Displays

OS4.7

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical states (including sea-ice and sea state) of the global ocean and the European regional seas. This capacity encompasses the description of the current situation (analysis and near-real time observations), the prediction of the situation a few days ahead (forecast), and the provision of consistent retrospective data records for recent decades (re-analysis and reprocessed datasets). CMEMS provides a sustainable response to private and public user needs, for academic, operational, policy and blue growth activities related to all sectors of the blue economy: polar environment monitoring, marine conservation & policies, science & climate, natural resources & energy, water quality, coastal monitoring, society & education, marine food, marine navigation and safety & disaster.

The session will cover research activities that are required to maintain CMEMS systems at the state of the art and prepare their long-term evolution (e.g. physical and biogeochemical modeling; coupling with coastal systems and hydrology; coupling with sea-ice, atmosphere & waves; data assimilation both for physics and biogeochemistry, probabilistic forecasting; big data, cloud computing and processing, artificial intelligence etc.). Presentations on the use, impact and design of in-situ and satellite (e.g. Sentinel missions) observing systems relevant to CMEMS are also much welcome.

Presentations are not limited to research teams directly involved in CMEMS and participation from external teams is strongly encouraged (e.g. from H2020 projects relevant to CMEMS and downstream applications, from projects on seasonal to multidecadal regional projections for the coastal ocean and marine ecosystems, from projects dealing with the monitoring and forecasting of river discharge of freshwater and nutrients).

We also welcome scientific presentations (i) on the verification, validation and uncertainty estimates of CMEMS products, (ii) on the use of CMEMS products for downstream applications (including support to maritime regulations and directives) and (iii) on the monitoring and long-term assessment of the ocean physical and biogeochemical states.

Public information:
Programme (Public on EGU website)
**TENTATIVE SCHEDULE FOR THE CHAT**
4-5 minutes per display, except for solicited ones

8:30-8:32 Introducing the session

8:32-8:50 Solicited presentations
D2426 Pierre-Yves Le Traon | Copernicus Marine Service: achievements, future challenges and long-term evolution
D2433 Mike Bell | Challenges in ocean modelling and data assimilation for CMEMS

8:50-9:15 Blue Ocean
D2428 Eric De Boissésson | Monitoring marine heatwaves in CMEMS ocean analysis systems
D2440 Shihe Ren | Intercomparison and validation of detected SST fronts based on CMEMS high-resolution reanalysis data and satellite observations in the South China Sea
D2431 Hao Zuo | Benefits of dynamically modelled river discharge input for ocean and coupled system
D2432 Yicun Zhen | An adaptive optimal interpolation based on analog forecasting: application to SSH in the Gulf of Mexico
D2434 Markus Meier | Sources of uncertainty of Baltic Sea future projections
D2427 Stéphane Law Chune | WAVERYS : A CMEMS global wave reanalysis during the altimetry period

9:15-9:19 White Ocean
D2435 Guillaume Boutin (Timothy Williams) | Impact of wave-induced sea ice fragmentation on sea ice dynamics in the MIZ

9:19-9:35 Green Ocean
D2429 Elodie Gutknecht | Modelling the marine ecosystem of IBI European waters for CMEMS operational applications
D2436 Paolo Lazzari | Simulating bio-optical properties in the Mediterranean Sea
D2437 Anna Conchon | Gathering knowledge on mesopelagic ecosystems: insights from a parsimonious modelling approach
D2430 Marine Bretagnon | CMEMS Primary production from satellite remote sensing: spatial and temporal evolution and comparison with other products

9:35-9:45 Brown (coastal) ocean
D2441 Encarni Medina-Lopez | High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data
D2442 Marc Mestres | CURAE – bridging the gap between regional CMEMS forecasts and coastal high-resolution applications

9:45-9:50 Validation
D2443 Marion Mittermaier | High-resolution model Verification Evaluation (HiVE). Part 2: Using object-based methods for the evaluation of algal blooms

9:50-10:05 Downstream applications
D2444 Miguel Inácio | The Copernicus Marine Environment Monitoring Service as a platform to map marine ecosystem services: a Lithuanian case study
D2438 Luis Rodriguez Galvez | Earth Observation services for Wild Fisheries, Oystergrounds Restoration and Bivalve Mariculture along European Coasts
D2439 Anne Vallette | Marine Litter Drift Monitoring (Forecast and Hindcast) in the Channel and the North Atlantic

10:05-10:15 Open Discussion

Break

Chat Time 10:45–12:30

10:45 Introduction to the session

10:45-10:50 Ocean Reporting
D2445 Karina von Schuckmann (Pierre-Yves Le Traon) | Ocean reporting of the Copernicus Marine Environment Monitoring Service

10:50-11:00 In-situ observations
D2453 Mélanie Juza (Tanguy Szekely) | How CMEMS INSTAC contributes to the monitoring of the ocean?
D2461 Jérôme Gourrion | A novel statistical approach for Near-Real Time Quality Control of hydrographic observations

11:00-11:30 Blue Ocean
D2462 Stefania Angela Ciliberti | Progresses in the CMEMS BS-MFC for improving forecasting capabilities and monitoring the Black Sea region through high quality modelling systems
D2447 Guillaume Reffray | A new version of the IBI near real time system for November 2020: what will be changed?
D2450 Anna Chiara Goglio | A baroclinic tidal forecasting model for the Mediterranean Sea - First validation results
D2456 Romain Escudier | A high resolution reanalysis for the Mediterranean Sea
D2454 Alvise Benetazzo | Towards a unified framework for maximum wave computation from numerical models: outcomes from the LATEMAR project
D2455 Rianne Giesen (Ad Stoffelen) | Improved ocean wind forcing products

11:30-11:35 White Ocean
D2449 Timothy Williams | The neXtSIM-F sea ice forecasting platform

11:35-11:50 Green Ocean
D2452 Julien Lamouroux | Assessment of the CMEMS global biogeochemical forecasting operational system, with assimilation of Ocean Colour data
D2460 Yeray Santana-Falcón | Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic ocean
D2448 Patrick Lehodey (Anna Conchon) | Zooplankton and Micronekton products from the CMEMS Catalogue for better monitoring of Marine Resources and Protected Species

11:50-11:55 Brown (coastal) Ocean
D2458 Francisco Campuzano | Framework for improving land boundary conditions in regional ocean products

11:55-12:05 Validation
D2446 Jan Maksymczuk | High-resolution model Verification Evaluation (HiVE). Part 1: Using neighbourhood techniques for the assessment of ocean model forecast skill
D2451 Malek Ghantous | Validation of the CMEMS-IBI wave model with data assimilation in a high resolution regional configuration

12:05-12:15 Downstream Applications
D2457 Nikolaos Kampanis (Katerina Spanoudaki) | The COASTAL CRETE downscaled forecasting system
D2459 Javier Bárcena (Javier García-Alba) | SOSeas: An assessment tool for predicting the dynamic risk of drowning on beaches

12:15-12:30 Open Discussion

12:30 Closing the session

Share:
Convener: Angelique Melet | Co-conveners: Stefano Ciavatta, Emanuela Clementi, Pierre De Mey
Displays
| Fri, 08 May, 08:30–12:30 (CEST)

Files for download

Download all presentations (258MB)

Chat time: Friday, 8 May 2020, 08:30–10:15

Chairperson: Angélique Melet, Stefano Ciavatta, Emanuela Clementi, Pierre De Mey
D2426 |
EGU2020-10322
| solicited
| Highlight
Pierre-Yves Le Traon

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical state, variability and dynamics of the ocean, ice and marine ecosystems for the global ocean and the European regional seas.  The Copernicus Marine Service has run a successful initial phase over the past five years.  Operational capabilities have been demonstrated, user uptake and user base have been steadily increasing and service evolution activities have allowed regular improvements of the products and services provided to users.  CMEMS now serves a wide range of users (more than 21,000 subscribers are registered to the service) and applications (maritime safety, marine resources, coastal and marine environment, weather, seasonal forecast and climate).  An overview of CMEMS achievements will be given and the presentation will highlight the essential role of R&D activities.  CMEMS priorities and scientific challenges for Copernicus 2 (2021-2027) will then be discussed.   

D2427 |
EGU2020-7062
Stephane Law Chune, Lotfi Aouf, Alice Dalphinet, Bruno Levier, and Yann Drillet

As part of the Copernicus Marine Core service, WAVERYS is the multi-year wave reanalysis that aims to provide global wave data with a grid resolution of 1/5°. The wave reanalysis covers the period of 1993-2018 and provides 3-hourly classical integrated wave parameters describing the sea state at the ocean surface. The wave model used is the V4 version of the model MFWAM, which is driven by atmospheric forcing (winds and ice fraction) from ECMWF ERA5 reanalysis. This latter has showed a significant improvement regarding to the previous reanalysis ERA-Interim. WAVERYS includes the assimilation of altimeters wave data available during the period starting from Topex-Poseidon until Sentinel-3A missions. Directional wave spectra from Synthetic Aperture Radar (SAR) of Sentinel-1A and 1B missions are also assimilated. This is the first time that such directional wave spectra are used in a global wave reanalysis.

