Copernicus Marine Environment Monitoring Service (CMEMS ...€¦ · CMEMS SERVICE EVOLUTION STRATEGY: R&D PRIORITIES June 2017 1 1 1 INTRODUCTION The Copernicus Marine Environment

CopernicusMarineEnvironmentMonitoringService(CMEMS)Service

EvolutionStrategy:R&Dpriorities

Version4November2018

DocumentpreparedbytheCMEMSScientificandTechnicalAdvisoryCommittee(STAC)andreviewed/endorsedbyMercatorOcean

CMEMSSERVICEEVOLUTIONSTRATEGY:R&DPRIORITIES June2017 00

Tableofcontent1 Introduction......................................................................................................................1

2 ScientificandtechnicalbackboneofCMEMS...................................................................3

3 KeydriversoftheServiceEvolutionandrelatedR&Dpriorities.......................................4

4 R&Dareasandrequireddevelopments............................................................................7

4.1Circulationmodelsfortheglobalocean,regionalandshelfseas...................................7

4.2Sub-mesoscale-mesoscaleinteractionsandprocesses.................................................8

4.3Coupledocean-marineweatherinformation,surfacecurrentsandwaves..................10

4.4Newgenerationofsea-icemodelling............................................................................11

4.5modellinganddataassimilationformarineecosystemsandbiogeochemistry...........12

4.6SeamlessinteractionsbetweenCMEMSandcoastalsystems......................................13

4.7Coupledocean-atmospheremodelswithassimilativecapability.................................15

4.8Oceanclimateproducts,indicatorsandscenarios........................................................16

4.9Observationtechnologiesandmethodologies..............................................................17

4.10Observingsystems:impactstudiesandoptimaldesign..............................................19

4.11Advancedassimilationforlarge-dimensionalsystems................................................20

4.12High-leveldataproductsandbigdataprocessing......................................................21

5 Theserviceevolutionroadmapfor2021andbeyond....................................................23

5.1ThetargetedCMEMSserviceinandafter2021............................................................23

5.2Tier-3R&DmarineprojectsofinteresttoCMEMS.......................................................25

5.3Tier-2R&DmarineprojectsselectedbyCMEMS..........................................................27

5.4Serviceevolutionroadmapandmilestonesuntil2021.................................................29

6 Appendices......................................................................................................................30

6.1ServiceEvolutionStrategicdocuments.........................................................................30

6.2CMEMSServiceEvolutioncall21-SE-CALL1projects....................................................30

6.3CMEMSServiceEvolutioncall66-SE-CALL2projects....................................................38

6.4CMEMSServiceEvolution:R&DcallsforGlobalMFC...................................................40

6.5ReportfromtheCopernicusCoastalWorkshop............................................................40

6.5.1Contextandworkshopobjectives..........................................................................41

6.5.2Mainoutcomesandrecommendations.................................................................41


1 INTRODUCTION

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular andsystematicreferenceinformationonthephysicalstate,variabilityanddynamicsoftheoceanandmarineecosystemsfortheglobaloceanandtheEuropeanregionalseas.Thiscapacityencompasses the description of the current ocean situation, the variability at differentspatial and temporal scales, thepredictionof theocean state a fewdays toweeks ahead(forecast), and the provision of consistent retrospective data records for recent decades(reprocessed observations and re-analyses). CMEMS provides a sustainable response toEuropeanuserneedsinfourareasofbenefits:(i)maritimesafety,(ii)marineresources,(iii)coastal and marine environment, (iv)weather, seasonal forecast and climate. A majorobjective of the CMEMS is to deliver and maintain a competitive and state-of-the-artEuropean service responding to public and private intermediate user needs, and thusinvolvingexplicitlyandtransparentlyusersintheservicedeliverydefinition.

ADelegationAgreement1hasbeensignedbetweentheEuropeanCommissionandMercatorOcean for the CMEMS implementation during the 2015-2021 period. It mandates thedevelopmentandmaintenanceofaServiceEvolutionStrategy2(seealsosection6.1)inorderto respond tonewneedsand takeadvantageof improvedmethodologies.This strategy isthemechanism bywhich lessons and knowledge derived during the past, aswell as newresearch results, are used to guide change and/or a potential service upgrade in thesubsequentphases.

The Service Evolution Strategy ismaintained on the basis of direct feedbacks from users,scientific and technical gaps analysis of emerging andexistinguser requirements, and thepotential to improve the Core Service elements. At the same time, the Service EvolutionStrategyidentifiesresearchneedsthatcanguideexternal(e.g.H2020)andinternal(CMEMS)R&Dpriorities. Consistentupdatesof the ServiceEvolution Strategyand itsR&Dprioritiesaremadeonanannualbasis.

ThisdocumentisthethirdupdateoftheR&DroadmapwhichidentifiestheessentialScienceandTechnologicaldevelopmentsneededfortheevolutionofCMEMSduringthe2015-2021period.Differentcategoriesofactivitiesareconducted,withdifferenttimehorizons,playersandobjectives:

1Seehttp://www.copernicus.eu/sites/default/files/library/CMEM_TechnicalAnnex_PUBLIC.docx.pdf2 http://marine.copernicus.eu/wp-content/uploads/2017/03/CMEMS-High-Level-Service-Evolution-Strategy-FV-September-20-2016.pdf


• long-termobjectives(Tier-3,beyond2yearsupto~10years),correspondingtothelong-termevolutionoftheCMEMSframework;

• mid-term objectives (Tier-2, 1 – 2-year cycle) addressed by dedicated R&D projectteams,forimplementingandassessingmajorscientificupgradesoftheserviceduringPhase-I (2015-2018) and mostly Phase-II (2018-2021) (see technical annex of theDelegationAgreement);

• short-term objectives (Tier-1, several months to 1 year cycle) with activities foraddressing issues requiring fast responses for rapid implementation within theexistingphasesofCMEMS.

Long-termR&Dactivities,althoughimplementedinadifferentframework,areascrucialasshortandmid-termactivitiesforthesustainableevolutionoftheService.Theshortandmid-termactivities areaddressedboth through internalCMEMSactivities andopenR&Dcalls,whilelong-termactivitiesarepromotedintheframeworkofexternalprojects(e.g.Horizon2020orotherEuropeanandnationalR&Dprogrammes).


2 SCIENTIFICANDTECHNICALBACKBONEOFCMEMS

The backbone of the CMEMS relies on an architecture of production centres both forobservations (Thematic Assembly Centres – TACs) andmodelling/assimilation (Monitoringand Forecasting Centres – MFCs) and a Central Information System (CIS). The CMEMSarchitectureforthephase2(2018-2021)consistsof:

• Eight Thematic Assembly Centres (TACs), including six “space” TACs organized byoceanvariables(seasurfacetopography,oceancolour,seasurfacetemperature,seaice, waves, and winds), one TAC for in situ observations, and one TAC for multi-observations (combining both in situ and satellite observation). TACs gatherobservational data and generate elaborated products, e.g. multi-sensor dataproducts,derivedfromtheseobservations.TACsarefedbyoperatorsofspaceandinsituobservationplatformsandinfrastructures;thedetailedoperationalrequirementsfor space and in situ observation data were initially specified in the Marine CoreService Strategic ImplementationPlan (Ryder, 2007 “GMES Fast TrackMarineCoreService,StrategicImplementationPlan,Finalversion24/04/2017”).

• Seven Monitoring and Forecasting Centres (MFCs), distributed according to themarine area covered (Figure 1, including Global Ocean, Arctic Ocean, Baltic Sea,NorthAtlanticWest Shelf,NorthAtlantic Iberia-Biscay-Ireland area,MediterraneanSeaandBlackSea).MFCsgeneratemodel-basedproductsontheoceanphysicalandbiogeochemicalstates,includingforecasts,analysesandreanalyses.

• A Central Information System (CIS), encompassing the management andorganization of the CMEMS information and products, as well as a unique UserInterface.

Figure1.RegionscoveredbyCMEMS.


3 KEYDRIVERSOFTHESERVICEEVOLUTIONANDRELATEDR&D

PRIORITIESThe guidelines to identify the R&D activities to be organized within Copernicus or incoordinationwiththeservicearemotivatedbyseveralkeydrivers:

• Requirements from core users in the fourmain areas of benefit as recalled in theIntroduction, accounting for bothexistingneeds andneeds likely to emerge in thefuture;

• RequirementsfromtheCMEMSproductioncentresbased,inparticular,ontheir3-6yearsR&Dplans,consideringthelimitationsinthedailyserviceduetoknowledgeortechnologicalgapsidentifiedtoelaboratetheproducts;

• RequirementsmotivatedbythedevelopmentofCopernicusdownstreamservices;• Requirements from European and international programmes dealing with Earth

observations,operationaloceanographyandCopernicus;notablyGODAEOceanView,the Copernicus Integrated Ground Segment (IGS) and GEOSS (Global EarthObservationSystemofSystems);

• Outcomes of EU projects implemented under the Space theme (R&D to enhancefutureGMES applications in theMarine andAtmosphere areas), such asMyWave,OSS2015, E-AIMS, OPEC and SANGOMA or under FP7 and H2020 projects (e.g.JERICO,PERSEUS,AtlantOS).

Additionaldriversthatreflectamorestrategicviewfortheserviceevolutionarealsotakenintoaccount,suchas:

• EUdirectives implemented to regulate activities in theMarine sector (e.g.,MarineStrategyFrameworkDirective,MaritimeSpatialPlanning);

• Newscientificandtechnologicalopportunitiesinthenext5-10years,regardingbothsatellite (e.g. theSentinelssuites)and insituobservations (e.g.plans fortheglobalBGC-Argo extension), modelling and assimilation capabilities, new communicationanddataprocessingtechnologies,etc.

• The need to maintain competitiveness w.r.t. international players in operationaloceanography, as keeping a high-level know-how and innovation capacity will bestrategictoattractnewusers;

• ForthcomingactivitiesinvolvingCopernicusdevelopmentsintheframeworkofotherEuropean (e.g. EMODnet, EuroGOOS, EOOS) and global initiatives or programmes


(e.g.theGlobalOceanObservingSysteminthecontextofGEOSS,GEOBluePlanet,GODAEOceanView);

• TheSustainableDevelopmentGoals(SDG)definedaspartoftheUNEP2030Agenda(especially SDG 14 - Conserve and sustainably use the oceans, seas and marineresourcesforsustainabledevelopment);

• Outcomes of the 21st Conference of the Parties (COP21) to the UNFCCC (UnitedNationsFrameworkConventiononClimateChange) including theneed foraglobalstocktaketosupportemissionagreements.

The constant dialogue with user communities as outlined above helped in identifyingoverarchingthemesfortheServiceEvolution,focusingon:

i. A better description of ocean biogeochemical and ecosystem parameters inparticular to support reporting onmarine environmental status in the regionalEuropeanseas,asrequiredbytheMarineStrategyFrameworkDirective(MSFD);

ii. The production of consistent ocean and wave information/products resultingfrom the coupling between the ocean state, the atmosphere, surface wavedynamicsandotherair-seainterfaceprocesses;

iii. A better interface between the CMEMS and coastal monitoring servicesoperated by Member States or private groups: this will require an improvedprocessingofsatelliteand insituobservationsinthecoastalzoneaswellastheprovisionof suitableconnectivityandboundaryconditionsbetweentheMarineServiceandthenationalcoastalsystems;

iv. A better monitoring and description of the ocean state and its variability,requiring thepreparationand releaseofannualOceanStateReportsdescribingthe state of the global ocean and the European regional seas, in particular forsupportingtheMemberStatesintheirassessmentobligation;

v. A better assessment of the quality of CMEMS products, which requirescontinuous upgrade of observation infrastructures and improved datainformationandaccessservices.

Moreover, it is identified that one overarching driver for service evolution remains acontinuousenhancementofmodelling, dataassimilationand forcing techniquesatboththeair-sea interfaceand lateralboundaries (coasts,openboundaries), inorder toexploitthe considerable investment in recent and upcoming observation technologies (e.g. SAR,SARin,SWOT,CFOSAT)andobservationinfrastructures.

These priorities provide guidelines to the short, medium and longer term developmentsoutlined in section 4 below. The proposed R&D roadmap was established following ananalysisofR&DprioritiesbytheCMEMSScientificandTechnicaladvisorycommittee(STAC)setuptoassistMercator-OceanfortheCMEMSimplementation.

This version (V4) released in November 2018 provides an update of R&D priorities anddevelops the Service Evolution roadmap and plans for the coming years. It takes intoaccount,inparticular,revised3-yearand6-yearevolutionplansfromtheCMEMSTACsand


MFCs, past and on-going H2020 Copernicus projects (service evolution, downstream)referredtoinsection5,outcomesofthefirstcallfortendersforCMEMSServiceEvolution(section6),andapresentationofprojectsselectedinthesecondcallfortendersforCMEMSServiceEvolution(section6).


