1. Introduction
A remarkable oceanic thermal dome, which is located near the Republic of Seychelles in the southwestern Indian Ocean [herein, the Seychelles Dome (SD); see Fig. 1 for its location], is important for several reasons. The region is known to show high biological productivity resulting from upwelling of nutrient-rich water (Xie et al. 2002). More importantly, the sea surface temperature anomaly (SSTA) over the SD region influences the onset of the Indian summer monsoon, thus influencing climate conditions in various regions in Asia. Using observational data and outputs from ensemble AGCM experiments, Annamalai et al. (2005) showed that the onset is delayed by about 1 week when the SSTA is above normal. This is because the warm SSTA keeps the intertropical convergence zone (ITCZ) from advancing into the Indian subcontinent. More recently, Izumo et al. (2008) suggested that the spring SST over the SD may be a potential predictor for the summer monsoon rainfall. Such an anomalous SST condition over the SD may even influence the climate in remote regions through atmospheric teleconnections (Hurrell et al. 2004; Annamalai et al. 2005). Also, the SD is located at an important pathway of the Indonesian Throughflow water, influencing the stratification and air–sea heat flux in the Indian Ocean (Song et al. 2004). Finally, the occurrence of the tropical cyclones is closely linked with the thermocline depth anomaly and the SSTA in the southwestern Indian Ocean (SWIO), including the southern part of the SD (Xie et al. 2002). To understand how the SSTA is determined in the SD region is one of the most interesting topics in the Indian Ocean.
Despite the importance of the SD, there are not many works that address its variation and formation. Using available observational data, Xie et al. (2002) recently examined the interannual variation of the SD. They showed that the westward-propagating downwelling Rossby wave forced by the easterly wind stress anomaly associated with El Niño–Southern Oscillation (ENSO) or the Sumatra, Indonesia, cooling events is the dominant mechanism for its interannual variation. This dynamic aspect explains why the surface heat flux anomalies, which thermodynamically cause the basin-wide warming associated with ENSO events (Klein et al. 1999), cannot explain the interannual variation in the SWIO region. Rao and Behera (2005) clarified that the interannual variations of the SWIO north of 10°S is mainly influenced by the interannual Rossby waves associated with the Indian Ocean dipole (Saji et al. 1999; Webster et al. 1999; Murtugudde et al. 2000; Rao et al. 2002; Yamagata et al. 2004), whereas the region south of 10°S is affected by ENSO. The above studies, however, did not discuss the seasonal variation of the SD. Most studies on the seasonal variation in the tropical Indian Ocean have focused so far on the Yoshida–Wyrtki jet (Yoshida 1959; Wyrtki 1973) and the Somali Currents (Schott and McCreary 2001). As far as we know, no study to date has quantitatively discussed the seasonal variation of the SD despite its importance in the Indian monsoon system. Considering the fact that understanding the seasonal variation is fundamental to that of nonseasonal variability (Rasmusson et al. 1999), the current situation is unfortunate; this is the main motive of the present study.
Using observational data, assimilation data, and OGCM outputs, we try to capture the seasonal variation of the SD in detail. The content is organized as follows. In the next section, a brief description of the model and data used in this study is presented. In section 3, the seasonal variation of the SD is described. In particular, results from the heat budget and upwelling speed analyses are presented in detail. Conclusions are given in the final section.
2. Model and data description
a. OGCM
The Indo-Pacific Ocean basin model used in this study is based on version 3.0 of the Modular Ocean Model (MOM3.0), which has been developed at National Oceanic and Atmospheric Administration (NOAA)/Geophysical Fluid Dynamics Laboratory (GFDL), and the basic equations are given in Pacanowski and Griffies (1999). Our model covers most of the Indian and Pacific Oceans from 14.75°E to 69.75°W and from 52°S to 30°N. The Indonesian Throughflow is explicitly included. The horizontal resolution is uniform with 0.5° in both zonal and meridional directions. There are 25 levels in the vertical with 8 levels in the upper 100 m. The bottom topography adopted in this model is based on the 5-minute gridded elevations/bathymetry for the world (ETOPO5) dataset and is smoothed to make the numerical calculation stable (Killworth 1987). The lateral eddy viscosity and diffusivity are calculated using the formula proportional to the horizontal grid spacing as well as the total deformation rate (Smagorinsky 1963). Within 3° of the southern and northern boundaries, the values of these coefficients are increased so that the damping time scale becomes 1 day at 52°S and 30°N, and the temperature and salinity are relaxed to the monthly climatology of the World Ocean Atlas 1994 (WOA94; Levitus and Boyer 1994; Levitus et al. 1994) in order to reduce artificial wall effects. The vertical eddy viscosity and diffusivity are parameterized in terms of the Richardson number as in Pacanowski and Philander (1981).
