1. Introduction

An oceanic thermal dome around the Republic of Seychelles in the southwestern Indian Ocean (Reverdin and Fieux 1987; Woodberry et al. 1989; McCreary et al. 1993) is referred to simply as Seychelles Dome (SD; Yokoi et al. 2008). Because the thermocline is very shallow over the SD, the region is known to be a center of strong ocean–atmosphere interaction. In fact, the sea surface temperature anomaly (SSTA) over the southwestern Indian Ocean including the SD region, which is induced by oceanic Rossby waves (Murtugudde et al. 2000; Xie et al. 2002; Rao et al. 2002; Huang and Kinter 2002; Rao and Behera 2005), influences the onset of the Indian summer monsoon (Annamalai et al. 2005; Izumo et al. 2008). When the dome is weak and the overlying SSTA is positive, a northward migration of the intertropical convergence zone (ITCZ) toward the Indian subcontinent is delayed. In addition, the SD is located close to the western pole of the Indian Ocean dipole (IOD; Saji et al. 1999; Webster et al. 1999; Yamagata et al. 2004; Chang et al. 2006), which plays an important role in the variability of the East African short rains (Black et al. 2003; Behera et al. 2005) and world climate (Guan and Yamagata 2003; Saji and Yamagata 2003). The SST variations in the dome region may even influence the Pacific–North America (PNA) pattern during El Niño years through atmospheric teleconnections (Annamalai et al. 2007b). Furthermore, the number of tropical cyclones is closely related to the strength of the SD (Xie et al. 2002). Thus, a successful simulation of the SD in coupled GCMs (CGCMs) is crucial to better climate predictability.

However, no study to date has compared the ability of different models to simulate the oceanic dome, even though it is of great importance to climate and ecology. Therefore, we believe that such an intercomparison study will enhance our understanding of the SD and the associated air–sea interaction and may contribute to the improvement of these coupled GCMs. In fact, it is a very challenging task for CGCMs to simulate the seasonal variation of the SD (Yokoi et al. 2008; Hermes and Reason 2008). Yokoi et al. (2008) demonstrated that it is dominated by a semiannual cycle due to the local Ekman upwelling. This semiannual nature is explained by unique contributions of two components of the Ekman pumping: one term proportional to the planetary beta and the zonal wind stress and the other term proportional to the wind stress curl. The former is determined by the seasonal change in the zonal component of the wind stress vector above the SD associated with the Indian monsoon and causes downwelling (upwelling) during boreal summer (boreal winter). On the other hand, the latter is due to the meridional gradient of the zonal wind stress and causes almost steady upwelling for 5 months from June to October even though the zonal wind stress itself varies significantly during this period. Thus, the semiannual variation, despite being remarkable, is due to a subtle balance between the seasonal variation of the zonal wind stress and the wind stress curl.

In this study, we have examined the ability of coupled GCMs to simulate the SD and its seasonal variation. The paper is organized as follows: A brief description of 22 coupled GCMs submitted to the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project, phase 3, (CMIP3) multimodel dataset is given in section 2. In section 3, we analyze the SD simulated in each model and discuss causes of model biases. A summary and discussion are presented in the final section.

2. Description of CMIP3 models

We here examine ocean temperature, precipitation, and wind stress data from the “twentieth-century climate in coupled models” (20c3m) control runs of 22 coupled GCMs submitted to the CMIP3. Among 23 models submitted at the time of the present analysis, we could extract both ocean temperature and wind stress data for 22 models, listed in Table 1 (details of these models are described at http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php). Using the available dataset, we have calculated the monthly mean climatology for the last 50 yr of the twentieth century to investigate the simulated seasonal cycle of the SD. For the Hadley Centre Global Environmental Model, version 1 (HadGEM1), only 45 yr of oceanic temperature data were available.

The model outputs are compared with various observational datasets including the World Ocean Atlas 1998 (WOA98; Antonov et al. 1998), the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data (Kalnay et al. 1996), and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) data (Xie and Arkin 1996). The WOA98 provides a global hydrographic temperature data with a uniform horizontal resolution of 1° and 24 vertical levels. The NCEP–NCAR reanalysis data are used for the wind stress data.

