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  • View in gallery

    Vertical profiles of salinity (blue), temperature (red), and σθ (black) observed by Argo float 5905359 at 8.15°N, 129.20°W on 28 Oct 2018. Dots and lines indicate raw values and vertically interpolated values on a 1-dbar interval using the Akima spline (Akima 1970), respectively. Horizontal dashed black and red lines indicate the MLD and ILD, respectively. The amplitude of the TI is indicated by ∆θ.

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    Distributions of BLT (colored dots) from Argo profiles in 2003–18 and mean SSS (contours) from SMAP in April 2015–March 2019 in (a) January, (b) April, (c) July, and (d) October. The rectangle indicates the box used later for the calculation in Figs. 4, 12, and 13 (5°–13°N, 140°–100°W).

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    Distributions of ∆θ (colored dots) from Argo profiles with BLT > 10 dbar in 2003–18 and mean SST (contours; °C) from OISST in April 2015–March 2019 in (a) January, (b) April, (c) July, and (d) October. Dotted lines indicate the contour of 27.5°C in (a) and 28.5°C in (b)–(d). The rectangle indicates the box used later for the calculation in Figs. 4, 12, and 13 (5°–13°N, 140°–100°W).

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    Monthly histogram (bars) of number of Argo profiles with (a) BLT > 10 dbar and (d) ∆θ > 0.1°C and their frequency (dots) in the box 5°–13°N, 140°–100°W (see Fig. 2). Also shown is the seasonal variation of (b) BLT and (c) MLD (filled circles) and ILD (open circles), averaged for the values from Argo profiles with BLT > 10 dbar in the box, and the seasonal variation of (e) ∆θ and (f) depth of temperature maximum, averaged for the values from Argo profiles with BLT > 10 dbar and ∆θ > 0.1°C in the box. Vertical bars indicate the 95% confidence interval based on the standard deviation. The calculations cover the period 2003–18.

  • View in gallery

    Distribution of BLT (colored dots) from Argo profiles in 2003–18 and −PSm/hm (contours; month−1) in (a) January, (b) April, (c) July, and (d) October. Gray shading indicates freshening where values are lower than −0.4 month−1.

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    Distribution of BLT (colored dots) from Argo profiles in 2003–18 and −uEkSm (contours; month−1) in (a) January, (b) April, (c) July, and (d) October. Gray shading (negative values) indicates freshening.

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    Distribution of mean wind stress (N m−2) from NCEP in 2003–18 in (a) January, (b) April, (c) July, and (d) October.

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    Distribution of ∆θ (colored dots) from Argo profiles in 2003–18 and −uEkTm (contours; °C month−1) in (a) January, (b) April, (c) July, and (d) October. Gray shading (negative values) indicates cooling.

  • View in gallery

    Distribution of regions where −uEkSm < 0 (month−1) and −uEkTm < 0 (°C month−1) (gray shading) and mean mixed layer temperature (contours; °C) from MOAA_GPV in 2003–18 in (a) January, (b) April, (c) July, and (d) October. The dashed line indicates the contour of ∂Tm/∂y = 0.

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    Distributions of BLT (colored dots) from Argo profiles in 2003–18 and −ugSm (contours; month−1) in (a) January, (b) April, (c) July, and (d) October. The contour interval is 0.2 month−1. Gray shading (negative values) indicates freshening.

  • View in gallery

    Distributions of ∆θ (colored dots) from Argo profiles in 2003–18 and −ugTm (contours; °C month−1) in (a) January, (b) April, (c) July, and (d) October. The contour interval is 0.5°C month−1. Gray shading (negative values) indicates cooling.

  • View in gallery

    Seasonal variation of net heat flux (downward heat into the ocean is positive) from OAFlux (red dots) and J-OFURO3 (blue dots), averaged in the box 5°–13°N, 140°–100°W (see Fig. 2) during 2003–09 and 2003–13, respectively. Vertical bars indicate the 95% confidence interval based on the standard deviation of the climatological values calculated using each grid point of the box.

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    Seasonal variation of (a) zonal (red dots) and meridional (blue dots) components and (b) magnitude of wind stress from NCEP, averaged in the box 5°–13°N, 140°–100°W (see Fig. 2) during 2003–18. Vertical bars indicate the 95% confidence interval based on the standard deviation of the climatological values calculated using each grid point of the box.

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    Distribution of Ry at the sea surface (colors) from SMAP and OISST and mean SSS (contours) from SMAP in April 2015–March 2019 in (a) January, (b) April, (c) July, and (d) October.

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    Meridional section of salinity (contours) and Ry (colors) along 125°W from MOAA_GPV in (a) January, (b) April, (c) July, and (d) October. Green and blue lines indicate the MLD and ILD, respectively.

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    (a) Seasonal variation of the rate of MLS change [left-hand side of Eq. (1); red curve] and the sum of forcing terms [right-hand side of Eq. (1); blue curve], averaged in the box 5°–13°N, 140°–100°W (see Fig. 2). (b) Seasonal variation of each forcing term on the right-hand side of Eq. (1), averaged in the box: ESm/hm (red curve), −PSm/hm (blue curve), −uEkSm (green curve), −ugSm (orange curve), and weSm/hm (purple curve). Vertical bars of the rate of MLS change in (a) and each forcing term in (b) indicate the standard deviation of the climatological values calculated using each grid point of the box. Vertical bars of the sum of forcing terms in (a) were estimated from the 95% confidence interval of each forcing term in (b) while assuming the errors are additive.

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Seasonality and Formation of Barrier Layers and Associated Temperature Inversions in the Eastern Tropical North Pacific

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  • 1 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
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Abstract

Seasonality and formation of barrier layers (BLs) and associated temperature inversions (TIs) in the eastern tropical North Pacific Ocean were investigated using raw and gridded Argo profiling float data, satellite data, and various sea surface flux data. BLs were observed frequently in boreal summer and autumn along the sea surface salinity (SSS) front south of the eastern Pacific fresh pool. TIs were found within the gap between the western and eastern Pacific warm pools in autumn when BLs were thickest. A mixed layer salinity budget was constructed to determine the formation mechanism responsible for BLs with TIs. This budget revealed that Ekman advection works to both freshen and cool the eastern tropical North Pacific in autumn and contributes to the formation of the thickest BLs with the warmest TIs through the tilting of the SSS front. Precipitation is a secondary contributor to BL formation in autumn. The BLs are also prevalent during summer but are thinner, are without associated TIs, and are primarily formed through precipitation. The largest rainfall associated with the intertropical convergence zone mostly occurred north of the band of thickest BLs in both summer and autumn. The geostrophic advection of salinity did not coherently contribute to the formation of BLs or TIs. The idea that Ekman advection contributes most to the formation of the thickest BLs with warm TIs was further corroborated because the horizontal salinity gradient was the dominant contributor to the density gradient and so is favorable for BL and TI formation.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shota Katsura, skatsura@ucsd.edu

