 Research article
 Open Access
 Published:
Spatiotemporal vertical velocity variation in the western tropical Pacific and its relation to decadal ocean variability
Progress in Earth and Planetary Science volume 9, Article number: 57 (2022)
Abstract
Prior to the cold phase of the Pacific decadal oscillation (PDO), sea surface water in the western tropical North Pacific is heated. To evaluate the impact of the subsurface water on the upperocean (\(<100\) m depth) temperature variation, we examined the vertical velocity variation. We applied altimetrybased gravest empirical mode (AGEM) method to Argo hydrographic profile (2001–2017) and altimetric sea surface height (1993–2017) data in the western tropical Pacific and obtained a timevarying threedimensional (3D) potential density field in the layer from 100 to 1000 m depth. From the AGEMderived potential density, we derived a 3D field of vertical velocity below 100 m depth by the Pvector inverse method, which is based on potential density and vorticity conservations, and examined the spatiotemporal characteristics, focusing on the seasonal to quasidecadal (QD) variations. In the mean state, substantial upward current (\(>10^{6}\,{\text {m\,s}}^{1}\)) is observed in the region of \(10^{\circ }\)–\(17^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\) in the North Equatorial Current, southern part of the North Pacific subtropical gyre. Possibly besides the Ekman upwelling, the upward current contributes to the cooling of the upper ocean in the western tropical North Pacific through the upward advection of the deep cold water. The upward current was found to be intensified (attenuated) by the strengthening (weakening) of the subtropical gyre interior southward flow on the QD timescale in association with frequent occurrences of La Niña (El Niño) events. The weakening of the upward current is responsible for the QD increase in upperocean temperature in the western tropical North Pacific, which is equivalent to or larger than \(0.2~^{\circ }\)C, several years after the warm phase of the PDO. The QD temperature variation may affect the phase reversal of the PDO through the heat transport of the western boundary current of the subtropical gyre, i.e., the Kuroshio.
1 Introduction
The subtropical gyre of the North Pacific is a basinscale anticyclonic circulation, consisting of the northward swift western boundary current, socalled the Kuroshio, and the southward weak flow in the interior region. The subtropical gyre transports a huge amount of heat northward (e.g., Bryden and Imawaki 2001; Nagano et al. 2009, 2010). Through the variation in the northward heat transport, the subtropical gyre of the North Pacific is considered to play a critical role in interannual and longer timescale midlatitude climate variations such as the Pacific decadal oscillation (PDO) (e.g., Kawai et al. 2008), the most dominant variation in sea surface temperature (SST) in the North Pacific, (e.g., Mantua et al. 1997; Mantua and Hare 2002). Similar decadal oscillations were produced in ocean and atmosphere coupled numerical models (Latif and Barnett 1994, 1996; Gu and Philander 1997).
The spatial pattern of the PDO is defined as the first empirical orthogonal function (EOF) mode of SST in the North Pacific. A map of regression slope of SST to the PDO index (the time coefficient of the first EOF mode of the SST) is illustrated in Fig. 1a. In the positive (warm) phase of the PDO, the pattern exhibits a negative SST anomaly in the central part of the North Pacific and a positive SST anomaly in the central and eastern equatorial Pacific (Mantua et al. 1997; Mantua and Hare 2002). Thermal disturbances excited at the northern periphery of the subtropical gyre due to the strengthening of the Aleutian Low are observed to subduct in the upper main thermocline (pycnocline), to propagate southward in the interior region of the subtropical gyre, and, longer than 10 years after the subduction, reaches a subsurface (up to approximately 100 m depth) layer around a latitude of \(18^{\circ }\)N of the western tropical North Pacific (e.g., Deser et al. 1996; Schneider et al. 1999).
By combined use of altimetric sea surface height (SSH) data, expendable bathythermograph temperature data, and Argo salinity data at lines from San Francisco to Hawaii and from Hawaii to Japan, Nagano et al. (2016a) studied the quasidecadal (QD) variation in the subtropical gyre net heat transport due to the variation of the interior flow with periods of 8–12 years (e.g., Tourre et al. 2001; White et al. 2003; Hasegawa and Hanawa 2003; Hasegawa et al. 2013). Up to 4 years after the positive (warm) PDO phase, the volume transportweighted temperature in the subtropical gyre interior region increases due to the enhancement in volume transport in a potential density layer between 25.0 and 25.5 \({\sigma }_{\theta }\) (\({\sigma }_{\theta } =\) potential density \( 1000~{\text {kg\,m}}^{3}\)). As a result, because the temperature difference of the water masses transported by the Kuroshio and the gyre interior flow decreases, the northward net heat transport decreases. The reduction in the northward heat transport accumulates heat in the western tropical North Pacific, where climatological SST is very high, and enhances the warm pool SST (Nagano et al. 2016a), as is recognized in the map of the regression slope with a 3year lag (Fig. 1d). In other words, the warm pool SST increases prior to the negative phase of the PDO.
The QD variation in the subsurface volume transport in the interior region of the subtropical gyre is suggested to contribute to the SST variation in the region of \(10^{\circ }\)–\(20^{\circ }\)N, \(125^{\circ }\)–\(160^{\circ }\)E in the western North Pacific warm pool (Nagano et al. 2016a) possibly through the enhancement of the upwelling of deep water from layers deeper than 100 m depth. If we obtain a threedimensional (3D) field of vertical velocity, we can confirm this idea. The spatiotemporal characteristics of upward currents in the western tropical North Pacific are, however, unknown because vertical current velocity is too small (\(<10^{4}\) \({\text {m\,s}}^{1}\)) to be directly measured by oceanographic instruments such as current meters, the accuracy of current measurements being of the order of \(10^{3}\) \({\text {m\,s}}^{1}\) at best for now (e.g., Thomson and Emery 2014). Therefore, vertical velocity should be estimated from other physical variables such as 3D water density field under some hydrodynamic assumptions.
Based on the conservations of potential density and potential vorticity, Needler (1985) derived a formula of 3D absolute velocity vector (\({{\varvec{v}}}\)), in the interior region of subtropical gyres as
where \({\rho }\) is the potential density, f is the Coriolis parameter, g is the gravitational acceleration, \(q=f/{\rho } (\partial {\rho }/{\partial }z)\) is Ertel’s potential vorticity neglecting the relative vorticity, and \({{\varvec{k}}}\) is the unit vertical vector. In addition to potential density and potential vorticity, the Bernoulli function of a water parcel advected by the flow is conserved if the flow is steady and the diffusion can be neglected (e.g., Needler 1985; Pedlosky 1987a, b). For the estimation of the 3D current velocity field from hydrographic data, several methods have been proposed by past investigators (e.g., Stommel and Schott 1977; Olbers et al. 1985; Killworth 1983, 1986; Chu 1995, 2006). These methods are expected to provide similar 3D current velocity fields because they are based on the same assumptions, i.e., conservations of potential density and potential vorticity.
Chu (1995, 2006) developed a sophisticated method, called the Pvector inverse method, to calculate 3D absolute velocity vector from observed hydrographic data, avoiding cases that isosurfaces of potential density and potential vorticity are parallel (i.e., \({\nabla }{\rho }\,{\times }{\nabla }{q}\) = 0), as described in the following section. For example, he applied the Pvector inverse method to the circulations in the upper layer of the North Atlantic and demonstrated a very strong downward velocity around a depth of the main pycnocline (500 m depth) in the area south of Iceland, where the subduction zone of the global ocean conveyor belt is possibly located (Schmitz 1995). Therefore, the Pvector inverse method provides reasonable 3D distributions of vertical velocity and may be used to diagnose vertical velocity in the western tropical and subtropical North Pacific.
By applying the Pvector inverse method to the 3D potential density field in the tropical and subtropical North Pacific, we expect to obtain a vertical velocity field in the upper ocean layer and to reveal its seasonal to decadal variations. In other words, we can identify the location where the QD variation in the subsurface volume transport in the interior region of the subtropical gyre reported by Nagano et al. (2016a) emerges in the western Pacific region and how the QD variation of the vertical transport to the east of the Philippines is related to the variation in the interior region. Further, the impact of the vertical transport on the upper ocean to the east of the Philippines may be estimated.
In general, potential vorticity is not conserved along streamlines with horizontal and vertical diffusion. To obtain the absolute velocity vector field by the Pvector method and other methods based on the potential vorticity conservation, a timevarying 3D gridded water density field with sufficient spatial resolution is required. As a candidate of such a dataset, we may use gridded hydrographic data such as the grid point value of the Monthly Objective Analysis using the Argo (MOAA GPV) data (Hosoda et al. 2008) based on global Argo float data collected since 2000. Using this dataset, we first attempted to compute the Pvectorderived vertical velocity. However, the vertical velocity computed from the MOAA GPV data applying the Pvector method is unreasonably large (\(>10^{4}\,{\text {m\,s}}^{1}\)) probably because the effective resolution (\({\sim }1000\) km) is too low to apply the conservation of potential vorticity. Hence, we must use a finer gridded dataset to obtain reliable vertical velocity estimates by the Pvector inverse method.
