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CMEMS requirements for the evolution of the Copernicus In Situ Component

Mercator Ocean, EUROGOOS, and CMEMS partners

Version 1 - December 2018

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Table of content

1 Introduction ...... 3 2 The role of In-Situ observations and its organization in CMEMS...... 4 3 CMEMS requirements for the evolution of the Copernicus In Situ Component ...... 7 3.1 Global Ocean ...... 8 3.2 Arctic Basin ...... 10 3.3 Baltic Basin ...... 11 3.4 Iberia-Biscay-Ireland Basin ...... 11 3.5 Black Sea Basin ...... 12 3.6 Mediterranean Basin ...... 13 3.7 North West Shelf Basin ...... 13 3.8 Requirements for the calibration/validation of satellite data products ...... 14 4 Summary Synthesis ...... 16 5 References ...... 17 6 Annex 1 ...... 18 6.1 Global MFC ...... 18 6.2 Arctic MFC ...... 21 6.3 Baltic Sea MFC ...... 27 6.4 Iberia-Biscay-Ireland MFC ...... 30 6.5 Black Sea-MFC ...... 33 6.6 Mediterranean MFC ...... 39 6.7 North West Shelf MFC ...... 45 6.8 Sea Level-TAC ...... 48 6.9 Ocean Color-TAC ...... 50 6.10 Sea Surface Temperature TAC ...... 54 6.11 Sea Ice TAC...... 57 6.12 Multi OBservations TAC ...... 58 6.13 Wave TAC ...... 63 6.14 Euro-Argo (Euro-Argo ERIC) ...... 65 6.15 EUROGOOS HF Radar Task Team ...... 69 6.16 EUROGOOS Ferry Box Task Team ...... 71 6.17 EUROGOOS Tide Gauges Task Team...... 73

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6.18 EUROGOOS Glider Task Team...... 75 6.19 BOOS ...... 80 6.20 IBI ROOS ...... 85 6.21 NOOS ...... 87 6.22 MONGOOS ...... 90 6.23 Arctic ROOS ...... 94

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1 INTRODUCTION

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic information on the state of the physical ocean at global and regional level. There are four main areas of benefits covered by the service: maritime safety, coastal and marine environment, marine resources, and weather seasonal forecasting and climate activities. CMEMS is based on a production structure covering two layers:

 Processing of space and in-situ observations and delivery of derived products: this is achieved through Thematic Assembly Centres (TACs) organized according to consistent parameters – or sets of parameters. CMEMS include eight TACs mainly handling space observations (Sea Level, Ocean Colour, Sea Surface Temperature, Sea Ice, Winds, Waves, and Multi-Observation Integration), and one TAC dedicated to In- Situ observations. Processing of models, for forecasts, hind-cast and reanalyses, fed by products derived from space and in-situ observations (to be provided by the TACs): these tasks are achieved by Monitoring and Forecasting Centres (MFCs), structured according to regional domains (6 European regional seas) and global ocean. The service provides, to several thousands of users, pioneering solutions, operational and scientifically assessed, to monitor the global ocean with a focus on the European seas. In agreement with the Delegated Regulation on Copernicus data and information policy, the products delivered by CMEMS are open and free of charge, and compliant with European regulations, such as INSPIRE. The products are accessible through a European one-stop-shop, the CMEMS web-portal, which includes a structured information catalogue monitored to ensure that it complies with its operational obligations to users. Earth observation data from In-situ components provide crucial and unique global ocean information along the water column. The provision of these data, used by CMEMS to derive more elaborated products, strongly depends on the proper specification of requirements, which have to be updated regularly to ensure the evolution of the service in response to user needs.

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2 THE ROLE OF IN-SITU OBSERVATIONS AND ITS ORGANIZATION IN CMEMS

In-situ data are of paramount importance for CMEMS as they can provide information of the ocean interior which cannot be observed from space. Delivery of CMEMS products strongly relies on timely provision of in-situ observations; global to regional scale observations are required by MFCs for model testing and calibration, and for assimilation and validation in operational phase. In addition in-situ observations are used by CMEMS TACs to elaborate and validate high level data products.

Figure 1: CMEMS marine sea-borne infrastructure.

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Operational oceanography relies on the observation of a well-defined number of variables (Essential Ocean Variables or EOVs) which have to be provided in a timely and sustained manner with appropriate sampling. CMEMS has set up a dedicated unit the “INS-TAC”, focused on collecting in-situ data from data providers (national and international networks), carrying out quality control in a homogeneous manner, and distributing them in near real time. Today the INS-TAC is one of the major aggregators of in-situ observation at global level, with a structure developed in coherence with other components of the pan European data management landscape such as SeaDataNet and EMODnet (see Figure 1). The INS- TAC is not only directly gathering observations but collaborates closely with the institutes operating the networks both at global scale (Argo, OceanSITES, DBCP, EGO, etc.) and at European scale through the EuroGOOS Regional Ocean Observing Systems (Arctic ROOS, BOOS, NOOS, IBI- ROOS, MOONGOOS and Black Sea GOOS). The main types of In-Situ observing systems aggregated by INS-TAC include all the platforms identified in the initial Strategic Development Plan [1], and many others, made available over the years, as soon as their quality was deemed appropriate to meet service requirements; they comprise:

 Drifting Argo Floats for the measurement of temperature and salinity profiles to ~2000m and, by tracking them, mean subsurface currents. In addition the latest model BGC-Argo allows measurements of biogeochemical properties of the oceans.  Research vessels which deliver complete suites of multidisciplinary parameters from the surface to the ocean floor, but with very sparse and intermittent spatial coverage and at very high cost of operations.  XBTs by research vessels and ships of opportunity underway for the measurement of temperature and salinity profiles to ~450-750m depth.  Surface Moorings capable of measuring subsurface temperature profiles in particular continuously over long periods of time. Currents are often monitored and meteorological measurements are usually made too. Biofouling restricts the range of measurements that can be made from long deployments in the photic zone but surface salinity and biogeochemical measurements are attempted.  Ferry-Box and other regional ship of opportunity measurement programs for surface transects which may include temperature, salinity, turbidity, chlorophyll, nutrient, oxygen, pH and algal types.  The network of tide gauges, which provides long term reference and validation sea level data.  Gliders, which complement floats and moorings and are able to perform transects of physical and biogeochemical parameters from the surface to 1000m at a lower cost than ships.  Surface drifters are cheap and light-weight platforms that passively follow the horizontal flow at the surface via a drogue/sail. They complement satellites for sea surface temperature and surface current measurements

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 Permanent long-range (up to 200 km) HF-radar monitoring systems in specific regions of national/international interests and importance, typically as part of an observatory.  Sea Mammals can be fitted with non-invasive miniaturized ocean sensors the can help collecting measurements in remote and extremely cold places such as Polar areas. Operational aspects of the in-situ provision are managed in close collaboration with EUROGOOS, while the high-level coordination points are coordinated together with the European Environment Agency (EEA), the entity entrusted by European Commission with the coordination of the Copernicus In-Situ Component.

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3 CMEMS REQUIREMENTS FOR THE EVOLUTION OF THE COPERNICUS IN SITU COMPONENT

CMEMS requirements for the in-situ observing system have been previously gathered in the framework of the GISC project [2] led by the European Environmental Agency. As CMEMS evolves some of these requirements have been met, while others need to be updated to the new commitments and capabilities of the service. Evolution of CMEMS is tightly linked to existing and future requirements of end-users. However, user requirements do not translate directly into observation requirements; they must go through the added value chain of the service; to this end, new scientific and technological advances in the fields of Earth observation, numerical modelling, and data processing techniques are taken into account to define the requirements needed for a correct evolution of the service. The latest update of CMEMS Service Evolution Strategy and its associated R&D priorities [3] has introduced a set of over-arching goals and associated actions and R&D priorities for evolving the service from its initial state toward a mature, state-of-the-art, leading and innovative Copernicus Service. Major evolutions are required, in particular, to monitor and forecast the ocean at fine scale and to improve the monitoring of the coastal zone. This is essential for key applications such as maritime safety, maritime transport, search and rescue, pollution monitoring and offshore operations. CMEMS must also improve its capacities to monitor and forecast the biogeochemical state of the ocean (e.g. ocean carbon uptake, acidification, deoxygenation, eutrophication, water quality, biological productivity). This is required for the Marine Strategy Framework Directive (MSFD), to guide decisions and actions by governments and industry and to inform the management of marine resources (fishery, aquaculture). Future evolutions of the in-situ observing system need to be aligned with such goals. Another important contribution to the definition of in-situ requirements and to the identification of area of potential improvement is also ensured by a close working relationship of CMEMS with EUROGOOS, ROOSes and the Euro-Argo ERIC. At the moment there is a quite general consensus about the requirements, and priorities needed to have in-situ observing systems which can fulfill present and future CMEMS demands. These have been discussed recently during several workshops organized by EEA, EuroGOOS, Euro-Argo and CMEMS partners. Latest updates have been collected during a dedicated workshop, held in Toulouse on July 3rd 2018 and organized by Mercator Ocean International in collaboration with EUROGOOS, which brought together all CMEMS TACs and MFCs, ROOSes representatives and EUROGOOS Task Team members. Overall it was agreed that the consolidation and sustainability of the global and regional in- situ observing systems remain a strong concern. There are critical sustainability gaps and major gaps for biogeochemical observations (carbon, oxygen, nutrients, chl-a).

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To follow the evolution of ocean general circulation models in term of spatial resolution, which in future will reach the kilometric scale at global level, there is a clear need of more sensors deployed at global and regional scale. Timeliness is also an important parameter to be improved, to ensure that data are available at each model run; this is particularly important for coastal applications where ocean dynamics evolve on a rather short time. In terms of platforms, maintenance of Argo core mission at the present level is a strong priority at global and regional level. Nowadays Argo is the key in-situ network for operational oceanography, providing thousands of daily measurements of ocean physics. Future improvements of Argo should include:

 BioGeoChemical (BGC) measurements (BGC Argo); a BGC Argo array in synergy with satellite ocean colour observations (Sentinel-3 A&B) will have a major impact on the Copernicus Marine Service BGC products (e.g. air/sea fluxes of CO2, acidification, deoxygenation, primary production).  Deep measurements (Deep Argo); the deep ocean (below 2000 meter) is impacted by climate change and should be taken into account for earth energy imbalance monitoring, heat storage and sea level rise computation. Deep measurements will prevent unrealistic model drift and will be crucial to constrain CMEMS models at depth through data assimilation.  Under ice measurements; under ice and high latitude data are also very sparse and the model forecast and analysis will benefit from more of them. Improving ROOSes and key observing systems such as ferry-boxes, gliders, tide gauges and HF radars is also critical for regional CMEMS products. The Global Ocean and each European basin have their specific needs, in terms of coverage and resolution; in addition they are characterized by a different degree of development in terms of sensors deployed or made available, often due to data policy. In the following of this section a highlight of the main requirements and current status for the Global Ocean and the six European regional seas is reported. A more detailed list of requirements for each basin can be found in Annex 1.

3.1 Global Ocean

At global scale INS-TAC collects in-situ data from the JCOMM networks (Argo, DBCP, OceanSites, GISUD, OceanGliders, GOSHIP) and main international (US-NCEI-WOD, US-NDBC, IMOS) and European (SeaDataNet, ICES, EMODnet) aggregators. For near real time and forecast applications, the most important source of profile data is the Argo network (about 4000 platforms cycling every 10 days) and its extensions to Deep Ocean, and BioGeoChemical parameters. It is complemented by XBT lines (about 50 lines half active in 2018) and sea mammals in high latitudes and in delayed mode the GOSHIP CTD lines (60 lines planned) and other research cruises observations from US-NODC and CCHDO. For Time- series, the most important source of observations is the DBCP network operating more than

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1400 drifters and 20 Arctic buoys as well as more than 500 moorings providing both atmospheric and oceanographic data. It is complemented by the GOSUD and VOS network on voluntary observing ships that provide both SST/SSS and also Surface Carbon data. Argo network also provides time-series of T&S at the surface and at drifting depth and derived velocity information. For REP products, the database is complemented by research data coming from US-NCEI-WOD database The global ocean monitoring systems are constrained by temperature and salinity profiles and time series from Argo floats, XBT, CTD, moorings, gliders and sea mammal measurements, coming from the CMEMS in-situ TAC databases. The assimilation of Argo observations provides an efficient constrain of the global large scale ocean temperature and salinity mean and variability but only down to 2000 m depth. They complement the satellite observations that provide a much higher spatial resolution but only of surface fields. Other assimilated platforms are sampling either locally, or during a short period the ocean. They help constraining the analysis in specific regions. In the present system, surface drifters, TSG and Ferry box observations are withheld from the analysis. Both assimilated and non-assimilated observations are used for the qualification of the analysis and forecasts. In particular, the model velocities are validated with observations from surface drifters, HF radars and moorings. Tide gauges allow validating sea level variability on coastal zones. Today operational BGC and ice model forecasts are respectively constrain by satellite ocean color observations, in situ observation are used for model development and analysis validation. Argo floats provide oxygen, Chlorophyll, pH and nitrates observations, as the GLODAP profiles and maps provide oxygen, nutrients and carbonate system observations from other platforms than the Argo floats. In situ ice observations, more specifically thickness of the different ice and snow categories from the unified CDR sea ice data base in the Arctic, are used to validate the ice model outputs. For the global wave product validation, Significant Wave Height and period (1D peak and mean) from buoys are used. Following the Service Evolution strategy, the Copernicus global system will evolve toward an increase spatial and temporal resolution from 1/12° to 1/36°. The model correction is computed using 7-day assimilation window (7 day of observations). This will be reduced to 2 to 3 days in the future. These evolutions will need: • NRT data available in less than a day. • Delayed time in situ database containing as much as possible qualified data. • More high frequency (up to hourly to observe the diurnal cycle in the upper ocean) and high resolution (up to a km resolution) in situ observations, first for validation purpose, later to be assimilated. In terms of variables to be observed with a better global coverage it is recommended to increase:

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• BGC measurements: oxygen, pH, Chl-a and nitrate; • Deep T&S measurements (below 2000 meters); • In situ velocity observations; • Under ice observations; • In situ ice observations including thickness per ice/snow category for the Antarctic region and from more platforms; • Wave measurements. Some of those observations are used today for validation purpose (e.g. velocity) but will be assimilated in the future systems. In situ BGC data coverage must be enhanced and this is a strong priority for CMEMS. Lack of in-situ BGC data is a major shortcoming that limits our ability to monitor and forecast the state of the “green” ocean. Some networks would benefit from a data management better organized at the international level to ensure homogeneity for the corrections, qualification procedures, format, and QC flag values.

3.2 Arctic Basin

Key variables to characterize the water masses and their variability over the Arctic are temperature, salinity, together with ice properties (thickness, drift). These variables are measured today by ice-tethered profilers (ITP), acoustic tomography, Argo floats, sea mammals, moorings, drifters, and when possible by research cruises CTD. Only research cruises provide the necessary biogeochemical data. At the moment the number of in situ platforms is very limited and not sufficient to monitor the entire region. In terms of observations, there are severe limitations with measurements over the seasonal ice zone, which is growing broader and where none of the platforms available today can collect data. More ice mass balance (IMB) buoys are needed to measure ice thickness and snow depth that are critical in particular for remote sensing algorithm calibration and validation. There is also a need for more BGC-Argo floats, since monitoring cruises in Polar Regions are seasonally biased. All the above calls for general improvement of the observing platforms and sensors, filling of gaps in the observing network and facilitation of year-round operations. Data harmonization and access need to be improved as well; specifically, data sampling, transmission, calibration, processing, archiving and retrieval of required variables shall be improved, using distributed and connected databases (INSPIRE-compliant). A large share of the Arctic in situ data is either unavailable or scattered on different national databases, especially when it comes to biological data. There is a clear need for improving the coordination and collaboration between data providers and stakeholders in the pan- Arctic region in order to better use existing systems and resources.

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3.3 Baltic Basin

For the Baltic Sea, in-situ ocean observations for operational oceanography include ferrybox lines, moorings, R/Vs (Research Vessels), tidal gauges, Argo floats and drifter. In recent years HF radars are tested in the transition waters and gliders tested in the central Baltic Sea. Although the deployed platforms are providing data with a sufficient spatial and temporal resolution, it is pointed out that coverage should be improved; the Baltic Sea includes 17 sub-basins with significant spatial variability in physical and biogeochemical characteristics. This requires in-situ observations in all sub-basins to ensure spatial representativeness in the cal/val of regional products. The BAL MFC is currently developing a new level of validation, Multi-model ensemble (MME), forecasting and reanalysis capacities. This will generate new needs on in-situ observations. At first, the validation will be extended to cover extreme events, e.g., high sea, major Baltic inflow, storm surge, upwelling, oxygen depletion and algae bloom etc. For this purpose, high resolution observations are needed to reveal detailed spatiotemporal features in synoptic scale. In general, more moorings for T/S are needed in the Danish strait and more stations for BGC measurements should be deployed over the central and north Baltic area (more than half of the sub-basins do not have sufficient data for the eutrophication assessment). An increase of Argo floats is also a priority for Baltic. Finally, Data management should be also improved; most of the ship data cannot be accessed timely, both for operational forecasting and ocean state assessment report, and currently BOOS and BAL MFC only receive near real time data from less than two-thirds of the total amount of tide gauge stations.

3.4 Iberia-Biscay-Ireland Basin

At the Iberia-Biscay-Ireland Monitoring and Forecasting Center (IBI-MFC) in-situ observations are critical for the generation of analysis and to validate the model forecast. In situ observations products are used for:

 Data Assimilation: Temperature and salinity vertical profiles data from Argo floats are assimilated in the ocean physics model to generate analysis outputs.  Qualification/Validation processes: Product quality metrics are computed using data from several platforms such as mooring buoys, Argo floats and BGC-Argo floats, drifters, gliders, XBTs, CTDs, and HF Radar. Although a recent inventory of the IBI basin, performed in the framework of the MyCOAST project, identified 272 items delivering data (among these 246 operational stations, 139 tide gauges, 17 HF Radars), the coverage of in situ data in coastal areas is still too scarce. The increase of the coverage in the coastal areas may positively impact on the data assimilation performance for generating better physical analysis. In the future, in situ products should be kept, at least, with the current specifications. It will be important to increase the number of in-situ observations in near real-time on shelf and coastal areas of the IBI regions. Particularly, some variables should be better covered. HF-Radar products are useful to

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provide a good view of the high frequency surface circulation dynamics. To complete the monitoring of circulation on shelf, more acoustic Doppler current profilers (ADCP) observations (if possible available in NRT) are needed. It is also quite important to count in the future with reliable NRT fresh water river discharge data in river mouths (if accompanied of biogeochemical data, the better). In-situ biogeochemical data coverage in IBI area must be certainly enhanced. Current lack of this in-situ data is certainly a present shortcoming and this limitation is certainly an open issue that can condition future IBI MFC developments (such as modelling progresses, estimation of meaningful biogeochemical monitoring indicators in the area).

