Applications of GODAS at NCEP/Climate Prediction Center
Quasi-global, 1x1 degree horizontal resolution enhanced to 1/3 degree in
the tropics, 40 vertical levels
KPP boundary layer mixing scheme, free surface
Wind stress, heat flux, E-P from Reanalysis 2,
surface salinity relaxed to Levitus monthly SSS climatology
3D VAR, analyzes temperature and salinity, error covariance
varies geographically and temporally
Temperature profile data from XBTs, profiling floats (Argo), moorings (TAO), synthetic salinity
from local Levitus T-S climatology
Applications of GODAS at Climate Prediction Center
model uses GODAS as oceanic initial conditions.
“Weekly ENSO Update” uses
GODAS pentad temperature and sea level fields to monitor intraseasonal
Bulletin” uses GODAS monthly temperature and sea level fields to
assess interannual oceanic variability.
Markov model uses GODAS
monthly sea level field as predictor.
CCA ENSO model uses GODAS
monthly sea level and depth of 20 degree as predictors.
Study relationship between ENSO and MJO-related oceanic Kelvin waves.
ocean-atmospheric modes in the Tropical Atlantic Ocean, and improve
forecast skill of SST and precipitation there.
Global Ocean Data Assimilation System (GODAS) became operational in August
2003 and the Coupled Forecast System (CFS03) became operational in
September 2004. With these two new systems in place NCEP has a greatly improved seasonal
to annual forecasting capability.
The role of
the GODAS is to provide an accurate estimate of the ocean state that is
used to initialize the seasonal forecasts made with the CFS. To estimate
the ocean state the GODAS combines ocean observations with an ocean model
(the Modular Ocean Model, version 3, MOM3, developed at GFDL).
Figure 1 illustrates the impact of assimilating data into the ocean model
in terms of the correlation and RMS difference between the sea surface
height in the model and the sea surface height observed by the TOPEX and
Jason I satellite missions. The top panels show the results for MOM3
forced by surface fluxes from the NCEP atmospheric Reanalysis 2 with no
data assimilated. The middle panels show the results for the same model
and forcing, but with temperature and salinity data assimilated. The
bottom panels show the results when the TOPEX and Jason1 data are
assimilated as well. The impact of the data on model sea surface height
operational version of GODAS corresponds the middle panels of the figure.
is based on GFDL’s MOMv3. When that model is forced by the same
surface forcing as used in GODAS, but no data is assimilated, the
temperature climatology is biased. In the tropics that bias is
mostly positive. The GODAS corrects most of the bias by
assimilating temperature profile data. Thus,
Figure 2 illustrates the bias in the average temperature
of the upper 400 meters of MOMv3 by subtracting the corresponding
field from GODAS.
In the Atlantic the bias is small on the equator, but
as large as 2oC at 10oN. In the tropical
Pacific the warm bias is mostly confined to the eastern half of the
basin. The entire tropical Indian Ocean is warm with the largest
values of about 1.5oC at 10oS.
principal, a model used in data assimilation should be unbiased,
i.e., an assimilation system should be used to correct the model
variability, not the model mean state. While this ideal is rarely
achieved, it is a goal worth pursuing.
the GODAS has some errors in its representation of surface currents
in the tropical Pacific Ocean.
3 compares two climatologies of the surface zonal currents
in the tropical Pacific for the period 1993-2003. The top panel
shows currents derived from surface drifters deployed and managed by
the Global Lagrangian Drifting Buoy Project at the Atlantic
Oceanographic and Meteorological Laboratory. The bottom panel shows
the equivalent currents from GODAS. The blank patches in the
drifter image occur where there is insufficient drifter data to
compute a reliable average. While there are sampling differences
between the two images, some general comparisons are useful. Off
the equator the overall pattern of the currents is much the same in
each picture, although the Equatorial Counter Current appears to be
too strong in the GODAS in the far western Pacific. A more serious
error occurs on the equator and west of the dateline. Here the
drifter data indicate eastward flow while the GODAS data indicate a
strong westward flow. The same error in the GODAS currents appears
in comparisons with current meter data at 165E on the equator (not
GODAS assimilates temperature and salinity, thereby correcting the
mass field in the model. However, the GODAS does not assimilate
current data. Thus, off the equator, where the flow is essentially
geostrophic, the GODAS is able to represent the currents
accurately. On the equator, where geostrophy breaks down, the GODAS
has difficulty computing the surface currents correctly.
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Assimilation of synthetic
salinity improves the salinity climatology in GODAS, but
underestimates the sea surface salinity (SSS) variability. An
improved estimate of the SSS variability could contribute to
improved surface currents.
Surface currents are
slightly improved when more weight is given to the model’s own
computation of the salinity in the mixed layer than to the
synthetic salinity data.
Assimilation of altimetry
appears to improve sea level analysis significantly in the
Atlantic and Indian Ocean. The next step is to conduct a set of
hindcast experiments to evaluate its potential impact on Seasonal
to Interannual forecasting.
Assimilation of altimetry,
however, does not improve deficiencies in GODAS surface currents.
underway to incorporate the satellite data assimilation into the
operational GODAS. There are, in addition, other potential ways to
improve GODAS and these will also be explored.