4 R&DAREASANDREQUIRED

DEVELOPMENTSIn thissection, the12R&Dareasrequiredtosupport theserviceevolutionguidedbyuserneedsaredescribed.Therequireddevelopmentsarecategorizedintermsoftimehorizons(short-tomid-term,andlong-term)andexpectedobjectives.Thedevelopmentswithshort-tomid-termobjectivesrefertoR&Dactivitiesthatareexpectedtodeliversignificantresultsin lessthan2years,while longer-termactivitiesarethoughttoneedmorethan2yearstobear results that will impact the service. The sections addressing R&D priorities of the 4over-arching themes that emerged from the strategic analysis made by the STAC areorganizedasfollows:oceancirculationmodelling,mesoscaleandotherinteractions,ocean-waveandocean-icecoupling (sections4.1 to4.4),biogeochemistryandecosystems in themarine environment (section 4.5), interactions with the coastal ocean (section 4.6) andocean-atmospherecoupling,reanalysisandindicators,andclimatechange(sections4.7and4.8). Finally, sections 4.9 to 4.12 correspond to cross-cutting activities andmethodologiesthatwillbenefitthe4overarchingthemes.

4.1 CIRCULATIONMODELS FOR THEGLOBALOCEAN, REGIONAL AND SHELFSEAS

Expectedevolutions

TosupportCopernicusmarineusersanddecision-makerstherewillbeanincreasingdemandformodel informationonfinespatialscales,higherfrequenciesandwithamorecompleterepresentationofdynamicalprocessesof the turbulentocean.This is relevant toall areasfromtheopenoceanwherethedynamicsaresignificantlyimpactedbymesoscaleeddies,totransition areas connecting coastal and shelf seas and to critical regions (e.g. straits,boundary layers) requiring high topographic resolution. Numericalmodels will be neededwithincreasedresolutionstorepresentthedynamicscapturedbyexistingandfutureEarthObservation platforms (e.g. high-resolution wide-swath altimetry, geostationary sensors,directestimationsof surface currents,high resolution sea surface temperature (SST), etc.)andconsistentwithimprovedestimatesoffluxesoffreshwaterandsuspendedmatteratthecoast.

Requireddevelopmentswithshort-tomid-termobjectives

• Development of advanced physical parameterizations, numerical schemes andalternative grids to improve the performance of ocean models resolving themesoscaleandsmallerscales;


• Adaptationofmulti-scale and seamless downscaling/nesting capabilities to achievekilometrictosub-kilometricresolutionoverspecificareas;

• Betterresolutionofsurfacecurrents(0-20mdepth)andhigher-frequencyprocesses(e.g. tides) includingtheireffects (e.g.rectification)andassociateduncertaintiesonthecirculationandonthetransportoftracers;

• Adaptation of forcing methods with consistent physical parameterizations thatbenefitthefinescales(fronts,filaments)ofsurfaceoceanpatterns;

• Improvedcapabilityin3Dstormsurgeforecastingforglobalandregionalmodels,inparticular new strategies to incorporate atmospheric surface pressure data, tidalforcingandcoupledocean-wave-atmosphereeffects;

• New strategies and algorithms to solve the model equations efficiently on nextgenerationcomputingsystems:thisshouldresultincodeperformanceimprovementson most intensive HPC applications, which is crucial to sustain operationalproduction;

• Developmentofempiricalorotheratmosphericmodellingtechniquestoimprovethespatialandtemporalresolutionofatmosphericforcing.

Requireddevelopmentswithlonger-termobjectives

• Modelling issues related to multi-scale and multi-physics and in particular modelnestingandunstructuredgrids;

• Developmentofnon-hydrostaticoceanmodelstorepresentthefullevolutionoffine-scaleoceanicstructuresinthethreespatialdimensions;

• Understanding of fine scale processes to guide the development of global oceananalyses and forecasts at increased resolution and complexity (e.g. includingrepresentationsoftidalphysics,mixingandwave-currentinteractions).

4.2SUB-MESOSCALE-MESOSCALEINTERACTIONSANDPROCESSES

Expectedevolutions

Ourknowledgeontherelationshipsbetweenthephysical,chemicalandbiologicalprocessesin the upper ocean is continuously improving. This is essential for understanding andpredicting how the ocean and the marine ecosystems respond to ocean dynamics andchanges in atmospheric forcing. Advection, mixing and enhanced vertical velocitiesassociatedwithmesoscale and sub-mesoscale oceanic features such as fronts,meanders,eddiesandfilamentsareoffundamentalimportancefortheexchangesofheat,freshwaterand for biogeochemical tracers between the surface and the ocean interior, but also forexchangesbetweentheopenoceansandshelfseas,andbetweenthepelagicoceanandthebenthos.

Thechallengesassociatedwithmesoscale(20-300km,Rossbynumbers<1,andtimescalesfromweekstoafewmonths)andsub-mesoscalevariability(between1-20km,RossbyandRichardson numbers O(1), and time scales from minutes to a few days) require high-


resolutionobservations(bothinsituandsatellite)andmulti-sensorapproaches.Accordingly,multi-platformsynopticexperimentshavetobedesignedinareascharacterizedbyintensedensity gradients and strong mesoscale activity to monitor and establish the verticalexchangesassociatedwithmesoscaleand sub-mesoscale structuresand their contributionto upper-ocean interior exchanges. In situ systems, including ships, profilers and driftersshould be coordinatedwith satellite data to provide a full descriptionof the physical andbiogeochemicalvariabilityandwillbecombinedwithresolutionnumericalsimulations.

Focus should be made on a range of scales (15-100 km) traditionally not resolved byconventional altimeters but in which the wide swath altimeter SWOT will make anunprecedented contribution. At the same time, the realistic representation of thegeneration and evolution of such processes in numerical ocean models remains verychallenging due to the chaotic nature of the oceanic flow, making direct model-datacomparisonsatthesmallestscalesproblematic.


• Predictability studies to assess the feasibility of developing open ocean andcoastal/regionalseas’forecastswithlead-timeofafewdaystoweeks,togetherwithassessmentoflikelypredictionskill;

• Methodstoassesstherelativeimpactofsub-mesoscalesandinternalwavesonseasurfaceheightandinsituobservations;

• Methods to account for turbulence-submesoscale interactions into numericalmodels, topographic interactions at small scales,mixing induced by internalwavesandotherinteractingprocessesbetweeninternalwavesandsubmesoscaleeddies;

• Development of new metrics adapted to the increased resolution in bothobservations/productsandmodels.


• Synthesisfrommultidisciplinaryfieldexperimentsandnumericalstudiestoassess(i)systematicerrorinoceanmodelsinducedbysub-mesoscaledynamics,(ii)impactofverticalexchangesassociatedwithmesoscaleandsub-mesoscalestructures,and(iii)operationalproductsinspecificareas;

• Process studies focused on physical-biological interactions at sub-mesoscale,implications for biogeochemistry, productivity, export, ecosystem structure anddiversity;

• Improved understanding of vertical exchanges associated with oceanic mesoscaleand sub-mesoscale features (e.g. fronts, meanders, eddies and filaments) throughthecombineduseofinsitu,satellitedataandnumericalmodels;

• Assess the impactofsolving theoceandynamicsatkilometricscalesontheroleofocean on climate (e.g. vertical exchange of heat and carbon, representation ofoverflows).


4.3COUPLEDOCEAN-MARINEWEATHERINFORMATION,SURFACECURRENTSANDWAVES

Expectedevolutions

Themost important oceanweather parameters delivered by CMEMS are ocean currents,temperature(inparticular inthenear-surfacelayeroftheocean),sea ice,sea level,wavesand surface winds. For several marine and coastal applications, information on surfaceparameters, including total and non-tidal currents and waves, is highly important. Oneexample is the off-shore industry, where information on wave height and period arefundamentalinthedecisionmaking(e.g.whenoceanplatformsneedtobeevacuatedduringviolent storms). Everyday activities along the coasts heavily depend onwave information.Informationon surfacewaves is also an essential input to the risk analysis in storm floodevents.

Wavesareabridgebetweentheoceanandtheatmosphere.Therefore,surfacefluxesandwinds used to drive the wave and ocean circulation should be consistent. The quality ofwave forecastsandanalyses is strongly linkedwith thequalityof thewind forcing.Wavesare also an important mixing agent with an active role in erosion and resuspensionprocesses.

In2012,theEUfundedprojectMyWavewasestablishedtolaythefoundationforafutureService thatalso includesoceanwaves.Additionalefforts fundedunder the1stCMEMSSEcall are underway to support the production of more consistent ocean-marine weatherinformation, including information on surface waves. However, the two-way couplingbetweenoceanandwaveprocesseshastobefurtherdevelopedandimproved.


• Improvedmethodsforanalysingandpredictingupperoceancurrents;• Improvedprocessingmethodstoretrievewaveandsurfacecurrents(totalandnon-

tidal) information from the relevant satellite data, such as wave height based onaltimeters (e.g. Sentinel 3) and wave information from SAR (e.g. Sentinel 1) andCFOSAT;

• Improved processing methods to retrieve wave information (including integratedparameterssuchassignificantwaveheight)frominsituplatforms(buoys,moorings,HFradars,etc);

• Developments to implement fully coupled ocean-wave models in which bothcomponents will exchange information at high frequency, and development ofcoupling parameterizations (bulk parameterizations for instance), both in the openoceanandneartheshoreline;

• Investigations of wave model error dependencies on swell parameters (usuallysourceofthelargesterrors)andimprovedparameterizations;

• Improvedwavemodellingneartheiceedgeandinice-infestedwaters.



• Improvedmodellingofextremeandroguewaves;• Implementationofadvancedtechniquestoassimilatedataintofullycoupledocean-

wave-atmospheremodelsystems;• Improvedcharacterisationoftheuncertainties incoupledocean-waveanalysesand

forecasts.

4.4NEWGENERATIONOFSEA-ICEMODELLING

Expectedevolutions

There is evidence of a number of shortcomings in modelling the coupled ocean-sea-ice-atmosphere system at high latitudes that continue to limit the quality of operationalproductsdeliveredtousers(bothreal-timeaswellasreanalyses).The10kmto100kmwideMarginal Ice Zone where ocean waves and sea ice processes are coupled is expected towideninawarmerArctic,butisstillnotwellrepresented,neitherindatanorinoperationalmodels.Additionally,productsforfisheries(havingaverystrongeconomicvalue)dependonseveralqualitiesofbothphysicalmodels(mixing,horizontalandverticaladvectionoflarvae)andprimaryandsecondaryproductionmodels.

Some shortcomings are inherent to the traditional viscous-plastic sea-ice rheology (andelastic-viscous-plastic aswell) used in the current sea-icemodels, deformations of sea icebeingnotcorrectlyrepresentedandinturnthevelocityofseaicedriftbeingalsoincorrect.Theabilitytoassimilateseaicedriftisalsoimpairedbysuchmodelbiases.Inaddition,theinclusion of more non-linear, chaotic processes are expected to become increasinglychallengingforassimilationmethods,inacontextwheremoreobservationswillbedeliveredfromspace,especiallythroughtheSentinelsuite.


• Newmethodsdesignedtoassimilatesatelliteseaiceobservations,suchasthetypeandthicknessofseaiceandmoredetailedinformationavailablefromSARimagery,togetherwithimpactassessments;

• Sea-icemodelsbasedonamore realistic rheology (for instance rheologicalmodelsbased on solid mechanics rather than fluid mechanics and their inclusion intooperationalmodels);

• Improvedcouplingbetweenoceanwavesandsea-iceprocesses;• Improvedphysicalmodellingofmixingbelowseaice;• Improvedseaicethermodynamicsthatwillincludeeffectsofsurfacemeltponds;• Couplingwithsnowmodelstoaccountforageingofsnowandblowingsnow;• Development of a new generation of sub-kilometer scale dynamic sea ice models

abletoresolveicefloes.



• Representationofbiologicalcycles insea ice, includingopticalpropertiesofsea iceandverticalmigrationofnutrientsinseaice;

• SpecificdevelopmentformodellingoftheMarginalIceZone,takingintoaccounttheheterogeneity insea icecoverageandcouplingeffectswithoceanandatmosphereandwavesatfinescales;

• Developmentsonsea-icemodelsanddataassimilationtargetedtoinitializecoupledocean-atmosphereandsea-iceforecastsforawiderangeoftimescales.

4.5MODELLING AND DATA ASSIMILATION FORMARINE ECOSYSTEMS ANDBIOGEOCHEMISTRY

Expectedevolutions

UsersofthefutureCMEMSwillbenefitfromthegradualimprovementofmodelsandtoolstomonitor thebiogeochemical stateof theoceanandmarineecosystems inoceanbasinsandmarginal seas, usingmodel-data integration and assimilationmethodologies. Thiswillrequire very significant strengthening of the observation system of the “green” oceancomponent at a range of scales, including in regional and coastal seas. In addition to theSentinel suite, the potential of future satellite sensors (e.g. imagers from geostationarysatellites) should induce a breakthrough in themonitoring of coastal areas and the land-oceaninterface.Moreover,monitoringtheconcentrationanddistributionofpollutantswillbeessentialforpredictingecosystemresponsestopollution.Otherchallengesonthemarinecomponentofthecarbon/greenhousegasescycleswillalsorequiresignificantR&Deffortassignificantgapsinmonitoringtoolsalsoexistinthisarea.

