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What Controls Seasonal Variations of the Diurnal Cycle of Sea Surface Temperature in the Eastern Tropical Indian Ocean?* YANG YANG College of Physical and Environmental Oceanography, Ocean University of China, and Center for Ocean and Climate Research, First Institute of Oceanography, State Oceanic Administration, Qingdao, China, and International Pacific Research Center, and School of Ocean and Earth Science and Technology, University of Hawai‘i at Manoa, Honolulu, Hawaii

TIM LI International Pacific Research Center, and School of Ocean and Earth Science and Technology, University of Hawai‘i at M anoa, Honolulu, Hawaii, and Climate Dynamics Research Center, and Earth System Modeling Center, International Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China

KUIPING LI AND WEIDONG YU Center for Ocean and Climate Research, First Institute of Oceanography, State Oceanic Administration, Qingdao, China (Manuscript received 4 December 2014, in final form 7 August 2015) ABSTRACT Recent in situ buoy observations revealed interesting seasonal features of the diurnal sea surface temperature cycle (DSST) in the eastern tropical Indian Ocean. Composite analysis shows that areas away from the equator exhibit stronger seasonal variations of DSST, while weaker seasonal variations appear near the equator. The most interesting characteristic is the distinctive contrast of the seasonal variations of DSST between the Bay of Bengal (BOB) and the region south of the equator (particularly around 128S). While the range of DSST is weakest in the BOB during boreal summer, it has its largest range around 128S in austral summer. Furthermore, BOB DSST exhibits two peaks that occur during the monsoon transitions (March– April and October), whereas DSST south of the equator shows only a single peak in its annual cycle. Using a one-dimensional, oceanic, mixed layer model, the authors examined the cause of the distinctive annual cycles of DSST north and south of the equator. Two parallel experiments were conducted at buoy sites 128N, 908E and 128S, 80.58E driven by surface forcing from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product. The results demonstrated that, in the BOB, both surface shortwave radiation and wind stress contribute to the March maximum, whereas the wind stress alone drives the October maximum. In contrast, the seasonal variation of DSST south of the equator is primarily caused by the annual cycle of the wind stress, which is extremely weak in austral summer near the intertropical convergence zone (ITCZ). How the monsoon and ITCZ modulate the distinctive annual cycles of DSST is discussed.

* School of Ocean and Earth Science and Technology Publication Number 9506 and International Pacific Research Center Publication Number 1150.

Corresponding author address: Tim Li, IPRC, SOEST, University of Hawai‘i at M anoa, 1680 East West Road, POST Bldg. 401, Honolulu, HI 96822. E-mail: [email protected] DOI: 10.1175/JCLI-D-14-00826.1 Ó 2015 American Meteorological Society

1. Introduction Sea surface temperature has been shown to play a fundamental role in regulating variations in air–sea interactions, since the sea surface is the lower boundary of the atmosphere. SST is also a vital/key parameter in the fields of marine biology and chemistry (Kawai and Wada 2007; Weihs and Bourassa 2014). Because of the strength of solar radiation and Earth’s rotation, the

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diurnal SST cycle (DSST) is one of the dominant processes in the coupled atmospheric–ocean climate system (Yang and Slingo 2001). The earliest records of the DSST can be traced back more than a half century (e.g., Sverdrup et al. 1942; Roll 1965). Recently, a large amount of DSST variability has been detected by both in situ and satellite observations (Stramma et al. 1986; Fairall et al. 1996; Clayson and Weitlich 2005; Ward 2006; Gentemann et al. 2003; Stuart-Menteth et al. 2003; Gentemann et al. 2008). Gentemann et al. (2008) claimed that large diurnal warming events could reach peaks around 58–78C in the extratropics. Even in the tropics, diurnal warming can be as large as 28–38C under clear-sky, low-wind conditions (e.g., Flament et al. 1994; Webster et al. 1996; Soloviev and Lukas 1997; Stuart-Menteth et al. 2003; Kawai and Wada 2007; Kennedy et al. 2007; Gille 2012). Because the heat, moisture and gas exchanges at the surface of the ocean are sensitive to the surface temperature, Fairall et al. (1996) and Ward (2006) pointed out that the large warming can lead to an incremental increase in the net surface heat flux toward the atmosphere of 50– 60 W m22. Further, Yu et al. (2004) suggested that the absence of high-frequency variations in SST in models degraded the accuracy of air–sea temperature and specific humidity differences. Therefore, the diurnal cycle of SST may have a great impact on the mean climate system and on ocean–atmosphere interaction in general (Chen and Houze 1997; Woolnough et al. 2000; Clayson and Chen 2002; Dai and Trenberth 2004). Therefore, recently, more attention has been paid to resolving the diurnal SST variability in climate model mean states, including the Indian monsoon (Terray et al. 2012) and low-frequency modes, such as the Madden– Julian oscillation (MJO; Woolnough et al. 2007; Bernie et al. 2005, 2008; Oh et al. 2013; Y. Li et al. 2013) and the El Niño–Southern Oscillation (ENSO; Danabasoglu et al. 2006; Masson et al. 2012). Through the comparison of results from the use of 2-h versus 24-h coupling intervals in a high-resolution coupled atmosphere–ocean general circulation model (CGCM), Terray et al. (2012) found that the Indian summer monsoon (ISM)–ENSO teleconnection was better represented with the 2-h coupling configuration. Woolnough et al. (2007) investigated the impact of diurnal mixing of the upper ocean on the MJO and demonstrated significant improvement in the prediction skill, especially for the phase of the MJO over the Indian Ocean and the western Pacific. Previous observations of the DSST variability mainly focused on the western Pacific warm pool (e.g., Price et al. 1986, 1987; Weller and Anderson 1996; Webster et al. 1996; Soloviev and Lukas 1997; Kawai and Kawamura 2002; Ward 2006). Many modeling studies

