Ocean and sea-ice data assimilation and forecasting in the TOPAZ system L. Bertino, K.A. Lisæter, I. Kegouche, S. Sandven NERSC, Bergen, Norway Arctic ROOS meeting, 18 th Dec. 2007

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

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.

Sequential data assimilation Recursive Monte Carlo method ForecastAnalysis Observations 1.Initial uncertainty 2.Model uncertainty 3.Measurement uncertainty Member1 Member2 …… Member99 Member100

The TOPAZ model system  TOPAZ: Atlantic and Arctic  HYCOM  EVP ice model coupled  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 atmos. forcing

Model upgrade Doubling the horizontal resolution TOPAZ2: 18 to 36 km TOPAZ3: 11 to 16 km TOPAZ2 TOPAZ3

System Validation Consistency? Accuracy? Performance?

Consistency: Against Climatology TOPAZ2TOPAZ3 Temperature anomalies at 30 m depths

Accuracy: against ice concentrations TOPAZ2TOPAZ3 Model minus obs.

Accuracy Against in-situ profiles from NPEO Aerial CTD casts TemperatureSalinity

Assimilation on 4 th and 11 th April Up to +10 days forecast

Forecast skills: Barents Sea - ice concentrations Average Winter 2007Average Summer 2007

Ice drift validation  In-situ  Ice drifting buoys (Statoil/CMR)  Manned expeditions  Remote sensing  ASAR (NERSC) WP2  QuickSCAT (Ifremer)  Modelling  TOPAZ V1, class 1  A good agreement [ J. Wåhlin]

Historical minimum Arctic sea-ice area, summer 2007 Observed sea-ice from SSM/I, NORSEX algorithm

Forecasting the ice minimum in TOPAZ Overlay of successive forecasts TOPAZ catches the freeze-up

Products Standards Delivery Timeliness

What products?  MERSEA products  Class 1:  3D daily fields  ocean and sea-ice  Anomalies to climatolgy  Class 2:  Predefined sections  Predefined moorings  Class 3:  Volume fluxes through sections  Salt and heat transports  Class 4:  Differences with observations,  Forecast skills  Other products (targeted)  Ensemble uncertainties,  Predicted drift  Icebergs

Class 2 metrics  Sections stored daily Moorings stored daily

Uncertainty estimates example sea-ice thickness Ensemble average 13 th March 2007 Ensemble standard dev. 13 th March 2007

Forecasting the drift of Tara  TOPAZ successive forecasts in red  Actual positions of Tara from DAMOCLES in black  Updated on Google Earth [ K. A. Lisæter]

8m draft Iceberg simulations  An iceberg is sensitive to  Winds  Waves  Currents  Ice drift  Ice thickness  Iceberg shape  Tides  Melting  … 13m draft 18m draft [ I, Keghouche, NERSC ]

Availability  Forecast updated every Thursday  10 days forecast horizon  Available freely via  Webpage (static pictures)  OPeNDAP (data)  No password required  But feedback is welcome  Available to date  TOPAZ2: October 2005 to October 2007  TOPAZ3: July 2007 to present

Plans  Ongoing projects (MERSEA, BOSS4GMES)  Assimilation of additional data (Argo)  Inclusion of ecosystem model  NORWECOM from IMR, Bergen.  RT exploitation of TOPAZ at met.no  Developments of TOPAZ at NERSC  Exploitation at met.no (ongoing)  Planned project MyOcean ( )  30-years reanalysis