OS4.8

The Copernicus Marine Service implemented by Mercator Ocean International (MOi) provides operational, regular, and systematic reference information on blue/white/green ocean state for the global ocean and European regional seas. The service is unique in the world for its coverage and comprehensiveness; its balance between state-of-the-art science and operational commitments; and the consistency of its portfolio where satellite and in situ observations, and 3D model simulations are proposed in coherence to describe, monitor, and forecast the ocean sate. Thirty-five thousand expert downstream services and users are connected to the service. The Copernicus Marine Service responds to public and private user needs and supports policies related to all marine and maritime sectors.

An overview of Copernicus Marine Service achievements during the period 2015-2021 will be first given. Major advances have been achieved.  The offer for the blue, green and white ocean has been regularly improved with new products and marine parameters (surface currents, waves, pH, CO2, icebergs), higher resolution and representation of more dynamical processes, improved product quality and product quality assessment, more satellite data (Sentinels) used as upstream inputs and improved algorithms,  longer time series of reprocessed in situ and satellite data and ocean reanalyses, ocean monitoring indicators and ocean state reports and new visualisation tools.  The uptake of Sentinel-1 (sea-ice, waves), Sentinel-3 (altimetry and surface currents, sea-surface temperature, ocean colour) data and Sentinel-2 (turbidity, ocean colour) has, in particular, greatly improved Copernicus Marine Service offer.  

Drivers and plans for Copernicus 2 (2021-2027) will then be presented. The objective is to further establish Copernicus Marine Service products as a worldwide reference, continue to foster the service uptake and respond to increasing and pressing user and policy needs for improved ocean monitoring and prediction capabilities. MOi in close interaction with the European Commission and member states and with the advice of its scientific and user committees has developed an ambitious plan for the next 7 years that allows a staged implementation depending on budget implementation, user needs and priorities and feasibility/maturity. Three levels of implementation have been identified: baseline, enhanced continuity and new services. Baseline will be implemented from the start of Copernicus 2 to ensure the continuity of the present service. The enhanced continuity and new services streams will build from present and future H2020 and Horizon Europe R&D projects and will be developed depending on budget and priorities. A strong priority is, in particular, to offer new services for the coastal ocean through a co-design and co-development approach between the EU Copernicus Marine Service and coastal marine services operated by member states.

The challenging issues to establish a comprehensive monitoring and forecasting of the global ocean requires international cooperation. The Copernicus Marine Service has established important partnerships (e.g., GOOS and IOC, OceanPredict, GEO and GEO Blue Planet).  The UN Decade of Ocean Science will provide a unique opportunity and framework to strengthen this very much needed international cooperation.

How to cite: Le Traon, P.-Y.: The Copernicus Marine Service: achievements and future plans, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1102, https://doi.org/10.5194/egusphere-egu22-1102, 2022.

In the framework of the Copernicus Marine Service (CMEMS), the Black Sea Monitoring and Forecasting Centre (BS-MFC) is the European reference service for the provision of ocean analyses, forecasts and reanalyses in the Black Sea basin. It ensures a high level of efficiency in terms of operations, science and technology for the Black Sea predictions and monitoring of the physical including waves and biogeochemical processes. This work provides an overview of the BS-MFC modelling systems together with a description of the main operational products delivered through CMEMS interfaces. The product catalogue includes near real time and multi-year datasets, including interim recently introduced in the offer and developed in the perspective of future provisioning of monitoring indicators. The BS Physics unit delivers analysis and forecast products over a domain of about 2.5 km in horizontal with 121 vertical levels, based on NEMO v4.0 online coupled to OceanVar for the assimilation of insitu and satellite observations. It implements open boundary conditions at the Marmara Sea by means of high resolution ocean fields provided by the Unstructured Turkish Straits System. The BS Biogeochemistry unit delivers near real time and multi year products over a domain of about 3 km resolution and 31 levels, based on NEMO v3.6 online coupled to BAMHBI, able to assimilate daily L3 satellite chlorophyll observation by using the Ocean Assimilation Kit developed as part of the SANGOMA project. The BS Waves unit delivers analysis and forecast products over the same domain of BS Physics. The model is based on WAM Cycle 6, forced by surface currents and sea surface height provided by the BS Physics forecasting system. BS-MFC near real time systems are forced by ECMWF IFS analysis and forecast atmospheric fields. BS-MFC multi year systems are instead forced by ECMWF ERA5 reanalysis atmospheric fields and provide past reconstruction of the ocean state in the Black Sea at the resolution of about 3 km horizontally. Since May 2021, interim datasets are also provided with the objective to support marine monitoring capacities in the area. The work focuses on the product quality assessment of relevant BS-MFC variables and on future upgrades for improving the accuracy of forecast and reanalysis.