The model is first spun up for 20 yr using the annual mean wind stress and heat flux from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data (Kalnay et al. 1996). The sea surface temperature (SST) and salinity (SSS) are restored to the annual mean climatology (Levitus and Boyer 1994; Levitus et al. 1994) with the relaxation time scale of 30 days. The initial condition is the annual mean temperature and salinity field with no motion. Then, the model is further integrated for 9 yr using the monthly climatology of the NCEP–NCAR reanalysis database for the period from 1950 to 1997, and the SST and SSS are restored to the monthly climatology with the relaxation time scale of 30 days. The simulation data are stored every 5 days and are used for the present analysis. Note that the contribution from each term in the heat budget equation is computed at every time step. Outputs from the last 5 yr are analyzed here.
b. Observation and assimilation data
For comparison, we have used other datasets in this study. As observational data, we have used the World Ocean Atlas 1998 (WOA98; see Antonov et al. 1998; Boyer et al. 1998; information also online at http://www.nodc.noaa.gov), which is the global historical hydrographic data with uniform horizontal resolution of 1° and 24 vertical levels. Also, the World Ocean Atlas 2001 (WOA01; Conkright et al. 2002) is used for the nitrate. We have also used the Simple Ocean Data Assimilation (SODA; Carton et al. 2000) and other assimilation data from Masina et al. (2004). The SODA is used for a global ocean using the GFDL MOM version 2 (MOM2) of 20 vertical levels and a horizontal resolution of 1° with increasing resolution up to 0.4° near the equator. This assimilates the upper-444-m temperature and salinity data from WOA94; hydrographic, satellite, and in situ SST data from Reynolds and Smith (1994); and altimeter sea level data. The wind stress data from the Comprehensive Ocean–Atmosphere Data Set (COADS) by da Silva et al. (1994) and the NCEP–NCAR reanalysis data (Kalnay et al. 1996) are used. The global assimilation product of Masina et al. (2004) uses the MOM, but it only assimilates the upper-250-m temperature data from the WOA94 data, the SST data (Reynolds and Smith 1994), and the altimeter sea level data. The atmospheric forcing is from the NCEP–NCAR reanalysis data (Kalnay et al. 1996). The horizontal resolution adopted by Masina et al. (2004) is 0.5° in the zonal direction and from 0.35° to 0.5° in the meridional direction, with 40 levels in the vertical.
3. Seasonal variation
a. Results from the OGCM
Figure 2 shows the annual mean temperature at a depth of about 100 m, whereas Fig. 3 shows the zonal and meridional cross sections of temperature near the core of the dome. As shown in Fig. 2, the OGCM is successful in reproducing the location of the SD. However, the simulated core temperature is about 4°C colder than that of the observation and assimilation data. This cold bias also exists throughout the tropical Indian Ocean, and it is related to the shallower thermocline in the OGCM (Fig. 3); the 20°C isotherm reaches about 30 m in the present OGCM along 62.5°E compared to 70 m in the observation and assimilation data. The difference is due to a model bias, particularly in the vertical mixing. To study the seasonal variation of the SD, we consider an artificial box (50°–75°E, 5°–10°S) in the upper 100 m (Fig. 2), which covers a major part of the SD simulated by the OGCM. Because the observed annual mean depth of the thermocline is located around the depth of 100 m, this depth is chosen here.
Figure 4 shows the seasonal variation of the upper-100-m heat content in the box. A remarkable feature is the semiannual signal. Two minima correspond to mature seasons of the SD: one in June and the other in December for the model and assimilation products, and one in January for the observation. In fact, a simple harmonic analysis indicates that the amplitude of the semiannual harmonics is larger than that of the annual harmonics (Table 1). Thus, the model is successful in reproducing this important characteristic, even though its core temperature is colder and its thermocline is shallower (Fig. 2). Also, this is clearly seen both in the cross section of the temperature across 62.5°E and in the subsurface temperature at a depth of 100 m (Fig. 5). This shows quite a contrast with other oceanic thermal domes, where the annual harmonic dominates (e.g., Umatani and Yamagata 1991; Masumoto and Yamagata 1991; Vinayachandran and Yamagata 1998). Quite interestingly, this semiannual variation is also seen in the nitrate data from WOA01; two maxima occur in June and January when the dome is most developed and nutrient-rich water from below upwells (Fig. 6). We need to clarify a root cause of the semiannual signal in the SD.
