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Argo and GODAE

Outline GODAE overview and status Links with Argo Use of Argo data in GODAE Conclusions - perspectives. Argo and GODAE. P.Y. Le Traon with contributions from the GODAE IGST Argo Symposium, November 12-14 2003. The Global Ocean Data Assimilation Experiment.

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Argo and GODAE

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  1. Outline GODAE overview and status Links with Argo Use of Argo data in GODAE Conclusions - perspectives Argo and GODAE P.Y. Le Traon with contributions from the GODAE IGST Argo Symposium, November 12-14 2003

  2. The Global Ocean Data Assimilation Experiment Objective:To provide a practical demonstration of real-time operationalglobal oceanography • Regular comprehensive description of the ocean circulation at high temporal and spatial resolution • Consistent with a suite of remote and in-situ measurements and appropriate dynamical and physical constraints Includes the main operational and research institutions from Australia, Japan, the United States, the United Kingdom, France, Norway, Europe Main demonstration phase : 2003 to 2005 - Consolidation phase 2006 - 2007 Climate and seasonal forecasting, marine safety, fisheries, the offshore industry, Navy applications and management of shelf/coastal areas are among the expected beneficiaries of GODAE The integrated description of the ocean that GODAE will provide will also be highly beneficial to the research community

  3. GODAE Development Organisation : Patrons group : representatives of agencies, International GODAE Steering Team (IGST) (8 meetings), Project office, Melbourne Strategic Plan (2001) and Implementation Plan (2002, under revision) Rationale, required inputs and outputs, evaluation Implementation: GODAE components, work plan Main activities (2002/2003) Pilot projects (Argo, GHRSST) => develop a specific component (with a broad perspective) (global in-situ observing system for Argo, global high resolution SST fields for GHRSST) Prototype systems (N Atlantic, N Pacific, equatorial Pacific, Global) • Test the whole chain from observing system to applications and users Continue development of ocean state estimation methodologies Development of data/product serving capability and product assessment and intercomparison

  4. MERCATOR Prototype Systems, Status From prototype systems to GODAE Target 1st prototype (1/3°): operational since January 2001. One bulletin per week since the beginning. Successful demonstration. Multivariate in Fall 2003. 2nd prototype : North Atlantic/Med Sea 1/15° prototype routinely operated since January 2003. A 2° global ocean component added in Spring. 3rd prototype = GODAE target. ¼° global ocean configuration. Summer 2004. #1 #2 #3 1/4° 1/15° 1/15° 2° 1/3° 1/4° 1/3°

  5. Applications • Increasing use of MERCATOR products by ocean service providers • Public Service : strong involvement in oil spill forecasting after the Prestige tanker disaster • Ocean bulletins by Mercator forecasters • System outputs available for coupling with oil spill dissemination models (Météo-France and Met.No) • Nautical Events : Ocean races such as Route du Rhum or (now) Transat race Jacques Vabre ; etc • Research (cruise planning) • Commercial : fisheries (Catsat service)

  6. 1/12° Atlantic HYCOMSSH in Gulf Stream region White/black line is the frontal analysis of MCSST observations performed at NAVOCEANO. Black line represents data more than four days old.

  7. DIADEM/TOPAZ and regional high resolution modelling for offshore applications Global => Regional => Coastal (physics and ecosystems) A new COOP/GODAE pilot project ? Courtesy of G. Evenssen

  8. An Example of Forecast Skill (1) 60-day Prediction of a Kuroshio Meander Temperature (°C) at 115m Assim/initial state Assim/verification Forecast Free-run simulation Free-run simulation 30 April 1998 29 June 1998 Japan Meteorological Agency COMPASS-K Ocean Prediction System (Kuragano & Kamachi, 2002)

  9. Comparison of NCOM, NLOM and SeaWIFS near the Arabian Peninsula Global NLOM : Oct 3 2002 Global NCOM: Oct 3 2002 SeaWIFS composite Sep 30-Oct 7 2002

  10. Mixed-layer heat budget: contrasting various climate events ECCO Lee et al. 2002 PDO ENSO IOD Courtesy T. Lee

  11. Data and Product Servers Specialized servers : SSALTO/DUACS (altimetry), Coriolis (in-situ), etc GODAE Monterey server established to provide real-time in situ observations, atmospheric forcing, assimilation products ECMWF analyses/forecasts available through ESSC (Reading University) Product servers for the different GODAE centers => use of OpenDaP (DODS) and Live Access Server (LAS)