Further, WAVERYS uses a 3 hour surface current forcing provided by ocean reanalysis GLORYS12 implemented by Mercator-Ocean in the frame of Copernicus Marine Service with a grid resolution of 1/12°. The wave reanalysis is high skilled for ocean regions with dominant wave-currents interactions. Preliminary validation tests have shown improvement by 15% in scatter index for large scale high currents areas. This paper will give detailed characteristics of the wave system and will insist on the benefits of taking into account ocean currents and a physics calibrated for realistic swell propagation.

 

D2428 |
EGU2020-19468
| Highlight
Eric de Boisseson

Since 2015, CMEMS has been providing near-real time and multi-year ocean analyses that describe both past and current ocean states. In the recent years, the increased frequency of marine heatwave events has raised the attention of the community. Strong and long-lasting events have been shown to have a significant impact on the marine ecosystems and industries. In the work presented here, recent cases of marine heatwaves have been analysed in the ECMWF ORAS5 reanalysis that is part of the CMEMS catalogue of multi-year products. Marine heatwaves are detected from time series of Sea Surface Temperature using a tool developed at CSIRO. Various aspect of the heatwaves are investigated in ORAS5 fields such as the strength and duration of the events and their propagation into the subsurface. The characteristics of recent heatwave events in the North Pacific and off New Zealand as captured in ORAS5 will also be discussed. Particular attention will be brought onto the Pacific Ocean 'Blob', the longest marine heatwave on record that lasted from 2014 to 2016. ORAS5 captured the ‘Blob’ and its propagation down the vertical column in coastal regions where the fishing industry usually strives. The extent of the ecological and economical impact of such an event is still felt to this day. The evolution of future marine heatwave events will be monitored in ORAS5. The predictability of these heat waves at monthly and seasonal range is under investigation.

D2429 |
EGU2020-5243
Elodie Gutknecht, Guillaume Reffray, Alexandre Mignot, Tomasz Dabrowski, and Marcos Sotillo

As part of the Copernicus Marine Environment Monitoring Service (CMEMS), a physical-biogeochemical coupled model system has been developed to monitor and forecast the ocean dynamics and marine ecosystem of the European waters. The model domain, called Iberia-Biscay-Ireland (IBI), covers the North-East Atlantic Ocean from the Canary Islands to Iceland, including the North Sea and the Western Mediterranean. Based on NEMO-PISCES with a horizontal resolution of 1/36°, the CMEMS IBI coupled model has provided 7-day weekly ocean forecasts for CMEMS since April 2018. But prior to its operational launch, a pre-operational qualification simulation (2010-2016) has allowed assessing the model's capacity to reproduce the main biogeochemical and ecosystem features of the IBI area. 
The objective is to evaluate the capacity of the coupled model to reproduce the spatial distribution and seasonal dynamics of the main biogeochemical variables, using this 7-year qualification simulation. The model results are compared with available satellite estimates as well as in situ observations, with a focus on BGC-Argo floats. 
The evaluation confirms that this model system can be a useful tool to better understand the current state and changes in the marine biogeochemistry of European waters and can also provide key variables for developing indicators to monitor the health of marine ecosystems. 

D2430 |
EGU2020-21903
Marine Bretagnon, Philippe Garnesson, and Antoine Mangin

Half of the global primary production is produced in the ocean by phytoplankton and the reaction of photosynthesis. For the marine environment, primary production is at the basis for the food web, by the supply of energy for higher trophic levels. Monitor primary production appears therefore to be a guideline to reach sustainable fisheries. In addition to its role on the trophic web, primary production is also important for its role on CO2 fluxes. Indeed, while phytoplankton creates matter from nutrients and CO2. The produced matter can be grazed by higher trophic levels or sink towards sediment. Amount of carbon sequestrated and exported out of the productive layer give some clues efficiencies of the oceanic biological carbon pump. Primary production is therefore important not only for economic resources, but also for climatic studies, to investigate if the ocean is a carbon sink or sources.

A strategy of algorithm validation / inter-comparison was used as part as the CMEMS project to identify most accurate primary production algorithm among the most used in the literature.

Primary production validation is based on the commonly used comparison with in situ data, as well as the frequency and the intensity of the annual bloom in different basin. Inter-comparison with model were performed at the basin scale of the Mediterranean Sea to assess the robustness and the consistency of different type of estimates.

Satellite estimate of primary production, as proposed by CMEMS, give now access to an archive of 21 years for user community, to investigate evolution of primary production at the global scale or in specific basin.

 

D2431 |
EGU2020-8564
Hao Zuo, Eric de Boisseson, Ervin Zsoter, Shaun Harrigan, Patricia de Rosnay, Fredrik Wetterhall, and Christel Prudhomme

River freshwater input is crucial in modelling global ocean. Most ocean models used in CMEMS services rely on climatological river discharge data with various deficiencies, which can lead to biased simulated ocean states. The Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) provides state-of-the-art global flood forecasts and downstream river discharge. A GloFAS-ERA5 global river discharge reanalysis dataset has been produced using the same system, modelled by routing runoff from ECMWF’s (European Centre for Medium- Range Weather Forecasts) atmospheric reanalysis ERA5 via a river network. As a global gridded data set that covers from 1979 to near-real-time, GloFAS-ERA5 reanalysis can provide an improved and standardized input of land freshwater input for global, regional and coastal ocean models. Evaluation results suggest that the overall performance of this new river discharge reanalysis is reasonably good in general when verified against a global network of 1801 discharge observation stations. A new method has been developed for conversion of the GloFAS-ERA5 reanalysis data into land freshwater input for the NEMO ocean model. This method has been tested with a climatology of GloFAS-ERA5 river discharge. Compared to the DRAKKAR climatology of land freshwater input (BT06, hereafter) used by most CMEMS services, this new data set has an increased global mean value of ~1.33 Sv, but with reduced seasonal variations. Assessment of this GloFAS-ERA5 land freshwater input has been carried out with the operational ECMWF ocean analysis system-OCEAN5, driven by the same ERA5 atmospheric forcing. Evaluation of simulated ocean state against in-situ observations show improvements in regions affected by Amazon freshwater input when use GloFAS-ERA5 instead of BT06, by reducing a negative sea surface salinity bias in these regions. However, negative impact from switching to GloFAS-ERA5 land freshwater input is also visible in several regions, e.g. in the Maritime Continent and west coast of central America, which is associated with a large positive bias in the GloFAS-ERA5 river discharge at these regions. This issue can be mitigated by applying bias-correction to the GloFAS-ERA5 land freshwater input, and by adding extra vertical mixing in several affected regions that are close to the river mouth. Assessments of module simulated ocean Essential Climate Variables (ECVs) have been carried out to quantify the benefit of this realistic freshwater time series input. Improvements in climate signals like the Atlantic Meridional Overturning transports is also recorded.

D2432 |
EGU2020-22004
Yicun Zhen, Pierre Tandeo, Stephanie Leroux, Sammy Metref, Thierry Penduff, and Julien Le Sommer

Because of the irregular sampling pattern of raw altimeter data, many oceanographic applications rely on information from sea surface height (SSH) products gridded on regular grids where gaps have been filled with interpolation. Today, the operational SSH products are created using the simple, but robust, optimal interpolation (OI) method. If well tuned, the OI becomes computationally cheap and provides accurate results at low resolution. However, OI is not adapted to produce high resolution and high frequency maps of SSH. To improve the interpolation of SSH satellite observations, a data-driven approach was recently proposed: analog data assimilation (AnDA). AnDA adaptively chooses analog situations from a catalog of SSH scenes -- originating from numerical simulations or a large database of observations -- which allow the temporal propagation of physical features at different scales, while each observation is assimilated. In this article, we review the AnDA and OI algorithms and compare their skills in numerical experiments. The experiments are observing system simulation experiments (OSSE) on the Lorenz-63 system and on an SSH reconstruction problem in the Gulf of Mexico. The results show that AnDA, with no necessary tuning, produces comparable reconstructions as does OI with tuned parameters. Moreover, AnDA manages to reconstruct the signals at higher frequencies than OI. Finally, an important additional feature for any interpolation method is to be able to assess the quality of its reconstruction. This study shows that the standard deviation estimated by AnDA is flow-dependent, hence more informative on the reconstruction quality, than the one estimated by OI.