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have also highlighted the effects of the diurnal ocean variation on the western Pacific warm pool (e.g., Shinoda and Hendon 1998; Bernie et al. 2005; Shinoda 2005; Soloviev and Lukas 2006), as well as on the Atlantic Ocean (Pimentel et al. 2008; Guemas et al. 2011, 2013). Less attention has been paid to the Indian Ocean, where the unique ocean–land configuration makes the Asian monsoon one of the most remarkable climate systems worldwide (Yu et al. 2012a). Previous studies have documented the spatial distribution of seasonally persistent DSST (e.g., Kawai and Wada 2007; Bellenger and Duvel 2009; Clayson and Weitlich 2005; Weihs and Bourassa 2014) based on in situ observation or numerical modeling. In general, the seasonal variability of DSST migrates with the seasonal cycle of sun, especially for the Pacific and Atlantic regions. However, in the Indian Ocean, the Asian monsoon makes the annual cycle of DSST quite unique. The present paper aims to reveal the seasonally and latitudinally dependent characteristics of the DSST over the eastern tropical Indian Ocean based on buoy observations and to further elucidate, with the aid of a one-dimensional oceanic mixed layer model, the relative importance of the various surface forcing factors in regulating the seasonal variations of DSST. The remaining part of the paper is organized as follows: The model and data are presented and described in section 2. Section 3 describes the time–latitude variation of the diurnal cycle of SST over the tropical eastern Indian Ocean. The distinctive seasonal features of DSST in the Bay of Bengal (BOB) and south of the equator are discussed in sections 4 and 5, respectively. Finally, a discussion and conclusions are given in section 6.

2. Data and model description a. Data The primary data used in this study are hourly sea surface temperatures from the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA; McPhaden et al. 2009) over the eastern tropical Indian Ocean. RAMA is the main component of the Indian Ocean Observing System (IndOOS; McPhaden et al. 2009). RAMA was initiated in 2004 for improved description, understanding, and prediction of the east Africa, Asian, and Australian monsoon systems. It was designed by the Climate Variability and Predictability Program (CLIVAR)/Global Ocean Observing System (GOOS) Indian Ocean Panel as a contribution to the Indian Ocean Observing System. High-frequency SST data collected from 16 buoys in the

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TABLE 1. The location and temporal record length of buoys. Lat, lon (degrees)

Period

Resolution

128S, 80.58E 88S, 80.58E 48S, 80.58E 1.58S, 80.58E

18 May 2010–3 Jul 2012 23 Aug 2008–8 Jul 2012 3 Aug 2011–23 Aug 2012 6 Sep 2006–25 Jul 2008 17 Aug 2008–21 May 2010 10 Aug 2008–4 Oct 2010 29 Jul 2011–23 Nov 2011 4 Sep 2006–5 Jun 2008 13 Aug 2008–31 Aug 2009 1 Jan 2002–8 Jun 2012 9 Nov 2004–27 Mar 2006 11 Sep 2006–19 Mar 2009 30 Jul 2009–2 May 2012 17 Sep 2006–24 Apr 2009 1 Aug 2009–26 May 2011 17 Nov 2006–22 Jul 2008 4 Jul 2010–5 Jul 2011 15 Nov 2006–1 May 2009 1 Nov 2010–3 Jul 2011 18 Oct 2008–13 Sep 2010 3 Nov 2010–1 Sep 2012 20 Oct 2008–7 Nov 2009 5 Nov 2010–30 Dec 2012 22 Mar 2011–25 Apr 2012 27 Oct 2001–15 Nov 2003 9 Jul 2004–28 Jul 2008 16 Mar 2009–29 Jan 2013 27 Oct 2001–15 Nov 2003 9 Jul 2004–28 Jul 2008 16 Mar 2009–29 Jan 2013

10 min 10 min 10 min 10 min

08, 80.58E 1.58N, 80.58E 1.58S, 908E 08, 908E

1.58N, 908E 48N, 908E 88N, 908E 128N, 908E 158N, 908E 128S, 938E 88S, 958E

58S, 958E

10 min 10 min 1h 10 min

10 min 10 min 10 min 10 min 10 min 10 min 1h

1h

eastern Indian Ocean were used in the current analysis. Some of this buoy data predates RAMA by a few years, with the longest records dating back to 2002. The locations and temporal coverage of the RAMA buoys are detailed in Table 1. All buoys used here measure SST at a depth of 1 m with a SeaBird Electronics SBE-37IM. While satellites measure the ocean skin temperature, which is sensed by the atmosphere, they are unable to adequately resolve the exact day-to-day hourly DSST (Kennedy et al. 2007). Although there is a small difference between the skin temperature and buoy SST, algorithms for satellite-derived SST are conventionally tuned with buoy-observed SST (Kawai and Wada 2007) so that the average of the satellite SST agrees with the buoy SST. Reanalysis data provide continuous, gridded global coverage. However, because of their coarser temporal resolution (usually a 6-h time interval), these products are not suitable for studies of the diurnal cycle. Bellenger and Duvel (2009) interpolated the 6-h-resolution 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data to an hourly time step and found that the interpolated data alias the

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TABLE 2. List of all sensitivity runs in EXP1. ‘‘A’’ represents the annual mean climatological diurnal cycle forcing, and ‘‘M’’ represents the monthly climatological diurnal cycle forcing. Surface fluxes Experiment

SW

WS

LH

SH

LW

EP

SW run WS run LH run SH run LW run EP run CTL run

A M M M M M M

M A M M M M M

M M A M M M M

M M M A M M M

M M M M A M M

M M M M M A M

high-frequency variability that is important to adequately resolve the diurnal cycle (Weihs and Bourassa 2014). The 3-hourly Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data, on the other hand, do not contain surface flux products. As a result, the surface flux and wind stress fields that are used to drive an ocean mixed layer model (introduced in the next subsection) are derived from the National Aeronautics and Space Administration (NASA) hourly Modern-Era Retrospective Analysis for Research and Applications (MERRA; Lucchesi R. 2012). This reanalysis dataset has a horizontal resolution of 2/3 degree in longitude and 1/2 degree in latitude, as well as 72 pressure levels vertically extending from 1000 to 0.01 hPa. Because the diurnal cycle of surface shortwave radiation (SW) and wind stress (WS) play important roles in DSST, we compared the diurnal SW and WS from MERRA with buoy observations from the 128N, 908E site for the period from November 2009 through October 2010 and at the 128S, 80.58E site for the period from June 2010 through June 2012 (not shown here). In general, the diurnal SW and WS fields from MERRA agree with that derived from the buoy observations, particularly in the monsoon transition seasons. Larger SW and WS errors appear in boreal summer, but during this period the SW and WS errors have less impact on the DSST in the BOB because there is a much deeper mixed layer there as a result of the strong summer wind stress forcing. The correlation coefficients of diurnal SW and WS time series between the two datasets are 0.988 and 0.976 at site 128N, 908E and 0.996 and 0.918 at site 128S, 80.58E. In addition, monthly shortwave radiation at the top of the atmosphere (TOA) and precipitation from MERRA are used to study the surface shortwave radiation difference between sites north and south of the equator. The SST from the TMI is also used for flux correction in model simulations.

