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Training Course 2009 – NWP-DA: Ocean Data Assimilation 1 Data assimilation in the ocean Magdalena A. Balmaseda.

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Presentation on theme: "Training Course 2009 – NWP-DA: Ocean Data Assimilation 1 Data assimilation in the ocean Magdalena A. Balmaseda."— Presentation transcript:

1 Training Course 2009 – NWP-DA: Ocean Data Assimilation 1 Data assimilation in the ocean Magdalena A. Balmaseda

2 Training Course 2009 – NWP-DA: Ocean Data Assimilation 2 Outline of lecture Applications of ocean data assimilation: Importance of ocean data assimilation on the seasonal forecasts. Ocean data assimilation and historical ocean reanalyses. The ocean observing system Typical ocean data assimilation system Background co-variances: spatial scales Balance relationships for salinity and velocity Altimeter and Salinity data Bias correction Future directions Ocean in the Medium Range forecast Ocean initialization for climate projections. The myth of coupled data assimilation

3 Training Course 2009 – NWP-DA: Ocean Data Assimilation 3 Why do we do ocean analyses? Climate Resolution (global ~1x1 degrees) To provide initial conditions for seasonal forecasts. To provide initial conditions for monthly forecasts To provide initial conditions for multi-annual forecasts (experimental only at this stage) To reconstruct the history of the ocean (re-analysis) High resolution ocean analysis (regional, ~1/3-1/9-1/12 degrees) To monitor and to forecast the state of the ocean Defense, commercial applications (oil rings…)

4 Training Course 2009 – NWP-DA: Ocean Data Assimilation 4 Ocean Data Assimilation Activities in the Community Operational real time high resolution, mostly regional, no reanalysis: FOAM (MO), MERCATOR, NRL,CSIRO,… Operational real time, global, reanalysis (seasonal/decadal forecasts): ECMWF, MO, Meteo-France/MERCATOR, NCEP, MRI, JMA… Research, mainly climate reanalysis: ENACT consortium, ECCO consortium, GSOP.

5 Training Course 2009 – NWP-DA: Ocean Data Assimilation 5 Delayed Ocean Analysis ~12 days Real Time Ocean Analysis ~Real time ECMWF: Weather and Climate Dynamical Forecasts ECMWF: Weather and Climate Dynamical Forecasts 18-Day Medium-Range Forecasts 18-Day Medium-Range Forecasts Seasonal Forecasts Seasonal Forecasts Monthly Forecasts Monthly Forecasts Ocean model Atmospheric model Wave model Atmospheric model Ocean model Wave model

6 Training Course 2009 – NWP-DA: Ocean Data Assimilation 6 Basis for extended range forecasts: monthly, seasonal, decadal The forecast horizon for weather forecasting is a few days. Sometimes it is longer e.g. in blocking situations 5-10 days. Sometimes there might be predictability even longer as in the intra- seasonal oscillation or Madden Julian Oscillation. But how can you predict seasons, years or decades ahead? The feature that gives longer potential predictability is forcing given by slow changes on boundary conditions, especially the to the Sea Surface Temperature (SST) Atmospheric responds to SST anomalies, especially large scale tropical anomalies El Nino/Southern Oscillation is the main mode for controlling the predictability of the interannual variability.

7 Training Course 2009 – NWP-DA: Ocean Data Assimilation 7 In the equatorial Pacific, there is considerable interannual variability. The EQSOI ( INDO-EPAC) is especially useful: it is a measure of pressure shifts in the tropical atmosphere but is more representative than the usual SOI (Darwin – Tahiti). Note 1983, 87, 88, 97, 98. Sea Level Pressure (SOI) Sea Surface Temperature (Nino 3) Nino 3 Nino 3.4 SST variability is linked to the atmospheric variability seen on previous slide suggesting a strongly coupled process.