D2433 |
EGU2020-5680
| solicited
Mike Bell

The ambition of CMEMS is to be the primary source of information for monitoring and prediction of the physical and bio-geochemical properties of the marine environment, both in the global deep ocean and in European coastal waters. This requires that the teams developing CMEMS identify and address several key scientific and technical challenges. Some challenges relate to the ocean model used to generate CMEMS products. For example to ensure that it simulates the most important aspects of the flow as faithfully as other ocean models and that it is able to perform efficiently on relevant High Performance Computer (HPC) systems. Some challenges relate to the data assimilation. For example to ensure that the ocean re-analyses are not compromised by problems caused by biases in the ocean model and to develop more effective collaboration within the team of data assimilation developers. Other challenges relate to biogeochemistry (BGC). For example to develop monitoring systems sufficient to support the validation and development of the BGC models, and suitable systems for joint assimilation of physical and BGC measurements. The presentation will focus on illustrating and discussing these challenges.

D2434 |
EGU2020-20070
Markus Meier, Christian Dieterich, and Matthias Gröger

In an ensemble of regional scenarios for the Baltic Sea we analyzed the sources of uncertainty in climate indices and environmental quality indicators. The ensemble is based on 32 regionalized scenarios where four different external drivers have been varied. Climate is represented by four different Earth System Models (ESMs). Uncertain future greenhouse gas emissions are represented by two different Representative Concentration Pathways (RCPs). Two nutrient load scenarios, broadly equivalent to two Shared Socio-economic Pathways (SSPs), describe two distinct evolutions of the regional population development, agricultural practices and food demand and two scenarios for global mean sea level rise (GMSL) measure the impact of the water level on the biogeochemical cycle in the Baltic Sea. The volume averaged temperature increase at the end of the century relative to the reference period 1976-2005 is 1.3 to 2.2 K (RCP 4.5) and 2.9 to 4.2 K (RCP 8.5). Averaged salinity changes by -2.1 and +0.2 g/kg (RCP 4.5) and -3.2 and -0.2 g/kg (RCP 8.5). For temperature, uncertainties before 2080 are dominated by natural variability and ESM biases. After 2080 the largest source of uncertainty is related to the unknown greenhouse gas concentrations. As expected, uncertainties related to either SLR or nutrient loads are negligible. For salinity, the dominating source of uncertainty during the entire 21st century is explained by the biases of the ESMs. However, natural variability and, in particular by the end of the century, uncertainties due to unknown greenhouse gas concentrations and sea level rises are important as well. For hypoxic area, uncertainties before 2040 are dominated by ESM biases. After 2040 the largest source of uncertainty is related to the unknown nutrient loads (SSPs). However, ESM biases, natural variability, unknown greenhouse gas concentrations and unknown sea level rises play an important role as well. Hence, the predictability of hypoxic area on long time scales requires accurate knowledge of various drivers and accurate quality of ESMs.

 

D2435 |
EGU2020-8657
Guillaume Boutin, Timothy Williams, Pierre Rampal, Einar Olason, and Camille Lique

The decrease in Arctic sea ice extent is associated with an increase of the area where sea ice and open ocean interact, commonly referred to as the Marginal Ice Zone (MIZ). In this area, sea ice is particularly exposed to waves that can penetrate over tens to hundreds of kilometres into the ice cover. Waves are known to play a major role in the fragmentation of sea ice in the MIZ, and the interactions between wave-induced sea ice fragmentation and lateral melting have received particular attention in recent years. The impact of this fragmentation on sea ice dynamics, however, remains mostly unknown, although it is thought that fragmented sea ice experiences less resistance to deformation than pack ice. In this presentation, we will introduce a new coupled framework involving the spectral wave model WAVEWATCH III and the sea ice model neXtSIM, which includes a Maxwell-Elasto Brittle rheology. We use this coupled modelling system to investigate the potential impact of wave-induced sea ice fragmentation on sea ice dynamics. Focusing on the Barents Sea, we find that the decrease of the internal stress of sea ice resulting from its fragmentation by waves results in a more dynamical MIZ, in particular in areas where sea ice is compact. Sea ice drift is enhanced for both on-ice and off-ice wind conditions. Our results stress the importance of considering wave–sea-ice interactions for forecast applications. They also suggest that waves likely modulate the area of sea ice that is advected away from the pack by ocean (sub-)mesoscale eddies near the ice edge, potentially contributing to the observed past, current and future sea ice cover decline in the Arctic. 

D2436 |
EGU2020-10281
Paolo Lazzari, Eva Alvarez, Elena Terzic, Stefano Salon, Emanuele Organelli, Fabrizio D'Ortenzio, and Vincenzo Vellucci

We present the results of a series of simulations performed by a multi-spectral bio-optical model developed in the framework of the BIOPTIMOD Service Evolution project for the Copernicus Marine Environment Monitoring System (CMEMS-SE). In this research, we integrate the CMEMS Mediterranean Sea biogeochemical model MedBFM (multi-stoichiometric, phytoplankton functional types -PFT- based) with a bio-optical model able to resolve light propagation along the water column with 25 nm resolution in the visible range. Recent optical data streams provided by novel observations platforms, such as the Biogeochemical Argo floats (BGC-Argo) and the multi-spectral satellite sensors (ESA-CCI and Sentinel-OLCI), are used for the model validation. Our approach aims to improve the quality and reduce the uncertainty of paramount CMEMS biogeochemical products such as phytoplankton biomass and primary production.  

We present: 1) the optical dataset gathered from BGC-Argo floats and satellite sensors; 2) the multi-spectral upgrade of the bio-optical model including specific PFT optical properties  and CDOM formulation; 3) the assessment of the new bio-optical model within the CMEMS quality framework with particular reference to Remote Sensing Reflectance (Rrs) data and to light attenuation as inferred from BGC-Argo floats.

In particular, the analysis of Rrs spatial and temporal variability allows to evaluate the skill of different parameterizations for PFT (e.g. photochemical efficiency) and CDOM dynamics (e.g. photobleaching rate), using an ensemble of multi-annual 3D simulations.

The role of the CDOM deep inventory and river discharge in modulating light attenuation is also evaluated. Results indicates that the novel bio-optical model allows to reconstruct the West-East DCM gradient as a self-emergent property.

Major impacts of the project, potentially strategic for CMEMS users, involve: the improvement of CMEMS biogeochemical products quality, the development of new optics-related biogeochemical products for CMEMS, and the strengthening of the scientific and technological links with the new generation of marine bio-optical sensors, which may also support a stronger collaboration between modelling, remote sensing and experimental communities.



D2437 |
EGU2020-5741
Anna Conchon, Olivier Titaud, Inna Senina, Beatriz Calmettes, Audrey Delpech, and Patrick Lehodey

SEAPODYM-LMTL is the Lower (zooplankton) and Mid (micronekton) Trophic levels model of the Spatial Ecosystem And POpulation DYnamic Modeling framework. Currently, there is one zooplankton and 6 micronekton functional groups defined according to their vertical behavior and development times. The model is global and spatially explicit with transport described through a system of advection-diffusion equations. The vertical dimension is simplified into three layers -- epipelagic, upper and lower mesopelagic -- defined relatively to the euphotic depth. There are three vertically migrant and three non-migrant functional groups. The model is parsimonious with only a few parameters (6 for the zooplankton and 11 for the micronekton) that control the energy transfer efficiency from the primary production and the mortality and time of development that are linked to the water temperature. A data assimilation framework has been implemented to estimate those parameters.  We present briefly the latest results and future challenges of this model. They include the validation of vertical layer boundaries, the first zooplankton and micronekton parameters estimation using existing biomass observations, and the developments needed to use large global datasets of acoustic data. 