Satellite observations have extensive coverage and sufficient resolutions in space and time to resolve the ocean variations on scales ranging from mesoscale to global scale. SSH maps with a horizontal resolution of \(1/4^{\circ } {\times } 1/4^{\circ }\) are available to examine current variations (Pujol and Mertz 2020). Particularly, because SSH is a physical parameter, which corresponds to integrated specific volume (i.e., reciprocal of water density) through the water column from the bottom to the sea surface, variations in altimetric SSH should be related to those in water density through the water column. Thus, combined use of the Argo hydrographic profile data and altimetric SSH data might produce a sufficiently fine resolution 3D hydrographic dataset.
The altimetrybased gravest empirical mode (AGEM) method has been applied to spatiotemporally sparse hydrographic data to estimate variations of hydrographic and current fields all over the world from SSH data (e.g., Sun and Watts 2001; Swart et al. 2010; Nagano et al. 2016a, b). Because AGEMderived hydrographic fields have the same horizontal resolution as that of the altimetric SSH data, they are sufficient to resolve regional scale circulations and their temporal variations. Nagano et al. (2016a, b) applied the AGEM method to hydrographic data and altimetric SSH data, and estimated sections of potential temperature and salinity (therefore, potential density) at a line from San Francisco to near Japan via Hawaii across the interior region of the North Pacific subtropical gyre and 3D hydrographic fields in the western subarctic North Pacific.
In this study, we compute the 3D monthly potential temperature, salinity, and therefore potential density fields from the AGEM for areas divided by applying cluster analysis to Argo hydrographic profile data. We then estimate the vertical velocity field by the Pvector inverse method and examine the variations on seasonal to decadal timescales. In Sect. 2, we describe Argo conductivity–temperature–depth (CTD) profile data, altimetric SSH data, and the AGEM method. In Sects. 3 and 4, we analyze the AGEMderived potential density and the Pvectorbased vertical velocity fields, focusing on their variations related to the QD variations in the tropical and subtropical North Pacific and examine the impact of the vertical velocity variation on the upperocean temperature through the vertical advection of the deep cold water. Summary and conclusion are provided in Sect. 5.
2 Data and methods
2.1 Argo data
To construct AGEM fields, we used more than 7000 temperature and salinity profiles from the near sea surface (10 dbar) to a pressure level of 1000 dbar (equivalent to a depth of approximately 1000 m) collected for each month by Argo floats (Advanced automatic QC Argo Data version 1.2) from January 2001 to May 2017 in the region of \(10^{\circ }\)S–\(25^{\circ }\)N, \(120^{\circ }\)E–\(180^{\circ }\). A total of 90192 hydrographic profiles were used in this study. The accuracies of the Argo temperature, salinity, and pressure data were reported to be \({\pm }0.005\) \(^{\circ }\)C, \({\pm }0.01\) (psu), and \({\pm }5\) dbar by Argo Science Team, respectively (Hosoda et al. 2008). Following the Akima (1970) method, temperature and salinity profiles were interpolated onto the vertical grids at intervals of 10 dbar from 10 to 1000 dbar.
From the Argo temperature and salinity profile data, we calculated spiciness parameter by using the polynomial proposed by Flament (2002), which isopleths are orthogonal to those of potential density on the temperature–salinity diagram. The orthogonal character of the spiciness parameter permits us to identify water masses in terms of potential density by the single parameter. Because of this, spiciness analyses are useful to distinguish water masses, as demonstrated in the Kuroshio and North Pacific subtropical gyre interior regions by Nagano et al. (2009, 2010, 2012). We performed cluster analysis, applying Ward’s criteria (e.g., Ward 1963; Anderberg 1973) to spiciness values averaged between isopycnal layers at intervals of 0.01 \({\text {kg\,m}}^{3}\) from 24.06 to 26.46 \({\sigma }_{\theta }\) and changing the number of clusters from 3 to 9. The number of clusters being set to 7, the classification based on the cluster analysis is found to be roughly consistent with the distributions of water masses in the western tropical and subtropical Pacific such as the North/South Pacific Tropical Waters and the North Pacific/Antarctic Intermediate Waters (e.g., Fine et al. 1994; Kashino et al. 2011; Talley et al. 2011). Thus, we classified Argo hydrographic profiles into 7 clusters.
We divided the study region (\(10^{\circ }\)S–\(25^{\circ }\)N, \(120^{\circ }\)E–\(180^{\circ }\)) into pieces of \(5^{\circ }\) on each side based on this cluster analysis result. Next, the dominant (the most frequent) cluster of hydrographic profiles for each piece was determined. Based on the dominant clusters in all pieces, the study region was separated into 7 areas in individual months except for January (6 areas) and February (5 areas). The area division based on the cluster analysis is found to be roughly consistent with the regional current and water mass pathways (Fig. 2) (e.g., Reid 1997; Talley et al. 2011).
Using the Argo temperature and salinity profiles, we calculated the dynamic height with the reference pressure level of 1000 dbar, \(H_{\text {CTD}}\), in meters as
where p is pressure and \({\delta }={\rho }^{1}  {\rho }^{1} (0, 35, p)\) is the specific volume anomaly relative to the specific volume of seawater with temperature 0 \(^{\circ }\)C and salinity 35 (e.g., Gill 1982) calculated from Argo CTD data. The vertical integral in Eq. (2) was discretized into the summation of 10dbar averaged \({\delta }\) multiplied by the vertical interval of 10 dbar from 10 to 1000 dbar. The error in the Argoderived \(H_{\text {CTD}}\) was estimated to be smaller than 0.8 cm, which is small compared with the altimetric SSH data error (\({\sim }3\) cm).
2.2 Altimetric SSH data
We used daily altimetric absolute SSH (\(H_{\text {ALT}}\)) \(1/4^{\circ }\) \(\times\) \(1/4^{\circ }\) gridded map data in the region of \(10^{\circ }\)S–\(25^{\circ }\)N, \(120^{\circ }\)E–\(180^{\circ }\) from January 1993 to May 2017. The absolute SSH data are derived from the delayedtime updated SSH (Level 4) anomaly data (DT_SLAH) (Pujol and Mertz 2020) added to the mean dynamic topography data (MDT_CNESCLS13) provided by Rio et al. (2011). Because the SSH data are erroneous near the coasts, we did not use SSH data in regions of water depths less than 1000 m to estimate AGEM fields and AGEMderived 3D potential temperature and salinity fields.
\(H_{\text {ALT}}\) is linearly correlated with the dynamic height derived from Argo CTD data, i.e., \(H_{\text {CTD}}\), with correlation coefficients of 0.60–0.98 (Fig. 2), which are much higher than the 1% significance level (i.e., within the 99% confidence limit) based on the Student’s t test. Therefore, we performed linear regressions and based on those equations, converted values of \(H_{\text {ALT}}\) to dynamic height, which is required to construct 3D potential temperature and salinity fields by the AGEM method described in the next subsection. Note that the correlation coefficients in the equatorial and South Pacific are relatively low possibly because of highfrequency (time scales shorter than days) variations by internal gravity waves or small scale patchy water masses as observed by Richards et al. (2015) and others, which cannot be fully resolved by the altimetric SSH observations.
2.3 Areawise AGEM method
Vertical structures of temperature and salinity are considered to be greatly different among the areas classified by the cluster analysis and seasons. Thus, to consider the spatial and seasonal variations, for each month and each area, we constructed AGEM fields, i.e., the relations between hydrographic profiles and dynamic height, \(H_{\text {CTD}}\), based on all the Argo CTD data in the area during the month. The AGEM fields were smoothed for \(H_{\text {CTD}}\) by a Gaussian weight function with an efolding scale of 2.5 cm to remove noisy features due to the highfrequency variability.
As an example, the AGEM potential temperature and salinity fields down to a depth of 1000 m for 6 areas in January are shown in Figs. 3a–f and 3g–l, respectively. The main thermocline illustrated by the sharp potential temperature decrease from 25 \(^{\circ }\)C to 8 \(^{\circ }\)C commonly deepens as the dynamic height \(H_{\text {CTD}}\) increases. Along with the main thermocline, the salinity maximum layer deepens with \(H_{\text {CTD}}\) although the salinity values are different for each area. Salinity in the areas in the Southern Hemisphere (Fig. 3i, k) is higher than that in other areas (Fig. 3g, h, j, l), as illustrated by Reid (1997) and others. Corresponding to the regional differences in salinity, there are also regional variations in potential temperature and density.
We identify differences in the vertical distributions of potential temperature and salinity for the season (not shown). In the upper layer shallower than 100 m, potential temperature, and salinity seasonally vary associated with the development of the mixed layer in winter and the stratification in summer. Because, in this AGEM estimation, we used Argo hydrographic data during approximately 17 years from 2001 to 2017, the constructed AGEM fields should account the interannual to decadal variations. Such spatial and seasonal variations of the AGEM fields beside the SSH variations are responsible for those of the horizontal circulation and vertical current and their interannual to decadal modulations, as described in the following sections.
