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The ECMWF new operational Ocean Re-Analyses System 4 (ORAS4). Magdalena A. Balmaseda , Kristian Mogensen, Anthony Weaver, and NEMOVAR consortium. Slide 1. Outline. The ORA-S4 ocean reanalyses

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  1. The ECMWF new operational Ocean Re-Analyses System 4 (ORAS4) Magdalena A. Balmaseda , Kristian Mogensen, Anthony Weaver, and NEMOVAR consortium Slide 1

  2. Outline • The ORA-S4 ocean reanalyses • 1st operational implementation of NEMOVAR • ORAS4 description • General performance • OSEs and other sensitivity experiments • Assessing Robustness of climate signals • Heat Uptake • Atlantic MOC • Summary

  3. NEMOVAR: Variational Data Assimilation system for the NEMO Ocean Model • Collaboration among several institutions: • CERFACS, Met Office, ECMWF, INRIA, Un of Reading • Incremental formulation: outer/inner loops • Covariances as in Weaver et al (OPAVAR), except for altimeter • Geostrophy, T/S relationship • Altimeter projection based on stratification • Automatic QC (consistent with EN3 data set) • Observation operator in NEMO • Bias correction algorithm • NEMOVAR in ORAS4: • Incremental 3Dvar FGAT. 10 day assim cycle. IAU

  4. ECWMF: ORAS4 Ocean Re-Analysis • It replaces the previous ORAS3 (based on HOPE/OI) • 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). Alongtrack altimeter • Strong relaxation to SST (OI_v2) until 2010. OSTIA SST thereafter • 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)

  5. Estimating Bias Correction From Argo Period The offline bias correction is estimated from Argo. The correction is applied during the data assimilation process in the production of long climate reanalysis (from 19570901 to present)

  6. Large spread in the deep ocean (poorly observed) 2000m 800m TEMP SAL

  7. Which SST product to use? OIV2_025_AVHRR: bias cold in the global mean (regional differences) Bias decreases with time. Weaker interannual variability Fit to insitu Temperature: bias cold in tropics, better in mid latitudes. DECISION: OIV2_1x1 until 2010 and OSTIA thereafter

  8. 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 (next) • Assimilation of Global Sea Level Trends (from gridded maps) • Global sea level is assimilated: FWF=SL_trendobs-SH_trendmodel • Prior to Alti era the closure is with clim SL. Smoothed daily values for real time • Choice of MDT (Mean Dynamic Topography) • External Product: Rio9, • 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 • Balance relationship between sea level and T/S is a linear formulation of the Cooper and Haines scheme, taking into account the stratification of the water column

  9. Multivariate balance for Altimeter IN NEMOVAR the balance is between sea level and a definition of steric height (vertical integration of density): Original formulation of NEMOVAR αref and βrefare calculated by linearizing the equation of estate using the background T/S values as reference. Comments: i) zref=1500m is arbitrary. An attempt to take into account that baroclinicity is low below this level. Can we account for the vertical stratification more universally? ii) this can lead to increments in model levels with large dz

  10. Modifications A):Weighting based on stratification. Use BV frequency to calculate αN and βN instead of equation of state B) Do not double-count balance-salinity corrections

  11. Assessment of the ORA-S4 re-analysis Choose a baseline: the CONTROL (e.i., no data assim) • Assim Intrinsic Metrics • Fit to obs (first-guess minus obs): Bias, RMS • Error growth (An-obs versus FG-obs) • Consistency: Prescribed/Diagnosed B and R This is insufficient to assess a Reanalysis product • Spatial/temporal consistency: long time series and spatial maps • Time correlation with Mooring currents • Correlation with altimeter/Oscar currents • Transports (MOC and RAPID): short time series • Quite limited records. Not always independent data • Skill of Seasonal Forecasts • Expensive. Model error can be a problem. • Process studies: Example impact of assimilation on the MOC

