Description: The Mediterranean community represented in this paper is the result of more than 30 years of EU and nationally funded coordination, which has led to key contributions in science concepts and operational initiatives. Together with the establishment of operational services, the community has coordinated with universities, research centers, research infrastructures and private companies to implement advanced multi-platform and integrated observing and forecasting systems that facilitate the advancement of operational services, scientific achievements and mission-oriented innovation. Thus, the community can respond to societal challenges and stakeholders needs, developing a variety of fit-for-purpose services such as the Copernicus Marine Service. The combination of state-of-the-art observations and forecasting provides new opportunities for downstream services in response to the needs of the heavily populated Mediterranean coastal areas and to climate change. The challenge over the next decade is to sustain ocean observations within the research community, to monitor the variability at small scales, e.g., the mesoscale/submesoscale, to resolve the sub-basin/seasonal and inter-annual variability in the circulation, and thus establish the decadal variability, understand and correct the model-associated biases and to enhance model-data integration and ensemble forecasting for uncertainty estimation. Better knowledge and understanding of the level of Mediterranean variability will enable a subsequent evaluation of the impacts and mitigation of the effect of human activities and climate change on the biodiversity and the ecosystem, which will support environmental assessments and decisions. Further challenges include extending the science-based added-value products into societal relevant downstream services and engaging with communities to build initiatives that will contribute to the 2030 Agenda and more specifically to SDG14 and the UN's Decade of Ocean Science for sustainable development, by this contributing to bridge the science-policy gap. The Mediterranean observing and forecasting capacity was built on the basis of community best practices in monitoring and modeling, and can serve as a basis for the development of an integrated global ocean observing system.

Description: The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean.

Description: Published

Description: s235–s320

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: HPC world is increasing incredibly fast.We can build more resolute and accurate models. Unfortunately,Big Data management pops up when trying to evaluate them.We set up a Python module that does automated assessment of a set of observations,extraction and aggregation in time of ocean model time,running evaluation methods. For the first time execution,we used ocean physics variables and CMEMS reprocessed moorings historical data,two model data from the RITMARE and from CMEMS. The results are promising,because we can process an historical or real time evaluation and it is portable,the evaluation results provides more reliable skill scores.

Description: Published

Description: Barcelona, Spain

Description: 4A. Oceanografia e clima

Description: Since 2016, the Copernicus Marine Environment Monitoring Service (CMEMS) has produced and disseminated an ensemble of four global ocean reanalyses produced at eddy-permitting resolution for the period from 1993 to present, called GREP (Global ocean Reanalysis Ensemble Product). This dataset offers the possibility to investigate the potential benefits of a multi-system approach for ocean reanalyses, since the four reanalyses span by construction the same spatial and temporal scales. In particular, our investigations focus on the added value of the information on the ensemble spread, implicitly contained in the GREP ensemble, for temperature, salinity, and steric sea level studies. It is shown that in spite of the small ensemble size, the spread is capable of estimating the flow-dependent uncertainty in the ensemble mean, although proper re-scaling is needed to achieve reliability. The GREP members also exhibit larger consistency (smaller spread) than their predecessors, suggesting advancement with time of the reanalysis vintage. The uncertainty information is crucial for monitoring the climate of the ocean, even at regional level, as GREP shows consistency with CMEMS high-resolution regional products and complement the regional estimates with uncertainty estimates. Further applications of the spread include the monitoring of the impact of changes in ocean observing networks; the use of multi-model ensemble anomalies in hybrid ensemble-variational retrospective analysis systems, which outperform static covariances and represent a promising application of GREP. Overall, the spread information of the GREP product is found to significantly contribute to the crucial requirement of uncertainty estimates for climatic datasets.

Description: Data from the reanalyses presented in this work are available from the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). Part of this work was supported by the EOS COST Action (“Evaluation of Ocean Synthesis”, http://eos-cost.eu/) through its Short Term Scientific Missions program. The full C-GLORS dataset is available at http://c-glors.cmcc.it. This work has received funding from the Copernicus Marine Environment Monitoring Service (CMEMS).

