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

Ocean Forecasting System: Components

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Provision of appropriate observations (remote sensing and in-situ) +

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Use of appropriate operational analysis/model/forecast systems =

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Services delivery system ( applications ) Monitoring Networks Satellites Ships Buoys etc.

Assimilation Forecast Model Application End User

Outline of Talk Three big Research Challenges and Opportunities:

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Progress in Ocean Modelling

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New Research Directions in Data Assimilation

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Enhanced and new Observing Systems

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Outlook

Progress in Ocean Modelling: Basin-Scale

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

Progress in Ocean Modelling: Regional and Coastal

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

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 o Background State Errors : need to know estimates of observation errors and source of model errors o Observations Errors : measurement and representation error o Uncertainty : need for a posteriori estimates

Data Assimilation: New Instruments

 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: 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

Skandrani et al., 2008

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

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 non-Gaussian) (e.g.  Which larger-scale model information

SLA Corr.

can be used and how? Coupled coastal-deep ocean models and unstructured grid models  Tides and DA

T200 Corr.

 Limits to predictability & skill assessment: (Oke et al., 2005, 2008) need for increased-range and higher-resolution NWP forcing

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

Observing Systems: Biogeochemical and Ecosystem OOS SST

 Incomplete bgc and ecosystem observing network  Issue: accuracy of obs., e.g. Chl: 30% or worse  currently limited forecasting capability

PAR Chl-a TPP

Robinson et al., 2008

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 Adapted from Prandle et al., 2005

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)

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?

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

In a Nutshell…

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

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

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

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