Duringthepastyears,asuiteofEUfundedprojectshasdevelopedtheprocessingofremote-sensing data for open ocean and coastal waters (see section 5) as well as prototypeecological marine forecast systems for ocean basins and European seas (Atlantic, Baltic,Mediterranean and Black Seas) which include hydrodynamics, lower and higher trophiclevels. More recentprojects fundedunder the1st CMEMSService EvolutionR&Dcallwilldeliverfurtheradvancesformulti-platformbiologicaldataassimilation,dataassimilationofphytoplankton functional types and delivery of ecosystem enriched products. There is anexpectation that the integratedAtlanticOceanobserving systemwill increase thenumberandqualityofinsituobservationsonchemistry,biologyandecologyoverthenextdecade.Aco-evolutionofthedatause inassessmentandpredictivemodelsholdsgreatpotential fornewproductsandusers.

It is required that results from these projects as well as similar advances in the field betransferredtoCMEMSinordertoconsolidatethecoreecosystemcomponentoftheservice.



• Improved modelling and assimilation capabilities for the representation of oceanbiogeochemistryandthemarinefoodwebfromprimaryproductiontohighertrophiclevels(planktontofish),includingestimationsofuncertainty;

• Relationships between optical properties and biomass for direct assimilation ofopticalpropertiesintobiogeochemicalmodels;

• Improving CMEMS monitoring (i.e. products derived from satellite and in situobservations) and modelling products to better support aquaculture managementapplications;

• Improved pH and carbon monitoring and forecasting: carbonate systemobservations,modellingandassessmentatregionalandglobalscales;

• Methods to generate operational products in addition to standard (T, S, Chl, –oxygen, pH, nutrients, light, plankton biomass) in support of predictive habitatforecasts, for ecological status and fisheries modelling and risk assessment (e.g.invasivespecies,harmfulalgalblooms,marineprotectedareaplanning);

• Multi-objective assimilation capabilities, e.g. combining state and parameterestimation,combiningoceancolourandsub-surfacedatafromBGC-Argo,glidersandotherrelevantecologicalobservationsespeciallyinregionalseas;

• Demonstrationofconsistentinterfacing(nesting,downscaling)betweenopenoceanbiogeochemical models and regional/coastal ecosystem models and downstreamapplications;

• Standardized validation methods for ecosystem model products/variables(particularlyrelatedtonon-assimilatedobservations/variables).


• Improveddescriptionofbenthic-pelagiccouplingonshort-term(seasonal)andlong-term(decadal)scales;identificationoftheimpactofinitialconditions;

• Improved methodologies for supplying operational information on sources ofnutrientsandpollution/chemicalstotheoceans;

• Develop a system for routinemodel estimates of the dose and direct and indirecteffectsofpollutiononcommunitiesofalgae,zooplanktonandfishlarvae.

4.6SEAMLESSINTERACTIONSBETWEENCMEMSANDCOASTALSYSTEMS

ThecoastalmonitoringservicesoperatedbyMemberStatesorprivategroupswill formanimportant and strategic group of users of the CMEMS. These activities will enhance thesocio-economic value of CMEMS by contributing to theMSFD, spatial planning and otherdownstream applications (offshore operations, coastal engineering, habitat monitoring,aquaculture,harmfulalgalbloommonitoring,adaptationandmitigationtoclimatechange).Downscaling also includes the integration of coastal stations, and the role of CMEMSregionalproductsisalsotofillgapsbetweencoastalobservatories.


However,the“one-way”visionofacoreservicedeliveringinformationtodownstreamuserswithoutfeedbacktoupstreamprovidershasanumberoflimitationssincethecoastalstripshould also be considered as an “active” boundary layer that influences the open oceanregion connected to coastal areas. Scientifically, interactions between land, littoral, shelf,regionalandabyssalseasarestillamajorunknown,poorlycharacterizedandmodelled.Itistherefore needed to develop the CMEMS in such a way as to enable more efficientinterfacing with a large variety of coastal systems describing the physical andbiogeochemicalcoastaloceanstatesandecosystems.Futureoperationalcirculationmodelsimplemented in the open ocean in CMEMS should enable more flexible coupling with avariety of model codes and regional configurations specifically customized for coastaldynamicsandbenefitingfromuserexperienceandpractices,includingthoseinterfacedwithnear-shore,estuarymodelsandhydrologicalmodels,unstructuredgridmodelsinareasthatrequireveryhighresolutionwithgoodrepresentationoftopographicfeatures.

These issues have been explored in a number of recent EU-funded and national projects,relying on the coordinating role of EuroGOOSwith the objective to consolidate, integrateand further develop existing European coastal and regional operational observing andforecasting systems into an integrated pan-European system targeted at detectingenvironmentalandclimatechanges,predictingtheirevolution,producingtimelyandqualityassured forecasts, and providingmarine information services (including data, informationproducts, knowledge and scientific advices). In the ongoing H2020 CEASELESS project forinstance, it will be demonstrated how the new Sentinel measurements will support thedevelopmentofacoastaldimension inCopernicusbyprovidinganunprecedented levelofresolution,accuracyandcontinuitywithrespecttopresentproducts.

Furthermore, it also becomes timely to explore the possible connections between theCMEMSandtheCopernicusLandmonitoringserviceascoastaluserswillrelyoninformationfrom both services. Therefore, an expert workshop was recently organized by EEA andMercatorOceanwith the objective to better address user needs in the coastal zone (seesection 6.5). The required developments listed below are partly inspired from the mainconclusionsofthisexpertworkshop.


• Improvedandstandardisedinputsoffreshwaterflowsandassociatedriverinputsofparticulateanddissolvedmatterandhomogenisedriverforcingapproachesinglobal,regionalandcoastalmodels;

• Improve the interfaces/interactions between coastal monitoring and modellingsystemsandCMEMS;

• Comprehensive impact studies of CMEMS boundary conditions on coastal systems(physics,biology)andtheirapplications(e.g.MSFD);

• Dataassimilationandensembleforecastingimprovementstobetterservethecoastalapplications;


• Development of flexible interfaces between regional and coastalmodels, includingnear-shoreandestuarinemodelsandwiththecapabilitytoconnectstructuredandunstructuredgrids.


• Adoption of robust standards to ensure compatibility between CMEMS anddownstreamsystems;

• Connectionandcouplingwithlandhydrologymodels.

4.7COUPLEDOCEAN-ATMOSPHEREMODELSWITHASSIMILATIVECAPABILITY

Expectedevolutions

Whilecoupledocean-atmospheremodelshavebeendevelopedforawhileinthecontextofclimateresearchandweatherforecasting,theMFCsystemsinCMEMSarecurrentlybasedon a “forcingmode” approach (except for the GLO-CPL forecasting system) in which theoceanisdrivenbysurfacemomentum,heatandfreshwaterfluxescomputedusingspecifiedatmosphericinformationatfairlycoarsespace-timeresolution.Thiscouldbeanissueduetothedifferentscalesofvariabilityintheatmosphereandoceansystems.Inparticular,thelackoffeedbackatfinescalesbetweenoceanandatmosphericmodels leadsto inconsistenciesand has implications for the practical forecasting capability of regional and coastalenvironments. Furthermore, the existence of observations at the ocean-atmosphereinterface is an opportunity to introduce assimilation constraints into coupled models,especiallyforregional/coastalapplicationsofinteresttoCopernicususers.


• Advancedassimilationmethods targeted toprovide improvedestimationsofupperoceanpropertiesconsistentwithsea-surfaceobservationsandair-seafluxes;

• Representationofdiurnalvariabilityandcoolskinlayerinforcedoceanandcoupledocean-atmospheremodels;

• Consistent numerical schemes to improve the representation of ocean-wave-atmosphereinteractionsforregionaloceanicapplications;

• Useofoceanwavespectratodeterminecontributionstoboundarylayermomentumfluxes.


• Novelocean forcingapproaches thatmay include simplifiedatmosphericboundarylayerdynamicsandoceanfeedbacksathighspatialresolution;

• Analyses of impacts and feedbacks resulting from coupling between ocean,atmosphereandwavesonthesurfaceoceandynamicsandbiology,includinginthecaseofextremeevents;

• Facilitate collaboration with the atmospheric community for exploitation of thesurface observations common to the coupled ocean-sea-ice-atmosphere interface,


suchas surfacewinds, surface currents, SST,waves, radiative, freshwaterandheatfluxcomponents,sea-iceconcentration,sea-icethicknessandsea-icetemperature;

• Multivariatedataassimilation intofullycoupledocean-wave-atmospheredynamicalsystems.

4.8OCEANCLIMATEPRODUCTS,INDICATORSANDSCENARIOS

Several CMEMS systems produce analyses and reanalyses or multi-year reprocessedobservationsthatdescribeoceanvariabilityandchanges.OceanMonitoringIndicators(OMI)canbederived fromtheseproducts for trackingoceanmechanismsand/orclimatemodesand trends, much as we use global greenhouse gas (GHG) concentrations to followanthropogeniceffects,andgloballyaveragedSSTtomonitorwarming.CMEMSisdevelopingOMIsformonitoringtheoceanstateatglobalandEuropeanscales(seetheCMEMSMulti-YearProductStrategyPlan,section6.1).

Manyusers are also interested in future trends andevolutionof themarineenvironmentunderdifferentclimatescenarios.Earthsystemandclimatemodelpredictions(seasonaltodecadal)andlonger-termprojectionsbasedonGHGscenarios,togetherwithcomprehensiveoceanreanalyses,canbeusedto inferpossiblechanges intheoceanstateatregionalandcoastallevels,includingbiogeochemicalandotheraspectsofthemarineenvironment(e.g.,relatedtoharmfulalgalbloomsandothermarineextremeevents).Inthefieldofecology,ourscientificunderstandingofecosystemshasmaturedtoapointthatwemaybeabletoproduce potential scenarios based on our knowledge of potential future change in thephysicalsystem(e.g.waterquality,coralbleaching).

Such outcomes require the production of improved ocean reanalyses and multi-yearreprocessed observations suitable for climate change detection, with reduced systematicerrors and improved dynamical and physical consistency and realism. Reanalyses productquality delivered by the CMEMS will have to be monitored on a continuous basis, usingproven,verifiableandrobustmethodologiesthatcanbeagreeduponwithexternalpartners(see section 6.1). This activity can rely on standard metrics (e.g. as defined in theinternationalGODAEOceanViewframework)oronnewapproaches.

TheseactivitieswillcontributetotheOceanStateReportsdeliveredbyCMEMS,andwillalsobenefitfromthedevelopmentoftheCopernicusClimateChangeService(C3S)line.


• Developmentofmethodsforinferringthefuturestate(fromseasonstodecades)ofthemarineenvironment(physicsandbiogeochemistry)atregionalandcoastalscales,as well as changes in ecosystems, based on climate model predictions andprojections(andassociatedtestsforqualityandreliability);

• DevelopmentofOceanMonitoringIndicatorsbasedonCMEMSandotherproducts;specific needs include indicators (i) for the physical ocean state, variability and


changemonitoring(ii)forthehealthoftheocean(e.g.acidification,deoxygenation,eutrophication), (iii) for fishery and aquaculture management and (iv) for otherapplications such as maritime transport, marine renewable energy, coastal zonemanagement;

• Frameworkforcoordination/intercomparisonswithinternationalcommunityefforts(EuroGOOS,GODAEOceanView,CLIVAR,CMIPactivities…);

• Methods toensurequality, homogeneity and robustuncertaintymeasures in long-termtime-seriesreconstructedfromdataormodelreanalyses.


• Developmentofmethodsforinferringthefuturestate(fromseasonstodecades)ofthemarineenvironment(physicsandbiogeochemistry)atregionalandcoastalscales,as well as changes in ecosystems, based on climate model predictions andprojections(andassociatedtestsforqualityandreliability)[continued].

4.9OBSERVATIONTECHNOLOGIESANDMETHODOLOGIES

Expectedevolution

The observing system, which today provides regular and systematic data on the physicalstate anddynamicsof theoceanandmarineecosystems, is expected toevolve in severaldirectionsduringthenextyears:

• Theglobalinsitucomponent,throughcoordinatedprogrammessuchasArgoanditsextensions(deepocean,polarseasandBGC),AtlantosintheAtlanticandTPOS2020inthePacificOcean,isexpectedtomoresystematicallycollectdataonnewchemicalandbiogeochemicalstatevariables(oxygen,bio-opticalproperties,nutrients,pH) inaddition to conventional temperature and salinity profiles, and to increase thesamplingofthedeep(below2000m)andice-coveredseas;

• The European regional and coastal seas will be monitored through morecoordinated, multi-sensors and multi-parameter networks including HF radars,cabled structures, fixed platforms, ferry boxes, surface drifters, gliders, and othernewtechnologies,e.g.undertakenunderEuroGOOS,EMODnet,theEOOS(EuropeanOceanObservingSystem)initiativeandotherprojects(e.g.JERICO-Next,AtlantOS);

• Thespacecomponentwill relyonamixtureofoperational (e.g. theSentinel suite)and exploratory missions, providing sea-level (both along-track and wide-swath),geoid information, SST, ocean colour, surface salinity, surface wind and currents,wavepropertiesandsea-iceparameters;

• Newcapabilitiesareexpandedtosampleavarietyofscalesthroughmulti-platformobserving systems that includemore andmore autonomous platformswithmulti-disciplinarysensors;

• Observations delivered through Copernicus or in cooperationwith other initiativeswill increase, especially those dedicated to validation measurements following


agreed protocols (e.g. ESA protocols, quality assurance framework for Earthobservationguidance);

• Oceanobservingsystemswillbemulti-purposeandhavemultipleuses,observingtheoceanatmultiplescalesand fromthecoastal to theopenocean,deliveringqualitycontrolleddatatoscienceandsociety.