How to cite: Peneva, E., Ciliberti, S. A., Gregoire, M., Staneva, J., Palazov, A., Coppini, G., Lecci, R., Matreata, M., Marinova, V., Masina, S., and Pinardi, N. and the Black Sea Monitoring and Forecasting Centre: Recent advancements in the evolution of the Black Sea Monitoring and Forecasting Centre, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11605, https://doi.org/10.5194/egusphere-egu22-11605, 2022.

This work presents the last version of the operational analysis and forecasting system of the physical variables and evolution plans in the Black Sea, as developed in the framework of the Copernicus Marine Service for the Black Sea Monitoring and Forecasting Centre. 

The modelling system consists of NEMO v4.0 hydrodynamical model at 1/40º resolution in horizontal and 121 vertical levels, online coupled to OceanVar for the assimilation of available insitu and satellite observations. Recently, the system has been upgraded to handle the operational historical and forecasting discharge data for the Danube River as provided by the NIHWM (Romania). Major details on the model setup and product are also available at https://resources.marine.copernicus.eu/product-detail/BLKSEA_ANALYSISFORECAST_PHY_007_001/INFORMATION. 

This contribution will focus on the description of the Black Sea Physics production unit operational capacity and on the accuracy of the delivered products by computing relevant metrics for analysis fields and forecasting skills.

How to cite: Ciliberti, S., Jansen, E., Azevedo, D., Causio, S., Ilicak, M., Gunduz, M., Matreata, M., Stefanizzi, L., Creti', S., Lecci, R., Lima, L., Aydogdu, A., Peneva, E., Coppini, G., Masina, S., and Pinardi, N.: Black Sea Physics Analysis and Forecasting System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7369, https://doi.org/10.5194/egusphere-egu22-7369, 2022.

How to cite: Escudier, R., Reffray, G., Hamon, M., Levier, B., and Gutknecht, E.: Improving the assimilation part of the near real time system of IBI, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11410, https://doi.org/10.5194/egusphere-egu22-11410, 2022.

European coasts are often exposed to severe storms that trigger extreme water-level conditions, leading to coastal flooding and erosion. With the objective to provide useful and timely information on coastal flood risk from extreme sea level events at European scale, a proof-of-concept for a European Coastal Flood Awareness System (ECFAS) is being developed as part of a European Union’s Horizon 2020 project. ECFAS could contribute to the evolution of the Copernicus Emergency Management Service.

ECFAS uses state-of-the-art coastal monitoring and forecasting technologies and datasets suited to regional-to-local scale assessments. For its early-warning component, ECFAS capitalizes on the ocean forecasting systems operated by the Copernicus Marine Service (CMEMS). Such forecasts are combined with a coastal-stretch-specific, pre-computed flood catalog to provide a mapping of the inundation depth and extent. Consecutively, the ECFAS-Rapid and Risk and Recovery Mapping component is activated which allows an operational assessment of the socio-economic impact of marine storms.

