HYBRID GODAS STEVE PENNY, DAVE BEHRINGER, JIM CARTON, EUGENIA KALNAY, YAN XUE
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1 STEPHEN G. PENNY UNIVERSITY OF MARYLAND (UMD) NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTION (NCEP) HYBRID GODAS STEVE PENNY, DAVE BEHRINGER, JIM CARTON, EUGENIA KALNAY, YAN XUE NOAA CLIMATE REANALYSIS TASK FORCE MEETING SEPTEMBER 23, 2015
2 OCEAN ASSIMILATION ADVANCEMENTS AT NCEP TO DATE: Oceanic Local Ensemble Transform Kalman Filter (Ocean-LETKF) system (Penny et al., 2013) Hybrid-Gain assimilation method (Penny 2014) Hybrid 3DVar/LETKF Global Ocean Data Assimilation System (Hybrid-GODAS) at NCEP (Penny et al., 2015) 21-Year Hybrid GODAS Reanalysis (Penny et al., in preparation) Collaborators: D. Behringer, J. Carton, E. Kalnay, T. Miyoshi, K. Ide, G. Chepurin, Y. Xue
3 21-YEAR HYBRID-GODAS REANALYSIS 4 Temperature 0.8 Salinity bias (ºC) rmsd (ºC) TEMPERATURE 0 AND SALINITY (O-F) 0 RMSD AND BIAS REDUCED Temperature USING THE HYBRID-GODAS 0.5(5-DAY FORECASTS) DVar Hybrid-GODAS 3-month moving averages Pre-Argo Argo-Era date rmsd (psu) bias (psu) DVar Hybrid-GODAS date
4 21-YEAR HYBRID-GODAS REANALYSIS 4 Temperature 0.8 Salinity THERE IS A CLEAR OBSERVING SYSTEM IMPACT bias (ºC) rmsd (ºC) TEMPERATURE 0 AND SALINITY (O-F) 0 RMSD AND BIAS REDUCED Temperature USING THE HYBRID-GODAS 0.5(5-DAY FORECASTS) DVar Hybrid-GODAS 3-month moving averages Pre-Argo Argo-Era date rmsd (psu) bias (psu) DVar Hybrid-GODAS date
5 NEAR SURFACE SALINITY Seasonal variability of the SSS is improved. Spinup
6 NEAR SURFACE SALINITY GODAS (Operational, ocean-only) CFSR (Operational, coupled) EN4 Hybrid
7 EQUATORIAL PACIFIC ADCP* *Acoustic Dopler Current Profilers RMSD (cm/s) Anomaly Correlation 3DVar Hybrid Hybrid-GODAS updates velocity field, 3DVar-GODAS does not. 3DVar Hybrid Improvement Improvement
8 NEAR SURFACE OCEAN CURRENTS Comparison to OSCAR* currents (~0-30m) from Mean zonal current differences (cm/s) Anomaly Correlation 3DVAR-GODAS HYBRID-GODAS *OSCAR currents derived from satellite altimeter and scatterometer data
9 INTERNATIONAL COMPARISON RMSD of anomaly correlations versus ensemble mean Thanks to Yan Xue
10 INTERNATIONAL COMPARISON RMSD of anomaly correlations versus ensemble mean MOM3/GODAS MOM4/GODAS MOM4/Hybrid Thanks to Yan Xue
11 26.5ºN, NORTH ATLANTIC Seasonal cycle captured well in both. The hybrid spins up volume throughflow towards observed levels. RAPID/MOCHA Array (Observed) ~18 SV 3DVar-GODAS Hybrid-GODAS ~10 Sv ~14 Sv
12 AMOC ( ) Sv Similar increase throughout Atlantic 3DVar-GODAS Hybrid-GODAS
13 EQUATORIAL ATLANTIC Guinea Fracture Zone Gulf of Guinea Amazon Outflow
14 AMAZON OUTFLOW PLUME
15 NEAR SURFACE SALINITY Both 3DVar and Hybrid miss SSS anomaly changes in the West Eq. Atlantic Amazon outflow region Hybrid captures salinification trend in the East Eq. Atlantic SSS anomaly
16 MODEL BIAS*, DIAGNOSED OMF, SSS, Averaged Smallest bias in Tropical Pacific Too fresh Gulf Stream Too salty around Europe Too fresh Eq. Atlantic Too fresh off Antarctica (*Model Bias = OGCM + Forcing) Split bias from Brazil/Malvinas Confluence through whole Southern Ocean
17 ENSEMBLE SPREAD 1/2ºx1/2º with refinement to 1/4º latitude at the equator (CFS GODAS resolution) 1/4ºx1/4º with increased vertical resolution near the surface Both with MOM4p1, from collaborator Hasibur Rahaman (INCOIS) -> Shift toward 1/4ºx1/4º global MOM6 with 2m resolution near SFC
18 C L I M AT E F O R E C A S T S Y S T E M V 3 Atmosphere Land Current CFSv3 components Wave Ocean Aerosol Sea Ice
19 W E A K LY C O U P L E D D ATA A S S I M I L AT I O N Atmosphere Atmos DA Land Land DA Current CFSv3 components Wave Aerosol DA Aerosol Wave DA Ocean Ocean DA Sea Ice DA Sea Ice
20 C O U P L I N G O N LY O N F O R E C A S T Atmosphere Land Current CFSv3 components Wave Ocean Aerosol Sea Ice
21 S T R O N G LY C O U P L E D D ATA A S S I M I L AT I O N Atmosphere EACH DOMAIN IS INFLUENCED BY O B S E R V AT I O N I N N O V AT I O N S FROM ALL OTHER DOMAINS Land Current CFSv3 components Wave DA Ocean Aerosol Sea Ice
22 STRONGLY COUPLED DA REDUCES ERRORS (vs. weakly coupled DA) For example, assimilating only atmospheric observations leads to significant improvements in ocean: a) % reduction b) weak coupling baseline c) % reduction d) weak coupling baseline global tropics Southern Hemisphere Northern Hemisphere tropics Improvement-> Improvement-> % reduction weak coupling baseline % reduction weak coupling baseline e) f) (Note: Observing System Simulation Experiments (OSSEs), not real data) Sluka, T., S.G. Penny, E. Kalnay, T. Miyoshi, 2015: Using Strongly Coupled Ensemble Data Assimilation to Assimilate Atmospheric Observations into the Ocean. Submitted to GRL.
23 STRONGLY COUPLED DA REDUCES ERRORS (vs. weakly coupled DA) Again, assimilating only atmospheric observations: b) Upper 500m ocn T 2.0 a) ocn S [C] [PSU] Temperature Pacific Atlantic depth [m] depth [m] d) f) ocn T (Pacific) ocn T (Atlantic) [C] [C] Improvement-> Improvement-> depth [m] depth [m] c) e) ocn S (Pacific) ocn S (Atlantic) [PSU] [PSU] Salinity Real-data experiments with the CFSv2 will come next.
24 NEXT STEPS Upgrade to GFDL MOM6 ocean model (1/4ºx1/4º, 75 vertical layers with 2m surface layers) (NGGPS/R2O) {complete, pending testing} New observational data in the Hybrid-GODAS (satellite data, surface drifters, atmospheric data) (NGGPS/R2O) {in progress} Transitioning Hybrid-GODAS to operations Implementing Strongly Coupled DA in CFSv2 (India Monsoon Mission) and prototype CFSv3. Contact:
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