3.5 Black Sea Basin

The Black Sea Monitoring and Forecasting Centre (BS-MFC) models assimilate T/S profiles from Argo floats (for NRT production), moorings, and XBT/MBT. At the moment there is a clear lack of data to be used regularly for improving forecasts products. In particular, to understand coastal dynamics and contribute in evolving physics model for NRT, coastal profilers and moorings regularly updated and continuous in time are requested. The BS- Physics team considers that in-situ observing system should evolve also accounting the importance to have coastal observatories, in particular wave buoys, ADCP and HF radar, to evolve next generation of BS physical and wave modelling; ad hoc and continuous monitoring is required in the shelf area, especially in the Black Sea Northern part and in the Danube Delta. BS-Biogeochemistry suffers from a lack of regional data to use regularly for improving the validation exercise and for assimilation in the biogeochemical model. In particular, as an anoxic basin, the dynamics of carbonates and the conversion of in-situ fluorescence into chlorophyll (e.g. from BGC-ARGO floats) requires particular monitoring efforts for supporting biogeochemistry modeling. Being a turbid area, with anomalous high colored dissolved organic matter, the Black sea shelf waters requires extensive observations for assessing its inherent optical properties for supporting the development of high quality ocean color data. The development of satellite products from the Copernicus Sentinels that will be assimilated in models needs to be supported by such data. To this end dedicated campaigns are required. Data like pH, dissolved inorganic carbon and alkalinity are critically lacking in the Black Sea and this would prevent a sound initialization of the models. So far, the biogeochemical variable that has the best spatial and temporal coverage is oxygen because it is provided by 4-5 BGC Argo floats. On the shelf the monitoring of oxygen is crucial and BGC Argo floats cannot go there. So cruises, mooring, glider could be a solution. In-situ wave measurements are extremely insufficient; it is requested a more organized in- situ observing network for monitoring the Black Sea and coastal area, with the best temporal and spatial resolution. Overall, the lack of NRT in-situ data, systematically maintained at regional level and quality controlled is considered as a weakness with respect to the other MFCs for improving data assimilation capabilities and for enforcing the validation of NRT and MYP products.

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3.6 Mediterranean Basin

Monitoring the Mediterranean basin with a proper observing system is important for scientific, societal and operational purposes. At the moment there is a recognized concern about the status of the Mediterranean observing system and its future. There are important gaps due to lack of instruments and poor data policy, in particular in Central-Eastern Med Sea ad Northern African Coast leading to a strong North-South West/east unbalance. Future of existing limited system is at risk due to funding based on project and affected in recent years by national crises. It is thus strongly recommended to trigger initiatives to grant funding to fill the mentioned gaps and to foster the evolution of the western Mediterranean. When looking more specifically at platforms type, at MED-MFC Argo floats, CTD and XBT platforms are assimilated into the system and also used for quality assessment, while moorings are used only to assess the quality of the system through and independent validation. The existing spatial coverage of Argo network is almost 20 per day in the Mediterranean domain. Full profile transmission could be considered as a major improvement for CMEMS operational analyses. There is also a need for an increased number of Argo floats displaced in high dynamical area (where the floats are suddenly moved away resulting in a poor coverage) and at deep ocean; a need to reintroduce XBT measurements; a need to increase the number of in situ velocity observations (e.g. ADCP, moorings, HF Radars, drifters) and NRT fresh water river discharge data at river mouths (if accompanied of biogeochemical data, the better). Wave buoy observations of significant wave height and mean wave period are used to calibrate and validate the Med-waves modelling system. It is required to maintain and increase the number of wave buoys and include wave measurements from HF Radars, in particular in the Central and Eastern Mediterranean and along the African coasts. There is also a clear need for increased coverage in the Eastern and Southern Mediterranean in terms of number of BGC-Argo floats and number of moorings equipped with BGC sensors.

3.7 North West Shelf Basin

NWS observing system is operated by a consortium of 23 governmental agencies from 9 countries bordering the NWS basin. Although a general effort in providing the largest possible number of measurements is recognized, there are still gaps for some platform/variables that need to be filled. In details sea level and meteorological observations are well sampled across the basin. Over long time scales sea temperature, salinity and waves measurements are considered mature but with some gaps in specific areas. Major efforts need to be made for salinity and currents observations both in terms of sustained coverage and, to address data access policy (data often freely accessible but access restricted to registered users). Biogeochemical and hydrological measurements are currently based on in- situ samples analyzed in laboratory which prevent automated Near Real Time dissemination. At the NWS-MFC in situ temperature and salinity profiles from Argo floats, underwater gliders, moored buoys, marine mammals, and manual profiling methods are regularly

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downloaded and quality controlled before being used for data assimilation. For verification purposes also drifter-derived currents, and tide gauges are used. There is a need to increase the in situ data coverage on shelf where data from Argo floats are not available. The number of biogeochemical observations is unsatisfactory and the BGC- Argo will not be available in key areas like the continental shelf. The sustained NRT data availability, for all type of observations, plays a key role especially for the data assimilation and this is therefore a clear requirement for a forecasting system.

3.8 Requirements for the calibration/validation of satellite data products

CMEMS TACs operationally use in-situ observations to elaborate and validate high level (gridded L3, and interpolated L4) data products. It is important to emphasize that the quality of these products strongly depends on the quality of upstream satellite data provided by space agencies. To this end, all activities aimed at supporting evolutions of the Copernicus satellite component, and calibration and validation of the upstream (L2) satellite data are under responsibility of ESA and EUMETSAT which are already working, through specific projects, to develop a dedicated in situ observation system to ensure the acquisition of high quality in situ measurements accompanied with their uncertainties, also referenced as “Fiducial Reference Measurements” (FRM). These measurements can be also used by CMEMS TACs to develop new products and validate them; it is thus important that CMEMS requirements are taken into account in the design and development of new platforms acquiring FRM. In the following part of this section are reported some specific requirements from the different TACs. Satellite-based sea level measurements are mainly validated using tide gauge networks, drifting buoys, and Argo profilers. While at global level the number of available in-situ observations is adequate to carry out routinely validation, at regional level and in particular for the Mediterranean Sea and Black Sea available dataset are still limited for a robust validation. In situ data are used in the OC-TAC routine operations to evaluate and calibrate the ocean color remote sensing algorithms and to validate the operational products distributed by CMEMS. To carry-out these activities FRM observations are needed. These measurements are acquired through a variety of systems and platforms such as automated in-water and tower-based radiometers, and from profiles acquired during dedicated research cruises. At the moment these observations are very sparse and limited. There is a clear need for improving the number of platforms able to perform such measurements. In addition, the future systems should be designed to allow NRT data stream and enable operational capabilities that meet CMEMS service needs. Due to the regional variability of the bio-optical characteristics for the water masses there is also an increasing need for measurements encompassing diverse regional, offshore and shelf waters across basins. Due to a potential large number of match ups, use of BGC Argo data offers complementary approaches for the routine validation of satellite ocean color products.

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Validation of sea surface temperature (SST) products is operationally done using in situ data as independent source of comparison acquired from surface drifting buoys, Argo floats, and moored buoys. To carry out a proper validation of SST products, dedicated in situ datasets based on accurate, near real time, quality controlled data are needed. In situ data need to be collected at the highest available frequency and they shall contain more metadata to better select and/or filtering out the data with confidence for validation. In terms of coverage, it is recommended to increase the measurements over the Black Sea, the Baltic Sea and the Arctic region due to the very low number (even missing in some cases) of observations. An additional important requirement for satellite validation is to have the in situ measurements available in near real time, e.g. 24 hours within the acquisition. Validation of sea-ice products derived by satellite measurements is carried out using ship measurements from ice breakers, ice mass balance buoy measurements, drifter GPS buoys, and Ice tethered profilers. At the moment there is a great lack of in situ data both over the Arctic and Antarctic regions, thus validation activities are limited to using a different type of satellite imagery (e.g. Sentinel-2) or aerial photography. As already stated in section 3.2 a general improvement of the observing platforms and sensors, to fill gaps in the observing network is urgently needed. Specifically, the necessary in situ observation parameters are temperature profiles in the ocean, ice, and snow, (and also ideally a weather station with the buoy). For winds and waves measurement validation it is recommended to increase the number of moored buoys, the main platform used for validation, to better represent regional geophysical conditions. In CMEMS higher level products, including salinity, currents and BGC properties of the ocean are generated by a specific production center, the “Multi-Observations” TAC, applying fusion techniques, and using as input a combination of satellite and/or in-situ data. These products rely on the timely provision of in-situ data. In particular, it is considered of great importance to keep the Argo network at the current standard, and to develop a BGC-Argo float array as proposed in the BGC Argo Science & Implementation Plan [4]. It is also considered important to maintain the current SOCAT network with extensions towards under sampled areas like the Southern Ocean.

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4 SUMMARY SYNTHESIS

Based on a recent update of the CMEMS requirements for the evolution of the Copernicus In-Situ Component, gathered during a dedicated workshop held in Toulouse on July 2018, the following recommendations have been formulated:

 Consolidation and sustainability of the global and regional in-situ observing systems remain a strong concern. There are critical sustainability gaps and major gaps for biogeochemical observations (carbon, oxygen, nutrients, chl-a). New mechanisms need to be set up between the EU and member states to address them.  To follow the evolution of ocean general circulation models in term of spatial resolution, which in future will reach the kilometric scale at global level, there is a clear need of more sensors deployed at global and regional scale.  Timeliness is also an important parameter to be improved, to ensure that data are available at each model run; this is particularly important for coastal applications where ocean dynamics evolve on a rather short time.  In terms of platforms, consolidation of the Argo core mission (T&S–0-2000 m) including the sampling of polar seas and marginal seas and developing its two major extensions (BGC Argo and Deep Argo) is a strong priority at global and regional level. Nowadays Argo is the key in-situ network for operational oceanography, providing thousands of daily measurements of ocean physics and progressively becoming the main source of biogeochemical observations in the open seas.  Improving ROOSes (Regional Ocean Observing Systems) and key observing systems such as ferry-boxes, gliders, tide gauges and HF Radars are strong priorities for regional CMEMS products.  A specific effort for the Arctic region is needed; there are severe limitations with measurements over the seasonal ice zone, which is growing broader and none of the platforms available today can collect data there. More ITPs, core and BGC Argo floats are needed. IMB buoys are needed to measure ice thickness and snow depth that is critical for remote sensing algorithm calibration and validation.  Data harmonization and their access need to be improved as well; specifically, data sampling, transmission, calibration, processing, archiving and retrieval of required variables shall be improved, using distributed and connected databases.  Development of a dedicated network able to collect Fiducial Reference Measurements for all the ocean variables estimated by the Copernicus Satellite component is also important for CMEMS, since these data are also used for the development of new products and their validation.

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5 REFERENCES

[1] R. P., « GMES Fast Track Marine Core Service Strategic Implementation Plan. Final draft for the Marine Core Service Implementation Group», Marine Core Service implementation group report., (2007)..

[2] G. Zeug, T. Blagoev, A. M. Hayes et H. Andersen, «Analysis of in situ requirements & Report on criteria to determine priorities for support,» Report of GMES in situ Coordination (GISC) Project , 2012.

[3] Mercator Ocean , «CMEMS High level Service Evolution Strategy». Document prepared with the support of the CMEMS Science and Technology Advisory Committee (STAC), http://marine.copernicus.eu/science-learning/service-evolution/service-ev, 2016.

[4] J. K. et C. H., «Biogeochemical-Argo Planning Group. 2016. The scientific rationale, design and Implementation Plan for a Biogeochemical-Argo float array», 2016.

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6 ANNEX 1

In the following are reported the full contributions provided for the preparation of the dedicated “CMEMS In-Situ Requirements Workshop”, held in Toulouse on July 3, 2018.

6.1 Global MFC

Authors: Elisabeth Remi1, Yann Drillet1;(1) Mercator Ocean International Mercator Ocean is running in real time the Copernicus global high resolution (1/12°) ocean forecasting and reanalysis system and a global ¼° system as well as regional configurations for R&D activities on the Iberia Irish and Biscay region. A wave model assimilating wave height measurements from satellites is operated in real time by Météo France. In situ wave measurements are up to now used for validation of the model outputs. In situ observations are required with a global coverage: • in real time, • in delayed time with a better qualification whenever possible, • for data assimilation, • for qualification and validation of the analysis and forecast.

6.1.1 Status of the In Situ observations used by your organization Presently, the in situ assimilated observations in the real time and reanalysis systems are: • Temperature and salinity observations (profiles/time series) from the CMEMS in-situ TAC (Coriolis) database: Argo floats, XBT, CTD, moorings, sea mammals, gliders. • Drifters, TSG and Ferry box data from Coriolis are withheld from the analysis up to now.

Observations are used at the measurement location, no in situ map product are used for Data Assimilation, except climatologies to relax the model fields toward it in specific regions/situations. There is no ice and BGC in situ observations assimilated presently in the operational systems. Both DT and NRT products are used. Other data (not assimilated) are used for the qualification of the analysis and forecasts, or to test new system developments: • ARMOR3D and GlobCurrent mapped products from observations, • BGC observations from Argo floats (oxygen, Chlorophyll, pH and nitrates), • WOA climatology is employed for Nitrate, Phosphate, Silicate and oxygen in the whole water column, • GLODAP V2 in situ data for oxygen, nutrients (nitrate, phosphate, Silicate), and variables of the carbonate system (dissolved inorganic carbon, alkalinity, and pH),

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• Velocities from drifters, HF radars and moorings, • Sea level from tide gauges.

Mapped products and discrete observation points are used for validation. In situ ice observations (thickness of the different ice and snow categories from the Unified CDR Sea Ice data base in the Arctic) are used to validate the ice model outputs. For the global wave product validation, Significant Wave Height and period (1D peak and mean) from buoys are used. There is no plan to assimilate in situ wave data as they are too sparse to have a significant impact on the analysis.

6.1.2 Present needs, and outlook on future requirements We identify some areas over the global ocean with a clear lack of physical observations: • The deep ocean (below 2000 meters) which is impacted by climate change and should be taken into account for heat storage computation. • Polar oceans are presumably undergoing the impact of climate change more than any other world oceans. In parallel, these oceanic areas are largely the most sub-sampled oceans. Sustaining, reinforcing and expanding (with novel technics such as Argo with ice sensor) actual networks (ITP, sea mammals) in ice covered oceans is strongly required. • On the shelf observations are also needed to improve the analysis (better boundary conditions for downscaling applications, better close to the shore/western boundary currents that influence the open deep ocean).

In terms of variables to observe with a better global coverage: • In situ velocity observations, • In situ ice observations including thickness per ice/snow category for the Antarctic region and from more platforms, • Wave measurements in the open ocean. In situ BGC data coverage must be enhanced. Lack of in-situ BGC data is a major shortcoming that limits our ability to monitor and forecast the state of the “green” ocean. Time and space coverage and resolution The global system resolution will evolve toward an increase spatial and temporal resolution from 1/12° to 1/36°. The model correction is computed using 7-day assimilation window (7 day of observations). This will be reduced to 2 to 3 days in the future. • NRT data should be available in less than a day • DT in situ database should contain as much as possible qualified data basis on delayed mode. • Both should cover the globe. More High Frequency (up to hourly to observe the diurnal cycle in the upper ocean) and High Resolution (up to a km resolution) in situ observations will be useful first for validation

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purpose, later to be assimilated. Up to now, they are subsampled or filtered when assimilated to reduce the number of observations. Sustained fixed platforms providing long term high accuracy time series of physical and BGC variables in the open ocean are crucial to monitor the quality of ocean analysis and forecasts. Requirements related to data management For operational systems, in situ observations are crucial to constrain the ocean interior.

The different types of in situ observations should be: • Managed at international level to ensure homogeneity for the corrections, qualification procedures, format, QC flag value… • Available in Real Time within a day, • Available in Delayed mode version updated regularly with an improved QC, • Easy to access, • Well maintained in time, • Well documented (inventory, correction, QC procedures, filtering,…) • Provided with an error estimate (useful for data assimilation) • Research / “local/coastal” observations should be made available.

Then they have to be gathered in a large database with a unified file format and easy to handle. Subsampling in time and space will depend on the use and model configuration.

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6.2 Arctic MFC

Authors: Laurent Bertino1, A. Samuelsen1; (1) NERSC

6.2.1 Status of the In-situ observations used by ARC MFC Table 1 gives an overview of the present usage of in situ products within the Arctic MFC; for the time being, only Temperature and Salinity profiles are assimilated routinely both in near real-time and reanalysis, nutrient profiles are being assimilated in an ongoing biological reanalysis. Most data are used for validating the products whether they are assimilated or not. The impact lines show that some data sources may be more important to the ARC MFC as an indirect resource for calibrating satellite data or because of their contributions to the theories that support models and remote sensing products. The Priority line however reflects the degree of urgency of intensifying the observation network. There is only one data under priority “C” (ice drift) because in the present situation the ice drift products from satellite data have good spatial coverage, but this does not means that the in situ time series should be interrupted. The data needs of process studies for model developments and remote sensing products are beyond the scope of this questionnaire. River data is not covered by this survey although it is an important topic for the Arctic.

Table 1: Overview of the use of different in situ data in the ARC MFC. NRT: Near Real Time, RAN: Reanalysis. CMEMS: data taken from CMEMS portal. Ext: external data source. Impact: “Real” = has been documented. “Ongoing” = is being documented. “Ind.”: Indirect through calibration of remote sensing products. “Science”: Data is used for process studies. “None”: Data not available or insufficient. Priority A = urgent, B= Second priority, C: Not a priority for the time being.

OBS T/S SST Surf. Current Ice Nutrients Waves SL Ice Snow Currents Prof. Drift Thick

Source CMEMS CMEMS CMEMS Ext Ext Ext Ext Ext Ext Ext + Ext

Assim. NRT + RAN RAN

Valid. NRT + NRT NRT RAN NRT + RAN + RAN RAN RAN RAN NRT

Impact Real Ind. Science Science Ind. Ongoing None Ind. Ind. None

Priority A B B B C A A B A A

Temperature and salinity:

• Platforms: all data from the Arctic INS TAC from Argo, Ice-Tethered profilers, research cruises CTD, acoustic tomography. Data from mammals and gliders are not

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used yet although they are available in CMEMS. Additional data from collaborative partners are now publicly available as part of the UDASH data from AWI. o In NRT INSITU_ARC_NRT_OBSERVATIONS_013_031 o In RAN INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b (Assim) o INSITU_GLO_TS_OA_NRT_OBSERVATIONS_013_002_a (Val) • Role: both for assimilation and validation in near real-time (NRT) and reanalysis (RAN) • Impact: better control of vertical profiles. Xie et al. 2017 have documented a reduction of bias and RMS errors up to 50% in both temperature and salinity in the whole Arctic. Temperature biases return within 1 year after the observations are gone. Citation: Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013, Ocean Sci., 13, 123-144, https://doi.org/10.5194/os-13-123-2017, 2017.

Figure 2: All assimilated Temperature (left) and Salinity (right) profiles in the ARC MFC Physical reanalysis in the whole period 1992-2016.

SST:

• Platforms: surface drifters (See Figure 3 for a typical example) • INSITU_GLO_NRT_OBSERVATIONS_013_030 • Role: validation of NRT forecasts • Impact: accuracy numbers

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Figure 3: Surface drifter coverage as from CMEMS INS TAC on 17th April 2018, deviations from the TOPAZ analysis. Data points in sea ice have been removed because it is not indicated which vertical level is measured (i.e., snow temperature, ice surface temperature, but not SST).

