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.

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

[email protected]

CFS03 (Climate Forecast System)will be implemented Aug. 24

--daily monthly, seasonal forecasts out to 10 months

Atmospheric component--operational global weather model (GFS03) of 2003 at T62, 64

levels

Ocean component--GFDL MOM3no flux correction

Ocean DataAssimilation

System (ODAS)OGCM

3D VAR

Coupled OceanAtmosphere

GeneralCirculation

Model(CGCM)

Ocean InitialConditions

SST ForecastUS ForecastsSurface 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-analysisfor 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

OGCMMOM

Global v.3

DataAssimilation

3D VAR

Observations:XBTsTAO

P-FloatsAltimetry

AnalyzedFields:

TemperatureSalinity

Ocean Data Assimilation System (ODAS)

Surface StressHeat Flux

P-EFrom ?

--NCEP-2 reanalysis 1979-present (CDAS-2)T62, 28 levels corrections and updates to NCEP-1Used in hind-castsolder 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-castsMore 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 2001-Dec. 2003

Zonal surface stress

GDAS-FSU

CDAS2-FSU

Jul 2001-Dec 2003

Zonal surface stress

Contour interval half of previous slide

1979-2000

NCEP2-FSU

ERA40-FSU

COADS-FSU

Zonal surface stress

Nino 3.4

5S-5N

190-240E

Equatorial east Pacific

Correlation monthly zonal surface stress anomalies 1979-2001

FSU-CDAS2

FSU-ERA40

CDAS2-ERA40

29S-29N 5S-5N

FSU-NCEP2 .63 .55

FSU-ERA40 .68 .65

NCEP2-ERA40 .85 .75

Correlation of monthly anomalies in zonal wind

stress 122-290E 1979-2001

29S-29N 5S-5N

FSU-NCEP2 .59 .58

FSU-GDAS .63 .67

NCEP2-GDAS .87 .77

Correlation of monthly anomalies in zonal wind

stress 122-290E July 2001-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.