Abstract

Seasonality and formation of barrier layers (BLs) and associated temperature inversions (TIs) in the eastern tropical North Pacific Ocean were investigated using raw and gridded Argo profiling float data, satellite data, and various sea surface flux data. BLs were observed frequently in boreal summer and autumn along the sea surface salinity (SSS) front south of the eastern Pacific fresh pool. TIs were found within the gap between the western and eastern Pacific warm pools in autumn when BLs were thickest. A mixed layer salinity budget was constructed to determine the formation mechanism responsible for BLs with TIs. This budget revealed that Ekman advection works to both freshen and cool the eastern tropical North Pacific in autumn and contributes to the formation of the thickest BLs with the warmest TIs through the tilting of the SSS front. Precipitation is a secondary contributor to BL formation in autumn. The BLs are also prevalent during summer but are thinner, are without associated TIs, and are primarily formed through precipitation. The largest rainfall associated with the intertropical convergence zone mostly occurred north of the band of thickest BLs in both summer and autumn. The geostrophic advection of salinity did not coherently contribute to the formation of BLs or TIs. The idea that Ekman advection contributes most to the formation of the thickest BLs with warm TIs was further corroborated because the horizontal salinity gradient was the dominant contributor to the density gradient and so is favorable for BL and TI formation.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shota Katsura, skatsura@ucsd.edu

1. Introduction

Salinity can be the dominant contributor to upper-ocean density stratification in some oceanic regions (e.g., de Boyer Montégut et al. 2007). In these regions, strong vertical salinity gradient layers, called haloclines, shape the pycnocline and inhibit the exchange of heat, momentum, and other properties between the surface and subsurface layers. This affects sea surface temperature (SST) and hence the heat exchange with the atmosphere that controls the heat content and budget of the mixed layer. Salinity stratification can also control the vertical distribution and exchange of properties such as nutrients and carbon dioxide (CO2), especially at higher latitudes, and can affect the biological production and air–sea exchange of CO2 (Andreev et al. 2002). Because of these effects, a knowledge of the role of salinity in setting the stratification within the upper ocean is important for understanding climate variability, air–sea interaction, and biogeochemical processes.

In many cases, the halocline coincides with the base of the density-defined mixed layer. However, in some regions the base of the mixed layer is shallower than that of the isothermal layer because of a strong halocline that occurs within an isothermal layer (e.g., Lindstrom et al. 1987; Sprintall and Tomczak 1992; Sato et al. 2006; de Boyer Montégut et al. 2007). The region between the bottom of the mixed layer and the depth of the isothermal layer is called a barrier layer (BL; Godfrey and Lindstrom 1989; Lukas and Lindstrom 1991) because it acts as a stable barrier against heat and momentum exchange with the subsurface. When a BL exists, cooling of the mixed layer through entrainment is reduced because the entrained water below the mixed layer base has the same temperature as within the mixed layer (e.g., Vialard and Delecluse 1998; Maes et al. 2002). Indeed, when the BL is associated with a vertical temperature inversion (TI; e.g., Sprintall and Roemmich 1999; Helber et al. 2012; Thadathil et al. 2002), entrainment works to warm the mixed layer. In addition, in the presence of a BL, momentum flux into the ocean by wind is trapped above the BL, resulting in acceleration of flow within the mixed layer (Roemmich et al. 1994; Vialard and Delecluse 1998).

The properties, formation, and impact of variability in BL thickness have been investigated in many different oceanic regions. In the western tropical Pacific Ocean, where the existence of a BL was first reported (Lukas and Lindstrom 1991), a BL persists throughout the year with seasonal fluctuations in thickness and horizontal extent (Sprintall and Tomczak 1992). BLs in this region are formed by heavy precipitation (Lukas and Lindstrom 1991; Sprintall and Tomczak 1992) and a tilting of the sea surface salinity (SSS) front at the eastern edge of the fresh pool (Cronin and McPhaden 2002). During El Niño events, the BL becomes thin, reflecting eastward displacement of the SSS front; conversely, during La Niña events, the BL becomes thick, reflecting westward displacement of the SSS front (Ando and McPhaden 1997; Delcroix and McPhaden 2002; Fujii and Kamachi 2003; Bosc et al. 2009; Qu et al. 2014). A coupled ocean–atmosphere general circulation model demonstrated that the BL contributes to the onset and buildup of El Niño by preventing the entrainment of cooler water below the mixed layer and trapping the momentum flux of the westerly wind burst (Maes et al. 2002, 2005). In the Indian Ocean, BLs are found in the Bay of Bengal, the Arabian Sea, and the eastern tropical region (e.g., Sprintall and Tomczak 1992). In the Bay of Bengal and the Arabian Sea, the seasonal and intraseasonal variation of BL thickness and distribution is controlled by the monsoon-driven surface circulation via Ekman pumping and the propagation of Rossby and Kelvin waves (Vinayachandran et al. 2002; Rao and Sivakumar 2003; Thadathil et al. 2007, 2008; Girishkumar et al. 2011). BLs in these Indian Ocean regions are often associated with TIs (Thadathil et al. 2002; Gopalakrishna et al. 2005) and have a significant impact on SST modulating the heat budget within the overlying mixed layer and potentially influencing the monsoon precipitation (Durand et al. 2004; Girishkumar et al. 2013). In the eastern equatorial Indian Ocean, BLs are maintained by heavy precipitation and river runoff (Masson et al. 2002; Qu and Meyers 2005) and modify the oceanic response to the intraseasonal Madden–Julian oscillation forcing (Drushka et al. 2014). In the tropical Atlantic Ocean, the BL is formed mainly in response to a combination of Amazon River runoff and precipitation (Pailler et al. 1999; Mignot et al. 2007) and is thought to play important role in driving climate variability in the Atlantic (Foltz and McPhaden 2009). In the subtropics of each ocean basin, BLs are distributed along SSS fronts on the equatorward side of SSS maxima in winter (Sato et al. 2004, 2006). BLs in the subtropics have a patchy and/or porous distribution (Sato et al. 2004, 2006; Mignot et al. 2009) and are formed through the tilting of SSS fronts that reflects the seasonality of mixed layer depth (Katsura et al. 2015).

