Key Future Research Priorities in Ocean Forecasting

1. Key Future Research Priorities in Ocean Forecasting Andreas Schiller, Pierre Brasseur, Pierre De Mey, Roger Proctor, Jacques Verron GODAE Final Symposium, 12 – 15 November 2008, Nice, France

2. Provision of appropriate observations (remote sensing and in-situ) + Use of appropriate operational analysis/model/forecast systems = Services delivery system (applications) Ocean Forecasting System: Components Monitoring Networks Satellites Ships Buoys etc. Forecast Model End User Application Assimilation

3. Three big Research Challenges and Opportunities: Progress in Ocean Modelling New Research Directions in Data Assimilation Enhanced and new Observing Systems Outlook Outline of Talk

4. Progress in Ocean Modelling: Basin-Scale • Areas for further improvement: - vertical mixing (mixed-layer, thermocline, deep ocean) - vertical velocity ( biogeochemical cycles) • Advanced numerical schemes improve eddy-topography interactions: NEMO (¼)° (Barnier et al., 2006) Mean Eddy Kinetic Energy

5. Progress in Ocean Modelling: Regional and Coastal • Rich dynamics (upwelling, tides, • bathymetry, strong gradients etc.) • various couplings (lateral BCs, • ocean-atm., sediments) Challenges: • Improved understanding of shelf edge and slope processes • Appropriate use of non-hydrostatic codes to resolve critical mixing processes • Coupling with hydrological models at the land boundary (complete water cycle) • Links: coastal monitoring and assimilation • Biogeochemical &Ecosystem Modelling less mature • than phys. modelling;coupling of BGC with Ecosystem Modelling Two-way coupling (1/3)° NEMO (1/15)° NEMO (Cailleau et al., 2008)

6. Data Assimilation: Concepts & Errors • Operational oceanography largely applies sequential • approaches but variational approaches are at verge of • being used (reanalyses and forecasting) • However: still unclear whether 4D-VAR is fully applicable • to ocean (e.g. meso-scale nonlinearities vs. linearity in • tangent linear models) •  Hybridisation an alternative (e.g. Robert et al., 2006)? • Ensemble methods, multivariate EOFs, phys.-based • coordinate transformations applied to • Background State Errors: need to know estimates of observation errors and source of model errors • Observations Errors: measurement and representation error • Uncertainty: need for a posteriori estimates

7. Salinity from space (SMOS, Aquarius) Satellite ocean colour data to constrain physical and bio- geochemical ocean properties in a consistent manner Lagrangian features of some instruments (often seen today as Eulerian), e.g. ARGO and gliders Enhanced applications of altimetry to shelf seas and/or high resolution (cases of SARAL/AltiKa and SWOT) Data Assimilation: New Instruments

8. Data Assimilation: Boundary Conditions DA powerful tool for guiding parameter estimation/error control: design of parameterizations and reducing wind & flux uncertainties. Example of reduced SST errors: Bias Std Deviation Control Run Run with opt. atmosph. forcing Skandrani et al., 2008

9. Data Assimilation: Biogeochemical • Data Assimilation in bgc/ecosystem models immature: • many variables, parameters, structure of ecosystem • models (functional groups) • Need for fully non-linear data assimilation methods Example: Towards Ocean Colour Assimilation (Brasseur et al.) Need to better understand the (non-linear) transfer functions between error sources and their signature on observations, and associated (non-linear) correlations between state variables of the coupled models Ensemble runs (200 members) with perturbed wind forcings: std deviation of surface phytoplankton after 15 days

10. Data Assimilation: Coastal Ocean • Complex physics & range of scales of variability, open system • Many data types potentially available for assimilation, some of • them with uncertain representation (error) • Complex statistics (e.g. • non-Gaussian) • Which larger-scale model • information can be used and how? • Coupled coastal-deep ocean • models and unstructured grid • models • Tides and DA • Limits to predictability & skill assessment: • need for increased-range and higher-resolution NWP forcing SLA Corr. T200 Corr. (Oke et al., 2005, 2008)

11. Observing Systems:New Types of Observations Liverpool Bay Coastal Observatory (Irish Sea) 2009: SMOS Sea-Surface Salinity • Coastal Observatories: • New observational data [e.g. tides, waves, river • flows, temperature, sediment, ecology] • Extending over longer periods  modelling accuracy • Requires comprehensive and expensive • operational observing platforms

12. Observing Systems:Biogeochemical and Ecosystem OOS • Incomplete bgc and ecosystem observing network • Issue: accuracy of obs., • e.g. Chl: 30% or worse • currently limited • forecasting capability PAR SST Chl-a TPP Robinson et al., 2008

13. Observing Systems:Observing System Design • OSEs and OSSEs: assessing existing and planning new observing systems • Issue: model-dependent multi-model ensembles? • Definition of common metrics for • Optimisation • (global/coastal) • External (for users) • Adaptive sampling Argo Adapted from Prandle et al., 2005

14. Outlook: Coupled Ocean-Atmosphere Systems • Coupled initialisation of ocean-atmosphere systems • Treating complex physical (and biogeochemical) • components as one system • Problem is very complex due to difference in scales • between ocean, atmosphere, sea-ice, (bgc) • Key theoretical and practical challenge: development of • associated data assimilation techniques for coupled • systems (en route to Earth system modelling)

15. Outlook • Other Key Challenges: • Verification of (re-)analysis and forecasts on all spatial and temporal scales (error estimates, uncertainty) • Continued convergence and consolidation of models internationally: community modelling efforts • Earth systems modelling (seamless: NWP to climate): • physics-chemistry-biology(ecology)-socio-economic?

16. Outlook Progress in science of ocean forecasting relies on • Access to advanced technology (supercomputers, • services for data management, visualisation, analysis) • Advances in global and coastal • observing networks, • telecommunication • Global ocean data centres and • coastal observatories (QC’d obs) • Education: integrated and multi-disciplinary approaches demand state-of-the-art science leadership; maintain and improve links between academic research and operational agencies

17. In a Nutshell… Coupled Initialisation (now)  Earth System Modelling (future, seamless) Ongoing/future: shelf seas & coastal, physics Ongoing: Intercomparison & Validation Ongoing: global, physics Ongoing/future: biogeochemistry (ecosystems) Ongoing: OSEs & OSSEs Systems (obs, models, assimilation)

18. Innovation is a Living Process: Opportunities and Challenges for Operational Oceanography and for the GODAE OceanView Science Team!

19. MERCI & THANK YOU! Innovation is a Living Process: Opportunities and Challenges for Operational Oceanography and for the GODAE OceanView Science Team!