1. An Arctic Ocean/Sea Ice Reanalysis Detlef Stammer, Nikolay Koldunov, Armin Köhl Center für Erdsystemforschung und Nachhaltigkeit Universität Hamburg page 1

2. Preamble A complete picture of the ocean for the purpose of climate research and applications will only come from a synergy between observations, modeling and data assimilation. Goal of ocean synthesis (reanalysis) is to obtain such best possible description of the ocean by combining all available data with the dynamics of an ocean circulation model. “Data assimilation” is mostly least-squares fitting of models to data. Different methods are variant algorithms used to find the minimum of an objective (or cost) function, the extent to which an approximation to that minimum is acceptable, and whether one seeks an error estimate.

3. time Smoothed Estimate: x(t+1)=Ax(t)+Gu(t) Filtered Estimate: x(t+1)=Ax(t)+Gu(t)+  (t+1) x: model state, u: forcing etc,  : data increment Model Physics: A, G Data increment:  Consistency of Assimilation Data The temporal evolution of data-assimilated estimates is physically inconsistent (e.g., budgets do not close) unless the assimilation’s data increments are explicitly ascribed to physical processes (i.e., inverted). Climate Syntheses need to preserve first principles (Bengtsson et al., 2007)

4. The ECCO Effort ECCO was established in 1998 as part of the World Ocean Circulation Experiment (WOCE) with the goal to combine a general circulation model with diverse observations to produce a quantitative description of the time-varying global ocean state. The dynamically consistent assimilation procedure uses the adjoint method to adjust the temperature and salinity initial conditions and the atmospheric state as well as model parameters to bring the model into consistency with the assimilated data and the prior model- data error weights. Various different solutions exist over various time spans. including the German contribution to ECCO, called GECCO. Seite 4

5. 50+ year long synthesis for global ocean. MITgcm dynamic ocean/sea ice model Global configuration, 1/3 o -1 o, 50 layers Data sets assimilated include: EN3 data base of temperature and salinity profiles from MBT,XBT,CTD and Argo, Reynolds and AMSR/E SST, AVISO altimetric SLA, CNES CLS11 - GOCO02s MDT, WHO9 climatological T and S. GECCO2 Ocean Synthesis

6. Observational Coverage Example: Temperature Profiles Example: Salinity Profiles 200519951965 19952005

7. Adjoint Optimization Seite 7 Iteration 1-9 1948 2009 Iteration 9-18 1948 2011 1948 1952 1956 20082011  SSS from WOA09 added Iteration 18-23: 5 year windows with one year overlap (GECCO2) 2013 2008 Annual updates for decadal predictions: Simulation 1948-2011 forced with NCEP RA1 6h atmospheric state Calculate the model data misfit formulated as a cost function. The adjoint calculates gradients of cost function wrs control parameters. Control parameters are change iteratively to reduce misfit.

8. GECCO2 and Church et al. 2004 SSH Trends 1955 to 2003 sea level trend total thermo steric 1950 to 2010 sea level trend total thermo steric mm/ yr

9. Hierarchy of GECCO Forward and Adjoint Runs Global 1° x 1/3° Atl. 32 km Atl. 16 kmAtl. 8 kmAtl. 4 km All simulations:  Ocean-sea ice coupled simulations (50 vert. levels);  Initial T/S conditions from WOA2005;  NCEP RA1 6-hourly atmospheric state + bulk formula. Atl. simulations only:  Open bound. cond. at 33°S and Bering St. from global model with imposed barotropic net throughflow of -0.9 Sv;  SSS relaxation to WOA2005; SST to monthly ERSST V3. SSH std (cm)

10. Mean sea surface height Farrell et al., 2012 (Knudsen and Andersen 2013) ATL12 Koldunov et. al., 2014, JGR, under revision.

11. Coastal sea level Koldunov et. al., 2014. subm. to JGR

12. Model Configurations targeting the Arctic ModelPeriod Horizontal resolution Boundary conditions AS forcing Vertical levels ATL12 1948-2009 ~8 kmGECCO NCEP reanalysis 50 POL06 2000-2009 ~15 kmATL06 NCEP reanalysis 50 ARCTIC401980-2009~40 kmClosedNCEP reanalysis 25 ATL12POL06ARCTIC40

13. Koldunov et al., 2013 Monthly means of mean September sea ice area sensitivities per grid cell to SAT Arctic adjoint sensitivity studies.

14. Arctic Ocean Data Assimilation Configuration Medium resolution coupled sea ice-ocean configuration: -Horizontal resolution ~ 15 km -Ocean boundary conditions are from the larger Atlantic Ocean setup (ATL06) -Atmospheric forcing – NCEP Reanalysis -There are 50 vertical levels Koldunov et al., 2014, in preparation

15. Data Constraints -Monthly PHC climatology (T, S). -Mean Dynamic Topography from DTU (GOCO03s). -Monthly SST from AMSRE. -Altimetry: TOPEX/Poseidon, ERS-1,2, Envisat. -Combined EN3 (include NABOS, CABOS, NPEO, Beaufort gyre experiment) and NISE hydrographic data. -Sea ice concentration: -EUMETSAT OSI-SAF Version 2 (constant uncertainties) -ESA CCI Sea-Ice-ECV project (variable uncertainties).

16. Seite 16

17. Control Paramters -Initial temperature and salinity -Atmospheric state: -Surface (2-m) air temperature -Surface (2m) specific humidity -Surface (10-m) zonal and meridional wind velocity -Precipitation -Downward shortwave and longwave radiation.......

18. Misfit changes for different variables wrs initial run

19. Sea ice concentration 2005 Satellite0 iterationLast iteration March September

20. Sea ice concentration 2007 March September Satellite0 iterationLast iteration

21. Model-observations difference for 2007 Satellite0 iterationLast iteration March September

22. Spatial distribution of air temperature corrections June 2005 Short Wave Radiation

23. Corrections of wind (2005) May 2005

24. Seasonal cycle of sea ice in 2005 Sea Ice Area Sea Ice Extent

25. Sea ice drift, October 2005 U componentV component Ice velocity

26. Sea ice thickness. October-November 2005 ICESat thickness0 iterationLast iteration

27. Sea ice thickness, November 2005 Before assimilationAfter assimilation

28. Summary First pilot attempt of a dynamic Arctic ocean/sea ice synthesis. First results are promissing and show that sea ice parameters can be used to constrain a coupled ocean/sea ice model. Results indicate that rectifying effect seem to happen in that sea ice thickness gets adjusted along with area and concentration. Results still need to be evaluated and compared with independent data before mechanisms can be studied. More data, esp. thickness, SSH and in situ data sets are required!!