The assimilated observational data for ocean in the experiment are monthly
mean temperature and salinity field derived from the World Ocean Database
2001 (WOD2001), 10-daily averaged Reynolds SST field and the
TOPEX/Poseidon 10-daily mean sea surface height anomaly. The continuous
fields yielded as “Kyousei K-7”ocean data assimilation product are also used as
quasi-observation to fill the gaps of very sparse observation. The atmospheric
data are the PREP BUFR dataset taken from the National Centers for Environmental
Prediction (NCEP) for air temperature, specific humidity and wind
vector after some processing to obtain 10-daily mean and to perform appropriate
quality checks. 10-daily mean 10m scalar wind from SSM/I observation are multipled
by ECMWF wind direction data to obtain wind vector quasi-observation
over the ocean.
The optimization problem to minimize the cost is solved by the iteration with
conjugate gradient method using the gradient calculated by the atmosphereocean
coupled adjoint code.
Because weather mode in the shorter period than 10-days is not controlled
in the optimization with the observational data, we performed coupled model
integrations under 11 different atmospheric initial conditions using optimized
ocean initial condition and adjustment factor. The ensemble of atmospheric
initial conditions are taken, at 1-day intervals, from the first 5 days of the first
month of the assimilation window and from the last 5 day of the month perior to
the first month of the assimilation window. An ensemble member which seems
to express the reality the best is selected for each window, and they are pieced
together as a continuous data for three years .
S. C. Bloom, L. Takacs, A. M. da Silva, and D. Ledvina. Data assimilation
using Incremental Analysis Updates. Mon. Wea. Rev., 124:12561271, 1996.
H. Bonekamp, G. van Oldenborgh, and G. Burgers. Variational assimilation of
Tropical Atmosphere-Ocean and expendable bathythermograph data in the
Hamburg Ocean Primitive Equation ocean general circulation model, adjusting
the surface fluxes in the tropical ocean. J. Geophys. Res., 106:16693
R. Giering and T. Kaminski. Applying TAF to generate efficient derivative code
of Fortran 77-95 programs. PAMM, 2(1):5457, 2003.
R. Giering and T. Kaminski. Recipes for Adjoint Code Construction. ACM
Trans. On Math. Software, 24(4):437474, 1998.
W. D. Hibler III. Modeling a variable thickness sea ice cover. Mon.
N. Komori, K. Takahashi, K. Komine, T. Motoi, X. Zhang, and G. Sagawa.
Description of sea-ice component of Coupled Ocean-Sea-Ice Model for the
Earth Simulator (OIFES). J. Earth Simulator, 4:3145, 2005.
D. Menemenlis, I. Fukumori, and T. Lee. Using Green’s functions to calibrate
an ocean general circulation model. Mon. Wea. Rev., 133:12241240, 2005.
T. Mochizuki, T. Miyama, and T. Awaji. A simple diagnostic calculation of
marine stratocumulus cloud cover for use in general circulation models. J.
Geophys. Res., 2006. submitted.
T. Nakajima, M. Tsukamoto, Y. Tsushima, A. Numaguti, and T. Kimura. Modeling
of the radiative process in an atmospheric general circulation model.
Appl. Opt., 39:48694878, 2000.
W. Ohfuchi, H. Nakamura, M. K. Yoshioka, T. Enomoto, K. Takaya, X. Peng,
S. Yamane, T. Nishimura, Y. Kurihara, and K. Ninomiya. 10-km mesh mesoscale
resolving simulations of the global atmosphere on the Earth Simulator:
Preliminary outcomes of AFES (AGCM for the Earth Simulator). J.
Earth Simulator, 1:834, 2004.