In this presentation, we focus on the skill of the CMEMS ocean hydrodynamic models that provide the marine hazard component to the system. We apply a methodology to detect storm-driven extreme sea level events from tide-gauge records and validate the event peak representation and forecast lead time impact. For best analyses, results show satisfactory results but a general underprediction of peak magnitudes of 10% for water levels and 18% for surges across the detected storm events. In average, the models are capable of independently flagging 76% of the observed events. Forecasts show insignificant lead time impact up to a 4-day lead time, demonstrating the suitability of the systems for early warning applications. Finally, by separating the surge and tidal contributions to the extremes, we identify the source of the prediction misfits and provide recommendations for the evolution of the CMEMS forecasting models for coastal flooding applications.

The ECFAS (European Coastal Flood Awareness System) project has received funding from the EU H2020 research and innovation programme under Grant Agreement No 101004211.

How to cite: Irazoqui Apecechea, M., Melet, A., Armaroli, C., Ciavola, P., and Fernandez Montblanc, T.: European Coastal Flood Awareness System (ECFAS): forecasting extreme coastal water-levels at European scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5729, https://doi.org/10.5194/egusphere-egu22-5729, 2022.

A new approach for the assessment of the quality of physics analysis and forecast products (model hereafter) of the Copernicus Marine Service (CMEMS) is proposed. The method is based on the machine learning K-means clustering algorithm. The main goal of the method is to perform clustering of the bivariate model sea water temperature and salinity errors. The model errors are defined by subtracting a measured value from the corresponding model value. We use the data from in situ near real-time observations of CMEMS. Sea water temperature and salinity products are evaluated with simultaneously measured temperature and salinity data by forming a two-dimensional error space (model minus measurements) and performing a clustering procedure in it. This method enables to consider all available measurements and assigns a quantitative quality measure to each spatial location and time instant where and when the measurements exist.

The quality assessment of physics analysis and forecast products is performed for the Baltic Sea, the Atlantic - European North West Shelf, the Atlantic - Iberian Biscay Irish region, the Mediterranean Sea and the Black Sea for the year 2021. For each regional sea, there are about 100 000 to 1 000 000 simultaneous temperature and salinity data pairs available for comparison. K-means clustering of model errors was done using five clusters for each region.

An error cluster of good quality of the model (location of dominant centroid with temperature and salinity bias close to zero) made up about 50% for the Baltic Sea, 65% for the Atlantic - European North West Shelf, 70% for Mediterranean Sea and Atlantic Iberian Biscay Irish region and 90% for the Black Sea of all comparison data pairs. We would like to note that shallow coastal areas were poorly covered by measurement data, which disabled assessment of model quality there. In the Baltic Sea, spatial distribution of model errors showed that simulated temperature and salinity fields in the Gulf of Finland had lower quality than in the rest of the Baltic Sea sub basins. In the Gulf of Finland, a significant share of model errors belonged to two clusters with overestimated salinity and temperature (dS=1.8, dT=2.0 °C and dS=0.5, dT=0.8 °C). In the Atlantic - European North West Shelf and in the Atlantic - Iberian Biscay Irish region, temperature and salinity were underestimated (dT=-2.7 °C, dS=-0.3 and dT=-1.8 °C, dS=-0.2, respectively) between a depth of 1000 m and 1300 m. In the Atlantic - Iberian Biscay Irish region, the Mediterranean Sea and the Black Sea, a separate cluster emerged in each region, which indicated a severe mismatch of the model and the measured data. A good quality of physics analysis and forecast products of the CMEMS is achieved using data assimilation of measured salinity and temperature profiles, which overlap with the data used in this assessment study.

How to cite: Raudsepp, U., Maljutenko, I., Verjovkina, S., and Lagemaa, P.: An assessment of the quality of physics analysis and forecast products of the European regional seas in 2021 using K-means clustering algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8218, https://doi.org/10.5194/egusphere-egu22-8218, 2022.