The semiannual signal is clearly seen in the total upwelling term and the Ekman pumping term. The contribution from the remote forcing is always negative because of the inflow of the Indonesian Throughflow; the simulated mean volume transport is 11 Sv, which is in good agreement with past observational estimates (Godfrey 1996). The remote forcing shows an annual signal as the simulated transport of the Indonesian Throughflow undergoes seasonal variation as in the observation, but the amplitude of the seasonal cycle in the remote forcing term is smaller compared with that of the Ekman pumping term. The semiannual variation of the Ekman pumping term results from the superposition of the contribution from the term related to wind stress curl and the term related to the planetary beta effect (Fig. 9). The latter causes a strong downwelling in boreal summer and an upwelling in boreal winter. This is associated with the change of the wind direction above the SD owing to the Indian monsoon; it is easterly (westerly) during boreal summer (boreal winter; see Fig. 10). On the other hand, the term related to the wind stress curl shows an annual variation with a strong upwelling during boreal summer and fall. Interestingly, the curl term is almost constant at 0.6 × 10−5 m s−1 for 5 months from June to October, resulting in a trapezoidal shape function in time, which is in contrast to the sinusoidal shape of the term related to the beta effect. This is why the semiannual signal appears in the total Ekman pumping even though both terms are dominated by the annual signal.
The trapezoidal shape function is somewhat peculiar considering the fact that the summer monsoon is stronger but shorter. To examine this, the curl term is further divided into two parts, that is, [1/(ρof )](∂τy/∂x) and −[1/(ρof )](∂τx/∂y). From Fig. 11, it is clear that the −[1/(ρof )](∂τx/∂y) term is more dominant than the [1/(ρof )](∂τy/∂y) term. This means that the meridional gradient of the zonal wind stress remains almost constant from June to October, even though the zonal wind stress itself varies significantly. This feature is closely related to the unique location of the SD (Fig. 12); it straddles two regions—one dominated by the Indian monsoon, and another dominated by the southeasterly trade wind throughout a year, even if its magnitude changes seasonally.
b. Sensitivity to wind stress data
To check the sensitivity of our results to the wind stress data, we have calculated the seasonal variation of the Ekman pumping velocity in the dome region using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data, the Southampton Oceanography Centre (SOC) surface flux climatology (Josey et al. 1998) based on ship observations, and the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO) data (Kubota et al. 2002) based on satellite observation (Fig. 13).
All four datasets show the dominant semiannual signal in the Ekman pumping velocity as shown in Table 2. Furthermore, this semiannual signal results from the superposition of the wind stress curl term, which remains almost constant over 5–7 months during boreal summer and fall, and the term related to the beta effect, which shows the sinusoidal variation. Thus, the results presented in the previous subsection are robust and are not sensitive to the wind stress data used to force the OGCM.
4. Summary and discussion
We have studied the seasonal variation of the SD in detail for the first time. The dominance of the semiannual signal in the SD is shown by analyzing the upper-ocean heat content in both observations (including the nitrate data) and the OGCM. This semiannual variation in the heat content is mainly caused by the vertical flux associated with the local Ekman pumping, which is caused by two wind forcing terms—one related to the wind stress curl and another related to the beta effect as well as the zonal wind stress. The seasonal variation in the term related to the beta effect is due to the change in the wind direction associated with the Indian monsoon, which causes downwelling (upwelling) during boreal summer (boreal winter). The curl term also shows a dominant annual cycle with strong upwelling during boreal summer and fall. However, the curl term, in particular the meridional gradient of the zonal wind stress, remains almost constant for five successive months from June to October, even though the zonal wind stress itself varies significantly during this period. This feature turns out to be closely related to the unique location of the SD; the seasonal variation in wind stress is dominated by the Indian monsoon to the north and the southeasterly trade winds prevail throughout a year to the south.
Thus, the present study proposes a new mechanism for the semiannual signal in the Indian Ocean. We note that two mechanisms for generating semiannual signals have been proposed previously. The most well-known mechanism is associated with two monsoon breaks during which the Yoshida–Wyrtki jets are driven by zonal winds (Yoshida 1959; Wyrtki 1973). The basin-wide resonance is considered to enhance this semiannual signal (Cane and Moore 1981; Jensen 1993; Han et al. 1999). Another mechanism is related to the asymmetry of the Indian summer and winter monsoons; the summer (winter) monsoon is stronger (weaker) but shorter (longer; see R. Suzuki et al. 2004, personal communication). This is especially effective in generating semiannual variation in the Arabian Sea and even affects the equatorial Indian Ocean through possible Kelvin–Munk waves (Godfrey 1975; McCreary 1983).
It is known, however, that the SST in the southwestern Indian Ocean, including the SD region, shows a dominant annual cycle, rather than a semiannual cycle (Levitus 1987). The observed temperature in the upper 30 m of the SD region is dominated by the annual harmonic, whereas that below 30 m is more dominated by the semiannual harmonic (Fig. 14a). However, the semiannual harmonic is also present in SST; its amplitude is about 40% of that for the annual harmonic. In fact, the SST in the SD region shows two peaks in April and December. When the heat budget is calculated for the upper 30 m, about 50% of the semiannual variation is actually explained by the variation in the SD (Fig. 14b). We note here that the mixed layer above a depth of about 30 m mostly influences the SST.