8 Training Course 2009 – NWP-DA: Ocean Data Assimilation 8 Need to Initialize the subsurface of the ocean

9 Training Course 2009 – NWP-DA: Ocean Data Assimilation 9 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

10 Training Course 2009 – NWP-DA: Ocean Data Assimilation 10 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):~ For Var-EPS/Monthly forecasts

11 Training Course 2009 – NWP-DA: Ocean Data Assimilation 11 Equatorial Atlantic: Taux anomalies Equatorial Atlantic upper heat content anomalies. No assimilation Equatorial Atlantic upper heat content anomalies. Assimilation ERA15/OPS ERA40 Uncertainty in Surface Fluxes: Need for Data Assimilation Large uncertainty in wind products lead to large uncertainty in the ocean subsurface The possibility is to use additional information from ocean data (temperature, others…) Questions: 1.Does assimilation of ocean data constrain the ocean state? 2.Does the assimilation of ocean data improve the ocean estimate? 3.Does the assimilation of ocean data improve the seasonal forecasts

12 Training Course 2009 – NWP-DA: Ocean Data Assimilation 12 ORA-S3 Main Objective: Initialization of seasonal forecasts Historical reanalysis brought up-to-date (12 days behind real time) Source of climate variability Main Features ERA-40 daily fluxes ( ) and NWP thereafter Retrospective Ocean Reanalysis back to 1959 Multivariate on-line Bias Correction (pressure gradient) Assimilation of temperature, salinity, altimeter sea level anomalies an global sea level trends. 3D OI, Salinity along isotherms Balance constrains (T/S and geostrophy) Sequential, 10 days analysis cycle, IAU Balmaseda etal 2007/2008

13 Training Course 2009 – NWP-DA: Ocean Data Assimilation 13 Correlation with OSCAR currents Monthly means, period: Seasonal cycle removed No Data Assimilation Assimilation:T+S Assimilation:T+S+Alt Data Assimilation improves the interannual variability of the ocean analysis

14 Training Course 2009 – NWP-DA: Ocean Data Assimilation 14 Ocean data assimilation also improves the forecast skill (Alves et al 2003) Data Assimilation No Data Assimilation Impact of Data Assimilation Forecast Skill

15 Training Course 2009 – NWP-DA: Ocean Data Assimilation 15 OCEAN REANALYSIS As well as initializing the seasonal forecasts, the ocean reanalysis is an important source for climate variability studies: Meridional Overturning Circulation Trends in the upper ocean heat content Attribution of Sea level rise

16 Training Course 2009 – NWP-DA: Ocean Data Assimilation 16 This cartoon shows the 3 dimension circulation of the ocean. Note the differences between the North Atlantic which has deep water formation and the North Pacific which does not. Note also the Indonesian throughflow. Thermohaline Circulation

17 Training Course 2009 – NWP-DA: Ocean Data Assimilation 17 Estimation of the Atlantic MOC Assimilation No-Data Bryden etal 2005 Cunningham etal 2007 From Balmaseda etal 2007

18 Training Course 2009 – NWP-DA: Ocean Data Assimilation 18 North Atlantic: T300 anomaly North Atlantic: S300 anomaly Climate Signals….

19 Training Course 2009 – NWP-DA: Ocean Data Assimilation 19 Uncertainty in Ocean Reanalysis: GSOP EQPAC EQATL EQIND TRPAC TRATL NPAC NATL GLOBAL Signal to noise ratio: T300S300

20 Training Course 2009 – NWP-DA: Ocean Data Assimilation 20 Impact on ECMWF-S3 Seasonal Forecast Skill S3 Nodata S3 Assim Balmaseda et al 2007

21 Training Course 2009 – NWP-DA: Ocean Data Assimilation 21 Ocean circulation: some facts The radius of deformation in the ocean is small (~30km). So one expects things to happen on much smaller scales than in the atmosphere where the radius of deformation is ~3000km. [Radius of deformation =c/f where c= speed of gravity waves. In the ocean c~<3m/s for baroclinic processes.] The time scales for the ocean cover a much larger range than for the atmosphere: slower time scales for adjustment. The ocean is strongly stratified in the vertical. The ocean is forced at the surface by the wind, by heating/cooling and by fresh water fluxes (precip-evap). The electromagnetic radiation does not penetrate into the ocean, which makes the ocean difficult to observe from satellites.

22 Training Course 2009 – NWP-DA: Ocean Data Assimilation 22 Ocean Circulation Wind Driven: Gyres Western Boundary Currents Density Driven: Thermohaline Circulation Sinking of water in localized areas Ubiquitous upwelling maintaining the stratification Deep circulation concentrated in the western boundary (Stommel) Adjusment processes Equatorial Kelvin waves (c ~2-3m/s) (months) Planetary Rossby waves (months to decades)

23 Training Course 2009 – NWP-DA: Ocean Data Assimilation 23 The Ocean Observing system ARGO floats XBT (eXpandable BathiThermograph) Moorings Satellite SST Sea Level

