The aim of this work is to study the influence of errors on the accuracy of estimation of absorbed solar energy in the visible spectral range in the water column of the Barents Sea occurring due to the use of various satellite ocean color chlorophyll-retrieval algorithms. The estimates of absorbed energy are based on data obtained during field experiments conducted in various parts of the Barents Sea in June-August 2021, as well as on modeling results. A new regional algorithm for evaluation of chlorophyll concentration in the Barents Sea (B22) is presented as a data source. This algorithm provides more accurate estimates compared to the standard MODIS algorithm under conditions of coccolithophore bloom. Comparing of the results of hydro-optical modeling to the data of shipboard hydro-optical measurements made it possible to validate the obtained data and confirmed the correctness of the selected models. Furthermore, we calculate the parameters of underwater light fields and the absorbed thermal energy in the visible spectral range in the seawater column, showing that differences in the chlorophyll concentration due to the accuracy of satellite bio-optical algorithms (30–50%) have little effect on the vertical distribution of solar energy absorbed in the seawater column.

Data on the light absorption by seawater and its components are needed in many theoretical and practical aspects of marine science and engineering. However, up to now, there is a lack of such data for the northeastern part of the Black Sea. This article presents the data on light absorption measured by a portable integrated cavity absorption meter (ICAM) spectrophotometer in the Gelendzhik region of the Black Sea during field studies in June 2017 and 2018, together with other bio-optical and oceanographic data from in situ measurements and satellite observations. In 2018, the elevated values of the colored dissolved organic matter (CDOM) absorption in the surface layer were observed concurrently with high values of salinity, contradicting the idea of river runoff being the main CDOM source. The vertical profiles of salinity differed in 2017 and 2018, especially in shallow waters; in the upper layer, the salinity increased from 17.1 psu in 2017 to 17.8 psu in 2018, while the values of CDOM absorption increased from 0.10 to 0.16 m⁻¹. The analysis of available hydrometeorological data pointed to intensive vertical mixing due to the strong wind forcing as a main factor in increasing values of both salinity and the CDOM absorption in the surface layer in 2018.

Summer blooms of potentially harmful cyanobacteria are common in the Baltic Sea. Under clear sky conditions, the cyanobacterial blooms are easily detectable from space. We propose a new regional algorithm for cyanobacteria biomass estimation from satellite data in the eastern part of the Gulf of Finland, developed on the basis of field measurements in July–August 2012–2014. The multi-regression equation defines the cyanobacteria biomass as a function of the particle backscattering coefficient and chlorophyll concentration. The use of this equation provides the best performance in comparison to the linear one, which is reflected in both R² and RMSE values (0.61 and 272 mg m⁻³ respectively). Unlike other algorithms, which determine only the cyanobacteria bloom area in the Baltic Sea, our algorithm allows the determination of both a bloom area and its intensity. Considering the algorithm errors, the bloom detection threshold has been shifted from the 200 mg m⁻³ determined by biologists to 300 mg m⁻³. Based on data from the MODIS-Aqua satellite ocean color scanner, the spatial and temporal variability of cyanobacterial blooms in this region from 2003 to 2022 was analyzed. Significant interannual variability of cyanobacteria biomass was revealed in the central part of the studied region, with minimum values in 2014 and maximum in 2004. The record bloom during the studied period occurred in July 2004 (the average cyanobacteria biomass was 780 mg m⁻³). The weakest blooms were observed in 2009, 2010, and 2014, when both in July and August, the bloom areas did not exceed 30% of the study region.