 Maes et al., 2005, 2006). Also, the spatial structure of BLT driven by special variation in Ekman drift is crucial for the formation of monsoon cyclones in the pre-monsoon season (Thadathil et al., 2007; Vinayachandran et al., 2002; Masson et al., 2005; Neetu et al., 2012).
The variability of BLT is mainly affected by the change of MLD and thermocline due to various mechanisms, such as heavy precipitation, oceanic currents, wind stress, and oceanic waves (Bosc et al., 2009; Mignot et al., 2007; Masson et al., 2002; Qu and Meyers, 2005). For instance, a thicker BL mainly presents in the areas beneath the Intertropical Convergence Zone (ITCZ) with decreasing sea surface salinity (SSS) due to abundant rainfall (Vialard and Delecluse, 1998) or large river discharge (Pailler et al., 1999). The strong wind stress anomalies could also contribute to thickening the BL via deepening the thermocline (Seo et al., 2009).
Compared to the tropical Pacific and the Atlantic Ocean, the tropical Indian Ocean (TIO) is characterized by a shallower thermocline in the west (Yokoi et al., 2012, 2008; Yu et al., 2005) and stronger interannual variation of upper-ocean temperature in the east (Li et al., 2003; Saji et al., 1999), which provides a unique region to evaluate the seasonal and interannual variabilities of BLT.
The strong seasonality of BLT has been observed in some subregions of the TIO, such as the southeastern Arabian Sea, the Bay of Bengal, and the southeastern TIO (Schott et al., 2009). These regions are also characterized by the strong seasonality of SSS due to different hydrological processes (Rao, 2003; Subrahmanyam et al., 2011; Zhang et al., 2016; Zhang and Du, 2012). Overall, the seasonality of BLT in the TIO is partly consistent with the change of SSS due to the impact of freshwater (Masson et al., 2002; Qu and Meyers, 2005).
The interannual variability of BLT in the southeastern TIO can be partly explained by Indian Ocean Dipole (IOD) events (Qiu et al., 2012). During positive IOD year (e.g., 2006), thinner BL in the southeastern TIO is mainly led by the shallower thermocline induced by the upwelling Kelvin wave in the presence of weakly shoaling MLD. In negative IOD year (e.g., 2010), a thicker BL is expected due to the extending of the thermocline. Furthermore, at the subseasonal scale, the zonal SSS gradient driven by the freshwater advection results in a thicker BL to sustain a fresh and stable MLD (Drushka et al., 2014).
Existing studies on the interannual variability of BLT were mainly focused on specific years and lacked long-term evaluation. More importantly, the interannual variability of both thermocline and SSS are supposed to be associated with ENSO events (Grunseich et al., 2011; Rao and Sivakumar, 2003; Subrahmanyam et al., 2011; Zhang et al., 2013), but relationships between BLT and ENSO are scarcely reported in the TIO. Also, the relative impact of SSS and thermocline depth on the variability of BLT is still unclear and is not systematically investigated in the TIO. Thus, the evolution of the seasonal and interannual variabilities of BLT and its relationship with SSS and thermocline anomalies are still highly desired. The Simple Ocean Data Assimilation (SODA) version 3 ocean reanalysis dataset covers time series data from 1980 to 2015, which may be adequate for such purpose.
The remainder of this paper is arranged as follows. In Sect. 2, we briefly describe the datasets and methods. Comparisons of the BLT variability interpreted from both observed and reanalysis datasets in the TIO are presented in Sect. 3. Section 4 presents the seasonal variability of the BLT in the TIO, while its interannual variability is shown in Sect. 5. Finally, a summary and discussions are given in Sect. 6.
where TTDDTm02 is the depth of the top of thermocline, which is defined as the depth at which the surface temperature is 0.2∘ cooler than the sea surface temperature and is hereafter referred to as the isothermal layer depth (ILD). MLD is the mixed layer depth defined by oceanic density at which depth the density is 0.03 kg m−3 larger than that of the surface (de Boyer Montégut et al., 2007; Mignot et al., 2007).
Another dataset is the latest released SODA version 3 reanalysis data (1980–2015) with a horizontal resolution of 0.5∘ which is hereafter denoted as SODA v3 data and can be accessed from the Asia-Pacific Data-Research Center (APDRC: http://apdrc.soest.hawaii.edu/datadoc/soda_3.3.1.php, last access: July 2020). SODA v3 has reduced systematic errors in the upper ocean and has improved the accuracy of the poleward variability in the tropic (Carton et al., 2018). It has 26 vertical levels with a 15 m resolution near the sea surface. We adopted the same Ifremer equation to calculate SODA BLT as the difference between density and temperature-defined MLD.
Salinity and temperature in the first level (5 m) are adopted as the SODA SSS and sea surface temperature (SST), respectively. The thermocline depth is defined as the depth of the 20∘ isotherms.
Monthly SST between 1980 and 2015 on a grid of 1∘×1∘ is acquired from Hadley Center Global Sea Ice and Sea Surface Temperature (HadISST: https://climatedataguide.ucar.edu/climate-data/sst-data-hadisst-v11, last access: July 2020) to calculate the Nino3.4 index. The Nino3.4 index is the average SST anomaly in the area of 5∘ N–5∘ S, 170–120∘ W. Monthly precipitation data are obtained from CMAP (Climate Prediction Center (CPC): http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=13195, last access: August 2020). Monthly zonal wind stress is obtained from SODA.
Figure 1Seasonal distributions of the BLT climatology obtained from Argo (a–d) and SODA (e–h) from 2005 to 2015 in the Indian Ocean. Units: m. The thicker green line is the zero BLT line from Argo and the dashed blue lines represent the areas of the western TIO (5∘ N–12∘ S, 55–75∘ E) and the eastern TIO (5∘ N–12∘ S, 85–100∘ E), respectively. The two thin black lines represent the latitudes of 12∘ S and 5∘ N, respectively.
The significance of simultaneous and lead–lag correlations is evaluated in this study with a Student's t test. In all the datasets, we removed the annual cycle of each parameter before the interannual correlation analysis. Composite analysis is also employed to evaluate the interannual variability of BLT using a Monte Carlo significance test. The Monte Carlo process is that the IOD/El Niño/La Niña years are randomly shuffled (10 000 times) for each month, and a mean Student's t test is used to calculate the t statistic for the selected areas. The mean of the t statistic generated by the random simulations exceeding that of the actual t value is determined and assessed at the 5 % significance level. The positive and negative IOD years are provided by the Bureau of Meteorology (http://www.bom.gov.au/climate/iod/, last access: July 2020), and the El Niño and La Niña years are obtained from the Golden Gate Weather Services (https://ggweather.com/enso/oni.htm, last access: August 2020). Monthly mean values are averaged over 3 sequential months for different seasons, e.g., December–January–February (DJF) for boreal winter, March–April–May (MAM) for boreal spring, June–July–August (JJA) for boreal summer, and September–October–November (SON) for boreal autumn. All the area-averaged parameters shown in this study are weighted by the cosine of the latitude.
Figure 2Seasonal cycle of the region-averaged BLT for SODA and Argo: (a) the western TIO (12∘ S–5∘ N, 55–75∘ E) and (b) the eastern TIO (12∘ S–5∘ N, 85–100∘ E).
BLT in the TIO calculated from SODA v3 is first validated against Argo float observations from 2005 to 2015. As shown in Fig. 1, the seasonal BLT climatology obtained from SODA v3 is biased thinner in the Bay of Bengal in all seasons compared to that derived from Argo. This thinner BL in SODA v3 is probably because it lacks the runoff