D2438 |
EGU2020-2714
Luis Rodriguez Galvez, Ghada El Serafy, Sonja Wanke, Daniel Twigt, and Nicky Villars

Sea related activities are set to increase and the growth in food production from sea enhancing global food security is already a reality. However, this growth must be aligned with increasing environmental constraints as well as complying and restoring regulations and frameworks. This requires the adoption of improved and efficient behaviors based on wider incorporation of available information and knowledge from the industry and citizens alike. Marine and coastal managers must make decisions to maintain the social, economic, and ecological health of marine and coastal areas in coastal and nearshore areas and to operate, plan and manage their activities at sea. The European funded FORCAST project represents a step forward in this direction by bringing the coastal water quality and met-ocean information closer to the target sectors: wild fisheries, oystergrounds restoration, and bivalve mariculture. FORCOAST will develop, test and demonstrate, in operational mode, novel Copernicus-based downstream information services that will incorporate and combine Copernicus Marine Environment Monitoring Service (CMEMS), Copernicus Land Monitoring Service (CLMS) and Climate Change Monitoring Service (CMS), local monitoring data and advanced modelling in the service. FORCOAST will provide consistent high resolution data products for coastal applications, based on a standardized data processing scheme. Furthermore, FORCOAST will make use DIAS which will help to develop the data access and cloud processing service. FORCOAST will provide those services in eight pilot service uptake sites covering five different regional waters (North Sea, Baltic Sea, Mediterranean Sea, Black Sea and the coastal Atlantic Ocean). The outcome of FORCAST is a novel commercial service that will provide Copernicus-based downstream information coastal services to a variety of stakeholders, which will result in an operation, planning and management improvement of different marine activities in the sectors of wild fisheries and aquaculture, having an economic and societal positive effect on the involved parties.

*This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870465

D2439 |
EGU2020-13029
| Highlight
Anne Vallette, Fatimatou Coulibaly, and Stephen Emsley

Meteorological events, such as storms and/or gale force winds, act as triggers to influx of macro litter into the hydrological cycle via run off from land into rivers. These rivers discharge into the sea and the marine litter is then transported through the region by currents and wind either becoming entrained in the sea, possibly sinking and/or disintegrating into micro marine litter or ending up being stranded at the coast then washed back ashore or flown on to the land. Thanks to a Copernicus Marine Environment Monitoring Service (CMEMS) grant, ARGANS Ltd has developed a web-based service, called Litter-TEP, that aims to track marine litter from the source. It uses a parametric model of riverine macro litter discharge, to seed drift models of the NE Atlantic Shelf Region (OSPAR II/III), providing to end-users a 5-day running forecast of macro-litter density in the sea, potential beach stranding at the coast and, inversely, where a beach litter event is identified to provide the likelihood of where the litter entered the sea. In order to determine drift trajectories, we use ocean current, wave and wind forecasts from Copernicus Marine Service high quality analysis and forecast products for the European North West Shelf seas. The main issues which have been identified, and for which we perform additional R&D, are the following: a) source’s modelling and estimation of volume of litter introduced to the sea, b) litter’s types for which the drift model should be adapted, and c) the spatial resolution of models in the littoral area (nearshore) vs. offshore. In fact, for the beaching & refloating models, we need of a bathymetry at the scale of 1/3000 and a coastal cartography at 1/1000 to obtain the beach profile, then calculate the runoff on the beach, the rip currents, etc. The next enhancement, driven by users’ requirements, is to improve the land discharge model vide collection of litter seeings with citizen crowdsourcing apps, and records of beach litter surveys, and beach cleaning campaigns. Another improvement, in the mid-term, targets the discharge models, using refined hydrologic schemes for the watersheds, and better estimates of habitats (rural, urban, industrial, …).  ARGANS Ltd service is the next-generation tool for planning beach cleaning and helping local authorities to track back the trash to their sources, leading the fight against litter pollution and for improvement of the river water quality. 

D2440 |
EGU2020-2521
Shihe Ren, Xueming Zhu, and Drevillon Marie

Oceanic fronts, as important mesoscale phenomena, are transition zones between two water mass with different hydrological features, which has important significances in various fields, such as fishery, environment protection and marine military. In order to effectively obtain the ocean front information, a gradient-based Canny edge detection algorithm is applied to detect the ocean front, and post-processing procedure specifically designed for high resolution hindcast and reanalysis is utilized to extract primary ocean fronts, ensuring the balance of frontal continuity and positioning accuracy. Based on CMEMS reanlaysis and satellite SST data and regional model hindcast from NMEFC in China, a long-term intercomparison and validation is performed to evaluate the frontal detection method and model behavior in aspect of mesoscale forecasting/hindcasting. Frontal statistical product include mean status, seasonal and interannual analysis are utilized to intercompare. We also develop ‘CLASS 4’ validation metric to validate daily results in different datasets. Further more, forecast error, bias and skill of operational ocean front product are also shown for potential users.

D2441 |
EGU2020-3465
Encarni Medina-Lopez

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

D2442 |
EGU2020-7234
Marc Mestres, Johannes Pein, Manuel Espino, Johannes Schulz-Stellenfleth, Lars Boye Hansen, Joanna Staneva, and Agustín Sánchez-Arcilla

The sustainable management of coastal areas with limited natural resources and subject to high anthropogenic pressures, based on complete forecast and analysis systems, is one of the main concerns of coastal authorities. Paradoxically, current oceanographic operational predictions are often insufficient to provide a good description of these regions due to low resolution/accuracy, the omission of relevant land-sea interactions, and the inadequate inclusion in the models of physical processes specific to the coastal fringe.

The CURAE project bridges this gap between the regional scales and the local coastal needs by establishing a 2-way connection between the existing CMEMS products and an innovative coastal information system (required by coastal water managers and littoral users alike) that considers hydro-morphodynamic interactions in an operational manner. The paper will present a new set of robust downscaling and coupling tools that have been developed to enhance the coastal dimension of present CMEMS products. The main advances refer to coastal processes not commonly incorporated in oceanographic predictions and that interact with the shelf sea, including the continental discharge in terms of water, sediment and nutrient fluxes.

The project is focused on two hydrodynamically contrasting sites with different uses (aquaculture and dredging) that demonstrate the added value and potential of CMEMS for coastal applications. The first case is the semi-enclosed microtidal Fangar Bay in the Ebro Delta (Spanish Mediterranean), in which bivalve farming coexists with tourism and nutrient- and sediment-laden freshwater discharges from rice fields in a hydrodynamically weak environment interacting with active domain geometries. The second study case is the macrotidal Wadden Sea/German Bight estuaries area, with concentrated plus distributed land discharge and significant dredging activities required for port operations. To ensure that the tools and conclusions derived from the project are as generic as possible, different numerical approaches (structured vs. unstructured grids) have been applied at each site and will be compared and discussed in the paper. The obtained results illustrate the benefits of quantitative forecasts for the healthy functioning of coastal systems, while proving the potential of CMEMS for supporting sustainable interventions in the coastal zone. The obtained advances are encouraging from scientific and application standpoints and underpin the need for a high-resolution coastal extension of CMEMS.

D2443 |
EGU2020-7799
Marion Mittermaier, Rachel North, Christine Pequignet, and Jan Maksymczuk

HiVE is a CMEMS funded collaboration between the atmospheric Numerical Weather Prediction (NWP) verification and the ocean community within the Met Office, aimed at demonstrating the use of spatial verification methods originally developed for the evaluation of high-resolution NWP forecasts, to CMEMS ocean model forecast products. Spatial verification methods provide more scale appropriate ways to better assess forecast characteristics and accuracy of km-scale forecasts, where the detail looks realistic but may not be in the right place at the right time. As a result, it can be the case that coarser resolution forecasts verify better (e.g. lower root-mean-square-error) than the higher resolution forecast. In this instance the smoothness of the coarser resolution forecast is rewarded, though the higher-resolution forecast may be better. The project utilised open source code library known as Model Evaluation Tools (MET) developed at the US National Center for Atmospheric Research (NCAR).

 

This project saw, for the first time, the application of spatial verification methods to sub-10 km resolution ocean model forecasts. The project consisted of two parts. Part 1 is described in the companion poster to this one. Part 2 describes the skill of CMEMS products for forecasting events or features of interest such as algal blooms.  

 

The Method for Object-based Diagnostic Evaluation (MODE) and the time dimension version MODE Time Domain (MTD) were applied to daily mean chlorophyll forecasts for the European North West Shelf from the FOAM-ERSEM model on the AMM7 grid. The forecasts are produced from a “cold start”, i.e. no data assimilation of biological variables. Here the entire 2019 algal bloom season was analysed to understand: intensity and spatial (area) biases; location and timing errors. Forecasts were compared to the CMEMS daily cloud free (L4) multi-sensor chlorophyll-a product. 

 

It has been found that there are large differences between forecast and observed concentrations of chlorophyll. This has meant that a quantile mapping approach for removing the bias was necessary before analysing the spatial properties of the forecast. Despite this the model still produces areas of chlorophyll which are too large compared to the observed. The model often produces areas of enhanced chlorophyll in approximately the right locations but the forecast and observed areas are rarely collocated and/or overlapping. Finally, the temporal analysis shows that the model struggled to get the onset of the season (being close to a month too late), but once the model picked up the signal there was better correspondence between the observed and forecast chlorophyll peaks for the remainder of the season. There was very little variation in forecast performance with lead time, suggesting that chlorophyll is a very slowly varying quantity.  