Newmulti-platform andmulti-disciplinary approaches combining both in situ (e.g. gliders,Argo, ships, drifters, fixed platforms) and satellite observations at high resolution will beneeded to resolve awide rangeof temporal and spatial scales, tomonitor key regionsorprocessesfortheoceanstateanditsvariabilityandtofillgapsinourknowledgeconnectingphysical processes to ecosystem response (e.g. BGC Argo, TPOS2020, Atlantos, GOOS). Inaddition, there is a huge potential to more efficiently access/use data from differentindustrialplatforms,includingoffshorepowerstations.

Thecoredatasetassimilated inCMEMSreal-timemodelsormulti-yearreanalysissystemsinclude SST, sea-level anomalies and T/S profiles (almost systematically), sea-iceconcentration(wheneverpossible),andchlorophyllconcentration(occasionally).Inadditionto consolidating the assimilation of such data, a broader list of parameters should beintegratedintomonitoringandforecastingsystemswithinthenext3-10years.


• Improved processing of Sentinel 1, 2, 3 data for coastal ocean products andapplications;

• Adaptation of existing quality-control methods to new observing systemcomponents,bothinsitu(forinstancenewbiogeochemicalparameters)andsatellite,withafocusonSentinelobservationsandfutureexploratorymissions(e.g.CFOSAT,SWOT); advancement and adoption of international calibration and quality-controlprocedures;

• Developmentsofadvancedprotocols for real-timequality checkingandautomatedflagging procedures that are more consistent with nowcast/forecast informationdeliveredbyMFCs;

• More consistent processing and assembly of data from different, heterogeneousobservation platforms and sensors for estimating derived quantities (e.g. sea-iceproducts, surface currents, mixed-layer depth, primary production, nutrients fromtemperature,salinityandO2verticalprofilesetc.);

• Incrementaldevelopmentofassemblycentrestoincludemoreobservationsrelevanttocoastalwatersbutalsoofinteresttothemonitoringofregionalseas.


• Exploitationof new technologies andplatforms tomore systematically observe pHandpCO2inoceanbasins;

• Adaptation of assembly centres procedures to take advantage of new sensors,communication technologies and unification of procedures, protocols, access anddownloadsystemsacrossassemblycentres.


4.10OBSERVINGSYSTEMS:IMPACTSTUDIESANDOPTIMALDESIGN

Expectedevolutions

Regarding future observing systems (from space or in situ), the best possible basis isrequiredinthedesignandexploitationofobservationnetworksandsatelliteconstellations.This goal can be ideally achieved through impact and design studies (Observing SystemExperiments – OSEs; Observing System Simulation Experiments - OSSEs) and dedicatedprocess experiments and pilots. OSEs and OSSEs represent a useful investment whenexpandinganexistingobservingsystem,defininganewone,orpreparingtheassimilationofnew data types, or data with improved resolution/accuracy. They are also required fordesignstudiestore-assessthesamplingofthepresentinsitunetworks,definetherequiredextensions,andpreparetheassimilationofnewinsitudatatypesconsideringthesynergieswith satellite observations. Such approaches enable a consistent approach to theassessmentoftheimpactofobservationdata,assimilationtechniquesandanalysis/forecastsystems,thedefinitionandoperationofspacemissions(upstreamanddownstream)andinsitunetworks.

The existing TAC andMFC capabilities provide an excellent opportunity to develop a newfunctionalitywithin theCMEMS, and conduct impact studies andobserving systemdesignbasedon rigorousandobjectivemethodologies.This functionalitywill consolidate the linkbetween CMEMS and the data providers by formalizing recommendations on futureobserving systems. In return, improving the design of future observing systemswill be ofgreatbenefittoCMEMSitselfgiventhelikelyimpactontheproductsquality.


• AdaptationoftoolsanddiagnosticmethodssuchasDegreesofFreedomSignal(DFS)and Forecast System Observation Impact (FSOI) to measure the impact ofobservationsintooceanmonitoringandforecastingsystems;

• Adaptation of assimilation interfaces (e.g., observation operators) to new satellitesensors:wide-swathaltimetry,oceancolour,SSTfromSentinels,surfacecurrents…;

• Developmentofmorerobustmethodologies(i.e.toensureresultsasindependentaspossiblefrommodelanderrorassumptions)andautomatictoolstoconductimpactstudies(includingtheOSEclass);

• Impact studies of new observation data types or products for ocean analyses,forecasts and reanalyses (e.g. SSS from space, Sea Ice thickness from Cryosat andSMOS,surfacecurrentdatasets,BGC-Argo,HFRadars,etc.);

• Networkstudiesthatwillprovideguidancetodeployments:thisincludesidentifyingcriticaloceanobservationpointsandregionswherethebenefittoforecastskillandoceananalysesandreanalysescanbemaximized.


• Designstudiestore-assessthesamplingofthepresent-dayinsituT/Snetwork(ArgoincludingBGC-Argo,ships,buoys,moorings);


• Guidance to the in situ observation communities on how to optimize observingstrategiesandthesynergieswithSentinelandfuturewide-swathaltimetrymissions(SWOT),complementingtheongoingAtlantOSeffort.

4.11ADVANCEDASSIMILATIONFORLARGE-DIMENSIONALSYSTEMS

Expectedevolutions

The operational suites implemented in the CMEMS MFCs essentially rely on traditionalassimilation methods inherited from the Kalman filter or the 3DVAR approach includingsimplifications and adaptations to make them tractable with large-dimensional systems.Today,onlytheArcticMFChasexplicitlyimplementedtheEnKFtopropagatethespreadofanensembleoflikelystatesbetweensuccessiveupdatingsteps,thoughseveralotherMFCsintend to implement ensemble approaches in the future (Global, Mediterranean, BalticMFCs,Figure1).

The current trend in oceanic and atmospheric data assimilation is a move towardprobabilisticmethods inherited fromtheBayesianestimation framework,as reflected inavariety of activities conducted in recent projects (e.g. FP7 SANGOMA), or reported inworkshops (e.g., GODAE Ocean View task teams) and conferences (WMO symposium onDataAssimilation).

At CMEMS level, it becomesurgent to take advantageof these developments in order to(i)further develop the assimilation methods to enable implementation into systemsinvolving different components (coupling with atmosphere, biology, biogeochemistry,cryosphere, etc.), (ii) improve the coupling between assimilative systems with differenttargetresolutionsanddifferentassimilationparameterizations, (iii) implementaneffectiveproductionofprobabilisticinformationasneededbyuserstosupportdecision-making,and(iv)improve the physical consistency of ocean state estimations, ensuring that the small-scaledynamicssimulatedbyoceanmodels is inbalancewiththe largerscalescapturedbytheobservingsystems.

In the NEMO framework (most MFCs systems are based on NEMO), a new assimilationcomponenthasbeen implemented in the referenceversion that includes severalmodules(observation operators, linear-tangent and adjoint of the circulation model, incrementalupdatingschemes)tofacilitatecouplingwithvariousassimilationsystems.Itisalsoplannedto include an ensemble simulation mode in future versions of NEMO. Other efforts onmodularsoftwaredevelopmenthavealsobeeninitiatedinEuropeaninstitutions,suchastheObject-OrientedPredictionSystem(OOPs)projectatECMWF.Aconvergencebetweentheseparallel efforts is expected in order to ensure an effective implementation of thesedevelopmentsintothelarge-scalesystemsbasedonNEMO.


• Metrics for ocean analysis/reanalysis and forecasting produced using ensembletechniques;


• Developmentandapplicationofadvancedassimilationmethods,includingtoidentifyandunderstandmodelbiasandforcingerrors,withafocusonoceanreanalyses;

• Adaptations of ensemble-based assimilation/forecasting systems, anddevelopmentofnewtechniquestoincreasetherangeandamountofobservationsassimilatedintoMFCs (e.g. ocean currents, ice thickness, ocean colour), making best use ofCopernicusSentinelmissions;

• CommondevelopmentsonassimilationmodulestoconnectmodelsandassimilationkernelssharedbetweendifferentMFCs;

• Extension of conventional assimilation schemes to include image data information(SST,OceanColour)inadditiontopixeldata;

• Efficientmethodstoaccountforcomplexobservationandmodellingerrorstructures(incl.non-Gaussianerrors)inassimilationalgorithms;

• Adaptation of analysis schemes to process different spatial and temporal scalesduringtheanalysisstage;

• Adaptation of analysis schemes to preserve the consistency of non-observedquantitiessuchasvorticity,diffusion,verticalvelocity,passivetracersetc.;

• Verification methods and intercomparison protocols (rank histograms, ContinuousRank Probability Score, …) suitable to assess the reliability of ensembles inprobabilisticassimilationsystems;

• Inverse methods that will incorporate simplified balance relationships (e.g.geostrophic and vorticity balance, omega equation) to ensure physically consistentproducts.


• Development of data assimilation tools for coupled models (ocean-atmosphereincludingwaves,physics-biogeochemistry,ocean-shelfmodels), includingconsistentcoupledinitializationapproachesandfluxcorrectionschemesatinterfaces;

• Developments of software infrastructure that can accommodate differentassimilation methods and facilitate the sharing of algorithms and optimization ofcomputercodes(models,assimilationschemes)onHPC.

4.12HIGH-LEVELDATAPRODUCTSANDBIGDATAPROCESSING

Expectedevolutions

CMEMSneedstoenrichitsofferwithimprovedaccessibilityandqualitycontrolleddata, inagreementwith scientific community standards. Users are also requesting advanced dataproductsthatshouldrelyonmorecomplexprocessingofmeasurementsofessentialoceanvariables at higher resolution, including the provision of composite products and theiruncertaintyestimates.Thosedataproductswillfeedmonitoringandforecastingsystemsorvalidate model outputs. The ESA Climate Change Initiative and the Copernicus Climate


Change Service (C3S), for instance, will deliver advanced products that will benefit theCMEMSreanalysesproduction.

The methodologies in place today for merging, filtering, compressing and interpolatingobservations (untilnowmostlybasedonstatistical interpolationmethods)willneed tobeadapted to face the challenges of big data streams and observing systems that areheterogeneousintermsofspace-timesampling,resolutionandaccuracy.Anadditionalissuetoaddress ishowtotakeadvantageofthehighqualitydelayedmodedatainordertogetimprovedreanalysesoftheoceanstateforassessingchangesandupdatingclimatologies.


• NewhighresolutionoceancolourproductsfromSentinel2andsynergywithSentinel3OLCIproducts;

• Development of advanced data products merging different type of observationsthroughmultivariateanalysis (e.g. forchlorophyll,surfacecurrents, icedriftand icethickness);

• Reprocessingofexistingdata setswithmoreadvancedquality controlmethods, tocontinuouslyimprovetheoceanreanalysiseffortincludingfordetectionofchange;

• Specific processing andmapping of data product uncertainties for both direct use,validationpurposes,anddataassimilation;

• Discriminationmethodsbetweenstericandnon-stericcomponents inobservedsealevelanomalysignals,bothfordataprocessingandassimilationintomodels;

• Preparationofcompositedataproductsandderivationofquantitiesthatcanbeusedtoinfertransportfromtracerinformation,orviceversa;

• New bathymetric data with very high resolution, and possibly information aboutchanges in ocean bottom, including impact assessment on ocean analyses andforecasts.


• Data mining and image processing techniques needed to facilitate the automaticextractionandanalysisofpatternsfrombigdatasets(producedbyobservingand/ormodellingsystems)andtobetterrespondtouser’sneeds.


5 THESERVICEEVOLUTIONROADMAP

FOR2021ANDBEYONDIn this section, the outline of the service framework anticipated in 2021 is describedtogetherwiththecompletedandongoingTier-3R&Dprojectstobetakenintoaccountforthe definition of priorities in future calls, and the main scheduling of service evolutionprojectsuntil theendof theCMEMS implementationperiod.TheCMEMS technical annexprovides the framework for the implementationof theCMEMS in termsof functionalitiesandoperationaltasks.