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TABLE 3. List of all sensitivity runs in EXP2. ‘‘A’’ represents the annual mean climatological diurnal cycle forcing, and ‘‘H’’ represents the observed hourly diurnal cycle forcing from MERRA. Surface fluxes Experiment

SW

WS

LH

SH

LW

EP

SW run WS run LH run SH run LW run EP run CTL run

A H H H H H H

H A H H H H H

H H A H H H H

H H H A H H H

H H H H A H H

H H H H H A H

b. Model and experiment design The classic one-dimensional mixed layer model developed by Price et al. (1986, hereafter PWP) was used to examine the relative impacts of wind stress and heat flux on DSST. This model has been used successfully for simulating the SST and DSST variation in the Indian Ocean (Shinoda and Hendon 1998; Kawai and Wada 2007; Mujumdar et al. 2011; Thushara and Vinayachandran 2014). Besides PWP, the nonlocal K profile parameterization model (KPP; Large et al. 1994) is also one of the main one-dimensional ocean models for DSST studies. Because of the capability of the boundary layer to penetrate well into a stable thermocline in both convective and wind-driven situations, the KPP model has been shown to simulate many such events very well, including convective boundary layer

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deepening, diurnal cycling, and storm forcing. However, the mixed layer computed by the KPP model is much deeper than that of the PWP model and field observations (Van Roekel and Maloney 2012). As the mixed layer is crucial to the DSST study, we finally chose the computationally inexpensive PWP model. The results from the control run in our study also demonstrate the good performance of the PWP model in reproducing the DSST variation in the Indian Ocean. In the model, vertical mixing is performed in three steps: 1) static instability, if it exists, is relieved in the upper-ocean mixed layer; 2) mixed layer entrainment is performed based on a bulk Richardson number criterion (Rb $ 0.65); and 3) shear instability mixing between adjacent model layers is performed based on a gradient Richardson number criterion (Rg $ 0.25). Penetrative solar radiation is calculated using a Jerlov (1976) water type with a bimodal exponential function (Paulson and Simpson 1977). The e-folding scale is 23 m for type I at the site 128N, 908E and 20 m for type IA at the site 128S, 80.58E. The vertical resolution of the model is 0.5 m, and the time step is 1 h. The forcing of this PWP model includes surface shortwave radiation, latent heat flux (LH), sensible heat flux (SH), net longwave radiation (LW), wind stress, and evaporation minus precipitation (EP). To reveal the relative roles of each forcing factor in inducing diurnal SST variations, we design two groups of numerical experiments, which include one control run and a series of sensitivity runs.

FIG. 1. The time–latitude plot of the range (difference between maximum and minimum) of the composite diurnal SST cycle derived from the 16 buoy observations over the tropical eastern Indian Ocean (units: 8C).

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FIG. 2. The seasonal variation of climatological mean DSST (blue line), maximum/minimum DSST (shaded), and standard deviation of DSST (error bars) from (top) buoy observations and (bottom) PWP simulations for (left) 128N, 908E and (right) 128S, 80.58E.

In the first group of experiments (EXP1), the control run is forced by monthly mean climatological diurnal cycle fields of surface heat fluxes and wind stress. For the sensitivity runs, we specify one of the six forcing fields with an annual mean climatological diurnal cycle, while other forcing fields are kept the same as in the control run. Then the difference between the sensitivity and the control runs may be regarded as the contribution from the monthly variations of the diurnal cycle of that specific forcing field. The details of the forcing conditions for each of the sensitivity experiments are shown in Table 2. In the second group of experiments (EXP2), the control and sensitivity runs parallel those in EXP1, but climatological monthly mean diurnal cycle forcing fields are replaced by real-time hourly data in all sensitivity runs. The details of the surface forcing conditions for each of the sensitivity experiments in EXP2 are shown in Table 3. The major difference between the two sets of experiments is that EXP2 includes higher-frequency forcing. For all experiments, the model is run for 10 yr over the

period 2003–12. The model simulation results from January 2008 to December 2012 and June 2010 to June 2012 are used for composite analyses to compare with the buoy observations from sites 128N, 908E and 128S, 80.58E, respectively. It is generally found that SSTs from one-dimensional models gradually drift to a new state that is far away from the realistic annual mean state. In that case, the simulated diurnal cycle would oscillate around an unrealistic mean state. To correct for this mean state error, a time-independent annual mean flux adjustment approach, adapted from Li and Hogan (1999), is used. The procedures are as follows: 1) as the buoy-observed SST is in good agreement with the TMI SST, we use the 10-yr (2003–12) mean TMI SST at the buoy sites as the initial observed mean; 2) the PWP model is integrated for 10 yr with a Newtonian damping term (with a relaxation time scale of 6 h) added in the SST equation that pulls the model SST toward the observed mean value; 3) the heat flux correction term is saved each hour during the integration, and the 10-yr mean value is calculated; 4) in the subsequent runs, the calculated mean

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FIG. 3. The monthly evolutions (1: January, 2: February, . . . , 12: December) of the diurnal SST cycle (with the diurnal mean removed; units: 8C) from (a) buoy observations, (b) CTL run in EXP1, and (c) CTL run in EXP2 at 128N, 908E. (d) The seasonal variation of the range (difference between diurnal maximum and minimum) of the diurnal SST cycle (units: 8C) from buoy observations (black), CTL run in EXP1 (blue), and CTL run in EXP2 (red) at 128N, 908E.

heat flux correction value is added into the SST equation as an additional flux term. As described by Li and Hogan (1999), this term only corrects for the unrealistic drift and does not damp the diurnal, seasonal, and interannual variations.

