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Diel vertical migration (DVM) is a survival strategy adopted by zooplankton that we investigated in the Corsica Channel using acoustic Doppler current profiler (ADCP) data from April 2014 to November 2016. The principal aim of the study is to characterize migration patterns and biomass temporal evolution of zooplankton along the water column. The ADCP measured vertical velocity and echo intensity in the water column range between about 70 and 390 m (the bottom depth is 443 m). During the investigated period, zooplanktonic biomass had a well-defined daily and seasonal cycle, with peaks occurring in late winter to spring (2015 and 2016) when the stratification of the water column is weaker. Zooplanktonic biomass temporal distribution in the whole water column is well correlated with biomass of primary producers, estimated with satellite data. Zooplanktonic blooming and non-blooming periods have been identified and studied separately. During the non-blooming period zooplanktonic biomass was most abundant in the upper and the deep layers, while during the blooming period the upper-layer maximum in zooplanktonic biomass disappeared and the deep layer with high zooplanktonic biomass became thicker. These two layers are likely to correspond to two different zooplanktonic communities. The evolution of zooplanktonic biomass is well correlated with chlorophyll, with phytoplankton biomass peaks preceding the upper-layer secondary production by a lag of about 3.5 weeks. Nocturnal DVM appears to be the main pattern during both periods, but reverse and twilight migration are also detected. Nocturnal DVM was more evident at mid-water than in the deep and the upper layers. DVM occurred with different intensities during blooming and non-blooming periods. One of the main outcomes is that the principal drivers for DVM are light intensity and stratification, but other factors, like the moon cycle and primary production, are also taken in consideration.

The use of Multi-model Super-Ensembles (SE) which optimally combine different models, has been shown to significantly improve atmospheric weather and climate predictions. In the highly dynamic coastal ocean, the presence of small-scales processes, the lack of real-time data, and the limited skill of operational models at the meso-scale have so far limited the application of SE methods. Here, we report results from state-of-the-art super-ensemble techniques in which SEPTR (a trawl-resistant bottom mounted instrument platform transmitting data in near real-time) temperature profile data are combined with outputs from eight ocean models run in a coastal area during the Dynamics of the Adriatic in Real-Time (DART) experiment in 2006. New Kalman filter and particle filter based SE methods, which allow for dynamic evolution of weights and associated uncertainty, are compared to standard SE techniques and numerical models. Results show that dynamic SE are able to significantly improve prediction skill. In particular, the particle filter SE copes with non-Gaussian error statistics and provides robust and reduced uncertainty estimates.