Ocean Data Assimilation Consortium: Forecasting Analysis

1. CDEP Consortium Ocean Data Assimilation Consortium for Seasonal-to-Interannual Prediction (ODASI) COLA, GFDL, IRI, LDEO, NCEP, GMAO(NSIPP) Ed Schneider (COLA) and Chaojiao Sun (GMAO) Michele Rienecker, Steve Zebiak, Tony Rosati Jim Kinter, Alexey Kaplan, Dave Behringer http://nsipp.gsfc.nasa.gov/ODASI

2. CDEP Consortium Ocean Data Assimilation Consortium for Seasonal-to-Interannual Prediction (ODASI) COLA, GFDL, IRI, LDEO, NCEP, GMA GMAO Michele Rienecker Chaojiao Sun Jossy Jacob Robin Kovach Anna Borovikov GFDL Tony Rosati Matt Harrison Andrew Wittenberg COLA Jim Kinter Ed Schneider Ben Kirtman Bohua Huang IRI Steve Zebiak Eli Galanti Michael Tippett LDEO Alexey Kaplan Dake Chen NCEP Dave Behringer http://nsipp.gsfc.nasa.gov/ODASI

3. ODASI Themes: • ODA product intercomparisons (models, assimilation methodologies, assimilation parameters) using a common forcing data set and common QC’d in situ data streams Models: MOM4, MOM3, Poseidon, Cane-Patton, LDEO4 Methodologies: 3DVAR, OI, EnKF, Reduced state KF and optimal smoother, bias correction strategies Coupled Forecast Sytems: CGCMs, Hybrid models, Intermediate models • Development of observational data streams • Validation of assimilation products in forecast experiments • Observing system impacts - focused on TAO: • TAO array was established for S-I forecasting. • Is it effective in its present configuration? • Could it be modified to provide better support for S-I forecasts? • what is its role c.f. other elements of the ocean observing system?

4. Coupled Data Assimilation Workshop, Portland, April 2003: • Assimilation of subsurface temperature improves Niño-3 forecast skill (usually), but we aren’t sure why (initialization of state, anomalies) • Forecast errors are dominated by coupled model shocks and drifts • It is not yet clear as to the “best” method for forecast initialization • consistent with observed state • consistent with CGCM climatological biases • initialize the model’s coupled modes • Can we use seasonal forecast skill to comment on observing system issues?

5. The Experiments: • * initial conditions for 1 January and 1 July, 1993 to 2002 • * Forecast duration: 12 months • * 6-member ensembles for each system • * The observations: assembled and QC'd by Dave Behringer at NCEP • historical XBTs from NODC, MEDS • TAO from PMEL web site • Argo profiles from GODAE/Monterey server • * Surface forcing: assembled by GFDL • NCEP GDAS daily forcing: momentum, heat, freshwater • surface wind climatology replaced by Atlas’s SSMI surface wind analysis • include a restoration to observed SST and SSS

6. The Experiments (ctd): • Initial conditions for forecast experiments prepared using • 1. All in situ temperature profiles, including the full TAO array • 2. Western Pacific (west of 170W) TAO moorings • 3. Eastern Pacific TAO moorings • Hypothesis: the Eastern Pacific data important for shorter lead forecasts and the Western Pacific data important for longer lead forecasts. Address uncertainty in the results by use of • ensembles • different assimilation systems • different CGCMs • different classes of models (CGCMs, hybrid, intermediate)

7. Outline • Niño 3 SST anomaly Forecast skill • from different models, assimilation systems, observational constraints • January consensus forecast from CGCMs • Reynolds SST is verification • Ensemble spread • Skill in the equatorial band (analysis is verification) • Impacts on the Analysis • Conclusions

8. CGCM1 CGCM2b CGCM2a All TAO moorings West TAO moorings East TAO moorings Obs (Reynolds) hybrid2a hybrid2b Intermed1 hybrid1 Niño-3 SST anomalies January Starts

9. All TAO moorings West TAO moorings East TAO moorings Obs (Reynolds) Niño-3 SST anomalies CGCM2a CGCM2b July Starts Intermed1 hybrid2a hybrid2b hybrid1 Intermed

10. All TAO moorings West TAO moorings East TAO moorings Obs (Reynolds) CGCM Forecast skill - January starts - multimodel ensemble

11. January starts July starts Niño3 Niño4

12. CGCM2a - forecast anomaly correlations SST - July start HC - Jan start HC - July start 3mo 6mo

13. Jan Jul

14. Analysis: Average Temperature in upper 300m

15. XBT profiles available per month Jun 1997: 2021 Dec 1996: 1440

16. Seasonal drift of NSIPP CGCMv1 as a function of forecast lead time June for each initialization month. January for each initialization month Niño 3 anomaly correlation of 0.9. Vintzileos et al. (GSFC)

17. Coupled Data Assimilation Workshop, Portland, April 2003: • Assimilation of subsurface temperature improves Niño-3 forecast skill (usually), but we aren’t sure why (initialization of state, anomalies) • Forecast errors are dominated by coupled model shocks and drifts • It is not yet clear as to the “best” method for forecast initialization • consistent with observed state • consistent with CGCM climatological biases • initialize the model’s coupled modes • Can we use seasonal forecast skill to comment on observing system issues?

18. Conclusions: • Early stage of the analysis - we have to study the results in more detail • Statistical significance of results - need more ensemble members and more cases of both warm and cold events for robust conclusions • Eastern array definitely improves forecast skill • Western array improves skill in central Pacific • Entire array • best results • probably associated with atmospheric response across the entire Pacific • some indication that get a tighter spread • results are subtle - complicated by coupled model shocks and drifts