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Observing System experiments with ECWMF operational ocean analysis (ORA-S3)

Observing System experiments with ECWMF operational ocean analysis (ORA-S3). The new ECMWF operational ocean analysis system Historical reanalysis and real time The ORA-S3 analysis system Impacts of data assimilation (mean/variability/forecast skill) Results from OSEs

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Observing System experiments with ECWMF operational ocean analysis (ORA-S3)

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  1. Observing System experiments with ECWMF operational ocean analysis (ORA-S3) • The new ECMWF operational ocean analysis system • Historical reanalysis and real time • The ORA-S3 analysis system • Impacts of data assimilation (mean/variability/forecast skill) • Results from OSEs - Impact on the ocean state - Impact on forecasts - Impact on climate variability Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  2. ECMWF: Weather and Climate Dynamical Forecasts 10-Day Medium-Range Forecasts Seasonal Forecasts Monthly Forecasts Atmospheric model Atmospheric model Wave model Wave model Ocean model Real Time Ocean Analysis ~Real time Delayed Ocean Analysis ~12 days Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  3. Real time Probabilistic Coupled Forecast time Ocean reanalysis Consistency between historical and real-time initial initial conditions is required Quality of reanalysis affects the climatological PDF Main Objective: to provide ocean Initial conditions for coupled forecasts Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  4. ORA-S3 Ocean Re-Analysis System • Ocean model: HOPE (~1x1, equatorial refinement) • Assimilation Method OI (3D OI). • ERA-40 fluxes to initialize ocean. • Retrospective Ocean Reanalysis back to 1959. • Assimilation of T • Assimilation of salinity data. • Assimilation of altimeter-derived sea level anomalies. • Multivariate on-line Bias Correction . • Balanced relationships (T-S, ρ-U) • 10 days assimilation windows, increment spread in time Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  5. Observations used in the S3 ocean analysis Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  6. Observation Monitoring Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  7. Altimeter product • Ingredients: • Assimilation of detrendend sea level, taking care of removing the spatial average from the altimeter data: Observed SLA from T/P+ERS+GFO+Jason+ENVISAT Respect to 7 year mean of measurements. Weekly anomalies, twice a week. Global gridded maps A Mean Sea Level Choice: MSL from an analysis where no altimeter has been assimilated There are MSL products derived from GRACE (Rio4/5 from CLS, NASA, …) but the choice of the reference global mean is not trivial and the system can be quite sensitive to this choice. Better assimilation methods are needed to make optimal use of the Gravity product Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  8. T/S conserved T/S conserved OI CH96 T/S Changed OI Sequential Assimilation of data streams • Assimilation of Sea level anomalies • Assimilation of Subsurface temperature • Assimilation of Salinity Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  9. Bias evolution vector-equation ¢ = + f f b b b ; - k k k 1 b k prescribed (constant/seasonal) Some notation (Temperature,Salinity,Velocity) Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Balmaseda et al 2007, QJRMS