The formation and erosion of BLs in the eastern tropical North Pacific (ETNP) have been far less studied than those in the western Pacific Ocean. It is not known whether the same mechanisms are responsible for BL formation and thickness in the ETNP although clearly the atmospheric and ocean conditions are different. The ETNP has the lowest SSS in the tropics corresponding to the eastern Pacific fresh pool, which extends westward north of the equator from the west of Costa Rica (Fiedler and Talley 2006). The eastern Pacific fresh pool is due to heavy precipitation associated with the seasonal march of the intertropical convergence zone (ITCZ; Alory et al. 2012) and the intra-American monsoon system (Amador et al. 2006). In the ETNP, BLs are zonally distributed beneath the ITCZ between the equator and 10°N (de Boyer Montégut et al. 2007; Mignot et al. 2007). However, the ETNP BLs are much thinner than those in the western tropical Pacific (Fiedler and Talley 2006) and the mixed layer (i.e., above the BL) is extremely shallow (Suga et al. 2004), suggesting that BLs may have a more significant impact on SST and hence climate in this region.

The aim of this study is to describe the characteristics and seasonality of the BL in the ETNP and to clarify the mechanism(s) for its formation as well as that of any associated TIs. The data and methods used in this study are described in section 2. The seasonality and formation of the BL and TI in the ETNP are examined within the framework of the mixed layer salinity budget in section 3. The mechanisms for BL and TI formation and their impact on air–sea interaction in the ETNP are discussed and summarized in section 4.

2. Data and methods

Temperature and salinity profiles from Argo profiling floats in the North Pacific during 2003–18 were quality checked as outlined in Oka et al. (2007). Argo profiles before and after March 2015 were from the Argo Global Data Assembly Center and the Advanced automatic QC Argo data version 1.2. by the Japan Agency for Marine-Earth Science and Technology, respectively. We also used the monthly gridded mean temperature and salinity data with a horizontal resolution of 1° latitude × 1° longitude known as the gridpoint value of the monthly objective analysis using the Argo data (MOAA_GPV) covering 2003–18 (Hosoda et al. 2008). This dataset was created by optimally interpolating the observed temperature and salinity anomalies from values of the World Ocean Atlas 2001 climatology (Conkright et al. 2002) onto grid points at each standard depth. In our study, the vertical profiles from both the individual Argo profiles and the gridded MOAA_GPV fields were interpolated to a 1-dbar interval using the Akima spline method (Akima 1970), and then potential temperature θ and density σθ were calculated.

SSS and SST were from the monthly L3 70-km version-3.0 product of the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP; Meissner and Wentz 2018) and the Advanced Very High Resolution Radiometer product of Optimum Interpolation Sea Surface Temperature (OISST; Reynolds et al. 2007), respectively. The horizontal resolution of these gridded data is 0.25° latitude × 0.25° longitude, and we used data from April 2015–March 2019 corresponding to the same data period available as the SMAP product.

Precipitation and wind stress were from the Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin 1996, 1997) at 2.5° latitude × 2.5° longitude and the National Centers for Environmental Prediction (NCEP; Kistler et al. 2001) at ~2° latitude × 1.875° longitude, respectively. For each month during 2003–18 these data were linearly interpolated to a 1° latitude × 1° longitude grid corresponding to the MOAA_GPV grid. We also used sea surface height data from Copernicus Marine Environment Monitoring Service (CMEMS) for 2003–17, with a horizontal resolution of 0.25° latitude × 0.25° longitude. Sea surface height was also averaged into the same 1° × 1° grid as MOAA_GPV. Net heat flux was from the Objectively Analyzed Air–Sea Heat Fluxes Project (OAFlux; Yu and Weller 2007; Yu et al. 2008) with a horizontal resolution of 1° latitude × 1° longitude during 2003–09 and from the Japanese Ocean Flux Datasets with Use of Remote Sensing Observations (J-OFURO3; Tomita et al. 2019) with a horizontal resolution of 0.25° latitude × 0.25° longitude during 2003–13.

Isothermal layer depth (ILD) was defined as the depth at which θ decreases by 0.2°C from the θ value at 10-dbar depth (de Boyer Montégut et al. 2007). Mixed layer depth (MLD) was defined as the depth at which σθ increases from the σθ value at 10-dbar depth by the σθ equivalent to a temperature decrease of 0.2°C from the 10-dbar salinity. Barrier layer thickness (BLT) was defined as the difference between the deeper ILD and shallower MLD. The amplitude of the TI (∆θ) associated within the BLs was defined as the difference between the temperature at the MLD and at the depth of the vertical temperature maximum within the BL (Fig. 1). In the analysis, the raw Argo profiles were used for all BLT, MLD, and ILD calculations and MOAA_GPV was used for the MLS budget (see section 3b). In this study, we only considered BLs with a BLT > 10 dbar and TIs for which ∆θ > 0.1°C (Ueno and Yasuda 2005).

Fig. 1.
Fig. 1.

Vertical profiles of salinity (blue), temperature (red), and σθ (black) observed by Argo float 5905359 at 8.15°N, 129.20°W on 28 Oct 2018. Dots and lines indicate raw values and vertically interpolated values on a 1-dbar interval using the Akima spline (Akima 1970), respectively. Horizontal dashed black and red lines indicate the MLD and ILD, respectively. The amplitude of the TI is indicated by ∆θ.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

3. Results

a. Distribution and seasonality of BLs and TIs

In the ETNP, BLs thicker than 10 dbar were distributed zonally from 5° to 15°N east of 150°W in boreal summer (July; Fig. 2c) and autumn (October; Fig. 2d) in association with the zonal SSS front to the south of the eastern Pacific fresh pool. These BLs extended westward toward the thick BLs in the western tropical Pacific. In boreal winter (January; Fig. 2a) and spring (April; Fig. 2b) the SSS front was weaker and BLs were sparser. In the western tropical Pacific, thick BLs were observed throughout the year albeit with seasonality in their thickness and spatial expanse. BLs thicker than >40 dbar were distributed south of 10°N and west of 160°W in boreal spring (Fig. 2b) and connected eastward to the zonal BL band in the eastern tropics in boreal summer–autumn (Figs. 2c,d). In boreal winter–spring, BL distribution is sparser and shrinks westward (Figs. 2a,b). In the subtropics, thick BLs were distributed around 10°–25°N in boreal winter zonally along the SSS front south of the SSS maximum (Fig. 2a), decaying in boreal summer (Fig. 2c). Another thick BL region was found in the eastern subtropics around 28°N, 130°W in boreal winter and spring (Figs. 2a,b). The spatial distribution and seasonality of BLs were consistent with previous studies (e.g., Sprintall and Tomczak 1992; de Boyer Montégut et al. 2007).

