WWOSC, August 16-21, 2014, Montreal 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 WWOSC, August 16-21, 2014, Montreal 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 Anamorphosis Approach Conclusions and Future developments Outline

3 WWOSC, August 16-21, 2014, Montreal 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 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 Assimilation of Sea Ice Concentration : Main features of the GLORYS2v3 simulation

4 WWOSC, August 16-21, 2014, Montreal 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) Assimilation of Sea Ice Concentration : Main features of the GLORYS2v3 simulation Model - Nemo 3.1, LIM2-EVP - Global ¼, 75 levels Sea Ice Concentration from CERSAT (IFREMER)

5 WWOSC, August 16-21, 2014, Montreal SAM2 Data Assimilation System : Pf: Fixed Basis Background error covariances Representation by a prior ensemble of anomalies  We use these anomalies to compute Pf in the analysis Model trajectory  We generate a pseudo-ensemble from a forced simulation Model trajectory anomaly Running mean of the model trajectory Temporal window Analysis date 2 Analysis date 1

6 WWOSC, August 16-21, 2014, Montreal Cersat ORCA025/LIM2noEVP (80N,150W)(80N,0W) Concentration (SIC) correlation fields from 1 single concentration observation (~280 modes, February) SAM2 Data Assimilation System : Background Error specification : Correlation fields

7 WWOSC, August 16-21, 2014, Montreal Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System Sea Ice Concentration on 15 th September 1992 (assimilation start in December 1991) Sea Ice Concentration Misfits to Observation (CERSAT) on 15 th September 1992 Sea Ice Concentration RMS misfits G2V3-NOASSIM/ICE G2V3-ASSIM/ICE Jan 1992 Sep 1992May 1993 Jan 1992 Sep 1992May 1993 CERSATGLORYS2V3-NOASSIM/ICE Global ¼°, 75 levels GLORYS2V3-ASSIM/ICE Global ¼°, 75 levels

8 WWOSC, August 16-21, 2014, Montreal Arctic September 2001March 2001 G2V3 - CERSAT G2V1 - CERSAT Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System

9 WWOSC, August 16-21, 2014, Montreal Extreme Events CERSAT data GLORYS2V3 Sept 1996 Sept 2007 Good behaviour of GLORYS2V3 during extreme events Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System

10 WWOSC, August 16-21, 2014, Montreal SIC >85% Correlation: 0.4  0.9 Correlation: 0.8 Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System Sea Ice extent anomaly 2001

11 WWOSC, August 16-21, 2014, Montreal Assimilation of Sea Ice Concentration : Impact in Arctic region with GLORYS2V3 System Monthly Volume Anomalies of Monthly Volume

12 WWOSC, August 16-21, 2014, Montreal Recent developments in the Sea Ice Analysis

13 WWOSC, August 16-21, 2014, Montreal Different Restart Strategies: IAU on SIC + … - … nothing using an implicit concept of thickness conservation  SIC i,  h i = 0, h i = cte  V i = h i.  SIC i Restart Strategies for the sea ice model update hihi SIC i +  SIC i = Analysis of Sea Ice Concentration (y2006m10d15, 3 days cycle) Innovation CERSAT- ORCA025/LIM2noEVP Model update using IAU method Residual Work in Progress

14 WWOSC, August 16-21, 2014, Montreal Different Restart Strategies: IAU on SIC + … - … nothing using an implicit concept of thickness conservation  SIC i,  h i = 0, h i = cte  V i = h i.  SIC i - correction on thickness using a reference thickness h*=1m (Tietsche et al.)  SIC i,  V i = h*.  SIC i  h i = ( h* - h i ).  SIC i / ( SIC i +  SIC i ) hihi SIC i +  SIC i = hihi SIC i +  SIC i = h*h* G2V3 TEST(G2V3 with  h) TEST – G2V3 Restart Strategies for the sea ice model update Work in Progress Thickness in March 1994

15 WWOSC, August 16-21, 2014, Montreal Development of a multivariate Sea Ice Analysis SIC Model updateThickness Model update Test_G2V4 Sea Ice Model update (y2011m11d11) FREE G2V4 SIC RMS Misfit Work in Progress 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

16 WWOSC, August 16-21, 2014, Montreal Development of a multivariate Sea Ice Analysis FREE G2V4 SIC RMS Misfit Work in Progress 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 G2V3TEST_G2V4TEST_G2V4 – G2V3 Thickness (y2011m11d11)

17 WWOSC, August 16-21, 2014, Montreal The Gaussian anamorphosis method consists to define an appropriate transformation T of the space leading to gaussian distribution of the variables. innovation Forecast error Kalman gain Error sub-space Observational update Analysis step in the physical spacein the anamorphosed space The Gaussian anamorphosis transformation T could be estimated from the ensemble forecast {x f } i SAM2 Parameterization in progress : The Gaussian Anamorphosis approach

18 WWOSC, August 16-21, 2014, Montreal IncrementInnovation Physical spaceAnamorphosed space The Gaussian Anamorphosis approach Physical space Local transformation of the sea ice concentration (SIC) pdf SIC SIC percentilesAnamorphosed SIC

19 WWOSC, August 16-21, 2014, Montreal 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 Anamorphosis Approach 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 WWOSC, August 16-21, 2014, Montreal Thank You!!