igst meeting june 2-4, 2008 the gmao’s ocean data assimilation & si forecasts michele...
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IGST MeetingJune 2-4, 2008
The GMAO’s Ocean Data Assimilation & SI Forecasts
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
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ODAS-1Algorithms:- 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
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ODAS-1Experiments: 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
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Salinity Variability along the Equatorial Pacific (2ºS-2ºN)24.5kg/m3 Density Surface
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165ºE 140ºW
EnKF Exp1 - All observations
EnKF Exp3 - No ArgoOI - No SSH
Observations - Argo
Salinity Variations along the Equatorial Pacific
95ºW
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Salinity analyses validated against CTD dataTAO servicing cruises (8ºS-12ºN)
2005
Niño-4 (160ºE-150ºW) Niño-3 (150ºW-110ºW)
EnKF - All observations
EnKF - No ArgoOI - No SSH
Control - No Assimilation
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GMAO CGCMv1 (Tier1) Forecast Ensembles 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 L34LSM: Mosaic (SVAT)OGCM: Poseidon v4, 1/3 x 5/8 x L27, with embedded mixed layer physicsCGCM: Full coupling, once per day
ODAS: Optimal Interpolation; Ensemble Kalman Filter“LDAS”: Offline forced land states (recalibrated)
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1-monthforecast
3-monthforecast
6-monthforecast
EnKF with SSH EnKF w/o SSH OI
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1-monthforecast
3-monthforecast
6-monthforecast
EnKF with SSH EnKF w/o SSH OI
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1-monthforecast
3-monthforecast
6-monthforecast
EnKF with SSH EnKF w/o SSH OI
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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 Observations EnKF - No Argo
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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 Observations EnKF - No Argo
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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
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Thank You!