By converting monthly altimetric SSH, \(H_{\text {ALT}}\), to \(H_{\text {CTD}}\) and referring to the AGEM fields, we estimated the temporal changes of the 3D fields of potential temperature and salinity, and that of potential density on the horizontal grids of \(1/4^{\circ }\) at the vertical grids of 10 m from 10 to 1000 m. Ranges of converted SSH, i.e., dynamic height, variations in individual areas were within those of the dynamic height of the AGEM fields (abscissa in Fig. 3). This suggests that the AGEMderived 3D potential temperature and salinity fields involve the seasonal to decadal variations that are consistent with the SSH variations. To eliminate noisy features and to suppress artificial gaps between the areas in AGEMderived potential temperature and salinity fields for reasonable estimation by the Pvector inverse method, spatial smoothing was performed using a Gaussian filter with a damping scale of 150 km. For the following analysis, we used the smoothed AGEMderived potential temperature and salinity fields. In Fig. 4, we show the rootmeansquare (RMS) potential density differences between Argo and AGEMderived data at depths of 100, 200, 400, and 800 m; the RMS potential density difference at each grid was computed by using data collected in the month by Argo floats within a radius of 1000 km. The RMS potential density differences are reduced with the depth from approximately 1.2 \({\text {kg\,m}}^{3}\) at 100 m to 0.1 \({\text {kg\,m}}^{3}\) at 800 m due to the weakening of the variations on shorter timescales than approximately 1 month. In addition, note that there are no artificial spatial gaps in the RMS potential density differences due to the area division based on the cluster analysis.
Figure 5a shows the horizontal distribution of potential density at a depth of 200 m averaged in all months during the study period. In addition, according to Rintoul et al. (2002) and Nagano et al. (2016b), we calculated the streamfunction defined as
The streamfunction in the mean state integrated from the sea surface to a pressure level of 1000 dbar (approximately 1000 m depth) is illustrated by contours in Fig. 5a. The estimated mean current circulations are similar to those observed by past investigators (e.g., Nitani 1972; Reid 1997; Qu et al. 1998; Qu and Lukas 2003). The North Pacific subtropical and tropical gyres to the north and south of approximately \(13^{\circ }\)N are characterized by high and low potential density. Associated with the northward sharp decrease in potential density at a latitudinal band between \(10^{\circ }\)N and \(15^{\circ }\)N, there exists a strong current toward the western boundary of the Pacific basin, i.e., the North Equatorial Current (NEC), which consists of the westward current of the southern part of the subtropical gyre and that of the northern part of the tropical gyre.
The Mindanao Dome (MD) is produced in the region off the eastern coast of the Mindanao Island (\(4^{\circ }\)–\(10^{\circ }\)N) and the eastern edge of the circulation extends to the east of \(140^{\circ }\)E in boreal spring (Fig. 5c) and to the east of \(150^{\circ }\)E in boreal autumn (Fig. 5e). The remarkable seasonal change of the circulation observed by Qu et al. (1998), Qu and Lukas (2003), and others, and produced in a numerical model by Tozuka et al. (2002) are reproduced in this AGEM estimation.
2.4 Pvector inverse method
By using a unit vector \({{\varvec{P}}}\), called the perfect vector (Pvector); and the magnitude of the velocity vector \({\gamma }\), called the speed parameter, at location \({{\varvec{x}}}=(x, y, z)\), Chu (1995, 2006) expressed Eq. (1) as \({{\varvec{v}}}({{\varvec{x}}})\) \(= {\gamma } {{\varvec{P}}}\). Here, using potential density \(\rho\) and potential vorticity \(q=f/{\rho } (\partial {\rho }/{\partial }z)\), \({{\varvec{P}}}\) is defined as
Using the thermal wind equation, \(\gamma\) at a depth level of \(z_{k}\) (\({\gamma }_{k}\)) is calculated as
where (\({\Delta }u_{k}\), \({\Delta }v_{k}\)) is the vertical shear vector of horizontal velocity relative to the reference depth level, \(z_{r}\). In this study, we set \(z_{r}\) to 1000 m.
Using the monthly 3D potential density fields estimated by the areawise AGEM method, we calculated the 3D velocity vector below a depth of 100 m, where the AGEMbased potential density is reliable. Note that we did not conduct the calculation in the equatorial region between \(3^{\circ }\)S and \(3^{\circ }\)N because f is close to zero approaching the equator. There are two necessary conditions for the solution to exist. Isosurfaces of potential density and those of potential vorticity are not parallel (\({\nabla }{\rho }\,{\times }{\nabla }{q}\) \(\ne\) 0), and the denominator of Eq. (5) does not vanish (for the existence of the Pspiral). By examining the two necessary conditions for the existence of the absolute velocity vector, we can remove inappropriate solutions. In this study, we did not calculate the Pvector and the speed parameter in the locations where these necessary conditions are not satisfied. The necessary conditions are satisfied in areas exceeding 86% of the calculation region every month. Values of \({{\varvec{P}}}\) and \(\gamma\) in areas with no data were interpolated with a spatial Gaussian weight function with a horizontal efolding scale of 150 km, which is much smaller than spatial scales of the phenomena of our interests (\(> 500\) km).
The estimation of the 3D absolute current velocity field was conducted under some assumptions. In particular, the neglect of relative vorticity may produce significant error in the vertical velocity. Taking the curl of the momentum equation and using the continuity equation for incompressible fluid, we obtain \({\partial }w/{\partial }z = (1/f)({\partial }{\zeta }/{\partial }t)\), where \({\zeta }={\partial }v/{\partial }x  {\partial }u/{\partial }y\) is relative vorticity (e.g., Gill 1982). The horizontal velocity is no more than 50 \({\text {cm\,s}}^{1}\) in the NEC and the horizontal scale of the variation is larger than 1000 km, so that the magnitude of \({\zeta }\) is order of \(10^{7}\) \({\text {s}}^{1}\) except in the western boundary region. Taking into account the vertical and time scales of the w variation, 100 m and 1 month, respectively, the contribution of relative vorticity except in the western boundary region is less than \(10^{6}\) \({\text {m\,s}}^{1}\), which is smaller than the w variation. In the western boundary region, the error larger than \(10^{6}\) \({\text {m\,s}}^{1}\) is considered to be included in the estimated vertical velocity data.
By using the thermal wind equation, the vortexstretching equation, \(v = (f/{\beta }) {\partial }w/{\partial }z\), gives \({\rho }{\partial }^2 w/{\partial }z^{2} =  ({\beta }g/f^2){\partial }{\rho }/{\partial }x\), where \(\beta\) is the latitudinal variation of f (Killworth 1983). On the basis of this equation, the error of the estimated vertical velocity attributed to the potential density error can be evaluated. The RMS difference between Argo and AGEMderived potential density data in layers deeper than the main thermocline, where variations possibly due to highfrequency internal gravity waves are relatively small, may be regarded as the AGEM potential density error. If we set the error of the potential density to 0.1 \({\text {kg\,m}}^{3}\), we evaluate the error of the w variation with a horizontal scale of 1000 km and a vertical scale of 100 m to be \(10^{7}\) \({\text {m\,s}}^{1}\). The error is one order of magnitude smaller than the variation in w on the seasonal and longer time scales, as will be discussed in the following sections.
To examine the characteristics of interannual and longer timescale variations in vertical velocity, we smoothed the data by a 15month running mean filter. We decomposed the interannual vertical velocity variation by EOF modes to reveal regionalscale variation patterns, as the EOF mode decomposition is helpful to examine the spatiotemporal characteristics of the variations (e.g., Thomson and Emery 2014). Based on the EOF mode patterns, we determine the upwelling region where we compute volume and temperature transports.
2.5 SST data
To examine the impact of vertical velocity on upperocean (\(<100\) m depth) temperature in the region east of the Philippines, we used monthly US National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature (SST) V2 (NOAA_OI_SST_V2) data (Reynolds et al. 2002) from January 1993 to May 2017 in the region of \(10^{\circ }\)S–\(25^{\circ }\)N, \(120^{\circ }\)E–\(180^{\circ }\). We smoothed the data by a 15month running mean filter to eliminate the seasonal variation and evaluated the temporal tendency of the temperature by taking the smoothed data to the water temperature averaged in the top 100 m.
2.6 Climate indices
We used the Niño 3.4 index from January 1993 to December 2016, which is defined as the monthly mean SST anomaly from the seasonal variation in the central and eastern equatorial region of \(5^{\circ }\)N–\(5^{\circ }\)S, \(170^{\circ }\)W–\(120^{\circ }\)W as an El Niño–Southern Oscillation (ENSO) index by Trenberth (1997). Anomalously high and low SST events in the central and eastern equatorial Pacific, i.e., El Niño and La Niña events, are indicated by the Niño 3.4 index. In addition, the PDO index from January 1993 to December 2016, defined as the time coefficient of the first EOF mode of SST in the region poleward of \(20^{\circ }\)N for the period from 1900 to 1993 (Mantua et al. 1997), was used to calculate the regression slope between the PDO index and SST in the Pacific (Fig. 1) and to conduct comparison with the time series of the vertical current transports in the western tropical North Pacific.
3 Results
3.1 Mean circulation and vertical velocity fields