The12R&Dareasdevelopedintheprevioussectionshouldbecoveredinsuchawayastoensure that CMEMS can evolve towards the targeted service lines foreseen for 2021 andafter. While the 12 R&D areas are all relevant and essential at present to reach theobjectivesinitiallydefinedfortheCMEMS,prioritiesarere-adjustedperiodicallyaccordingtoupdates of user requirements, the status of scientific developments achieved within andoutsidetheCMEMScommunity,andtothehighlevelCMEMSevolutionstrategy.

5.1THETARGETEDCMEMSSERVICEINANDAFTER2021

The CMEMS is currently designed to deliver products to users in four main domains ofapplication:

i. marinesafety,whichrequiresinformationatveryhighresolutioninfocusedareasallover the globe to supportmarine operations,marine weather forecasting, sea iceforecasting, combating oil spill, informing ship routing and search and rescueoperations,andallactivitiesrequestingoffshoreoperations;

ii. marineresources,withapplicationstothesustainablemanagementoflivingmarineresources,throughfisheriesandaquaculture;

iii. coastalandmarineenvironment,withapplicationsthatrequiregoodknowledgeofenvironmental status in coastal areas in terms of their physical, chemical andbiologicalcomponentsandoftheirvariability;

iv. weather,seasonalforecastingandclimate,withneedsforaccurateinformationnearthesurfaceandsub-surfaceocean,onadailyorshortertimebasis,inrealtimeanddelayedmode.

WithrespecttotheexistingCMEMSofferandbasedonexistingandfutureuserneeds,thefollowingevolutionsoftheserviceintermsoftechnicalandscientificcontentofitsproductsareproposed:


• Stayingaworld-leadingreferencesourceofinformationforoperationalplayersinthemarine domain through improved product consistency (especially of reprocessedtime series and reanalyses), quality accuracy and assessment,multi-data approachwithscientificinter-calibrationandassessment;

• Improving the physical information content of CMEMS products by increasingresolutioninspaceandtimeofmodelsandobservations,addingtherepresentationof physical processes, going towards more coupled systems (ocean circulation –waves – sea ice – atmosphere – biogeochemistry – ecosystems) to produceconsistentestimatesofthefulloceanstate;

• Improving assimilation schemes, in particular for biogeochemical data, takingadvantageoftheconsiderableinvestmentinobservationinfrastructureandincreaseofdataflow,especiallythroughtheSentinelconstellationthatwillbeinplacein2021in tandem with the in-situ component; these techniques should also take intoaccountnewHPCarchitecturesandcomputingfacilitiesavailablein2021;

• Meetingthe“marinebiology”demandandreachthelevelofexcellenceCMEMShasnow for the “marine physics”. This also includes providing more information onbiogeochemistryandhigher trophic levels (fromplanktonto fish) tobettersupportfisheriesmanagement,livingresourcesprotection,sustainableexploitationofmarinebiomass;

• Enhancingoceanclimateandoceanhealthmonitoring(physicsandbiogeochemistryincluding O2 and pH), supported by the release of annual Ocean State Reports3,informed by annual analyses of the state of the global ocean and the Europeanregional seas, in particular for supporting theMember States in their assessmentobligation;

• Assessing past and future climate change impacts on the ocean environment:transform the high level CMEMS expertise on the ocean physical, biogeochemicalstates,oceanhealthandecosystems,intoastrongassessmentcapacityontheoceanclimate,proposeassessmentcapacityandwhat-ifscenarios,includingprojectionsoftheoceanstate(physical,biogeochemical,ecosystems)atregionalscalesfromweekstodecadesforclimatechangescenarios;

• Supporting coastal area monitoring by Member States through the provision ofsuitableinterfacesandboundaryconditionsbyCMEMSforefficientEUleverageforsupporting coastal environment knowledge and economy. Co-production andoperation between the CMEMS and the national coastal systems should beencouraged, with adaptations at the level of coastal seas benefiting from userexperienceandpractices(seealsosection6.5).

3Thefirst issueoftheOceanStateReport isavailablehere (doi:10.1080/1755876X.2016.1273446),andthesecond issue here (doi: 10.1080/1755876X.2018.1489208). See also section 6.1 for the CMEMS Multi-YearProductStrategyPlan.


TheseobjectiveswillhavetobecoveredbyCMEMSTier-2projects(seesection6)and3-6yearsR&DplansoftheTACs/MFCs.TheCMEMStechnicalannexidentifiesadditionalservicelinesmorespecificallymotivatedbyMemberStates’requirements.

The CMEMS service is operated today in an overarching framework that includes otherCopernicusservices4,suchastheAtmosphereservice(CAMS,ECMWF),Landservice(CLMS,EEA, JRC), Emergency service (EMS, JRC), Security service (EMSA, FRONTEX, SATCEN) andClimateChange service (C3S, ECMWF), aswell as a communityof newusers consolidatedthrough dedicated user uptake calls. This landscape will smoothly modify the profile ofcapabilities andusers and service needs, including newusers at the intersectionbetweendifferentCopernicusservices.However,existingR&DprojectsandserviceprioritiescurrentlylargelyalignwiththeMarineServiceoverarchingobjectives.

5.2TIER-3R&DMARINEPROJECTSOFINTERESTTOCMEMS

This section provides a status of Tier-3 (i.e., long-term objectives) R&D projects that areidentifiedin2018asrelevantfortheCMEMSserviceevolution.

Completedprojects

Anumberofprojects, including those implementedunder theSpace theme in2012,havebeencompleted:

• E-AIMS:improvedEuropeancontributiontotheinternationalArgoobservingsysteminsupporttoscientificandoperationalchallengesforinsitumonitoringoftheworldocean.

• MyWave:preparationofthescientificgroundsforawavecomponentofthemarineservice.

• OSS2015:consolidationofbiogeochemicalproducts,informationandservicesbasedonmodels,insituandsatelliteobservations.

• OPEC: improvement of operational services for biochemistry and ecologicalparametersattimescalesfromdaystodecades,withafocusonecologicalstatusofcoastalandshelfseas.

• SANGOMA: preparation of the new generation of stochastic data assimilationmethodsforoceanphysicalandbiologicalmodelapplications.

• HIGHROC:FP7project(2014-2017)toconductR&Dnecessaryforthenextgenerationofcoastalwaterproductsandservicesfromoceancolourspace-bornedatabygivinganorderofmagnitudeimprovementinbothtemporalandspatialresolution.

4http://www.copernicus.eu/main/services


OngoingandnewlydecidedEUprojects

• JERICO-NEXT: H2020 project (2015-2019) which aims at providing a solid andtransparent European network for operational services delivering high qualityenvironmental data and information products related to marine environment inEuropeancoastalseas;

• AtlantOS: H2020 project (2015-2019) which aims at optimising and enhancing theintegratedobservingsystems in theAtlanticOcean (northernandsouthernbasins),withafocusoninsituplatformsandconsideringEuropeanaswellasnon-Europeanprojects(TPOSinthetropicalPacific,andSOOSintheSouthernOcean)andpartners.

• IntarOS: a new H2020 project (2016-2020) to build up an integrated and unifiedholistic monitoring system for the different parts of the Arctic, including tools forintegrationofdatafromatmosphere,ocean,cryosphereandterrestrialsciences.

• ODYSSEA:H2020project (~2017-2021)downstreamofCMEMStodevelop,operateand demonstrate an interoperable and cost-effective platform that fully integratesnetworks of observing and forecasting systems across the Mediterranean basin,addressingboththeopenseaandthecoastalzone.

• SPICES: H2020 project (2015-2018) which aims at developing new methods toretrieve sea ice parameters from existing satellite sensors to provide enhancedproductsforpolaroperatorsandpredictionsystems.

• CEASELESS:H2020project(2016-2019)thatwilldemonstratehowthenewSentinelmeasurementscansupportthedevelopmentofacoastaldimensioninCopernicus.

• CoReSyf: H2020 project (2016-2018) that aims at supporting the development ofcoastalresearchthroughsatellitedatabycreatinganonlineplatformdeliveringEarthobservationdataforcoastalwatermonitoring.

• COASTOBS:H2020project(2018-2020)focusedondevelopingauser-relevantserviceplatformforcoastalwaterqualitymonitoringwithvalidatedproductsderived fromEarthObservation.

• MyCoast: INTERREG project (2017-2020) to enhance the capability of riskmanagement systems in the Atlantic region by improving co-operation between,observationalandforecastingsystems,andendusers.Thetechnicalnetworkingandspecific synergies will strengthen the use and the dissemination of downstreamapplicationsofCMEMSinordertoaddressthecommonchallengeofresilienceofthecoastalzonetorisk.

• KEPLER:H2020project(2019-2020)topreparearoadmapforCopernicustodeliveran improved European capacity for monitoring and forecasting thePolarRegions.

• IMMERSE: H2020 project (2019-2022) aiming at developing the next generationNEMO reference code with improved performances in the context of CMEMSsystems (includingdevelopments for improvedoceanstateestimatesand forecastsatkilometric scale,exploitationof thenextgenerationofhigh resolutionobservingnetworks,developmentofaflexibleandgenericsoftwaretoolsseriesforinterfacingCMEMSobservationandmodel-basedproducts).


Avarietyofprojectssupportingmainlythedevelopmentofdownstreamservicesarealsoofpotential interesttotheevolutionoftheCMEMSandshouldbemonitoredtosomeextentbyCMEMSexperts.

R&DgapsforfutureH2020calls

The guidance document published by the EC in 2015 (“Research needs of Copernicusoperational services”) identified 4 key actions needed to implement the CMEMSover theperiod of 2014-2020, taking into account evolving user needs and technologicaldevelopments.

These4keyactions(thatwerebasedonthefirstversionofthisroadmap)areassociatedtothefollowingscientificandtechnicalchallenges:

1. Solvingoceandynamicsatkilometricresolution,2. Designingfutureobservingsystemsandrelatedassimilationmethods,3. Developing seamless information chains linking dynamics, biogeochemistry and

ecosystemessentialvariables,4. SeamlessinteractionsbetweenCMEMSandcoastalmonitoringsystems.

ThetablebelowshowsthatanumberofH2020projectsrecentlyimplementedwilladdresssomeof thesechallenges (mainlyaction2and4),whileotheractions [(1)and(3)]arestillneededtomeettheobjectives.

1. Solvingoceandynamicsatkmresolution IMMERSE2. Designing future observing systems and

relatedassimilationmethodsAtlantOS, Jerico-NEXT, IntarOS, SPICES,CEASELESS

3. Developing seamless chains linkingdynamics, biogeochemistry andecosystem

4. Seamless interactions between CMEMSandcoastalmonitoringsystems

CEASELESS,IMMERSE,COASTOBS

5.3TIER-2R&DMARINEPROJECTSSELECTEDBYCMEMS

ThissectionprovidesanoverviewoftheTier-2(i.e.,medium-termobjectives)R&DprojectsthatwereselectedfortheCMEMSServiceEvolutioncall21-SE-CALL(2016-2018)andcall66-SE-CALL2(2018-2020).Themainoutcomesofthe12projectsofthe1stcall(21-SE-CALL)canbefoundonline5andinsection6.2.Themainoutlinesofthe18on-goingprojectsforthe2ndcall(66-SE-CALL2),includingprojectsparticipantsandabstracts,canbefoundonline4andinsection6.3.

5http://www.mercator-ocean.fr/en/mercator-ocean/copernicus/service-evolution/


ThetwoCMEMSR&DServiceEvolutioncallsincluded5lots:

• Oceancirculation,ocean-waveandocean-icecoupling:o 3 projects selected in call 1: proposals funded under this theme mainly

addressed sub-mesoscale processes (R&D area 4.2), ocean-wave (R&D area4.3)andocean-seaicecoupling(R&Darea4.4).

o 4projectsselectedincall2:selectedproposalsaddressforecastingoflargestwaves(R&Darea4.3),wave-ocean-seaicecoupling(R&Darea4.4,4.3),newgeneration of sea-icemodelling (4.4) and tides and numerical mixing (R&Darea4.1).

• Marineecosystemsandbiogeochemistry:o 3 projects selected in call 1: the funded projects covered several R&D

objectives identified under area 4.5, specificallymodelling of higher trophiclevelsinecosystemsanddataassimilationcapabilities.

o 4 projects selected in call 2: two projects address assimilation of opticalproperties in biogeochemical models, and two projects address modellingfromlowtohightrophiclevels(R&Darea4.5).

• Linkswithcoastalenvironment:o 2 projects selected in call 1: one project addressed upscaling issues under

R&D area 4.6, while the other focused on regional data assimilation andensembleforecasting.

o 4projectsselectedincall2:oneprojectfocusesonimprovedmeandynamictopographyinthecoastalzone,twoprojectsaddressriverdischargesandtheland-ocean boundary, and one project focuses on development of flexibleinterfacesbetweenregionalandcoastalmodels(R&Darea4.6).

