Passive ocean acoustic tomography: theory and experiment

In this paper the Passive Ocean Acoustic Tomography (P-OAT) methodology is presented. This technique, avoiding the use of a dedicated active sound source, estimates the sea water temperature spatial distribution from the received noise emitted from ships of opportunity. The feasibility of the proposed methodology has been confirmed both by test-runs on semi-synthetic data and by the use of real acoustic and environmental data collected during INTIMATE00 experiment performed on October 2000 in the Atlantic Ocean off the Portuguese coasts

Preliminary deployment of Grid-assisted oceanographic applications

Abstract. Grid integration of OGS oceanographic remote instruments and coupled physical-biogeochemical model has been explored in the framework of the EC-FP7 DORII project. We discuss here the first preliminary results achieved, describing the different tools developed with the support of the project consortium. A general background of the Grid technology for the e-Science is also provided.</p

Pre-operational short-term forecasts for Mediterranean Sea biogeochemistry

Operational prediction of the marine environment is recognised as a fundamental research issue in Europe. We present a pre-operational implementation of a biogeochem- ical model for the pelagic waters of the Mediterranean Sea, developed within the framework of the MERSEA-IP Euro- pean project. The OPATM-BFM coupled model is the core of a fully automatic system that delivers weekly analyses and forecast maps for the Mediterranean Sea biogeochem- istry. The system has been working in its current configura- tion since April 2007 with successful execution of the fully automatic operational chain in 87% of the cases while in the remaining cases the runs were successfully accomplished af- ter operator intervention. A description of the system devel- oped and also a comparison of the model results with satel- lite data are presented, together with a measure of the model skill evaluated by means of seasonal target diagrams. Future studies will address the implementation of a data assimila- tion scheme for the biogeochemical compartment in order to increase the skill of the model’s performance

Model Order Reduction for Rotating Electrical Machines

The simulation of electric rotating machines is both computationally expensive and memory intensive. To overcome these costs, model order reduction techniques can be applied. The focus of this contribution is especially on machines that contain non-symmetric components. These are usually introduced during the mass production process and are modeled by small perturbations in the geometry (e.g., eccentricity) or the material parameters. While model order reduction for symmetric machines is clear and does not need special treatment, the non-symmetric setting adds additional challenges. An adaptive strategy based on proper orthogonal decomposition is developed to overcome these difficulties. Equipped with an a posteriori error estimator the obtained solution is certified. Numerical examples are presented to demonstrate the effectiveness of the proposed method

Modeling Carbon Budgets and Acidification in the Mediterranean Sea Ecosystem Under Contemporary and Future Climate

We simulate and analyze the effects of a high CO2 emission scenario on the Mediterranean Sea biogeochemical state at the end of the XXI century, with a focus on carbon cycling, budgets and fluxes, within and between the Mediterranean subbasins, and on ocean acidification. As a result of the overall warming of surface water and exchanges at the boundaries, the model results project an increment in both the plankton primary production and the system total respiration. However, productivity increases less than respiration, so these changes yield to a decreament in the concentrations of total living carbon, chlorophyll, particulate organic carbon and oxygen in the epipelagic layer, and to an increment in the DIC pool all over the basin. In terms of mass budgets, the large increment in the dissolution of atmospheric CO2 results in an increment of most carbon fluxes, including the horizontal exchanges between eastern and western sub-basins, in a reduction of the organic carbon component, and in an increament of the inorganic one. The eastern sub-basin accumulates more than 85% of the absorbed atmospheric CO2. A clear ocean acidification signal is observed all over the basin, quantitatively similar to those projected in most oceans, and well detectable also down to the mesopelagic and bathypelagic layers

Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design

Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these additional degrees of freedom into fidelity assessment has become increasingly challenging. Here, we propose a new method to provide information on the model predictive skill in a concise way. The method is based on the conjoint use of a k-means clustering technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means algorithm and the assessment metrics reduce the number of model data points to be evaluated. The metrics evaluate either the model state accuracy or the skill of the model with respect to capturing emergent properties, such as the deep chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo observations as the sole evaluation data set ensures the accuracy of the data, as it is a homogenous data set with strict sampling methodologies and data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine Service. The model performance is evaluated using the model efficiency statistical score, which compares the model–observation misfit with the variability in the observations and, thus, objectively quantifies whether the model outperforms the BGC-Argo climatology. We show that, overall, the model surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and the mixed layers as well as silicate in the mesopelagic layer. However, there are still areas for improvement with respect to reducing the model–data misfit for certain variables such as silicate, pH, and the partial pressure of CO2 in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed here can also aid in refining the design of the BGC-Argo network, in particular regarding the regions in which BGC-Argo observations should be enhanced to improve the model accuracy via the assimilation of BGC-Argo data or process-oriented assessment studies. We strongly recommend increasing the number of observations in the Arctic region while maintaining the existing high-density of observations in the Southern Oceans. The model error in these regions is only slightly less than the variability observed in BGC-Argo measurements. Our study illustrates how the synergic use of modeling and BGC-Argo data can both provide information about the performance of models and improve the design of observing systems.</p

Challenges for Sustained Observing and Forecasting Systems in the Mediterranean Sea

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