Interannual variabilities of eastern subtropical mode waters
in the North and the South Pacific identified from reanalysis datasets
obtained by 4DVAR data assimilation.
Takahiro Toyoda1, Shuhei Masuda1, Nozomi Sugiuara1, Hiromichi Igarashi2, Yuji Sasaki,
Yoichi Ishikawa3, Toshiyuki Awaji13
1

Frontier Research Center for Global Change (FRCGC), Japan Agency for Marini-Earth Science and Technology(JAMSTEC), Japan2
Marine-Earth Data and Information Department, JAMSTEC, Japan3
Department of geophysics, Kyoto University, Japan
Correspondence: ttoyoda@jamstec.go.jp

INTRODUCTION
Surface water masses, such as mode waters in subtropics (see Hanawa and Talley, 1999), are important because they
characterize the interior circulation according to their volumetric modes. Moreover, since they circulate in thethermocline remaining their properties, they are important for the climate variability in the interannual to decadal timescale. Therefore the understanding of mechanism responsible for their dynamical behaviors is necessary for theclimate study. However, the interannual variability of subtropical mode waters in the North Pacific is not understood
sufficiently especially for the eastern subtropical mode water (ESTMW) identified recently by Hautala and Roemmich(1998).
The climatological formation mechanism of the ESTMW has been studied by Toyoda et al. (2004) and theimportance of the preconditioning of mixed layer in the formation region has been demonstrated (Fig. 1). In
summer and autumn, strong easterly winds enhance evaporation and enrich salinity of surface water. Subsequently,
this salty water is carried northward by easterly induced Ekman transport. In addition, the low level cloud coverprevent insolation and suppress the SST increase in the eastern part of the subtropics. By these superimposedeffects, the cold salty surface water appears in the ESTMW formation region before winter and, eventually, thelocally deep mixed layer corresponding to the ESTMW is generated in winter there in spite of the moderatecooling flux. In this study, we investigate whether the interannual variability of this mode water can be explainedby variations in these factors of surface forcing.
In doing so, we use a newly-derived reanalysis dataset obtained by our 4-dimensional variational (4D-VAR) data
assimilation experiment that synthesizes all available observational data within the framework of an ocean generalcirculation model (OGCM). Since the advantage of the 4D-VAR approach is that it creates a dynamically
self-consistent dataset, it provides the best possible time-trajectory fit to the observations offering greater
information content on the oceanic state than can be derived from models or data alone (e.g., Stammer et al.,
2002; Masuda et al., 2003).
Following a description of the methodology and dataset, features of the interannual variability of the ESTMW in
the North Pacific is described using the reanalysis dataset. After summary, preliminary results on the difference
between the North and the South Pacific ESTMWs are presented.

Figure 1. Schematic view of the preconditioning of winter mixed layer in the formation of the ESTMW, shown by
Toyoda et al. (2004).
DataThe reanalysis dataset used in this study was derived from the 4D-VAR assimilation system used by Masuda etal. (2003) and Awaji et al. (2003). The OGCM is version 3 of the GFDL Modular Ocean Model (MOM3;
Pacanowski and Griffies, 1999). Our assimilation system has been constructed for the global ocean with ahorizontal resolution of 1 degree both zonally and meridionally and has 36 vertical levels.
To generate the first guess field for our data assimilation experiments, this model was firstly performed without
assimilation (hereafter referred to as the "simulation run") by using 10-daily interannual forcings. For the surface
momentum, sensible, long-wave radiative, and fresh water fluxes in this simulation run, data from the 6-hourly
National Centers for Environmental Prediction Department of Energy Atmospheric Model Intercomparison
Project (NCEP-DOE-AMIP-II) dataset have been used. These are provided on a 1.9o latitude by 1.875o longitudegrid. Latent heat flux was estimated from NCEP's Optimally Interpolated Sea Surface Temperature (OISST) fieldby applying the commonly used bulk formulae. In addition, the International Satellite Cloud Climatology Project(ISCCP; Kidwell and Poltar, 1988) dataset was used for the short-wave radiative flux.
In our 4D-VAR approach, an optimized 4-dimensional dataset is sought by minimizing a cost function (Masudaet al., 2003). The assimilation covers a time window of 10.5 years starting from 1990. We chose the initialconditions of model variables and air-sea fluxes (heat, fresh water, and momentum fluxes) as the control variables
with the latter modified within the assimilation period as 10-day mean values (see Masuda et al. (2003) for moredetail).
The assimilated elements in this study are temperature and salinity from the Fleet Numerical Meteorology andOceanography Center (FNMOC) dataset, OISST values, and sea-surface dynamic-height anomaly data derived
from TOPEX/Poseidon altimetry. In addition, the climatologies of the World Ocean Database 1998 (WOD98) areused as background data. All the observational data are averaged onto 1 degree by 1 degree bins and thencompiled as series of 10-day means for the surface data and monthly means for the subsurface data. The
resolution after 120 iterations of an adjoint calculation covering a total of 10.5 years is acceptable for our purposessince values of the cost function are then almost equivalent to errors in the assimilated input fields.
The dataset in the 1990s derived from the last cycle of the reanalysis run is used. An initial "shock-effect" isinevitably produced in association with the establishment of the initial conditions and although this almostdisappears within one month, we have nevertheless rejected the whole of the first year (1990) and thus theanalysis period runs from January 1991 to June 2000 in the following work. In addition, when validating anddiscussing the surface fluxes of the assimilated data, we use the 40-year European Centre for Medium-Range
Weather Forecasts reanalysis (ERA40; Klinker, 1997) as well as the NCEP--National Center for AtmosphericResearch reanalysis--2 (NCEP2; Kalnay et al., 1996). Also, we use the ISCCP dataset to investigate thesummertime low-level cloud cover in the ESTMW region.
Results
Our 4D-VAR product reproduce the local deepening of wintertime mixed layer in the eastern subtropical North
Pacific consistent with the previous observational studies, such as Hautala and Roemmich (1998). In addition,
temperature-salinity-volume diagrams show that properties in this region are consistently reproduced with theWOA climatologies. These features provide an appropriate benchmark for assessing the applicability of our datato the investigation of the interannual variation. In this study, we shall define the ESTMW as the component witha volume greater than 2x1012 m3 in each salinity class (in steps of 0.05 psu) and in each temperature class (in stepsof 0.2oC) and ranges between 16-22oC. In order to gain an insight into the mechanisms controlling the interannual
variation in the ESTMW, we define a newly formed ESTMW volume as the maximum value of the existing
ESTMW in winter minus the minimum value from the preceding autumn. The time series of this quantity isshown in Fig. 2a.