 

Comparing an analysis which included the assimilation of observed chlorophyll shows that it is much closer to the observed L4 product than the non-biological assimilative analysis. It must be concluded that if the forecast were started from a DA analysis that included chlorophyll, it would lead to forecasts with less bias, and possibly a better detection of the onset of the bloom.  

 

D2444 |
EGU2020-8083
Miguel Inácio, Marius Kalinauskas, Katarzyna Miksa, Eduardo Gomes, and Paulo Pereira

Oceans and seas have always played an important role in supporting human wellbeing through the deliverance marine ecosystem services (MES). Nevertheless, the anthropogenic driven environmental degradation coupled with changes in socio-economic dynamics affected the capacity to deliver MES in quantity and quality. While it is of the utmost importance and need to map and assess MES, data deficiencies, data standardization, lack of knowledge on the functioning of multiple MES and poor spatial and temporal coverage, hinder its operationalization. The objective of this work was to test the applicability of databases and platforms like the Copernicus Marine Environment Monitoring Service (CMEMS) to be used as a unified point to map and assess MES in Lithuanian marine area. To map different indicators of MES such as wave weight and direction as well as chlorophyll-a concentration, data was extracted from physical and biogeochemical model outputs within CMEMS, covering the whole extent of the Lithuanian Exclusive Economic Zone. From a user perspective, the use of CMEMS to map and assess MES, allows: (1) to overcome complex challenges such as ecological modelling, utilizing outputs directly; (2) to cover spatial and temporal extent in areas where information is scarce and (3) to have vertically resolved data, important for the understanding and mapping of MES. In this perspective, CMEMS plays and will potentially play a higher role towards the operationalization of MES, contributing to better and more informed decision in the sphere of marine environmental management.

This work has received funding from the European Social Fund project Lithuanian National Ecosystem Services Assessment and Mapping (LINESAM) No. 09.3.3-LMTK-712-01-0104 under grant agreement with the Research Council of Lithuania (LMTLT).

Chat time: Friday, 8 May 2020, 10:45–12:30

Chairperson: Emanuela Clementi, Pierre De Mey-Frémaux, Angélique Melet, Stéfano Ciavatta
D2445 |
EGU2020-8626
| Highlight
Karina von Schuckmann and Pierre-Yves Le Traon

The Copernicus Marine Environment Monitoring Service (CMEMS) ocean state-of-the-art ocean reporting for the global ocean and European seas is part of the production center service elements in order to establish a unique reference of value-added expert information at a regular frequency. This is achieved through two principal activities:

  1. Annual release of the peer-reviewed CMEMS Ocean State Report containing a state-of-the-art value-added synthesis of the ocean state, variability and change from the past to present
  2. Ocean Monitoring Indicators and related operational framework on the CMEMS web portal. In particular, CMEMS has developed several indicators based on global or regional ocean reanalyses. For a series of indicators, consistency estimates are available, based on a multiproduct approach inherited from CLIVAR/GODAEIV-TT ORA IP.

This activity is aiming to reach a wide audience from the scientific community, over climate and environmental service and agencies, environmental reporting bodies, decision maker to the general public. Currently, the ocean state report activity is in its 5th cycle, and a huge number of indicators have been made freely available via the CMEMS web portal, including numerical data, scientific and quality context and product documentation. We will give here an overview on the CMEMS ocean reporting activity, highlight main outcomes, and introduce future plans and developments.

D2446 |
EGU2020-8681
Jan Maksymczuk, Ric Crocker, Marion Mittermaier, and Christine Pequignet

HiVE is a CMEMS funded collaboration between the atmospheric Numerical Weather Prediction (NWP) verification and the ocean community within the Met Office, aimed at demonstrating the use of spatial verification methods originally developed for the evaluation of high-resolution NWP forecasts, with CMEMS ocean model forecast products. Spatial verification methods provide more scale appropriate ways to better assess forecast characteristics and accuracy of km-scale forecasts, where the detail looks realistic but may not be in the right place at the right time. As a result, it can be the case that coarser resolution forecasts verify better (e.g. lower root-mean-square-error) than the higher resolution forecast. In this instance the smoothness of the coarser resolution forecast is rewarded, though the higher-resolution forecast may be better. The project utilised open source code library known as Model Evaluation Toolkit (MET) developed at the US National Center for Atmospheric Research. 

 

This project saw, for the first time, the application of spatial verification methods to sub-10 km resolution ocean model forecasts. The project consisted of two parts. Part 1 describes an assessment of the forecast skill for SST of CMEMS model configurations at observing locations using an approach called HiRA (High Resolution Assessment). Part 2 is described in the companion poster to this one.  

 

HiRA is a single-observation-forecast-neighbourhood-type method which makes use of commonly used ensemble verification metrics such as the Brier Score (BS) and the Continuous Ranked Probability Score (CRPS). In this instance all model grid points within a predefined neighbourhood of the observing location are considered equi-probable outcomes (or pseudo-ensemble members) at the observing location. The technique allows for an inter-comparison of models with different grid resolutions as well as between deterministic and probabilistic forecasts in an equitable and consistent way. In this work it has been applied to the CMEMS products delivered from the AMM7 (~7km) and AMM15 (~1.5km) model configurations for the European North West Shelf that are provided by the Met Office. 

 

It has been found that when neighbourhoods of equivalent extent are compared for both configurations it is possible to show improved forecast skill for SST for the higher resolution AMM15 both on- and off-shelf, which has been difficult to demonstrate previously using traditional metrics. Forecast skill generally degrades with increasing lead time for both configurations, with the off-shelf results for the higher resolution model showing increasing benefits over the coarser configuration. 

D2447 |
EGU2020-8804
Guillaume Reffray, Mounir Benkiran, Bruno Levier, Elodie Gutknecht, Roland Aznar, Karen Guihou, and Marcos Garcia-Sotillo

The IBI (Iberian-Biscay-Ireland) MFC (Monitoring and Forecasting Centre) is in charge of providing ocean forecasts for the IBI region in the framework of the Copernicus Marine Environment Monitoring Service (CMEMS). To do so, a coupled model system is operated daily to monitor and forecast the ocean dynamics and marine ecosystem. The domain of the model is a subset of the ORCA grid at the resolution of 1/36° and covers the North-East Atlantic Ocean from the Canary Islands to Iceland, including the North Sea and the Western Mediterranean.

The IBI system is operated since 2010 and regularly released. The coupled model is based on NEMO-PISCES. A data assimilation method based on SEEK Kalman filter is applied to the physical component. This coupled system with data assimilation has been operational since April 2018, providing 5-day physical forecasts on a daily basis and 7-day ecosystem and carbon forecasts on a weekly basis.

A new release of this system is planned for November 2020, and includes a lot of changes about the physical NEMO model especially on its vertical component (turbulence, solar penetration, bulk formulaes, ...), a new tuning for the data assimilation method and a new CMEMS global biogeochemical system to initialise and force at the open boundaries of the IBI system. The objective is to summarize these changes and to illustrate their impacts by confronting the model results to in-situ data profiles, SST satellites products, BGC-Argo floats ...

D2448 |
EGU2020-9016
Patrick Lehodey, Olivier Titaud, Anna Conchon, inna Senina, and Jacques Stum

Since 2019, the Copernicus Marine portfolio is providing a new model-based product on Zooplankton and Micronekton, representing the low and mid-trophic levels (LMTL) of the ocean food chain (Global Ocean low and mid trophic levels biomass multi year product: ). Zooplankton are organisms in the size range of less than 1 to a few millimeters (e.g., copepods) constituting prey of all fish larvae and small pelagic fish species (e.g herring, sardines and anchovies). Micronekton are also relatively small but actively swimming organisms such as crustaceans, fish, and cephalopods that are typically about 1 to 10 centimeters in size. Micronekton are prey of large fish and other oceanic predators. They are also predators of fish larvae. Therefore, zooplankton and micronekton distributions are key explanatory variables to understand fish recruitment mechanisms, individual behaviour of satellite tracked individuals of protected species (e.g., marine turtles, seabirds, sharks and marine mammals), and dynamics of large populations of fish species either targeted by fisheries (mackerel, tuna, swordfish, etc.) or strictly controlled in by-catch (e.g., bluefin tuna or sharks). The product includes 2-dimensional (latitude-longitude) maps of biomass for zooplankton and groups of micronekton on a weekly basis from 1998 to 2018 at a 25 km horizontal resolution. It is a model-based estimation of micronekton biomass, relying on the most possible realistic forcings (temperature, currents and primary production) assimilating satellite and in situ data. Continuous progress in the development and validation of these new models and products will help to reduce our knowledge gaps and will also support the Science & Climate sector as zooplankton and micronekton are key to better quantifying the carbon uptake and storage in the ocean, known as the “biological carbon pump”.