3. Seasonal variation

Figure 1 shows the distribution of the annual mean depth of the 20°C isotherm (D20) in the tropical Indian Ocean. Except for four models [CGCM3.1 (T47), CGCM3.1 (T63), the Goddard Institute for Space Studies Atmosphere–Ocean Model (GISS-AOM), and the GISS Model E-R (GISS-ER)], all other models are successful in reproducing the SD; the D20 of GISS-AOM and GISS-ER is too deep (their D20 is deeper than 200 m), whereas the upwelling of CGCM3.1 (T47) and CGCM3.1 (T63) appears as coastal upwelling in the eastern Indian Ocean along Sumatra (Saji et al. 2006). However, the core of the dome in many models is located too far (more than 10° in longitude) to the east [the Commonwealth Scientific and Industrial Research Organisation Mark, version 3.0 (CSIRO-Mk3.0); the Geophysical Fluid Dynamics Laboratory Climate Model, version 2.0 (GFDL-CM2.0); the Model for Interdisciplinary Research on Climate 3.2 (MIROC3.2), in both the high-resolution (hires) and medium-resolution version (medres) versions; and the Community Climate System Model, version 3 (CCSM3)] or to the west (the Institute of Numerical Mathematics Coupled Model, version 3.0 (INM-CM3.0) and ECHAM and the global Hamburg Ocean Primitive Equation model (ECHO-G)] when compared with that of the WOA98, whose center is located at 62°E, 7.5°S (Fig. 2). Here, we have defined the core by the location of minimum D20 between 12° and 5°S in the Indian Ocean, excluding coastal regions. Only six models [the Centre National de Recherches Météorologiques Coupled Global Climate Model, version 3 (CNRM-CM3), the GFDL Climate Model, version 2.1 (GFDL-CM2.1), GISS Model E-H (GISS-EH), the Istituto Nazionale di Geofisica e Vulcanologia SXG (INGV-SXG), the Meteorological Research Institute Coupled General Circulation Model, version 2.3.2a (MRI-CGCM2.3.2), and HadGEM1] simulate the center within 5° of the observed position. In addition, the dome is too deep in MIROC3.2 (hires) and the third climate configuration of the Met Office Unified Model (HadCM3), whereas it is too shallow in the Parallel Climate Model (PCM).

Next, the seasonal variation of the D20 within the simulated SD and the amplitudes of the annual and semiannual harmonics are shown (Figs. 3 and 4). Here, we have calculated average D20 in a 16° (longitude) × 4° (latitude) box; it is shifted so that it can cover the simulated SD in each model. It turns out that most models except for CNRM-CM3, L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL-CM4), HadGEM1, and INM-CM3.0 underestimate the amplitude of the semiannual harmonic (Fig. 4b). In contrast, most models except for ECHO-G, the Flexible Global Ocean–Atmosphere–Land System Model, gridpoint version 1.0 (FGOALS-g1.0), and GISS-EH overestimate the amplitude of the annual harmonic (Fig. 4a). As a result, the ratio between the semiannual harmonic and the annual harmonic is much smaller in most coupled GCMs when compared with that of the WOA98 (Fig. 4c), suggesting that most CMIP3 models have difficulties in simulating the realistic seasonal variation of the SD.

To identify the origin of these model biases, we have examined the seasonal variation of the Ekman pumping in the SD region because it plays the dominant role in the seasonal variation of the SD (Yokoi et al. 2008). As expected, the ratio between the semiannual harmonic and the annual harmonic for the local Ekman pumping and that for D20 in the dome region have good correspondence (Fig. 5). The models with a stronger semiannual component in Ekman pumping over the dome region have a better skill in the semiannual cycle of the SD.