24 Training Course 2009 – NWP-DA: Ocean Data Assimilation 24 Time evolution of the Ocean Observing System XBTs 60s Satellite SST Moorings/Altimeter ARGO PIRATA TRITON Gravity info: GRACE GOCE 2008 SSS info: Aquarius SMOS

25 Training Course 2009 – NWP-DA: Ocean Data Assimilation 25 Data coverage for Nov 2005 Changing observing system is a challenge for consistent reanalysis Todays Observations will be used in years to come Moorings: SubsurfaceTemperature ARGO floats: Subsurface Temperature and Salinity + XBT : Subsurface Temperature Data coverage for June 1982 Ocean Observing System

26 Training Course 2009 – NWP-DA: Ocean Data Assimilation 26 The background error correlation scales are highly non isotropic to reflect the nature of equatorial waves- Equatorial Kelvin waves which travel rapidly along the equator ~2m/s but have only a limited meridional scale as they are trapped to the equator. Length scales for a typical climate model: ~2 degree at mid latitudes ~15-20 degrees along the eq. Typical cycling: 10 day window Complex structures an smaller length scales near coastlines are usually ignored. Background errors

27 Training Course 2009 – NWP-DA: Ocean Data Assimilation 27 Density dependent correlation scales The 3-D background length scales can also depend on both geographical distance and density differences.

28 Training Course 2009 – NWP-DA: Ocean Data Assimilation 28 From, a salinity increment by preserving the water mass characteristics (Troccoli et al, MWR,2002) S(T) scheme: Temperature/Salinity relationship is kept constant From,velocity is also updated by introducing dynamical constraints (Burgers et al, JPO 2002) It prevents the disruption of the geostrophic balance and the degradation of the circulation. Important close to the equator, where the time scales for inertial adjustment are long. Updates to Salinity and Velocity

29 Training Course 2009 – NWP-DA: Ocean Data Assimilation 29 Balance relationship for Salinity We have temperature data to assimilate but until recently, no salinity data. Velocity data remain scarce. Unfortunately leaving salinity untouched can lead to instabilities. The following slide shows the problems that can occur if salinity is not corrected. A partial solution is to preserve the water-mass (T-S) properties below the surface mixed layer. In the last decades there have been 3 generations of ocean data assimilation systems: G1: salinity was not corrected. G2: is corrected but not analysed in system-2 (Troccoli etal 2002). G3: is corrected and analysed in system-3 (Haines etal 2006).

30 Training Course 2009 – NWP-DA: Ocean Data Assimilation 30 3 months into assimilation Stratified Temp at I.C Meridional Sections (Y-Z) 30W Temperature Salinity Constraint: To update salinity to preserve the water mass properties (Troccoli et al 2002) Temperature Salinity Spurious Convection Develops

31 Training Course 2009 – NWP-DA: Ocean Data Assimilation 31 Updating Salinity: S(T) SCHEME S A) Lifting of the profile Tanal Tmodel B) Applying salinity Increments Sanal Smodel

32 Training Course 2009 – NWP-DA: Ocean Data Assimilation 32 Sea Level Anomaly from Altimetry El Nino 197/98 Sea Level anomaly Equatorial Temperature anomaly

33 Training Course 2009 – NWP-DA: Ocean Data Assimilation 33 Balance relationship: vertical projection of sea level anomaly Vertically stratified fluid: Lets take a 2 layer model, where the density of the second layer is only a little greater than that of the upper layer. Typically A 10cm displacement of the top surface is associated with a 30m displacement of the interface (the thermocline). 10cm 30 m If we observe sea level, one can infer information on the vertical density structure

34 Training Course 2009 – NWP-DA: Ocean Data Assimilation 34 A linearized balance operator for global ocean assimilation Define the balance operator symbolically by the sequence of equations Treat as approximately mutually independent Temperature Salinity SSH u-velocity v-velocity Density Pressure (Weaver et al., 2005, QJRMS)

35 Training Course 2009 – NWP-DA: Ocean Data Assimilation 35 Components of the balance operator Salinity balance (approx. T-S conservation) SSH balance (baroclinic) u-velocity balance (geostrophy with β-plane approx. near eq.) v-velocity (geostrophy, zero at eq.) Density (linearized eq. of state) Pressure (hydrostatic approx.)

