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, 18th 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 assimilationRecursive Monte Carlo method

Forecast Analysis

Observations

1. Initial uncertainty

2. Model uncertainty

3. Measurement uncertainty

12

3

Member1

Member2

……

Member99

Member100

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 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

TOPAZ2 TOPAZ3

Temperature anomalies at 30 m depths

Accuracy: against ice concentrations

TOPAZ2 TOPAZ3

Model minus obs.

AccuracyAgainst in-situ profiles from NPEO

Aerial CTD casts

Temperature Salinity

Assimilation on 4th and 11th April

Up to +10 days forecast

Forecast skills: Barents Sea - ice concentrations

Average Winter 2007 Average 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

2007Observed sea-ice from SSM/I, NORSEX algorithm

Forecasting the ice minimum in TOPAZ

• Overlay of successive forecasts

• TOPAZ catches the freeze-up

Products

StandardsDelivery

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 estimatesexample sea-ice thickness

Ensemble average

13th March 2007

Ensemble standard dev.

13th 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 http://topaz.nersc.no (static pictures) OPeNDAP http://topaz.nersc.no/thredds (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 (2008-2011) 30-years reanalysis