  12. In early 2001 several assimilation centres made their assimilation products available on the web in “real-time”: Mercator, NRL, DIADEM/TOPAZ, FOAM

  13. Intercomparison - Metrics • Definition of common metrics (e.g. transports,…) (model and assimilation) • Agree on common formats/grids and fields to be compared (e.g. SST, T, S) • Use of LAS/OpenDap to facilitate the intercomparison/exchange of products • Projects starting to intercompare products (MERSEA in Europe) with prototype systems (North Atlantic, North Pacific, equatorial Pacific)

  14. Distributed via OPENDAP & LAS Distributed via OPENDAP Distributed on FTP Distributed on FTP Intercomparison/Internal MetricsProduct organised in 4 classes • Class 1: Standard products (primary products, daily fields) T, S, U, V, SSH, MLD, BSF, TX, TY, Qtot ,E-P-R (2D and 3D interpolated fields on regular common horizontal grids and standard reduced depth levels, NetCdf CF format) • Class 2: Other 2-D model outputs (high resolution sections and moorings, daily fields) T,S,U,V • Class 3: Integrated transports (mass and heat) through sections (daily fields),meridional overturning,computed at original model grid • Class 4: Assimilation metrics (weekly products) to assess the quality of the forecasts (forecast-analysis)

  15. July mean salinity at 200m depth (ci=0.2psu) MERCATOR HYCOM-US TOPAZ LEVITUS01 North Atlantic Ready for the intercomparison (MERSEA Strand 1 project) FOAM

  16. GODAE - Links with ARGO • GODAE = Best use of data is when data are integrated using effective assimilation techniques. One of the objectives of GODAE is to maximize the benefits from the data (in particular altimetry and ARGO) • ARGO => GODAE • provide data complementary to remote sensing data to constrain ocean models • expertise for the use of Argo data in models • feedback on GODAE analyzed fields • GODAE => ARGO • provide analyzed fields to improve QC • help the data interpretation • feedback on ARGO contribution (impact in models, consistency/complementarity with other data sets, testing the utility of ARGO data in an integrated framework)

  17. Use of Argo data by GODAE All GODAE modelling/assimilation centers use Argo data ! Start using T profiles with exisiting assimilation techniques (e.g. XBT) and/or use of Argo data for model validation. Development of multivariate data assimilation techniques (2002 – on going) : => modelling of the background model error (multivariate) covariance => crucial for an effective use of Argo data in models => and for improving S fields when assimilating T fields Salinity profile assimilation (2002 – on going) Impact studies (starting) : results with and without Argo data assimilation Use of deep velocity data for model validation. Research on velocity/trajectory assimilation. How to best combine high resolution altimeter/SST data with sparse Argo data is still a research issue. GODAE groups are working on it.

  18. a a b b OLD NEW Systematic validation with available T and S profiles (here two different runs of the first MERCATOR prototype system) Model-XBT/Argo temperature differences averaged from 100-500m (first six months of 2001) Red: model is warmer. Blue: Model is cooler than XBT observations.

  19. PSY1v2 : altimetry + Argo (Coriolis) with a fully multivariate scheme versus PSY1v1 (altimetry only) (see MERCATOR poster – Bahurel et al) Salinity at 1000 m 29-10-2003 PSY1v2 All data Reynauld PSY1v1 Only SLA

  20. Reynaud PSY1v2 All data PSY1v1 Only SLA Salinity section, 29-10-2003 Florida-Gibraltar

  21. 81m 227m 6m 215 179 181 30m 112m 356m 187 179 176 Depth 55m 155m 572m 182 175 151 Number Data PSY1V2 : comparison with in-situ data not yet assimilated 22-10-2003

  22. Salinity increments from ARGO assimilation at ECMWF (K. Haines) • New S(T) assimilation leads to 2 increments • Balancing increment S1associated with • T assimilation preserves S(T) in model • (already operational at ECMWF for past • 2 years, Troccoli et al 2002) • Salinity assimilation increment S2 • associated with observed S(T) changes • (under test, 1 year assimilation complete) First assimilation increments Aug02 (averaged over upper 300m) S1 S1 + S2 S2 Mean N Atl. Salinity Top 300m S1 only Keith Haines Arthur Vidard Aug02 Aug03

  23. Use of Argo float velocity to validate intermediate circulation of the Japan COMPASS-K assimilation systemsee posters by Iwao et al., and Kamachi et al.