36 Training Course 2009 – NWP-DA: Ocean Data Assimilation 36 Example: 3D-Var assimilation of a single T obs. at 100m on the eq. The β-plane geostrophic approximation results in a continuous zonal velocity increment across the equator. Multivariate structures implied by the balance opt. Courtesy of A. Weaver

37 Training Course 2009 – NWP-DA: Ocean Data Assimilation 37 Projecting altimeter data into the subsurface via the balance operator (and its adjoint) Example: 3D-Var assimilation of a single SSH obs. at the eq. The anisotropic response is a combination of a background state dependence in 1) the salinity balance; 2) the linearized equation of state; and 3) the temperature error variance parameterization. Courtesy of A. Weaver

38 Training Course 2009 – NWP-DA: Ocean Data Assimilation 38 Assimilation of altimeter data Ingredients: The Mean Sea Level is a separate variable, and can be derived from Gravity information from GRACE (Rio4/5 from CLS, NASA, …) and future GOCE. But the choice of the reference global mean is not trivial and the system can be quite sensitive to this choice. Active area of research. The GLOBAL sea level changes can also assimilated: Ocean models are volume preserving, and can not represent changes in GLOBAL sea level due to density changes (thermal expansion, ….). The difference between Altimeter SL and Model Steric Height is added to the model as a fresh water flux. Observed SLA from T/P+ERS+GFO Respect to 7 year mean of measurements A Mean Sea Level

39 Training Course 2009 – NWP-DA: Ocean Data Assimilation 39 Mean Dynamic Topography differences Systematic differences between MODEL-MDT and GRACE derived products: Pacific-Atlantic SL gradient is steeper in MODEL-MDT We need appropriate methods to treat this systematic difference in the assimilation scheme. If correct, the information from GRACE-MDT could be used before the altimeter era. For System 3 we have chosen the MODEL_MDT

40 Training Course 2009 – NWP-DA: Ocean Data Assimilation 40 Results are very sensitive to the MDT Not good for consistent historical reanalysis

41 Training Course 2009 – NWP-DA: Ocean Data Assimilation 41 Why a bias correction scheme? A substantial part of the analysis error is correlated in time. Changes in the observing system can be damaging for the representation of the inter-annual variability. Part of the error may be induced by the assimilation process. What kind of bias correction scheme? Multivariate, so it allows to make adiabatic corrections (Bell et al 2004) It allows time dependent error (as opposed to constant bias). First guess of the bias non zero would be useful in early days (additive correction rather than the relaxation to climatology in S2) Generalized Dee and Da Silva bias correction scheme Balmaseda et al 2007 Bias Correction Scheme

42 Training Course 2009 – NWP-DA: Ocean Data Assimilation 42 Impact of data assimilation on the mean Assim of mooring data CTL=No data Large impact of data in the mean state: Shallower thermocline PIRATA

43 Training Course 2009 – NWP-DA: Ocean Data Assimilation 43 The systematic error may be the result of the assimilation process T-Assim incr (C.I=0.05 C/10 days) Vertical velocity (C.I=0.5m/day) No-data assim Mean Analysis – Obs NINO 3: Eastern Pac 100m 200m

44 Training Course 2009 – NWP-DA: Ocean Data Assimilation 44 The Assimilation corrects the ocean mean state Free model Data Assimilation z (x)Equatorial Pacific Data assimilation corrects 2 sorts of systematic errors: The depth of the equatorial thermocline: Probably diabatic. Possible to correct The slope of the equatorial thermocline Adiabatic: not easy to correct by diabatic corrections.

45 Training Course 2009 – NWP-DA: Ocean Data Assimilation 45 Bias evolution vector-equation Some notation (Temperature,Salinity,Velocity) prescribed (constant/seasonal) k f kk f k b bbb ; 1

46 Training Course 2009 – NWP-DA: Ocean Data Assimilation 46 Bias and circulation T-Assim incr (C.I=0.05 C/10 days) Vertical velocity (C.I=0.5m/day) Standard Balmaseda etal 2007 Bias corrected: pressure

47 Training Course 2009 – NWP-DA: Ocean Data Assimilation 47 Effect of bias correction on the time-evolution Assim of mooring data CTL=No data Bias corrected Assim

48 Training Course 2009 – NWP-DA: Ocean Data Assimilation 48 Future directions: Pros and Cons of uncoupled versus coupled data assimilation: A feasible solution is to have a 2-tier approach: atmosphere initialization including and ocean model. It is desirable to use the same model in the assimilation and in the forecast Interactive ocean in the medium range weather forecasts? Lead/lag relationship between SST and tropical convection The initialization at high of the ocean subsurface may be important in the prediction of tropical cyclones. Ocean initialization for decadal forecasts. Is it relevant? How to do it?

