1. OceanObs 09, Venice 21-25 September 2009 1 THE ECMWF Seasonal Forecasting system

2. OceanObs 09, Venice 21-25 September 2009 2 Overview OCEANOBS 09 & EUROBRISA  Applications most welcome for the concept of End To End Seasonal Forecasting Systems The ECMWF S4  Better skill in the Equatorial and South Atlantic  Mixed results everywhere else (large biases) EUROBRISA PROJECT  Subseasonal time scales (WCRP workshop in Exeter)  Decadal time scales (Paco’s talk)  NCEP part of EUROSIP  Corean Centre?

3. OceanObs 09, Venice 21-25 September 2009 3 End-To-End Seasonal forecasting System ENSEMBLE GENERATION COUPLED MODEL Forecast PRODUCTS Initialization Forward IntegrationForecast Calibration OCEAN PROBABILISTIC CALIBRATED FORECAST

4. OceanObs 09, Venice 21-25 September 2009 4 A decade of progress on ENSO prediction Steady progress: ~1 month/decade skill gain How much is due to the initialization, how much to model development? S1 S2 S3 Half of the gain on forecast skill is due to improved ocean initialization OceanObs09 plenary paper

5. OceanObs 09, Venice 21-25 September 2009 5 1.No observing system is redundant Example: the Pacific, where Argo, moorings and altimeter still complement. Lessons for other basins. Implications of the missing TAO data for the on-going El Nino 2.The altimeter is the only OS contributing to the North Subtropical Atlantic. Argo is the only OS contributing the skill on the Indian Ocean. 3.There are obvious problems in the Eq Atlantic: model error, assimilation, and possibly insufficient observing system Assessing the Ocean Observing System The assessment depends on the quality of the coupled model Sign of progress: a decade ago the OSES with Seasonal Forecasts were not considered a useful evaluation tool. Long records are needed for results to be significant: Any observing system needs to stay in place for a long time before any assessment is possible. So far impact on forecasts of SST only. Impact on atmospheric variables next From Balmaseda and Anderson 2009 See also Fujii et al 2008

6. OceanObs 09, Venice 21-25 September 2009 6 What is the value of a long historical record? Example from the Medium Range Weather Forecasts (TIGGI) Impact of Increased ensemble size versus longer calibration period (Continuous Rank Probability Skill Score, T-2m Europe) A longer calibration period has larger impact than increasing the ensemble size. From Hagerdorn 2008

7. OceanObs 09, Venice 21-25 September 2009 7 Predicting for users: end-to-end 63 ………… 62 4 3 2 1 Climate forecast ………… 63 62 4 3 2 1 Downscaling 63 ………… 62 4 3 2 1 Application model 0 Forecast probability of T or PP Forecasts probability of e.g. crop yield 0 non-linear transformation

8. OceanObs 09, Venice 21-25 September 2009 8 5-month lead fcst Obs Corr. skill Prediction of Dengue Risk transmission: 5 month lead time From EUROBRISA http://eurobrisa.cptec.inpe.br/ Numerical Model+ Calibration + Dengue model Forecast issued in Nov 1997, valid for Apr 1998

9. OceanObs 09, Venice 21-25 September 2009 9 ECMWF S4 NEMO (ORCA1)+CY36R4 Increased atmos resolution (to T255 + 91 levels) [S3 was T159+62 levels) Initial conditions with NEMOVAR, ERA-INTERIM, and…

10. OceanObs 09, Venice 21-25 September 2009 10 ECWMF: COMBINE Ocean Re-Analysis Used to initialized EC-EARTH decadal forecasts It uses NEMO/NEMOVAR, ORCA1 configuration, 42 levels (ORCA1_Z42_v2) NEMO V3.0 + Local Modifications. Forced by ERA40 (until 1989) + ERA Interim (after 1989) Assimilates Temperature/Salinity from EN3 (corrected XBT’s). Strong relaxation to SST (OI_v2) Offline+Online model bias correction scheme (T/S and pressure gradient):  Offline bias term estimated from Argo Period  Latitudinal dependence of the P/T/S bias: P strong at the Eq, weak at mid latitudes. Viceversa with T/S 5 ensemble members (perturbations to wind, initial deep ocean, observation coverage)

11. OceanObs 09, Venice 21-25 September 2009 11 Assessment of the COMBINE re-analysis Compared with the CONTROL (e.i., no data assim) Better fit to T/S profiles No degraded Equatorial Currents Spread in the deep ocean Improvement in ENSO forecasts Correlation with altimeter data as a measure of interannual variability: Improvements in the tropics, slight degradation at mid latitudes (especially North East Atlantic) Atlantic MOC? Further developments for the next operational system (due end of this year): Altimeter, revised assimilation parameters, partition of bias,SST,…