Fig. 2.
Fig. 2.

Distributions of BLT (colored dots) from Argo profiles in 2003–18 and mean SSS (contours) from SMAP in April 2015–March 2019 in (a) January, (b) April, (c) July, and (d) October. The rectangle indicates the box used later for the calculation in Figs. 4, 12, and 13 (5°–13°N, 140°–100°W).

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

TIs occurred much less frequently than BLs but typically were associated with regions where the BL was thick (Fig. 3). In the ETNP, TIs appeared around 10°N, 120°W in boreal summer (Fig. 3c). They were more densely distributed in autumn covering the region between the western Pacific warm pool and the eastern Pacific warm pool (between the 29°C SST contours; Fig. 3d). TIs were less frequent in winter and spring (Figs. 3a,b). TIs in the western tropical Pacific did not show a distinct seasonality and were more widely distributed relative to the eastern tropical region. They extended eastward south of 10°N in autumn (Fig. 3d) and shrank westward from winter to summer as the thicker BLs were located more westward (Figs. 3a–c). In the subtropics, TIs were observed around Hawaii only in boreal winter when the BLs were thickest (Figs. 2a and 3a). In the eastern subtropical region, strong TIs were frequently observed around 28°N, 135°W in boreal winter–spring when the thickest BLs occur (Figs. 3a,b).

Fig. 3.
Fig. 3.

Distributions of ∆θ (colored dots) from Argo profiles with BLT > 10 dbar in 2003–18 and mean SST (contours; °C) from OISST in April 2015–March 2019 in (a) January, (b) April, (c) July, and (d) October. Dotted lines indicate the contour of 27.5°C in (a) and 28.5°C in (b)–(d). The rectangle indicates the box used later for the calculation in Figs. 4, 12, and 13 (5°–13°N, 140°–100°W).

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

In the ETNP box (5°–13°N, 140°–100°W; see Fig. 2) BLs occurred in more than 20% of Argo profiles from May through December with two peaks (~40%) occurring in July and October (Fig. 4a). The observed mean BLT was around 20 dbar from December to August. The BLT was largest in October exceeding 25 dbar when BLs are prevalent but BLT did not show a corresponding peak in July when BLs are also most prevalent (Fig. 4b). This seasonal variation of BLT mainly reflects that of the ILD (Fig. 4c). While both ILD and MLD shoal slightly in boreal winter, ILD deepens from August through November being deepest in October when the BL is thickest, while MLD remains relatively constant during this period.

Fig. 4.
Fig. 4.

Monthly histogram (bars) of number of Argo profiles with (a) BLT > 10 dbar and (d) ∆θ > 0.1°C and their frequency (dots) in the box 5°–13°N, 140°–100°W (see Fig. 2). Also shown is the seasonal variation of (b) BLT and (c) MLD (filled circles) and ILD (open circles), averaged for the values from Argo profiles with BLT > 10 dbar in the box, and the seasonal variation of (e) ∆θ and (f) depth of temperature maximum, averaged for the values from Argo profiles with BLT > 10 dbar and ∆θ > 0.1°C in the box. Vertical bars indicate the 95% confidence interval based on the standard deviation. The calculations cover the period 2003–18.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

Frequency of TIs observed in BLs within the ETNP box was highest in September–November with a peak in October exceeding 15% (Fig. 4d). TIs were seldom observed in January–April (<2%). Although the TI frequency showed a small peak in July, it was only about 7% and less than half the frequency peak in October. The TI mean amplitude was lowest in March (~0.18°C) and then increased gradually throughout the remainder of the year (Fig. 4e). TIs were warmest (exceeding 0.25°C) in October–November when TIs most frequently occurred (Fig. 4d) and BLs were thickest (Fig. 4b). TI amplitude remained around 0.24°C in December–January (Fig. 4e) although TIs and BLs occurred in relatively few profiles during these months (Figs. 4a,d). The temperature maximum was shallowest in May–July, and deepest (~50 dbar) in March–April and again in October (Fig. 4f) when the TIs are warmest (Fig. 4e) and BLs are thickest (Fig. 4b).

b. A mixed layer salinity budget to determine formation mechanisms of BLs and TIs

The BL formation through salinity change can be attributed to two processes: freshening near the sea surface and salinification in the subsurface (Katsura et al. 2015). Here, to determine the formation mechanism of BLs through the surface process in the eastern tropical Pacific, we constructed a mixed layer salinity (MLS) budget (Ren and Riser 2009; Katsura et al. 2013):
Smt=(EP)SmhmuEkSmugSmweH(we)ΔShm,
where Sm is the MLS, t is time, E is evaporation, P is precipitation, hm is MLD, uEk is Ekman velocity, ug is geostrophic velocity, ∇ is the horizontal differential operator, we is entrainment velocity, H is the Heaviside step function, and ∆S is the salinity difference between the mixed layer and the layer 20 dbar below the mixed layer (former minus latter) (all terms have units of month−1). The MLS budget using Eq. (1) almost closes and explains the seasonal variation of MLS in the ETNP box (see the appendix). Of the forcing terms on the right-hand side of Eq. (1), evaporation works to increase MLS and hence does not contribute to BL formation. Entrainment also does not result in BLs because it works to destabilize salinity stratification below the base of mixed layer. Thus, in the following we only focus on precipitation, Ekman and geostrophic advection, which all potentially contribute to BL formation through surface freshening. Equation (1) does not include the effect of horizontal and vertical diffusion. Vertical diffusion does not contribute to BL formation since it destabilizes salinity stratification like entrainment. Although horizontal diffusion may have a significant contribution to the MLS budget in the ETNP we did not have sufficiently accurate or the high-resolution data needed to estimate it as a factor of BL formation. These caveats are discussed further in section 4. Ekman velocity uEk was calculated as
uEk=1ρ0fhm(τy,τx),
where τx and τy are zonal and meridional component of wind stresses, respectively (positive eastward and northward), f is the Coriolis parameter, and ρ0 is the reference density of seawater, taken to be 1025 kg m−3. Geostrophic velocity ug was calculated from sea surface height assuming thermal wind as
ug=gf(ηy,ηx),
where g is the gravity constant and η is sea surface height. We estimated the three terms on the righthand side of Eq. (1) using monthly climatological values for each variable. MLS and MLD were constructed using MOAA_GPV. The budget terms constructed using MLS from MOAA_GPV for Sm in Eq. (1) were not significantly different when we constructed the budget terms using SSS from SMAP as MLS (not shown).

