Ocean and seaice data assimilation and forecasting in the TOPAZ system

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Ocean and sea-ice data assimilation and
forecasting in the TOPAZ system

• L. Bertino, K.A. Lisæter, S. Sandven
• And the Mohn-Sverdrup Center at NERSC

Arctic ROOS meeting
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(No Transcript)
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Motivation

• Objective
• Provide short-term (10 days) forecasts of
  physical and biogeochemical ocean parameters to
  the public at large and intermediate users.
• Strategy
• Focus on advanced data assimilation techniques
• Gradual increase of resolution (as affordable)
• Nesting on regions of higher interest

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Method

• The ice-ocean system has two sources of
  information
• A nonlinear ice-ocean model
• A regular flow of observations
• Uncertainties arise primarily from
• The initial state
• Surface boundary conditions
• Measurements errors
• Monte Carlo methods can handle non-linear
  dynamics.
• Provide the best estimate
• Provide the residual uncertainty
• Each source of uncertainty must be simulated
  realistically.

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Sequential data assimilationRecursive Monte
Carlo method
Forecast
Analysis
Member1
Member2
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Member99

• Initial uncertainty
• Model uncertainty
• Measurement uncertainty

Member100
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Observations
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The TOPAZ model system

• TOPAZ Atlantic and Arctic
• HYCOM
• EVP ice model coupled
• 11- 16 km resolution
• 22 hybrid layers
• EnKF
• 100 members
• Sea Level Anomalies (CLS)
• Sea Surface Temperatures
• Sea Ice Concentrations (SSM/I)
• Sea ice drift (CERSAT)
• Runs weekly since Jan 2003
• ECMWF forcing

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Ice concentration multivariate updatesummer
K.A. Lisæter et al. 2003
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Error estimates

• Examples

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Error estimates ice thickness (no observations)
Ensemble average 13th March 2007
Ensemble standard dev. 13th March 2007
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Ensemble VariancesTemporal evolution (variance
of ice concentrations)

• 1st March 2006

• 13th Sept 2006

http//topaz.nersc.no
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System Validation

• Consistency?
• Accuracy?
• Performance?

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Validation against hydrographic data
June07
Sept07
Topaz2
Topaz3
IMR
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Sparse profiles under iceNPEO deployment 2006
--- TOPAZ NPEO
North Pole Environment Observatory
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TOPAZ against Coriolis data North Atlantic Aug.
2006
F. Høydalsvik

• Temperature (100m)
• Control run
• Assimilation run

• Salinity (upper 100m)
• Control run
• Assimilation run

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Sea-ice fluxes Fram Straits
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Water fluxes
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Live Access Server

• Data service
• NetCDF Files
• OPeNDAP / THREDDS

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Assimilation every week Up to 10 days forecast
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Forecast skills Example of ice concentrations,
8th November 2006
Barents Sea
Greenland Sea
Laptev Sea
Bering Strait
Kara Sea
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Conclusions

• The EnKF HYCOM combination shows some skills in
• Accuracy
• Consistency
• Performance
• The numerical data are served freely on
  http//topaz.nersc.no (OPeNDAP server)
• Interpolated 3D fields, polar-stereographic grid
• Sections and time series (ice volume transport )
• Any feedback is welcome

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Ocean ecosystemTOPAZ-ECO

• Problem
• Coupled 3-dimensional physical-biological model
• Slow model, many tracers
• Non-Gaussian variables
• Data
• Satellite, ocean colour
• Little in-situ data available
• Utility
• Methodological developments
• Perspective of high-resolution assimilative
  models
• Environment monitoring
• Fisheries

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Plans

• Assimilation of Argo data
• Inclusion of ecosystem model
• NORWECOM, IMR
• RT exploitation of TOPAZ at met.no
• Operational since March 2008.
• 20-years reanalysis (as part of FP7 MyOcean)

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Open positions

• Post-doc (sea-ice modeler) modelling of the
  Marginal Ice Zone (MIZ)
• Develop / test a new sea-ice rheology
• Validation in a realistic model of the Fram
  Strait against observations
• Duration 18 months, funding by TOTAL EP
• Application deadline 1st December 2008
• Contact laurent.bertino_at_nersc.no and
  johnny.johannessen_at_nersc.no