• Ocean-Atmospherecouplingandclimate:o 1projectselectedincall1:theprojectfocusedonthemomentumexchange

across theocean-atmosphere interface in regionaloceanpredictionsystemsand thevalueofprovidingCMEMSproducts fromocean-wave systems thatarefullycoupledwithanatmosphericcomponentrelativetothoserunninginforcedmode(R&Darea4.7).

o 3 projects selected in call 2: proposals fundedunder this theme address (i)satelliteSSTassimilationwithaproperaccountofthediurnalcycle(R&Darea4.7), (ii) advanced ensemble based data-assimilation approaches for oceanreanalysesinatmosphericforcedandcoupledmode(R&Darea4.7),and(iii)regionalclimateprojections(R&Darea4.8).

• Cross-cutting developments on observation, assimilation, and product qualityimprovement:

o 3 project selected in call 1: funded proposals addressed (i) satellite SSTassimilationwithaproperaccountofthediurnalcycle,(ii)theintegrationofexistingEuropeanhigh-frequencyradaroperationalsystemsintotheCMEMS,and (iii) synergistic useof satellite and in situ data to infer the stateof theupperoceanandassociatedfluxoftracersandenergyatfinescale.


o 3 projects selected in call 2: proposals fundedunder this theme address (i)data-driven data assimilationmethods (R&D area 4.11, 4.12), (ii) advancedmethods for comparisonofobservations andmodels athigh-resolution, (iii)ensemble data-assimilation, stochastic forecasting and model-ensembleverification(R&Darea4.11).

5.4SERVICEEVOLUTIONROADMAPANDMILESTONESUNTIL2021

TheCMEMSR&Droadmapuntil2021willbeimplementedasillustratedbelow.Inadditionto programmatic aspects, a number of milestones relevant to the service evolution R&Dcomplemented the roadmap, e.g. the Copernicus Marine Week held in Brussels inSeptember2017.

AtTier-2level,the12CMEMSR&Dprojectsselectedinearly2016(from21-SE-CALL1)endedinFebruary2018,attheendofCMEMSPhase1,soastoprovideguidancetotheTier-1R&DworkplansthatwillhavetobedevelopedintheTACs/MFCstenderscoveringphase2(seesection 6.2. for the main outcomes of the projects). The second Tier-2 CMEMS call fortenders (66-SE-CALL2) will mainly impact the CMEMS after 2021: this will set a clearprogrammatic constraint on the definition of the long-term CMEMS strategy for the post2021period.Eighteen2-yrR&DprojectshavebeenselectedandstartedinApril2018(seesection6.3.foralistofselectedprojects).

AtTier-3level,ongoingH2020projectsareexpectedtodeliverresultsofinteresttoCMEMS,butnotbefore2018.TheEU-fundedprojects (e.g.undertheSpaceEO-3-2016,EO-3-2018)areexpectedtoprovideelementsforthefutureCMEMSperiodstartingin2021.


6 APPENDICES6.1SERVICEEVOLUTIONSTRATEGICDOCUMENTS

The CMEMS Service Evolution strategy is described in the CMEMS High-Level ServiceEvolutionStrategydocument1.

TwootherdocumentsprovidespecificdetailsontheCMEMSStrategiesforproductqualityassessmentandmonitoringandmulti-yearreprocessedandreanalysisproducts.

ThesedocumentshavebeenpreparedbyMercatorOceanwiththesupportoftheCMEMSSTAC.

6.2CMEMSSERVICEEVOLUTIONCALL21-SE-CALL1PROJECTS

Twelveprojectswerefundedunderthe1stServiceEvolutionR&Dcall.TheR&Dperformedin these projects are impacting and will impact CMEMS systems and products in thefollowingyearsthrough:

• TheadditionofnewproductsinCMEMScatalogueduring2018-2021:

Observations from European high-frequency radars will be delivered by CMEMS during2018-2021. High-frequency radars provide observations of ocean surface currents overcoastal areas with high spatial and temporal resolution that are needed for manyapplications related to ocean surface transports such as hydro-dynamical characterizationandsearchandrescueactivities.

Data on phytoplankton functional types derived from satellite surface chlorophyllobservations will be produced on near-real time at global scale. Observation-derivedphytoplankton functional types are of particular interest for the ecosystem modellingcommunity, including for assimilation in biogeochemical models and for their evaluation.They should contribute to improve the capability of operational systems to simulate thebiogeochemicaloceanstateandtoenhancethecapabilityofCMEMStomonitorindicatorsofthehealthoftheocean.

Finally,micronektonproductswill be distributedby CMEMS to better address themarineresources area of benefit. Micronekton is a prey for most large marine species that areeither exploited or protected. As such, it appears as a key ecosystem component atmid-trophicleveltounderstandandmodelthehabitatsandpopulationdynamicsofmostlargemarinespecies.

• An upgrade of CMEMS systems to produce the best possible ocean informationand/ortopreparethenextgenerationofoperationalsystems:


Advances from Service Evolution projects could lead to upgrades of CMEMS operationalmodellingsystemsthrough:

• Enhanced representation of coupling effects between ocean-wave-sea-ice-atmosphere components (including Stokes-Coriolis drift, surface wave inducedmixing, modified momentum exchanges, heat and freshwater exchanges, floeadvection by currents, floe lateral melting, sea ice drift due to wave loss ofmomentuminseaice).

• DirectupgradesofCMEMSoperationalforecastingsystemsthroughamorecompleterepresentationofdynamicalprocessesinoceanandwavemodels.

• Upgradeddataassimilationcapabilitiesofsatellitedaytimeseasurfacetemperatureobservations,ofsatellitederivedphytoplanktonfunctionaltypes,ofmulti-platformsbiogeochemical variablesdelivered fromsatelliteandBGC-Argo floats,andofhigh-resolutionobservations.

• Enhanced capabilities in regional ocean uncertainty quantification and modelensembleconsistencyverificationtomovetowardensembleassimilationcapabilities.

The corresponding gradual scientific and technical improvements of CMEMS integratedsystemswillcontributetodeliveroceanforecastsandreanalysesofincreasedaccuracyforabettermarineenvironmentmonitoring.

Inaddition,CMEMSproductshavebeenanalysedinseveralprojectsleadingto:

• Improved estimates of ocean surface currents through combined used of satellitealtimetryandseasurfacetemperatureobservations.

• Estimatesofnewderivedvariables(e.g.verticalvelocitiesintheupperoceanusingaquasi-geostrophy framework, frontogenetic terms) to bring more knowledge onoceandynamics.

• AbettercharacterizationofthemesoscaleeddyactivityintheMediterraneanSea.

Theprojectshavealsoprovidedabetterscientificunderstandingonoceandynamics,whichprovides insightsontheprocesses forwhichanenhancedrepresentation insystemscouldallowabetterrepresentationandmonitoringoftheoceanstate.

Asummaryofeachprojectoutcomesisprovidedbelow.

ARCTICMIX. IMPACT OF ADDITIONAL CONTRIBUTIONS TO THE VERTICAL MIXING FOR THESIMULATIONOFARCTICOCEANANDSEA-ICESTATES

PI:FabriceArdhuin(CNRS/LOPS).

Co-Is:CamilleLique(IFREMER),ClaudeTalandier(CNRS),MickaelAccensi(IFREMER),MarionHuchet(CNRS).

Abstract: The Arctic Ocean is experiencing some rapid transformations, including a fastshrinking of the sea-ice cover, with potential impact on global ocean and climate. Aseasonally ice-free Arctic would allow new prospects for socio-economic activities in theregion. Understanding and forecasting these changes thus requires an accurate model


simulationoftheArcticoceanandsea-icestates.Yet,mostArcticmodelsarelargelybiased,and verticalmixing has been identified as a key parameter controlling the realism of theoceanandsea-icestates.Here,weproposetoevaluatetheeffectoftheimplementationinahigh-resolutionmodel of different sources of vertical mixing (namely, the surface waves,tide,anddoublediffusivemixing)onthesimulationofoceanandsea-icestate.Thesurfacewavecontributionwillbeimplementingfollowingpreviousworkonthewavesimpactinice-free regions, and on-going studies on the impact of waves on sea-ice rheology in themarginalsea-icezone,andthecharacterizationofthepotentialfeedbackonthesea-icemeltviatheoceanmixing.AllmodeldevelopmentsandtheevaluationoftheimpactofdifferentmixingparameterizationsresultingfromthisprojectwillbedirectlyapplicabletotheglobalNEMOORCA12configurationusedbyCMEMS.

DIMUP. DIAGNOSE, INTERPRET, MONITOR UPPER OCEAN CIRCULATION: NOVEL DATASYNERGIESVIADYNAMICALEXPLORATION

PIandorganization:FabriceCollard(OceanDataLab).

Co-Is:LucileGaultier(OceanDataLab),ErwanHascoët(OceanDataLab).

Steeringcommittee:AurélienPonte(IFREMER),PatriceKlein(IFREMER),JoeLaCasce(Univ.Oslo),BertrandChapron(IFREMER),VladimirKudriavtsev(Univ.StPetersburg).

Abstract:Themainobjectivesofthisproposalaretodevelopandapplyinnovativemethodsthat infer, from satellite and in situ observations, the state of the upper ocean andassociatedhorizontalandverticalfluxesoftracersandenergy.Thesemethodsleverageanapriori knowledgeof thedynamics (embedding inaquasi-geostrophicmesoscaleeddy fieldtoppedbyaturbulentmixed-layer)whosevalidation,intermsofspatio-temporalscalesandbackground environmental conditions, represent the cornerstone of the proposal. ThisprojectdirectlyaddressestheR&Dpriority4.6(“Sub-mesoscale-mesoscaleinteractionsandprocesses”)oftheCMEMSServiceEvolutionStrategy,andwillcontributetotheR&Dpriority4.2(“Frombigdatastreamstohigh-leveldataproducts“)throughthedevelopmentofnewproducts and physical diagnostics. Several very high resolution basin scale numericalsimulations have recently suggested the strong dynamical impact of fine scale (below theRossby deformation radius) ocean processes. In this context, we propose to thoroughlyassess the role and observability of these fine scales by an approach combining remotesensing, realistichigh-resolutionmodellingand idealizeddynamicalmodels.Thiseffortwillprovide CMEMS with dedicated practical tools and improved products to better resolvethesescales.

GREENUP. GREEN MATRIX UPLOADED: A NEW ECOSYSTEM VARIABLE FOR MARINERESOURCESSECTOR

PIandorganization:PatrickLehodey(CLS).

Co-Is:PedroAfonso(IMAR),MarkPayne(DTU).


Abstract:TheobjectiveofthepresentprojectistoextenttheCMEMSproductscataloguebydevelopinganewproductcoveringakeyecosystemcomponentatthemid-trophiclevel,i.e.themicronekton,tobetteraddresstheMarineResourcesareaofbenefit.Indeed,providingbothrealtimeandhindcastsimulationofmicronektondistributionsproducedfromCMEMSphysical (temperature, currents)andbiogeochemical (primaryproduction,euphoticdepth)existing products would assist in the development of new management tools by variousresearch and fisheries organisations andwould pave the way for new approaches to themanagementandmonitoringofmarineresources.TheworkwillconsistfirstlyindevelopingandsetupthemicronektonmodelforthenorthAtlanticocean,toproducedemonstrationproductsthatwillthenbeusedwithintwoUseCases.

INCREASE. INNOVATION ANDNETWORKING FOR THE INTEGRATIONOF COASTAL RADARSINTOEUROPEANMARINESERVICES

PIandorganization:JulienMader(AZTI).

Co-Is:AnnaRubio(AZTI),AinhoaCaballero(AZTI),LuisFerrer(AZTI),AntonioNovellino(ETT),GiuseppeManzella(ETT),PaoloD’Angelo(ETT),MarcoAlba(ETT).

Abstract: The accurate monitoring of surface transport, which is inherently chaotic anddependsonthedetailsofthesurfacevelocityfieldatseveralscales,iskeyfortheeffectiveintegratedmanagementofcoastalareas,wheremanyhumanactivitiesconcentrate.Thishasbeen the driver for the growth of coastal observatories along the global ocean coasts.Amongthedifferentmeasuringsystems,coastalHighFrequencyRadar(HFR) is theuniquetechnologythatoffersthemeanstomapoceansurfacecurrentsoverwideareas(reachingdistancefromthecoastofover100km)withhighspatial(afewkmsorhigher)andtemporalresolution (hourly or higher). Consequently, the European HFR systems are playing anincreasingroleintheoveralloperationaloceanographymarineservices.TheirinclusionintoCMEMS is crucial to ensure the improved management of several related key issues asMarineSafety,MarineResources,CoastalandMarineEnvironment,Weather,ClimateandSeasonal Forecast. In this context, INCREASEwill set thenecessarydevelopments towardstheintegrationoftheexistingEuropeanHFRoperationalsystemsintotheCMEMS,followingfourmainobjectives: (i) ProvideHFRquality controlled real-time surface currents andkeyderivedproducts;(ii)Setthebasisforthemanagementofhistoricaldataandmethodologiesfor advanceddelayedmodequality-control techniques; (iii) Boost theuseofHFRdata forimproving CMEMS numerical modelling systems; and (iv) Enable an HFR EuropeanoperationalnodetoensurethelinkwithoperationalCMEMS.