Surface currents:

• Platforms: drifting buoys (drogue at 15m). There are HF radars in Northern Norway but these are better assimilated by downstream services (MET Norway’s coastal ROMS model). Their coverage is not worth the effort for a large area like the Arctic. • INSITU_GLO_NRT_OBSERVATIONS_013_030 • Role: validation of NRT forecasts • Impact: Accuracy numbers. Process studies (horizontal diffusion) Current profiles:

• Platforms: NABOS moorings from UAF (MMTP profiler), IMR moorings in the Barents Sea Opening, Bering Strait moorings from APL-UW. • Role: Validation of physical RAN • Impact: none (data QC and representatives issues). Scientific contributions to Arctic oceanography and fluxes. Ice drift:

• Platforms: drifting buoys (IABP) • Role: validation of the physical reanalysis • Impact: validation of the ice drift acceleration, of the seasonal cycle. Nutrients (Nitrate, Silicate, Phosphate and Oxygen):

• Platforms: research cruises from the Norwegian NMDC and other sources (SEADATANET, ICES, GLODAP). • Role: Assimilation in BIO reanalysis. Validation of BIO reanalysis and BIO NRT. • Impact: Better constraint on the vertical structure of nutrients but only in the Norwegian Sea (insufficient data elsewhere). See the typical East-West imbalance in Figure 4.

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Figure 4: Nutrients profiles density from three databases: ICES, GLODAPv2 and NMDC (IMR) accessed in September 2017. Note the temporal coverage is unequal. The plot from SeaDataNet shows only a few data points and has been omitted.

Waves (Significant wave heights, peak period, mean direction):

• Platforms: Moored wave buoys (Only from oil platforms and on Iceland) • Role: model calibration and validation • Impact: Improved wave statistics at the buoys positions. Sea level:

• Platforms: tide gauges (publication by Calafat et al. 2014). Only Norwegian tide gauges enter CMEMS. • Role: validation of the physical RAN. • Impact: They have documented that the RAN product is adequate to study sea level rise, although the seasonal cycle has too low amplitude. Ice thickness:

• Platforms: Airborne campaigns (Operation Ice Bridge), Drifting Ice Mass Balance Buoys (from CRREL, Dartmouth, USA), Moorings in the Beaufort Sea (Beaufort Gyre Experiment Programme, BGEP from WHOI, USA). • Role: Validation of the Physical RAN. • Impact: Accuracy numbers but limited coverage. Snow depth on ice:

• Platforms: Research campaigns. IAOOS buoys from LOCEAN. • Role: Validation of the physical RAN • Impact: none (too few data)

6.2.2 Present needs, and outlook on future requirements in terms of:

Platforms:

• More ITPs are needed in the Central Arctic, the numbers are presently down to 1-2 ITPs and Xie et al. 2017 showed that the density should be at least that of IPY (5-10 active ITPs).

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• A new type of platform is needed that can work in the seasonal ice zone: Argo work in open ocean and ITPs work in permanent ice cover. But the seasonal ice zone is growing. Adaptations of Argo are undergoing. • Sea mammals are autonomous and thrive specifically in the seasonal ice zone. The data needs drift correction and is mostly useable in delayed-mode. • Acoustic tomography data works in the seasonal ice zone but its use for assimilation is not yet common. We still recommend more acoustic data in the central Arctic, hopefully from the CAATEX (with MOSAiC) and CANAPE projects. • Bio-Argo and BGC-Argo data are needed because monitoring cruise data are seasonally biased. A few Bio-Argo from the NorArgo programme will be a good start, but spatial variations have very small scales for biology. • Sea ice thickness is far from being a solved problem, thus more IMB buoys are needed, in particular for the snow depth variable that is critical for remote sensing.

Coverage: • As mentioned above, the seasonal ice zone is growing broader and none of the platforms we have today can survive there. This will be an increasing problem as the ice retreats. • IABP ice drifting buoys are not covering the Arctic homogeneously. Satellite products provide better spatial coverage but poorer temporal coverage (data is less trustworthy in summer). • Ice thickness measurements (IMB, BGEP) are mostly in the American Basin, the IAOOS drifting systems on the European side are not publicly available. • There are no operational wave buoys in the Arctic except for Iceland, islands like Jan Mayen, Bear Island and Svalbard should be having permanent ones. • Research cruises from IMR tend to follow fish cohorts so the sampling is seasonally biased. We insist here on autonomous measurements. Spatial resolution:

• There should be one T/S profile every 200 km in the ocean, higher resolution in key areas (the Fram Strait and the Barents Sea opening for example). • The spatial resolution for biological variables should even be higher. • The drifting buoys shown in Figure 3 do not provide dense enough sampling for robust validation of SST nor surface currents (4 points in the whole Barents Sea for example). The spacing of 200 km is necessary for them as well. Temporal resolution:

• It would be good if all platforms (Argo, ITPs) could report data at least once a week in order to catch the shoaling of the mixed layer and the onset of the primary production bloom. Timeliness:

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• For NRT assimilation, data can be assimilated with a latency up to 10 days old, but not older.

6.2.3 Requirements related to data management • A large share of the Arctic in situ data is either unavailable or scattered on different national databases, especially when it comes to biological data. Our first requirement is that a systematic work of standardization, removal of duplicates and distribution is carried out. There is also a suspicion of ill-will (no intention to distribute the data) that is deleterious for the community at large. • The access to data should not require the installation of specific software (like OceanDataView, Matlab or other) that are not practical for operational NRT runs. The users should be allowed to extract data automatically by date and allowed to download the whole time-series by basic scripts on the command line. This is fine for the CMEMS INS TAC, but not all data is available there yet. • QC should not be delaying the public release of data. It is understandable that some advanced quality checks cannot be carried out in NRT but the delays are more often caused by lack of personnel or priority. The Arctic MFC also has internal QC checks in order to be robust against potential errors. • CMEMS requires MFCs to update the reanalysis regularly until 1-month behind NRT, so the QC’ed data would be most useful if it comes within 1 month and 1 week from present time.

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6.3 Baltic Sea MFC

Author: Jun She1; (1) Danish Meteorological Institute The purpose of the Baltic MFC is to provide timely, high quality physical (ocean-ice-wave) and biogeochemical reanalysis and forecast. The Baltic Sea in-situ observations are used in BAL MFC for i) calibrating and validating the correspondent numerical models both in offline and online modes, ii) data assimilation for historical reanalysis, interim reanalysis and forecast. Currently in-situ data on waves, sea level, water temperature and salinity, currents, oxygen, nutrients, chl-a and secchi depth are used in model calibration and validation (Cal/Val). The spatiotemporal requirements on in-situ data for the cal/val activity are closely related to the cal/val methodology used which currently mainly focuses on the model error statistics. Spatiotemporal coverage of observations for cal/val: the Baltic Sea includes 17 sub-basins with significant spatial variability in physical and biogeochemical characteristics. This requires in-situ observations in all sub-basins to ensure spatial representativeness in the cal/val. Assume 15 profiles per season are needed for T/S, dissolved oxygen, nutrients, chl-a, in total 1020 profiles per variable are needed. For surface parameters e.g., sea level, SST, significant wave height, sea ice concentration and thickness and ocean optics, since remote sensing also provides a significant amount of data, the requirements of in-situ data should be identified jointly with the remote sensing data. Timeliness of observations for cal/val: the offline validation in Baltic Sea provides prequalification for an Figure 5: HELCOM open sea sub-basins used for this eutrophication assessment. Source: updated model version based on validation of the model www.helcom.fi (with added sub-basin for a selected two year period, normally 3 years before numeration). the current year. The timeliness of the in-situ observation is 3 years, i.e., the in-situ observations have to be made available at the latest 3 years after the measurements. For online monthly and daily validation, the timeliness is much shorter, i.e. 1-30days. Data requirements for Multi-model ensemble (MME) prediction: in the BAL MFC, in-situ observations are also used in MME to estimate the single model forecast skill and its relative weight which will be used for making an ensemble forecast. The MME is currently performed for sea level, SST, SSS and subsurface T/S. This activity needs observations in 1-7 days before the MME prediction. Observation requirements for data assimilation: currently data assimilation is used to generate historical reanalysis (longer than 25 years). In situ observations assimilated are profile data of water T/S and nutrients. Impacts of assimilating in-situ data have been

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assessed by a series of Observing System Experiments (OSEs). She et al. (2007) studied impacts of assimilating different types of in-situ and satellite SST data. It was found that RMSE (Root Mean Square Error) of the model SST can be reduced by 43% with SST assimilation; however, with presence of the satellite SST, impacts of the in-situ SST are negligibly small. Assimilating SST does not change subsurface ocean state. Significant improvements have been identified by assimilating profile observations. A 20- year physical ocean reanalysis during 1990 – 2009 was generated by assimilating ICES T/S profiles (Fu et al., 2012). The results showed that, below 60m, RMSE of water temperature is reduced by 35%, mean bias of water salinity by 80% and RMSE by 52%. Fu et al. 2011 shows that the horizontal e-folding correlation lengths for the Baltic Sea subsurface temperature and salinity range from 25 – 150 km. Zhuang et al (2011) showed that the temporal impact scale of T/S profile assimilation is about 3 weeks in the Baltic Sea. Assuming that 1 profile is needed per horizontal e-folding scale every 3 weeks, the above results suggest that in total 1743 – 10456 profiles per year are needed. A more comprehensive reanalysis was carried out by Liu et al. (2016) by assimilating profile observations on T/S, nutrients, oxygen and ammonium (30 years) from SMHI SHARK database. It was found that RMSD of the model products is reduced by 59%, 46%, 78% and 45% for oxygen, nitrate, phosphate and ammonium, respectively. OSEs on water level were also made by Madsen et al. (2016) for a two-year period. The results showed that, by assimilating blended in-situ and satellite water level, the RMSE of the model sea level is reduced by 35%. However, no improvements are found in extreme sea level. Observation requirements for emerging needs: BAL MFC is currently developing a new level of validation, MME, forecasting and reanalysis capacities. This will generate new needs on in-situ observations. At first, the validation will be extended to cover extreme events, e.g., high sea, major Baltic inflow, storm surge, upwelling, oxygen depletion and algae bloom etc. For this purpose, high resolution observations are needed to reveal detailed spatiotemporal features in synoptic scale. Secondly, forecasting errors should be improved for the model system used in BAL MFC Phase2 by using data assimilation. NEMO-Nordic model is going to replace HBM model in 2019 in BAL MFC, major challenges of the current NEMO-Nordic operational version (running at SMHI) are relatively large sea level forecast errors in Danish and German coasts, more accurate Baltic-North Sea water exchange prediction and inter-sub-basin transport. Hence sea level data are needed for assimilation. T/S profiles will be assimilated in the BAL MFC NEMO forecasting system. The time window for the assimilation can be 1-7days. According to a previous OSE study (Zhuang et al., 2011), the positive impact of assimilating T/S profile data from ICES database can last about 3 weeks. Thirdly, emerging forecasting and reanalysis capacities will be developed in BAL MFC, e.g., sea skin temperature forecast, MME for currents and biogeochemical parameters, oxygen depletion and algae bloom forecast, interim reanalysis for the past 1-6 months etc. This raises requirements on in-situ observations of SST and sea skin temperature in open waters, currents and biogeochemical variables in each sub-basin. The timeliness for

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these emerging tasks is 1-7 days for near real time applications and 1-6 months for offline applications. References: Fu, W., Høyer, J. L., and She J. (2011). Assessment of the three-dimensional temperature and salinity observational networks in the Baltic Sea and North Sea, Ocean Sci., 7, 75-90 Fu, W., She J., and Dobrynin, M. (2012): A 20-year reanalysis experiment in the Baltic Sea using three-dimensional variational (3DVAR) method. Ocean Sci., 8(5), 827–844. Liu, Y., Meier, H. E. M., and Eilola, K. (2017): Nutrient transports in the Baltic Sea – results from a 30-year physical–biogeochemical reanalysis, Biogeosciences, 14, 2113-2131 Madsen, K.S., Høyer J.L., Fu W. and Donlon C. (2015), Blending of satellite and tide gauge sea level observations and its assimilation in a storm surge model of the North Sea and Baltic Sea, J. Geophys. Res. Oceans, 120, 6405–6418 She J., Jacob L. Høyer, and Jesper Larsen (2007). Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea. J. Mar. Sys. 65, 314-335. Zhuang, S. W. Fu, and J. She (2011). A pre-operational 3-D variational data assimilation system in the North/Baltic Sea, Ocean Sci., 7, 771-781.

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6.4 Iberia-Biscay-Ireland MFC

Authors: Alfonso Alonso-Muñoyerro, Marta1, García Sotillo1, Marcos, Pérez Gómez, Begoña1; (1) Puertos del Estado

6.4.1 Status of the In-situ observations used by your organization At the Iberia-Biscay-Ireland Monitoring and Forecasting Center (IBI-MFC), in situ observations products are used for: • Data Assimilation. Temperature and salinity vertical profiles data from Argo floats assimilated in the ocean physics model to generate analysis outputs. • Qualification/Validation processes. Product quality metrics computed using data from several platforms such as mooring buoys, Argo floats, drifters, gliders, XBTs, CTDs. So, in-situ observations are critical for the generation of IBI analysis and to validate the model forecast generated. Parameters and platforms used: For Qual/Val procedures, the following in situ obs products are utilized, depending on the studied variable: • CMEMS INSITU_IBI_TS_REP_OBSERVATIONS_013_040 (Moorings) data are used for Sea Surface Temperature (SST), Sea Surface Salinity (SSS), Sea Surface Currents (SSC) – Zonal/Meridional velocity, Sea level, Significant Wave Height, Mean Wave Period, Mean Wave Direction. • CMEMS INSITU_IBI_TS_REP_OBSERVATIONS_013_040 (Argo floats) data are utilized for Temperature and Salinity profiles. • CMEMS INSITU_IBI_NRT_OBSERVATIONS_013_033 (Argo floats) data are employed for Temperature and Salinity profiles. • CMEMS INSITU_GLO_NRT_OBSERVATIONS_013_030 (Buoys) data are used for Sea Surface Currents (SSC) – Zonal/Meridional velocity. • WOA 2009 climatology is employed for Temperature in the whole water column. • High Frequency (HF) CODAR SeaSonde radars (Galicia, Gibraltar Strait, Ebro Delta and Huelva-Algarve) data are used for Sea Surface Currents (SSC) – Zonal/Meridional velocity. • High Frequency (HF) CODAR SeaSonde radar data in Galicia are employed for Significant Wave Height, Mean Wave Period and Mean Wave Direction. • Bio-Argo floats. As in the case of Temp&Sal ARGO profiles, BIO-ARGO are used for IBI BIO product validation (Chlorophyll, Nitrate and Oxygen). Using those floats available in the covered periods in the IBI domain (19.0ºW, 5.0ºE, 26.0ºN, 56.0ºN). • Stations occupied by the Celtic Explorer (Irish Marine Institute) on Cruise CE1408, between 31/5/2014 and 3/6/2014 data are used for Nitrate, Phosphate and Silicate.

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• Data for 2014 from WOA13 V2, WOD, the Western Channel Observatory, and BODC for the surface layer in the North Sea, Irish Sea, English Channel and Celtic Seas are utilized for Nitrate, Phosphate and Silicate. • L4 and E1 moorings data from the Western Channel Observatory are employed for Nitrate and Phosphate. Coverage • Observations are used in the Qualification/Validation procedures when they are available in the Iberia-Biscay-Ireland area covered by the IBI-MFC products. (19.0ºW, 5.0ºE, 26.0ºN, 56.0ºN). Temporal resolution Observational data used by the IBI-MFC are downloaded with the maximum temporal sample available. Later, data is pre-processed to generate hourly and daily averages used in the IBI validation processes. Timeliness In-situ observational data are downloaded most of them on a daily basis to be used on the IBI online model validation procedures. Reprocess data or historic time-series, as well as other off-line data from specific campaigns, used only on delayed mode validations (monthly, quarterly and annual basis) are download when validation procedure is produced. IBI regional ocean analyses are produce on a weekly basis. Consequently, in-situ observations (T&S profiles) are collected, preprocessed and assimilated with this weekly frequency.

6.4.2 Present needs, and outlook on future requirements

Parameters, platforms & coverage In the future, in situ products should be kept, at least, with the current specifications. It will be important to increase the number of in-situ observations in NRT on shelf and coastal areas of the IBI regions. Particularly, some variables should be better covered: • HF-Radar products are quite useful to provide a good view of the high frequency surface circulation dynamics. To monitor the circulation on the shelf at depths, more ADCP observations (if possible in NRT) are required. More current observations are needed to compute transports, particularly, in Straits and along specific sections useful to characterize circulation features such as slope currents. • It is quite important to obtain in the future reliable NRT fresh water river discharge data in river mouths (if accompanied of biogeochemical data, the better). • More periodic in-situ observations covering the entire water column on shelf and coastal areas would be needed. It would be highly valuable to have CTD/glider periodically repeated missions. • BGC Argo data coverage in IBI area must be enhanced.

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In-situ observations should cover both open deep waters and shelf and coastal areas for a seamless monitoring. There is a strong need to acquire profiles in NRT on the shelf (such as Argo for open ocean).

6.4.3 Requirements related to data management Regarding future requirements, it would be nice to integrate in Near-Real-Time more gliders observations and HF radar data in the current existing or future In Situ TAC products. It would be also valuable to count with more data from large specific campaigns occurred or developed within projects in the reprocess product. The more data available in these reprocess in-situ products, the more valuable for later data assimilation and positive impacts on the reanalysis production. It would be great if it is possible to ease this integration of these data from extensive multi-parameter in-situ observations from specific regional campaigns in the existing reprocess products. It is certainly quite practical for us to use single standardized products. The coverage of in situ data in coastal areas (at least in the IBI domain) is still too scarce. The increase of the coverage in these coastal areas may positively impact on the data assimilation performance for generating better physical analysis.

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6.5 Black Sea-MFC

Authors: S. Ciliberti1, B. Lemieux-Dudon1, S. Masina1, G. Coppini1, J. Staneva2, A. Behrens2, M. Grégoire3, E. Peneva4, A. Palazov5; (1) CMCC, (2) HZG, (3) University of Liege, (4) University of Sofia, (5) IO-BAS.

6.5.1 Rationale The document gives an overview of in-situ data used by the CMEMS Black Sea Monitoring and Forecasting Centre (BS-MFC) for assimilation and product quality purposes. It highlights also some issues in data availability, which may have a huge impact for improving models and operations.

Experts from Black Sea Production Units – Physics, Biogeochemistry and Waves – will present the updated status of in-situ data used in the NRT and MY system, to define a strategy for strengthening the observing network in the Black Sea area towards the coastal areas, as part of the evolution foreseen in Phase 2.

In the next sections, according to recommendations provided by CMEMS Coordination of Earth Observation Data, the main BS-MFC requirements will be described and discussed.