The areawise AGEM estimation produced the horizontal upperocean circulations in the western parts of the North Pacific subtropical gyre and the North/South Pacific tropical gyres, as illustrated by the streamfunction in Fig. 5a. Feeding the subtropical gyre interior southward flow, the westward flowing NEC is strengthened toward the western boundary from \(148^{\circ }\)E to \(135^{\circ }\)E (Fig. 5a). The cyclonic circulation of the MD centered around \(8^{\circ }\)N, \(130^{\circ }\)E is separated from the eastern tropical cyclonic circulation east of \(165^{\circ }\)E. Associated with the westward shrinkage of the MD in boreal winter (Fig. 5b) and spring (Fig. 5c), the westward current of the southern part of the NEC around a latitude of \(8^{\circ }\)N is attenuated in longitudes between \(145^{\circ }\)E and \(168^{\circ }\)E as illustrated by the widening and undulation of the \(\chi _{1000}\) contours. When the MD is weak, the NEC approaches the North Equatorial Countercurrent (NECC), and it generates wavy patterns between the currents. The wavy patterns may be attributed to fluctuations due to the barotropic instability (e.g., Pedlosky 1987a), which can occur in the enhanced horizontal shear between the opposing currents, as observed in the central and eastern equatorial regions (e.g., Tanaka and Hibiya 2019). In boreal summer (Fig. 5d) and autumn (Fig. 5e), the MD extends eastward and strengthens the NEC.
Upward (positive) velocities of approximately \(1 {\times } 10^{6}\) \({\text {m\,s}}^{1}\) are found in a part of the NEC, 10–\(16^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\), in layers of 100 m (Fig. 6a) and 200 m (Fig. 6b) depths. This upwelling region is located in the boundary between the subtropical and tropical gyres, where the meridional gradients of the potential density and streamfunction are remarkable (Fig. 5a). Meanwhile, at a depth of 400 m, there is no systematic pattern of vertical velocity. As indicated by the vertical velocity maps at 100 m depth in Fig. 7, the upward velocity is extensively observed in the region north of approximately \(10^{\circ }\)N, mostly in the subtropical gyre region, in boreal winter (Fig. 7a) and spring (Fig. 7b) but is limited in the latitudes south of \(15^{\circ }\)N in summer (Fig. 7c) and autumn (Fig. 7d). The magnitudes of the vertical velocity tend to be amplified in low latitudes near the western boundary regions because of \(\gamma {\sim } f^{1}\) as derived from Eq. (5). Although values of vertical velocity at individual grids are similar to or a little larger than the error (\(10^{6}\) \({\text {m\,s}}^{1}\)) estimated in Sect. 2.4, the error of mean value across such a regional scale (\(>2000\) km) is evaluated to be \(10^{8}\) \({\text {m\,s}}^{1}\). Therefore, we focus on the variations with spatial scales equivalent to or larger than the upwelling region in the NEC. In Sect. 3.3, we will discuss the vertical volume and temperature transports across the upwelling region.
The seasonal changes in vertical velocity are also identifiable in the zonal and meridional sections; for example, we show zonal sections at the latitude of \(12.6^{\circ }\)N (Fig. 8) and meridional sections at the longitude of \(145.4^{\circ }\)E (Fig. 9). The upward and downward velocities were produced in a layer down to a depth of approximately 350 m in association with the undulation of the isopycnal surfaces of 25.0–26.5 \({\sigma }_{\theta }\) in the longitudinal band between \(140^{\circ }\)E and \(170^{\circ }\)E. The intensity of the vertical velocity is greatest in boreal winter (Figs. 8b, 9b) and spring (Figs. 8c, 9c). In the region from \(140^{\circ }\)E to \(160^{\circ }\)E, the upward velocity appears to be stronger than the downward velocity; particularly, in the layer shallower than 150 m, upward velocity is predominant (Fig. 8).
The considerably large vertical velocity (\(> 5 {\times } 10^{6}\) \({\text {m\,s}}^{1}\)) is generated in winter and spring around a latitude of \(12^{\circ }\)N, where the main pycnocline sharply shoals toward the equator along with the strongly baroclinic NEC, as illustrated in Fig. 9b, c. The strong vertical velocity in boreal winter to spring is considered to be driven by the winter intensification of the NEC, which may be partly attributed to the variation of the subtropical gyre interior flow volume transport in a potential density layer of 25.0–25.5 \({\sigma }_{\theta }\) reported by Nagano et al. (2012, 2016a). Despite the great seasonal change in a layer between 150 and 350 m depths, the seasonal change in the shallower layer around a latitude of \(10^{\circ }\)N between \(140^{\circ }\)E and \(180^{\circ }\) is remarkably small. Beneath a depth of approximately 350 m, where the vertical potential density gradients are less than those in the overlying layer, the magnitudes of the vertical velocity are unsubstantial.
3.2 Interannual and longer timescale variations in vertical velocity
The vertical velocity field varies on various time scales longer than 1 year. As described in Sect. 2.4, we obtained the interannual variation in vertical velocity by a 15month running mean filter and calculated the EOF modes. The percentages of variance explained by the first and second EOF modes are 66% and 11%, respectively, and the sum of these modes explains 77% of the total variance. Each higher EOF mode accounts for no more than 3% of the total variance. Therefore, the leading two EOF modes can explain most of the interannual to decadal vertical velocity variations.
The horizontal patterns of the first EOF mode at depths of 100 and 200 m are shown in Fig. 10a, b, respectively. At 100 m depth, apart from the western boundary region, the first EOF mode varies with a zonal scale exceeding 2000 km such as in the negative loading region of \(10^{\circ }\)–\(15^{\circ }\)N, \(150^{\circ }\)E–\(180^{\circ }\) (Fig. 10a), where the mean vertical velocity is largely upward, containing smaller scale (\({\sim }1000\) km) variations (Fig. 6a). The positive and negative alternating large loadings are present to the east of Mindanao Island (\(3^{\circ }\)–\(10^{\circ }\)N, \(125^{\circ }\)–\(140^\circ\)E), but, because relative vorticity was not taken into account, the vertical velocity estimated in the western boundary current region may be unreliable.
At a depth of 200 m (Fig. 10b), remarkably large positive and negative loadings are aligned alternatively along a latitude of \(12^{\circ }\)N with a zonal scale of approximately 500 km. The zonal scale of the vertical velocity variation at this depth is smaller than that at 100 m depth. The area of either sign of the loading at 200 m depth appears not to be dominant in the region \(10^{\circ }\)–\(15^{\circ }\)N, \(150^{\circ }\)E–\(180^{\circ }\). In both zonal (Fig. 10c) and meridional (Fig. 10d) sections of the EOF mode, the remarkable variation is observed down to a depth of approximately 350 m, below which vertical gradient of potential density is small and the vertical velocity variation is weak. Therefore, the first EOF mode variation of the vertical velocity is responsible for the intensity change of the upward current above approximately 350 m depth in the region of \(10^{\circ }\)–\(17^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\) shown in Figs. 6, 8a, and 9a.
The second EOF mode of the vertical velocity at a depth of 100 m has a large negative loading in a region similar to the first EOF mode but is limited within the region of \(10^{\circ }\)–\(15^{\circ }\)N, \(155^{\circ }\)E–\(180^{\circ }\) (Fig. 11a). Regions equatorward of \(10^{\circ }\)N and \(10^{\circ }\)S show mostly positive values. At a depth of 200 m (Fig. 11b), positive and negative loadings with spatial scales smaller than approximately 500 km are sporadically distributed in the region south of \(15^{\circ }\)N. The spatial patterns of the vertical velocity are identifiable in the zonal and meridional sections down to a depth of 290 m, but in layers deeper than 300 m, we find noisy features with vertical scales of 10–20 m (Fig. 11c, d).
The time coefficient of the first EOF mode (black solid line in Fig. 12a) has peaks several months after peaks of the Niño 3.4 index (gray solid line), i.e., El Niño events, as in 1997–1998, 2002–2003, 2004–2005, 2009–2010, and 2015–2016. Similarly, troughs of the time coefficients are found after La Niña events such as in 1995–1996, 1998–2001, 2007–2008, and 2010–2011. The timelagged correlation coefficient between the first EOF mode and the Niño 3.4 index shows the maximum of 0.52 with a lag of 8 months (black solid line in Fig. 13, Table 1). The maximum correlation coefficient is higher than the 5% significance level (i.e., within the 95% confidence limit) based on the Student’s t test. Therefore, the first EOF mode variation is significantly related to the ENSO.
Peaks of the time coefficient of the second EOF mode are found almost simultaneously with those of the Niño 3.4 index (Fig. 12b). With a lag of 1 month, the correlation coefficient reached the maximum of 0.74, which is higher than the 5% significance level and is therefore statistically significant (black dashed line in Fig. 13, Table 1). The second EOF mode variation is excited by the ENSO before the first EOF mode variation and is more highly correlated with the ENSO than the first EOF mode variation. The first EOF mode lags 9 months behind the second EOF mode with the maximum correlation coefficient of 0.71, which is much higher than the 5% significance level and is statistically significant (pink solid line in Fig. 13).
3.3 Impacts of vertical velocity variation on SST
In this subsection, we examined the impact of the interannual and longer timescale variations in the vertical velocity on the upperocean temperature in the region 10–\(17^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\), referred to as Region A in what follows, where the vertical velocity at 100 m depth is prominently upward on average (shown by a solid square in Fig. 6a) and quite variable (Fig. 10a).
We calculated vertical volume transport (\(V_{z}\)) and temperature transport (\(F_{z}\)) at a depth of 100 m in Region A, which are defined as
and
where \(C_p\) is the specific heat capacity of seawater at constant pressure and \(\theta\) is the potential temperature. \({\rho } C_p\) is set to \(3.99 {\times } 10^6\) \({\text {J\,K}}^{1}\,{\text {m}}^{3}\).