For more than 23 years, the multisatellite DUACS system has been providing  high quality multi-mission altimetry Sea Level products for oceanographic applications, climate forecasting centers, geophysics and biology communities. They consist in directly usable and easy to manipulate Level 3 (along-track cross-calibrated Sea Level Anomaly SLA) and Level 4 (multiple sensors merged gridded gap-free) products. Global and regional datasets (Arctic Ocean, European Seas ...) are available.

A full reprocessing of these products is carried out almost every 3 years, based on the state-of-the-art Level 2 to Level 4 algorithms. In Decembre 2021, a new version will be available within the Copernicus Marine Environment and Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S)  covering 28 years of altimetric data (i.e. almost a century of cumulated data using 24 altimetric missions). This version benefits from major improvements associated with new altimeter and mapping standards.

Here, we report the first results of this DUACS DT2021 multi-mission reprocessing. We first describe the main steps of the altimeter production system. Then, we discuss the characteristics and limits of the different products (C3S, CMEMS) in order to help the ocean and climate communities on their optimal use for validation, assimilation activities and other scientific studies. Several comparisons with independent datasets (along-track, drifters, tide gauges) show that a significant improvement has been achieved at mesoscale with this new version: almost 20% of SLA error reduction at wavelengths [65, 500km] and around 10% of geostrophic currents error reduction compared to the previous version (DT2018). At decadal time scale, the trend of the global mean sea level has been estimated to 3.4 +/- 0.4 mm/yr. (90% Confidence Interval), in line with other estimates and previous reprocessing DUACS-DT2018. New altimeter corrections are also available for the users (internal waves, correction for the Topex-A instrumental drift, flag ice).

How to cite: Faugère, Y., Taburet, G., Ballarotta, M., Pujol, I., Legeais, J. F., Maillard, G., Durand, C., Dagneau, Q., Lievin, M., Sanchez Roman, A., and Dibarboure, G.: DUACS DT2021: 28 years of reprocessed sea level altimetry products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7479, https://doi.org/10.5194/egusphere-egu22-7479, 2022.

The Copernicus Marine Services is operationally producing forecasts and reanalysis of biogeochemical properties such as nutrients, phytoplankton biomass and carbon chemistry, in the ocean. A key part of these model products is the assimilation of observed biogeochemical properties. Both the methods for assimilating biogeochemical observations and the observation systems are constantly evolving. In this talk we will give an overview over the present capabilities of biogeochemical data assimilation within the Copernicus Marine Services. Presently, biogeochemical data assimilation services cover a range of spatial scales, from global ocean to regional seas, methods, and observations assimilated, which are based both on in situ and remote satellite measurements. We will also present the future evolution that is presently being prepared and researched within Copernicus Marine Services as well as in research projects, such as H2020 SEAMLESS. This includes exploring the prospect of assimilating new types of observations from BGC-Argo, gliders and from remote sensing.  We will also present plans for moving towards ensemble assimilation and, given the tight connection between physical and biogeochemical variability, steps towards simultaneously constraining physical and biogeochemical model properties.

How to cite: Samuelsen, A. and Teruzzi, A.: Biogeochemical data assimilation in Copernicus Marine Services - status and future evolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4979, https://doi.org/10.5194/egusphere-egu22-4979, 2022.

The presentation focuses on two key points in bio-geochemical ensemble based data assimilation: the ensemble generation methods as well as the impact of the data assimilation on various bio-geochemical processes. We conduct the data assimilation experiments from October 2014. These are preparations for a future reanalysis product provided by the Baltic Monitoring and Forecasting Centre (BAL-MFC), which will cover the years 1993 – 2021. The Local Error Subspace Kalman Transform Filter (LESKTF) algorithm in the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) is applied for data assimilation of profile data from the SHARK database on a daily basis. After the daily analysis is performed, the mean of the ensemble members is used in the Nemo-ERGOM model system. This method is used operationally by the BAL-MFC.