Because the SST in the SD region affects the Indian summer monsoon (Annamalai et al. 2005) and the tropical cyclone activity (Xie et al. 2002), it is important for coupled GCMs to resolve the SD in the Indian Ocean. In particular, resolving its seasonal cycle suitably may provide a benchmark test for the performance of CGCMs.
Acknowledgments
We thank Prof. Yukio Masumoto for helpful comments and suggestions. The OGCM was run on HITACHI SR11000/J1 of Information Technology Center, the University of Tokyo under the cooperative research with Center for Climate System Research, the University of Tokyo. The present research is supported by the 21st century COE grant from the Ministry of Education, Culture, Sports, Science, and Technology of Japan for the “Predictability of the Evolution and Variation of the Multi-Scale Earth System: An Integrated COE for Observational and Computational Earth Science” of the University of Tokyo, and the Japan Society for Promotion of Science through Grant-in-Aid for Scientific Research (A) 17204040.
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Enlarged map of the western Indian Ocean and the Seychelles Dome. WICC: West Indian Coast Current, SMC: Southwest Monsoon Current, NMC: Northeast Monsoon Current, SC: Somali Current, NEMC: Northeast Madagascar Current, and SEC: South Equatorial Current.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Annual mean subsurface temperature at a depth of 100 m from (a) World Ocean Atlas 1998, (b) SODA, (c) Masina et al. (2004), and (d) the OGCM. Contour interval is 1.0°C. Temperatures less than 20°C for WOA98, SODA, and Masina et al. and 17°C for OGCM are shaded. The box used in the analyses is also shown.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Annual mean cross section of the SD across 7.5°S and 62.5°E from (a) the WOA98, (b) SODA, (c) Masina et al. (2004), and (d) OGCM (°C). Contour interval is 1°C and temperatures less than 20°C are shaded.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Annual march of the upper-100-m heat content deviation from the annual mean calculated from four data in the box (109 J m−2): WOA98 (long–short dashed line), SODA (solid line), Masina et al. (2004) (long dashed line), and OGCM (dotted line).
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
(a) Bimonthly cross section of the SD across 62.5°E from the WOA98. Contour interval is 1°C and temperatures less than 20°C are shaded. (b) Bimonthly subsurface temperature at a depth of 100 m from the World Ocean Atlas 1998. Contour interval is 1°C and temperatures less than 20°C are shaded.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Annual march of the nitrate concentration in the box from the WOA01 (mg m−3)
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Monthly heat budget calculated from the OGCM outputs in the box (J m−2 s−1). Here, “d(HC)/dt” denotes the rate of change of heat content, “hor_adv” denotes the horizontal advection term, “ver_adv” denotes the vertical advection term, “diff” denotes the diffusion term, and “shf” denotes the surface heat flux term.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Monthly upwelling speed calculated from the OGCM for the box (10−5 m s−1). Positive values mean upwelling.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Monthly Ekman pumping speed for the box (10−5 m s−1). Positive values mean upwelling.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Annual cycle of wind stress vectors (N m−2) and wind stress curl (10−8 N m−3) in the tropical Indian Ocean. Contour interval is 5 × 10−8 N m−3 and negative values are shaded.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Annual march of the curl term [1/(ρof )]∇ × τ = [1/(ρof )](∂τy/∂x − ∂τx/∂y) in the equation of the Ekman pumping speed for the box (10−5 m s−1). Also, “dτy/dx” denotes [1/(ρof )](∂τy/∂x) and “−dτx/dy” denotes −[1/(ρof )](∂τx/∂y). Positive values mean upwelling.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
(a) Zonal wind stress (10−1 N m−2) and (b) the meridional gradient of the zonal wind stress (10−7 N m−3), averaged between 50° and 75°E. Negative values are shaded.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
As in Fig. 9, but for (a) NCEP–NCAR reanalysis data, (b) ECMWF reanalysis data, (c) SOC data, and (d) J-OFURO data in the SD region (10−5 m s−1).
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
(a) Seasonal variation of the vertical temperature profile (from WOA98) in the SD region. Contour interval is 1.0°C and temperatures less than 20°C are shaded. (b) Heat budget for the semiannual component of the upper 30 m calculated from WOA98 and NCEP–NCAR reanalysis data in the SD region (J m−2 s−1). Here, the thick solid line denotes the rate of change of heat content, the thin solid line denotes the advection term, and dashed line denotes the surface heat flux term.
Citation: Journal of Climate 21, 15; 10.1175/2008JCLI1957.1
Ratio between the semiannual and annual harmonics for the upper-ocean heat content in the SD.
Ratio between the semiannual and annual harmonics for the Ekman pumping velocity in the SD region calculated from four different wind stress datasets.