12. OceanObs 09, Venice 21-25 September 2009 12 Assimilating Altimeter Data Assimilation of sea level anomalies: along track (new) SuperObbing: rms of superobs used to account for representativeness error Remove global sea level prior to assimilation Multivariate relationship: How to project sea level into the subsurface T and S. Essential to impose constrains on vertical stratification of the water column. Assimilation of Global Sea Level Trends (from gridded maps) Global sea level is assimilated: FWF=SL_trend obs -SH_trend model Choice of MDT (Mean Dynamic Topography) External Product: Rio9, using GRACE data Tried, but not good results, due to the mismatch between model and Rio9 It needs more work to have an “observation” bias correction For S4: MDT from an assimilation run using T and S

13. OceanObs 09, Venice 21-25 September 2009 13 CONTROL ASSIM: T+S ASSIM: T+S+Alti EQ Central PacificEQ Indian Ocean TROPICAL PacificGLOBAL Altimeter Improves the fit to InSitu Temperature Data RMSE of 10 days forecast

14. OceanObs 09, Venice 21-25 September 2009 14 Correlation with Altimeter COMBINE ASSIM T+S+Alti

15. OceanObs 09, Venice 21-25 September 2009 15 Impact of Ocean Assim in SST forecasts Prototype of S4: latest NEMOVAR+36r4 ASSIM CONTROL NEMOVAR consistent improves the forecast skill of SST at different lead times and different regions, at SEASONAL TIME SCALES. See Later for Decadal

16. OceanObs 09, Venice 21-25 September 2009 16 Combine project – Strategies for dealing with systematic errors in a coupled ocean-atmosphere forecasting system Project concept Nature climate Model climate Flux correction Anomaly initialisation Normal initialisation Linus Magnusson et al.

17. OceanObs 09, Venice 21-25 September 2009 17 Anomaly initialisation Initial state = Model climate + (Analysis – Analysis climate) = Analysis + (Model climate – Analysis climate) (i.e adding the systematic error to the analysis) Full state vector Rationale – avoiding model drift and over-shooting Red –Control, Purple - AnoIni Example – N. Atl 300 m heat content

18. OceanObs 09, Venice 21-25 September 2009 18 Momentum flux correction - rationale Systematic wind error (example October)

19. OceanObs 09, Venice 21-25 September 2009 19 Experiments Seasonal (14-month forecasts), 1989-1999, Start dates November and May Decadal (10-year), 1960-2005, Start dates November every 5 th year Control forecast Anomaly initialisation Momentum flux correction Heat and momentum flux correction Model cycle 36r1, Nemo version 3, sampled sea-ice 3 ensemble members

20. OceanObs 09, Venice 21-25 September 2009 20 SST bias in decadal integrations (fc year 2-10) Control (“or” Anomaly initialisation) U-flux correctionU- and H-flux correction

21. OceanObs 09, Venice 21-25 September 2009 21 T bias cross section, equatorial Pacific (fc year 2-10) Control (“or” Anomaly initialisation) U-flux correctionU- and H-flux correction

22. OceanObs 09, Venice 21-25 September 2009 22 Nino3.4 SST forecasts November 1995 – November 1998 Control Anomaly Initialisation U-flux correction U- and H-flux correction 969798 99 969798 99

23. OceanObs 09, Venice 21-25 September 2009 23 Model drift during the first year (10 start dates, 3 members)

24. OceanObs 09, Venice 21-25 September 2009 24 ENSO statistics – seasonal cycle (year 2-10) Re-analysis Control Anomaly Init. U- flux corr. U- and H-flux corr. Nino 3.4 SST meanNino 3.4 SST st. dev.

25. OceanObs 09, Venice 21-25 September 2009 25 Inter-annual variability of SST (1960-80 above, 1980-2000 below) Reanalysis Control fc Heat and mom flux correction 1980-2000 1960-1980

26. OceanObs 09, Venice 21-25 September 2009 26 Regression of rainfall anomalies on NINO3.4 OBS AnoIni U+H flux corr

27. OceanObs 09, Venice 21-25 September 2009 27 Regression of rainfall anomalies on NINO3.4 AnoIni U+H flux corr

28. OceanObs 09, Venice 21-25 September 2009 28 Opinions At WCRP there is “thirst” for examples of applications:  EUROBRISA is very well placed!  Should continue The FORECAST ASSIMILATION project is very powerful  THERE is a lot of science to do.