MASSIMILI.DEVELOPMENTOFABIOGEOCHEMICALMULTI-DATAASSIMILATIONSCHEMETOINTEGRATEBIO-ARGODATAWITHOCEANCOLOURDATAINTOCMEMS-MFCs

PIandorganization:GianpieroCossarini(OGS).

Othermembersoftheproposingteam:StefanoSalon(OGS),HeloiseLavigne(OGS),LauraMariotti(OGS),FabrizioD’Ortenzio(UPMC-LOV),VincentTaillandier(UPMC-LOV),AlexandreMignot(UPMC-LOV).


Externalexperts:ChristelPinazo(MIO),ClémentFontana(Geoid-Ocean).

Abstract:InMASSIMILIweaimtodevelopamulti-dataassimilationframeworktointegrateBio-Argo data with ocean colour data into the biogeochemical modelling systems of theCMEMSMFCs.Wewill start by qualifying themulti-data andmultivariate biogeochemicaldatasets (Bio-Argoandoceancolour) tobeusedby theassimilationscheme.Thenwewilldevelop the numerical components of a new multi-data scheme which assimilateschlorophyll, nitrate and oxygen in the frame of the 3DVAR variational scheme alreadyimplemented in the biogeochemical component of the CMEMSMED-MFC. Finally,wewillassesstheimpactsofthenewmulti-dataassimilationschemetotheCMEMSbiogeochemicalproductsfollowingaprotocolofdifferenttests,byadoptingnewskillperformancemetricsspecifically developed in the project to validate themulti-data assimilation with selectedobservational datasets. The proposed activities of MASSIMILI will benefit the users byincreasingthequalityoftheCMEMSbiogeochemicalproductswithabetterquantificationofthemodelerrors.WewillalsocontributetothecapacitybuildingforCMEMSTACsandMFCsinvolvedinbiogeochemicalmulti-datauseandassimilation.Further,MASSIMILIwillprovidefeedback/suggestions on the efficiency/performances of the existing and future Bio-Argonetworks.

MEDSUB.UNDERSTANDINGMESOANDSUBMESOSCALEOCEANINTERACTIONSTOIMPROVEMEDITERRANEANCMEMSPRODUCTS

PIandorganization:SimonRuiz(CSIC/IMEDEA)

Co-Is:AnandaPascual(CSIC/IMEDEA),AntonioSanchez-Roman(CSIC/IMEDEA),EvanMason(CSIC/IMEDEA),JoaquinTintoré(SOCIB),MélanieJuza(SOCIB),BaptisteMourre(SOCIB).

Abstract: The objective of this project is to contribute to the improvement of CMEMSproducts based on new understanding of the fine-scale ocean circulation. To achieve theobjective, amulti-platform approach, combining in situ and satellite data in synergywithnumericalmodels,isproposed.Theunderlyingscientificgoalistoenhanceourknowledgeof2D/3D (including vertical exchanges) mesoscale and submesoscale processes and theirinteractionsatdifferentscales.TheprojectfocusesontheMediterraneanSea,characterizedby intensemeso and submesoscale features (e.g fronts,meanders, eddies and filaments),and where the team has a broad experience. Main actions of the project include (1) toanalyzeCMEMSMFCs and TACsproducts and (2) to assess and validateCMEMSproductsusinghigh-resolutionmulti-platformobservations(glider,drifters,shipCTD,ADCP,Argo,etc)fromrecentfieldexperiments.ParticularattentionwillbedevotedtoprovidingfeedbacktotheMFCs.ThebenefitforCMEMSwillbetheevaluationofmodelproductsfromMonitoringandForescastingCenters(MFCs)andinsituandsatelliteThematicAssemblyCenters(TACs),to assess their capacity to investigatemeso and submesocale processes. Finally, a set ofrecommendationswillemergetoimproveCMEMSproducts,withparticularemphasisonthe2D/3Doceancirculationatmesoandsubmesoscale.

OWAIRS.OCEAN-WAVE-ATMOSPHEREINTERACTIONSINREGIONALSEAS

PIandorganization:HuwLewis(MetOffice)


Othermembers of the proposing team: Chris Harris, Andy Saulter, Juan Castillo Sanchez,TamzinPalmer(MetOffice).

Abstract:Focusedresearchthroughocean,waveandatmosphericsciencedisciplineswillimprovetherepresentationofmomentumexchangeacrosstheocean-atmosphereinterfaceinregionaloceanpredictionsystems.Thisstudyobjectivesare:•ImprovetheconsistencyofoceanandwavepredictionsystemswithinCMEMSbetweenthosederivedfromeither“forced”,partiallyorfully“coupled”modes,•Examinethesensitivityofocean-wave-atmospherefeedbackstomodelphysicsandsystemresolutionchoices,•DemonstratethevalueofprovidingCMEMSproductsfromocean-wavesystemsthatarefullycoupledwithanatmosphericcomponentrelativetothoserunninginforcedmode.

ThisprojectwilldelivertechnicaldevelopmentstoNEMOinterfaceroutinesandcasestudyevaluations focused on the North-West Shelf. The research builds on unique modellingcapability and a fully self-consistent and flexible evaluation frameworkwithwhich to runtraceable experiments. Following thiswork the CMEMS communitywill have available anenhancedoceanmodelinfrastructureanddemonstrationofamorerobustmethodologyforassessing forced and fully coupled prediction systems. Improved understanding andevidenceoftheimpactsofclosercouplingwillbedelivered.Thesewillinformfuturesystemconfiguration choices. Developments will readily translate into CMEMS service evolutionacrossMFCs,improvingthequalityofinformationprovidedtousers.

SCRUM. STOCHASTIC COASTAL/REGIONAL UNCERTAINTY MODELLING:SENSITIVITY, CONSISTENCY AND POTENTIAL CONTRIBUTION TO CMEMS ENSEMBLE DATAASSIMILATION

PIandorganization:SarantisSofianos(Univ.ofAthens).Othermembersoftheproposingteam:VassiliosVervatis(Univ.ofAthens),PierreDeMey(CNRS/LEGOS),NadiaAyoub(CNRS/LEGOS).Externalexpertinvolved:GeorgeTriantafyllou(HCMR),Charles-EmmanuelTestut(MercatorOcean).Abstract:TheproposedworkaimsatstrengtheningCMEMSintheareasofregional/coastalocean uncertainty modelling, Ensemble consistency verification, and Ensemble dataassimilation. The work is based on stochastic modelling of ocean physics andbiogeochemistry, in the context of coastal/regional Ensemble Data Assimilation (EDA)forecasting systems, and includes novel Ensemble consistency verification methods. Thework is designed for the Service Evolution framework within “Lot 3: links with coastalenvironment”. In a first step, we will use stochastic modelling to generate Ensemblesdescribinguncertaintiesincoastal/regionaldomains.Inasecondstep,wewillintroduceandtest twomethodologies aimed at checking the consistency of the above Ensembles withrespecttoTACdataandarrays.Inaddition,wewishtoshowcasetheuseofthoseEnsemble-modelleduncertainties inapilotdataassimilationexercise, andcontribute to theCMEMS


DA team guidance in support of upcoming decisions regarding the evolution ofregional/coastaldataassimilationschemesinCMEMS.SOSSTA.STATISTICAL-DYNAMICALOBSERVATIONOPERATORFORSSTDATAASSIMILATION

PIandorganization:AndreaStorto(CMCC).

Co-Is: Gerasimos Korres (HCMR), Sam Pimentel (TWU), Nadia Pinardi (CMCC), IsabelleMirouze(CMCC),EricJansen(CMCC),FrancescaMacchia(CMCC).

Abstract: Advancing capability to effectively assimilate satellite observations of SST withproperaccountofdiurnalcycletogetherwiththermo-dynamicalcouplingwithnear-surfaceoceanstateishighlyrelevanttoincreasingaccuracyofoceananalysisandprediction.

To ensure benefits of advanced SST assimilation in the future for diverse operationalsystems,aswellastoenhancecapacitytomakeoptimalassessmentsof impactofcurrentandfuturesatelliteSSTobservationsinstepwithprogressintheoceansatelliteobserving,itis desirable to investigate more efficient approaches. The objective of this project is todevelop and test a new technique for producing dynamically-based but highly efficientstatisticalobservationoperatorsforassimilationofdailysatelliteSSTobservationsthatcouldbe easily implemented with any data assimilation technique. The technique consists ofapplicationofstatisticalmethodofcanonicalcorrelationanalysistojointdatafromaprocessmodelofnearsurfaceoceanthermo-dynamicsandthecorrespondingsatelliteretrievalsofSSTfromdifferentsensors.ThisapproachmaystreamlinetheprogresstowardimprovingtheSST-DA in different operational DA systems in CMEMS, enriching overall DA technologyavailableinCMEMSinsupportofimprovingthecapacitytoperformoptimalassessmentsofSSTobservationimpacts.

TOSCA.TOWARDSOPERATIONALSIZE-CLASSCHLOROPHYLLASSIMILATION

PIandorganization:StefanoCiavatta(PML).

Co-Is:RobertBrewin,SusanKay,BenjaminTaylor(PML).

Abstract: This project will evolve the capability of CMEMS to monitor and simulatebiogeochemical indicators of the health of European shelf-seas, by developing the firstsystem assimilating ocean colour data of size-class chlorophyll (SCC) into pre-operationalecosystem models. This project goes beyond state-of-the-art operational models, whichassimilate ocean colour data of total chlorophyll, and which are currently challenged bysimulating reliably the phytoplankton groups and their biogeochemical and ecologicalfeedbacks.ThenewassimilationsystemwillbeanupgradetotheoperationalmodelinuseattheCMEMSNorthWestShelf(NWS)MonitoringandForecastingCentre(MFC).Firstly,anew algorithm will produce time series of SCC in the NWS; secondly, an operationalassimilation system will be upgraded to assimilate SCC; thirdly, the new system will beapplied to produce a quality- assessed pre-operational reanalysis of biogeochemicalindicatorsintheNWS.WeexpecttheapproachtobeexportabletootherCMEMSMFCsandto support CMEMS in assessing the state of the ocean. Ultimately, by enhancing CMEMS


capability tomonitorand simulatebiogeochemical indicators, thisprojectwill evolveboththeabilityofend-userstoachievetheirrequirementsandmarinepolicytosafeguardmarineecosystemsthroughcutting-edgeCMEMSproducts.

UPSCALING. PROPAGATING INFORMATION BACK FROM COASTAL/REGIONAL MODELS TOCMEMS

PIandorganization:AlexanderBarth(Univ.ofLiège).

Co-Is:L.Vandenbulcke(Seamod/SCJailoo).

Abstract: Many high-resolution (regional and coastal) models use CMEMS forecast asboundary conditions. However, no feedback mechanism is presently implemented in theCMEMS-MFCstobenefitfromthehigh-resolutionforecasts.

In this proposal, part of the nested models forecasts are extracted, and assimilated aspseudo-observationsinthebasin-scalemodel.Theprocedureiscalledupscaling.Itischeap(dataassimilation isperformedanyway inthebasin-scalemodels),butverybeneficialasaform of nesting feedback is realized. From the basin-scaleMFC point-of-view, the nestedmodels can be interpreted as «measurement devices» complementing too-sparse (real)observations. TheMFCswill alsobenefit fromdata assimilationof local and/orultra-high-resolutionobservations(e.g.SST)bythenestedmodels.Byimprovingthebasin-scalemodel,intime,throughmoreconsistentopenseaboundaryconditions,thenestedmodelswillalsobe improved. In thisproject, theupscalingprocedurewill be triedoutusingNEMO in theMediterraneanSeaanditsNorth-Westernbasin.

WAVE2NEMO.COUPLEDOCEAN-WAVEMODELDEVELOPMENTINFORECASTENVIRONMENT

PIandorganization:JoannaStaneva(HZG).

Co-Is:OyvindBreivik(MetNo),LuigiCavaleri(CNR),VictorAlari(TUT).

Abstract:Thisproposaldealswithimprovingthecouplingbetweenoceancirculationmodelsystemsandwavemodelsinaforecastenvironment.Theproposedactivitywillfocusontheimplementation of a coupled system in the operational short-term forecasting frameworktakingadvantageofthedevelopmentsintheframeofMyWaveandMyOceanprojectsandsupported by regional operating forecasting systems. The proposal aims at furtherdevelopment of theNEMOoceanmodel and the forcingwhichwill explicitly include: theeffectofwavesfromwavemodelsontheupperoceandynamics;providingtotheCMEMSforecastservicessoftwareforadditionalparameterswhichhavetobeexchangedbetweenwaves and hydrodynamic models; improved validation methods by retrieved waveinformationfromsatellitedataandinsituplatforms;demonstratingtheinteractionofwavesandcurrentsatsmallscalesbothinthemiddleofoceansandneartheshoreline.MostoftheCMEMStargetfields–marinesafety,marineresources,marineenvironmentandforecasting–willdirectlybenefitfromtheproposedR&Dworkproposedhere.Thus,itisexpectedthattheproposalwillmakeconnectionstoCMEMSinordertosupportthefutureproductionof


more consistentocean-marineweather information includingalso surfacewaves, asoftenrequestedbyusers.

