A global 4D-Var data assimilation experimentwith a fully coupled GCMNozomi Sugiura1, Toshiyuki Awaji1,2, Shuhei Masuda1, Takashi Mochizuki1,Takahiro Toyoda1, Hiromichi Igarashi3, Yoichi Ishikawa2
1

Frontier Research Center for Global Change, JAMSTEC, Japan2
Department of Geophysics, Kyoto University, Japan
3
Marine-Earth Data and Information Department, JAMSTEC, Japan
Correspondence: nsugiura@jamstec.go.jp

A four-dimensional variational (4D-Var) data assimilation system with a fully coupled global
ocean-atmosphere-land surface model has been successfully developed with an aim to better define both mean
structure and temporal evolution of coupled seasonal to interannual phenomena. By using oceanic andatmospheric adjoint codes with special considerations for preventing from the blow-up of fast growing
sensitivities [Hoteit et al., 2005], the system optimizes the oceanic initial conditions and the adjustment factors forbulk formulae of mass and heat and momentum exchanges [Mochizuki et al., 2007] so as to fit the (9-monthlong) trajectories of the model’s coupled field to the observational data for both ocean and atmosphere (see Table
1 for the experimental setting). Application to state estimation of global climate during the 1990s shows, forexample, that the structure in the tropical Pacific region becomes extremely realistic (Figs. 1b and 2) and hence
the atmospheric condition associated with El Nino events is better represented (Fig.3). And, in the analysis field(Fig. 1c), the departures of the ensemble mean from the observation are comparable to the ensemble spread, whichmeans that the analysis field is well optimized by the system. Also the errors do not grow in the 9-month longassimilation windows, which means that the system works properly as a smoother and seasonal to interannual
evolution is well constrained by the observation. An optimal synthesis of observational data and coupled model
provides suitable oceanic initial conditions to the El Nino evolution together with the adjustment of coupling
intensities regarding the air-sea exchange of fresh water, momentum, and heat. These data well reflect the realistic
trend of seasonal to interannual variabilities, thereby enabling us to offer almost 1.5year-long lead predictabilityfor the El Nino (Fig. 4), at least in the preliminary studies for the period from 1996 to 1998. These results suggestthat our 4D-Var coupled data assimilation system has an ability to provide superior state representation andforecast potential than earlier assimilation methods.
Table 1. The experimental setting of coupled data assimilation
Coupled model

CFES (coupled model for the earth simulator) [Ohfuchi et al., 2004], [Pacanowskiand Griffies, 1999]. Resolutions are T42L24 for atmosphere, and 1degree by1degree with 45 vertical levels for ocean.
Preliminary parameter tuning Green’s function method [Menemenlis et al., 2005]
Assimilation method
4D-variational method equipped with both oceanic and atmospheric adjoint codes.
Control variables
Oceanic initial conditions, and the adjustment factors for bulk formulae.
Observational data
WOD 2001 data, FNMOC data, OISST data, T/P sea surface height anomaly,
NCEP’s BUFR data, and SSM/I wind data, together with our ocean reanalysis
product [Masuda et al., 2006].
Assimilation window
9-month long windows starting from each January and July.

(a)

(b)

(c)

Figure 1 (a) Time change of Nino 3.4 SST for the first guess field starting from January (red) or July (blue),and observation (black curve). The error bars showing the ensemble spreads are of 1 sigma in width. Theunits are deg C. (b) The same as (a) but for the analysis field of the 1990s CDA experiment. (c)The
averaging bahavior of error and spread of the analysis Nino 3.4 SST for assimilation windows startingfrom January (red), or July (blue). Solid curves show the departures of the ensemble mean from theobservation, whereas dashed curves show the spreads among ensemble members.

(a)(b)(c)

Figure 2 Hovmoeller diagrams of SST along the equator averaged between 2S-2N for the 1990s. (a) The Reynolds
product, (b) the analysis field, and (c) first guess field. The units are degree C.
(a)

(b)

(c)

Figure 3 Hovmoeller diagrams of zonal wind stress along the equator averaged between 2S-2N for the 1990s.
(a) NCEP2 product, (b) the analysis field, and (c) The first guess field. The units are dyn/cm2.

Figure 4 NINO3.4 SST anomaly correlation coefficient for preliminary prediction experiment. Red line:
prediction from optimized oceanic initial conditions and with climatological bulk adjustment factors [Mochizukiet al., 2007] by CDA, blue line: prediction from oceanic initial condition by IAU and dashed line: persistenceexperiment. The statistics are from 6 cases of ensemble runs which start from October and April of 1996 to 1998,
each with 11 different atmospheric initial conditions.
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