  10. Mean Assimilation Temperature Increment Without bias correction Mean Assimilation Temperature Increment With bias correction Effect of the pressure-gradient correction • The information from the temperature assimilation increment (above left) can be used to estimate a correction to the pressure gradient. • The equivalent correction to the wind stress from the bias term appears below right (~5-10%). Units are 10^-2 N/m2. • By applying the correction in the pressure gradient the temperature increment is reduced (above right) Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  11. Analysis minus Observations Western Pacific Equatorial Indian DATA ASSIM NO DATA ASSIM The Assimilation corrects the ocean mean state Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  12. …improves the interannual varaibility No Data Assimilation Assimilation:T+S Assimilation:T+S+Alt Correlation with OSCAR currents Monthly means, period: 1993-2005 Seasonal cycle removed Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  13. And the skill of Seasonal Forecasts of SST Data assimilation improves the seasonal forecast of SST Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  14. Observing System experiments with ECWMF operational ocean analysis (ORA-S3) • The new ECMWF operational ocean analysis system • Historical reanalysis and real time • The ORA-S3 analysis system • Impacts of data assimilation (mean/variability/forecast skill) • Results from OSEs - Impact on the ocean state - Impact on forecasts - Impact on climate variability Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  15. - = ARGO effect (when ALTI) ALL NO_ARGO = - ALTI effect (when ARGO) ALL NO_ALTI - = ARGO effect (when no ALTI) NO_ALTI NEITHER - = NO_ARGO NEITHER ALTI effect (when no ARGO) Observing System Experiments • Period 2001-2006: ALL NO_ARGO NEITHER NO_ALTI (no argo/no alti) Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  16. Effect of ALTI Effect of ARGO (when alti is present) Effect of ALTI (when ARGO is not present) Effect of ARGO (when alti is not present) OSES: Effect on Salinity In the Tropical Atlantic/Indian, altimeter data helps ARGO Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  17. Effect of ALTI Effect of ARGO (when alti is present) Effect of ALTI (when ARGO is not present) Effect of ARGO (when alti is not present) OSEs:Effect on Sea Level Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  18. OSEs:Effect on T300 Effect of ARGO when Alti is present Effect of ARGO when Alti is NOT present Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  19. Eastern Equatorial Pacific North Sub Tropical Atlantic South Pacific Fit to the observations (rms error)Temperature ALL NO_ALTI NO_ARGO NEITHER Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  20. Fit to the observations (rms error)Salinity Equatorial Indian Equatorial Atlantic ALL NO_ALTI NO_ARGO NEITHER Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  21. Impact on Seasonal Forecast skill • Moorings: only the effect of anomalies is measured, since the effect of the mean state is included indirectly in the altimeter assimilation. • Observing systems are complementary • Altimeter has larger effect on Atlantic and Eastern Pacific • Argo has larger effect on Indian Ocean and Western Pacific Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  22. 1993-2007 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  23. Impact of Observing System in the climate variability ORA-S3 = Ocean reanalysis using “all” observing system ORA-nobs= Ocean model forced by surface fluxes NOARGO = No Argo data 2001-2006 NOSOLO = No SOLO/FSI floats 2001-2006 • Heat content • Attribution of Sea Level Change • Salinity Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  24. Ocean Heat Content at 300/700/3000 m • Upper 300m, there is a large degree of coherence in ORAS3, ORA-nobs, Lev. The largest signals are in ORAS3 (SYNERGY?) • Deeper Ocean: In ORA-nobs the decadal signals do not penetrate deep enough? • OSEs indicate that 2002-2003 upper ocean cooling is robust • Cooling after 2003 in ORAS3 is a consequence of ARGO in the Southern Oceans.The ARGO SOLO/FSI are not responsible for the post-2004 cooling Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  25. Spatial distribution of trends in heat content 1982-2006 mean minus 1959-1981 mean SST (deg C) Taux (x 0.01N/m2) T300 (deg C) Tauy (x 0.01N/m2) How reliable are the trends in ERA40 winds? Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  26. Comparison with ocean observations IPCC-AR4 (LEVITUS) ORA-S3 CI=0.05 deg/decade • Similarities • Equatorial cooling • Warmer subtropics • Cooling at ~60N • Comments • Trends in ERA40 winds seem robust • Stronger features in ORA-S3, more structure • Circulation changes as well as mixed layer changes Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  27. Comparison with ocean observations Atlantic and Indian IPCC-AR4 (LEVITUS) ORA-S3 CI=0.05 deg/decade Largest warming is in the Atlantic Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  28. Attribution of Sea level changes Trends 1961-2003: SL (IPCC) =1.8 mm/yr SH (IPCC) =0.5 mm/yr SH ORA-S3 (1960-2003)=0.9mm/yr SH ORA-nobs “ =0.5mm/yr ORAS3 gets closer….. 1993-2003: SL (IPCC) =3.1 mm/yr SH (IPCC) =1.6 mm/yr SH ORA-S3 (1993-2003)=2.1mm/yr SH ORA-nobs “ =1.1 mm/yr consistent with others 2002 onwards?? Effect of ARGO? Altimeter problems? Sea level changes= Mass + Volume (SH) Steric Height (SH) can be estimated from ORAS3 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  29. Attribution of Sea Level Change (OSES) • Argo is responsible for the decay in SH in ORAS3 • SOLO/FSI have little impact • But even without Argo, the trend in SH stabilizes after 2002 • While the SL from altimeter keeps increasing…If we believe the altimeter • This would imply a mass increase of 2mm/yr (twice as large as the latest IPCC) • Worrying: either the estimates are wrong, or a lot of continental ice is melting Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  30. ORAS3 ORA-nobs Bryden05 Cunningham07 ORAS3 ORA-nobs Impact of data assimilation in the MOC • Assimilation improves the estimation of the MOC • Downward trend ~4% decade in ORAS3, ~2% decade in ORA-nobs RMS fit to observations in the NATL Balmaseda et al, GRL 2007 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  31. Salinity in ORA-S3 Large spin up/down in the first 2-3 years. Large effect of ARGO Large uncertainty in fresh water fluxes Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  32. Summary • State estimation: • Both ARGO temperature and salinity have a large information content. • Argo is instrumental in correcting the salinity of the ORA-S3 analysis • The ARGO data is best used in combination with the altimeter information. • Seasonal forecast skill: • Argo/Altimeter/Moorings contribute to the improvement of the skill of seasonal forecast of SST. • Their contribution is often complementary: Argo has larger effect in the Western Pacific and Indian Ocean. Altimeter’s impact is larger in Atlantic and Eastern Pacific • Climate variability: • The profound impact of Argo on the analysis should be taken into account when analysing the climate variability from ORA-S3. • OSEs indicate a deceleration in the ocean warming and global SH after 2003. • The variability in the ORA-S3 salinity may not be reliable • Other comments: • A new observing system SHOULD NEVER HAVE a negative impact. • In the Seasonal Forecast, the inability to improve predictions in the Equatorial Atlantic is symptomatic of errors in the model/analysis. • In future reanalysis, the information provided by Argo could be used in retrospect, for instance via bias-correction algorithms (or improved models). Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

  33. What if the Observations have negative impact? • In the Analysis? • Model error not taken into account • Wrong Specification of Background error • Wrong Specification of Observation error • In the forecast? • The analysis error has not been reduced • The analysis error has been reduced in total, but the error has increased in the directions of larger error growth. • There is model error Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007

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