6.5.2 BS-Physics

Status of the In-situ observations used by BS-PHY The BS-Physics produces two types of products: NRT and MYP. Concerning NRT products, the BS-Physics hydrodynamic core model is coupled with 3DVAR scheme (Storto et al., 2011; Storto et al., 2014), which assimilates satellite data (SST and SL) and available in-situ observations. The NRT BS-Physics assimilates quality controlled temperature and salinity profiles, available through profiler-glider platform (ARGO). In-situ products used are: • INSITU_GLO_NRT_OBSERVATIONS_013_030 product; • INSITU_BS_NRT_OBSERVATIONS_013_034 product; NRT in-situ data are used also for performing quasi-independent validation, to compute the Estimated Accuracy Numbers (EAN), and to perform independent validation in hydrodynamic core model developments. Concerning MYP RAN products, the BS-Physics uses the 3DVAR scheme as the NRT system. The RAN BS-Physics assimilates UK MetOffice EN4.1.1 data available since 1990 to the present. Details are https://www.metoffice.gov.uk/hadobs/en4/. The BS-PHY RAN system assimilates, in particular, temperature and salinity profiles from ARGO, BUOYS, TESAC, XBT/MBT. Delayed mode in-situ data are used also for performing quasi-independent validation.

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Present needs, and outlook on future requirements BS-Physics suffers from a lack of regional data to use regularly for improving forecast products. In particular, to understand coastal dynamics and contribute in evolving BS-Physics model for NRT, coastal profilers and moorings regularly updated and continuous in time are requested. The BS-Physics team considers really important to have ad hoc measurements in the shelf, especially in the Northern part and in the Danube Delta (Figure 6). For RAN, the BS-Physics team is planning to test the CMEMS reprocessed data for producing the next RANs versions (from 2019 to 2021). Requirements related to data management To improve and guarantee reliability of NRT system, the BS-Physics requires NRT in-situ data in the Black Sea area (including coastal buoys), quality controlled, to use for assimilation and for validation purposes.

Figure 6: Distribution of Argo TS profiles for the period 2009-2018. Data are taken from the Coriolis database.

6.5.3 BS-Biogeochemistry

Status of the In-situ observations used by BS-BIO The BS-BGC produces two types of products: NRT and MYP (from 1992 up to 2017). The BS-BGC core model for NRT uses a SEEK Filter to assimilate in-situ profiles of temperature and salinity, and separately, to assimilate in-situ profiles of dissolved oxygen. Temperature, salinity and dissolved oxygen are measured from ARGO platforms (INSITU_BS_NRT_OBSERVATIONS_013_034). Historical in-situ data (Figure 7) and satellite CMEMS products (OCEANCOLOUR_BS_CHL_L3_NRT_OBSERVATIONS_009_044) are used to validate the MYP and NRT products. The MYP does not assimilate any data (except for a small nudging term for oxygen after 2010) and hence, these data sets can be considered as

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independent observations from which Estimated Accuracy Numbers (EANs) can be estimated. Present needs, and outlook on future requirements In the following, present needs and outlook on future requirements in terms of: • Parameters and platforms: optics (e.g. Chlorophyll, CDOM), carbonate/acidification (e.g. pH, alkalinity, DIC), others: sulfide, nitrate, phosphate, • Platforms: in-situ (for accurate validation of model/satellite algorithms) and BGC ARGO (when possible) • Coverage: basin scale (for sulfide: deep basin) • Spatial resolution: in situ data need to be collected at a sufficiently high resolution in order to allow the validation of satellite and BGC ARGO algorithms in the typical Black Sea’s regions. BS-Biogeochemistry suffers from a lack of regional data to use regularly for improving the validation exercise and for assimilation in the model. Figure 7 shows that after 1995 the number of data available for biogeochemistry drastically dropped (compared to 80s and 90s). Hopefully, the ARGO program and Sentinel 3 are expected to partially compensate for the lack of large scale cruises. In-situ data are absolutely needed in order to calibrate the algorithms used to retrieve the variables (e.g. mainly chlorophyll but also nitrate and sulfide) from the information provided by satellite (e.g. reflectance) and BGC-ARGO (e.g. fluorescence). These links are not straightforward and require the development of dedicated equations adapted to the particularities of the Black Sea environment (e.g. anoxia, Case2 water, presence of aerosols). For instance, the retrieval of the chlorophyll value from the fluorescence data delivered by Argo sensors cannot use standard algorithms but rather requires the development of specific equations tuned for low oxygen environments (work ongoing). Similarly, an optimal exploitation of the information provided by Sentinel 3 would require the collection of dedicated optical data and chlorophyll in order to fine-tune the algorithms used to retrieve chlorophyll from the satellite reflectance (the Black Sea has been identified as a region of high CDOM concentrations which complicates the retrieval of chlorophyll from fluorescence). One of the aims of the CMEMS Phase 2 is to assess the carbonate system but data like pH, DIC and alkalinity are critically lacking in the Black Sea and this would prevent event a sound initialization of the model. We expect that the presence of anoxic waters impacts the pH dynamics and data are needed in order to calibrate the dynamics of pH.

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Figure 7: Distribution of data points per decade available in the NATO and WOD data base (the largest data bases in the Black Sea). Only sites where one of the modelled variable (oxygen, NH4, NO3/NO2, POC, PON, Si, TOC, and chlorophyll) has been collected are represented. The last plot shows the position of the Argo floats where oxygen has been collected. The BS- Waves

So far, the biogeochemical variable that has the best spatial and temporal coverage is oxygen because it is provided by 4-5 BGC Argo floats. These data can be used for CLASS4 validation: • Horizontal 2D fields of oxygen concentration • 2D field of the oxygen penetration depth (i.e. the depth at which the oxygen concentration is 20 µmol/l). Requirements related to data management An optimal integration of radiometric measurements provided by remote sensing and field data like BGC-Argo, AERONET-OC and BIOMAP would contribute to improve the quality of the chlorophyll data in the Black Sea delivered by model and satellite. High quality carbonate data need also to be available from CMEMS. There are projects that have collected such data in the past (e.g. Geotraces, Sesame, KNORR) and some data are stored in international databases (e.g. WOD, Emodnet) but are not in CMEMS (no pH data in CMEMS for the Black Sea).

6.5.4 BS-Waves

Status of the In-situ observations used by BS-WAV The BS-Waves produces two kinds of products: NRT and MYP. In-situ wave measurements are extreme insufficient. For the evolution of the NRT system, the BS-Waves Team has been also decided that satellite data that will be provided by Sea

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level IN-Situ TAC will be further used for systemic validations of the wave products. Recently, only 2 buoys are supposed to be available, at the locations Burgos and Varna through. Unfortunately, Burgas buoy does not operate since January, 2017. Even more, the data quality of Varna buoy needs additional proofs. CMEMS In situ TAC has been informed about the issue on Burgas Buoy. Other in-situ measurements for wave parameters are, unfortunately, not available for the Black Sea (Schulz-Stellenfleth and Staneva, 2018). The NRT BS-Waves uses, in particular: • INSITU_BS_NRT_OBSERVATIONS_013_034 product, if available. NRT in-situ data are used also for performing quasi-independent validation for BS-WAVE NRT and MYP, to compute the Estimated Accuracy Numbers (EAN), to perform independent validations. Actually, the major advancements in the frame of BS-Waves are due thanks to the modelling activities and the use of satellite data. Present needs, and outlook on future requirements The BS-Waves requests a more organized in-situ observing network for monitoring the Black Sea and coastal area, with the best temporal and spatial resolution. Requirements related to data management To improve and guarantee reliability of NRT system, the BS-Waves requires NRT in-situ data in the Black Sea area (including coastal buoys), quality controlled, to use for validation purposes, especially for the Black Sea in-situ data category.

6.5.5 Conclusions The BS-MFC Team recommends an urgent action in in-situ observations to use for the advancing of the modeling infrastructure for Physics, Biogeochemistry and Waves. The lack of NRT in-situ data, systematically maintained at regional level and quality controlled is considered as a weakness with respect to the other MFCs for improving data assimilation capabilities and for enforcing the validation of NRT and MYP products. It is advisable that the INS-TAC Team keeps constant contact with the data providers and insists on maintenance of the measuring equipment, considering the importance of the continuity of the service. The BS-MFC Team strongly recommends that available data from other databases are integrated in the CMEMS INS TAC data collection.

References Storto, A., S. Dobricic, S. Masina, and P. Di Pietro, 2011: Assimilating along-track altimetric observations through local hydrostatic adjustments in a global ocean reanalysis system. Mon. Wea. Rev. 139, 738-754.

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Storto A., Masina S., Dobricic S., 2014. Estimation and impact of non-uniform horizontal correlation length scales for Global Ocean physical analyses. J. Atmos. Ocean. Technol., 31: 2330-2349. Schulz-Stellenfleth J. and J. Staneva (2018): A multi collocation method for coastal zone observations with applications to SENTINEL-3a altimeter wave height data , OSD, https://doi.org/10.5194/os-2018-124 Wiese, A., Staneva, J., Schultz-Stellenfleth, J., Behrens, A, Fenoglio-Marc, L., and Bidlot J. 11 (2018). Synergy between satellite observations and model simulations during extreme events, 12 Ocean Sci. Discuss., doi.org/10.5194/os-2018-87.

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6.6 Mediterranean MFC

Authors: Med-Physics: E. Clementi1, J. Pistoia1, A. Grandi1, G. Coppini1; (1) CMCC - Centro Euro- Mediterraneo Sui Cambiamenti Climatici. Med-Biogeochemistry: A. Crise2, L. Feudale2, G. Cossarini2; (2) OGS- Istituto Nazionale di Oceanografia e di Geofisica Sperimentale. Med-Waves: A. Zacharioudaki3, M. Ravdas3, G. Korres3; (3) HCMR - Hellenic Centre for Marine Research. This document provides a synthetic overview of the use/role of in-situ data in the CMEMS Mediterranean Sea Monitoring and Forecasting Centre (Med-MFC) and highlights the main requirements for the present and future modeling systems. Table 2 lists, for each in-situ parameter and platform, the role and the impact in the three Med-MFC components (Physics, Biogeochemistry and Waves).

Table 2: Role and the impact in the three Med-MFC components (Physics, Biogeochemistry and Waves) of each in-situ parameter and platform.

Parameters Platforms Role Impact

Temperature Vertical profiles from Assimilated in both analysis Impacts on the numerical solution of the Salinity ARGO, CTD, XBT (only and reanalysis systems and system since these data are assimilated. T) used in the validation These data are also used to estimate assessment quality of model results Temperature GLIDERS, Validation assessment Data used to estimate quality of model Salinity Moorings results SL Moorings Validation assessment Data used to estimate quality of model UV results Chlorophyll, Vertical profiles from Validation assessment Data used to estimate the uncertainty of Nitrate, Oxygen BGC-ARGO* model results Nitrate, Measurements from Validation assessment and Data used to estimate and reduce model Phosphate, casts (scientific calibration of BGC model uncertainty Oxygen, DIC, cruises/surveys) * Alkalinity Significant Wave Moorings Calibration and validation Estimate and reduction of model- Height of the WAM modeling observation differences Mean Wave system Period * BGC data from moorings (NRT and high frequency data) are almost not available

6.6.1 Present needs and outlook on future requirements Med-Physics The physical component of the Med-MFC is implemented over the whole Mediterranean Basin producing NRT and Reanalysis products. The hydrodynamic NEMO (v3.6) model solutions are corrected by a variational data assimilation scheme (3DVAR) with a daily cycle.

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The assimilated data are: temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations, moreover satellite SST data are used to correct surface heat fluxes. The system is forced by ECMWF winds at 1/8° resolution and is nested, in the Atlantic Sea, within the CMEMS GLO-MFC products. It provides weekly analysis (on Tuesday) for the previous 15 days and 10-day forecast is produced every day. Parameters and platforms: Actually, salinity and temperature vertical profiles from ARGO, CTD and XBT platforms are assimilated into the system and also used for quality assessment (see examples in Figure 8), while moorings are used only to assess the quality of the system through and independent validation. The CMEMS In-situ datasets used are: • INSITU_GLO_NRT_OBSERVATIONS_013_030 • INSITU_MED_NRT_OBSERVATIONS_013_035

Coverage: The existing spatial coverage of Argo network is almost 20 per day in the Mediterranean domain. Full profile transmission could be considered as a major improvement for CMEMS operational analyses. Need of increased number of Argo floats in high dynamical areas (where the floats are suddenly moved away resulting in a poor coverage) and at deep ocean. Need of XBT measurements (not anymore available in the Mediterranean Sea). Need to increase the number of in situ velocity observations (e.g. ADCP, moorings, HF Radars, drifters) and NRT fresh water river discharge data at river mouths. Spatial resolution:

One Argo profiler in each 3° 3° bin should be at least maintained for the future. Increased coverage in the Eastern and Southern Mediterranean Sea and in shallow water areas. Temporal resolution: Since Argo communicates usually every 5 days (or less for some float) in the Mediterranean domain, this time resolution ensures sequential observation spaced enough (more than 10km), i.e. independent measurement. Timeliness Part of Near Real Time data are usually lost (almost the 30% of the incoming signal) due to transmission issues.

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Figure 8: Left: time series of weekly Temperature RMS misfits at 8m depth with corresponding number of assimilated observations in 2016-2017. Right: Spatial distribution of ARGO data assimilated in the period 2016-mid 2018 (resolution 0.5o).

Med-Waves The Med-waves modelling system is based on the WAM Cycle 4.5.4 wave model. It consists of a wave model grid covering the Mediterranean Sea at a 1/24° horizontal resolution, nested to a North Atlantic grid at a 1/6° resolution. The system is forced by ECMWF winds at 1/8°. Refraction due to surface currents is accounted for with the input currents originating from CMEMS Global (1/12°) and Med (1/24°) MFCs. The system assimilates altimeter along- track significant wave height obtained from the CMEMS Wave TAC. It provides 1-day analysis and 5-day forecast hourly wave parameters. Currently, wave buoy observations of significant wave height and mean wave period obtained from the CMEMS INS-TAC are used to calibrate and validate the Med-waves modelling system. Model-buoy comparisons at individual wave buoy locations and/or for selected wave buoy groups (e.g. offshore/coastal) are performed. Figure 9 (Left) is an example of model-buoy comparison. Parameters and platforms: Provision of the water depth at the location of the mooring. Provision of the wave spectra from the wave buoys. Maintain and increase the number of wave buoys and include wave measurements from HF Radars. Coverage: Need for increased coverage in the Central and Eastern Mediterranean and along the African coasts (see Figure 9 Right). Need for open sea wave buoys in key areas of the Mediterranean Sea. Spatial resolution: Dense network of coastal wave buoys (ISPRA example). At least 8-10 open sea wave buoys operating in key areas of the WMED & EMED. 1-2 km spatial resolution of HF Radar wave measurements to support wave modelling in coastal areas.

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Temporal resolution: Hourly data for both wave buoys and HF Radars Timeliness: NRT (within 3-6 hours). Reprocessed wave observations should occur after 6-12 months.

Figure 9: Left: Merged QQ-plot (black crosses) and density scatter-plot of observed versus modelled Hs (Full Med). Superimposed are the 45° line (dashed red line) and the least-square linear regression line forced though the origin (solid red line). The linear r

Med-Biogeochemistry The Med-Biogeochemistry system provides both NRT and Reanalysis products. The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24 degree are produced by means of the MedBFM v2.0 modeling system (i.e. the physical-biogeochemical OGSTM-BFM model coupled with the 3DVARBIO assimilation scheme). MedBFM uses as physical forcing the outputs of the Med-Physics products. Seven days of analysis/hindcast and ten days of forecast are bi-weekly produced on Wednesday and on Saturday, with the assimilation of surface chlorophyll concentration from satellite observations (provided by the CMEMS-OCTAC). In-situ data are mainly used to estimate and reduce model uncertainty (see Figure 10).

Figure 10: Left: Hovmoller diagrams of chlorophyll for a selected float and model output for the period 2016-2017. BGC-Argo trajectory float is reported in the upper panel with deployment position (blue). Right: Skill indexes for model (blue) and float data (red):

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Parameters and platforms: Maintain and increase the number of BGC-Argo floats that provide chlorophyll, nitrate, oxygen pCO2 and pH. Nutrients (nitrate, ammonia, phosphate, and silicate), chlorophyll, oxygen and biomass of phytoplankton are provided from scientific cruises/surveys with a delay of years. Chlorophyll, oxygen, pCO2 high frequency data from open-sea moorings are nowadays practically absent. Coverage: Need for increased coverage in the Eastern and Southern Mediterranean (e.g., in terms of n. of BGC-Argo floats, n. of moorings, retrieval of historical in-situ datasets from no-European countries). Increase the number of the open-sea moorings (high frequency data) equipped with BGC sensors. Timeliness:

BGC-Argo floats communicates every 5 days; data are available within 1-2 days. Need for some moorings providing high-frequency (hourly) data. Reprocessed BGC data (from scientific surveys) should occur and made available after 1 year.

6.6.2 Requirements related to data management (i.e. data access, real time and delayed mode quality control and standardization) Med-Physics • Data access should be via ftp and thredds • Since ARGO communicates every 5 days (or less for some floats), it is very important to reduce delay time and transmission problems to avoid data loss • Include GTS data availability • Once floats reach the coast, or in presence instrumental issues, the delivered data should be checked and properly flagged • Improved quality control of sparse data at depth (some profiles present a high frequency data up to a certain depth and few sparse data at high depth which should be removed from the profile or properly flagged) Med-Waves • Introduce existing historic data that are currently missing from the INS TAC (e.g. Italian ISPRA buoys) • Still need for some additional info/standardization in wave buoy naming with respects to the historic data. For example, wave buoys at same location with different names in time. What are the differences? Can one merge their measurements in time? • Need for standardization concerning the wave spectra (i.e. In all buoys same data processing methods should be applied for determining of wave parameters and spectral estimates should be calculated on the same range of frequencies). • Need for some criteria to separate wave buoys to coastal and offshore

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• Med-Biogeochemistry • NRT data from BGC-Argo floats still need some standardization; Maintain and increase the number of BGC-Argo floats equipped with chlorophyll, nitrate, oxygen, pCO2 and pH sensors • Need for a single (certified and standardized) point of access for BGC historical and NRT data; • Continue and reinforce the dialogue with EMODNet Chemistry to ensure a timely and open access to historical data (i.e. cruise and scientific surveys); • Make available the historical biogeochemical datasets of in-situ measurements from scientific cruises/surveys; • Convince MS and EC to include in the program calls, as a requirement, the nRT distribution of decimated profiles of BGC variables acquired by the scientific cruises/surveys supported by them. • Establish a good relation with marine research infrastructure other than ARGO (e.g. EMSO, marine component of ICOS, and less urgent EMBRC) to have access to their data and to possible influence their observing strategy and priorities; • Contact the appropriate actors to assess the accessibility and fit-for-purpose of MSFD datasets for CMEMS applications. • Identify and support a limited number of open sea marine multi-disciplinary observatories able to provide high-frequency data from surface to sea bottom. Special emphasis on ECVs relevant to acidification and de-oxygenation.

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6.7 North West Shelf MFC

Author: Marina Tonani1; (1) Met Office

6.7.1 Status of the In-situ observations used by your organization The Met Office ocean forecasting systems are using in situ observations for data assimilation and products validation/verification. In situ SST and vertical profiles are regularly downloaded and quality controlled before being use for data assimilation and verification. Data we use for assimilation are: SSTs from moored buoys, drifting buoys and ships, temperature and salinity profiles from Argo profiling floats, underwater gliders, moored buoys, marine mammals, and manual profiling methods. For verification purposes we do use also drifter (and Argo) derived currents, tide gauges, HF radar derived surface currents, wave measurements, and river gauge data.

