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

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Presentation on theme: "Slide 1 The ECMWF new operational Ocean Re-Analyses System 4 (ORAS4) Magdalena A. Balmaseda, Kristian Mogensen, Anthony Weaver, and NEMOVAR consortium."— Presentation transcript:

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

2 Outline The ORA-S4 ocean reanalyses  1 st 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 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 ORAS4 Ocean Reanalysis M.A. Balmaseda 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 to present)

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

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 ORAS4 Ocean Reanalysis M.A. Balmaseda 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_trend obs -SH_trend model 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 α ref and β ref are 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 α N and β N 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) 1.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 2.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 3.Skill of Seasonal Forecasts Expensive. Model error can be a problem. 4.Process studies: Example impact of assimilation on the MOC

12 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

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

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

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

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17 RMS FirstGuess – Obs. Temperature Global MidLat: North MidLat: South TropicsEquator

18 Relative RMS (%) FirstGuess – Obs. Temperature Global MidLat: North MidLat: South TropicsEquator

19 Global MidLat: North MidLat: South TropicsEquator RMS FirstGuess – Obs. Salinity

20 Global MidLat: North MidLat: South TropicsEquator Relative RMS (%) FirstGuess – Obs. Salinity

21 Relative RMS (%) FirstGuess – Obs. Temperature Global MidLat: North MidLat: South TropicsEquator

22 Global MidLat: North MidLat: South TropicsEquator Relative RMS (%) FirstGuess – Obs. Salinity

23 OSES for Assessing Robustness of Climate Signals Ocean Heat Uptake MOC Sea Level

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

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26 300m 700m total Without Argo the ocean heat trends are weaker….

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

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

29 NEMO CONTROLNEMOASSIM-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? Zref=700m

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

31 In S4: MOC at 26N ASSIM1 CONTROL BC ASSIM-NoCoast ORA-S4 RAPID

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

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

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

35 Comparison with RAPID DATA

36 ORAS4 Ocean Reanalysis M.A. Balmaseda Summary ORAS4 is the new operational ocean reanalysis at ECMWF 1 st operational implementation of NEMOVAR Generally good performance: NEMOVAR reduces 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

37 ORAS4 Ocean Reanalysis M.A. Balmaseda Progress: MOC in NEMOVAR Footer-text Slide 37 COMBINE: first T/S NEMOVAR reanalysis ORAS4: Operational NEMOVAR reanalysis Bias correction, assim near coast, thinning of data…

38 ORAS4 Ocean Reanalysis M.A. Balmaseda Still: ASSIM versus CONTROL Footer-text Slide 38

39 ORAS4 Ocean Reanalysis M.A. Balmaseda Offline bias estimation Levitus 05 1-y relax No relaxation Additive Offline 1.Spin up of model with relaxation to climatology (1-yr) until the system stabilizes (~15 years) 2.Offline bias: Relaxation terms during last year. They vary with the seasonal cycle The additive bias correction works: blue and black curves converge

40 ORAS4 Ocean Reanalysis M.A. Balmaseda Upper Ocean Heat content

41 ORAS4 Ocean Reanalysis M.A. Balmaseda Trends in the Equatorial Pacific thermocline Shallowing of the EQ Pac thermocline. Robust (signal is in the winds and in ocean obs) Decadal signal or global warming trend? Do climate models reproduce it in the XXC runs? Related to changes in ENSO properties (Modoki ENSO): under investigation

42 ORAS4 Ocean Reanalysis M.A. Balmaseda

43 Footer-text Slide 43 Depth/Time MOC at 26N Drift in Control Trend in all: weakening and shallowing Maxima in strength correspond to deepening ORAS4 has deeper MOC ORAS4CONTROL INI CONTROL

44 ORAS4 Ocean Reanalysis M.A. Balmaseda Mixed Layer Depth Anomaly ORAS4 Footer-text Slide MLD over Labrador Sea Consistent with MOC variations


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