49 Training Course 2009 – NWP-DA: Ocean Data Assimilation 49 Initialization: uncoupled versus coupled Advantages: It is possible The systematic error during the initialization is small(-er) Disadvantages: Model is different during the initialization and forecast Possibility of initialization shock No synergy between ocean and atmospheric observations Full Coupled Initialization: No clear path for implementation in operational systems due to the different time scales. Difficult to initialize the atmosphere and the ocean simultaneously Weakly-coupled initialization IT IS FEASIBLE Atmosphere initialization with a coupled model Ocean initialization with a coupled model. Need of a good algorithm to treat model error. Uncoupled: Most common Other Strategies

50 Training Course 2009 – NWP-DA: Ocean Data Assimilation 50 Example: Phase between SST and tropical convection Composites of SST anomalies (contours) and OLR (colours) of MJO events. SST and convection are in quadrature. The lead-lag relationship between SST and deep convection seems instrumental for setting the propagation speed of the MJO. A two way coupling is required, but may not be enough. Thin ocean layers are needed to represent this phase relationship.

51 Training Course 2009 – NWP-DA: Ocean Data Assimilation 51 From Ginis 2008 Ocean Initial Conditions may be important

52 Training Course 2009 – NWP-DA: Ocean Data Assimilation 52 Future developments at ECMWF NEMOVAR: Variational DA for the NEMO model Incremental approach 3d and later 4dvar Different resolutions in inner-outer loop Ongoing scientific developments: Ensemble methods for flow dependent covariances (with 3d-var) Choice of control variables (density/spiciness) Treatment of Observation and Model bias

53 Training Course 2009 – NWP-DA: Ocean Data Assimilation 53 Summary Data assimilation in the ocean serves a variety of purposes, from climate monitoring to initialization of coupled model forecasts. Compared to the atmosphere, ocean observations are scarce. The main source of information are Temperature and salinity profiles (ARGO/moorings/XBTs), sea level from altimeter, SST from satellite/ships and Geoid from gravity missions. Assimilation of ocean observations reduces the large uncertainty(error) due to the forcing fluxes. It also improves the initialization of seasonal forecasts, and it can provide useful reconstructions of the ocean climate. Data assimilation changes the ocean mean state. Therefore, consistent ocean reanalysis requires an explicit treatment of the bias. More generally, we need a methodology that allows the assimilation of different time scales. The DA activities in the ocean are reaching maturity, after a steep learning curve during the 90s. However, the results provided by the different assimilation methods is too diverse. Work is needed for the consolidation and development of methodologies. The separate initialization of the ocean and atmosphere systems can lead to initialization shock during the forecasts. A more balance coupled initialization is desirable, but it remains challenging.

54 Training Course 2009 – NWP-DA: Ocean Data Assimilation 54 Some references related to ocean data assimilation at ECMWF The ECMWF System 3 ocean analysis system, Balmaseda et al To appear in Mon. Wea. Rev. See also ECMWF Tech-Memo 508. Three and four dimensional variational assimilation with a general circulation model of the tropical Pacific. Weaver, Vialard, Anderson and Delecluse. ECMWF Tech Memo 365 March See also Monthly Weather Review 2003, 131, and MWR 2003, 131, Balanced ocean data assimilation near the equator. Burgers et al J Phys Ocean, 32, Salinity adjustments in the presence of temperature adjustments. Troccoli et al., MWR.. Comparison of the ECMWF seasonal forecast Systems1 and 2. Anderson et al ECMWF Tech Memo 404. Sensitivity of dynamical seasonal forecasts to ocean initial conditions. Alves, Balmaseda, Anderson and Stockdale. Tech Memo 369. Quarterly Journal Roy Met Soc February 2004 A Multivariate Treatment of Bias for Sequential Data Assimilation: Application to the Tropical Oceans. Q. J. R. Meteorol. Soc., Balmaseda et al. A multivariate balance operator for variational ocean data assimilation. Q.J.R.M.S, 2006, Weaver et al. Salinity assimilation usinfS(T) relationships. K Haines et al Tech Memo 458. MWR, Impact of Ocean Observing Systems on the ocean analysis and seasonal forecasts, MWR. 2007, Vidard et al. Impact of ARGO data in global analyses of the ocean, GRL,2007. Balmaseda et al. Historical reconstruction of the Atlantic Meridional Overturning Circulation from the ECMWF ocean reanalysis. GRL Balmaseda et al.


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