Downscaling hydrodynamics features to depict causes of major productivity of Sicilian-Maltese area and implications for resource management.

Chlorophyll-a (CHL-a) and sea surface temperature (SST) are generally accepted as proxies for water quality. They can be easily retrieved in a quasi-near real time mode through satellite remote sensing and, as such, they provide an overview of the water quality on a synoptic scale in open waters. Their distributions evolve in space and time in response to local and remote forcing, such as winds and currents, which however have much finer temporal and spatial scales than those resolvable by satellites in spite of recent advances in satellite remote-sensing techniques. Satellite data are often characterized by a moderate temporal resolution to adequately catch the actual sub-grid physical processes. Conventional pointwise measurements can resolve high-frequency motions such as tides or high-frequency wind-driven currents, however they are inadequate to resolve their spatial variability over wide areas. We show in this paper that a combined use of near-surface currents, available through High-Frequency (HF) radars, and satellite data (e.g., TERRA and AQUA/MODIS), can properly resolve the main oceanographic features in both coastal and open-sea regions, particularly at the coastal boundaries where satellite imageries fail, and are complementary tools to interpret ocean productivity and resource management in the Sicily Channel.

Self-Organizing Maps-based ocean currents forecasting system.

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

Marine Sediments Remotely Unveil Long-Term Climatic Variability Over Northern Italy.

A deep understanding of natural decadal variability is pivotal to discuss recently observed climate trends. Paleoclimate proxies allow reconstructing natural variations before the instrumental period. Typically, regional-scale reconstructions depend on factors like dating, multi-proxy weighting and calibration, which may lead to non-robust reconstructions. Riverine records inherently integrate information about regional climate variability, partly overcoming the above mentioned limitation. The Po River provides major freshwater input to Eastern Mediterranean, as its catchment encompasses a large part of Northern Italy. Here, using historical discharge data and oceanographic measurements, we show that Po River discharge undergo robust decadal fluctuations that reach the Ionian Sea, ~1,000 km South of Po River delta, through propagating salinity anomalies. Based on this propagation, we use a high-resolution foraminiferal δ(18)O record from a sediment core in the Ionian Sea to reconstruct North Italian hydrological variability on millennial-scale for the first time. The reconstruction reveals highly significant decadal variability that persists over the last 2,000 years. Many reconstructed extremes correspond to documented catastrophic events. Our study provides the first millennial-scale reconstruction of the strength of decadal hydrological variability over Northern Italy. It paves the way to assess the persistence of large-scale circulation fingerprints on the North Italian climate.