Effects of the number of ensembles, transformations, generation techniques and deflation of the ensemble are explored and verified in our ensemble generation studies. Rank histograms, skewness and kurtosis of the ensembles before and after assimilation are computed.

The influences of dissolved oxygen profile data assimilation on the nutrients in deep layers are studied and compared with the integral influences of univariate data assimilation of dissolved oxygen, nitrate, phosphate and ammonium on the same variables. Validation results of the univariate data assimilation scheme are presented and discussed in regards to quality enhancement for the future reanalysis product.

How to cite: Düsterhöft-Wriggers, W., Spruch, L., Lindenthal, A., Li, X., Lorkowski, I., and Team, B.: Ensemble based data assimilation of bio-geochemical profile data in the Baltic Sea with a Nemo-ERGOM model system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9599, https://doi.org/10.5194/egusphere-egu22-9599, 2022.

We introduced feedback from the simulated biogeochemistry to physics in the framework of the CMEMS operational physical-biogeochemical model of the North-West European Shelf. Using this development we demonstrate that light attenuation by the biogeochemical tracers has a significant impact on ocean heating in the upper 200m of the water column. We also show that the simulated temperature is sensitive to the modelling scheme representing the underwater light attenuation, i.e in how it resolves spectra, direction and the optically active tracers. We will discuss in detail the impact of these developments on the research version of the CMEMS operational model that includes assimilation of temperature, salinity and chlorophyll.

How to cite: Skakala, J., Bruggeman, J., Ford, D., and Ciavatta, S.: The impact of marine biogeochemistry on physics and its consequences for the modelling of North-West European shelf seas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1523, https://doi.org/10.5194/egusphere-egu22-1523, 2022.

Large aggregations of the copepod Calanus finmarchicus occur each spring in the shelf-slope-oceanic regions off the Lofoten-Vesterålen Islands where productive fisheries have traditionally supported local and global economies. The retention and off-shelf transport of C. finmarchicus populations were studied by analyzing ocean color remote sensing and satellite altimetry data between 2010 and 2019 and employing a Lagrangian Coherent Structures (LCS) model. Results revealed the existence of a transport barrier reoccurring at the shelf break that retains C. finmarchicus on the shelf for 30-70 days in the spring when C. finmarchicus were seasonally ascending to the surface layer. The analysis of baroclinic and barotropic energy conversions indicated that the topographically steered Norwegian Atlantic Current (NwAC) is the primary mechanism in the formation of the transport barrier, which restricts exchanges of C. finmarchicus populations between shelf and oceanic waters. In the mid- or late April, an increase in baroclinicity leads to an increase in mesoscale eddies generated on the shelf break near Lofoten-Vesterålen Islands, breaking down transport barriers and causing off-shelf transport of C. finmarchicus. The transport barrier predictably reoccurs in early spring which supports the entrapment of C. finmarchicus in the shelf region.

How to cite: Dong, H., Zhou, M., Hu, Z., Zhang, Z., Zhong, Y., Basedow, S., and Smith, W.: Transport Barriers and the Retention of Calanus finmarchicus on the Lofoten Shelf in Early Spring , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6005, https://doi.org/10.5194/egusphere-egu22-6005, 2022.

 Advanced data assimilation methods can improve the forecast of Arctic sea ice, which has been widely used in climate modeling systems to merge observations into simulations. We apply the deterministic Ensemble Kalman filter (DEnKF) to a Lagrangian sea ice model, neXtSIM for sea ice forecast. neXtSIM is computationally solved on a time-dependent evolving mesh, causing a key challenge for applying the EnKF since the mesh grid number and positions are generally different in each ensemble member. The DEnKF analysis is performed on a fixed reference mesh, where model variables are interpolated between the reference mesh and the individual ensemble meshes before and after the assimilation. An ensemble-DA forecasting system for Arctic sea ice forecast based on neXtSIM is built by assimilating the OSI-SAF sea ice concentration (SIC) and the CS2SMOS sea ice thickness (SIT). The ensemble is generated by perturbing atmospheric and oceanic forcing online throughout the forecast. We evaluate the impact of sea-ice assimilation on the Arctic winter sea-ice forecast skills against the satellite observations and a free run during the 2019-2020 Arctic winter. Significant improvements in modeled SIT indicate the importance of assimilating CS2SMOS thickness. While the improvement of SIC and ice extend are clearly observed only in the case with daily assimilating OSI-SAF SIC, which avoids the constraint of daily loaded ocean variables. We found that assimilating a special observation gives the best forecast skill of the relevant variables. With a proper assimilation strategy, neXtSIM as a stand-alone sea ice model could perform computationally efficiently and maintain good forecast skills compared with coupled models.