  12. 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

  13. NEMOVAR re-an: verif. against altimeter data NEMO NoObs NEMOVAR T+S NEMOVAR-S4 T+S+Alti

  14. Impact of NEMOVAR in SST forecasts Prototype of S4: latest NEMOVAR+36r4. Anomaly Correlation NEMOVARNEMO-NoObs CENTRAL EQ. PACIFIC CENTRAL EQ. ATLANTIC EQ. INDIAN N SubTrop PACIFIC N SubTrop ATLANTIC S SubTrop ATLANTIC NEMOVAR consistently improves the forecast skill of SST at different lead times and different regions

  15. Experiments Conducted • ORAS4: 1958-onwards • ORAS4 NoBC: as ORAS4 no Bias Correction. 1958-2010 • CONTROL: No assimilation. 1958-2010 • CONTROL BC: Control with offline Bias Correction. 1958-2010 • CONTROL INI: Starting from CONTROL 2008. 1958-2010 • NoAlti: ORAS4 removing Altimeter • NoMoor: ORAS4 removing Mooring • NoArgo: ORAS4 removing Argo

  16. RMS FirstGuess – Obs. Temperature Global MidLat: South MidLat: North Tropics Equator

  17. Relative RMS (%) FirstGuess – Obs. Temperature Global MidLat: South MidLat: North Tropics Equator

  18. RMS FirstGuess – Obs. Salinity Global MidLat: South MidLat: North Tropics Equator

  19. Relative RMS (%) FirstGuess – Obs. Salinity Global MidLat: South MidLat: North Tropics Equator

  20. Relative RMS (%) FirstGuess – Obs. Temperature Global MidLat: South MidLat: North Tropics Equator

  21. Relative RMS (%) FirstGuess – Obs. Salinity Global MidLat: South MidLat: North Tropics Equator

  22. OSES for Assessing Robustness of Climate Signals • Ocean Heat Uptake • MOC • Sea Level

  23. After 2000, the deep ocean warms faster than the upper ocean. How robust is this?

  24. 700m 300m Without Argo the ocean heat trends are weaker…. total

  25. total 700m 300m total 700m But still the deep ocean warms faster… 300m

  26. NEMOVAR ASSIMILATION AND AMOC Assimilation decreases MOC South of 40N. In Increases MOC in the North Atlantic NEMOVAR-NEMONoObs NEMO -NoObs

  27. Zref=700m NEMO CONTROL NEMOASSIM-CONTROL HOPE/OI Assim- Control: Integral(vdz) CI:1m2/s • In NEMOVAR: • North of 20N, Assim produces stronger/narrower WBC • At 26N, Assimilation reduces the FST • In HOPE/OI • Assim increases WBC at all latitudes. • Effect of bathimetry?

  28. Barotropic Stream Function Assimilation (B) has stronger Subpolar and Subtropical Gyres Assim tends to shift the subtropical gyre northward. Discontinuity off the Florida Coast A) CONTROL B) ASSIM C) ASSIM-NoCoast ASSIM-CONTROL NoCoast- CONTROL

  29. In S4: MOC at 26N ASSIM1 CONTROL BCASSIM-NoCoast ORA-S4 RAPID

  30. Atlantic MOC at 26 North 800m Slide 32 Footer-text

  31. Latitude/Time: 1000m Southward Propagation? Equatorial Events. How deep?

  32. Latitude/Time at 3000m Southward propagation is more clear. Post 2000 –ve unusually long Slide 34 Footer-text

  33. Comparison with RAPID DATA

  34. Summary • ORAS4 is the new operational ocean reanalysis at ECMWF • 1st operational implementation of NEMOVAR • Generally good performance: NEMOVARreduces subsurface biases, improves the interannual variability and forecast skill • Still a challenge: how to assimilate data near the coast • Evaluating Ocean Reanalysis is more than just fit to data. An evaluation of the temporal consistency of the signals is needed. • OSEs are a good diagnostic tool for robustness of climate signals • They are also a diagnostic for data assimilation systems • The OSES conducted show positive impact of different observing systems, although, in the presence of bias correction schemes is not always easy to isolate the effects. • NEXT: • Nice webpage • ¼ of degree ocean reanalysis (next 2-3 years) • Exploitation of ensemble information • More coupling

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