This study examines the impact of different turbidity products on the Aegean Sea surface physical characteristics, by performing twin-experiment simulations using a high-resolution regional ocean model. The turbidity products used include an in-situ based diffuse attenuation coefficient dataset at 490 nm (kd490, in m− 1) and a satellite derived kd490 product. Satellite turbidity products are broadly used in ocean simulations due to their spatiotemporal coverage and algorithm universality. Their validation and empirical components are trained mainly in phytoplankton driven regions and this may cause systematic differences in oligotrophic areas of variable optical properties’ composition. In the Aegean Sea, the in-situ based turbidity product accounts for the contribution of suspended particles in the solar heating profile, having further implications in the surface characteristics. The Aegean Sea upper-ocean thermohaline characteristics and general circulation patterns, reveal distinct differences between the twin-experiment simulations, showcasing mesoscale to locally induced impact of the turbidity variations. The turbidity impact on the air-sea interaction fluxes affects both thermodynamic processes i.e., solar radiation penetration and absorption in the water column, as well as dynamic processes i.e., momentum fluxes due to changes of the sea surface temperature and subsequently to the momentum drag coefficient. The Aegean Sea surface characteristics in the in-situ based turbidity product simulation, show a stronger decoupling between the North and the South Aegean Sea, when compared with the satellite derived turbidity product simulation. These results highlight the importance of incorporating more realistic turbidity products in ocean models, especially for optically complex regions such as the Aegean Sea.

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments.

Accurate modelling of air-sea processes is essential for reliable forecasts of Mediterranean tropical-like cyclones (also known as “Medicanes”). Medicanes occasionally develop in the Mediterranean causing extreme weather conditions with catastrophic potential due to excessive precipitation, windstorms, and coastal flooding. In this work, we investigate how the complexity of ocean-wave-atmosphere coupling and model initialization affect the simulated track and intensity of the Medicane Ianos (2020). Results indicate that the model's initial conditions and the cyclone's development stage are the main drivers of track position errors, while ocean and wave feedback have a significant impact on the intensity and evolution of the cyclone. Compared with an atmosphere-only simulation, an atmosphere-ocean coupled system reproduces the cyclone's SST cooling effect (up to 3.7 °C), in agreement also with the satellite observations thus, reducing the cyclone intensity, as estimated by the minimum MSLP, the 10-m wind speed and the surface enthalpy flux. Adding a wave model to the coupled system, further increases the magnitude of ocean cooling (by about 1.2 °C), due to increased sea surface roughness leading to increased wind stress and enhanced upper ocean mixing. Overall, surface waves are shown to have competing effects on cyclone intensity i.e., negative feedback via increasing the surface momentum flux and positive feedback via increasing the enthalpy flux, the latter being more sensitive to surface roughness rather than to SST modifications brought by the wave coupled system. The turbulent air-sea fluxes under high winds, appear to be very sensitive to sea-state patterns resolved by the coupled models, highlighting the need to improve forecasting systems for extreme weather events in the Mediterranean.