1. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Ocean Initialization for seasonal forecasts ECMWF CAWCR Met Office JMASTEC NCEP MERCATOR-Ocean MRI JPL GMAO NOAA/GFDL University of Hamburg Magdalena A. Balmaseda Oscar Alves Alberto Arribas T. Awaji David Behringer Nicolas Ferry Yosuke Fujii Tony Tee Michele Rienecker Tony Rosati Detlef Stammer

2. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Outline Background  The basis of seasonal forecasts  Standard practice  Different operational efforts around the world Ocean Model initialization  Impact of assimilation on ocean estate  Impact on seasonal forecast skill. Overview Towards “coupled” initialization: ongoing efforts This talk only deals with prediction of SST. But seasonal forecasts products go beyond SST:  Temperature, Precipitation  Tropical cyclones and hurricanes  Applications such as hydropower, agriculture and health

3. GODAE Final Symposium, 12 – 15 November 2008, Nice, France The basis for seasonal forecasts Atmospheric point of view: Boundary condition problem –Forcing by lower boundary conditions changes the PDF of the atmospheric attractor “Loaded dice” –The lower boundary conditions (SST, land) have longer memory o Higher heat capacity (Thermodynamic argument) o Predictable dynamics Oceanic point of view: Initial value problem –Prediction of tropical SST: need to initialize the ocean subsurface. –Examples: A well established case is ENSO A more tantalizing case is the importance subsurface temperature in the North Subtropical Atlantic for seasonal forecasts of NAO and European Winters. Indian Ocean Dipole

4. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Need to Initialize the subsurface of the ocean

5. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Typical Seasonal Forecasting System: dealing with model error & forecast uncertanty Ocean reanalysis Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis Real time Probabilistic Coupled Forecast time Consistency between historical and real-time initial initial conditions is required Quality of reanalysis affects the climatological PDF

6. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Common features of the SF initialization systems Emphasis on upper ocean thermal structure and SST Climate configuration: Global domain, resolution ~1 deg with equatorial refinement. 30-50 vertical levels. Usually rely on previously analyzed SST field. Balance relationships (T and S, density and velocity) Assimilation cycle; 5-to-10 days Some control of the mean state: –Relaxation to climatology –Online bias correction (T, S, prssure gradient) –MDT: either prescribed (from free model, or T+S analysis) or estimated (corrected) online Reanalysis period (15-20-50 years). Usually 2 products: –Delayed: 7-30 days – NRT : (0-7 days) Some have an ensemble of analyses (3-5)

7. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Operational efforts: routine production of seasonal forecasts and ocean analysis MRI-JMA: MOVE/MRI-COM.G : http://ds.data.jma.go.jp/tcc/tcc/products/elnino/index.html ECMWF: ORA-S3 http://www.ecmwf.int/ products/forecasts/d/charts/ocean http://www.ecmwf.int/products/forecasts/d/charts/seasonal/http://www.ecmwf.int/products/forecasts/d/charts/seasonal/ CAWCR(Melbourne): POAMA-PEODAS http://www.bom.gov.au/climate/coupled_model/poama.shtml NCEP (GODAS): http://www.cpc.ncep.noaa.gov/products/GODAS/ Mercator/Meteo-France: http://bulletin.mercator-ocean.fr/html/welcome_en.jsp MetOffice GLOSEA3: http://www.metoffice.gov.uk/research/seasonal / http://www.metoffice.gov.uk/research/seasonal / GMAO: ODAS-1 http://gmao.gsfc.nasa.gov/research/oceanassim/ODA_vis.php http://gmao.gsfc.nasa.gov/cgi-bin/products/climateforecasts/index.cgi

8. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Reducing Uncertainty Equatorial Atlantic upper heat content anomalies. No assimilation Equatorial Atlantic: Taux anomalies Equatorial Atlantic upper heat content anomalies. Assimilation A simple way of producing ocean initial conditions is to force and ocean model with atmospheric fluxes But large uncertainty in wind products lead to large uncertainty in the ocean subsurface The possibility is to use additional information from ocean data (temperature, others…) Questions: 1.Does assimilation of ocean data constrain the ocean state? 2.Does the assimilation of ocean data improve the ocean estimate? 3.Does the assimilation of ocean data improve the seasonal forecasts

9. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Ocean observations assimilated XBT’s 60’s Satellite SST Moorings/Altimeter ARGO 19821993 2001 The ocean observing system has slowly been building up… Its non-stationary nature is a challenge for the estimation of interannual variability

10. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Example of potential problem: Assim of mooring data CTL=No data Large impact of data in the mean state: Shallower thermocline PIRATA From an Old DA system

11. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Impact of assimilation on the ocean state Alves et al Fit to TAO observations (RMSE) Temperature Salinity Zonal velocity POAMA: Only T, univariate (1 st generation) PEODAS: T+S, multivariate (2 nd generation) ORA-S3: T+S+, “ “ (2 rd generation) CONTROL : no data assimilation Improvements was slow to achieve. But progress is evident Alves et al 2008

12. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Importance of Salinity Results from MRI Fujii et al 2008 T+S: both temperature and salinity corrections NOS: No Salinity corrections, only temperature

13. GODAE Final Symposium, 12 – 15 November 2008, Nice, France barrier layer and warm water content Fujii et al 2008 The WWC, function of the barrier layer thickness, plays an important role on ENSO Barrier layer thkness T+S WWC: T+S -NOS

14. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Until very recently seasonal forecasts skill was considered a “blunt” tool to measure quality of ocean analysis: coupled models were not discerning enough. Examples of good impact were encouraging, but considered a strike of good luck. Improvements in the coupled ocean – atmosphere models also translate on the ability of using SF as evaluation of ocean initial conditions. In this presentation there are several examples showing the positive impact of data assimilation on the skill of seasonal forecast. There are even results with observing system experiments, where the seasonal forecasts show significantly different behaviour Need good coupled models to gauge the quality of initial conditions The initialization problem is different from the state estimation problem. –“Initialization shock” can be detrimental if non linearities matter. Impact on Seasonal Forecasts Skill

15. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Progress is not monotonic ERA15/OPS S2 NOdata S2 Assim ERA40/OPS DEM NOdata DEM Assim The quality of the initial conditions is not always the limiting factor on the skill

16. GODAE Final Symposium, 12 – 15 November 2008, Nice, France ALL NO-OCOBS SST-ONLY Impact of Initialization strategy on SF ECMWF S3 Relation between drift and Amplitude of Interannual variability. Possible non linearity: is the warm drift interacting with the amplitude of ENSO? Drift and variability depend on initialization!! More information corrects for model error, and the information is retained during the fc. Need better (more balanced) initialization Relation between drift and Amplitude of Interannual variability. Upwelling area penetrating too far west leads to stronger IV than desired.

17. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Impact on Initialiazation on SF Skill ECMWF S3 NINO3.4 RMS ERROR ALL NO-OCOBS SST-ONLY Adding information about the real world improves ENSO forecasts

18. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Impact on forecast skill (ECMWF-S3) NINO-W EQATL EQ3 STIO WTIO Balmaseda et al 2008 Reduction (%) in SST forecast error Range 1-3 months In Central/Western Pacific, up to 50% of forecast skill is due to atmos+ocean observations. Sinergy: > Additive contribution Ocean~20% Atmos ~25% OC+ATM~55%

19. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Impact of Different Ocean Observations JMA-MRI Fujii etal 2008 NINO-W EQATL EQ3 STIO WTIO OSEs in JM-MRI confirm the complementary nature of the observing systems (moorings and floats) on the skill of SF.

20. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Impact of initialization SF skill CAWCR POAMA OLD POAMA initial conditions New PEODAS initial conditions No Data Assimilation In the CAWCR system, an improved data assimilation system improves the seasonal forecast skill.

21. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Improvements in SF: Mercator-MeteoFrance S3 The new Meteo-France SF system 3 shows improved skill. A large contribution to the improvement is likely due to better ocean initial conditions

22. GODAE Final Symposium, 12 – 15 November 2008, Nice, France The ECCO-JPL / UCLA example Improvement on SF by using ECCO-JPL. Baseline is a forecast from ocean initial conditions without data assimilation. From Cazes-Boezio et al 2008. RMS ERROR on SF of SST

23. Initialization and non linearities Forecast lead time phase space Model Attractor (MA) non-linear interactions important Real World (RW) Initialization shock a b c

24. GODAE Final Symposium, 12 – 15 November 2008, Nice, France More balance intialization Coupled Data Assimilation “Assimilation of ocean data with a coupled model” –Coupled 4D-var: JAMSTEC –EnKF: GMAO, GFDL Coupled Breeding Vectors: –generation of the ensemble by projecting the uncertainty of the initial conditions on the fastest error-growth modes of the coupled system Anomaly Initialization: –Depresys (Met Office) –GECCO

25. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Towards more balanced Initialization (I) Coupled 4D-var: JAMSTEC Sugiura et al 2008 OBS First guess Analysis Control: initial conditions (IC) Control: Parameters (PRM) Control: IC+PRM

26. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Towards more balanced coupled initialization (II): Breeding Vectors in GMAO Yang et al 2008 May starts Nov starts 4 BV ens Control 4BV-Control ACC of 9-month lead FC of SST BV can also be used to formulate flow dependent covariances in the ocean data assimilation

27. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Summary Ocean data assimilation is used operationally in several centres around the world to initialize seasonal forecasts with coupled models Improving the seasonal forecasts by assimilating ocean data has been a slow process. Limiting factors have been (are) Balance constraints between variables Spurious inter-annual variability due to non-stationary nature of observing system Quality of coupled models With the current generation of ocean data assimilation systems and coupled models it is possible to demonstrate the benefits of assimilating ocean data for the seasonal forecast skill The initialization shock remains a problem. There are currently several initiatives aiming at a more coupled initialization. Another challenge is the initialization of a seamless prediction system: from days-months to decades.