###### Abstract  
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 Nino/La Nina). 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 Nino/La Nina). These results will allow for the construction of consistent regionally adapted time series and, therefore, will demonstrate the importance of El Nino/La Nina events on the bio-optical properties of the area. Chlorophyll-_a_; remote sensing; ocean color algorithms; El Nino-Southern Oscillation (ENSO) +
Footnote †: journal: remote sensing  
0000-0001-9000-0000-0000]B. A. A.  
each sensor [5]. Because of all of these facts, continuous climate data records cannot be constructed [6]. To enhance the satellite data's time coverage and, therefore, provide consistent time series, the merging of data from different sensors becomes imperative [2; 7; 8]. Operational satellite sensor constellations, such as Copernicus, provide ocean color data as elements of an integrated, sustained observing system [8] that has better global coverage and allows for the study of phenomena of a larger timescale than what an individual sensor provides [3]. The efficacy of such merged data must be regionally validated to ensure that the algorithms used are optimal. Bio-optical algorithms that relate ocean surface Chlorophyll-\(a\) concentration (Chl-\(a\)) with satellite data are predominantly empirical, deriving their parameters from in situ data [9]. Level-\(3\) Chl-\(a\) images available on the NASA Ocean Color website employ an OCx algorithm merged with the color index algorithm (CI) [10] with different parameters for different sensors [5]. However, these global algorithms might not be regionally optimal [6]. For instance, some algorithms underestimate the values of Chl-\(a\) in the California Current area [6; 11], while others overestimate them in the Northern Bering Sea and Chukchi Sea [12; 13]. Thus, evaluating global algorithms' performance at regional levels becomes paramount, especially when using merged satellite data. Such evaluations pave the way for robust, long-term data series, enhancing our understanding and management of ocean ecosystems. This research aims to evaluate various bio-optical algorithms for the southern part of the California Current System (CCS), offshore from the Baja California peninsula, and, if need be, to provide a regionally optimal working algorithm. The CCS, a region known for its coastal upwelling, eddies, and fronts, has been under scrutiny since 1949 via the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program focusing on the California coast, and since 1997 via the Mexican Research Program of the California Current (IMEOCAL), which centers on the region off the coast of Baja California. This region is a unique transitional zone where colder subarctic waters mingle with warmer tropical and subtropical currents [14; 15].