3. Seasonal and latitudinal dependence of the diurnal SST cycle over the eastern Indian Ocean Over the tropical Indian Ocean, there are a total of 16 buoy sites that have at least a 1-yr-long record of hourly SST. To illustrate the seasonal characteristics of DSST, we computed a monthly composite of DSST at each of these sites. A common feature of all sites is that the DSST composites all display a cosine-shaped curve, with a maximum between 1400 and 1600 and a minimum between 0600 and 0800 local time. It is further noted (not shown here) that, for any given latitude band, the seasonal variation of DSST is fairly consistent. Thus, a composite was made in each latitude band over the eastern Indian Ocean. Figure 1 shows a

time–latitude section of the composite DSST from the buoy observations. The most striking feature of the DSST is that in the northern Indian Ocean (at 88, 128, and 158N) there are two peaks, a strong one in March and a weaker one in October, separated by the annual minimum in DSST that occurs in boreal summer. This is in contrast with the seasonality of DSST in the southern Indian Ocean (particularly at 128S), where a single maximum of DSST appears during austral summer (January). Near the equator, the seasonal variation of DSST is, in general, weaker. As the DSST was averaged over several years, we check the spread of the monthly mean DSST range to determine whether or not the seasonal variation of the climatological DSST seen in Fig. 1 is robust. Figure 2 shows the results for the buoys at 128N, 908E and 128S, 80.58E. These results show that, even though there are year-to-year variations, the seasonal contrasts of the DSST from observations and PWP simulations are both robust. The maximum variability of the DSST at the 128N, 908E site appears in March and April when the DSST is strongest. The cause of such a

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direction, the seasonal variation of the DSST yields a similar conclusion (not shown here). Also, as the MERRA data cover a banded spatial domain, this verifies that the conclusions are nearly the same within a few degrees of latitude of the study sites. As documented by Weihs and Bourassa (2014), the seasonal variation of DSST is spatially homogeneous both in the central BOB and in the southern Indian Ocean. Hence the sites used here are representative of the broader domain. The seasonal variability of DSST in the BOB (88, 128, and 158N) and at 128S are the main focus of this study.

4. Causes of seasonal variation of DSST in the Bay of Bengal

FIG. 4. The seasonal variation of the range of the diurnal SST cycle (units: 8C) from PWP CTL and sensitivity experiments in (a) EXP1 and (b) EXP2 at 128N, 908E.

strong variation in the BOB may be attributed to strong interannual variability of the monsoon onset process (Yu et al. 2012a). In the following, we will investigate the processes responsible for the distinctive annual cycles of DSST in the northern and southern Indian Ocean using the PWP mixed layer model. The PWP model was run for all 16 buoy sites and forced by the MERRA data, and the results were consistent with the observations (not shown here). To check the representativeness of site 128N, 908E, we run the PWP model on five sites [e.g., (128N, 908E), (128N, 888E), (128N, 928E), (158N, 908E), and (88N, 908E)] around this site. The results suggest that even if the study were shifted by 28 or more in any

Because the RAMA buoy at 128N, 908E had the longest time series of hourly SST data (2008–12), we chose this site for further DSST analysis. Figure 3a illustrates the monthly variation of DSST observed at this site. The largest DSST range occurs in March, with a secondary peak occurring in October. Thus, maximum DSST variability occurs in the monsoon transition periods (Yu et al. 2012b). As expected, the weakest DSST variability appears during the summer monsoon season [June– August (JJA)], when deep convective clouds associated with the monsoon rains reduce diurnal shortwave radiation forcing. Various dynamic (e.g., wind stress) and thermodynamic (e.g., shortwave and longwave radiation and latent heat fluxes) factors may affect the distinctive seasonal variation of DSST in the BOB. Figures 3b and 3c show the one-dimensional model simulation results forced by the hourly MERRA data [i.e., control (CTL) runs in EXP1 and EXP2]. For each run, the model was integrated for 10 yr, and the last 5 yr (2008–12) of the simulation results were used for analysis and for comparison with the observations. In general, both experiments simulated realistic seasonal variations of DSST in the BOB with maximum DSST amplitudes occurring during the monsoon transition periods. However, there are some slight, but significant, differences as well. For example, the simulated DSST range in February is about 0.058C larger than the observed range, while in October it is about 0.038C smaller than observed. Nevertheless, the model has successfully captured the main features of seasonal variation of the observed DSST. Figure 3d shows the seasonal cycle of DSST range (defined as the difference between the maximum and minimum of the monthly averaged SST diurnal cycle). The modelsimulated and observed DSST ranges are quite close. This provides confidence that the sensitivity experiments using the PWP model will help to uncover the

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FIG. 5. The contributions (units: 8C) of each surface forcing factors [(left) EXP1; (right) EXP2] in (top) March and (bottom) October at 128N, 908E. The bar represents the difference of the DSST range between CTL and the sensitive run. The number above each bar represents the percentage contribution of each forcing factor.

specific processes that give rise to the seasonal variation of DSST in the BOB. Note that the large DSST range in April was well simulated in EXP2 but was underestimated in EXP1. Since the major difference between the two experiments is that EXP2 uses high-frequency forcing, we infer that high-frequency variations play a key role in DSST during the transition periods. This will need to be explored further in the future. As described in section 2, we conducted various sensitivity experiments to isolate the contributions of different processes to the DSST variability. Figure 4 shows the simulation results when each of the six forcing fields is individually set to its annual mean climatological value. In both EXP1 and EXP2, the SW and WS runs exhibit significant differences in the DSST range throughout the year when compared to the corresponding CTL runs, while the remaining forcing runs have much smaller differences. For example, in March, the SW run and the WS run produce DSST ranges of 0.348 and 0.338C in EXP1 and 0.388 and 0.338C in EXP2, respectively, both of which are roughly half of the CTL run values (0.728C in EXP1

and 0.778C in EXP2). These sensitivity experiments indicate that the monthly variability of the shortwave radiation and the wind stress are two dominant forcing factors that govern the large seasonal variation of DSST range in the BOB. To quantitatively assess the contributions from each of the six factors, we calculated the actual change of DSST range and the percentage change contributed by each factor in the two peak DSST months (i.e., March and October). The percentage change result is presented in Fig. 5. Here, the percentage change is defined as the ratio of the actual change relative to the sum of all six changes. Note that the wind stress forcing and the surface shortwave radiation forcing play dominant roles (accounting for 63% and 62%, respectively, in EXP1 and 53% and 47% in EXP2) in causing the large DSST in March. The latent heat flux, on the other hand, has only a weak positive effect (8%), whereas the net longwave radiation and the freshwater flux have weak negative effects (222% and 212%, respectively). The sensible heat flux has almost no effect.