Time series of the vertical volume and temperature transports are shown by thick black and green lines in Fig. 14a, respectively. Both modes of transports in Region A are always positive, i.e., upward, and fluctuates inversely related to the PDO and Niño 3.4 indices with correlation coefficients of \(0.80\) and \(0.79\), respectively (Table 1), being consistent with the EOF analysis in Sect. 3.2. In addition, the transport is relatively low (high) due to frequent occurrences of El Niño (La Niña) events during periods such as 1994–1998 and 2002–2005 (1999–2001 and 2007–2014). This relationship is clearly shown by the QD component of the Niño 3.4 index smoothed by a 37month running mean filter (Hasegawa et al. 2013) (thin magenta line in Fig. 14a), which is inversely related to the vertical volume transport (thin black line in Fig. 14a).
When the volume transport reached the maximum over 1.5 Sv in summer (July) of the 2008 La Niña year (downward arrow in Fig. 14a), the upward current velocity exceeded \(1 {\times } 10^{6}\) \({\text {m\,s}}^{1}\) in the region from \(150^{\circ }\)E to \(180^{\circ }\) around \(12^{\circ }\)N (Fig. 15b). The strong upwelling was driven principally by the southward current greater than 1 \({\text {cm\,s}}^{1}\) at 100 m depth (contours in Fig. 15b) existing above the isopycnal zone of 25.0–25.5 \({\sigma }_{\theta }\) at 200 m depth (vertical line mesh), which impinged on the slope of the isopycnals shoaling southward. In this period, the southward current in the isopycnal layer of 25.0–25.5 \({\sigma }_{\theta }\) was mostly distributed in the subtropical gyre interior region (\(10^{\circ }\)–\(25^{\circ }\)N, \(145^{\circ }\)E–\(180^{\circ }\)) (Fig. 16b). The westward velocity of the NEC in this isopycnal layer was higher than 15 \({\text {cm\,s}}^{1}\) to the east of \(160^{\circ }\)E.
Meanwhile, in the summer (July) of the 1997 El Niño year (upward arrow in Fig. 14a), the vertical velocity in Region A was weakly upward or downward, and the corresponding southward current was observed only in a limited region centered around \(14^{\circ }\)N, \(175^{\circ }\)E (Fig. 15a). The area of the subtropical gyre interior southward current in the isopycnal layer of 25.0–25.5 \({\sigma }_{\theta }\) and the intensity of the NEC are both smaller than those in the 2008 summer (Fig. 16a). The QD variations in the vertical velocity just beneath a depth of 100 m are expected to contribute to the QD variation in SST in the western tropical North Pacific related to the PDO as illustrated in Fig. 1.
Because the water temperature and vertical velocity at 100 m depth are not horizontally uniform, the temperature of upwelling water is not necessarily identical to the areaaveraged temperature within Region A. The water temperature of water mass transported by the vertical current can be examined by the volume transportweighted temperature (\({\Theta }\)) at a depth of 100 m defined as
The volume transportweighted temperature (gray solid line in Fig. 14b) varies on the QD timescale in opposite phase with the Niño 3.4 index (magenta line in Fig. 14b) because of the anomalously warm (cold) water in the upper layer of the western Pacific warm pool in La Niña (El Niño). Therefore, the increase in \(\Theta\) just below a depth of 100 m in La Niña years acts to reduce the cooling effect of the enhancement of the upward velocity. Because of the nonuniformity of w, \(\Theta\) is approximately 0.1 \(^{\circ }\)C higher than the areaaveraged temperature, \(T_{100}\) (gray dashed line in Fig. 14b). However, note that, as will be shown below, the volume transportweighted temperature variation is much less effective in the upperocean temperature variation than the upward current variation.
The upward volume and temperature transports in Region A decrease with the depth because, in layers deeper than 150 m, upward currents are compensated by similar magnitude downward currents (Fig. 8). For example, the volume and temperature transports at 200 m depth are mostly positive (upward) but smaller than 0.8 Sv and 50 TW, respectively. Their correlation coefficients with respect to the Niño 3.4 index are \(0.44\), absolute values of which are smaller than the 5% significance level.
To examine the impact of the vertical velocity and temperature variations at 100 m depth on the temperature in the overlying layer, we estimate temperature tendency (\({\partial } T_{w}/{\partial } t\)) averaged in the top 100 m due to the vertical velocity variation, w, i.e., vertical advection of temperature (\(w{\partial }T/{\partial }z\)), by the following formula:
where \(h_{\text {u}}\) is the depth of the uppermost level of the estimation of w (i.e., 100 m), \(T_{\text {d}}\) is the potential temperature just below 100 m depth, and \(T_{\text {u}}\) is the upperocean (\(<100\) m depth) temperature. \(T_{\text {d}}\) and \(T_{\text {u}}\) are set to the AGEMderived potential temperature at the 110 m depth and monthly gridded value of SST prepared by Reynolds et al. (2002), respectively. We assigned to w the timevarying vertical velocity at a depth of 100 m derived from the Pvector inverse method. When the vertical velocity is negative (or downward), w is set to zero, i.e., \(w=0\).
The temperature tendency, \({\partial } T_{w}/{\partial } t\), based on Eq. (9) and averaged in Region A is shown in Fig. 14c. The QD variability (red line in Fig. 14c) is remarkable and is almost in phase with the PDO index (cyan line in Fig. 14b) with a significant correlation coefficient of 0.63 (Table 1). Associated with the enhancement of the vertical volume transport (Fig. 14a), the temperature tendency has substantial troughs in 2000, 2009, and 2014, which occur 6–8 years after the peaks of the volume transport in the isopycnal layer between 25.0 and 25.5 \({\sigma }_{\theta }\) in the subtropical gyre interior region in 1994, 2001, and 2006 estimated by Nagano et al. (2016a). The minimum value of the temperature tendency (\(0.04\) \({^{\circ }{\text {C}}}\,{\text {month}}^{1}\)) in the spring of 2000 is approximately 30% of the annual mean cooling rates of the mixed layer due to the vertical entrainment and the net surface heat flux, which were individually estimated to be approximately \(0.13\) \({^{\circ }{\text {C}}}\,{\text {month}}^{1}\) by Qu (2003); thus, the variation in the upward advection of the deep cold water due to the vertical velocity variation has a critical influence on the QD upperocean temperature variation.
4 Discussion
4.1 Relation of the upward current cooling to the PDO
The temperature variation obtained by temporally integrating the anomaly of \({\partial }T_{w}/{\partial }t\), i.e., \(T^{\prime }_{w} = \int {\partial }T^{\prime }_{w}/{\partial }t \,{\text {d}}t\), exhibits a smooth QD variation with peaks in 1999 and 2006 (blue line in Fig. 14c). These peaks largely coincide with those of the QD SST variation in the North Pacific warm pool region calculated by Nagano et al. (2016a) in the region marked by the dotted square (Figs. 6, 7, 10a, 11a) and the troughs of the QD variation in the net heat transport examined by Nagano et al. (2016a). The peak values of \(T^{\prime }_{w}\) reach or exceed 0.2 \(^{\circ }\)C, which is equivalent to the regression slope in the western tropical North Pacific with a lag of 3 years as illustrated in Fig. 1d. \(T^{\prime }_{w}\) inversely varies leading 34 months to the PDO index with a significant correlation coefficient of \(0.65\) (Table 1). The leading time (34 months) is a little longer than a leading time (2 years) of the variation in coral nitrogen isotope concentrations in Tatsukushi Bay, Japan, a possible proxy for the surface Kuroshio water temperature, to the PDO index reported based on 36year time series by Yamazaki et al. (2016). The discrepancy (8 months) between the time leads of this study and that of Yamazaki et al. (2016) can be explained by the advection time of approximately 9 months evaluated by Nagano et al. (2014, 2017) from the northward propagation of sea surface salinity variation along the western subtropical North Pacific.
Therefore, 6–8 years after the variation in the volume transport in the isopycnal layer of 25.0–25.5 \({\sigma }_{\theta }\) in the subtropical gyre (Nagano et al. 2016a), the vertical volume transport in the region east of the Philippines (\(10^{\circ }\)–\(17^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\)) changes and it modifies the upperocean temperature through the vertical advection of the cold deep water to the upper ocean. Considering the horizontal advection time scale of water masses in the subtropical gyre interior region (\({\sim }10\) years) (e.g., Sasaki et al. 2010), the time lag between the variations in the vertical volume transport to the east of the Philippines and the volume transport in an isopycnal layer of 25.0–25.5 \({\sigma }_{\theta }\) (6–8 years) appears to be reasonable.
The relations of the upwelling strength in the NEC to the positive and negative phases of the PDO are summarized in Fig. 17a, b, respectively. In the positive phase of the PDO, the subtropical gyre interior flow in the isopycnal layer between 25.0 and 25.5 \({\sigma }_{\theta }\) is strengthened (Fig. 17a). The strengthened interior flow disturbance proceeds southwestward and, 6–8 years after, reaches the east of the Philippines, and cools the upper ocean in the western tropical North Pacific (Fig. 17b), leading to the negative phase of the PDO.
The excessive accumulated heat in the western tropical North Pacific during approximately 4 years by the attenuation of the vertical velocity is considered to be advected northward by the Kuroshio (Fig. 17a) as inferred from the extension of the positive regression slope (high SST after the positive PDO phase) in the western subtropical Pacific (Fig. 1d). Note that the high SST anomaly is attributable not to the positive PDO phase 3–4 years before but to the negative PDO phase approximately 10 years before. The anomalously warm water is transported to the midlatitude region of the North Pacific by the Kuroshio and the Kuroshio Extension, yielding the next negative phase of the PDO. The strengthening (weakening) of the upward transport in Region A in the negative (positive) phase of the PDO is critically related to the reversal of the PDO phase. Note that, in the region east of the Philippines, there are mixed layer depth and barrier layer thickness variations (Sato et al. 2004; Nagano et al. 2018), which may change responses of SST to the surface heat flux and vertical current variations but cannot be accounted in this study. In particular, it is unclear how the barrier layer thickness varies and affects the SST variation. Future comprehensive studies are necessary to better understand the SST variation in the western North Pacific related to the reversal of the PDO phase.
