C.-E. Testut , G. Garric, L. Parent, C.Bricaud (Mercator-Ocean, Toulouse, France)

1. Recent Advances in the Mercator-Ocean reanalysis system : Assimilation of sea-ice concentration in the Arctic sea C.-E. Testut, G. Garric, L. Parent, C.Bricaud (Mercator-Ocean, Toulouse, France) G. Smith (CMC, Environnement Canada), Y. Lu (BIO, DFO Canada)

2. Outline Assimilation of Sea Ice Concentration in Glorys2v3 Main features of the Glorys2v3 simulation Overview of the results in the Arctic sea Recent developments in the Sea Ice Analysis • Restart strategies for the sea ice model update • Use of multivariate analysis • Use of Gaussian AnamorphosisApproach Conclusions and Future developments

3. Assimilation of Sea Ice Concentration : Main features of the GLORYS2v3 simulation GLORYS:GLobal Ocean ReanalYses and Simulations - French Reanalysis project, supported by GMMC (Mercator, Coriolis). PI: B. Barnier - main partners: Drakkar consortium, CORIOLIS, MERCATOR - project started at national level in 2008 + cooperation with EU funded FP7 MyOcean project MOTIVATION : The need for a realistic description of the ocean state and variability over the recent decades, at the global scale, and at the scale of the ocean basins and regional seas. OBJECTIVES : - Produce an eddy permitting global ocean/sea-ice reanalysis spanning the “altimetric + ARGO" era 1992-today - To iterate / produce different reanalysis along the 1992-today time period - Start to design the ERA-Interim reanalysis scenario : 1979-today - Promote the use of reanalysis products in the climate community

4. Assimilation of Sea Ice Concentration : Main features of the GLORYS2v3 simulation Model • Nemo 3.1, LIM2-EVP • Global ¼, 75 levels • 1992-2012 Assimilation • Analysis based on a 2D local multivariate SEEK filter • Weakly-coupled DA system using 2 separate analyses : • Ocean Analysis (SLA, InSitu Data from CORA3.2, SST) , IAU on (h,T,S,U,V) • Ice Analysis (SIC), IAU on (SIC) • SIC Error: 1% open ocean, linear from 25% to 5% for SIC values between 0.01 and 1 • Forecast error covariances are built from a prior ensemble of Sea Ice Concentration anomalies => Fixed basis background error • Temperature and salinity bias correction using Argo (3DVar method) Sea Ice Concentration from CERSAT (IFREMER)

5. anomaly 2005 2003 2001 2002 2006 2004 Model trajectory SAM2 Data Assimilation System : Pf: Fixed Basis Background error covariances Representation by a prior ensemble of anomalies • We generate a pseudo-ensemble from a forced simulation Model trajectory Temporal window Running mean of the model trajectory • We use these anomalies to compute Pf in the analysis Analysis date 1 Analysis date 2

6. SAM2 Data Assimilation System : Background Error specification : Correlation fields (80N,150W) (80N,0W) ORCA025/LIM2noEVP Cersat Concentration (SIC) correlation fields from 1 single concentration observation (~280 modes, February)

7. Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System Sea Ice Concentration on 15th September 1992 (assimilation start in December 1991) CERSAT GLORYS2V3-ASSIM/ICE Global ¼°, 75 levels GLORYS2V3-NOASSIM/ICE Global ¼°, 75 levels Sea Ice Concentration RMS misfits G2V3-NOASSIM/ICE Jan 1992 Jan 1992 Sep 1992 Sep 1992 May 1993 May 1993 G2V3-ASSIM/ICE Sea Ice Concentration Misfits to Observation (CERSAT) on 15th September 1992

8. Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System September 2001 March 2001 G2V3 - CERSAT Arctic G2V1 - CERSAT

9. Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System GLORYS2V3 CERSAT data Extreme Events Sept 1996 Good behaviour of GLORYS2V3 during extreme events Sept 2007

10. Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System Sea Ice extent anomaly SIC >85% 2001 Correlation: 0.8 2001 Correlation: 0.4  0.9

11. Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System Anomalies of Monthly Volume Monthly Volume

12. Recent developments in the Sea Ice Analysis

13. Restart Strategies for the sea ice model update Different Restart Strategies: IAU on SIC + … • … nothing using an implicit concept of thickness conservation DSICi, Dhi = 0, hi = cte <=> DVi = hi . DSICi Work in Progress + = hi SICi DSICi Analysis of Sea Ice Concentration (y2006m10d15, 3 days cycle) Innovation CERSAT- ORCA025/LIM2noEVP Residual Model update using IAU method

14. Restart Strategies for the sea ice model update Different Restart Strategies: IAU on SIC + … • … nothing using an implicit concept of thickness conservation DSICi, Dhi = 0, hi = cte <=> DVi = hi . DSICi • correction on thickness using a reference thickness h*=1m (Tietsche et al.) DSICi , DVi = h* . DSICi <=> Dhi = ( h* - hi ) .DSICi / ( SICi +DSICi ) Work in Progress + = hi SICi DSICi + = hi h* SICi DSICi G2V3 TEST(G2V3 with Dh) TEST – G2V3 Thickness in March 1994

15. Development of a multivariate Sea Ice Analysis Monovariate state vector for G2V3 sea ice analysis [SIC] with (SIC) observations Multivariate state vector for G2V4 sea ice analysis [SST,SIC,Thickness] with (SST,SIC) observations SST restricted to open ocean close to the marginal zone SIC RMS Misfit Work in Progress FREE G2V4 Test_G2V4 Sea Ice Model update (y2011m11d11) SIC Model update Thickness Model update

16. Development of a multivariate Sea Ice Analysis Monovariate state vector for G2V3 sea ice analysis [SIC] with (SIC) observations Multivariate state vector for G2V4 sea ice analysis [SST,SIC,Thickness] with (SST,SIC) observations SST restricted to open ocean close to the marginal zone SIC RMS Misfit Work in Progress FREE G2V4 G2V3 TEST_G2V4 TEST_G2V4 – G2V3 Thickness (y2011m11d11)

17. SAM2 Parameterization in progress : The Gaussian Anamorphosis approach The Gaussian anamorphosis method consists to define an appropriate transformation T of the space leading to gaussian distribution of the variables. Analysis step in the physical space in the anamorphosed space innovation Error sub-space Forecast error Kalman gain Observational update The Gaussian anamorphosis transformation T could be estimated from the ensemble forecast {xf}i

18. The Gaussian Anamorphosis approach SIC percentiles Anamorphosed SIC SIC Local transformation of the sea ice concentration (SIC) pdf Physical space Anamorphosed space Physical space Innovation Increment

19. Conclusions and future developments Assimilation of Sea Ice Concentration only in G2V3 • Good representation of the sea ice extent and the sea ice concentration • Lack in the time evolution of the sea ice thickness Work in progress on the Sea Ice analysis • Identify of a more efficient restart strategy for the sea ice thickness • Use of multivariate analysis where the state vector is extent to [SST,SIC,thickness] • Use of Gaussian AnamorphosisApproach to improve the sea ice thickness update => obtain a more robust Sea Ice analysis for the next reanalysis and for the real time Next steps of the development • Coupling between SAM2 and NEMO3.6/LIM3 • Use of Arctic-Northern Atlantic Configurations (CREG4 and CREG12 with NEMO3.6) which are in development in partnership with Canada (Env. Can. and Fisheries and Oceans) • CREG4 : research application, dev. of the sea ice analysis and/or an Ensemble approach • CREG12 : benchmark reanalysis over the last 10 years in order to prepare the Mercator global 1/12°reanalysis Source: G. Smith, Env. Canada, Montréal

20. Thank You!!