6.3CMEMSSERVICEEVOLUTIONCALL66-SE-CALL2PROJECTS

Eighteen2-yrprojectswereselectedfortheperiodApril2018-March2020.Theprojectstitle,PIandlinktotheabstractareprovidedbelow.

• [DIVOST-COM] DIURNAL VARIABILITY OF THEOCEAN SURFACE TEMPERATURE FORCOMMUNITYMODELLING.PI:I.Karagali(DTUWindEnergy).Co-Is:J.She(DMI),J.Hoyer(DMI),J.Murawski(DMI).Abstract.

• [HiVE]HIGH-RESOLUTIONMODELVERIFICATIONEVALUATION.PI:J.Maksymczuk&M.Mittermaier(UKMetOffice).Co-Is:R.Crocker,C.Pequignet,A.Ryan(UKMetOffice)Abstract.

• [SCRUM2]STOCHASTICCOASTAL/REGIONALUNCERTAINTYMODELLING(SCRUM)2:CONSISTENCY,RELIABILITY,PROBABILISTICFORECASTING,ANDCONTRIBUTIONTOCMEMSENSEMBLEDATAASSIMILATION.PI:S.Sarantis(Univ.ofAthens)Co-Is:V.Vervatis(Univ.ofAthens),M.Kailas(Univ.ofAthens),P.deMey(CNRS/LEGOS),N.Ayoub(CNRS/LEGOS).Abstract.

• [CLIMSEA]REGIONALLYDOWNSCALEDCLIMATEPROJECTIONSFORTHEBALTICANDNORTHSEAS.PI: H. Andersson(SMHI).Co-Is: M. Meier, C. Dieterich, M. Gröger (SMHI). Abstract.

• [GENOA]DEVELOPMENTOFANADVANCEDENSEMBLE-BASEDOCEANDATAASSIMILATIONAPPROACHFOROCEANANDCOUPLEDREANALYSES.PI:E.deBoisséson(ECMWF).Co-Is:H.Zuo,M.Balmaseda,P.deRosnay(ECMWF).Abstract.

• [LAMBDA]LAMBDAPROJECT(λ)–LAND-MARINEBOUNDARYDEVELOPMENTSANDANALYSIS.PI:R.Neves(MARETEC-IST).Co-Is:F.Campuzano,A.I.Oliveira,L.Simionesei(MARETEC-IST).F.Braunschweig,D.Brito(ActionModulers).A.Turiel,E.Olmedo(BEC-ICM).A.Novellino,G.Manzella,A.Paolo(ETT).Abstract.

• [BRONCO]BENEFITSOFDYNAMICALLYMODELLEDRIVERDISCHARGEINPUTFOROCEANANDCOUPLEDATMOSPHERE-LAND-OCEANSYSTEMS.PI:H.Zuo(ECMWF).


Co-Is:P.DeRosnay,F.Wetterhall,E.Zsoter,E.deBoisseson(ECMWF).Abstract.

• [CURAE]COASTALUNSTRUCTUREDRESOLUTIONFORAQUATICENVIRONMENTS.PI:A.Sanchez-Arcilla(UPC).Co-Is:M.Espino(UPC),M.Mestres(UPC),D.Gonzales-Marco(UPC),J.Schulz-Stellenfleth(HZG),Johannes-Pein(HZG)J.Staneva(HZG),L.BoyeHansen(DHI-GRAs),S.Huber(DHI-GRAs),M.OlanderRasmussen(DHI-GRAs).Abstract.

• [COMBAT]COMBINATIONOFALTIMETRYANDHFROBSERVATIONSFORCOASTALDATAASSIMILATION.PI:A.Caballero(AZTI)Co-Is:A.Rubio(AZTI),J.Mader(AZTI),L.Ferrer(AZTI),C.Dufau(CLS),M-H.Rio(CLS),G.Larnicol(CLS),N.Ayoub(LEGOS).Abstract.

• [OPTIMA]OPTICALDATAMODELLINGANDASSIMILATION.PI:S.Ciavatta(PML).Co-Is:R.Brewin(PML),J.Skakala(PML),B.Taylor(PML),T.Smyth(NERC/PML),J.Bruggeman(PML),D.Ford(UKMetOffice).Abstract.

• [BIOPTIMOD]INTEGRATIONOFNOVELOPTICALOBSERVATIONSINCMEMS–BGCMODELSTOIMPROVETHECMEMSBGCPRODUCTS.PI:P.Lazzari(OGS).Co-Is:G.Cossarini(OGS),S.Salon(OGS),C.Solidoro(OGS),F.D’Ortenzio(LOV),V.Vellucci(LOV).Abstract.[ZOOMBI]ZOOPLANKTONFORMULATIONSINMARINEBIOGEOCHEMICALMODELS.PI:U.Daewel(HZG).Co-Is:J.Schulz-Stellenfleth(HZG),C.Schrum(HZG),A.Samuelsen(NERSC),V.ÇağlarYumruktepe(NERSC).Abstract.

• [LMTL] MARINE ECOSYSTEM LOW TO MID TROPHIC LEVELS.PI:P.Lehodey(CLS)Co-Is:I.Senina,O.Titaud,P.Gaspar,B.Calmettes,M.Lalire(allCLS).Abstract.

• [WIZARD]WAVE-OCEAN-SEA-ICEINTERACTIONSINTHEARCTIC.PI:F.Ardhuin(LOPS,CNRS).Co-Is:C.Lique(LOPS,IFREMER),P.Rampal(NERSC),T.Williams(NERSC),L.Brodeau(OceanNext).Abstract.

• [RENUMERATE]REDUCINGNUMERICALMIXINGRESULTINGFROMAPPLYINGTIDESEXPLICITLYINAGLOBALOCEANMODEL.PI:A.Megann(NOC).Co-Is:M.Luneva(NOC).Abstract.


• [WAVEFLOW]CONSISTENTWAVE-MEANFLOWMODELLINGINCOUPLEDMODELS.PI:O.Breivik(METNo)Co-Is:J.Staneva(HZG),J-R.Bidlot(ECMWF),G.Nurser(NOCS)Abstract.

• [LATEMAR]LARGESTWAVESINMARINEENVIRONMENT:NEWPRODUCTSFORWAVEMODELFORECAST.PI:A.Benetazzo(CNR/ISMAR).Co-Is:L.Cavaleri,M.Sclavo,F.Barbariol,L.Bertotti,P.Pezzutto(CNR/ISMAR).Abstract.

• [3DA]DATA-DRIVENDATAASSIMILATION.PI:P.Tandéo(IMTAtlantique).Co-Is:RonanFablet(IMTAtlantique),StéphanieLeroux(OceanNext).Abstract.

6.4CMEMSSERVICEEVOLUTION:R&DCALLSFORGLOBALMFC

Callswereopenedin2016toanswerspecificneedsofthehigh-resolutionglobalforecastingsystemofCMEMS.Projectsareon-going.

CALL22-GLO-HRLot1:Globaloceanmodelling

The objective is to improve the global high-resolution configuration currently used in theGlobalMFC,whichisbasedontheNEMOcommunitymodelinaglobal1/12°configuration(e.g. parametrizations, numerical schemes, effective resolution). Another objective is topreparetheevolutionofCMEMSglobalforecastingsystemstowardsstochasticapproaches.

CALL22-GLO-HRLot2:High-Resolutionocean,waves,atmosphereinteractions

Theobjectiveistoimprovehigh-resolution(mesoscale)ocean/atmosphereinteractioninthehigh-resolutionglobalanalysisandforecastingsystemrunbytheGLOMFC,bycouplingtheocean model to an atmospheric boundary layer model. A further step is to couple thissystemwithaglobalwavemodel.

Call36-GLO-HRLot1:Assimilationofbiogeochemicaloceandataonaglobalscale

Theobjective istoassimilatebiogeochemicaldata,asapriorityfromsatelliteobservationsbut also from in situ data in the global MFC biogeochemical model (PISCES coupled toNEMO)usingastochasticapproach.

Call36-GLO-HRLot2:SeparationofscalesinOceandataassimilationattheglobalscale

The objective is to propose a data assimilation method which can be used to processdifferent spatial and temporal scalesduring theanalysis stage,whichare compatiblewiththeMercatorOcéanAssimilationSystem(SAM2)basedonthereducedorderSEEKfilter.

6.5REPORTFROMTHECOPERNICUSCOASTALWORKSHOP

EvolutionofCopernicusMarineandLandServicestobetterhandlethecoastalzone


Summary andmain outcomes of an expertworkshop held onDecember 13& 14, 2016 inBrussels

6.5.1ContextandworkshopobjectivesTheworkshopwasorganizedbyEEAandMercatorOceanasafirststeptoanalysemid-term(2018-2020)andlongterm(post2021)prioritiesfortheevolutionoftheCopernicusMarine(CMEMS)andLand(CLMS)servicetobetteraddressuserneedsinthecoastalzone.Theworkshopwasafirststeptoaddressthecomplexissueofusers/servicesinthecoastalareasandinterfacesbetweencore/Europeananddownstreamservices.SeveralMarineandLand Copernicus service coastal experts, representatives of coastal monitoring systems,representativesoftheprivatesectorandPIsofEuropeanprojectsrelatedtocoastalservicesparticipatedtotheworkshop.

6.5.2MainoutcomesandrecommendationsImproving themonitoring and forecasting of coastal zones building onCopernicusMarineand Land services and making the best use of existing and future Copernicus satelliteobservations is both needed and feasible. This requires both a significant evolution ofCopernicusMarineandLandcoreEuropeanservicesanda strengtheningof the linkswithdownstreamcoastalmonitoringactivitiesorganizedbothinthepublic(memberstates)andprivatesectors.Theworkshopidentifiedseveralimportantgapsorareasofimprovements.Aseriesofshort-term/mid-termactionsandrecommendationswereidentified.Theytakeintoaccount the necessary delineation between core and downstream activities. The mainrecommendationscanbesummarizedasfollows:

• Better processing (i.e. specific algorithms) of satellite observations is required toimprove the quality of coastal products. This could be organized as part of theevolution of CLMS and CMEMS from 2018 onwards. This applies, in particular, forCMEMS to ocean colour products in the coastal zone (Sentinel 3 and Sentinel 2,MSG/SEVERI).

• New satellite products to better characterize the state of the coastal zone (e.g.coastline,coastalerosion)anditsevolution(e.g.onseasonaltoannualbasis)shouldbeproposed.

• Improved Digital Elevation Models (DEM) and Bathymetries are basic corerequirementsforthecoastalzone.

• Newsatellitedata/productsarealsorequiredfortheRegionalSeasConventions(e.g.BarcelonaConvention/Mediterranean).Manyneighbourhoodpolicies,theMSFDandMSPDirectivesrelyontheavailabilityofsuchdata.

• BasedonananalysisofexistingcapabilitiesandrequirementsfromotherCopernicusservices(inadditionofCMEMSandCLMS)(e.g.emergency,C3S),theneedtoextendthemarineandlandserviceactivitiestothemonitoringandforecastingofallmajorEU rivers (river outflows, nutrient and sediment loading) should be analysed. Byextensionotherlinkagesbetweenlandrelatedandsearelatedparametersshouldbeexplored.

• Similarly, themonitoring, short and long-term prediction of sea level close to thecoastsshouldbeimproved.Thisisrequiredforawiderangeofapplications(flooding,coastalerosion,coastalzonemanagement).

• Stronger interfaces between CMEMS and downstream coastal marine monitoringsystemsshouldbedeveloped.Thisincludesharmonizationandstandardizationissues


(e.g.formats,qualityassessmentmethods,documentation,datadistribution)andofthe use of consistent and improved bathymetry, atmospheric forcing and riverinputs.

• Pilotstudiesshouldbeconductedforrepresentativesub-regional/localcoastalareasspanningtherelevantfields(physical,biological)ofthecoastalmarineenvironment(cfCMEMSUserUptakeactivities).

• Links/interfaces with EMODnet portals and activities (e.g. bathymetry, seabedhabitats,chemistry)shouldbereinforced.

• Products from national/member states coastalmonitoring systems could bemadeavailablethroughCMEMS,CLMSorCopernicus(e.g.DIAS)dataportals(coproductionEU&MemberStates).

• Copernicuscouldincludeinthelongerruncore/common/genericcoastalactivitiestosupport the development of coastal downstream services and strong operationalinterfaceswith theCopernicusMarineandLandservices (e.g. short-term/mid-termR&D on modelling, data assimilation and coupling, satellite observations andproductsfromSentinels,insitudatagatheringandprocessing).

• Capacitybuildinganddevelopmentofbestpracticesarealsoveryimportantforthecoastalzonemonitoringactivitiesdevelopedbymemberstatesandlocalauthorities.ThisisanareawhereaEuropeanapproachwouldbebeneficial.

Documents

Copernicus Marine Environment Monitoring Service (CMEMS ...€¦ · CMEMS SERVICE EVOLUTION STRATEGY: R&D PRIORITIES June 2017 1 1 1 INTRODUCTION The Copernicus Marine Environment