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FIG. 6. The seasonal variation of monthly mean climatological surface shortwave radiation (closed circles; units: W m22), shortwave radiation at the top of atmosphere (opened squares; units: W m22), and precipitation (Xs; units: mm day21) at 128N, 908E. The data are derived from MERRA.

A distinct difference in the relative roles of the surface forcing factors appears between October and March. Unlike in March, the surface shortwave radiation does not play a major role in the formation of the large DSST range in October. The wind stress, on the other hand, remains a dominant factor (75% for EXP1 and 57% for EXP2). The second most important factor in October is the surface latent heat flux forcing, which accounts for 16% of total DSST range in EXP1 and 26% in EXP2. How does the seasonal variation of diurnal WS, SW, and LH forcing fields influence the annual cycle of DSST range? In the following, we investigate the physical cause of each of the forcing factors. First, we examine the seasonal variation of the shortwave radiation. Figure 6 shows the annual cycle of monthly mean climatologies for the shortwave radiation at the TOA, the shortwave radiation at the surface, and the precipitation at 128N, 908E. Even though the solar radiation at the TOA follows the sun’s migration, the solar radiation at the sea surface reaches its maximum in March and its minimum in June/July, which could be attributed to the effects of clouds. Much stronger monsoon precipitation occurs in boreal summer (June–August) than in boreal winter (January–March). The cloud processes associated with this strong rainfall lead to a minimum surface shortwave radiation in boreal summer. As will be seen in the next section, this precipitation modulation effect is very different in the Southern Hemisphere, where

maximum surface shortwave radiation appears in the local summer season. The distinctive annual cycle of the surface shortwave radiation with a maximum in March provides a straightforward explanation of why DSST range is greatest in the BOB. From January to March, the sun moves from the Southern Hemisphere to the equator. The solar radiation received at TOA increases during that period over the BOB. However, the rainfall belt remains south of the equator during this period, and there is little precipitation over the BOB. Therefore, without the obstruction of extensive cloud cover, the sea surface receives its maximum shortwave radiation in March. As the sun moves northward in April, the solar radiation at the TOA over the BOB continues to increase. However, the rainfall belt also begins to move northward in line with the onset of the monsoon (K. Li et al. 2013), and this leads to increased cloud cover. As a result, the surface shortwave radiation begins to gradually decrease. During boreal summer (JJA), strong precipitation and deep convective clouds over the BOB block roughly half of the shortwave radiation from reaching the sea surface. After summer, the rainfall belt over the monsoon region starts to retreat, although there is still a weak rainfall belt over the BOB. In October, the precipitation at 128N, 908E is still quite large (roughly 4 times as large as in March). As a consequence, the surface shortwave radiation in October is much smaller than that in March.

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In addition to the shortwave radiation, the wind stress forcing also greatly contributes to the observed seasonal DSST variation. The effect of the wind stress forcing on the diurnal cycle of SST has been addressed in previous studies. For example, PWP described the diurnal SST variation as occurring in three stages. Starting at sunrise, the upper few meters of the ocean begins to warm from incoming shortwave radiation. This surface warming grows rapidly up to midday, as the heat received is confined to the upper 5 m. In the first few hours after the midday, wind mixing begins to mix the heat stored in the upper layer downward. After sunset, the surface flux reverses and is dominated by heat loss to the atmosphere, and the SST falls until the following sunrise. Gentemann et al. (2003) also showed the importance of the diurnal variation of the wind speed in the SST. In the presence of strong heating but with no wind, the warming is confined to the upper 1-m layer, and the DSST range can be as large as 28–38C (e.g., Halpern and Reed 1976; Stramma et al. 1986; Price et al. 1986). However, under strong wind conditions, the heat received by the surface flux can be mixed to a greater depth (Weller and Anderson 1996), and the DSST range can be one order of magnitude smaller. In general, both the seasonal variations of the mean wind stress and the amplitude of the diurnal cycle of the wind stress may affect DSST. Figure 7a shows the seasonal evolution of both of these components of the wind stress. It is interesting to note that both the mean and the amplitude of the diurnal cycle of wind stress are smallest in March–April. Theoretically, both of these factors may be responsible for the minimum mixed layer depth at that time, which would, in turn, cause a larger DSST for the same given surface solar radiation forcing. To investigate the relative importance of these two wind stress factors, we conducted two sensitivity experiments. In the first experiment, we removed the annual cycle of the wind stress and imposed the annual mean wind stress year-round with the observed seasonally varying diurnal wind stress cycle superimposed. In the second experiment, we imposed the observed annual cycle of the mean wind stress but superimposed a fixed-amplitude diurnal cycle of wind stress. The former is identified as the monthly wind stress effect (MOE), and the latter is identified as the diurnal wind stress effect (DIE). Figure 8 shows the simulation results from the MOE and DIE experiments. For comparison, the changes of the DSST range derived from the WS control run (in which both the above factors were considered) are also shown. The results show that the DSST range due to the

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FIG. 7. (a) The seasonal variation of surface wind stress (units: N m22) from MERRA at 128N, 908E (the error bars represent the amplitude of the wind stress diurnal cycle); and the seasonal variation of mixed layer depth derived from the CTL run (closed circles; units: m) and the WS run (closed squares; units: m) for (b) EXP1 and (c) EXP2.

wind stress forcing is primarily caused by the annual cycle of the mean wind stress, while the seasonal change of diurnal amplitude of the wind stress plays only a minor role. As a result, in the following, we will focus on the effects of the seasonal variation of the mean wind stress. The monthly mean wind stress has minimum values in March–April and October (Fig. 7a). As mentioned previously, these two periods are often considered to be the monsoon transition periods over the BOB. The light wind stress causes extremely weak vertical mixing. As a result, the ocean mixed layer is very shallow during these two periods. This weak wind stress combined with strong diurnal solar radiation forcing during the monsoon transition leads to the enhanced DSST variations. Conversely, during the monsoon season, strong monsoonal winds lead to strong vertical mixing in the upper ocean and a much deeper mixed layer depth. This shallow mixed layer,