The local Ekman pumping can be further decomposed into two components:

View Expanded

where ρ_o is the density of seawater, f is the Coriolis parameter, τ is the wind stress, β is the meridional gradient of the Coriolis term, and τ^x is the zonal component of the wind stress (Fig. 6). The first term on the right-hand side is proportional to the wind stress curl (hereinafter the curl term) and the second term is proportional to the planetary beta and the zonal wind stress (hereinafter the beta term). Both show the strong dominance of the annual harmonic, but their sum shows dominant semiannual variation. The curl term remains almost steady for 5 months from June to October in the observation. This is due to the unique location of the SD; it lies between the region dominated by the Indian monsoon and the southeasterly trade winds (Yokoi et al. 2008). On the other hand, the beta term is more closely influenced by the seasonal reversal of the wind associated with the Indian monsoon, whose summer (winter) monsoonal winds are stronger (weaker) but last less long (longer).

For quantitative comparison of these two terms, we have calculated the skewness using the following formula:

View Expanded

where A is the skewness and N is the number of data points (An and Jin 2004). The beta term in most models is positive, which is inconsistent with the observation (Fig. 7a); the model summer (winter) monsoon tends to last longer (less long). Only five models [GISS-EH, FGOALS-g1.0, GFDL-CM2.1, CCSM, and MIROC3.2 (medres)] are successful in this aspect. On the other hand, the curl term is relatively well reproduced in most models, except for four models [CCSM, FGOALS-g1.0, CSIRO-Mk3.0, and MIROC3.2 (medres)] showing an opposite sign (Fig. 7b). Thus, simulating the seasonal asymmetry of the Indian monsoon seems to be a key to the realistic simulation of the SD.

Because the seasonal variations in the curl and beta terms are closely related to the Indian monsoon and the meridional shift of the ITCZ, we have constructed time–latitude diagrams of the monthly climatology for precipitation, SST, and wind stress averaged over 60°–70°E (Fig. 8). The ITCZ remains to the north of the equator until December in some models (e.g., CSIRO-Mk3.0) even though that in the observation lies over the SD. One reason for this bias is the warmer Arabian Sea. As shown by Izumo et al. (2008), the strong Ekman upwelling in the western Arabian Sea off Oman associated with the summer monsoon leads to the cooling of SST there. It seems that this upwelling along the western boundary of the Arabian Sea is weaker in some models. In contrast, the ITCZ shifts too far south during boreal winter in some models (e.g., INGV-SXG and PCM). This results in erroneously strong westerly winds over the SD and leads to the positive skewness of the beta term.

4. Summary and discussion

Using outputs from 22 CMIP3 coupled GCMs, we have examined their skill in simulating the SD and its seasonal variation. Six models are successful in simulating the center of the dome within the accuracy of 5° in its location. However, almost all models underestimate the dominance of the semiannual harmonic when compared with the observation. This is because the simulated semiannual harmonic in the local Ekman pumping over the SD is weak. The main cause for this bias is the model’s inability to simulate the asymmetric nature of the monsoonal winds; the summer (winter) monsoon is shorter (longer) in its duration but stronger (weaker) in its magnitude.

It is interesting to note that Annamalai et al. (2007a) have shown that only GFDL-CM2.0, GFDL-CM2.1, ECHAM/Max Planck Institute Ocean Model (MPI-OM), MRI-CGCM2.3.2, PCM, and HadCM3 were successful in simulating the June–September rainfall climatology and seasonal evolution of the all-India rainfall. However, in our analysis, some of these models do not show high skill in simulating the semiannual cycle in the Ekman pumping over the SD, which is closely linked with the monsoon. The major reason for the discrepancy is the difference in the metrics used to evaluate the monsoon; those in Annamalai et al. (2007a) focused on the rainfall pattern correlations and root-mean-square differences during the summer monsoon, whereas ours take into account several factors including the monsoon transitions and the winter monsoon. It seems that models with a good skill in simulating the summer monsoon rainfall pattern do not necessarily succeed in simulating the winter monsoon.

The SST in the SD region may influence the interannual variation in the Indian summer monsoon, as discussed by Annamalai et al. (2005) and more recently by Izumo et al. (2008), and the tropical cyclone activity, as discussed by Xie et al. (2002). Because the realistic simulation of the SD is crucial for reproducing the climatic effects of the SD successfully, resolving the SD and its seasonal cycle may suitably provide a benchmark test for the performance of CGCMs.