6.7.2 Present needs and outlook on future requirements The in-situ data coverage on shelf where data like Argo are not available (see Figure 11) needs to be increased. Most of the long-term observing platforms, like moorings and ferry boxes, are close to the coast. The off-coast part of the North Sea is generally measured only by research initiatives like the gliders from the AlterECO project and the CTD profiles from a Norwegian research vessel, as shown in Figure 11. These measurements are very important but can’t guarantee continuity in time. In this area, marine mammals are a very good source of data in specific locations, but they are seasonal due to the seals behavior. In general, gliders and sea mammals could be a good alternative to Argo in shelf areas. The number of currents measurement at present is limited to a few coastal ADCPs, a couple of surface buoys and HF radar surface measurements along narrow stretch of coastlines. Currents data could be very useful for product validation and in future for data assimilation, if available. The number of biogeochemical observations is unsatisfactory and the BGC-Argo will not be available in key areas like the continental shelf. The NRT data availability, for all type of observations, play a key role especially for the data assimilation and this is therefore a clear requirement for a forecasting system. Increasing the number of deep Argo and BGC-Argo floats will not improve the North-West European shelf observing system but has the benefit to increase the quality of the lateral boundaries forcing this model. The North Sea basin has an extend area covered by oil and gas platforms, see Figure 12; we can increase the number of available measurements incorporating these existing assets and maybe also wind farm instrumentation we are not aware of. The addition of atmospheric data required in the forcing of ocean models, such as wind mean sea level pressure, would also be valuable as they are needed to understand the hydrodynamics. In particular, collocated measurements (e.g. wind, wave and current measurements are all carried out for the same location) are needed, and should be considered in future deployment.

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Figure 11 Temperature and salinity observations from 19th July to 20th August 2018 (modified map from CMEMS INSITU TAC www.marineinsitu.eu). Blue dot: mooring; red-dot: gliders; pink-dot: marine mammals; green line: research cruise; blue- line: ferry box.

6.7.3 Requirements related to data management Data access: download interfaces are fine. Improved graphical/interactive interface could help in better understanding the availability of different types of observations. There is a need for a subsetter-like interface to make it easier to select observations for a specific area, time, parameters and platform. The subsetter is now available only for the last 30 days with a very reduced number of options for parameters/platform. Emodnet-physics portal is much more advanced from this point of view. RT-DT QC: we will be able to provide a full assessment after September when we will be using CMEMS data in the operational production. Standardization: recommendation: try to keep the ‘depth’ information as consistent as possible across all the GLO in-situ obs files, as this will make it easier for users to extract the data they need (there were issues in consistency in the OceanSITES files between use of depth and pressure as the vertical ‘axis’). There are many sources where observations can be acquired from (e.g. EmodNET, CMEMS, OceanSites, GTS) and it is hard to know what is overlapping. Is there a standard station number common to all (WMO ID is not available or not used) it could simplify the detection of duplicates across the different web-interfaces (Emodnet, CMEMS, …).

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Figure 12: Oil and gas fields, from Wikipedia. https://en.wikipedia.org/wiki/List_of_oil_and_gas_fields_of_the_North_Sea

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6.8 Sea Level-TAC

Authors: Marie Isabelle Pujol1, Yannice Faugère1, Guillaume Taburet1, Maxime Ballarotta1; (1) CLS

6.8.1 Status of the In-situ observations used Tide gauge networks To validate sea level gridded product (L4), we use in-situ measurements from the GLOSS/CLIVAR (https://uhslc.soest.hawaii.edu/network/) and PSMSL (http://www.psmsl.org/data/) tide gauge networks. The most consistent state of the ocean between both data time series within a certain distance is considered. After extracting co- located altimeter and tide gauge data, additional quality control is performed to compute SSH difference for the most reliable time series. More details can be found in Valladeau et al., Comparing Altimetry with tide Gauge and Argo Profiling Floats for Data Quality Assessment and Mean Sea Level Studies, 2012. Comparisons with Tide Gauges are performed globally and cover the period spanning from January 1993 to present time. We do not use the CMEMS tide gauges network since too many inconsistencies have been detected in the past (moving localization of tide gauges…). The data are then hardly comparable since they are not stable in time. However, an assessment of SL-TAC operational products with CMEMS in-situ observations from tide gauges from routine observations is planned in 2019 in collaboration with IMEDEA. Drifter network Drifter data from the AOML dataset are used. More information about this dataset can be found in the PUM and QUID of the In-Situ TAC (http://cmems- resources.cls.fr/documents/QUID/CMEMS-INS-QUID-013-044.pdf and http://cmems- resources.cls.fr/documents/PUM/CMEMS-INS-PUM-013-044.pdf). Two types of drifters can be considered: with or without their drogue. When the drogue is lost, the drifter goes back to the surface and is directly under the influence of the wind stress. A wind slippage correction is used to adjust this kind of data. Drifters velocity data are then compared with maps of absolute geostrophic current derived from altimetry. Argo & CTD profiling floats The in-situ Argo vertical profiling floats derived from the Coriolis Global Data Assembly Centre provide dynamic heights anomalies from which can be derived the steric sea level. Relative comparisons can be carried out between these in situ independent measurements and the altimeter total sea level. The aim is to assess the impact of a new altimeter standard used in the sea level computation and to detect potential jumps, drift or anomalies in the altimeter data on a global and basin scale (Valladeau et al., 2012 and Legeais et al., 2016).

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6.8.2 Present needs, and outlook on future requirements The number of available dataset (tides gauges, drifters and Argo floats, HF Radar) is still limited for a robust validation of the regional-Mediterranean Sea and Black Sea products. Data are either sparse or concentrated in a limited area of these closed basins.

Parameter and coverage Spatial resolution Temporal Timeliness platform resolution Tide gauges : H global Daily/monthly Delayed time Drifters: currents Global, marginal hourly Delayed time seas, high latitudes ARGO: T/S profile marginal seas, 10-day Delayed-time high latitudes sampling for each instrument CTD T/S profiles marginal seas, - Delayed-time high latitudes HF Radar TBC

6.8.3 Requirements related to data management

Parameter and platform Quality control required Other Tide gauges : H yes Detection/correction of calibration jump Drifters: currants yes Drog loss detection; correction for inertial currants; ARGO: T/S profile yes Sampling of the deep ocean (< -2000m) CTD: T/S profile yes

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6.9 Ocean Color-TAC

Authors: Gianluca Volpe1, Vittorio E. Brando1, Rosalia Santoleri1;(1) CNR-ISMAR

6.9.1 Status of the In-situ observations In situ data are used in the OCTAC routine operations to evaluate and calibrate the ocean colour remote sensing algorithms and to validate the operational products distributed by CMEMS. To this end and for a proper assessment of the product quality both input (L2) and output of the algorithms (L3) need to be available from in situ observations which are known to be less affected by uncertainty given that they are collected using consolidated and shared protocols. The requirement is therefore on high quality in situ measurements accompanied with their uncertainties; these data are referenced as Fiducial Reference Measurements (FRM) by the ocean colour community. For the development and qualification of ocean colour primary variables the following FRM observations are used: spectral remote-sensing reflectance, concentration of chlorophyll a, spectral inherent optical properties (light attenuation and backscattering) and spectral diffuse attenuation coefficients. These observations are acquired through a variety of systems and platforms such as:

• Automated in-water radiometry from moored Fixed-Point Observatories (i.e. MOBY and BOUSSOLE) • Automated above-water radiometry from Tower Fixed-Point Observatories (AERONET-OC) • In-water radiometry from profiles acquired during dedicated research cruises • Inherent Optical Properties (IOPs) from profiles acquired during dedicated research cruises • Biogeochemical measurements (i.e. Chlorophyll, TSM) and IOPs from samples acquired during dedicated research cruises. These observations are very sparse and limited. Moreover, they are acquired and analysed following stringent measurements and data reduction protocols as defined by the space agencies and ocean colour community. OCTAC relies on available FRMs ocean colour in situ database such as SeaBaSS and MERMAID and AERONET-OC as well bio-optical data acquired by the OCTAC partners during their research cruises: AMT cruises (PML), Mediterranean data (CNR), Baltic data (SyKe).

In situ chlorophyll data are obtained through fluorometric or spectrophotometric techniques and from HPLC (high-performance liquid chromatography) measurements. In situ chlorophyll fluorescence methods are not deemed of adequate quality for product development or qualification, unless periodically calibrated with HPLC sample measurements.

AERONET-OC is an automated system, the only one among all these FRMs data streams, and as such able to provide radiometric data in near-real time. All the other FRMs are non-

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automated systems strongly dependent on analysis and post-processing therefore they are only available delayed time mode of days, weeks or months from data acquisition.

The evolution of the ocean colour catalogue will include ocean colour water quality and ecological derived products such as primary production, phytoplankton functional groups and size, particle size distribution. The observation for these variables are scant, cannot be obtained using automated system and requires analyse water samples not performed in all research cruises.

Due to the lack of sufficient number of in situ data for variables other than reflectance and chlorophyll at the present the assessment of the quality of OC global and regional products is limited to Chl and Rrr variables.

6.9.2 Present needs, and outlook on future requirements The position of the fixed location platforms (coastal or open ocean) as well as the way measurements are taken (automatic systems or human operated) somehow discriminate the processes and their scales of variability that can be monitored through the derived in situ measurements. Therefore, an important point is that these platforms cannot be considered as mutually exclusive but must be considered instead as complementary systems to cover the widest possible range of processes and scales of variability. This in turn enables the CMEMS products whose quality have benefited of the use of such in situ data to be valuable and useful for a wider user community.

Platforms and Parameters Moored Fixed-Point Observatory for high precision radiometric measurements for system vicarious calibration The quality of CMEMS Ocean Colour products strongly depends on the quality of upstream satellite data provided by space agencies. The Post launch system vicarious calibration (SVC) using highly precise and accurate ground radiometric measurements is an essential step in the process of achieving sufficient satellite ocean colour product quality to meet the needs of CMEMS upstream. Presently, the entire OC mission relies on MOBY fixed point observatory for vicarious calibration. The creation of an OC-SVC infrastructure dedicated to Copernicus Programme is under discussion as part of the Copernicus space component, this a key point to the best performance of OLCI ocean colour missions that meet the long-term and high-quality standards underpinning marine bio-geochemistry data products delivered by CMEMS. EUMETSAT identified a series of steps to achieve this goal and is currently working on preliminary design of the Copernicus OC-SVC infrastructure. It is important to highlight that there are specific CMEMS requirements that should be taken into account in the design. In particular, the system should be designed to allow NRT data stream and enable operational capabilities that meet CMEMS service needs. Data of the future OC-SVC Infrastructure should be available to CMEMS in near real time to be also used for assessment and validation purposes.

Tower Fixed-Point Observatories with Automated above-water radiometry

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AERONET-OC is currently a network of multi-spectral sensors thus allowing the validation of a limited number of spectral bands (e.g., of the twenty-one OLCI bands only nine or twelve of them can be validated). These sites are located in coastal waters and provide data in NRT. An evolution towards hyper-spectral sensors is strongly envisaged as it would allow the validation of all remote sensors across the entire spectral range (VIS and/or NIR). At these sites CTD, fluorometry, IOPs as well as biogeochemical water samples should be routinely acquired to allow the full characterization of the environment and the validation of the geophysical parameters distributed by CMEMS.

Moored Fixed-Point Observatories with Automated in-water radiometry for OC validation Currently there are only a few sites in which in-water radiometry is routinely performed using commercial off-the-shelf instrumentation (e.g., BOUSSOLE, Western Channel Observatory and Lampedusa Buoy). These sites routinely acquire upward and downward radiometry at several fixed depths as well as CTD, fluorimeter and IOPs measurements. These sites are located in open waters and therefore are the only sites in which continuous measurements for case 1 product validation are available. At part of Lampedusa (still in the development phase) the data are not yet available in NRT. NASA is developing the MOBY- NET system that will allow transmitting the data in NRT. At these sites, biogeochemical water samples are routinely acquired these allow to contribute to bio-optical FRM dataset required to algorithm calibration and validation of OC derived geophysical parameters (chlorophyll, transparency, PFTs, etc).

Similarly, to the very positive AERONET-OC experience, a network of automated moored fixed-point observatories for OC validation should be established on the basis of the above infrastructures, with the further aim of providing data in near real time to the OC community.

Ship based Radiometric, IOPs and biogeochemical data from research cruises To ensure continuity of the in-water radiometry, IOP and biogeochemical measurements from profiles acquired during dedicated research cruises access to berths needs a long term coordinated effort at the European scale thus enabling sustained coverage of regional seas. In addition, an evolution from profiles acquired during dedicated research cruises to continuous measurement systems is envisaged. Research cruise data are the only once covering different areas of the ocean and therefore different bio-optical region.

Ship based Biogeochemical measurements from water samples Currently biogeochemical FRMs (i.e. Chlorophyll, TSM) and IOPs derive from samples acquired during dedicated research cruises and are strongly dependent on post-cruise chemical laboratory analysis or post-processing therefore they are only available delayed time mode of days, weeks or months from data acquisition. A wide community effort is needed to reduce the time from sampling to data availability on shared databases.

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Furthermore, the routine acquisition of parameters needed for the evolution of the ocean colour catalogue (e.g., primary production, phytoplankton functional groups and size, particle size distribution, etc) requires a coordinated community effort.

6.9.3 Spatial and temporal coverage and resolution Due to the regional variability of the bio-optical characteristics for the water masses there is an increasing need for measurements encompassing diverse regional, offshore and shelf waters across basins.

The introduction of automated above-water radiometry and continuous IOP measurement systems on ships of opportunity would dramatically increase data availability in general and also in near-real time. Bio-geochemical measurements acquired on fixed platforms, ferryboxes or Bio-Argos will surely contribute to increase data spatial and temporal coverage; alas they can only become FRMs if a sustained calibration activity is carried out (e.g., through HPLC samples for fluorimetric Chlorophyll a). This necessary step degrades the data availability from near-real time to a delayed-time mode.

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6.10 Sea Surface Temperature TAC

Authors: Jean-François Piollé1, Emmanuelle Autret1, Anne O’Caroll2, Igor Tomazic2,Stéphane Saux-Picart3, Emma Saux-Picart3,Bruno Buongiorno-Nardelli4, Andrea Pisano4, Gary Corlett5, Jacob L. Hoyer6; (1) Ifremer, (2) Eumetsat, (3) Meteo-France, (4) CNR, (5) GHRSST Project Office / EUMETSAT, (6) DMI.

6.10.1 Status of the In-situ observations Within the SST TAC, in situ data are currently used to support the validation and monitoring of the operational and reprocessed L3 and L4 SST products. The baseline for the validation of the SST TAC products is to use in situ temperature measurements as independent source of comparison. The validation procedure is based on the compilation of a matchup dataset, i.e. co-located (in space and time) measurements of SST data and in situ measurements. The accuracy is then quantified in terms of statistical metrics, such as the mean bias and the root mean squared difference (RMSD), estimated from the temperature differences of the matchup samples. The in situ data currently used are the NRT data files acquired from surface drifting buoys, Argo floats and moored buoys. The main used variable is the water temperature. The GHRSST group on satellite SST validation (STVAL) recommends using surface drifting buoy measurements only as reference. However, in some regional seas, the number of drifting buoy measurements is very low (e.g., Baltic Sea and Arctic Sea), and even equal to zero (e.g., Black Sea). In the future, other data sources (moored buoys, ARGO floats, etc.) could be considered for validation statistics to support the sparse (missing in some cases) availability of surface drifters.

6.10.2 Present needs, and outlook on future requirements

Parameters, platforms and coverage Whatever the platform and data stream considered, in addition to measurement values and standard variables (coordinates, time, etc.), additional information related to the in situ acquisition system is required to allow the selection of data with confidence for validation. It would be useful to include metadata detailing the network and/or project to which the platform belongs, the instrument/sensor model type, the deployment date, the sampling frequency, the depth of the sensor (mainly for surface platforms) and the drag loss. Moreover, the upcoming HR SST drifters from Trusted project will report additional variables that must be included in the data files, as e.g. the statistical moments and/or percentiles of the subsample distribution, the complete set of high frequency data and the near surface water pressure which can be used to estimate the depth.

In terms of platforms, more metadata are required to better select or filter out the platforms to be used with confidence for validation. The current CMEMS NetCDF data files contain very few metadata on their associated buoy or float, while buoy or float metadata are assembled and available from JCOMMOPS web site (through an API). However crossing both sources in near real time is neither hardly practicable nor very integrated. The satellite community

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wishes therefore the metadata related to the in situ acquisition system to be propagated throughout the netCDF data files delivered by CMEMS. This includes:

• Network or project to which the platform belongs : for instance we want to select GTMBA moored buoys (excluding other extra tropical or coastal buoys) or the upcoming set of FRM buoys funded by Copernicus Eumetsat funded Trusted project • Instrument model types: for instance to select buoys equipped with HR SST sensors • Deployment date: to assess the age of the buoy and possible issues • Averaging period: the period over which individual samples of a sensor are averaged to construct the reported value • Sampling frequency: the frequency at which a sensor samples (for the subsamples used to construct the reported value) • Depth of the sensor: the depth of the sensor for surface platforms (drifters) • Drag loss : as estimated from data analysis or reported by the platform itself • Calibration information: as provided by the buoy manufacturer • Manufacturer These platform/instrument metadata may be provided for instance in global attributes or in netCDF groups for files containing measurements from different platform or instruments.

In terms of parameters, the upcoming HR SST drifters from Trusted project will report additional variables that must be included in the data files, for instance:

• The statistical moments and/or percentiles of the subsample distribution • The complete high frequency data (that will be broadcasted in some cases) • The manufacturer • The near surface water pressure sensor (can be used to estimate depth) In terms of temporal resolution, the highest temporal resolution available is required, in particular for moored buoys. HR SST drifters from Trusted project will also occasionally report the full samples (instead of a 5 minutes average every hour), these high resolution data shall be reported in CMEMS product. In terms of coverage:

• there have been no more drifter data in the Black Sea available from in-situ TAC since 2016, which is likely due to no recent deployments (to be confirmed). This is critical for the validation of black sea products. • Similar issues occur in Baltic Sea with very few drifting buoys available • sea ice surface temperature observations (iSVP buoys) from drifting buoys for the validation of the Arctic SST/IST product are also required. These of course fall in the water and report SSTs when the ice melts. Requirements related to data management (i.e. data access, real time and delayed mode quality control and standardization)

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More generally there is a need for a dedicated “surface” dataset with improved quality control and selection of “good” surface data:

• Possibly one single daily file per type of in situ source (drifters, Argo, moorings,...)rather than individual buoy files, though they are different practices among the users. • Selection of surface measurements, down to 5m depth: the proper selection of surface data should be especially investigated for Argo floats (surface profiles are often split in two or different profiles, pump off/on cases shall also be addressed). • Better depth reporting in particular when the pressure is missing (using for instance buoy metadata) or sometimes several depths reported. • Clear identification of buoys located in sea ice areas (based on some ancillary data?) and clear indication (flag) of buoys on sea ice (and therefore reporting sea ice temperature instead of sea surface temperature). This requires dedicated data screening procedures. • Improved quality control o Usage of blacklists (based on a monitoring of each buoy wrt to external sources such as satellite SST): some blacklists are already maintained by Meteo-France or UK Met Office o Specific checks over sea-ice areas and arctic areas (given the very high temperature anomalies in these areas over the last years) o Identification of “good” Argo surface measurements (as the temperature sensor may actually be off water) o A specific check (at least for drifting buoys) based on SST and position history. Anomalous values of SST or position have been observed in the past on drifters (e.g. position becomes lat=0 and lon=0). They can be flagged out by simple tests. o Buoy position measurement timestamp and data measurement timestamp or a flag if they are too distant. • Uncertainties. The requirement for satellite validation is to have the in situ measurements available, for a given day, the next day. Moreover the daily files shall be updated every day as long as new measurements are collected (meaning that if some buoy measurements are received 3 days after acquisition, the corresponding daily file shall be appended with these newly collected measurements). Users can then choose to wait (to have more data or better data) or not depending on their application.