D2449 |
EGU2020-9136
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Olason

The neXtSIM-F forecast system consists of a stand-alone sea ice model, neXtSIM, forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation.

neXtSIM is a novel sea ice model which is able to reproduce sea ice deformation properties and statistics, such as spatial localisation and temporal intermittency,
even at relatively low resolutions. For our forecast we run it at 10km resolution, over a pan-Arctic domain. We assimilate OSISAF SSMI and AMSR2 sea ice concentration products and the SMOS sea ice thickness product by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. 

We present an evaluation of the platform over the period from November 2018 to present, looking at sea ice drift and concentration and extent, and thin ice thickness.

neXtSIM-F is scheduled to become part of the CMEMS Arctic Marine Forecast Center in June 2020.

D2450 |
EGU2020-9400
Anna Chiara Goglio, Emanuela Clementi, Massimiliano Drudi, Alessandro Grandi, Rita Lecci, Valentina Agresti, Simona Masina, Giovanni Coppini, and Nadia Pinardi

In the framework of the Copernicus Marine Environment Monitoring Service (CMEMS) Mediterranean

Analysis and Forecasting Physical System (MedFS), a specific modeling upgrade has been carried out

by including the main lunisolar tides.

Mediterranean tides, even if characterized by small amplitudes, play an important role on the dynamics

of the Mediterranean sea and the introduction of tides in the hydrodynamic numerical model simulations

represent the first step in the development of a numerical forecasting model that considers explicitly the

tidal dynamics and the mesoscales.

MedFS is an operational system that produces weekly analysis and daily 10-days forecasts of the main

physical fields with a resolution of around 4.5km over the whole Mediterranean basin including the

Atlantic Ocean adjacent area (Clementi et al., 2018).

Baroclinic high resolution numerical experiments have been performed including a tidal potential and

forcing the model at the Atlantic boundaries with tidal elevation downscaled from a global model

FES2014 and tidal velocity derived from the TUGOm (http://sirocco.omp.obsmip.

fr/ocean_models/tugo) ocean hydrodynamic model. The experiments have been carried out

including the 8 most relevant tidal constituents in the Mediterranean Sea, namely M2, S2, K1, O1, K2,

N2, P1 and Q1.

In this work, first results of baroclinic tidal model experiments are presented together with their

validation with respect to insitu and satellite data as well as comparing with available literature studies.

In particular the harmonic analysis of tidal amplitudes and phases highlight the model ability to

correctly represent the tide gauges observations in the whole basin and in the areas of large tidal signal.

D2451 |
EGU2020-9873
Malek Ghantous, Lotfi Aouf, Alice Dalphinet, Cristina Toledano, Lorea García San Martín, Ernesto Barrera, Pablo Lorente, and Marcos García Sotillo

One of the challenges of the Iberia-Biscay-Ireland (IBI) Monitoring Forecasting Centre in CMEMS phase 2 is the implementation of the assimilation of altimeter wave data in the wave forecast system.  In this work we explored the impact of the assimilation of altimeter wave data in the IBI domain.  We ran the Météo France version of the WAM wave model (MFWAM) in the IBI domain for 2018 and 2019, with data assimilated from the Jason 2 and 3, Saral, Cryosat 2 and Sentinel 3 altimeters.  This high-resolution (0.05 degree) configuration was forced by 0.05 degree ECMWF winds, and boundary conditions were provided by a 0.1 degree global model run.  We also included refraction from currents generated with the NEMO-IBI ocean circulation model.  We present results with and without wave–current interactions.  Validation against both buoy data and the HaiYang 2 altimeter shows that the assimilation of data leads to a marked reduction in scatter index and model bias compared to the run without data assimilation; the gains from including currents meanwhile are modest.  

The data assimilation scheme presently implemented in MFWAM uses an optimal interpolation algorithm where constant model and observational errors are assumed.  To add some sophistication, we experimented with non-constant background errors derived from a model ensemble.  Though the effect was small, the method suggests a way to improve the data assimilation performance without substantially altering the algorithm.

D2452 |
EGU2020-13293
Julien Lamouroux, Alexandre Mignot, Coralie Perruche, Giovanni Ruggiero, Julien Paul, Charles-Emmanuel Testut, Jean-Michel Lellouche, and Yann Drillet

The operational production of data-assimilated biogeochemical state of the ocean is one of the challenging core projects of the Copernicus Marine Environment Monitoring Service (hereafter CMEMS). In that framework, Mercator Ocean International is in charge of improving the realism of its global 1⁄4° coupled physical-biogeochemical simulations, analyses and re‐analyses, and to develop an effective capacity to routinely estimate the biogeochemical state of the ocean, including, amongst others, the implementation of biogeochemical data assimilation. Primary objectives are to enhance the time representation of the seasonal cycle in the real time and reanalysis systems, and to provide a better control of the production in the equatorial regions.

In that framework, Mercator Ocean International has successfully updated its global biogeochemical analysis and forecasting system with an Ocean Color data assimilation capability. In this system, successfully commissioned in September 2019, the biogeochemical model (NEMO/PISCES) is offline coupled with the dynamical ocean (1/12° coarsened to 1/4° resolution) from the CMEMS global physical analysis and forecasting operational system (PSY4), at a daily frequency, and benefits from the assimilation of satellite (SSH-SST-SIC) and in situ physical data. Nevertheless, a biogeochemical climatological damping is activated in the biogeochemical model in order to mitigate the impact of some misconstrained processes (vertical velocities) from this physical data-assimilated forcing (under investigation). The dedicated assimilation of biogeochemical data relies on a simplified version of the SEEK filter, where the forecast error covariances are built from a fixed-basis - but seasonally variable - ensemble of anomalies computed from a multi-year numerical experiment (without biogeochemical data assimilation) with respect to a running mean. Regarding Ocean Colour observations, the system relies, as a first step, on the CMEMS Global Ocean surface chlorophyll concentration products, delivered in NRT.

The objective of this presentation is thus to provide (1) a short description of the implementation of the aforementioned data assimilation methodology in the forecasting system; (2) a synthesis of the assessment of this global biogeochemical forecasting system, by cross-comparing the assimilated solution with various datasets, both spatial (Ocean Colour) and in situ (BGC-Argo, GLODAP), and (3) a synthetic overview of the impact/benefit of the assimilation of the Ocean Colour data.

D2453 |
EGU2020-13432
Mélanie Juza, Tanguy Szekely, Jérôme Gourrion, Inmaculada Ruiz-Parrado, Sylvie Pouliquen, and Joaquin Tintoré

CMEMS IN SITU TAC (INSTAC) collects in situ observations from various platforms (e.g. Argo floats, gliders, drifters, ships, fixed stations, marine mammals, high-frequency radar). It provides free, open and quality-controlled physical and biogeochemical ocean data in both delayed mode and near-real time, in support to the operational oceanography, the ocean health and the climate change. Monitoring the 4-dimensional ocean at various spatial and temporal scales, the INSTAC multi-year products provide an essential information on the ocean state, variability and changes. Hence, the INSTAC group contributes substantially to the elaboration of the annual CMEMS Ocean State Report (Von Schuckmann et al., 2016, 2018, 2019, 2020), in collaboration with internal and external scientific experts, as well as with other CMEMS TACs and MFCs.

A general overview of the INSTAC contributions to the CMEMS Ocean State Report is presented, highlighting its capacity to describe, analyze and understand the ocean state and variability of both physical and biogeochemical components from the sea surface to the deep ocean, from the coastal to open sea waters at both short-term (event) and long-term temporal scales. The INSTAC team contributes to the CMEMS Ocean Monitoring Indicators (e.g. temperature, salinity, ocean heat content, water mass and heat exchange, extreme event detection), investigates the ocean circulation variability (e.g. cold and fresh blob in North Atlantic, mesoscale eddy anomaly), analyses the impacts of climate change on marine ecosystem and ocean circulation (e.g. water mass responses to climate warming, cyclones), and develops operational applications and services (pollution risk, search-and-rescue, storm and wave alerts, river discharges).

D2454 |
EGU2020-13683
Alvise Benetazzo, Francesco Barbariol, Paolo Pezzutto, Luciana Bertotti, Luigi Cavaleri, Silvio Davison, and Mauro Sclavo

Reliable prediction of oceanic waves during severe marine storms has always been foremost for offshore platform design, coastal activities, and navigation safety. Indeed, many damaging accidents and casualties during storms were ascribed to the impact with abnormal and unexpected waves. However, predicting extreme wave occurrence is a challenging task, at first, because of their inherent randomness, and because the observation of large ocean waves, of primary importance to assess theoretical and numerical models, is limited by the costs and risks of deployment during severe open-ocean sea-state conditions.