How to cite: Cheng, S., Chen, Y., Aydogdu, A., Bertino, L., Carrassi, A., Rampal, P., and K. R. T. Jones, C.: Improved Arctic sea ice forecasting by combining ensemble Kalman filter with a Lagrangian sea ice model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1113, https://doi.org/10.5194/egusphere-egu22-1113, 2022.

This study aims to analyse long-term interannual changes in extreme winds and waves in the Black Sea. Severe wave conditions from 1979 to 2020 are detected using the 99th percentile of the significant wave height (SWH), based on the method proposed in (Weisse and Günther, 2007; Staneva et al., 2020a). Long-term spatial wave statistics of the Black Sea are then obtained based on the annual trend of 99th percentile SWH and the number, lifetime, and intensity of extreme events occurring between 1979 and 2020. In addition, the variability of these extreme event characteristics is demonstrated. Besides, wave reanalysis of the Black Sea is used to investigate intraannual variation and long-term wave energy potential change. Hence, wave power and wind statistics are shown for Black Sea CMEMS multiyear products to identify the most suitable areas for wave energy exploitation and offshore wind power potential and to determine the safe and efficient design, installation and operation of marine energy sector assets. The results reveal that the average number of storm events is the highest in the eastern basin. In contrast, the average lifetime reaches a maximum on the southwestern coast. Intensity peaks in the same region as the lifetime but is also high in the basin interior. Spatial mean extreme event analyses show a slight increase in event numbers and intensity, but decreasing trends for the event lifetime and maximum area of storm events. In regions where wave conditions are strong, there have been increases in extremes relative to normal conditions in recent years. This can significantly affect designs. In terms of wave energy, mean wave power peaks in the southwestern area of the Black Sea. The wave power trend follows a pattern similar to that of the SWH with a pronounced east–west difference; its variation is higher, resulting in a coefficient of variation (CoV) of ~2.5.

How to cite: Staneva, J., Ricker, M., Akpinar, A., Behrens, A., Giesen, R., and von Schuckmann, K.: Long-term interannual changes in extreme winds and waves in the Black Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6629, https://doi.org/10.5194/egusphere-egu22-6629, 2022.

Monitoring and assessment in the Baltic Sea region have been coordinated by HELCOM (Helsinki Commission) since 1979, with the first periodic assessment published in 1986. HELCOM latest assessments are based on indicators that reflect the achievement or non-achievement of good environmental status (GES) in the sub-basins of the Baltic Sea. For this purpose, HELCOM has developed a set of core indicators, with each of them having a quantitative GES threshold.

This comprehensive indicator-based system requires long-term and systematic measurements to minimize the influence of natural (spatial and temporal) variability on the trend estimates. State indicators and background hydrographic characteristics (as drivers of natural variability) have to be calculated and evaluated to distinguish pressure-induced changes/trends. However, routine environmental monitoring is carried out with a low spatial and temporal resolution. While model data could be used as a substitute, they are not applied yet largely due to shortcomings in accuracy and overall mistrust of model products.