The precipitation term was largest in the zonal band corresponding to the location of the ITCZ with a meridional width of about 10° and displayed a small meridional discrepancy when compared with the BL distribution in the ETNP (Fig. 5). In boreal winter, freshening due to precipitation was largest in the zonal band 0°–10°N, with the maximum occurring between 120° and 85°W and largely coinciding with the distribution of BLs in the ETNP (Fig. 5a). The precipitation increased during spring–summer, and the zonal band of freshening moved northward to 5°–15°N (Figs. 5b,c). In boreal spring, while BLs occurred within the region associated with the large precipitation band, the BLs thicker than 30 dbar were frequently observed north of the strongest precipitation at 10°N, 115°W (Fig. 5b). In summer, the precipitation band shifted northward and the thickest BLs (>30 dbar) were concentrated between 3° and 8°N, south of the strongest precipitation region (Fig. 5c). Similarly, the thickest BLs in autumn were also concentrated south of the maximum freshening by precipitation (Fig. 5d).

Fig. 5.
Fig. 5.

Distribution of BLT (colored dots) from Argo profiles in 2003–18 and −PSm/hm (contours; month−1) in (a) January, (b) April, (c) July, and (d) October. Gray shading indicates freshening where values are lower than −0.4 month−1.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

Unsurprisingly, the distribution of the Ekman salinity advection term in the ETNP has seasonality corresponding to the wind stress. Ekman advection contributes to BL formation only when Ekman salinity advection term is negative, and this term likely contributed to BL formation along with precipitation especially in autumn (Fig. 6d). In winter, the Ekman salinity advection term was positive south of 10°N (Fig. 6a), reflecting the northward Ekman advection of saltier water lying south of the fresh pool (Fig. 2a) in response to the westward trade wind (Fig. 7a). This effect did not contribute to BL formation through the surface freshening in the ETNP in winter because it worked to increase the MLS and to destabilize the salinity stratification. In spring, the Ekman salinity advection term is negative in the vicinity of 10°N, 120°W (Fig. 6b) since the wind stress is directed southwestward (Fig. 7b) and so water in the fresh pool is advected northwestward (Fig. 2b). Although the Ekman advective term value was smaller than the precipitation term (Fig. 5b), the region of Ekman advective freshening directly corresponds to where the Argo profiles display thick BLs (Fig. 6b), implying that Ekman advection plays an important role in BL formation together with precipitation in boreal spring. A zonal band of freshening formed along the SSS front at 5°–10°N east of 130°W in summer (Fig. 6c), resulting from the shift in wind direction to northeastward (Fig. 7c) and southward Ekman advection of fresher water from within the fresh pool (Fig. 2c). Freshening along the SSS front strengthened in autumn, and the thickest BLs were found in the area of maximum freshening centered at 8°N, 120°W (Fig. 6d). This period coincided with the enhancement of wind stress (Fig. 7d) indicating that Ekman advection plays a primary role in the formation of autumn BLs in the ETNP through the tilting of the SSS front. BLs were also distributed south of 10°N and west of 130°W in summer and winter although the Ekman advection term was positive, implying BL formation in this region during these seasons mainly occurred through freshening by precipitation (Figs. 6c,d). The Ekman salinity advection term was negative throughout the year north of 15°N, and the thickest BLs in the subtropical North Pacific were also distributed north of 15°N in winter and spring (Figs. 6d,a). This is consistent with BL formation through the tilting of the SSS front caused by northward Ekman advection of fresher water (Katsura et al. 2015).

Fig. 6.
Fig. 6.

Distribution of BLT (colored dots) from Argo profiles in 2003–18 and −uEkSm (contours; month−1) in (a) January, (b) April, (c) July, and (d) October. Gray shading (negative values) indicates freshening.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

Fig. 7.
Fig. 7.

Distribution of mean wind stress (N m−2) from NCEP in 2003–18 in (a) January, (b) April, (c) July, and (d) October.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

Since the freshening by Ekman advection in autumn corresponded not only to the distribution of BLs (Fig. 6d) but also to the region of warm TIs (Fig. 3d), the Ekman driven contribution to mixed layer temperature was estimated as −uEkTm, where Tm is mixed layer temperature (Fig. 8). In winter, Ekman advection works to cool or warm the ocean south or north of 7°N, respectively, and west of 120°W (Fig. 8a). Westward wind stress causes northward Ekman flow, which advects cooler or warmer water northward (Figs. 3a and 7a). In summer and autumn, cooling through Ekman advection occurred from 7°N, 130°W to 15°N, 105°W (Figs. 8c,d). This cooling was due to the southeastward advection of cooler water from north of the warm pool caused by the northeast winds (Figs. 7c,d). In autumn, mixed layer cooling larger than 0.1°C month−1 occurred around 10°N, 125°W where the warmest TIs are located (Fig. 8d).

Fig. 8.
Fig. 8.

Distribution of ∆θ (colored dots) from Argo profiles in 2003–18 and −uEkTm (contours; °C month−1) in (a) January, (b) April, (c) July, and (d) October. Gray shading (negative values) indicates cooling.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

In Fig. 9 we show the regions where Ekman advection worked to both freshen and cool the mixed layer. During summer and autumn (Figs. 9c,d) the region between the western and eastern Pacific warm pools is identified as a region where southward Ekman advection likely contributed to formation of the BLs with TIs. To identify whether the gap between the warm pools is favorable for southward Ekman advection to induce cooling we plot the zero contour of the meridional gradient of temperature (∂Tm/∂y = 0) in Fig. 9. The zero contour line (∂Tm/∂y = 0) that traverses the western and eastern warm pools (Fig. 9) separates the cooler waters where ∂Tm/∂y < 0 to the north from the cooler waters where ∂Tm/∂y > 0 to the south (Fig. 9). The zero-contour shifted southward at the gap between the warm pools during both summer and autumn (Figs. 9c,d). The position of this southward shift corresponded to the region with both freshening and cooling by Ekman advection, indicating that the gap between warm pools is favorable for southward Ekman advection of cooler water and hence formation of TIs in summer and autumn. Although the climatological area corresponding to freshening and cooling (gray shading in Fig. 9) was small compared to the broader TI distribution (Fig. 3), its position and extent somewhat varies interannually as does the TI distribution (not shown). Freshening and cooling also occurred in winter and spring around 10°N, 100°W although BLs with TI were not observed during those seasons (Figs. 9a,b). Even if both freshening and cooling occurred by Ekman advection, BLs and TIs cannot be formed when freshening is insufficient to result in density stratification. It is likely that freshening by Ekman advection is not sufficiently large enough to form BLs and TIs during winter and spring (Figs. 6a,b). In this sense, TIs were only associated with BL formation rather than contributing to BL formation.

