Support the Copernicus Med-MFC activities (2025-2028).
Ocean Sciences, Climate
Research area
The Mediterranean Monitoring and Forecasting Center (MED MFC) of the Copernicus Marine Service provides essential ocean state information for the Mediterranean Sea, focusing on physical, biogeochemical, and wave variables. It supports marine policy, Blue Growth, and scientific innovation. The service delivers real-time and delayed-time products, including ocean analysis, forecasts, and reanalyses from 1987 onwards. The system integrates a coupled hydrodynamic-wave model (NEMO-WW3) and the OceanVar assimilation scheme to improve the quality of forecasts and reanalysis. Key system upgrades include enhanced vertical dynamics, wave-current coupling, and river runoff representation, providing crucial data for climate monitoring and ocean variability studies.
Project goals
My work within the MED MFC project focuses on two research lines. The first involves an intercomparison of CMS Net Primary Production products in the Mediterranean Sea (i.e., three biogeochemical model and two satellite ocean colour products), to understand product consistency and link discrepancies to differences in algorithms or model assumptions. In parallel, I am supporting the development of an Ocean Monitoring Indicator (OMI) for NPP for the Copernicus Ocean State Report 11. In addition, as part of the STRATMA project, I assess the capacity of the current Italian station network to capture the variability of key biogeochemical variables (i.e., chlorophyll, dissolved inorganic nitrogen, total phosphorus, oxygen, salinity). By decomposing time series into different temporal signals and computing correlations between CMS products and in-situ data, I evaluate where and how well these products represent coastal conditions, to improve the monitoring plan.
Computational approach
As I am at the beginning of my work experience within Terabit, one of the key challenges I will face is mastering the necessary computational skills to effectively handle large-scale ocean data. Since the project involves working with extensive datasets, efficient data processing and analysis are critical. To do this, I will rely on high-performance computing resources, such as the G100 and Leonardo clusters, which are designed to handle the substantial computational load. To make the most of these resources, I will need to continuously develop my skills in Linux-based systems and improve my programming abilities, especially in languages like Python. These skills will be essential to navigate the technological challenges of ensuring data quality and managing complex models. Additionally, I I guess I will need to address the challenge of optimising computational performance when working with large observational datasets. I will have to develop a deep understanding of the computational infrastructure and a proactive approach to troubleshooting and optimisation. My role will involve making sure that our products are generated efficiently, accurately, and in a way that can support the demands of real-time forecasting and long-term climate monitoring.
Image
Annual mean net primary production (g/m²/yr) from OC TAC data (cmems_obs-oc_med_bgc-pp_my_l4-multi-4km_P1M). This figure is a draft, part of an initial qualitative comparison of OC TAC data with modelled and satellite data of net primary production (NPP). The comparison aims to provide insights into the agreement and discrepancies between different estimates of NPP across the Mediterranean Sea.
Key results
For the NPP intercomparison, preliminary results from climatologies over 1999–2022 show that the five CMS products broadly agree in terms of monthly magnitudes, but differ in the timing of NPP peaks. Some models show maxima in late winter, consistent with a winter bloom, while others peak in summer, in line with satellite-based estimates. These differences point to structural divergences between modelling systems and satellite-based algorithms, and provide a basis for guiding product selection and interpretation of spatiotemporal trends. For the STRATMA project, we assessed how well the current Italian MSFD station network captures the temporal variability of key biogeochemical variables using CMS products as a reference field. Spatial correlations were computed across multiple temporal signal windows and aggregations. Results show that the fraction of the Italian EEZ well-covered by the network increases with the length of the signal window, with chlorophyll reaching near-complete coverage at 14 months. Oxygen showed the lowest coverage, constrained by data availability at depth. Including or excluding the central monitoring stations had little impact on the results, suggesting they are largely redundant in terms of spatial representativeness.
Resource usage
Within TeRABIT, I used the HPC resources available through CINECA, specifically G100 and Leonardo, primarily for storing and processing CMS datasets. The main activities involved downloading and managing large NetCDF files covering the Mediterranean Sea across multiple years, variables, and products, organising the data into a structured archive, and running preprocessing pipelines including regridding, temporal subsetting, and masking.
What's next
After TeRABIT, the plan is to complete and consolidate the work on both research lines. For the NPP intercomparison, the next steps include extending the analysis to include non-CMS products, finalising the comparison across the full dataset, and preparing a peer-reviewed publication. In parallel, the Ocean Monitoring Indicator for NPP will be completed and submitted for inclusion in the Copernicus Ocean State Report 11. For the STRATMA project, the monitoring plan evaluation will be finalised, including the preparation of the technical reports required by the project, among other tasks such as the definition and evaluation of new suggested transects. These activities rely on the processing of large multi-year, multi-variable CMS datasets, which require substantial computational resources. Access to HPC infrastructure through CINECA is essential to handle the volume of data involved and to run analyses at the spatial and temporal scales required by both projects.
Image
Monthly climatology of NPP (integrated over 0–200 m) and CHL (surface concentration) for the Mediterranean Sea over the common period (January 1999–December 2022) for the Mediterranean Sea (MED REA) and Global (GLO REA) biogeochemistry reanalysis products and the Mediterranean Sea (MED OCTAC) and Global (GLO OCTAC) Ocean Colour products.
Francesco D'Adamo
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale
I am an environmental scientist specializing in forest ecology, climate resilience, and ecosystem services. My experience includes research on forest dynamics, biodiversity, and the impacts of climate change, using data analysis, spatial modeling, and statistical approaches. I have worked with large datasets and multidisciplinary teams, contributing to international research projects. My background also includes fieldwork, scientific communication, and collaboration with stakeholders to support evidence-based environmental policies. Passionate about sustainable management, I combine analytical skills with a broad ecological perspective to address complex environmental challenges.