1. Introduction
In the central to southwestern subtropical gyre of the North Pacific Ocean, there are three distinct subtropical fronts (STFs), each identified as a poleward-shoaling temperature and density front above 300 m (Kobashi et al. 2006). According to their relative geographical locations, the three fronts are named the northern STF (NSTF; 22°–25°N, 130°E–180°), the southern STF (SSTF; 19°–21°N, 130°E–180°), and the eastern STF (ESTF; 24°–26°N, 180°–160°W; Xu et al. 2022). Each STF is associated with a subtropical countercurrent (STCC) based on the thermal wind relationship (Uda and Hasunuma 1969; Kobashi and Kubokawa 2012). Changes of the STCC and STF significantly affect the North Pacific climate variability (Xu et al. 2012a,b) and the deep atmospheric convection (Kobashi et al. 2008; Xie et al. 2011). Xu et al. (2022) detected a weakening and poleward-shifting trend of the ESTF from 1980 to 2018, whereas there is no significant change in the NSTF and SSTF in recent decades. Yet it is still unclear whether the STFs have east–west asymmetric variability on multidecadal time scales and, if so, what causes the difference.
Previous studies have suggested that the interannual variability of the STFs is mainly associated with wind forcing (Qiu and Chen 2010), while the decadal to multidecadal variability of the STFs is primarily related to mode waters (Xie et al. 2011; Kobashi et al. 2021; Wu et al. 2022). Here, we focus on multidecadal time scales in the present study, and hence mode water would be more important. Mode water is a thick layer of water with homogeneous properties between the seasonal and permanent thermocline (Hanawa and Talley 2001). Characterized by the low potential vorticity properties, the North Pacific Subtropical Mode Water (STMW; Masuzawa 1969) and the central mode water (CMW; Nakamura 1996; Suga et al. 1997) originate from the winter deep mixed layer in the Kuroshio–Oyashio Extension (KOE) region. After formation, they migrate southwestward via the background mean flow (Liu and Hu 2007) and mesoscale eddies (Xu et al. 2016, 2017). Due to the beta spiral effect (Kubokawa 1997), mode waters of different density classes overlap vertically to the north of STCC/STF. Such thick mode water layers give rise to the northward-shoaling upper pycnocline, leading to the eastward shear and STFs (Kobashi and Kubokawa 2012). Overall, strength of the ESTF is mainly affected by the CMW (Sugimoto et al. 2012), while the NSTF and SSTF [hereinafter western STF (WSTF)] are primarily controlled by the STMW (Kobashi et al. 2021; Wu et al. 2022; Xu et al. 2022).
Based on a simple isopycnal model simulation, Ladd and Thompson (2002) suggested that the decadal variability of CMW is associated with the Pacific decadal oscillation (PDO). A positive phase of the PDO leads to deeper model mixed layers and the formation of denser and thicker CMW, and a negative phase of the PDO leads to shallower model mixed layers and the formation of lighter and thinner CMW. On the other hand, more recently, Wu et al. (2020a,b) found that the multidecadal variability of the STMW is controlled by the Atlantic multidecadal oscillation (AMO). A positive phase of the AMO corresponds to an anomalous warmer and thinner STMW, and a negative phase of the AMO corresponds to an anomalous colder and thicker STMW. The previous findings described above imply a zonally asymmetric variability of the WSTF and ESTF.
To our knowledge, the east–west asymmetric low temporal variability of the STFs remains to be clarified and quantified. Our recent work (Wu et al. 2022, hereinafter WU22) demonstrates that the multidecadal variation of the WSTF is controlled by the AMO-induced STMW variability. The AMO-induced multidecadal anomalies are embedded in the STMW. The STMW formed east of 160°E could propagate southwestward along the thermocline circulation and reach the WSTF region in about 5 yr. During the AMO positive phase the thinner STMW flattens the upper pycnocline, reduces the meridional density gradient, and weakens the WSTF intensity, and, during the AMO negative phase the thicker STMW sharpens the upper pycnocline, enhances the meridional density gradient, and strengthens the WSTF intensity, with both lagging ∼5 yr behind the AMO index. While the emphasis of WU22 was on the AMO-induced WSTF variation, the present study exploits the relationship between the PDO and ESTF from an observational viewpoint and compares the asymmetric variations between the STMW (WSTF) and CMW (ESTF).
The present study demonstrates that the multidecadal variability of the CMW is very different from that of STMW. The PDO controls both the position and strength changes of the CMW. A positive state of the PDO is associated with deeper winter mixed layers in the midlatitude central North Pacific, together with the outcrop area of CMW extending equatorward. Subsequently, the CMW volume increases and its southern edge shifts equatorward, which finally results in the strengthening and southward-migrating ESTF. Conversely, a negative state of the PDO is associated with shallower winter mixed layers in the midlatitude central North Pacific, together with the outcrop area of CMW extending poleward. Subsequently, the CMW volume decreases and its southern edge shifts poleward, which finally results in the weakening and northward-migrating ESTF. Our study is for the first time to systematically investigate the different dominant factors for the multidecadal variability of the WSTF and ESTF, with the former controlled by the AMO via the STMW changes and the latter controlled by the PDO via the CMW changes.
The rest of the paper is organized as follows: section 2 describes the data and methods used in this study. Section 3 presents major results. Section 4 is a summary with discussions.
2. Data and methods
a. Data
In the present study, we use three observational and one oceanic reanalysis data to examine the east–west asymmetric changes of the mode water and STFs on multidecadal time scales. The three observational products are Ishii data (Ishii et al. 2017), the Institute of Atmospheric Physics, Chinese Academy of Sciences data (hereinafter IAP data; Cheng et al. 2016), and the gridded Argo data from the International Pacific Research Center (hereinafter IPRC Argo data; http://apdrc.soest.hawaii.edu/projects/argo/). The Ishii, IAP, and IPRC Argo datasets all have a horizontal resolution of 1° × 1°. The Ishii data (version 7.3) have 28 levels in the upper 3000 m from 1955 to 2020. The IAP data have 41 vertical levels in the upper 2000 m from 1940 to present. The monthly climatological IPRC Argo data have 27 vertical levels in the upper 2000 m, which are mainly used to identify the mean distribution of STMW, CMW, and STFs because of their short time span. The reanalysis product is from the global Simple Ocean Data Assimilation, version 2 (SODA v2.2.4; Carton and Giese 2008), which relies on the Parallel Ocean Program (POP) physics ocean model and assimilates the ocean observational data from 1871 to 2010. The SODA v2.2.4 has monthly mean outputs with horizontal resolution of 0.5° × 0.5° and 40 vertical layers in the upper 5000 m.
The surface heat flux and wind stress fields are based on the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al. 1996). The AMO index is obtained from the sea surface temperature (SST) anomaly averaged over the North Atlantic basin (0°–65°N, 80°W–0°; Enfield et al. 2001). The PDO index is defined as the leading principal component of the monthly SST variability in the North Pacific poleward of 20°N (Mantua et al. 1997).
b. