Abstract Ocean water clarity, influenced by marine chlorophyll concentration, significantly alters the distribution of shortwave radiation in the water column. This work aims to assess the effects of varying chlorophyll on the upper-ocean physical properties and their subsequent impact on the atmosphere, using a coupled ocean-atmosphere regional model for the Mediterranean and Black Seas. We performed 11-year (2011–2021) twin-simulation experiments based on different chlorophyll concentrations to estimate the penetration of solar radiation in the ocean. The first simulation used a monthly climatology field of chlorophyll concentrations derived from satellite observations, while in the second experiment, the chlorophyll concentration was kept constant at 0.05 mgm−3 \$\mathrm{m}\mathrm{g}\ {\mathrm{m}}^{-3}\$, representing clear water conditions. Results show that radiative heating driven by chlorophyll amplifies the seasonal cycle of temperature in the upper layers, leading to increased surface warming in summer and surface cooling in winter. Also, higher surface chlorophyll contributes to cooling in subsurface layers throughout the year due to its shading effect. The temperature response to chlorophyll variations is controlled by the mixed layer depth and a balance between (a) direct near-surface radiative heating due to the chlorophyll absorption and (b) indirect cooling resulting from vertical turbulent mixing processes with subsurface waters. The atmosphere moderates the seasonal sea surface temperature (SST) response caused by chlorophyll differential heating primarily through changes in latent heat flux. Ultimately, our simulations suggest that increased surface chlorophyll concentrations enhance the Mediterranean overturning circulation, highlighting the necessity of incorporating realistic optical forcing into regional climate modeling studies.

A Bay of Biscay model configuration is used as a test case to assess the data-based consistency of ensemble-based ocean model uncertainties of several types: [A] built-in stochastic parameterizations at regional ocean scales, [B] ocean model response to a global atmospheric model ensemble and [C] both A and B simultaneously. Ensembles of varying length were generated. In addition to a seasonal-range ensemble, three medium-range ensembles were carried out over successive overlapping segments permitting to compare consistency metrics for different lead times. The largest spread was obtained for the C case, although most of the model uncertainties were attributable to the stochastic ocean parameterizations in A. We addressed the question of which ensemble type and lead time was able to provide the most realistic model uncertainties given observations of SST, sea level, and Chlorophyll a, using a theoretical and diagnostic consistency analysis framework expanded from Vervatis et al. (2021a). In our results, consistency was satisfactory for the stochastic ensembles of types A and C, for the “aged” error cases (but only marginally with respect to the “young” error cases), and whenever physical and biogeochemical uncertainty processes were active in the region and could be detected by the observational networks, such as the onset of the spring shoaling of the thermocline and the phytoplankton abundance primary bloom. Sea level empirical consistency was improved when a wide range of low- to high-frequency errors were included in the signal of dynamic atmospheric process in the data and in the model inverse barometer. These findings provide additional insight that can help configure ensemble-based methods in academic studies and in operational ocean forecasting systems.