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6.11 Sea Ice TAC.

Authors: A Fleming1, J Buus Hinkler2, M Brandt Kreiner2, J Karvonen3, F Dinessen4, T Lavergne4, C Wettre4; (1) BAS, (2) DMI, (3) FMI, (4) Norwegian Meteorological Institute.

There is a great lack of in situ data both for the Arctic and the Antarctic Ocean.

Ship measurements from ice breakers in the Baltic are important. Ship-, land-, or aircraft- based in situ observations of icebergs are very useful. IMO Polar Code requirements have prompted ship operators, including a larger number of tour vessels, to start making routine sea ice observations to support the mandated risk assessment tools. Such systematic measurements could be used in e.g. validation of sea ice concentration, and lead to improvements in SI TAC algorithms and products.

Sea ice producers need improved access to ice drifting buoys. Currently we only have a few, which are used to validate our products as well as improving and developing detection algorithms. For the algorithms there is a special focus on ice thickness and snow on ice.

Specifically, the necessary in situ observation parameters are: temperature profiles in the ocean, ice, and snow, (and also ideally a weather station with the buoy). Such profiles are available from IMB (Ice Mass Balance) buoys and also Ice Tethered Profilers (ITPs). These two buoy types were used more extensively in the period around the International Polar Year (2007-2009), but since that period such buoys have become scarce.

When measuring ice drift, the most important in situ information comes from having precise GPS positions. E.g. ARGO or Iridium positions are not precise enough.

Gaining access to polar observations in near real-time (NRT) can be difficult. Arctic and Antarctic buoys are not coordinated through e.g. an overarching program, and this makes it challenging to access NRT data.

6.12 Multi OBservations TAC

Authors: S.Guinehut1, S. Mulet1, H. Claustre2, M. Gehlen3, B. Buongiorno Nardelli4; (1) CLS, (2) LOV, (3) LSCE, (4) CNR.

6.12.1 Introduction This short note aims at providing input related to requirements for in-situ observations for MULTIOBS TAC products. MULTIOBS TAC products are the following:

They cover the physical and biogeochemical state of the ocean, at the surface and at depth. They are constructed using in-situ and satellite observations and data fusion techniques. Requirements are presented in the following sections by type of parameter.

6.12.2 Temperature and salinity profiles and near-surface observations Temperature and salinity full profiles (for the ARMOR3D method), or near-surface observations (for the SSS/SSD method) come from CMEMS INSITU TAC, both in real-time and delayed-time. They cover all platforms such as Argo profiling floats, moorings, research cruise CTDs, XBTs, gliders, surface measurements from TSG, surface drifting buoys, as made available by international program or national efforts. Temperature and salinity historical observations are first used for the development of the methods to compute the statistics, such as covariances and correlation scales used in the data fusion techniques. They are then used in NRT (near real-time) and DT (delayed-time) for integration in the data fusion techniques and for validation. Their impact is high since the developed method has been tuned to stay close to the observations.

As the ARMOR3D and SSS/SSD methods rely on data fusion techniques with no dynamical propagation, neither in space (horizontal & vertical) nor in time, the impact of the observations is localized in their surroundings. This is on one side strength of the methods since they stay close to the observations, but on the other hand a limitation since they rapidly react to any decrease in the observing system. The lack of vertical propagation for short profiles might introduce inconsistency between the layers. The lack of temporal propagation means that the system does not keep the memory of previous analyses. This could cause jumps into the solution if in-situ observations are not regularly available, particularly for the deep layers. A strong recommendation from the ARMOR3D and SSS/SSD systems would thus be to have complete profiles on the vertical (up to at least 1500-m depth), regularly spaced over the global ocean (such as 3x3) and regularly available in time (such as 10 days). Furthermore, ARMOR3D method is a platform to perform Observing System Simulation Experiment (OSSEs) and Observing System Experiment (OSEs) to study the impact of the different observing systems: in-situ and satellite, existing and future. Recent experiments have been conducted in the frame of the European H2020 AtlantOS project to study enhancement of the Argo Observing System (double sampling at the equator and western boundary regions (ArgoX2) and full depth sampling (Deep Argo)). Results show a better restitution of the variability with the additional observations provided where the Argo observing system is increased to twice and that the Deep Argo observing system complete well the current observing system by correcting the bias that exist at depth, and their interannual variability. The ARMOR3D system is ready to ingest those additional observations. As we rely on one provider: CMEMS INSITU TAC, this provider should collect as much as possible observations and perform the standardization in terms of format, content, QC procedure, documentation. Full QC, including adjusted values is expected in DT. At least basic QC are expected in NRT since the methods have been constructed to get rid of observations that are too far away from the first guess solutions. In NRT, QC should not delay the release of the data by more than 24 hours. In DT, as CMEMS requires regular update for the long time series, DT in-situ T&S profiles should be available regularly.

6.12.3 Surface currents Surface velocities (15m depth) computed from surface drifter displacement come from CMEMS INSITU TAC in NRT and AOML Global Drifter Program (GDP, http://www.aoml.noaa.gov/phod/dac/index.php) in DT. Surface currents historical observations are first used for the development of the method which compute the surface currents, and specifically for the development of the Ekman current model parameters. They are then used in NRT and DT for validation purposes. Note that SST observations from surface drifters are also used to constrain an experimental version of the surface current product.

As for T&S observations, a regularly spaced array over the global ocean is required, and regularly available in time. As we currently rely on two providers: CMEMS INSITU TAC and AOML with two different format and content and two different QC procedures, standardization is required to have all in one place. For surface currents computed from surface drifter displacement, care should be particularly taken for the drogue loss detection and associated wind slippage correction if any required. Specific corrections might be required to get rid of specific signals such as tidal currents, inertial oscillations.

6.12.4 Currents at depth Current at depth are used for validation purposes. Unlike previous observation types, they are not acquired regularly through the operational chains. YoMaHa velocities at 1000-m depth computed from Argo drifts at parking depth are used to validate ARMOR3D geostrophic velocities (http://apdrc.soest.hawaii.edu/projects/Argo/data/trjctry/). Current profiles from current meters equipped on tropical mooring arrays (TAO/TRITON, PIRATA, RAMA) are downloaded from PMEL to also validate ARMOR3D geostrophic velocities. WM-ADCP observations taken onboard French vessels, processed and provided by SISMER/Ifremer (http://doi.org/10.12770/60ad1de2-c3e1-4d33-9468-c7f28d200305) have also been used recently to validate ARMOR3D geostrophic velocities. Data from Global Multi-Archive Current Meter Database (http://stockage.univ- brest.fr/~scott/GMACMD/gmacmd.htm) have also been used a long time ago to validate ARMOR3D geostrophic velocities. Apart from YoMaHa product, observations of currents at depth are located along research cruise lines or at specific mooring sites. As far as we know, the Southern Ocean is highly under sampled and there might also be a seasonal bias. It seems that current meters, ADCP and WM-ADCP data are scattered on different databases. The increase of these observing systems towards global and regular sample is of course a need since velocity observations are currently important independent observations of physical ocean state estimate either from data fusion techniques or models. However, a lot of work is already needed to bring together the historical existing datasets in one standardized, validated and documented database. This kind of observations might additionally require specific processing to correct for high frequency signals such as tidal currents and inertial oscillations.

6.12.5 Carbon observations at the surface

Surface Ocean fugacity of carbon dioxide (fCO2) observations are collected from the SOCAT Atlas (Surface Ocean CO2 Atlas, https://www.socat.info/) which distribute quality-controlled observations along ship route as well as gridded fields. SOCAT Atlas should enter the CMEMS

INSITU TAC in 2019. SOCAT Atlas is released annually with new datasets as well as updates of older one. fCO2 observations and fields are first used for the development of the method to train the neural network method. They are then used to force the neural network method and compute surface pCO2 (partial pressure of CO2) and FCO2 (Flux of CO2). pCO2 is then used together with fields of Alkalinity (computed from empirical relationships with surface ocean salinity) to estimate pH. Independent in-situ observations of pCO2 and pH at the surface, as available through the Global Ocean Data Analysis Project version 2 (GLODAPv2), or through long time series at fixed point, such as the ones available through FixO3 (Fixed-Point Open Ocean Observatories, http://www.fixo3.eu/), BATS and HOT sites are used for validation purposes. When available and fully calibrated, pH observations taken by pH sensors onboard BGC-Argo floats such as the ones deployed as part of the SOCCOM project (https://soccom.princeton.edu/) will also be a complementary source of data for validation first, and to constrain pH estimates in a second step. A first requirement is to maintain the current SOCAT network with extensions towards under sampled areas like the Southern Ocean. The development of BGC-Argo float as proposed in the Biogeochemical-Argo Science & Implementation Plan should then fill some gaps in space, in time and towards NRT processing.

6.12.6 Biogeochemical profiles (nutrients, oxygen, chla, backscattering coefficient, carbon) Vertical profiles of nutrients (nitrate, phosphate and silicate) and oxygen are collected from the Global Ocean Data Analysis Project version 2 (GLODAPv2). They are used for the development of the method to train the neural network method and for its validation. GLODAP database should enter the CMEMS INSITU TAC in 2019. Temperature, salinity and Oxygen profiles from the Argo GDAC (Global Data Assembly Center), together with geolocation, are then used as input variables of the neural network method to retrieve full profiles of the concentrations of nitrate, phosphate and silicate. High quality calibrated and validated Oxygen Argo profiles are currently available only in DT, so are the reconstructed nutrient profiles. With the development of new floats equipped with oxygen sensor programed to take in air measurement at each cycle, high quality oxygen profiles should also be available in NRT, through CMEMS INSITU TAC.

Vertical profiles of Chla fluorescence (FChla), backscattering coefficient (bbp) [a proxy for Particulate Organic Carbon (POC)] and density from the Argo GDAC are, together with their ocean color products surface counterpart (Chla, bbp), used to train and validate the neural network method. The neural network method is then used together only with satellite ocean color observations of bbp and Chla and (Argo) density profiles to reconstruct 3D fields of bbp and Chla.

As for carbon at the surface, independent in-situ observations of nutrients, bbp and Chla are available through long time series at fixed point, such as the ones available through FixO3

(Fixed-Point Open Ocean Observatories, http://www.fixo3.eu/), BATS and HOT sites are used for validation purposes. Even more than for physical measurements, a large share of BGC in-situ data, including carbonate system parameters, nutrients, FChla and bbp might still be unavailable or are scattered in different databases, even if huge recent progress have been made. A first requirement is to maintain and broaden the GLODAP database (i.e. collect as much as possible observations and perform the standardization in terms of format, content, QC procedure, documentation) with the integration of all available high qualified ship-based hydrographic survey, as the ones carried out through GO-SHIP (Global Ocean Ship-Based Hydrographic Investigations Program) standards. The development of BGC-Argo float as proposed in the Biogeochemical-Argo Science & Implementation Plan should fill some gaps in space, in time and towards NRT processing.

6.12.7 General requirements The general requirements are here to emphasis the need of the following complementary observing systems (for physical and biogeochemical variables): • Autonomous platforms such as Argo and its extensions (Deep, BGC) and surface drifters as they provide NRT and regularly distributed (in time & space) observations over the global ocean; • Long time series at fixed points from moorings, acting as references; • Ship based hydrographic survey with the best quality standards, as the ones provided through GO-SHIP standards, acting also as references (BGC, deep ocean).

6.13 Wave TAC

Author: Romain Husson1; (1) CLS

6.13.1 Status of the In-situ observations used The validation activity against in situ measurements in performed within the WAVE-TAC by IFREMER. The wave information used for validation depend on the dataset: • For SAR data, which involves directional spectral wave observations, only buoys measuring and providing the 5 parameters mandatory for the 2D spectrum reconstruction are considered. • For the altimeter data, which involves the total significant wave height observations, all buoys measuring this parameter can be considered. To validate wave products (L3 SAR and Altimeter products), we use in-situ measurements from buoys that can be provided by a variety of source networks: NODC, MEDS, UKMET, CDIP, NDBC, CETMEF, OPPE, Poseidon and Meteo-France. They have hourly resolution, are placed at global scale and operate since 1991 until now. The data provider is the IFREMER/CERSAT GlobWave database, based on various external sources, which harmonized the data in terms of format and performs quality control on the data content. Restriction depending on original provider. Availability is also very dependent on the source and cannot always be automated. The reliability of the in situ sources is therefore very heterogeneous and we rely on the most reliable ones (NODC/NDBC, CDIP, Meteo-France, NOOS) for short-term cal/val activities. At present, we do not use the CMEMS in situ network for several reasons, two of them being that: • It only includes data provided in NRT, and therefore, this database does not take advantage of the reprocessing performed by the different sources. • The NRT data does not enable reconstructing the full 2D spectra, which is a necessary input for comparing with the L3 SAR spectral products. However, it is expected that before the end of 2018, directional wave spectra measured by in situ buoys will be gathered and qualified using a basic quality check by CMEMS IN SITU TAC. We will therefore change or reference dataset to rather use this one. Yet, in-house additional treatments will be used such as swell tracking algorithms to select best quality swell measurements from ins situ measurements.

6.13.2 Present needs, and outlook on future requirements The use of available in situ dataset is mainly limited by the buoys locations, which are mostly located in coastal areas, which does not necessarily fairly represent the open ocean situations that satellite measurements more often sample. The only one located far off-

shore being “Stratus” buoy, with ID 32012, operated by Woods Hole Oceanographic Institution. Directional wave measurements form this buoy are unavailable since February 2018 (still unavailable today 25/06/2018).

Parameter and platform Coverage Spatial resolution Temporal Timeliness resolution Total significant wave Global N/A hourly Delayed time height – wave buoys and NRT 2D wave spectra – Global N/A hourly Delayed time directional wave buoys and NRT

6.13.3 Requirements related to data management Parameter and platform Quality control required Other Total significant wave height Yes – wave buoys 2D wave spectra – Yes Specific treatment applied for directional wave buoys swell monitoring (temporal smoothing + adaptive partitioning)

6.14 Euro-Argo (Euro-Argo ERIC)

Authors: S. Pouliquen1, and Euro-Argo ERIC Management Board; (1) Ifremer

6.14.1 Argo Network In May 2018 the Argo network is 3825 floats, 21% being deployed by Euro-Argo members (820 floats)

Among those floats:

 nearly 300 floats measures oxygen, 200 Chl-a and Suspended Particles, 100 Nitrate , 90 PH and 70 Downwelling irradiance  about 50 are deep floats , profile between 4000m and 6000m. These floats are presently in a Pilot phase mode and data below 2000m are flagged at 3 (i.e. data to be used after careful check)

The status of the Argo network is monitored by JCOMMOPS (http://www.jcommops.org/board?t=Argo) in term of network implementation, coverage in time and space, data availability, instrumentation reliability and metrics are computed on a monthly basis both at global and regional scale.

T&S Argo network coverage - BGC Argo coverage

% data delevered with 24h to French GDAC - Monitoring early failure (less than 10 cycles,) 3- year lifetime( 100 cycles), 5-year (250 cycles )

6.14.2 Approximate level of investment and Man Power The Euro-Argo ERIC monitor the European contribution to the Argo network both for the core mission and for the extensions that are developping through resarch projects .

Table 3: Monitoring the float purchase per year for the Euro-Argo members

The annual investment to maintain the Euro-Argo contribution to the Argo network it’s about 5 M€ per year ( 200 T&S 2000m floats per year, shipment, transmission cost, data processing in Near Real Time and in Delayed mode) and represent more than 19 equivalent full time. The Ero-Argo ERIC is a team of 5 persons (3.5 equivalent full time) that ensure the monitoring and the coordination of the European contribution to Argo

6.14.3 European Strategy The “Strategy for the Evolution of Argo in Europe” published in December 2015 and updated in 2017 taking into account Euro-Argo ERIC Scientific and Technical Advisory Group recommendations and last estimations of national deployments

(http://doi.org/10.13155/48526). It presents a plan for a European initiative for the future development of this international programme aiming at: • strengthening Europe’s role in and contribution to the global Argo programme, • supporting the implementation of the EU Marine Policy through the development and subsequent incorporation of biogeochemical sensors into the programme, • extending spatially the observations into the European and Polar Seas, as well as into the abyssal parts of the oceans, • further developing the existing data management system, and • maximising the relevant knowledge of the Seas and Oceans, e.g. their role in a changing climate. Euro-Argo needs to meet requirements from the research and operational (Copernicus) oceanography community in Europe with a focus on European Seas. This requires adaption of the ‘Core Argo’ design to these needs and evolutions in array design float technology and data systems. In this Euro-Argo Strategy document the following upgrades of the Argo program have been identified: • Monitoring of marginal seas: Baltic, Mediterranean Nordic and Black Seas, • Monitoring of high latitudes: Arctic and Antarctic oceans, • Monitoring of the near surface oceans, • Monitoring of the abyssal oceans, • Monitoring of ecosystem parameters. With the following, implementation guidelines have been defined: • Keep the Argo « Core mission » as the global array (T&S) with systematic acquisition of near surface measurements, • Implement an « extended mission » for marginal seas and high latitudes, • Define a strategy for O2 and Bio-Argo « extended mission » implementation both in marginal seas and global ocean, • Define a strategy for Deep-Argo.

The Euro-Argo ERIC and its members have prepared a strategic plan for the evolution of Argo in Europe and are now developing an implementation plan for the next decade. The target for the new operational phase of Argo in Europe is to deploy every year 250 standard Argo floats, 50 BGC floats and 50 Deep Argo floats. This corresponds to a contribution of about 25% of the international effort.

Table 4: Monitoring the implementation of the strategy and identifying the gaps

6.14.4 Assessment of sustainability of current system From the very start of Euro-Argo and Copernicus (GMES) (e.g. GMES Marine Core Service Implementation Group, EEA GMES In-Situ Coordination project, Euro-Argo preparatory phase and E-AIMS FP7 projects), it has been recognized that, given the global and international scope of Argo and the essential role of Argo for Copernicus, European contributions to Argo should be shared between member states and the EU. Based on existing and planned contributions from member states to Argo (about 5 MEuros/year), a direct additional EU contribution to Argo of 5 MEuros/year is required to implement the new phase of Argo over the next decade.