  24. FOAM 1° global model driven by 6 hourly fluxes from Jan-May 2003 • Only profile data assimilated in these integrations (no SSH or SST) Impact of salinity data on FOAM Differences between model and observations yet to be assimilated T assim T & S assim Climatology No assim S assim

  25. Improvement of upper ocean T by assimilating T profiles data ECCO – T. Lee Improvement of T relative to unconstrained model at 156 m 240 m 390 m

  26. Rms Misfit (Temperature) Rms Misfit (Salinity) All Data No Is Situ °C Psu Impact of Argo data on MERCATOR T and S forecasts vs Argo data not yet assimilated (2 month period – September/October 2003)

  27. (b) (a) (c) (d) 0 2 4 6 8 10 12 14 16 18 20 °C Merging of altimeter, SST and Argo data using statistical techniques (see poster Guinehut et al.) – MERCATOR/ARMOR Instantaneous T field at 200 m (02/10/02) from, (a) individual in-situ T data, (b) synthetic T (i.e. from downward extrapolation of altimetry/SST), (c) the merging of synthetic and in-situ T data and (d) the mapping of in-situ T data.

  28. Observations used to validate the regression method – year 2002 Use of altimetry (and SST) to infer 3D Temperature fields Rms of the in-situ T fields (red) relative to the Levitus climatology. Rms of the differences between the in-situ T fields and the synthetic profiles deduced from total SLA (green) and steric SLA (blue), and from a simple (SLA) or a multiple (SLA + SST) regression method. With simple statistical techniques, about half of variance of T fields can be deduced from altimetry (mesoscale). Results will be improved with advanced data assimilation techniques.

  29. Merging altimetry and Argo : validation with external data sets Separate Argo data into two sets (1 and 2) Compare T profiles deduced from altimetry, Argo (set 1) and altimetry+Argo (set 1) with set 2 Argo data North Atlantic – year 2002 Red: Rms of T field (anomalies/Levitus) Rms error in predicting subsurface T anomalies using only in-situ profiles from set 1 (Turquoise), using synthetic profiles (Green) and using in-situ combined to synthetic profiles (Blue).

  30. Conclusions/Perspectives • Very good progress in developing the use of Argo data in GODAE • All GODAE centers use Argo data (validation, assimilation). • Assimilation techniques have been adapted/improved to use Argo data. • Argo data provide already a unique data set for model validation. • Impact studies are starting. They show a significant impact. Assimilation techniques should be developed further to better merge the high resolution altimeter/SST data with Argo data (joint GODAE/Argo task) As GODAE develops, feedback on Argo initial design is expected. Meanwhile it is important to keep the global coverage objective of Argo (GODAE global models are progressively replacing regional models). Reanalysis activities : require high quality delayed mode data sets Salinity data required (with/without drift correction) Deep velocity data and fields from Argo are needed for validation but also for improving the use of altimetry (mean dynamic topography estimation).

  31. cm Left, altimeter/in-situ SLA scatter plot. Right, altimeter/in-situ SLA differences. Observations correspond to one month of data (26/08/03  25/09/03) and dynamic height are calculated relatively to 700 m. (Unit: cm) Provides a characterisation of errors and non-steric sea level signals (should be carried out over long time series)

  32. Impact of salinity errors on Altimetry/Argo comparison (North Atlantic) (Larnicol et al., 2003) (T,Scal) (T,S) (T,Sint) 1 2 3 std rms mean 5.38 5.49 1.11 5.11 5.24 1.16 5.43 5.43 -0.20

  33. Delta temperature (800m) Delta salinity (800m) Delta HBAR Delta SLA +7cm -7cm Delta HBAR Delta HBAR Delta S Delta S Delta T Delta T Multivariate assimilation: How it works

  34. Reynolds Safo PSY1v2 PSY1v1 Surface Sea Temperature 22/10/2002

  35. (a) (b) (c) (d) 5 7 9 11 13 15 17 19 21 °C T field from (a) the XBT section, (b) Levitus, (c) the vertical projection of SLA/SST (d) the combination of synthetic and Argo data. Rms error = 0.5°C to be compared with 1°C if Levitus is used.

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