We assessed the possibility of using the model and remote sensing data provided by Copernicus Marine Environment Monitoring Service (CMEMS) to calculate HELCOM eutrophication indicators and influencing hydrographic characteristics or extreme events like marine heatwaves and coastal upwelling events. Reanalysis products BALTICSEA_REANALYSIS_BIO_003_012 and BALTICSEA_REANALYSIS_PHY_003_011 together with remote sensing product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 were used for state indicators and hydrographic characteristics, respectively.

Selected HELCOM eutrophication indicators were calculated following HELCOM methodology as average concentrations in the surface layer (0 – 10 m) for winter (DIN and DIP), summer (Chl-a) and whole year (TN and TP). Indicator calculations show a steady decline in concentrations of all nutrient compounds in recent years for all basins in the north-eastern Baltic Sea that is not confirmed by the measurements. We suggest a way forward for harvesting monitoring data prior to their official submission deadline and producing interim reanalysis products to improve the confidence of assessments based on CMEMS products.

For occurrence and intensity of heatwaves, a climatology (1986-2020) of sea surface temperature (SST) and the 90^th percentile was calculated for each grid cell in the Baltic Sea. The heatwave was identified when SST exceeded the 90^th percentile value for a site and date. For upwellings, the SST data were analyzed along transects from coast to coast in either North-South or East-West direction. Every grid point with the local SST value >2 °C colder than the transect average was assigned to a coastal upwelling event. The results based on two selected products (reanalysis and remote sensing) agree well except in years/seasons when the seasonal thermocline was very shallow (e.g. 2018). We demonstrate that CMEMS products covering the surface layer dynamics in the Baltic Sea (e.g. SST) can be used in describing long-term trends and inter-annual variability in hydrographic conditions (also extreme events) and serve as background information for indicator-based eutrophication assessments.

How to cite: Samlas, O., Stoicescu, S.-T., She, J., and Lips, U.: Potential of CMEMS products for assessing eutrophication status and natural variability in the north-eastern Baltic Sea., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11280, https://doi.org/10.5194/egusphere-egu22-11280, 2022.

A fleet of more than 50.000 cargo ships worldwide has an enormous demand for energy resulting in considerable emissions. According to the 4th International Maritime Organization (IMO) global greenhouse gas (GHG) study, maritime transport emitted around 1,056 million tonnes of CO₂ in 2018 and was responsible for about 2.9% of the global anthropogenic CO₂ emissions. While the emissions per tonne and nautical mile have been reduced by almost 30% in the last decade, the overall emissions of cargo ships increased by more than 10% (up to 30% in some models) due to the growing demand. In order to tackle this increase, the MariData project conducts research on how an improved hydrodynamical modeling and the use of detailed predictions and data of sea state and environmental conditions can reduce the energy demand and hence emissions of cargo ships. 

In this set-up, a cloud-based geo data platform takes on a central role, which combines different data sets from CMEMS, GFS, recorded ship trajectories and further data sources. The geo platform acts as a data broker and provider as well as a machine learning environment for data mining and route predictions. One of its use cases is a data driven machine learning approach where freely available records of AIS data are combined with sea state and weather information and serve as a training set for a random forest regression. This model is capable of predicting the expected speed of cargo ships (characterized by width, length and draught) based on the sea state and weather forecasts. Due to the lack of detailed data on fuel consumption or energy demand, we need to exploit a heuristic. Under the assumption of a constant engine load for free sailing areas, the achieved speed depends on the resistance due to environmental conditions. Pixels with a low resistance are then favored for an energy optimized route. The geo platform also collects and provides data that is used by partners of the research project in their own routing application or to enhance and test their hydrodynamical analysis. 

We will present the technical set-up combining the data sources and facilitating the subsequent data mining and data analysis. Preliminary results of models and optimized routes will be presented. Finally, limitations of the approach and the data availability will be discussed.

How to cite: Pontius, M., Zaabalawi, S., Jürrens, E. H., and Gräler, B.: Reducing cargo ship emissions through energy demand optimized routing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9927, https://doi.org/10.5194/egusphere-egu22-9927, 2022.