and Prospects for the Future Michele Rienecker Global Modeling and Assimilation Office NASAGSFC With many contributions borrowed from the Ocean Data Assimilation Community e specially Coauthors of Rienecker et al OceanObs09 ID: 1026603
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1. Ocean Reanalyses- An Overview and Prospects for the FutureMichele RieneckerGlobal Modeling and Assimilation OfficeNASA/GSFCWith many contributions borrowed from the Ocean Data Assimilation CommunityespeciallyCo-authors of Rienecker et al. OceanObs’09Stammer et al. OceanObs’09Smith et al., Mercator Ocean Newsletter, January 2010Masina et al., Mercator Ocean Newsletter, January 2010Heimbach and Ponte, CLIVAR Decadal Workshop, 2010Yan Xue, this workshopGuillaume Vernieres & Robin KovachMagdalena BalmasedaUS CLIVAR IESA Workshop1
2. 2Early ocean reanalysis drivers: seasonal forecasts (~1980 onwards) and decadal prediction (EU ENACT and follow-on ENSEMBLES project; CMIP5: 1960 onwards)GODAE and CLIVAR: major advances through international cooperation, connections with the observationalists and with users – especially evident in the EUA variety of estimation methods: Optimal interpolation (OI), asymptotic Kalman filters, 3DVar Ensemble methods – state-dependent error estimates Smoothers: 4Dvar; Kalman smoothersMany different models Surface fluxes from various sources: atmospheric reanalyses or RT NWP analysesUS efforts: CSFR (NCEP), GFDL, GMAO, SODA (UMD&TAMU), ECCO (JPL&MIT) The quality of the analyses depends on: the quality of the model the quality of the forcing the quality of the data and how we use them i.e., the error statistics used in the estimation machinery US CLIVAR IESA Workshop
3. 3 Uneven observational coverage in space and time Deep ocean and ice covered regions are poorly observed. OS in marginal seas is declining OS in coastal areas needs attention However ….. Argo has had a dramatic impact on ocean analysesThe Observing System – one of the greatest challenges for ocean reanalysesUS CLIVAR IESA WorkshopGlobalNumber of temperature profiles per month
4. US CLIVAR IESA Workshop4In situ profiles available for assimilation in January 2010Argo: 8853 XBT: 938 TAO: 1907 PIRATA: 265
5. 5Ocean Analysis Systems for Climate: global, 1/4° or coarser horizontal grid spacing comparisons of many metrics: http://www.clivar.org/organization/gsop/gsop.php Many applications: monitoring climate - diagnostics of Earth’s climate variability, including unobserved quantities initialization of climate forecasts observing system studies SODAECMWFGECCOSODAECMWFECMWFGECCOGECCOSSH trends 1962-2001 *Thermosteric (upper)Halosteric (lower)* Note these analyses did not use corrected XBT dataFrom Stammer et al. OceanObs’09 CWPAtlantic MOC at 26N⎯ ECMWF analysis⎯ ECMWF – no data assim● Bryden et al. (2005)● Cunningham et al. (2007)US CLIVAR IESA WorkshopFrom Balmaseda et al., GRL, 2007
6. 6Heimbach et al. OceanObs’09 CWPImpact of SST, SSH, TSSSH, given SST, TS, BImpact of the observing system on MOC (Sv) in the ECCO systemRMS differences for 2006SST, given SSH, TS, BTS, given SSH, SST, BECMWF S3 (1-7 mo forecast)Balmaseda & Anderson (2009)US CLIVAR IESA WorkshopImpact of the observing system on Seasonal SST Forecast SkillHeimbach & Ponte:(1) Climatology only poor constraint - compare topre-1990’s era!(2) Large impact of altimetry & Argo(3) Complementary nature of Argo, altimetry(4) Modest impact of SST
7. 7 Argo: Biggest impact in less well-observed regions (Indian, S. Atlantic, S. Pacific, Southern Ocean) Argo salinity also improves estimated temperature Smith and Haines (2009) T impact on S can differ from S data impact on S Balmaseda et al. (2007)⇒ an issue for pre-Argo era ECMWF S3Impact of Argo on av. salinity in upper 300mBalmaseda et al., GRL 2007US CLIVAR IESA WorkshopImpact of Argo T & S on analysis salinity (0-300m)Impact of Argo T only on analysis salinity (0-300m)Argo
8. 8From Stammer et al. OceanObs’09 CWPNorth Atlantic heat contentUpper 700m* Note these analyses did not use corrected XBT dataUS CLIVAR IESA Workshop(Obs)From Masina et al., 20101601284-4-8-1220051960197019801990Global heat contentUpper 700mT300T300 Anomaly 70S – 70N30N – 70NFrom Yan Xue’s presentation& Guillaume Verniere’s posterUpdated diagnostics - Upper oceanAverage T in upper 300mComparison across systems – uncertainty or discriminator?A single system – uncertainty?
