Subject No. 7: Advanced Four-Dimensional Data Assimilation
System Using a Coupled Model Toward Construction of High-Quality
Our research goal is to construct an innovative four-dimensional coupled
data assimilation system capable of providing a high-quality comprehensive
dataset. This is stimulated by the recent remarkable progress in the
earth observing system and numerical models and by the emergence of
a gigantic computer, "the Earth Simulator" For this purpose,
we have been constructing the four-dimensional variational data assimilation
system (4D-VAR) on the coupled atmosphere-ocean-sea ice-land model.
Since the beginning of our project, we have (1) improved the coupled
model, (2) completed each adjoint model of the assimilation system,
and (3) started experiments by the coupled data assimilation. (April
We use the Coupled Atmosphere-Ocean-Sea Ice model for the Earth Simulator
(CFES). The atmospheric component is based on the CCSR/NIES model.
The ocean and the ice components are from the GFDL/MOM3 and the IARC
Ice model respectively. We also integrated the land parameterization
scheme MATSIRO (Minimal Advanced Treatments of Surface Interaction
and Runoff) into the CFES instead of the current one layer model so
called "bucket model"
We added many modifications and tunings to the CFES to improve its
performance. One example of such tunings is shown in Figure 1. It
is well known that in many coupled models the precipitation over the
tropical Pacific Ocean is too symmetric about the equator (so called
"Double ITCZ" as is shown in Figure 1 (a). Our tuning to
the cloud parameters, however, lessened the Double ITCZ structure.
Figure 1. Precipitation over tropical
Pacific Ocean from the CFES. (a) Before tuning. (b) After Tuning.
After tunings, the CFES reproduces important climatological phenomena
reasonably well. This is the good base for further perfection of the
datasets by the assimilation system. Figure 2 confirms that the interannual
variation pattern of the sea surface temperature (SST) of the CFES
is close to the one of observation.
Figure 2. Liner regression of SSTs
at each grid box against Nino 3.4 SST anomalies. (a) From CFES. (b)
From the observation (ERSST).
Assimilation of each component:
We have constructed the assimilation system of each component. Figure
3 shows the assimilation results of the atmospheric model to the SSM/I
observed wind speed. The difference of the observed 10m wind speed
between the simulation and the observation (Figure 3(a)) decreased
well after the assimilation to the 1-month averaged data (Figure 3(b)).
This figure demonstrates that the assimilation to the climatological
(beyond weather) data is possible by our system.
Figure 3. The difference of the 10m
wind speed. (a) Between the simulation and the observation. (b) Between
the assimilation and the observation.
Figure 4 shows that the SST along the Equator of the
1997/1998 El Niño is improved by the assimilation system of
the ocean model.
Figure 4. The SST along the Pacific
equator from January 1996 to June 1999. (a) Simulation. (b) Observation.
We have started the experiments of the assimilation on the coupled
model. Our preliminary result shows that the cost function to the
observational data successfully decreases by controlling the bulk
coefficient for the air-sea fluxes.
Our 4DVAR coupled data assimilation system is expected to be better
able to represent the nature of climate variability and thereby offer
greater information and forecast potential than do existing models
and data alone.