THE BEST ANALYZED AIR- SEA FLUXES FOR SEASONAL FORECASTING 2.12 Glenn H. White, W. Wang, S. Saha, D. Behringer, S. Nadiga and H.-L. Pan Global Climate.

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Presentation transcript:

THE BEST ANALYZED AIR- SEA FLUXES FOR SEASONAL FORECASTING 2.12 Glenn H. White, W. Wang, S. Saha, D. Behringer, S. Nadiga and H.-L. Pan Global Climate and Weather Modeling Branch Environmental Modeling Center National Centers for Environmental Prediction DOC/NOAA/NWS Camp Springs MD

CFS03 (Climate Forecast System) will be implemented Aug daily monthly, seasonal forecasts out to 10 months Atmospheric component--operational global weather model (GFS03) of 2003 at T62, 64 levels Ocean component--GFDL MOM3 no flux correction

Ocean Data Assimilation System (ODAS) OGCM 3D VAR Coupled Ocean Atmosphere General Circulation Model (CGCM) Ocean Initial Conditions SST Forecast US Forecasts Surface Temp Precip Seasonal Forecasting at NCEP Atmospheric Initial Conditions

23 years of hind-casts to provide a) bias corrections b) estimates of forecast skill for real-time forecasts NCEP-2 reanalysis a) initializes atmospheric model b) forces ocean re-analysis for ocean initial conditions for hind-casts

GODAS (MOM V.3) Forced by wind stress, heat flux, precipitation- evaporation SST is relaxed to weekly NCEP SST analysis surface salinity is relaxed to Levitus monthly SSS climatology. Wind stress is thought to have most impact

OGCM MOM Global v.3 Data Assimilation 3D VAR Observations: XBTs TAO P-Floats Altimetry Analyzed Fields: Temperature Salinity Ocean Data Assimilation System (ODAS) Surface Stress Heat Flux P-E From ?

-- NCEP-2 reanalysis 1979-present (CDAS-2) T62, 28 levels corrections and updates to NCEP-1 Used in hind-casts older atmospheric model than CFS What are best air-sea fluxes to initialize ocean assimilation for real-time forecasts?

--GDAS T254, 64 levels better data assimilation and atmospheric model than NCEP-2 better fluxes?? Not consistent with hind-casts More consistent with CFS than NCEP2

NCEP2 too many easterly waves

Eastern equatorial Pacific Zonal surface stress every 6 hours June 2004

Correlation of zonal surface stress every 6 hours June 2004 GDAS and CDAS2

Normalized RMS difference of monthly mean stress over 3 years Normalized bias in 3-year mean stress magnitude GDAS vs. CDAS

CDAS2 GDAS FSU July Dec Zonal surface stress

GDAS-FSU CDAS2-FSU Jul 2001-Dec 2003 Zonal surface stress

Contour interval half of previous slide NCEP2- FSU ERA40 -FSU COADS -FSU Zonal surface stress

Nino 3.4 5S-5N E Equatorial east Pacific

Correlation monthly zonal surface stress anomalies FSU-CDAS2 FSU-ERA40 CDAS2-ERA40

29S-29N5S-5N FSU- NCEP FSU- ERA NCEP2 -ERA Correlation of monthly anomalies in zonal wind stress E S-29N5S-5N FSU- NCEP FSU- GDAS NCEP2 -GDAS Correlation of monthly anomalies in zonal wind stress E July Dec. 2003

CONCLUSIONS -- GDAS, ERA40 surface stresses agree more with independent estimates than NCEP2, suggesting progress --disagreement between different estimates in equatorial Pacific implies substantial uncertainty in surface stress --CFS will use fluxes from NCEP-2 reanalysis to force ocean data assimilation, for consistency with hind-casts

Future CFS will conduct reanalyses with new CFS models for consistency of system as well as consistency with hind-casts. New CFS every 3-5 years. New global reanalyses every 3-5 years in support of seasonal forecasting. EMC plans to make CFS fields available to community.