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Ocean Initialization for seasonal forecasts

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

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Ocean Initialization for seasonal forecasts

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  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 • Higher heat capacity (Thermodynamic argument) • 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 Ocean reanalysis Real time Probabilistic Coupled Forecast time Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis Consistency between historical and real-time initial initial conditions is required Quality of reanalysis affects the climatological PDF Typical Seasonal Forecasting System: dealing with model error & forecast uncertanty

  5. 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)

  6. 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/ • 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/ • GMAO: ODAS-1 http://gmao.gsfc.nasa.gov/research/oceanassim/ODA_vis.php http://gmao.gsfc.nasa.gov/cgi-bin/products/climateforecasts/index.cgi

  7. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Equatorial Atlantic upper heat content anomalies. No assimilation Equatorial Atlantic upper heat content anomalies. Assimilation Reducing Uncertainty • 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: • Does assimilation of ocean data constrain the ocean state? • Does the assimilation of ocean data improve the ocean estimate? • Does the assimilation of ocean data improve the seasonal forecasts Equatorial Atlantic: Taux anomalies

  8. GODAE Final Symposium, 12 – 15 November 2008, Nice, France 1982 1993 2001 XBT’s 60’s Satellite SST Moorings/Altimeter ARGO Ocean observations assimilated The ocean observing system has slowly been building up… Its non-stationary nature is a challenge for the estimation of interannual variability

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

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

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

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

  13. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Impact on Seasonal Forecasts Skill • 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.

  14. GODAE Final Symposium, 12 – 15 November 2008, Nice, France Progress is not monotonic The quality of the initial conditions is not always the limiting factor on the skill ERA15/OPS S2 NOdataS2 Assim ERA40/OPS DEM NOdata DEM Assim

  15. GODAE Final Symposium, 12 – 15 November 2008, Nice, France ALL NO-OCOBS SST-ONLY Impact of Initialization strategy on SFECMWF S3 • 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. • Relation between drift and Amplitude of Interannual variability. • Possible non linearity: is the warm drift interacting with the amplitude of ENSO?

  16. GODAE Final Symposium, 12 – 15 November 2008, Nice, France NINO3.4 RMS ERROR ALLNO-OCOBS SST-ONLY Impact on Initialiazation on SF SkillECMWF S3 Adding information about the real world improves ENSO forecasts

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

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

  19. 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

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

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

  22. 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

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

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

  25. 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.

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