TOPAZ evaluation L. Bertino, F. Counillon, P. Sakov Mohn-Sverdrup Center/NERSC GODAE workshop, Toulouse, June 2009

TOPAZ System overview System description Validation of TOPAZ Data Assimilation

Uncertainty estimates Uncertainty estimates Hindcast studies Atmospheric Data Atmospheric Data Satellite Data SLA, SST, Ice, In Situ Data Analyze the ocean circulation, sea-ice and biogeochemistry. Provide real-time forecasts to the general public and industrial users EnKF Data assimilation system User-targeted ocean forecasting User-targeted ocean forecasting Ocean Primary production Ocean Primary production Gulf of Mexico model Atlantic and Arctic model Sea-Ice model Eco- system model

The TOPAZ model system  TOPAZ3: Atlantic and Arctic  HYCOM + EVP sea-ice model  km horizontal resolution  22 hybrid layers  EnKF  100 members  Observations  Sea Level Anomalies (CLS)  Sea Surface Temperatures (NOAA)  Sea Ice Concentr. (AMSR, NSIDC)  Sea ice drift (CERSAT)  Argo T/S profiles (Coriolis)  Runs weekly, 10 days forecasts  ECMWF forcing   (MERSEA…)

EnKF Correlations 3 rd Jan th Nov 2006

The HYCOM model  3D numerical ocean model  Hybrid Coordinate Ocean model, HYCOM (U. Miami)  US Navy global forecasts  Hybrid coordinate  Isopycnal in the interior  Z-coordinate at the surface  Terrain following (sigma)  Nesting capability  Coupled  Sea-ice model  Ecosystem models  Large community (

Nesting  Bring dynamically consistent information from large-scale circulation to coastal seas  One-way nesting  “Flather” condition for barotropic mode  Avoids reflection of waves at the boundary  Simple relaxation for the baroclinic mode  And for the tracers  Arbitrary resolution and orientation of the nested grids

Effect of the upgrade Weekly SSS in Dec. 1999, free run TOPAZ3TOPAZ4 MICOM BCM

TOPAZ System overview System description Validation Data Assimilation

3 Validation criteria cf weather forecasting (Murphy, 93)  Consistency  Are the operational forecasts in agreement with known processes of the ocean circulation?  Accuracy  How close to reality are the results?  Performance (value)  Advantage over any trivial forecast?  climatology, persistence

Validation Metrics  Problems:  Validating and comparing GODAE systems consistently  Different model horizontal grids / Vertical coordinates  Large amounts of 4D data  Large data transfers  Solutions adopted (during Mersea Strand 1, )  4 Classes of output products (3D, 2D, time series, residuals)  Common output grids (1/8th deg, projection...)  Self-documented file format (NetCDF)  Inter-operable file access (OPeNDAP/THREDDS)

Arctic Metrics

Validation against hydrographic data Topaz2Topaz3IMR June07 Sept07

Online comparison to Argo profiles

Sparse profiles under ice NPEO deployment TOPAZ — NPEO *: North Pole Environment Observatory

Water fluxes

Sea-ice edge Visual comparison  Ice concentration from model in color, SSMI 15% ice contour in black. Ice drift is overlaid.  Good overall correspondence between model and data  Visual comparison allows identification of problematic regions  West of Novaya Zemlya - a tendency for the ice edge to drift too little to the north during a forecast cycle  South of Svalbard (Bear Island) model ice edge too far to the north  Issues related to model physics  Ice-ocean momentum exchange  Ice models neglect physics which may be important on small scales  Fast ice  MIZ

Forecast skills by region Alaska Barents Sea Bering Strait Central Arctic Greenland Sea Kara Sea

SLA assimilation residuals Azores box

MERSEA sections updated  Blue: MERSEA Class2 sections  Red: Sections from IMR

TOPAZ System overview System description Validation Data Assimilation

Assimilation of Ocean Color in HYCOM-NORWECOM Data: Satellite Ocean Color (SeaWIFS) Coupled Model: HYCOM-NORWECOM (7 compartments) Problems: Coupled 3-dimensional physical-biological model. High-dimension. Non-Gaussian variables. Perspectives: Environment monitoring. Fisheries. Methodological developments for future coastal HR systems.

Gaussian anamorphosis with the EnKF Simon & Bertino (OSD, 2009) Anamorphosis: prior transformation of the variables in a Gaussian space (Bertino et al. 2003) Twin experiments (surface chlorophyll-a synthetic observations) Surface CHL a RMS error EnKF Cut-off of neg. values Gaussian Anamorphosis EnKF