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FIG. 8. The changes of the DSST range from the WS run (WSE), MOE run and DIE run in (a) March and (b) October. Red bar represents EXP1, and blue bar represents EXP2.

along with weakened diurnal solar radiation forcing, reduces the DSST range during the monsoon season. Figures 7b and 7c show the annual cycles of simulated mixed layer depth in the PWP model for the CTL run (when an annual cycle wind stress field is specified) and the WS run (in which an annual mean wind stress field is specified). The seasonal wind stress control process is clearly seen in these results from the seasonal variation of the mixed layer depth. When a seasonally varying wind stress is specified, the mixed layer depth shows a pronounced annual cycle, with minimum values in March–April (;10 m) and October (;20m). When the seasonal cycle in the wind is removed and only an annual mean wind stress is specified, the seasonal contrast

diminishes. These results are consistent in both EXP1 (Fig. 7b) and EXP2 (Fig. 7c). Compared to the wind stress and shortwave radiation forcing, the other heat fluxes (including LH, LW, SH) appear to play a minor role in the seasonal DSST variation in the BOB. This is because the annual variation of these fluxes is much smaller than that of the shortwave radiation (Fig. 9). As a result, their effect in changing ocean mixed layer depth is also relatively small. A weak positive contribution of the latent heat flux in March is attributed to a minimum of latent heat flux release in this month, which promotes a more stable stratification in the upper ocean and, thus, a shallower mixed layer. The surface longwave radiation, which has a maximum in

FIG. 9. The seasonal variation of surface shortwave radiation (closed circles; upward negative; units: W m22), latent heat flux (opened squares; upward positive; units: W m22), sensible heat flux (opened circles; upward positive; units: W m22) and net longwave radiation (Xs; upward positive; units: W m22) derived from MERRA at 128N, 908E.

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FIG. 10. The monthly evolutions of the diurnal SST cycle (with the diurnal mean removed; units: 8C) from (a) buoy observation, (b) CTL run in EXP1, and (c) CTL run in EXP2 at 128S, 80.58E. (d) The seasonal variation of the range of the diurnal SST cycle (units: 8C) from buoy observation (black), CTL run in EXP1 (blue), and CTL run in EXP2 (red) at 128S, 80.58E.

March, has the opposite, as it tends to cool the ocean surface more and promote a less-stable stratification.

5. Causes of maximum amplitude of DSST in January at 12°S In contrast to the BOB (128N), where a minimum DSST appears during the summer season, a maximum DSST appears at 128S in southern summer (January) (Fig. 1). What mechanisms are responsible for these distinctive summertime behaviors of the DSST in the Northern and Southern Hemisphere? Because of a relatively short observational period, we made a monthly climatological composite of the DSST at the 128S, 80.58E buoy site from June 2010 to June 2012 (Fig. 10a). Again, a minimum SST occurs at 0700 local time (LT), followed by a maximum SST at 1400 LT. Comparing the composite DSST ranges for each month, it is seen that the peak in DSST range occurs in January, with a range of 0.658C. To reveal the fundamental cause of the seasonal variation of DSST in the Southern Hemisphere, we used the

same modeling approach as in section 4. A number of numerical experiments with the PWP model were conducted at the 128S, 80.58E site. The surface forcing data derived from the MERRA dataset were used to force the mixed layer model. Consistent with the observations, CTL runs in EXP1 and EXP2 reproduced the largest DSST range in austral summer and the smallest range in austral winter (Fig. 10d). The average DSST ranges derived from EXP1 and EXP2 are around 0.78C in January and 0.18C in July, which matches well with the observations. Given that the mixed layer model simulates the annual cycle of DSST reasonably well, we further examined the relative roles of various forcing factors that give rise to the seasonal variation through sensitivity experiments (listed in Tables 1 and 2). The results (Fig. 11) indicate that the wind stress is the dominant factor driving the DSST, with the surface shortwave radiation being a considerably smaller secondary factor. As was done previously, we quantitatively assess the contributions from each of the six factors by calculating the percentage change of the DSST range caused by

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FIG. 11. The seasonal variations of the range of the diurnal SST cycle (units: 8C) derived from the control and sensitivity experiments from (a) EXP1 and (b) EXP2 at 128S, 80.58E.

each factor in the peak DSST month: January. As in section 4, the percentage change here is computed as the ratio of the individual factor change to the sum of all 6 factor changes, and this result is shown in Fig. 12. Note that the wind stress forcing accounts for 61.0% of the DSST range in EXP1 and 63.1% in EXP2, while the surface shortwave radiation forcing explains only 21.9% in EXP1 and 22.8% in EXP2. The latent heat flux, on the other hand, explains 15.5% in EXP1 and 13.0% in EXP2. The effects of the other factors on DSST are negligible. Why is the factor that controls DSST different between the Northern and Southern Hemispheres? In the following, we will address this question by focusing on