6.15 EUROGOOS HF Radar Task Team

Author: Julien Mader1; (1) AZTI The accurate monitoring of surface currents and transport is key for the effective integrated management of coastal areas, where many human activities concentrate. Among the different measuring systems, coastal High Frequency Radar (HFR) is the unique technology that offers the means to map ocean surface currents over wide areas (reaching distances from the coast of over 200km) with high spatial (a few kms or higher) and temporal resolution (hourly or higher). Consequently, the European HFR systems are playing an increasing role in the overall operational oceanography marine services (Rubio et al. 2017). The inventory of the different HF radar systems operating in Europe has been gathered thanks to a survey launched by the EuroGOOS HFR Task Team, in the framework of INCREASE (CMEMS Service Evolution) and JERICO-NEXT (H2020 Research Infrastructure) projects (Mader et al. 2016). 28 institutions provided information. From updated data (Mader et al. 2018), the use of HFR systems is growing in Europe with over 58 HFRs currently deployed and a number in the planning stage (12). The average growth rate since 2009 is 6 new stations per year (Figure 13).

Figure 13: Network evolution and map of the HF Radars stations The survey consisted in 46 questions oriented to provide information on four areas: • Contact people for each network or system • Technical information on the network, number, names, locations, working parameters of the sites (including questions on maintenance procedures and experience of interference problems) • Technical information about the data formats, sharing protocols and policies, QA/QC and processing • Areas of application of the data and identified users (including specific questions related to data assimilation)

No information has been gathered about costs, number of people involved in running the systems. References: Mader et al. (2016). The European HF Radar Inventory, EuroGOOS publications (Available at http://eurogoos.eu/download/publications/EU_HFRadar_inventory.pdf) Rubio et al. (2017). HF Radar Activity in European Coastal Seas: Next Steps toward a Pan- European HF Radar Network. Front. Mar. Sci. 4:8. doi: 10.3389/fmars.2017.00008 Mader et al. (2018). HF radar: a new observing system of the CMEMS INSTAC. Poster in GEO Blue Planet Symposium 2018, Toulouse, 4-6 July 2018.

6.16 EUROGOOS Ferry Box Task Team

Author: Andrew King1; (1) NIVA Current number and type of platform for FB Task Team

Table 5:Current number and type of platform for FB Task Team

Institution FerryBox lines Ship type Cefas 1 research vessel Ifremer 3 passenger ship, research vessel HCMR 1 car/passenger ferry HZG* 3 cargo ship, passenger ship IMR 2 passenger ship, research vessel MSI/TUT* 2 passenger ship NIVA* 5 cargo ship, passenger ship SMHI 1 cargo ship SYKE* 2 car/passenger ship European FB's not part of FB 9 cargo ship, passenger ship, research TT vessel TOTAL 20 + 9 FB TT + non TT FerryBoxes *Denotes those who provided responses to this request for “requirements”. Several previous requests were made as part of JERICO FP7 and the preparation of the EuroGOOS FerryBox Whitebook (2017).

6.16.1 Approximate level of investment (if known) Installations per line range from 60-100 kEuro depending on sensor configuration. Average investment reported in EuroGOOS FerryBox Whitebook was 110 kEuro. Additional person hours, travel, and consumables can vary depending on which ship FerryBoxes are installed on (distance between ship and lab, level of prep work needed, passenger fares/cabin costs depending on ship type, etc.).

6.16.2 Number of people (Full time equivalents) involved in running the system FTEs ranged from ~0.75-2 per FerryBox line. EuroGOOS FerryBox Whitebook reported 50 kEuro in personnel costs per year per line which is roughly equivalent to 1 FTE technician in most parts of Europe. The FTE/FerryBox ratio can of course vary depending on the complexity of the FerryBox setup (where the ship is located, the number of sensors/samplers, the amount of work a particular sensor may need for calibration/groundtruthing), and it can also depend on how many lines each institute runs – there can be an economy of scale for these operations.

6.16.3 Assessment of sustainability of current system From those who reported (NIVA, SYKE, MSI, HZG), sustainability was between 3-5 years. A short discussion between Andrew King and Wilhelm Petersen agreed that most operations are funded by research funding and this typically is on a ~3 year cycle (research grants from national and European level funding agencies). Some institutes have institute/government- level funding that ranges from <10% to 50% per line, but for the most part FerryBox operations are sustained by project funding.

6.16.4 Future plans e.g. new platforms, parameters Some Task Team members envision either completely new design and construction of the FerryBox systems and/or replacement of old systems. There are plans for new FerryBox lines to be launched on new ships and research vessels. And there are plans to install operate new sensors and samplers to provide new (or more comprehensive) observations on the carbonate system chemistry, greenhouse gases (e.g., CH4, N2O, etc.), nutrients, phytoplankton biodiversity, and microplastics and pollutants. Much of this work is funded by national and European infrastructure projects (e.g., H2020 JERICO-NEXT and INTAROS).

6.17 EUROGOOS Tide Gauges Task Team.

Author: Begoña Pérez1; (1) Puertos del Estado, Coastal sea level is still today mainly measured by tide gauges, some of them in operation since the nineteenth century (main European harbours). These in-situ observations complement therefore the sea level variability information provided by the satellite altimeters in the open ocean for the last decades, while still being used for many regional and local applications (geodetic studies, harbour operation and aid to navigation, tidal forecasts, storm surge and tsunami warning, dredging activities, etc). The current number of tide gauges in Europe is very high (674 stations reported in 2016 – see Figure 14 below-, most of the mooring sites of CMEMS are in fact tide gauges, which may be misleading for many users). However, despite this large number of stations and their long tradition, several problems and need of future work have been observed, mainly related to their multi-purpose character and the new requirements of data flow and quality:

6.17.1 Approximate level of investment bla • Tide gauges are operated in different countries by different kind of organizations: geodetic and hydrographic offices, oceanographic institutes, harbors, meteorological agencies, universities and even private companies. For this reason, it is difficult to estimate the amount of money and people involved in their operation (a detailed study of this should be accomplished in the future).

6.17.2 Data flow issues • The increasing number of data portals dealing with tide gauge data, usually the same stations for different purposes, implies that there is an urgent need of coordination. This is particularly important and needed between CMEMS/EMODnet and GLOSS and the Permanent Service for Mean Sea Level. • Access to tide gauge data in CMEMS must be improved. This task team has been working in collaboration with GLOSS, PSMSL and CMEMS In-Situ TAC experts to prepare a list of recommendations on data storage/metadata (http://eurogoos.eu/download/NetCdf_Recommendations_forCMEMS_EuroGOOSTG TT_October_2017.pdf). These are being implemented at this moment by the CMEMS In-Situ TAC’s. These recommendations contain some of the concerns of the altimetry community, after a side meeting with them in La Rochelle in November 2016 (http://eurogoos.eu/download/EuroGOOS_TGTT_LaRochelleNovember2016.pdf).

6.17.3 Sustainability issues • Sustainability problems have been detected after a survey launched by the task team in 2016 (http://eurogoos.eu/2016/01/15/eurogoos-tide-gauges-task-team-launches- a-survey-on-status-and-needs-of-the-existing-stations-in-europe/), sometimes even

in old and well stablished tide gauge networks, in well developed countries. The results of this survey were reflected in a note to policy makers (http://eurogoos.eu/download/publications/EuroGOOS-Tide-Gauge-Note-to-Policy- 2017-v.2.pdf), emphasizing the need of maintaining these stations for an adequate management of increasing sea level related coastal hazards. The main problem of the network of tide gauges is the funding for maintenance, a critical point for ensuring high-quality time series.

6.17.4 Future developments and other issues • Collocating tide gauges with permanent GNSS (Global Navigation Satellite System) stations is required for common reference of sea level data (the ellipsoid) and for monitoring vertical land movement and its impact on long-term mean sea level changes. This is already feasible in many tide gauges around Europe, but there is a need to improve access to these data and to know details about distance, levelling, etc. The task team prepared a document and first list of stations with GNNS data information, in collaboration with SONEL, in May 2018 (http://eurogoos.eu/download/TG_GNSS_2018.pdf). • Difficulty of access to tide gauge data in the Mediterranean Sea, especially in the North African coast. This difficulty is related both to problems of data policy (many tide gauges are operated by Hydrographic Offices and data are not public) and funding for maintaining permanent stations. • Understanding the impact of waves and high-frequency sea level oscillations (e.g. meteotsunamis) in tide gauge data is required for an adequate study and characterization of extreme sea level and coastal flooding events.

Figure 14:Status of European tide gauge network. Colours indicate whether platforms are at risk of decreased funding in the near future (Source: EuroGOOS Tide Gauge Task Team)

6.18 EUROGOOS Glider Task Team.

Author: Víctor Turpin1; (1) CNRS Underwater Gliders are commonly used in Europe for many scientific purposes. The capabilities of these autonomous platforms allow a wide range of applications, from coastal to open ocean. Glider payload is also quickly evolving from the classical set of sensors (CTD, Dissolved Oxygen, and optical backscatter sensor) to new sensor capacities, including ADCP, turbulence or nitrate sensors. The observing capability of the European fleet is also evolving toward deeper ocean observation with the arrival of the first deep glider reaching 6000m depth. The current number of operational gliders in Europe is approximately 120 distributed in the five ROOSes. The number of deployments registered within the Glider Task Team is continuously increasing. In 2017 we registered 70 deployment corresponding to about 2600 days at sea and around 30.000 T-S profiles. However, despite this rising activity and the development of news tools to support gliders data management, several needs for future work have been raised, mainly related to the data management in real time, delayed mode, QC and best practices to include new EOVs. Approximate level of Investment Gliders are operated independently by around 20 groups in different countries. For this reason, it is always difficult to estimate the amount of money and people involved in their operation. However, we recently try to collect information to estimate the global investment in the glider activity. This survey should be renewed every year to track the evolution of investment. In 2017, the approximate level of investment was: • Scientists: It is not easy to evaluate the time spend by scientist working with gliders data. The methodology should be clarified. It could be evaluated by the number of scientific publications (about 40 to 50 publications in 2017 referring to European gliders data). • Engineer / Technician: about 60 people involved in 2017. • Platform: 120 operational platform • Sensors in the network: common set of sensors: CTD, O2, optical backscatter sensor – other sensors: SUNA, MICRO, ADCP, PAR, Microstructure, Acoustic sensors, PAM. • Number of deployment: 70/ days at sea: 2.600 This should also include the investment on data management at the EGO/Coriolis level. Data flow issues • Today the data management system in Europe does not harvest the entire observations acquired by gliders. This gap is mainly due to the difficulty of the different groups to invest the required time for data management issues. In order to

solve this issue, data management tools have been developed and are now freely accessible. • Management of delayed mode data set is not developed yet. This is one of the tasks for EuroGOOS glider data management task team for next year 2019. • Procedure to include new variables in the data management system has also to be developed. Sustainability issues • To summarize, the glider activity in Europe is funded by national budgets for personnel salaries and for running costs (walls, infrastructure, sensor calibration and refurbishment) and by project (national and European) money for observations. Funding for investment funding comes from both project (national and European) and national found supporting the national glider infrastructure. • Most of the groups involved in the European glider network will continue to deploy gliders during the next 5 years. • 15 gliders sections of the network can be considered as repeated sections (with different repetition rate). Sustainability of these section is not guaranteed and an effort should be made for the maintenance of these section and also for the development of new recurrent sections (stressed by many groups). Future plans for the glider task team : • data management: an important effort will be made in the coming year on the organization of the glider data management system in real and delayed mode to increase the rate of data collected by the glider GDAC (Global Data Assembly Center). • sustainability assessment: A particular effort is planned within the EuroGOOS glider team to answer the question of endurance line and sustainability. • EuroGOOS glider task team is engaged in a complete review of best practices documentation. This review will lead to a Journal publication in Frontiers for Marine Sciences special issue on Best practices , expected by March 2019. Map of the network In the following maps, glider transects covered in 2017 both for the Mediterranean and Atlantic Ocean are shown. Glider tracks cover all the ROOSes in 2017. Issues remain in gathering gliders data into common European databases (CMEMS, EMODnet or SeaDataNet).

Figure 15: glider transects covered in 2017 for the Mediterranean

Figure 16:glider transects covered in 2017 for the Atlantic Ocean

The Table 6 below lists the contribution of the different institutions to the EUROGOOS glider task team.

Table 6: contribution of the different institutions to the EUROGOOS glider task team.

INSTITUTE Glider fleet Payload Scientific Area of interest

FMI 1 slocum 200m - CTD : SBE Water exchange - Dissolved Oxygen : Aanderaa Optode 4831 Blooming - Fluorometer : Wetlab (Chla, turbidity, CDOM)

Tallin University 1 slocum G2 - CTD : Pumped CTD system G-1451 Biogeochemical cycles of Technology shallow water - Dissolved Oxygen : aanderaa Optode 4318 Mixing and dispersion of particles and - Fluorometer : Wetlab (Chla, turbidity) pollutants

GEOMAR 12 Slocum - CTD : SBE Oxygen Minimum Zone gliders: Water Transformation - Dissolved Oxygen: Aandera 6 deep G1 2 shallow G1 - Fluorometer : Wetlab; Chl-a, Turbidity, CDOM 3 deep G2 - Nitrate Sensor : Seabird Satlantic SUNA 1 deep G3 (2 Gliders are - Turbulence sensor : Rockland Microrider equipped with

thrusters) Microstructure packages

HCMR 1 SeaExplorer - - CTD; Mixing 700m Endurance lines 1 SeaExplorer - - Dissolved Oxygen; 1000m, - Another 1000m - Fluorometer : Chlorophyll-A/Turbidity pack, glider is expected pCO2 to be purchased during 2019. OGS 2 slocums (200 - CTD : GPCTD, (CT) Water transformation and/or 1000m) - Dissolved Oxygen : Aanderaa Optode Endurance lines 2 Seagliders - Fluorometer : Wtelabs triplet (Chl-a, CDOM, Backscattering)

DTINSU 7 Slocum (5 G1, 2 - CTD : SBE - Water transformation G2) - Dissolved Oxygen : Aanderaa Optode - deep convection - Fluorometer : Wtelabs triplet (Chl-a, CDOM, - sub-mesoscale processes 1 Seaglider Backscattering) - endurance lines - ADCP 1Spray (in test) - acoustimertrics : hydrophones

SOCIB 4 G1 Slocum - CTD : SBE Endurance lines gliders - Dissolved Oxygen : Aanderaa Optode Wildlife surveys 4 G2 Slocum - Fluorometer : Wtelabs triplet (Chl-a, CDOM, Multiplatform experiments gliders Backscattering) 2 Seaglider 1KA

IOLR 3 SeaExplorers - CTD: SBE GPCTD Submesocale physical process study - Dissolved Oxygen : SBE GPCTD + DO Endurance line planned - Fluorometer : Wetlabs triplet - Methane Sensor : Franatech Hydrocarbure sensor : Alseamar Minifluo

Marine 1 slocum SG1 - CTD : SBE Seeking for funding Institute

NOC / MARS 60 – 70 gliders - CTD : SBE Multiplatform MARS (35 gliders) High latitude 1 deepglider - Dissolved Oxygen : Aanderaa optode, Contros Submesoscale physical process studies Environmental assessment - Fluorometer : WetLabs optics endurance lines

Turbulence sensors : Rockland microprofiler

PAR sensor (biospherical and satpar)

- Phosphate sensors : PSEG

- acoustics : hydrophones, Echosounders

- ADCP SAMS 2 SeaGliders + - CTD, Endurance lines: Access to the - Dissolved Oxygen : optode 2009 – 2017: Extended Ellett Line (one

MARS facility - Fluorometer : Wetlabs puck, deployment ~every winter, Scotland to - RSI microstructure, - PAM (Sea Mammal Iceland) Research Unit, St Andrews) 2014 – 2018: OSNAP (continuous deployments of 2 gliders, Scotland to Hatton Bank) 2018 – 2021: Ellett Array (continous deployments of 1 glider, Scotland to Hatton Bank) Process studies: 2012 – 2015: FASTNet (Fluxes Across Sloping Topography of the North East Atlantic) 2015: SSB (Shelf Sea Biogechemistry) 2016 - 2018: MASSMO (Marine Autonomous Systems in Support of Marine Observations) 2018: Arctic PRIZE (Arctic Productivity in the Seasonal Ice Zone) 2018 – 2021: COMPASS (Collaborative Oceanography and Monitoring for Protected Areas and Species)

University of 2 SeaGliders - CTD Seagliders will be integrated into new Gothenburg - Dissolved Oxygen : Aanderaa oxygen optode, Swedish national programme, SCOOT - Fluorometer : Wetlabs ecopuck, (Swedish Centre for Ocean Observing - PAR sensor Technologies). - ADCP(?) Strong collaboration with South Africa Glider observatories.

6.19 BOOS

Author: Jun She1; (1) Danish Meteorological Institute

6.19.1 Inventory of BOOS observations Established in 1995, the Baltic Sea Operational Oceanographic System (BOOS, http://boos.eurogoos.eu/) is a network of governmental agencies and research institutions that develops and operates operational marine data, modelling and information services for the public safety, blue growth, ecosystem-based management and climate change adaptation and mitigation in the Baltic Sea. The network currently consists of 23 member agencies coming from nine countries around the Baltic Sea. The in-situ ocean observations operated by BOOS partners include both online and offline data from ferrybox lines, moorings, R/Vs (Research Vessels), tidal gauges, Argo floats etc. In recent years HF radars are tested in the transition waters and gliders tested in the central Baltic Sea. The availability and operational use of these data are summarized in Table 7 (She, 2018). These data are delivered in near real time (NRT).Table 7 shows that only NRT T/S and part of the SST data have been used for forecast production.

Table 7: Availability of Baltic Sea in-situ observations to operational oceanography. SST: Sea Surface Temperature; SSS: Sea Surface Salinity; SSC: Sea Surface Currents; Chl-a Chlorophyll a; DO: Dissolved Oxygen; NRT: near real time; Val.: Validation; FC: forecas

Instruments Variables Amount* Delivery time Oprational time Existing operational window Use

Tidal gauges Water level 148 1-10min. 6H/3M/1Y Val.

SST 24 1D/3M/1Y Val.

Moorings Waves 12 <1hour 6H/3M/1Y Val.

SST 26 1D/3M/1Y Val., RAN

T/S 8 3W/3M/1Y FC, RAN

Currents 7 6H/3M/1Y Val.

Chl-a 7 1D/3M/1Y None

DO 7 1D/3M/1Y None

Ferrybox SST 15 NRT 1D/3M/1Y Val.

SSS 4 1D/3M/1Y Val.

Surf-DO 3 1D/3M/1Y None

Surf-Chl-a 3 1D/3M/1Y None

SSC 11 6H/3M/1Y None

Argo T/S 8 NRT 3W/3M/1Y None

R/V BOOS T/S ~100 NRT 3W/3M/1Y Val. FC, RAN

*Amount of observations: for NRT observations, number of stations is given for the past 30 days. The number can vary with time.