Fig. 9.
Fig. 9.

Distribution of regions where −uEkSm < 0 (month−1) and −uEkTm < 0 (°C month−1) (gray shading) and mean mixed layer temperature (contours; °C) from MOAA_GPV in 2003–18 in (a) January, (b) April, (c) July, and (d) October. The dashed line indicates the contour of ∂Tm/∂y = 0.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

The geostrophic salinity advection term in Eq. (1) in general showed little correspondence to the BL distribution (Fig. 10). The magnitude of the geostrophic salinity advection term was smaller than 0.2 month−1 in winter and spring in most of the ETNP (Figs. 10a,b). In summer and autumn, the contribution of geostrophic advection became larger between 5° and 10°N (Figs. 10c,d), reflecting the seasonality of the eastward North Equatorial Countercurrent (Hsin and Qiu 2012; Guimbard et al. 2017). However, the geostrophic advection displayed much small-scale structure consisting of a train of positive and negative values with no coherent zonal distribution of freshening in BL region. This is because the eastward North Equatorial Countercurrent flows almost along the SSS front and so small meridional perturbations in either the front or the countercurrent would result in the incoherent structure of alternating freshening and salinifying (Figs. 10c,d). The contribution of geostrophic advection to mixed layer temperature as −ugTm (Fig. 11) tended to cool the mixed layer north of 10°N since southward flow in the eastern part of the subtropical gyre advected cooler water from the north. Although cooling by geostrophic advection also occurred between 5° and 10°N in summer and autumn (Figs. 11c,d), similar to the geostrophic advection of salinity, the temperature geostrophic advection also did not have a coherent zonal distribution.

Fig. 10.
Fig. 10.

Distributions of BLT (colored dots) from Argo profiles in 2003–18 and −ugSm (contours; month−1) in (a) January, (b) April, (c) July, and (d) October. The contour interval is 0.2 month−1. Gray shading (negative values) indicates freshening.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

Fig. 11.
Fig. 11.

Distributions of ∆θ (colored dots) from Argo profiles in 2003–18 and −ugTm (contours; °C month−1) in (a) January, (b) April, (c) July, and (d) October. The contour interval is 0.5°C month−1. Gray shading (negative values) indicates cooling.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

Deeper ILD rather than changes in the MLD resulted in the thick BLTs in autumn (Fig. 3c). Within the ETNP box, net heat flux (downward positive) was positive throughout the year with two peaks in spring (March) and in autumn (September) (Fig. 12), indicating that net heat flux worked to intensify the stratification when it was strongest in these seasons. Thus, deeper ILD in autumn was not caused by the seasonality of net heat flux. Wind stress in the ETNP box shifted direction in June–July and again in October–November (Fig. 13a) and showed two seasonal peaks in intensity in March and September (Fig. 13b). These two peaks in wind stress intensity correspond well to the two peaks of ILD in March and September (Fig. 3c), suggesting that deeper ILD in autumn mainly reflected the stronger wind stress. Considering the primary role of Ekman advection in BL formation in autumn, the ILD deepening and the BL formation through tilting of the SSS front occurred by seasonal wind events in autumn similar to that found in the Pacific subtropical region (Katsura et al. 2015). Although Ekman advection worked to freshen the ETNP region in July (Fig. 6c), the contribution to the presence of BLs during this summer season was mainly through precipitation (Fig. 5c). The region of freshening by precipitation was much broader in summer than in autumn (Figs. 5c,d). Thus, the peak of BL frequency in July (Fig. 4a) reflected the broader freshening region through precipitation even though the BLT itself was thinner than during October (Fig. 4b).

Fig. 12.
Fig. 12.

Seasonal variation of net heat flux (downward heat into the ocean is positive) from OAFlux (red dots) and J-OFURO3 (blue dots), averaged in the box 5°–13°N, 140°–100°W (see Fig. 2) during 2003–09 and 2003–13, respectively. Vertical bars indicate the 95% confidence interval based on the standard deviation of the climatological values calculated using each grid point of the box.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

Fig. 13.
Fig. 13.

Seasonal variation of (a) zonal (red dots) and meridional (blue dots) components and (b) magnitude of wind stress from NCEP, averaged in the box 5°–13°N, 140°–100°W (see Fig. 2) during 2003–18. Vertical bars indicate the 95% confidence interval based on the standard deviation of the climatological values calculated using each grid point of the box.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

c. Investigating the salinity contribution to the horizontal density

The mixed layer budget analysis suggested the formation of BLs and TIs through freshening and cooling was caused mainly by Ekman advection with a minor contribution from precipitation. For the BL formation through Ekman advection, it is necessary that the contribution of the salinity gradient to the density gradient is larger than that of temperature gradient in the horizontal direction (Katsura et al. 2015). In addition, for TI formation through Ekman advection, horizontal salinity and temperature gradients must provide a compensating contribution to the horizontal density gradient. To further investigate the possibility of BL and TI formation through Ekman advection, the meridional density ratio Ry (Tippins and Tomczak 2003; Katsura et al. 2015), which is the measure of the relative contribution of temperature and salinity gradient to the seawater density gradient in the meridional direction, was calculated as
Ry=αT/yβS/y,
where α is the thermal expansion coefficient and β is the salinity contraction coefficient that were determined using OISST and SMAP, respectively. For BL and TI formation through the tilting of an SSS front, −1 < Ry < 1 and 0 < Ry < 1 must be satisfied, respectively. The magnitude of Ry at the sea surface was small in a zonal band around 5°N along the SSS front south of the eastern Pacific fresh pool in boreal summer and autumn and so satisfying the condition for BL formation (Figs. 14c,d). Sea surface Ry was also positive around 8°N in the northern part of the SSS front and so the condition for TI formation was also satisfied. The distribution of sea surface Ry in summer and autumn corresponded well to the distribution of BLs and TIs.
Fig. 14.
Fig. 14.

