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Slide 1 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Observing System experiments with ECWMF operational ocean analysis (ORA-S3) The new.

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Presentation on theme: "Slide 1 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Observing System experiments with ECWMF operational ocean analysis (ORA-S3) The new."— Presentation transcript:

1 Slide 1 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 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

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

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

4 Slide 4 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 D1 Time (days) BRT ocean analysis: D1-12NRT ocean analysis: D1 Assimilation at D1-12 Assimilation at D1-5 Operational Ocean Analysis Schedule BRT ( Behind real time ocean analysis): ~12 days delay to allow data reception For seasonal Forecasts. Continuation of the historical ocean reanalysis NRT (Near real time ocean analysis):~ 8 hours delay For Monthly forecasts

5 Slide 5 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Ocean model: HOPE (~1x1, equatorial refinement) Assimilation Method OI (3D OI). ERA-40 fluxes to initialize ocean. Retrospective Ocean Reanalysis back to 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 ORA-S3 Ocean Re-Analysis System

6 Slide 6 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Observations used in the S3 ocean analysis

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

8 Slide 8 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Altimeter product 1.Ingredients: 2.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

9 Slide 9 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Sequential Assimilation of data streams T/S conserved T/S Changed T/S conserved 1.Assimilation of Sea level anomalies 2.Assimilation of Subsurface temperature 3.Assimilation of Salinity

10 Slide 10 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Bias evolution vector-equation Some notation (Temperature,Salinity,Velocity) prescribed (constant/seasonal) k f kk f k b bbb ; 1   Balmaseda et al 2007, QJRMS

11 Slide 11 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Effect of the pressure-gradient correction Mean Assimilation Temperature Increment Without bias correction Mean Assimilation Temperature Increment With bias 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)

12 Slide 12 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 The Assimilation corrects the ocean mean state Western PacificEquatorial Indian Analysis minus Observations DATA ASSIM NO DATA ASSIM

13 Slide 13 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Correlation with OSCAR currents Monthly means, period: Seasonal cycle removed No Data Assimilation Assimilation:T+S Assimilation:T+S+Alt …improves the interannual varaibility

14 Slide 14 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 And the skill of Seasonal Forecasts of SST Data assimilation improves the seasonal forecast of SST

15 Slide 15 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 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

16 Slide 16 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Observing System Experiments Period : ALL NO_ARGO NEITHER NO_ALTI (no argo/no alti) ALLNO_ARGO - = ARGO effect (when ALTI) NO_ALTIALL - ALTI effect (when ARGO) = = - NEITHERNO_ALTI ARGO effect (when no ALTI) = - ALTI effect (when no ARGO) NEITHERNO_ARGO

17 Slide 17 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 OSES: Effect on Salinity Effect of ALTI Effect of ARGO (when alti is present) Effect of ARGO (when alti is not present)Effect of ALTI (when ARGO is not present) In the Tropical Atlantic/Indian, altimeter data helps ARGO

18 Slide 18 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 OSEs:Effect on Sea Level Effect of ALTI Effect of ARGO (when alti is present) Effect of ARGO (when alti is not present)Effect of ALTI (when ARGO is not present)

19 Slide 19 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 OSEs:Effect on T300 Effect of ARGO when Alti is present Effect of ARGO when Alti is NOT present

20 Slide 20 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Fit to the observations (rms error) Temperature Eastern Equatorial Pacific North Sub Tropical Atlantic ALL NO_ALTI NO_ARGO NEITHER South Pacific

21 Slide 21 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 ALL NO_ALTI NO_ARGO NEITHER Fit to the observations (rms error) Salinity Equatorial IndianEquatorial Atlantic

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

23 Slide 23 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November

24 Slide 24 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 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 NOSOLO = No SOLO/FSI floats Heat content Attribution of Sea Level Change Salinity

25 Slide 25 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 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 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

26 Slide 26 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Spatial distribution of trends in heat content Taux (x 0.01N/m2) Tauy (x 0.01N/m2) T300 (deg C) mean minus mean How reliable are the trends in ERA40 winds? SST (deg C)

27 Slide 27 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Comparison with ocean observations ORA-S3 IPCC-AR4 (LEVITUS) CI=0.05 deg/decade Similarities Equatorial cooling Warmer subtropics Cooling at ~60N Comments Trends in ERA40 winds seem robust St ronger features in ORA-S3, more structure Circulation changes as well as mixed layer changes

28 Slide 28 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Comparison with ocean observations ORA-S3 IPCC-AR4 (LEVITUS) CI=0.05 deg/decade Atlantic and Indian Largest warming is in the Atlantic

29 Slide 29 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Attribution of Sea level changes Trends : SL (IPCC) =1.8 mm/yr SH (IPCC) =0.5 mm/yr SH ORA-S3 ( )=0.9mm/yr SH ORA-nobs “ =0.5mm/yr ORAS3 gets closer… : SL (IPCC) =3.1 mm/yr SH (IPCC) =1.6 mm/yr SH ORA-S3 ( )=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

30 Slide 30 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 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

31 Slide 31 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Impact of data assimilation in the MOC ORAS3 ORA-nobs ORAS3 ORA-nobs Bryden05 Cunningham07 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

32 Slide 32 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 Salinity in ORA-S3 Large spin up/down in the first 2-3 years. Large effect of ARGO Large uncertainty in fresh water fluxes

33 Slide 33 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 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 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).

34 Slide 34 Magdalena A. Balmaseda, OSE Workshop, Paris 5-7 November 2007 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


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