9. 9From Stammer et al. CWP Freshwater contentUpper 700mUS CLIVAR IESA WorkshopFrom Masina et al., 2010
10. US CLIVAR IESA Workshop10GMAO ODAS-3From Smith et al., 2010, Mercator Ocean NewsletterUsing Water masses as a validation metric: pdf of salinity mis-fits in S(T)
11. US CLIVAR IESA Workshop11Detection & Attribution of Atlantic salinity changes, P. Stott, R. Sutton, D. Smith, GRL, 2008
12. US CLIVAR IESA Workshop12Issues for ocean climate reanalyses: Error statistics – both model and observations (representation errors) Reliable estimates of uncertainty in the analyses Understanding the analysis differences, esp. in modern “well-observed” era: controlled experiments – same data, QC, forcing, …. Model and forcing biases – esp. important in the early periods Treatment of salinity in the pre-Argo era Freshwater fluxes – imbalance impacts SSH variability and trends as well as salinity SSH biases in the assimilation of altimetry Corrections to data bases (e.g., XBT, Argo) – need for continual updates to re-analyses & careful documentation
13. 13Expected improvements in ocean state estimation in the next 10 years: Reduced model biases Increased model resolution (both horizontal and vertical) Improved parameterizations Improvements in NWP analyses and re-analyses ⇒ improved forcing products Improved error covariance modeling – the basis of the estimation procedure Improved RT and DM QC for input data streams Easy access to data bases with appropriate metadata ⇒ all data will be available to be assimilatedUS CLIVAR IESA WorkshopProspects for the Future:
14. 14Emerging generation: “coupled” analyses or Integrated Earth System Analyses State estimates consistent across components Current efforts target improved initialization of climate forecastsSome current examples: 3DVar (NCEP) EnKF (GFDL, GMAO) MvOI (GMAO) Future: atmosphere-ocean-land-sea-ice-chemistry-biologyGFDL CDA - EnKF Impact on Forecast Niño-3 SST anomalies RMS as a fn of initial time and forecast leadCourtesy Tony Rosati Initial month3Dvar CDA -EnKFForecast lead (months)1.01.80.01.01.80.0JF M A M J J A S O N DJF M A M J J A S O N D51119735111973US CLIVAR IESA WorkshopT300T300 Anomaly 70S – 70N30N – 70NFrom Yan Xue’s presentation& Guillaume Verniere’s posterUpdated diagnostics - Upper oceanAverage T in upper 300m
15. 15GMAO: GEOS-5 “Coupled” Ocean Assimilation using Atmospheric ReplayModel predicted changeReplay Correction from Existing AnalysisTotal “observed change” 09Z12Z15Z18Z00Z21Z03Z06Z09ZAnalysis from MERRA, ECMWF JRA-25, etcAtmospheric Replay BackgroundAny existing atmospheric analysisAssimilated atmos analysisNew ocean analysisCoupled model forecast“Initialized”States for Coupled forecast03Z06ZNext coupledanalysis cycleCoupled analysis cycleUS CLIVAR IESA WorkshopNCEP: 1st guess for both ocean and atmosphere are from AOGCM integrationGFDL: assimilate NCEP reanalysis; “All coupled components adjusted by observed data through instantaneously‐exchanged fluxes”Weakly coupled atmosphere-ocean assimilation
16. US CLIVAR IESA Workshop16Several examples of coupled ocean-sea-ice assimilation using co-variances estimated from EnKFLisæter et al. Ocean Dynamics, 2003 Lisæter et al. JGR, 2007 – CryoSat OSSECaya et al. JAOTech, 2010Adjoint investigation of sea-ice export sensitivities:Heimbach et al., Ocean Modelling, 2010Lisæter et al. JGR, 2007Coupled Ocean-Sea-Ice AnalysesNo AssimilationAssimilation of synthetic CryoSat sea-ice concentrationIce thicknessIce thicknesssalinitysalinity
17. μMmg m-3NCEP Forcing20092009MERRA Forcing20092009Courtesy, Watson GreggUpper-ocean vertical fluxes are critically important to biological activity. Issues for assimilation: Assimilation perturbations to ocean physics Surface forcingOcean BiologyUS CLIVAR IESA Workshop17
18. Assimilated Global Annual Median Chlorophyllfor MODIS-Aqua mg m-3Global Annual Median Chlorophyll for MODIS-AquaCourtesy, Watson GreggReduces sampling biasesfrom satellite in the annualdata18US CLIVAR IESA Workshop
19. US CLIVAR IESA Workshop19Summary/Comments A lot of progress in ocean data assimilation! For some metrics, current syntheses diverge in the modern “well-observed” era – somewhat surprising! We need to understand these differences: controlled experiments – same data, QC, forcing, …. Missing reliable estimates of uncertainty in the analyses We need to track assimilation increment contributions to budgets – a measure of quality and of progress. There is a push for “coupled” atmosphere-ocean assimilation where observations from one medium constrain the state in the other. We need to make sure that we don’t ignore the issues still to be addressed in the uncoupled system: representation error treatment of biases ….
20. US CLIVAR IESA Workshop20Thank you for your attention!