Distribution of Ry at the sea surface (colors) from SMAP and OISST and mean SSS (contours) from SMAP in April 2015–March 2019 in (a) January, (b) April, (c) July, and (d) October.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

To examine the subsurface contribution of the horizontal salinity gradient to the density gradient and BL formation in the ETNP we show a meridional slice of Ry with depth along 125°W (Fig. 15) calculated using the MOAA_GPV. We note that the MOAA_GPV fields show a BLT in April of 10 dbar comparable to that in October (Figs. 15b,d). This BLT is most likely an artifact produced by the interpolation process that created the gridded MOAA_GPV data, since the majority of individual Argo profiles did not detect BLs of this magnitude during April (Figs. 2 and 4b). In fact, it is well known that BLTs calculated from individual profiles are typically thicker than those calculated from the smoothed and interpolated profiles of gridded products (e.g., de Boyer Montégut et al. 2007; Mignot et al. 2007). The small values of Ry associated with the SSS front were observed below the sea surface within the mixed layer (Figs. 15c,d). This strongly supports the formation of BLs with TIs in the ETNP through the tilting of the SSS front and the advection of cooler water caused by Ekman flow. In winter, the SSS front became weak, and the regions with 0 < Ry < 1 showed a patchy distribution without distinct structure (Figs. 14a and 15a). Although small Ry was distributed along the SSS front within 0°–5°N during spring (Figs. 14b and 15b), the wind stress was weak and westward in this region, and Ekman advection did not work to freshen or cool (Figs. 7b and 9b). The SSS front is known to be strongest in boreal autumn (Kao and Lagerloef 2015; Yu 2015), and the associated seasonality of Ry also suggested that the presence of BLs and TIs reflects not only the wind direction but also the SSS front intensity. Although small values of Ry were found in the northern part of the eastern Pacific fresh pool between 10° and 15°N in autumn (Fig. 14d), BLs were not frequently observed during this season. This is probably because the easterly trade wind is weak and so Ekman advection of freshwater is also weak during this season (Figs. 6d and 7d).

Fig. 15.
Fig. 15.

Meridional section of salinity (contours) and Ry (colors) along 125°W from MOAA_GPV in (a) January, (b) April, (c) July, and (d) October. Green and blue lines indicate the MLD and ILD, respectively.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

4. Discussion and summary

Seasonality and formation of BLs and associated TIs in the ETNP were investigated using Argo profiling float data, satellite data, and various surface flux products. BLs were frequently observed in boreal summer and autumn along the SSS front south of the eastern Pacific fresh pool. Their frequency showed two peaks of about 40% in July and October and while mean BLT was highest in October exceeding 25 dbar, there was no corresponding peak in BLT during July. TIs in the ETNP were also found in autumn within the gap between the western and eastern warm pool regions. Frequency and amplitude of TIs were highest in October and exceeded 15% and 0.25°C, respectively, corresponding to the seasonality of the thickest and most prevalent BLs. Seasonal variation of both BLT and TI depth reflected the seasonality of ILD.

To investigate the formation of BLs and TIs in the ETNP, an MLS budget was constructed and analyzed. While winter BLs were fewer and thinner, they were observed within the region freshened by precipitation, implying their formation by precipitation. In contrast, although freshening by precipitation occurred in summer and autumn, maximum precipitation lay slightly north of the thickest BLs. The BLs during autumn were associated with warm TIs while fewer TIs were found during summer. A zonal band of freshening by Ekman advection formed along the SSS front in summer and autumn, reflecting the shift of wind direction to northeastward, and corresponded to the distribution of both BLs and TIs. Ekman advection also worked to cool at the gap between the western and eastern warm pools. Geostrophic advection did not show a zonal coherent distribution since the North Equatorial Countercurrent flows along the SSS front and small meanders in these two features resulted in alternating patchy contributions of warming/cooling and freshening/salinifying.

These results strongly suggested that Ekman advection plays the primary role in the BL and TI formation in the ETNP, particularly during the autumn season when BLs are most prevalent and thickest and TIs are warmest. The meridional SSS gradient was the dominant contributor to the meridional sea surface density gradient along the SSS front in summer and autumn, which is favorable for BL formation through the tilting of the SSS front. In addition, in the northern part of the SSS front the meridional SSS gradient showed a compensating contribution with the meridional SST gradient, indicating that TIs are formed in association with BL formation during autumn. This supports the idea that BLs and TIs in autumn are formed mainly by Ekman advection through the tilting of the SSS front and that their seasonality reflects the wind direction and the intensity of the SSS front.

The MLS budget to determine the mechanism of the BL formation included both Ekman and geostrophic advective terms and surface forcing through precipitation. The budget neglected the effect of evaporation, entrainment and both vertical and horizontal diffusion. Of these missing terms, it is possible that horizontal diffusion contributes to BL formation. We estimated the horizontal diffusion term as −κ2Sm, where κ is horizontal diffusivity and is assumed to be 8.0 × 103 m2 s−1 for the entire ETNP (Zhurbas and Oh 2004). In summer and autumn, the horizontal diffusion term was negative along the SSS front with a magnitude comparable to the Ekman advection term (<−0.1 month−1; not shown). However, the SSS front has no vertical gradient within the mixed layer in summer and autumn (Figs. 15c,d) implying that the contribution of horizontal diffusion to BL formation is likely very small. Higher spatial resolution of diffusivity would be needed to determine if BL formation through horizontal diffusion is possible.

This study revealed that Ekman advection works to both freshen and cool the ETNP in autumn and contributes to the formation of the thickest BLs with the warmest TIs through the tilting of the SSS front while precipitation is a secondary contributor to BL formation in autumn. Recent satellite observations of SSS revealed that tropical instability waves also cause strong SSS anomalies and fronts in the eastern tropical Pacific (Lee et al. 2012; Yin et al. 2014). Thus, it could be that tropical instability waves can also contribute to BL and TI formation by causing the meander and tilting of the SSS front. In the tropical North Pacific, mesoscale eddies were found to contribute to SSS variability through horizontal advection causing large SSS anomalies at their edges (Delcroix et al. 2019). At present, the temporal sampling of Argo profiles and the spatial resolution of the gridded Argo products are insufficient to investigate such small-scale processes below the surface layer that might be responsible for BL and TI formation. High-resolution ocean models will be useful to better understand these processes.

Seasonality and distribution of BLs and associated TIs in the western tropical and subtropical North Pacific were largely consistent with the previous studies. In our study, BLs with TI were additionally found in the eastern subtropical North Pacific around 28°N, 135°W in winter and spring (Figs. 2 and 3). The 0 < Ry < 1 required for TI formation is satisfied in this region in association with the strong SSS front (see Figs. 15a and 15b in Katsura et al. 2015) suggesting that the tilting of the SSS front is an important factor of BL and TI formation in the eastern tropical North Pacific. This region also corresponds to the formation region of the Eastern Subtropical Mode Water (ESTMW; Hautala and Roemmich 1998), and the SSS front plays an important role in preconditioning of the deepening of winter mixed layer and hence the formation of ESTMW (Katsura 2018). Our study further suggests that BLs with TI formation may also play a role in ESTMW formation, although this requires further investigation.

