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THE ECMWF Seasonal Forecasting system

THE ECMWF Seasonal Forecasting system. 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

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THE ECMWF Seasonal Forecasting system

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  1. THE ECMWF Seasonal Forecasting system

  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. COUPLED MODEL Atmosphere model Atmosphere model Atmosphere model Ocean model Ocean model Ocean model PROBABILISTIC CALIBRATED FORECAST ENSEMBLE GENERATION Forward Integration Forecast Calibration Initialization End-To-End Seasonal forecasting System Forecast PRODUCTS OCEAN

  4. Half of the gain on forecast skill is due to improved ocean initialization S1 S2 S3 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? OceanObs09 plenary paper

  5. 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

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

  7. ECMWF S4 • NEMO (ORCA1)+CY36R4 • Increased atmos resolution (to T255 + 91 levels) [S3 was T159+62 levels) • Initial conditions with NEMOVAR, ERA-INTERIM, and…

  8. ECWMF: COMBINE Ocean Re-AnalysisUsed 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)

  9. 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,…

  10. RMSE of 10 days forecast EQ Central Pacific EQ Indian Ocean CONTROL ASSIM: T+S ASSIM: T+S+Alti TROPICAL Pacific GLOBAL Altimeter Improves the fit to InSitu Temperature Data

  11. Correlation with Altimeter COMBINE ASSIM T+S+Alti

  12. Impact of Ocean Assim in SST forecasts Prototype of S4: latest NEMOVAR+36r4 ASSIMCONTROL NEMOVAR consistent improves the forecast skill of SST at different lead times and different regions, at SEASONAL TIME SCALES. See Later for Decadal

  13. Combine project –Strategies for dealing with systematic errors in a coupled ocean-atmosphere forecasting systemProject concept Nature climate Flux correction Normal initialisation Anomaly initialisation Model climate Linus Magnusson et al.

  14. Momentum flux correction - rationale Systematic wind error (example October)

  15. Experiments Seasonal (14-month forecasts), 1989-1999, Start dates November and May Decadal (10-year), 1960-2005, Start dates November every 5th 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

  16. SST bias in decadal integrations (fc year 2-10) Control (“or” Anomaly initialisation) U-flux correction U- and H-flux correction

  17. T bias cross section, equatorial Pacific (fc year 2-10) Control (“or” Anomaly initialisation) U-flux correction U- and H-flux correction

  18. Nino3.4 SST forecasts November 1995 – November 1998 Control Anomaly Initialisation U-flux correction U- and H-flux correction 99 96 97 98 99 96 97 98

  19. Model drift during the first year (10 start dates, 3 members)

  20. ENSO statistics – seasonal cycle (year 2-10) Re-analysis Control Anomaly Init. U- flux corr. U- and H-flux corr. Nino 3.4 SST mean Nino 3.4 SST st. dev.

  21. Regression of rainfall anomalies on NINO3.4 AnoIni U+H flux corr

  22. 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.

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