4.2 Effect of vertical eddy diffusion
The upward transport in Region A can be attributed to both the adiabatic and diabatic processes. The adiabatic process contributes to the upward transport through the upward displacement of the main pycnocline (around \(10^{\circ }\)N latitude) associated with the intensification of the northern part of the NEC. On the other hand, the isopycnal layer of 25.0–25.5 \({\sigma }_{\theta }\), where Nagano et al. (2016a) found the QD volume transport variation in the subtropical gyre interior region, does not reach the mixed layer base (\(<100\) m depth) (de Boyer Montégut et al. 2004) in Region A, although the upward velocity in this region extends from 250 to 100 m in all seasons (Figs. 8, 9). The mechanism of the water mass transport across the isopycnal surfaces, i.e., the diabatic process, is considered to be of importance in the process that the subsurface watermass volume fluctuations are entrained into the sea surface mixed layer.
In this subsection, we examine whether or not the eddy diffusive mechanism is important, by estimating the order of the magnitude of diapycnal eddy diffusivity from the AGEM potential density and vertical velocity. Assuming that the vertical advection is balanced with the diapycnal diffusion:
where \(K_{\rho }\) is diapycnal eddy diffusivity (e.g., Pedlosky 1987b), we can estimate \(K_{\rho }\). On the basis of the AGEMbased potential density, we set \({\partial }{\rho }/{\partial }z\) and \({\partial }^2{\rho }/{\partial }z^{2}\) to \(1.0 \times 10^{2}\) \({\text {kg\,m}}^{4}\) and \(1.0 \times 10^{4}\) \({\text {kg\,m}}^{5}\), respectively, and for \(w=\) \(1.0 \times 10^{6}\) \({\text {m\,s}}^{1}\), \(K_{\rho }\) is estimated to be \(1.0 \times 10^{4}\) \({\text {m}}^{2}\,{\text {s}}^{1}\). Considering that the isopycnals can move adiabatically in the real ocean, this value is considered to be the upper limit of the diffusivity that should be observed.
Whalen et al. (2012) estimated global distribution of diapycnal eddy diffusivity based on Argo hydrographic data obtained in the period from 2006 to 2011, during which La Niña events frequently occurred. The estimated value of \(K_{\rho }\) in the present study is of the same order of magnitude as globally averaged diffusivity at a depth of 200 m (Fig. S.5a of Whalen et al. 2012) calculated by Whalen et al. (2012). Note that the vertical transports are relatively large (black and green lines in Fig. 14a) in Whalen et al.’s study period (2011–2016). There exists noticeably enhanced diffusivity in a layer from 250 to 500 m depths over the rough ocean bottom in the western tropical North Pacific (Figs. 2a and S.1 of Whalen et al. 2012), which covers Region A. Even though the diapycnal process is not accounted for in the Pvector inverse method, the enhancement of the eddy diffusivity in La Niña condition by the rough bottom topography is consistent with the La Niñarelated strengthening of the vertical velocity estimated in this study. Future observations of vertical eddy diffusion by microstructure profilers in addition to CTD data in both phases of the ENSO may be useful to clarify the relationship between the vertical velocity in Region A and the bottom topography.
4.3 Vertical currents by the Ekman current divergence near sea surface
Note that the vertical currents near the sea surface cannot be studied by the Pvector method because they are considered to be strongly affected by the Ekman currents. Near the sea surface in the tropical (south of \(13^{\circ }\)N) and subtropical (north of \(13^{\circ }\)N) western North Pacific, the winddriven Ekman currents are divergent and convergent, causing upwelling and downwelling, respectively (e.g., Fig. 7 of Tozuka et al. 2002). The upward velocity induced by the Ekman currents is particularly strong in winter and reaches the order of \(10^{6}\) \({\text {m\,s}}^{1}\). Although the thickness of the Ekman layer is approximately 20 m even in the tropical North Pacific (Fig. 4 of Ralph and Niiler 1999), which is remarkably less than 100 m depth, the upward current at 100 m depth in the western tropical region may be strengthened by the Ekman upwelling.
5 Conclusions
Prior to the negative (positive) phase of the PDO, the SST in the western tropical North Pacific is heated (cooled). The SST variation is suggested to be related to the QDtimescale (8–12 years) variation in the volume transport in the subsurface layer of 25.0–25.5 \({\sigma }_{\theta }\) (Nagano et al. 2016a). To reveal where the QD variation in the volume transport in the subsurface layer emerges in the western tropical North Pacific and how great the impact of the variation on the upperocean temperature is, we examined the spatiotemporal characteristics of the 3D field of vertical velocity. By the areawise AGEM method, we estimated 3D potential temperature and salinity fields in the region of \(10^{\circ }\)S–\(25^{\circ }\)N, \(120^{\circ }\)E–\(180^{\circ }\) during the period from January 2001 to May 2017 using altimetric SSH data and CTD profile data collected by Argo floats. The 3D potential density field was computed from the AGEMderived potential temperature and salinity. The streamfunction in the top 1000 dbar calculated from the AGEMderived potential density is consistent with previous studies.
Applying the Pvector inverse method to the 3D AGEMderived potential density field, we obtained the timevarying 3D vertical velocity field. Vertical velocity is observed in a layer down to a depth of approximately 350 m. Associated with the seasonal westward shrinkage of the MD in boreal winter and spring, the westward flowing NEC approaches to the NECC and wavy patterns of the streamfunction and corresponding vertical velocity fluctuations are generated between the currents possibly through the barotropic instability due to the enhanced horizontal shear. Furthermore, the regionalscale upward current was found in the NEC, \(10^{\circ }\)–\(17^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\), called Region A in this paper. In this region, the southward shoaling of the isopycnal surfaces between 25.0 and 26.5 \({\sigma }_{\theta }\) produces a mean substantial upward velocity of approximately \(1 {\times } 10^{6}\) \({\text {m\,s}}^{1}\) at a depth of 100 m depth. Possibly in addition to the Ekman upwelling in the western tropical North Pacific, the upward current driven by the southward current impinged on the isopycnal slope advects the deep cold water to the upper ocean.
To examine the spatiotemporal characteristics, the interannual to decadal variations in the vertical velocity were decomposed by the EOFs. The first and second EOF modes represent the ENSOtimescale enhancement (attenuation) of the upward velocity at 100 m depth in the region of \(10^{\circ }\)–\(17^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\) (Region A) with peaks approximately 8 and 1 months after La Niña (El Niño) events, explaining 66% and 11% of the total variance, respectively. Furthermore, due to frequent occurrences of La Niña (El Niño) events, the upward velocity was observed to vary on the longer timescale, i.e., the QD timescale. The volume and temperature transports of the vertical velocity in the region of \(10^{\circ }\)–\(17^{\circ }\)N, \(140^{\circ }\)E–\(180^{\circ }\) vary on the QD timescale. The maxima of the volume and temperature transports are estimated to be approximately 1.5 Sv and 150 TW in the La Niña year of 2008, respectively. At this time, the upward current reached the maximum exceeding \(1 {\times } 10^{6}\) \({\text {m\,s}}^{1}\) in Region A, being forced by the southward current greater than 1 \({\text {cm\,s}}^{1}\) impinged on the sloping isopycnal surfaces.
The cooling effect of the upper ocean (\(<100\) m depth) due to the upward current advection was examined. The attenuation (intensification) of the upward transport in the positive (negative) phase of the PDO keeps the upperocean temperature tendency, i.e., temporal change in the temperature, anomalously high (low) approximately 6–8 years after the decrease (increase) in the volume transport in an isopycnal layer of 25.0–25.5 \({\sigma }_{\theta }\) in the subtropical gyre interior region, as reported by Nagano et al. (2016a), and increase (decrease) SST in the western tropical North Pacific, as schematically illustrated, respectively, for the positive and negative PDO phases in Fig. 17. The decreases in the vertical volume transport cause the upperocean temperature increase by 0.2 \(^{\circ }\)C or greater, which is equivalent to the SST change associated with the PDO. The QD variation in the vertical velocity in Region A can be critically related to the reversal of the PDO phase through the heat transport of the Kuroshio. This result, as well as atmospheric teleconnections from the tropical region (Alexander et al. 2002), suggests that there is a significant oceanic link connecting the mid and highlatitude regions to the tropical region.
Availability of data and materials
Qualitycontrolled Argo profile data (Advanced automatic QC Argo Data version 1.2) were provided in the Japan Argo database (ftp://ftp2.jamstec.go.jp/pub/argo/). Daily altimetric SSH data and monthly NOAA Optimum Interpolation SST V2 data were produced by the EU Copernicus Marine Environment Monitoring Service (CMEMS) (ftp://my.cmemsdu.eu/) and the US NOAA/OAR/ESRL (https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html).
Abbreviations
 3D:

Threedimensional
 AGEM:

Altimetrybased gravest empirical mode
 CTD:

Conductivity–temperature–depth
 ENSO:

El NiñoSouthern Oscillation
 EOF:

Empirical orthogonal function
 MOAA GPV:

Grid point value of the Monthly Objective Analysis using the Argo
 MD:

Mindanao Dome
 NEC:

North Equatorial Current
 NECC:

North Equatorial Countercurrent
 NOAA:

US National Oceanic and Atmospheric Administration
 PDO:

Pacific decadal oscillation
 QD:

Quasidecadal
 RMS:

Root mean square
 SSH:

Sea surface height
 SST:

Sea surface temperature
References
Akima H (1970) A new method of interpolation and smooth curve fitting based on local procedures. J Assoc Comput Mach 17:589–602
Alexander MA, Bladé I, Newman M, Lanzante JR, Lau NC, Scott JD (2002) The atmospheric bridge: the influence of ENSO teleconnections on airsea interaction over the global oceans. J Clim 15:2205–2231
Anderberg MR (1973) Cluster analysis for applications: probability and mathematical statistics. Academic Press, New York
Bryden HL, Imawaki S (2001) Ocean heat transport. In: Siedler H, Church J, Gould J (eds) Ocean circulation and climate: observing and modeling the global ocean. Academic Press, London, pp 455–474
Chu PC (1995) Pvector method for determining absolute velocity from hydrographic data. Mar Technol Soc J 29(3):3–14
Chu PC (2006) Pvector inverse method. Springer, Berlin. https://doi.org/10.1007/9783540333869
de Boyer Montégut C, Madec G, Fischer AS, Lazar A, Iudicone D (2004) Mixed layer depth over the global ocean: an examination of profile data and a profilebased climatology. J Geophys Res. https://doi.org/10.1029/2004JC002378
Deser C, Alexander MA, Timlin MS (1996) Upperocean thermal variations in the North Pacific during 1970–1991. J Clim 9:1840–1855
Fine RA, Lukas R, Bingham FM, Warner MJ, Gammon RH (1994) The western equatorial Pacific: a water mass crossroads. J Geophys Res 99(C12):25063–25080. https://doi.org/10.1029/94JC02277
Flament P (2002) A state variable for characterizing water masses and their diffusive stability: spiciness. Prog Oceanogr 54:493–501
Gill AE (1982) Atmosphereocean dynamics. Academic Press, London
Gu D, Philander SGH (1997) Interdecadal climate fluctuations that depend on exchange between the tropics and extratropics. Science 275:805–807. https://doi.org/10.1126/science.275.5301.805
Hasegawa T, Hanawa K (2003) Decadalscale variability of upper ocean heat content in the tropical Pacific. Geophys Res Lett. https://doi.org/10.1029/2002GL016843
Hasegawa T, Ando K, Ueki I, Mizuno K, Hosoda S (2013) Upperocean salinity variability in the tropical Pacific: case study for quasidecadal shift during the 2000s using TRITON buoys and Argo floats. J Clim 26:8126–8138. https://doi.org/10.1175/JCLID1200187.1
Hosoda S, Ohira T, Nakamura T (2008) A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations. JAMSTEC Rep Res Dev 8:47–59
Kashino Y, Ishida A, Hosoda S (2011) Observed ocean variability in the Mindanao Dome region. J Phys Oceanogr 41:287–302. https://doi.org/10.1175/2010JPO4329.1
Kawai Y, Doi T, Tomita H, Sasaki H (2008) Decadalscale changes in meridional heat transport across 24°N in the Pacific Ocean. J Geophys Res. https://doi.org/10.1029/2007JC004525
Killworth PD (1983) Absolute velocity calculations from single hydrographic sections. DeepSea Res 30(5A):513–542
Killworth PD (1986) A Bernoulli inverse method for determining the ocean circulation. J Phys Oceanogr 16:2031–2051
Latif M, Barnett TP (1994) Causes of decadal climate variability over the North Pacific and North America. Science 266:634–637
Latif M, Barnett TP (1996) Decadal climate variability over the North Pacific and North America: dynamics and predictability. J Clim 9:2407–2423
Mantua NJ, Hare SR (2002) The Pacific decadal oscillation. J Oceanogr 58:35–44
Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteorol Soc 78(78):1069–1079
Nagano A, Ichikawa K, Ichikawa H, Konda M, Murakami K (2009) Synoptic flow structures in the confluence region of the Kuroshio and the Ryukyu Current. J Geophys Res. https://doi.org/10.1029/2008JC005213
Nagano A, Ichikawa K, Ichikawa H, Tomita H, Tokinaga H, Konda M (2010) Stable volume and heat transports of the North Pacific subtropical gyre revealed by identifying the Kuroshio in synoptic hydrography south of Japan. J Geophys Res. https://doi.org/10.1029/2009JC005747
Nagano A, Ichikawa H, Yoshikawa Y, Kizu S, Hanawa K (2012) Variation of the southward interior flow of the North Pacific subtropical gyre, as revealed by a repeat hydrographic survey. J Oceanogr 68(2):361–368. https://doi.org/10.1007/s1087201201023
Nagano A, Uehara K, Suga T, Kawai Y, Ichikawa H, Cronin MF (2014) Origin of nearsurface highsalinity water observed in the Kuroshio Extension region. J Oceanogr 70:389–403. https://doi.org/10.1007/s1087201402375
Nagano A, Kizu S, Hanawa K, Roemmich D (2016a) Heat transport variation due to change of North Pacific subtropical gyre interior flow during 1993–2012. Ocean Dyn 66(12):1637–1649. https://doi.org/10.1007/s1023601610072
Nagano A, Wakita M, Watanabe S (2016b) Dichothermal layer deepening in relation with halocline depth change associated with northward shrinkage of North Pacific western subarctic gyre in early 2000s. Ocean Dyn 66(2):163–172. https://doi.org/10.1007/s1023601509178
Nagano A, Hasegawa T, Ueki I, Ando K (2017) El NiñoSouthern Oscillationtime scale covariation of sea surface salinity and freshwater flux in the western tropical and northern subtropical Pacific. Geophys Res Lett 44:6895–6903. https://doi.org/10.1002/2017GL073573
Nagano A, Ueki I, Hasegawa T, Ando K (2018) Oceanatmosphere observations in Philippine Sea by moored buoy. In: OCEANS  MTS/IEEE Kobe TechnoOcean (OTO), Kobe, Japan, pp 1–6. https://doi.org/10.1109/OCEANSKOBE.2018.8558886
Needler GT (1985) The absolute velocity as a function of conserved measurable quantities. Prog Oceanogr 14:421–429
Nitani H (1972) Beginning of the Kuroshio. In: Stommel H, Yoshida K (eds) Kuroshioits physical aspects. University of Tokyo Press, Tokyo, pp 129–163
Olbers DJ, Wenzel M, Willebrand J (1985) The inference of North Atlantic circulation patterns from climatological hydrographic data. Rev Geophys 23(4):313–356