The present study revealed that BLs with TIs are common in the ETNP particularly in boreal autumn. Since the MLD is very shallow in this region, the presence of BLs and TIs potentially may have a higher impact on SST and the atmosphere than in other regions where BLs exist. Previous studies have shown that BLs work to maintain and intensify tropical cyclones reducing the sea surface cooling caused by the storm-induced vertical mixing (Balaguru et al. 2012; Yan et al. 2017). Indeed, Jin et al. (2014) showed that the number and the intensity of tropical cyclones critically depend on the interannual surface layer temperature anomalies in July–November in the ETNP, corresponding to the exact region of our study. Since BLs in the ETNP are thicker in boreal summer–autumn and associated with stronger TIs in autumn, especially during October, BLs and TIs in the ETNP could be a significant contributor to the genesis, maintenance and intensification of tropical cyclones by modifying the heat budget in the surface layer.

The trade-wind-driven Ekman processes are thought to play a primary role in the seasonality of the SSS front (Yu 2015). At the same time, precipitation associated with the ITCZ also contributes to the seasonality of the eastern Pacific fresh pool and the SSS front (Kao and Lagerloef 2015). In our study, we note that the seasonality of BLs and TIs reflects the wind direction and the intensity of the SSS front. Thus, BLs and TIs may be closely coupled to the trade wind; that is, their presence and thickness are not only affected by the trade wind but in turn can also affect the wind field by modifying the SST. Further research is needed to evaluate the impact of BLs and TIs on not only SST, but also on the atmosphere to better understand their role in climate variability and coupled air–sea interactions.

Acknowledgments

Author Katsura is supported by JSPS Overseas Research Fellowships. Authors Sprintall and Katsura are supported by NASA Grant 80NSSC18K1500. The authors are thankful to two anonymous reviewers for their valuable comments. The Argo float data used in this study were collected and made freely available by the International Argo Project and the national programs that contribute to it (http://www.argo.ucsd.edu; http://www.jcommops.org/argo). Argo is a pilot program of the Global Ocean Observing System. The Argo profiles were from the Argo Global Data Assembly Center (ftp://usgodae.org/pub/outgoing/argo; ftp://ftp.ifremer.fr/ifremer/argo) and Advanced automatic QC Argo data version 1.2. by the Japan Agency for Marine-Earth Science and Technology (http://www.jamstec.go.jp/ARGO/argo_web/argo/?page_id=100&lang=en). The MOAA_GPV dataset was provided by the Japan Agency for Marine-Earth Science and Technology (http://www.jamstec.go.jp/ARGO/argo_web/argo/?page_id=83&lang=en). The L3 70-km version-3.0 product of the NASA Soil Moisture Active Passive was provided by NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC; https://podaac.jpl.nasa.gov/dataset/SMAP_RSS_L3_SSS_SMI_MONTHLY_V3_70KM). The Advanced Very High Resolution Radiometer product of OISST was provided by the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (https://www.ncdc.noaa.gov/oisst). The precipitation data from CMAP and wind stress (momentum flux) from NCEP were provided by the NOAA/National Weather Service (ftp://ftp.cpc.ncep.noaa.gov/precip/cmap and https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surfaceflux.html, respectively). Sea surface height data provided by CMEMS were obtained online (http://marine.copernicus.eu/). The net heat flux and evaporation from OAFlux were provided by the WHOI OAFlux project (http://oaflux.whoi.edu) funded by the NOAA Climate Observations and Monitoring (COM) program. The J-OFURO3 net heat flux data were obtained online (https://j-ofuro.scc.u-tokai.ac.jp/en/).

APPENDIX

Mixed Layer Salinity Budget in the ETNP Box

In section 3b, we estimated the contribution of precipitation, Ekman and geostrophic advection to BL formation through surface freshening. Here, we assessed whether the MLS budget using Eq. (1) is closed and explains the seasonal variation of MLS in the ETNP box. Entrainment velocity we in Eq. (1) was estimated as
we=wEk+(hmt+·hmu)=×τρ0f+(hmt+·hmu),
where wEk is the Ekman vertical velocity generated by the convergence and divergence of the horizontal Ekman transport, and u is the sum of Ekman and geostrophic velocities (Yu 2011).

In the ETNP box, the sum of forcing terms [right-hand side of Eq. (1)] corresponded well to the rate of MLS change [left-hand side of Eq. (1)] in terms of phase and amplitude, with a high correlation (coefficient R = 0.77), indicating that the seasonal MLS budget in the ETNP was almost closed and can be explained by Eq. (1) (Fig. A1a). Freshening by precipitation had a dominant contribution to the MLS budget, exceeding salinification by evaporation (Fig. A1b). Ekman advection averaged within the ETNP box was positive and had a minor contribution to the MLS budget relative to precipitation throughout the year (Fig. A1b). This is because of the narrow meridional width of the band of freshening by Ekman advection in autumn (Figs. 6c,d) relative to the width of the ETNP box. Similarly, although the geostrophic advection was comparable to Ekman advection within the larger and wider ETNP box (Fig. A1b), it showed a minor contribution to the MLS budget, consistent with the results in section 3b. There are small discrepancies between the right-hand and the left-hand side of Eq. (1), especially in July and November (Fig. A1a). This difference may be assumed as due to the lack of the diffusion term in Eq. (1).

Fig. A1.
Fig. A1.

(a) Seasonal variation of the rate of MLS change [left-hand side of Eq. (1); red curve] and the sum of forcing terms [right-hand side of Eq. (1); blue curve], averaged in the box 5°–13°N, 140°–100°W (see Fig. 2). (b) Seasonal variation of each forcing term on the right-hand side of Eq. (1), averaged in the box: ESm/hm (red curve), −PSm/hm (blue curve), −uEkSm (green curve), −ugSm (orange curve), and weSm/hm (purple curve). Vertical bars of the rate of MLS change in (a) and each forcing term in (b) indicate the standard deviation of the climatological values calculated using each grid point of the box. Vertical bars of the sum of forcing terms in (a) were estimated from the 95% confidence interval of each forcing term in (b) while assuming the errors are additive.

Citation: Journal of Physical Oceanography 50, 3; 10.1175/JPO-D-19-0194.1

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