was conducted in the deep-water region of the GM. The best station separation was produced when the NN concentration was integrated between the surface and 200 m depth, which was supported by the station classification based on the BFVs. Our classification system produces a better separation between station groups when compared to other classifications that rely on the use of altimetric variables and hydrographic criteria that have been previously employed to study biogeochemical and physical processes in the GM. We obtained parameterizations that accurately predicted the NN profiles between 100–500 m of stations sampled under stratified conditions in two other XIXIMI cruises in the gulf, although the parameterization has to be adapted to obtain accurate predictions under winter mixing conditions. Our results can be used to predict nitrate stocks and profiles based on a single BFV value obtained from the existing hydrographic databases of the GM as well as from CTD data at the time of sampling. The analysis of the CLIVAR Section A22 in the Caribbean Sea indicates that the nitracentric and hydrographic classification methodology developed in this study can also be applied to other oligotrophic basins where mesoscale eddies play important roles in controlling the distributions of hydrographic and biogeochemical properties.

This study proposes a method to detect ocean fronts from in situ temperature and density glider measurements. This method is applied to data collected along the CalCOFI Line 90, south of the California Current System (CCS), over the 2006–2013 period. It is based on image-processing techniques commonly applied to sea surface temperature (SST) satellite data. Front detection results using glider data are consistent with those obtained in other studies carried out in the CCS. SST images of the Multi-scale Ultra-high Resolution (MUR) dataset were also used to compare the probability of occurrence or front frequency (FF) obtained with the two datasets. Glider and MUR temperatures are highly correlated. Along Line 90, frontal frequency exhibited the same maxima near the transition zone (~130 km offshore) as derived from MUR and glider datasets. However, marked differences were found in the bimonthly FF probability with high (low) front frequency in spring-summer for glider (MUR) data. Methodological differences explaining these contrasting results are investigated. Thermohaline-compensated fronts are more abundant towards the oceanic zone, although most fronts are detected using both temperature and density criteria, indicating a significant contribution of temperature to density in this region.