Pedlosky J (1987a) Geophysical fluid dynamics, 2nd edn. Springer, New York. https://doi.org/10.1007/9781461246503
Pedlosky J (1987b) Thermocline theories. In: Abarbanal HDI, Young WR (eds) General circulation of the ocean. Springer, Berlin, pp 55–88
Pujol MI, Mertz F (2020) Product user manual for sea level SLA products. CMEMS, CMEMS
Qu T (2003) Mixed layer heat balance in the western North Pacific. J Geophys Res. https://doi.org/10.1029/2002JC001536
Qu T, Lukas R (2003) The bifurcation of the North Equatorial Current in the Pacific. J Phys Oceanogr 33:5–18
Qu T, Mitsudera H, Yamagata T (1998) On the western boundary currents in the Philippine Sea. J Geophys Res 103(C4):7537–7548
Ralph EA, Niiler PP (1999) Winddriven currents in the tropical Pacific. J Phys Oceanogr 29(9):2121–2129
Reid JL (1997) On the total geostrophic circulation of the Pacific Ocean: flow patterns, tracers, and transports. Prog Oceanogr 39:263–352
Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625
Richards KJ, Natarov A, Firing E, Kashino Y, Soares SM, Ishizu M, Carter GS, Lee JH, Chang KI (2015) Sheargenerated turbulence in the equatorial Pacific produced by small vertical scale flow features. J Geophys Res 120:3777–3791. https://doi.org/10.1002/2014JC010673
Rintoul SR, Sokolov S, Church J (2002) A 6 year record of baroclinic transport variability of the Antarctic Circumpolar Current at 140°E derived from expendable bathythermograph and altimeter measurements. J Geophys Res. https://doi.org/10.1029/2001JC000787
Rio MH, Guinehut S, Larnicol G (2011) New CNESCLS09 global mean dynamic topography computed from the combination of GRACE data, altimetry, and in situ measurements. J Geophys Res. https://doi.org/10.1029/2010JC006505
Sasaki YN, Schneider N, Maximenko N, Lebedev K (2010) Observational evidence for propagation of decadal spiciness anomalies in the North Pacific. Geophys Res Lett. https://doi.org/10.1029/2010GL042716
Sato K, Suga T, Hanawa K (2004) Barrier layer in the North Pacific subtropical gyre. Geophys Res Lett. https://doi.org/10.1029/2003GL018590
Schmitz WJ Jr (1995) On the interbasinscale thermohaline circulation. Rev Geophys 33(2):151–173. https://doi.org/10.1029/95RG00879
Schneider N, Miller AJ, Alexander MA, Deser C (1999) Subduction of decadal North Pacific temperature anomalies: observations and dynamics. J Phys Oceanogr 29:1056–1070
Stommel H, Schott F (1977) The beta spiral and the determination of the absolute velocity field from hydrographic station data. DeepSea Res 24:325–329
Sun C, Watts DR (2001) A circumpolar gravest empirical mode for the Southern Ocean hydrography. J Geophys Res 106(C2):2833–2855
Swart S, Speich S, Ansorge IJ, Lutjeharms JRE (2010) An altimetrybased gravest empirical mode south of Africa: 1. Development and validation. J Geophys Res. https://doi.org/10.1029/2009JC005299
Talley LD, Pickard GL, Emery WJ, Swift JH (2011) Descriptive physical oceanography: an introduction, 6th edn. Academic Press, Boston. https://doi.org/10.1016/C20090243224
Tanaka Y, Hibiya T (2019) Generation mechanism of tropical instability waves in the equatorial Pacific Ocean. J Phys Oceanogr 49(11):2901–2915. https://doi.org/10.1175/JPOD190094.1
Thomson RE, Emery WJ (2014) Data analysis methods in physical oceanography, 3rd edn. Elsevier, Amsterdam. https://doi.org/10.1016/C20100663620
Tourre YM, Rajagopalan B, Kushnir Y, Barlow M, White WB (2001) Patterns of coherent decadal and interdecadal climate signals in the Pacific Basin during the 20th century. Geophys Res Lett 28(10):2069–2072. https://doi.org/10.1029/2000GL012780
Tozuka T, Kagimoto T, Masumoto Y, Yamagata T (2002) Simulated multiscale variations in the western tropical Pacific: the Mindanao Dome revisited. J Phys Oceanogr 32:1338–1359
Trenberth KE (1997) The definition of El Niño. Bull Am Meteor Soc 78:2771–2777
Ward J (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244. https://doi.org/10.2307/2282967
Whalen CB, Talley LD, MacKinnon JA (2012) Spatial and temporal variability of global ocean mixing inferred from Argo profiles. Geophys Res Lett. https://doi.org/10.1029/2012GL053196
White WB, Tourre YN, Barlow M, Dettinger M (2003) A delayed action oscillator shared by biennial, interannual, and decadal signals in the Pacific Basin. J Geophys Res. https://doi.org/10.1029/2002JC001490
Yamazaki A, Watanabe T, Tsunogai U, Iwase F, Yamano H (2016) A 150year variation of the Kuroshio transport inferred from coral nitrogen isotope signature. Paleoceanography 31:838–846. https://doi.org/10.1002/2015PA002880
Acknowledgements
The 3D hydrographic field estimation by the AGEM method applying to Argo profile data owes a lot to discussion with Prof. Kimio Hanawa and Prof. Shoichi Kizu (Tohoku University). The authors are grateful to the editors, Dr. Michio Kawamiya (JAMSTEC) and Prof. Eiji Ohtani (Tohoku University), and anonymous reviewers for constructive review comments.
Funding
This work was partly supported by the Japan Society for the Promotion of Science, GrantinAid for Scientific Research (Grant Nos. JP15H04228, JP17K05660, JP20K04072, JP20H02236, JP20H04349, JP20KK0097).
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TH currently belongs to Japan Meteorological Agency. He contributed to this study when he belonged to Tohoku University and Hokkaido University. AN proposed the topic, conceived, designed the study, analyzed the data, and constructed the manuscript. TH analyzed the data and collaborated with the corresponding author (AN) in the construction of manuscript. MW discussed the results in cooperation with AN and TH. All authors read and approved the final manuscript.
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Nagano, A., Hasegawa, T. & Wakita, M. Spatiotemporal vertical velocity variation in the western tropical Pacific and its relation to decadal ocean variability. Prog Earth Planet Sci 9, 57 (2022). https://doi.org/10.1186/s40645022005133
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DOI: https://doi.org/10.1186/s40645022005133
Keywords
 Western tropical Pacific
 ENSO
 PDO
 Vertical velocity
 Pvector inverse method
 AGEM