In the context of the EU-based Copernicus Marine Environment Monitoring Service (CMEMS) evolution, the LATEMAR project (https://www.mercator-ocean.fr/en/portfolio/latemar/) aimed at improving the modelling of large wave events during marine storms. Indeed, at present, operational systems only provide average and peak wave parameters, with no information on individual waves whatsoever. However, developments of the state-of-the-art third-generation wave models demonstrated that using the directional wave spectrum moments into theoretical statistical models for wave extremes, forecasters are able to accurately infer the expected shape and likelihood of the maximum waves during storms.

The main purpose of the activity is therefore to provide the wave models WAM and WAVEWATCH III with common procedures to explicitly estimate the maximum wave heights for each sea state. LATEMAR achieved this goal by: performing an extensive assessment of the model maximum waves using field observations collected from an oceanographic tower; comparing WAM and WAVEWATCH III maximum wave estimates in the Mediterranean Sea; investigating the sensitivity of the maximum waves on the main sea state parameters. All model developments and evaluations resulting from this research project will be directly applicable to the wave model forecasting systems to expand their catalogue.

D2455 |
EGU2020-15559
Rianne Giesen, Ana Trindade, Marcos Portabella, and Ad Stoffelen

The ocean surface wind plays an essential role in the exchange of heat, gases and momentum at the atmosphere-ocean interface. It is therefore crucial to accurately represent this wind forcing in physical ocean model simulations. Scatterometers provide high-resolution ocean surface wind observations, but have limited spatial and temporal coverage. On the other hand, numerical weather prediction (NWP) model wind fields have better coverage in time and space, but do not resolve the small-scale variability in the air-sea fluxes. In addition, Belmonte Rivas and Stoffelen (2019) documented substantial systematic error in global NWP fields on both small and large scales, using scatterometer observations as a reference.

Trindade et al. (2019) combined the strong points of scatterometer observations and atmospheric model wind fields into ERA*, a new ocean wind forcing product. ERA* uses temporally-averaged differences between geolocated scatterometer wind data and European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis fields to correct for persistent local NWP wind vector biases. Verified against independent observations, ERA* reduced the variance of differences by 20% with respect to the uncorrected NWP fields. As ERA* has a high potential for improving ocean model forcing in the CMEMS Model Forecasting Centre (MFC) products, it is a candidate for a future CMEMS Level 4 (L4) wind product. We present the ongoing work to further improve the ERA* product and invite potential users to discuss their L4 product requirements.

References:

Belmonte Rivas, M. and A. Stoffelen (2019): Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT, Ocean Sci., 15, 831–852, doi: 10.5194/os-15-831-2019.

Trindade, A., M. Portabella, A. Stoffelen, W. Lin and A. Verhoef (2019), ERAstar: A High-Resolution Ocean Forcing Product, IEEE Trans. Geosci. Remote Sens., 1-11, doi: 10.1109/TGRS.2019.2946019.

D2456 |
EGU2020-15573
Romain Escudier, Emanuela Clementi, Massimiliano Drudi, Jenny Pistoia, Alessandro Grandi, Andrea Cipollone, Rita Lecci, Mohamed Omar, Ali Aydogdu, Simona Masina, Giovanni Coppini, and Nadia Pinardi

In order to be able to predict the future ocean climate and weather, we need to understand what happened in the past and the mechanisms responsible for the ocean variability. This is particularly true in a complex area such as the Mediterranean Sea with diverse dynamics such as deep convection and thermohaline circulation or coastal hydrodynamics. To this end, effective tools are reanalyses or reconstructions of the past ocean state. 

Here we present a new physical reanalysis of the Mediterranean Sea at high resolution, developed in the CMEMS Med-MFC framework. The hydrodynamic model is based on the Nucleus for European Modelling of the Ocean (NEMO) combined with a variational data assimilation scheme (OceanVAR). A series of system developments have been carried out to upgrade the current Med-MFC reanalysis to the new one with high resolution, including new NEMO version and configuration, the new version of atmospheric forcing (ERA-5) datasets and revised OceanVAR scheme.

The model has a horizontal resolution of 1/24° and 141 vertical z* levels and provides daily and monthly 3D values of temperature, salinity, sea level and currents. Hourly ERA-5 atmospheric fields force the model and daily boundary conditions in the Atlantic are taken from the global CMCC C-GLORS reanalysis. 39 rivers model the freshwater input to the basin plus the Dardanelles. The reanalysis covers 30-years, initialized from World Ocean Atlas climatology in January 1985, getting to a nominal state after a two years spin-up and ending in 2018. In-situ data from CTD, ARGO floats, XBT are assimilated into the model in combination with satellite altimetry data.

This reanalysis has been validated and assessed through comparison to in-situ and satellite observations as well as literature climatologies. The results show good agreement with observations and a better representation of the main dynamics of the region compared to the previous, lower resolution (1/16°) reanalysis. The new reanalysis will allow the study of physical processes at multi-scales, from the large scale to the transient small mesoscale structures.

D2457 |
EGU2020-17659
Nikolaos Kampanis, Katerina Spanoudaki, George Zodiatis, Maria Luisa Quarta, Marco Folegani, and George Galanis

The island of Crete is known to be at the crossroads of historic sea routes that served as conveyors of trade, knowledge and culture throughout history, linking some of the world's earliest sophisticated civilizations and currently attracts millions of tourists and cruise passengers. At the same time, the coastal area of Crete is an area of increasing interest due to the recent hydrocarbon exploration and exploitation activities in the Eastern Mediterranean sea and the increase of the maritime transport after the enlargement of the Suez Canal. National and local authorities, like ports and the coast guard, who are involved in maritime safety, such as oil spill prevention, safety of ships, the tourism industry and policy makers involved in coastal zone management, are only few of end users groups seeking high spatial and temporal resolution forecasting products and information to support their maritime activities in the coastal sea area of the island. To this end, a high-resolution, operational forecasting system, namely COASTAL CRETE, has been development for the coastal area of Crete to assist local end users and response agencies to strengthen their capacities in maritime safety and marine conservation. COASTAL CRETE implements advanced numerical hydrodynamic and sea state models, nested in the Copernicus Marine Environmental Monitoring Service of the Mediterranean Sea –CMEMS Med MFC. COASTAL CRETE produces, on a daily basis, 5-days hourly and 6-hourly averaged high-resolution forecasts of important marine parameters, such as sea currents, temperature, salinity, as well as waves. The COASTAL CRETE high-resolution (~1km) hydrodynamic model is based on a modified POM novel parallel code previously implemented by the CYCOFOS  in the Eastern Mediterranean and the Levantine Basin, while for wave forecasts, the last ECMWF CY46R1 parallel version  including  a number of new features,  a state-of-the-art wave analysis and prediction model with high accuracy in both shallow and deep waters has been implemented with a resolution of 1km. The harvesting of the CMEMS Med MFC products has been set in an automatic way and managed through the EODATASERVICE technology developed by MEEO, i.e. ADAM (Advanced geospatial Data Management platform - https://adamplatform.eu/). This application provides automatic data exchange management capabilities between the CMEMS Med MFC and the COASTAL CRETE models, enabling data visualization, combination, processing and download through the implementation of the Digital Earth concept. The downscaled high-resolution COASTAL CRETE forecasts will be used to deliver on demand information and services in the broader objectives of the maritime safety, particularly for oil spill and floating objects predictions.

Acknowledgements: Copernicus Marine Environment Monitoring Service (CMEMS) DEMONSTRATION COASTAL-MED SEA. COASTAL-CRETE, Contract: 110-DEM5-L3.

D2458 |
EGU2020-17977
Francisco Campuzano, Flávio Santos, Ana Isabel Ramos de Oliveira, Lucian Simionesei, Rodrigo Fernandes, David Brito, Estrella Olmedo, Antonio Turiel, Marco Alba, Antonio Novellino, Marina Tonani, Huw Lewis, Marcos García Sotillo, Arancha Amo Baladrón, Tomasz Dabrowski, Benjamin Jacob, Joanna Staneva, and Ramiro Neves

Currently hydrological models are not generally coupled to coastal and regional ocean models because, even if regarded as a powerful and useful tool, they do not fully accomplish to estimate accurately the right volume of water reaching the coastal zone for many reasons including water management activities such as human consumption, irrigation, etc. For this reason, many coastal and ocean models continue to use river climatologies as boundary conditions for representing such an active boundary. Furthermore, continuous salinity observations in the coastal area are scarce and sensors are highly unreliable while current Earth Observation (EO) products for salinity poorly represents the coastal gradients.