Figure 2. Newly formed volume of the ESTMWs in each winter form our reanalysis data (a) in the North Pacific and(b) in the South Pacific.
We carefully investigate the interannual variabilities in the forcing fields (see Toyoda et al. (for submission in2007) for more detail). The salt convergence by Ekman transport in the ESTMW formation region increase
remarkably from the middle of 1996 to the beginning of 1999, which correspond well to the large formations inthese years. Next, the temperature difference anomaly between surface and 100 m depth in the preconditioning
phase, which is a good measure for the strength of seasonal thermocline stratification shows that the strong (weak)stratifications correspond very well to the small (large) formations. In addition, by analyzing the ISCCP clouddataset, we found this thermal stratification variability is explained mostly by low-level cloud, especiallystratocumulus. The surface flux variability is also validated by comparing with NCEP2 and ERA40. Finally, thenet heat flux anomaly in winter shows that weak (strong) cooling anomalies correspond well to the small (large)
formations. NCEP2 and ERA40 validate our net heat flux anomaly there and, moreover, they indicate that morethan half of the net heat flux variation is explained by the latent heat. In summary, as appears in Table 1, theinterannual variability of the young formation of the ESTMW in the North Pacific is well explained by the interannual
variability of these 3 surface forcing factors. This fact is consistent with the climatological formation mechanism.
Table 1. Interannual variabilities of the young formation of the ESTMW and the surface forcing factors in theeastern subtropical North Pacific.
YearYoung
Salt
ThermalWinterFormation
Convergence
Stratification
Cooling
1992
1993
1994
++
1995
+
1996
1997
++
1998
+++
1999
+++
2000
In the South Pacific, there also exists the ESTMW (Wong et al., 2003). The associated mixed layer is deeper anddistributes in broader region than in the North Pacific, which is mostly attributed to the fresh water flux, strong
negative value in the subtropical South Pacific, while it is affected by the ITCZ in the North Pacific. The resultant
surface salinity is more than 36 psu, whereas about 35.2 psu at maximum in the North Pacific. We analyze in asimilar way on the interannual variability in the South Pacific ESTMW and compare the North and the South
Pacific ESTMW.
Figure 2b shows the interannual variability in the new formation of the South Pacific ESTMW. It is shown that,though it may be imagine that they show similar variations affected by the strong equatorial interannual variability, theyoung formation of the North and the South Pacific ESTMWs do not show similar interannual variability. In addition,by analyzing the density variation, it is suggested that salinity contribution to density variation is larger in the North
Pacific (43%) than in the South Pacific (30%). Moreover, in the South Pacific, temperature and salinity vary largely
coherently. The correlation coefficient is 0.75 in the South Pacific while 0.06 in the North Pacific. This indicates theimportance of the “speciness” anomalies in the South Pacific (e.g., Nonaka and Sasaki, 2007). Clarification of themechanism responsible for these variabilities and the difference between the North and the South Pacific ESTMWs
remains a subject for future work. In particular, the variability related to the ENSO is of interest.
REFERENCES
Awaji, T., S. Masuda, Y. Ishikawa, N. Sugiura, T. Toyoda, and T. Nakamura 2003: State estimation of the North
Pacific Ocean by four-dimensional variational data assimilation experiment. J. Oceanogr., 59, 931-943.
Hanawa, K., and K. Kameda 2001: Variability of core layer temperature (CLT) of the North Pacific subtropicalmode water. Geophys. Res. Lett., 28, 2229-2232.
Hautala, S. L., and D. H. Roemmich 1998: Subtropical mode water in the northeast Pacific Basin. J. Geophys.
Res., 103, 13055-13066.
Kalnay, E., et al. 1996: The NCEP/NCAR 40-year Reanalysis Project. Bull. Am. Meteorol. Soc., 77, 437--471.

Kidwell, K. B., and D. R. Poltar 1988: International Satellite Clould Climatology Project (ISCCP), U.S. Dept.
Commerce, NOAA, Suitland, MD, available online at http://www2.ncdc.noaa.gov/docs/isccp/intro.htm.
Klinker, E. 1997: Diagnosis of the ECMWF model performance over the tropical oceans, in Proceedings ofSeminar on Atmosphere-Surface Interaction, pp. 53--66, Eur. Cent. for Medium-Range Weather Forecasts,Reading, UK.
Masuda, S., T. Awaji, N. Sugiura, Y. Ishikawa, K. Baba, K. Horiuchi, and N. Komori 2003: Improved estimatesof the dynamical state of the North Pacific Ocean from a 4 dimensional variational data assimilation. Geophys.
Res. Lett., 30, 1868, doi:10.1029/2003GL017604.
Nonaka, M., and H. Sasaki 2007: Formation mechanism for isopycnal temperature-salinity anomalies propagatingfrom the eastern South Pacific to the equatorial region. J. Clim., 20, 1305-1315.
Pacanowski, R. C. and S. M. Griffies 1999: The MOM 3 Manual, Geophysical Fluid Dynamics
Laboratory/NOAA, Princeton, USA, pp. 680.
Stammer, D., et al. 2002: Global ocean circulation during 1992--1997, estimated from ocean observations and ageneral circulation model. J. Geopys. Res., 107, doi:10.1029/2001JC000888.
Toyoda, T., Y. Ishikawa, T. Nakamura, and T. Awaji 2004: Preconditioning of winter mixed layer in theformation of North Pacific eastern subtropical mode water. Geophys. Res. Lett., 31, L17206,
doi:10.1029/2004GL020677.
Toyoda, T., T. Awaji, S. Masuda, N. Sugiura, H. Igarashi, T. Mochizuki, and Y. Ishikawa 2007: Interannualvariability of North Pacific eastern subtropical mode water formation in the 1990s derived from a 4-dimensionalvariational ocean data assimilation experiment. J. Geophys. Res., for submission.
Wong, A. P. S., and G. C. Johnson 2003: South Pacific Eastern Subtropical Mode Water. J. Phys. Oceanogr., 33,1493-1509.