Description: Published

Description: 287-312

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: The long-term warming of the ocean is a critical indicator of both the past and present state of the climate system. It also provides insights about the changes to come, owing to the persistence of both decadal variations and secular trends, which the ocean records extremely well (Hansen et al., 2011; IPCC, 2013; Rhein et al., 2013; Trenberth et al., 2016; Abram et al., 2019). It is well established that the emission of greenhouse gasses by human activities is mainly responsible for global warming since the industrial revolution (IPCC, 2013; Abram et al., 2019). The increased concentration of heat-trapping greenhouse gases in the atmosphere has interfered with natural energy flows. Currently there is an energy imbalance in the Earth’s climate system of almost 1 W m−2 (Trenberth et al., 2014; von Schuckmann et al., 2016, 2020a; Wijffels et al., 2016; Johnson et al., 2018; Cheng et al., 2019a; von Schuckmann et al., 2020a). Over 90% of this excess heat is absorbed by the oceans, leading to an increase of ocean heat content (OHC) and sea level rise, mainly through thermal expansion and melting of ice over land. These processes provide a useful means to quantify climate change. The first global OHC time series by Levitus et al. (2000) identified a robust long-term 0−3000 m ocean warming from 1948−98. Since then, many other analyses of global and regional OHC data have been performed. Here, we provide the first analysis of recent ocean heating, incorporating 2020 measurements through 2020 into our analysis.

Description: Published

Description: 523–530

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: The formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation, and it has crucial implications on the ecosystem. The objective of the proposed work is to revise the main water mass formation events occurred during the latest 30 years (1987-2016) in the Mediterranean Sea and analyse their impact on biogeochemical properties considering the CMEMS Med-MFC physical and biogeochemical reanalysis data sets (https://doi.org/10.25423/medsea_reanalysis_phys_006_004 and https://doi.org/10.25423/MEDSEA_REANALYSIS_BIO_006_008 respectively). The analysis takes into consideration the four regions where events of intermediate and deep water formation are known to occur: 1) the Gulf of Lions for the Western Mediterranean Deep Waters; 2) the Southern Adriatic Pit for the Eastern Mediterranean Deep Waters; 3) the Cretan Sea for Cretan Intermediate Waters and Cretan Deep Waters; 4) the Rhodes Gyre, the area of formation of the so- called Levantine Intermediate Waters and Levantine Deep Waters. Annual water mass formation rates have been computed using daily mixed layer depth estimates considering the annual maximum volume of water above mixed layer depth with potential density within or higher than specific thresholds, and then divided by seconds per year. The use of different density thresholds reported in literature to identify water masses allows to detect variations of their characteristics over time, as observed from observations. The adopted methodology might underestimate the actual water mass formation rate but, thanks to the assimilation of in situ temperature and salinity profiles and the interactive heat flux correction based on observed satellite sea surface temperature, it permits an accurate estimation of the mixed layer depth and to detect the main open ocean convection events from the selected reanalysis data set. The analysis of chlorophyll and nutrient dynamics during the most significant episodes of water mass formation in North West Mediterranean and South Adriatic Sea highlights that these intense physical processes have an impact on the biogeochemical properties. We verified that dense water formation changes the structure of nutrients fields and might cause precondition for chlorophyll blooms, highlighting a strong link between vertical transport mechanisms and primary productivity. The CMEMS Mediterranean Sea physical reanalysis is able to reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in literature. This result is promising because it allows a constant monitoring of the open ocean deep convection process in the Mediterranean Sea, a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales and the possible effects on the Mediterranean ecosystems.

Description: Published

Description: Vienna

Description: 4A. Oceanografia e clima

Description: We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.

Description: This project has received funding from the European Union Horizon 2020 and Seventh Framework Programmes under grant agreement number 730960 SeaDataCloud.

Description: Published

Description: 611742

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: Ocean temperature observations are crucial for a host of climate research and forecasting activities, such as climate monitoring, ocean reanalysis and state estimation, seasonal-to-decadal forecasts, and ocean forecasting. For all of these applications, it is crucial to understand the uncertainty attached to each of the observations, accounting for changes in instrument technology and observing practices over time. Here, we describe the rationale behind the uncertainty specification provided for all in situ ocean temperature observations in the International Quality-controlled Ocean Database (IQuOD) v0.1, a value-added data product served alongside the World Ocean Database (WOD). We collected information from manufacturer specifications and other publications, providing the end user with uncertainty estimates based mainly on instrument type, along with extant auxiliary information such as calibration and collection method. The provision of a consistent set of observation uncertainties will provide a more complete understanding of historical ocean observations used to examine the changing environment. Moving forward, IQuOD will continue to work with the ocean observation, data assimilation and ocean climate communities to further refine uncertainty quantification. We encourage submissions of metadata and information about historical practices to the IQuOD project and WOD.

Description: Published

Description: 689695

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.

Description: None

Description: Published

Description: Rome, Italy

Description: 4A. Oceanografia e clima