In this presentation, the current state-of-the-art and the results of the LAMBDA Project (λ) (LAnd-Marine Boundary Development and Analysis) will be shown. The main aim of the project was to demonstrate an improvement in the thermohaline circulation in coastal areas by a better characterisation of the land-marine boundary conditions, with special regard to the salinity fields. The LAMBDA project analysed the opportunity of improving the land-marine boundary conditions by exploring the capacities of state-of-the-art hydrologic models. In order to achieve those objectives, the project strategy used an integrated approach that went from watershed models to validation in the coastal area by fit-for-purpose EO products, developed by SMOS, and passing through methods and proxies for integrating the freshwater flows into regional mesoscale grids. The watershed and estuarine proxies were modelled using the MOHID Water modelling system (http://www.mohid.com/) an open source model capable of simulating a wide range of processes, i.e. hydrodynamics, transport, water quality, oil spills, in surface water bodies (oceans, coastal areas, estuaries and reservoirs).

The project products were evaluated in Portugal Continental waters, tested in CMEMS regional products and evaluated by local experts in Germany, Ireland, Portugal, UK and Spain.

D2459 |
EGU2020-18033
Javier F. Bárcena, Javier García-Alba, Felipe Fernández, Javier Costas-Veiga, Marcos Mecías, María Luisa Sámano, David Szpilman, and Andrés García

This study focuses on the development of an operational service to prevent one of the major public health problems worldwide, drowning (https://soseas.ihcantabria.com/). Approximately, there are 360,000 annual deaths from drowning all around the world, although, global estimates may significantly underestimate the real values. In order to reduce the third leading cause of unintentional injury death worldwide, there is an urgent need to increase the understanding of drowning causes in highly, nonlinear and complex dynamic systems, such as beaches.

Usually, process-based models have been directly used to provide information for risk analysis or by means of hybrid downscaled systems in local areas. Nevertheless, this type of modelling is not operative to generate a worldwide system capable to offer a forecasting risk of drowning as a function of hydrodynamic information, suggesting that computational requirements will be an impediment to applications where a quick answer is required, e.g., managing temporary closures of bathing sites. Accordingly, different techniques have been proposed to overcome the large computational burden associated with process-based models, called dynamic emulation modelling. From a technical perspective, Artificial Neural Networks (ANN) models have a strong predictive ability for nonlinear systems allowing it to be synchronized with another system, and can enhance the overall reliability and applicability of process-based models simplifying the mathematical descriptions of the physical structure and mechanism of chaotic systems. From the operational perspective, the implementation of ANN models is highly efficient at a very low cost compared to the implementation of process-based models.

The catalogue of events included the information of metocean conditions provided by the global reanalysis and forecasts of Copernicus Marine Service (CMEMS) and the information provided by the Brazilian Life Saving Society (SOBRASA) about drowning in Santa Catarina beaches (Brazil). During the last 18 years, lifeguards have collected more than 132,000 observations from 139 coastal beaches, which have 346 lifeguard posts. This information has been provided in two databases: (1) a database of events (drownings or similar) and (2) a database of status flag at each lifeguard post. Events database contained 52,712 records from January 2001 to May 2019. The Flags database contained 79,487 records from November 2016 to July 2019.

From these databases, the chosen key-variables to predict the dynamic risk of drowning on beaches using electronic flags were geomorphological variables: morphological modal state, beach orientation, presence of estuary/river mouth, and metocean variables: Waves (significant total height, mean wave period, direction of waves), Wind (magnitude of wind velocity, direction of winds), Water Level (water level variation), and Currents (magnitude of marine currents).

The application to the Santa Catarina beaches demonstrated ANN models are viable surrogates of highly nonlinear process-based models and highly variable forcings to understand the synchronization between metocean conditions and drowning risks at beaches. The results showed that the neural networks conveniently reproduced the status flag of beaches. Finally, it is worthy to mention this service has created the availability of non-existent tools that enhance safety in these aquatic spaces, generating economic assets as a sign of high quality tourism.

D2460 |
EGU2020-18332
Yeray Santana-Falcón, Pierre Brasseur, Jean Michel Brankart, and Florent Garnier

Satellite-derived surface chlorophyll data are daily assimilated into a three-dimensional 24 member ensemble configuration of an online-coupled NEMO-PISCES model for the North Atlantic ocean. A one-year multivariate assimilation experiment is performed to evaluate the impacts on analyses and forecast ensembles. Our results demonstrate that the integration of data improves surface analysis and forecast chlorophyll representation in a major part of the model domain, where the assimilated simulation outperforms the probabilistic skills of a non-assimilated analogous simulation. However, improvements are dependent on the reliability of the prior free ensemble. A regional diagnosis shows that surface chlorophyll is overestimated in the northern limit of the subtropical North Atlantic, where the prior ensemble spread does not cover the observation's variability. There, the system cannot deal with corrections that alter the equilibrium between the observed and unobserved state variables producing instabilities that propagate into the forecast. To alleviate these inconsistencies, a one-month sensitivity experiment in which the assimilation process is only applied to model fluctuations is performed. Results suggest the use of this methodology may decrease the effect of corrections on the correlations between state vectors. Overall, the experiments presented here evidence the need of refining the description of model's uncertainties according to the biogeochemical characteristics of each oceanic region.

D2461 |
EGU2020-22241
Jérôme Gourrion, Delphine Dobler, and Tanguy Szekely

This study concerns a quality check (QC) test in which temperature and salinity observations are compared to the range of variability as known from a reference dataset.

For delayed-time QC, Gourrion et al. (2019) have shown that a validity range built from local minimum and maximum values performs better than the standard one estimated from the local mean +/- N standard deviations. It allows to account for the assymetry and peakedness of the local parameter distribution. The performance of the test is significantly improved, reducing strongly the number of hydrographic stations to be checked by a delayed-time operator.

Nevertheless, for near-real-time applications, the available operator time is severely reduced ; the method needs to be automatized and the number of erroneous detections further reduced.

Here, we first present the impact on the performance brought by the increase of the temporal extent of the reference dataset. Then, an adhoc widening of the validity interval is proposed and shown to further improve the performance, reaching a point of applicability for NRT processing. Finally, performance differences between temperature and salinity are highlighted. 

 

D2462 |
EGU2020-22304
Stefania Angela Ciliberti, Atanas Palazov, Marilaure Gregoire, Joanna Staneva, Elisaveta Peneva, Simona Masina, Rita Lecci, Veselka Marinova, Marius Matreata, Sergio Creti, Luc Vandenbulcke, Arno Behrens, Francesco Palermo, Eric Jansen, Leonardo Lima, Laura Stefanizzi, Farshid Daryabor, Diana Azevedo, Nadezdha Valcheva, and Paola Agostini and the CMEMS Black Sea Monitoring and Forecasting Center Team

The BS-MFC (Black Sea Monitoring and Forecasting Centre) delivers near real time and multi-year products for the Black Sea region with the scope to describe its physical, biogeochemical and wave conditions in the frame of CMEMS. This is done through 3 Production Units – Physics, Biogeochemistry and Waves – that implement state-of-the-art and accurate modelling approaches for forecasting and monitoring purposes. In 2019, the BS-MFC offer has been updated to include i) updated versions of the BS-PHY and BS-WAV NRT products and new BS-BIO product, ii) update of the MY products timeseries up to Dec 2018 and iii) inclusion of Ocean Monitoring Indicators (OMI).

Considering NRT systems, the systems are performing assimilation of in-situ and satellite products provided by CMEMS TACs with PHY and BIO products centered to 12:00Z and WAV product instantaneous fields covering 10-days forecast. BS-BIO offers new product since Jul 2019, including CHL, PHYC, O2, NO3, PO4, Primary Production and carbonate system components (pH, DIC, Alkalinity, air-sea flux of CO2). To support ocean monitoring purposes, describing the current state of the Black Sea physical dynamics, environmental and extreme events, the BS-MFC implements a set of OMI: a) vertically integrated oxygen content, b) oxygen penetration density and depth, c) sea surface temperature and salinity anomalies, d) significant wave height extremes.

To improve forecasting capabilities and prepare the next generation of BS products, the BS-MFC is working on several scientific topics, the most challenged are the increased resolution in vertical of the physical system, the problem of the Bosporus Strait as boundary condition, improved data assimilation capabilities, coupling strategies among PHY, BIO and WAV and improvement of upstream data ingestion in NRT and MY systems, including the usage of hourly forcing in WAV production system and forecast data of the Danube River discharge and nutrients in the PHY and BIO systems. Furthermore, the BS-MFC is working on enforce operational capacities and define pre-operational evaluation to estimate accuracy of operational and new products.