IGST Meeting June 2-4, 2008 The GMAO’s Ocean Data Assimilation & SI Forecasts Michele Rienecker, Christian Keppenne, Robin Kovach Jossy Jacob, Jelena Marshak.

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Presentation transcript:

IGST Meeting June 2-4, 2008 The GMAO’s Ocean Data Assimilation & SI Forecasts Michele Rienecker, Christian Keppenne, Robin Kovach Jossy Jacob, Jelena Marshak Global Modeling and Assimilation Office (GMAO) NASA/Goddard Space Flight Center

ODAS-1 Algorithms: - Univariate optimal interpolation (UOI) - contributed to USGODAE LAS products - Multivariate EnKF - temperature assimilation also corrects salinity and currents Model: Poseidon v4 OGCM (Schopf and Loughe, 1995) : Quasi-isopycnal vertical coordinate Prognostic variables are H, T, S, u and v Sea surface height (SSH) is diagnostic 1/3° x 5/8° x L27 Observations: - T(z) from XBTs/Moorings + synthetic S(z) from T-S climatology - T(z), S(z) from Argo drifters - SSH from Topex/Poseidon and Jason-1 (only for EnKF) Forcing: - SSM/I and QuikSCAT surface wind stress products (Atlas & Ardizzone) - NCEP reanalysis surface heat fluxes - GPCP monthly precipitation - Reynolds & Smith SST relaxation - Levitus SSS relaxation Next system: ODAS-2 Implemented with ESMF under GEOS-5 modeling system MOM4 (collaboration with NCEP and GFDL) GMAO’s Atmospheric analyses for forcing GMAO Ocean Data Assimilation Systems

ODAS-1 Experiments: 1993-present - Argo impacts (2003  ) - Application: Seasonal Forecasts with GMAO CGCMv1 Conducted “operationally” every month Contributed to US consensus forecast GMAO Ocean Data Assimilation Experiments

Salinity Variability along the Equatorial Pacific (2ºS-2ºN) 24.5kg/m 3 Density Surface

165ºE 140ºW EnKF Exp1 - All observations EnKF Exp3 - No Argo OI - No SSH Observations - Argo Salinity Variations along the Equatorial Pacific 95ºW

Salinity analyses validated against CTD data TAO servicing cruises (8ºS-12ºN) 2005 Niño-4 (160ºE-150ºW) Niño-3 (150ºW-110ºW) EnKF - All observations EnKF - No Argo OI - No SSH Control - No Assimilation

GMAO CGCMv1 (Tier1) Forecast Ensembles 12 month Coupled Integrations: 6-30 ensemble members AGCM (AMIP forced with Reynolds SST; NCEP Analyses) Ocean DAS (Surface wind analysis, GPCP precipitation; Reynolds SST, Temperature profiles; synthetic salinity profiles; Argo; altimetry) Ocean state estimate perturbations:  ’s randomly from snapshots Atmospheric state perturbations:  ’s randomly from previous integrations AGCM: NSIPP1 AGCM, 2 x 2.5 x L34 LSM: Mosaic (SVAT) OGCM: Poseidon v4, 1/3 x 5/8 x L27, with embedded mixed layer physics CGCM: Full coupling, once per day ODAS: Optimal Interpolation; Ensemble Kalman Filter “LDAS”: Offline forced land states (recalibrated)

1-month forecast 3-month forecast 6-month forecast EnKF with SSH EnKF w/o SSH OI

1-month forecast 3-month forecast 6-month forecast EnKF with SSH EnKF w/o SSH OI

1-month forecast 3-month forecast 6-month forecast EnKF with SSH EnKF w/o SSH OI

March Starts Impact of Argo on Seasonal Forecasts Each forecast is verified against its own analysis Forecast Anomaly Correlations - Global Heat Content (25ºS-25ºN) Forecast lead (month) EnKF - No SSH EnKF - All ObservationsEnKF - No Argo

March Starts Impact of Argo on Seasonal Forecasts Each forecast is verified against its own analysis Forecast Anomaly Correlations - Global Salt Content (25ºS-25ºN) Forecast lead (month) EnKF - No SSH EnKF - All ObservationsEnKF - No Argo

Summary ODAS-1 Multivariate EnKF generally outperforms the OI implementation - both analysis and forecasts Argo - an invaluable data set to correct salinity Argo and Altimetry seem to work in tandem to improve upper ocean forecasts, but occasionally also work against each other in the GMAO system. For GODAE: (1) GMAO ODAS-1 analyses through LAS (2) State of the Ocean Climate Next steps: Use MERRA atmospheric state replay in GEOS-5 coupled model with ODAS-2 - generate better balanced IC for seasonal forecasts

Thank You!