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the comparison of the wind stress and surface shortwave radiation forcing characteristics between the two hemispheres. The seasonal variation of the surface wind stress at the 128S, 80.58E buoy site is shown in Fig. 13a. Note that the minimum wind stress occurs in January and maximum wind stress occurs in August. The amplitude of the wind stress is more than one order of magnitude smaller in January (0.01 N m22) than in August (0.14 N m22). The light January wind stress forcing would cause weak vertical mixing and could lead to a very shallow oceanic mixed layer, which would lead to an increase of the DSST for a given diurnal solar forcing. In contrast, a very strong wind stress would quickly mix the solar heat input into a much thicker layer, tending to diminish the DSST. The effect of the wind stress on the mixed layer depth can be readily seen from the comparison of the CTL and WS sensitivity experiment (Figs. 13b,c). Note that the sole difference between the two experimental simulations lies in the wind stress forcing: the CTL case specifies a seasonally varying wind stress forcing, while the WS case specifies an annual mean wind stress forcing. A shallow mixed layer (;10 m) results from the weak wind stress forcing) in January (;0.01 N m22), whereas a deep mixed layer (;70 m) results from the strong wind stress forcing in July (;0.14 N m22). When an annual mean wind stress field was specified in the WS experiment, the contrast between the summer and winter mixed layer depth became less obvious. Thus, the sensitivity experiment demonstrates the importance of the seasonally varying wind stress in regulating the diurnal SST variability through its control of the thickness of the mixed layer. An interesting question is why 128S exhibits a minimum monthly wind stress in austral summer (January) while 128N shows minima in wind stress in both spring (March–April) and fall (October). The difference is primarily attributable to the climatic asymmetry relative to the equator in the tropical Indian Ocean (Schott et al. 2009). In northern summer, because of the thermal contrast between the heated Asian Continent and the ocean to the south, strong monsoon heating drives northward cross-equatorial flow, which accelerates westerly winds to the north of the equator and easterly winds to the south of the equator. As a result, the surface wind stress is strong in July on both sides of the equator. In northern winter, the cold Asian Continent leads to pronounced northeasterly winds over East Asia and the northern Indian Ocean. The northeasterlies become northerlies after crossing the equator and converge into the intertropical convergence zone (ITCZ) that lies west-southwestward along 58–158S. Along the ITCZ, the

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FIG. 12. As in Fig. 5, but for January at 128S, 80.58E.

wind is generally weak, blowing from west to east. As a result, a weak wind stress results around 128S in southern summer. The combined effect of the strong southeasterlies in northern summer in association with the Indian monsoon and weak westerlies along the ITCZ in southern summer leads to a clear annual cycle of wind stress at 128S, as shown in Fig. 13a. In the Northern Hemisphere, a minimum in wind stress appears in the transitional seasons, both prior to and after the monsoon season, while the wind stress in northern winter, in association with the Asia winter monsoon, is slightly stronger than that in the transitional seasons (Fig. 7a). Why is the surface shortwave radiation not as important in regulating the DSST at 128S as it is at 128N? To understand this question, we plotted the annual cycle of daily maximum shortwave radiation (MSW) at both sites (Fig. 14). It is obvious that the amplitude of seasonal variation of MSW at 128S, 80.58E is much smaller than that at 128N, 908E. The standard deviation of MSW at 128S, 80.58E is 86.2 W m22, while it is nearly twice as large (140.8 W m22) at 128N, 908E. Recall that the sole difference between the SW run and CTL run is that the former specified an annual mean solar forcing, while the latter specified a seasonally varying field. A stronger annual cycle of shortwave radiation at 128N would lead to a larger DSST in its peak phase. This is simply because stronger solar forcing has two effects on the DSST: 1) it directly impacts the DSST through the enhanced diurnal cycle of shortwave radiative flux, and 2) it indirectly influences the DSST by strengthening the upper-ocean stratification, thereby inhibiting the thickening of the oceanic mixed layer. Thus, the observed difference in the amplitude of the annual cycle of MSW between the two hemispheres explains why the seasonally varying solar forcing has a greater impact on the DSST at 128N than at 128S. Although the shortwave radiation at the TOA both has a maximum value in local summer in each hemisphere,

the surface shortwave radiation has a minimum value in northern summer at 128N but a maximum value in southern summer at 128S. This counterintuitive behavior can be explained by considering the differences in the annual cycle of rainfall between the two sites. The monthly mean climatological shortwave radiation at the TOA, the surface shortwave radiation, and precipitation at 128S, 80.58E are shown in Fig. 15. Comparing this figure with Fig. 6, one can see that, although peaks always occur in local summer, the amplitude of the annual cycle of rainfall at 128N is at least twice as large as the amplitude at 128S. The strong monsoon rains and associated deep convective clouds in the Northern Hemisphere modulate the shortwave radiation in such a way that the shortwave radiative flux reaching the surface in summer is an annual minimum. This is quite different than the cycle of surface shortwave radiation at 128S because the cloud cover in the Southern Hemisphere is relatively sparse. Therefore, the shortwave radiation is relatively unaffected by atmospheric attenuation so that it reaches a maximum in local summer. The seasonally varying surface latent heat flux also contributes, to a certain extent, to the maximum DSST in January at 128S (Fig. 12). Figure 16 shows the annual cycles of the monthly mean climatological heat fluxes at this site. Note that the minimum in the surface latent heat flux occurs in austral summer (January), in phase with the maximum in surface shortwave radiation. Both the minimum in the latent heat flux (loss) and the maximum in the shortwave radiation (gain) have a positive contribution to the maximum DSST in January, through the enhanced upper-ocean stratification and decreased mixed layer depth. The weaker amplitude of the annual cycle of the surface latent heat flux relative to the shortwave radiation is consistent with the simulation results, showing that the former has a weaker effect on DSST than the latter. The annual cycles of surface longwave radiation and sensible heat flux are very small at 128S.

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FIG. 13. (a) The seasonal variation of surface wind stress (units: N m22) from MERRA at 128S, 80.58E (the error bars represent the amplitude of the wind stress diurnal cycle); and the seasonal variation of the oceanic mixed layer depth derived from the CTL run (closed circles; units: m) and the WS run (opened squares; units: m) for (b) EXP1 and (c) EXP2.

As a result, neither of the two factors has a significant effect on the seasonal variation of DSST.