6.19.2 Assessment of BOOS data adequacy for BAL MFC In-situ observations are only useful for the BAL MFC activities (cal/val, MME and data assimilation) when they meet the correspondent quality and timeliness requirements. Gaps can be caused by either non-existence of observations or low quality of data or too long delivery time. A review on the assessment of BOOS observations can be found in She (2018). Sea level: Currently there are about 220 tide gauge stations operated along the Baltic Sea coastline. This amount represents a mean sampling distance of about 40 km, with high sampling density in transition waters (~10 km sampling distances) and low in Bothnian Sea (~100-200 km sampling distances). Around 8 stations are located in offshore islands. All the operated stations are able to deliver data in near real time with a data sampling frequency between 10 – 60 minutes. The amount of sea level stations is assessed as adequate for BAL MFC purpose. However, not all stations are available for BAL MFC. Currently BOOS and BAL MFC only receive near real time data from less than 2/3 of the total amount of tide gauge stations. Currents: BOOS partners in the Baltic Sea are currently operating 7 moorings for current profile measurements and 3 fixed platforms for surface current measurements. The data are provided in NRT. Most of the stations are located in the Baltic-North Sea transition area. Only 3 moorings are in the open Baltic Sea. This is not adequate for validating BAL MFC model performance on inflow and water exchange between the sub-basins. Other surface variables: This includes SST, sea surface salinity (SSS), chl-a, dissolved oxygen, waves, snow depth and sea ice thickness. Almost all of these parameters are measurable through satellite. Therefore one of the major uses of these data is to calibrate and validate satellite data. On the other hand, the in-situ surface observations are also used for validating the model products. Currently CMEMS receives SST/SSS/Chl-a/DO from 11 ferrybox lines and a number of coastal stations for SST. Hourly wave measurements are available from 13 mooring in the Baltic Sea. Most of them do not measure wave spectra. In general the in-situ surface observations fit for the purpose of BAL MFC applications. T/S profiles: The observations are mainly made by using research vessels (R/V), mooring and Argo floats. Recently, gliders are also used in the Baltic Sea. Most of the R/V observations can be found in ICES Oceanography database. In 2015, 5401 profiles are found in the Baltic Sea which corresponds to a mean sampling density of 2.8 profiles per 100 km by 100 km grid per week. However, most of the data is delivered more than one-year later, therefore does not fit for the purpose of BAL MFC NRT and interim applications. In addition to the R/V data, T/S

profiles are also available from 8 moorings and 8 Argo floats operated by the BOOS partners, generating up to 70080 T/S (hourly) profiles annually at the mooring locations and 2920 (daily) profiles at the Argo locations. According to the previous estimation of the BAL MFC data needs, the total amount of T/S profiles in the Baltic Sea should be sufficient for BAL MFC cal/val, forecast and reanalysis applications. There still exist two major challenges: one is to deliver the R/V T/S profile data in near real time. Currently only less than 10% of the R/V T/S profiles are accessible in near real time by BAL MFC. The other challenge is spatial inhomogeneity. The Gulf of Riga, Gulf of Finland and Gulf of Bothnia cover 42.8% of the Baltic Sea but the amount of R/V observations (about 810 profiles) only takes account of 15% of the total. Nevertheless this amount still counts for about 29 profiles per sub-basin per season for the 7 sub-basins in the region. Biogeochemical profiles: The investigated parameters include dissolved oxygen (DO), chlorophyll-a (chl-a), total nitrate and phosphate (N/P), pH, secchi depth, plankton and primary production (PP). In ICES database, the amount of R/V profiles is 4574 (DO), 3164 (chl-a), 3350 (N/P) and 1766 (pH). For these parameters, a reasonable amount of profile observations are available. Major challenges are still NRT data delivery and spatial inhomogeneity. For other parameters, the number of other observations from HELCOM COMBINE network is 702 (secchi dpeth), 257 (zooplankton), 141 (phytoplankton) and 24 (primary production). In this case, lack of observations is a major challenge. It should be noted that major part of HELCOM and ICES profile database in the Baltic Sea is provided by BOOS partners. Timeliness: most of the biogeochemical data are delivered in 1-3 years, which are too late for BAL MFC NRT and interim applications. The DO and chl-a are measured in most of the case together with CTD, therefore it is possible to make a NRT delivery, as already done by Swedish Meteorological and Hydrological Institute (SMHI). For the nutrient and pH, the data delivery should be made available within 1 month time after the cruises. Spatial inhomogeneity: Figure 17 shows the spatial distribution of the chl-a and total N/P stations in 2015. Significant gaps for chl-a are found in Bothnian Bay, western Bothnian Sea and south of Gotland.

Figure 17 Distribution of chl-a (left) and total N/P (right) profile stations in 2015, extracted from ICES Oceanography database.

In addition to the R/V data, at least 6 moorings in the Baltic Sea deliver NRT hourly DO and chl-a observations. This generates up to 52560 profiles annually at the mooring locations. All moorings are operated by Germany and Swedish partners in BOOS.

6.19.3 Recommendations for BOOS observing integration The data adequacy assessment is summarized in Table 8: Adequacy assessment of the Baltic Sea in-situ observations for operational oceanography and recommendations, together with recommendations. From BAL MFC side, future focus should be on i) developing advanced data assimilation schemes which can use more in-situ data, e.g. sea level, currents and biogeochemical variables and ii)developing optimal sampling strategy through OSE/OSSEs dedicated for forecasting error reduction. From BOOS side, future focus should be on i) near real time delivery of the ship data, ii) apply new cost-efficient monitoring technologies, e.g., shallow water Argo and bio-Argo floats, to fill the gaps and iii) to improve the spatial homogeneity by optimal use of existing monitoring infrastructure.

Table 8: Adequacy assessment of the Baltic Sea in-situ observations for operational oceanography and recommendations

Variables Cal/Val Historical Interim Online NRT Recommendations reanalysis reanalysis val./MME forecast

Water level FFU TBU, FFU TBU, FFU FFU TBU, FFU Less than 2/3 of the data shared. More data collection are needed

SST FFU FFU FFU FFU FFU More stations in open waters, with skin temperature

Waves FFU Not used Not used FFU Not used Directional spectra are needed

Currents GFU Not used Not used GFU Not used More stations to monitor inter- sub-basin water exchange

T/S FFU FFU GFU GFU GFU Near real time ship data delivery,

Chl-a GFU TBU, GFU TBU, GFU GFU GFU improve spatial inhomogeneity, esp. in south of Gotland, Gulf of DO FFU FFU GFU GFU GFU Riga, Gulf of Bothnian and Gulf of

Finland

N/P FFU FFU TBU, GFU TBU, GFU TBU, GFU Gaps are mainly caused due to delayed data delivery pH FFU Not used Not used Not used Not used Near real time ship data delivery,

improve spatial inhomogeneity,

Secchi Dep. GFU TBU, GFU TBU, GFU GFU TBU, GFU Reasonable amount of data is only found in Kattegat, Bornholm Basin, E. Gotland, Gdansk Basin, NB Proper and Bothnian Sea

Plankton TBU, Not used Not used Not used Not used More observations are needed GFU for emerging needs PP Not used Not used Not used Not used

*FFU: Fit for the use; GFU: Gaps identified for the use; TBU: To be used References: She J. 2018. Assessment of Baltic Sea observations for operational oceanography. P79-87 in Operational Oceanography serving Sustainable Marine Development. Proceedings of the Eight EuroGOOS International Conference. 3-5 October 2017, Bergen, Norway. E. Buch, V. Fernández, D. Eparkhina, P. Gorringe and G. Nolan (Eds.) EuroGOOS. Brussels, Belgium. 2018. ISBN 978-2-9601883-3-2. 516 pp.

6.20 IBI ROOS

Authors: Julien Mader1, Manuel Ruiz2; (1) AZTI, (2) IEO A recent inventory has been performed in the framework of the MyCOAST project (Coordinated Atlantic Coastal Operational Oceanographic Observatory. INTERREG Atlantic Area. 2017-2020). MyCOAST is led by AZTI and gathers almost all the main partners of IBIROOS operating in situ platforms and HF Radars. The following items have been fulfilled: Institution, platform type, name, station description, variables, depth, position, status, time sampling, time coverage, url, integration in EU network services or catalogs, contact. The following comments can be highlighted from the available information: • 272 items have been gathered, 246 operational stations, 139 tide gauges, 17 HF Radars • 15 institutions in charge of the observing systems. • Listed stations are operated by IBIROOS partners but in NOOS or MED. • Most of the observing systems provide measurements with a time sampling lower than 1 hour. • Half of the fixed platforms are tide gauges. • Others sources include deep and costal moorings, waverider buoys, profilers (ARGO and coastal profilers), CTD repeated cruises, drifters, fishery observing systems (RECOPESCA Programme) and HF Radars. • More detailed analysis of the information and a gap analysis of the coastal observatory will be performed in MyCOAST project.

Plans for future include: To add ADCP in a mooring (PdE), New deep water station (CTs & ADCP), Carbon monitoring in Irish Weather buoy network, GLOSS standards included in tidal gauges, deeper waters measurements by CEFAS for industry, High frequency tidal gauges (AZTI), new HF Radar stations (France, Portugal).

No information has been recently gathered about costs, number of people involved in running the system, or sustainability.

Figure 18: (Left) Map of MyCoast in-situ observing systems compiled (Triangles represent in-situ stations, red boxes identify areas where periodic repeated measurement campaigns are available); (Right) Map of Operational HF Radar observing systems operated by MyCoast partners. The areas depicted are not a realistic representation of HF radar coverages

6.21 NOOS

Author: S. Legrand(1), (1) Royal Belgian Institute of Natural Sciences. Established in 2001 as one of the EuroGOOS regional systems -ROOS-, the North West European Continental Shelf Operational Oceanographic System -NOOS (http://noos.eurogoos.eu/)- is a network of governmental agencies and research institutions that develops and operates on-line real-time operational marine data and information services for the North West European Continental Shelf and its Atlantic margin. The network is currently made of 23 member agencies coming from Norway, Sweden, Denmark, Germany, the Netherlands, Belgium, France, United Kingdom and Ireland, Since its creation, NOOS has been striving to integrate the different national observing networks, first by sharing in near real time key observations between all NOOS members (i.e. a fully distributed sys-tem), then by establishing “assembly centres” that have been collating these observations, consolidating and redistributing them to the NOOS community (partly centralized system), and finally by establishing in 2008 a unique data portal. Continuously growing, the NOOS data portal currently gives access to more than 500 different near-real- time data flows (Figure 19) covering 120 tide gauge sensors ; 80 temperature sensors; 70 waves buoys (significant waves height, mean waves direction, mean waves period, etc.); 50 meteorological sensors (wind, air temperature, sea surface atmospheric pressure, etc.); 40 salinity sensors; river discharges for 30 rivers; 13 ferry-boxes; 9 moorings for measuring biogeochemical data and only 3 ADCP sensors for currents.

Figure 19: The NOOS data portal is a central gateway to more than 500 near-real-time data flows.

A closer look at the distribution of the different sensors (Figure 20) shows some geographical gaps that might be partly filled in with existing sensors that have not been yet ingested in the NOOS data portal due to data policy issues. A solution to these issues is actively sought, maybe through the implementation of the European Directive INSPIRE. Another challenge for the future development of the NOOS data portal is the deployment of relative new technologies such as HF-Radar, gliders or animal-borne sensors. The current national

observing networks have limited investment mean but, thanks to the connection with EuroGOOS task teams could speed up the deployment of these technologies in the North West Shelf area. Finally, the deployment of near-real-time bio-geo-chemical sensors is still a research topic. However, to improve the assimilation of optical properties in bio-geo- chemical models, the NOOS modelling community encourages the deployment of in-situ optical sensors.

Tide gauges Meteorological sensors

Sea surface temperature Sea surface salinity

Waves Currents

Bio-geo-chemical variables River discharge

Figure 20: Geographical distribution of the observations available in the NOOS data portal per variables.

6.22 MONGOOS

Author: Giovanni Coppini1; (1) CMCC - Centro Euro-Mediterraneo Sui Cambiamenti Climatici. Access to MONGOOS monitoring systems is granted via the MONGOOS Data Center, fully integrated into MONGOOS web page. This system provides access to real time data from most of the existing operational oceanography instrumentation in the region thanks and is integrated with Copernicus Marine Service In-Situ TAC and EMODNET physics. The numbers and descriptions presented in this section correspond to the result of several iterations with all the institutions and MONGOOS and, on occasion, with some other actors with activity in the region, but not yet part of the organization. In spite of evident gaps (i.e. no tide gauges reported at Greece, North Africa gap), it constitutes the most comprehensive catalogue of available oceanographic measurements in the region. Figure 21 shows the distribution of wave, sea level, currents, temperature, and salinity sensors.

Figure 21: Maps of sensors for waves (upper left panel), sea level (upper right panel), current meters (bottom left panel) and water temperature and salinity (bottom right panel)

The MONGOOS Showcase Tool (http://www.mongoos.eu/in-situ-and-forecasts) contains links to the web page section of data providers offering measured data in real time, often in graphical format. The following figures, derived from the analysis of the information, describe the situation: • Waves. In total, there are 58 buoys capable of measuring waves, most of them directional. Coverage is more complete in the western basin, and there is an almost total absence on the African coast, with the exception of Ceuta and Melilla stations. • Circulation and Hydrography. Water temperature is the most frequently measured variable in regular basis, with 113 stations. Only 37 current meters are operational in

the whole region. 50 salinity stations are reported. No stations are present on the African coast. • Sea level. A total of 100 sea level stations are reported in the region. There is a significant gap in Eastern Europe that is due to lack of data in the Showcase Tool, rather than the absence of stations. MONGOOS community is working on solving this coordination issue. Once more, there is a total absence of stations on the North African coast, except for t the Spanish cities. • Atmospheric variables over sea. Several buoys and towers are able to measure atmospheric variables over the sea: The tool contains information of 80 Meteorological stations capable of measuring Air pressure, temperature and wind. • HF Radar Systems. High frequency (HF) radar systems measure the speed and direction of ocean surface currents in nearly real time. This technology is based on the emission of electromagnetic waves and the study of the echo after reflection by the sea surface. The velocity of the surface current can be derived from the change in frequency between the emitted and reflected signal. This is a result of the Doppler effect, which is the apparent change in frequency of a wave produced by the movement of the source with respect to the observer. A total of 10 HF radar systems have been or are currently in operation in the area. More information can be found at http://www.mongoos.eu/hf-radars. • Argo. MedArgo’s main responsibility is the overall coordination of profiling float operations in the Mediterranean and Black Seas. As such, MedArgo serves as a Delayed Mode Operator (DMO) for the delayed-mode processing of Argo data with specific QC tailored for the Mediterranean and Black Seas. The Number of Argo floats in the basin changes on time (70 at the time of writing this publication, March 2018). More info can be found at http://www.mongoos.eu/profilers-and-drifters-med-argo

Figure 22: known HF Radar Systems that are in operation in the Mediterranean Sea

Figure 23: Medargo network - March 2018

• Gliders. A glider is a underwater vehicle that uses small changes in its buoyancy in order to move up and down in the ocean. Unlike a float, a glider uses wings to convert that vertical motion to horizontal, propelling itself forward with very low power consumption. The high efficiency of the propulsion system enables gliders to be operated for several months, during which they can cover thousands of kilometers. They allow the autonomous and sustained collection of physical measurements, such as pressure, temperature and conductivity data and, depending on the model, some biogeochemical measurements (like fluorescence, oxygen or optical backscattering) of the upper 1km of the ocean. Registered measurements are transmitted by satellite every time the gliders comes up to surface. The same communication channel is used to transmit navigation commands to the gliders (heading, angle of ascent/dive, max depth,…). Although they are a relatively new platform in oceanography, in the Mediterranean Sea gliders are deployed regularly for ocean observations. Most of the active and archived glider missions can be tracked on the owners’ websites and also on EGO (Everyone's Gliding Observatories) website. There are 7 glider facilities at the Mediterranean Sea. More info can be found at http://www.mongoos.eu/glider-facilities

Figure 24: Glider facilities

For some variables, and in certain regions, the monitoring system is quite satisfactory. For example, the number of tide gauges on the western European Mediterranean coast is sufficient for most of the applications (study of sea level trends, storm surge monitoring, etc.). Similar situations can be found for wave buoys. Nevertheless, the absence and limitations of the monitoring system are clear and often dramatic. First of all, the African coast presents an almost total absence of data. There is a radical North-South imbalance. The only exceptions are the stations located in the Spanish cities of Ceuta and Melilla. Even on European coasts, there is an obvious unequal distribution of sensors because of lack of global planning. Other variables, like currents, are clearly under-sampled in the whole region. Same situations apply for salinity and water temperature, although for these variables data from gliders and Argo serve to mitigate the situation. In any case, owing to the size of mesoscale structures in the region, the limited number of sensors is clearly insufficient to properly contribute to data assimilation in numerical models, as previously pointed out. The solution to this problem is complex because of the high cost of maintaining these sensors at sea. Only a proper combination of satellite data (SST and altimeter), additional in- situ stations, an increased glider fleet and HF-radar data could provide the data needed to reduce model uncertainty to a fully satisfactory level for applications, such as Search and Rescue In any case, almost all the known stations are providing data to MONGOOS data centre, so coordination is not a major gap. The problem lies in the absence of data.

6.23 Arctic ROOS

Authors: Stein Sandven1, Jari Haapala2; (1) Nansen Environmental and Remote Sensing Centre, (2) Finnish Institute of Marine research In Table 9 are listed the current available In-situ ocean observations in the Arctic, with data transmission; the list does not includes coastal stations (tide gauges, HF radar, etc.) and new platforms (wavegliders, sailbuoys, etc.).

Table 9: In-Situ Arctic observations with data transmission

Platforms* Open Ice-covered Surface Vertical profiles Ocean area

Surface buoys (ocean & ice) x X x (x)

Argo floats x x x

Ice-tethered platforms X x x

Ice Mass Balance buoys X x x

Gliders x x x

Ferrybox x x

Sea mammals x X x x

Ship surveys (CTD, XBT, ADCP) x (x) x x with data transmission

Figure 25: Arctic ROOS in situ data (Last 30 days from 29 June 2018)

Contribution from INTAROS to in situ ocean observations To ensure collection of more data and build up knowledge on Arctic climate and environment, the INTAROS H2020 project will develop an efficient integrated Arctic Observation System by extending, improving and unifying existing and evolving systems in the different regions of the Arctic. Implementation of such observing system has to tackle challenges linked to: • Organization: Develop coordination and collaboration between data providers and stakeholders in the pan-Arctic region in order to better use existing systems and resources • Technology: Improvement of the observing platforms and sensors, filling of gaps in the observing network and facilitate for year-round operation, how to go from research to operational systems • Data dissemination, data management: Data sampling, transmission, calibration, processing, archiving and retrieval of required variables and build distributed and connected databases • Funding: How to develop sustainability of the observing systems, and what are the funding mechanisms?

With the objective of increasing temporal and geographic coverage and usefulness of observational data in the Arctic the project will provide the following Near real-time data: • 2 IAOOS platforms: 2018-2019-2020 (IOPAN) • 4-8 SIMBA Ice Mass Balance Buoys (FMI) • Argo Floats in Baffin Bay with bio-optical sensors (CNRS Takuvik) • Ferrybox between Tromsø and Longyearbyen (NIVA) • Glider experiments in the Fram Strait (CNRS LOCEAN) • AWIPEV observatory in Kongsfjorden, data transmission via cable, BGC variables, pH, CO2 (CNRS LOV) And the following mode data: • Moorings north of Svalbard with physical and biogeochemical sensors, ULS, acoustic instruments (UIB, NERSC, IOPAN, CNRS-LOCEAN, IMR) • Observatories in Hausgarten incl. BGC variables, pH, CO2 (AWI) • Greenland Ecosystem Monitoring Programme (Univ. Aarhus) • Contributions from US partners (Alaska Ocean Obs System +) • Contributions from Japan, China, South Korea