6. Conclusions and discussion The unique features of seasonal variation of the diurnal SST cycle (DSST) over the eastern tropical Indian Ocean have been investigated with new observational data collected from 16 RAMA buoys. The results show

that DSST exhibits remarkable seasonal and latitudinal variations, with a strong DSST range appearing away from the equator. The seasonal variation of DSST north of equator (88–158N) shows a distinctive double-peak feature, with maximum DSST appearing in March– April and October at the transitions of the monsoon but with minimum DSST during boreal summer. South of the equator (at 128S), the DSST shows a single-peak annual cycle, with a maximum occurring in January

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FIG. 14. The monthly evolutions of climatological diurnal maximum shortwave radiation (unit: W m22) at 128N, 908E (closed circles) and 128S, 80.58E (opened squares).

and a minimum in July, which largely follows the seasonal cycle of the heat input from surface shortwave radiation. The processes that control the distinctive seasonal variations of the DSST north (128N) and south (128S) of the equator have been investigated through a series of experiments with the PWP one-dimensional mixed layer model. The forcing fields (including surface wind stress and surface heat fluxes) were derived from the MERRA hourly product. The oceanic mixed layer model sensitivity experiments show that the surface shortwave radiation and wind stress fields play the dominant role and are responsible for the large DSST at 128N in March. Other heat flux terms play a relatively minor role. The cause of the secondary peak in DSST in October is primarily attributed to the relatively weak wind stress forcing. Wind stress affects the DSST indirectly through its role in determining the thickness of the ocean mixed layer. Strong wind stress induces strong vertical mixing that both deepens the mixed layer and redistributes the surface shortwave heat inputs downward. The distinctive seasonal variations of DSST north and south of the equator are partially attributed to differences in seasonally varying surface solar radiation. The amplitude of the seasonal variation of surface shortwave radiation at 128S is about half of the seasonal variation at 128N, while the amplitude of the annual cycle of wind stress is approximately the same at both two sites. Consequently, both surface heating and wind stress control the seasonal cycle of DSST at 128N, while the

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main factor that regulates the seasonal cycle of DSST at 128S is the wind stress forcing. The cause of the distinctive annual cycles in the surface shortwave radiation at 128N and 128S are attributed to differences in the atmospheric modulation of the solar radiation at the TOA on either side of the equator. While the shortwave radiation at TOA reaches similar maximum values in summer over both hemispheres, surface shortwave radiation has a minimum value in summer (July) at 128N but a maximum in summer (January) at 128S. This counterintuitive feature is explained by differences in the annual cycles of rainfall at the two sites. Note that the amplitude of the annual cycle of rainfall at 128N is much larger than that at 128S. At 128N during the summer monsoon, rains reduce the transmission of shortwave radiation, leading to an annual minimum in the shortwave radiative flux at the surface. At 128S, rainfall is considerably lower than at 128N so that the shortwave radiative fluxes reach their annual maximum in January coincident with the maximum in the shortwave radiation at the TOA, despite the fact that rainfall is highest in January. These results indicate that the intensity of summer rains makes a marked difference in the annual cycles of shortwave radiative fluxes between 128N and 128S. Another important factor that contributes to the distinctive seasonal variations of DSST north and south of the equator is the difference in annual cycles of the surface wind stress field. At 128S a minimum monthly wind stress appears in January, whereas, at 128N, a minimum wind stress appears in spring (March–April) and fall (October) coincident with the transition phases of the monsoon. The cause of this wind stress difference is primarily attributed to the climatic asymmetry about the equator in the tropical Indian Ocean. In boreal summer, because of a large thermal contrast between the heated Asian Continent and the cool ocean to the south, strong monsoon diabatic heating drives northward cross-equatorial flow, which accelerates westerlies to the north of the equator and easterlies to the south of the equator. As a result, the surface wind stress is strong in July on both sides of the equator. In boreal winter, the Asian winter monsoon leads to pronounced northeasterlies over East Asia and the northern Indian Ocean. The monsoon flow crosses the equator and converges into the ITCZ south of the equator with weak westerlies appearing along the ITCZ. As a result, a weak wind stress occurs at 128S in January. Calm wind appears in the BOB during the transitional seasons (March and October), when the SST is quite warm (K. Li et al. 2013). Thus, the combined effect of the summer and winter monsoons leads to different annual cycles of wind stress at 128N and 128S, with a single minimum at 128S

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FIG. 15. The seasonal variation of monthly mean climatological surface shortwave radiation (closed circles; units: W m22), shortwave radiation at the top of atmosphere (opened squares; units: W m22), and precipitation (Xs; units: mm day21) from MERRA at 128S, 80.58E.

occurring in January but twin minima at 128N occurring in March and October. In this study, we have examined the seasonal variations of DSST over the tropical eastern Indian Ocean. We found that monsoon rainfall and wind stress, primarily through their effects on radiative heating and upper-ocean mixing, greatly modulate the amplitude of

DSST. This study did not examine the interesting question of how the seasonally varying DSST might feed back to the monsoon seasonal cycle, including any potential impacts on the onset of the monsoon (Lau and Yang 1997; Wang and LinHo 2002; Wu et al. 2012; Yu et al. 2012b; K. Li et al. 2013). In addition, this study mainly focused on the large DSST away from the

FIG. 16. The seasonal variation of surface shortwave radiation (closed circles; upward negative; units: W m22), latent heat flux (opened squares; upward positive; units: W m22), sensible heat flux (opened circles; upward positive; units: W m22), and net longwave radiation (Xs; upward positive; units: W m22) from MERRA at 128S, 80.58E.

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study using a longer time period might be needed to confirm the conclusions derived from our analysis. A longer period of in situ observations would also be needed for addressing the interannual variability of DSST. Although Zhang and Anderson (2003) have argued that advection plays a negligible role in SST variation in the tropics, where the SST gradients are small, advection does influence some regions. Unfortunately, at present, a data record of sufficient temporal and spatial resolution (current and SST) does not exist to estimate these effects. Further, because a 1D model cannot represent horizontal advection, a 3D model will be needed to study the effect of horizontal advection on DSST. Acknowledgments. This study was supported by NSFC-Shandong Joint Fund for Marine Science Research Centers Grant U1406404, by Chinese Ministry of Science and Technology under Contracts 2015CB453200 and 2012DFB20210, by NSFC Grants 41475084 and 41005032, and by the International Pacific Research Center (IPRC), which is sponsored by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We thank the reviewers for their elaborate comments, which greatly improve the manuscript. REFERENCES

FIG. 17. The seasonal variation of monthly mean climatological (a) sea surface shortwave radiation (W m22) and (b) sea surface wind stress (units: N m22) from MERRA on different latitude, both averaged over 808–908E.

equator, and little attention was paid to the smaller seasonal variation of near-equatorial DSST. However, comparing the annual cycles of surface shortwave radiation and wind stress on and off the equator (Fig. 17), we found that both factors show much smaller seasonal variations on the equator, which may account for the small annual cycle of DSST. Further studies will be needed to resolve these issues. The present analysis of the buoy observations covers a relatively short period (in the range of 1–4 yr). A further

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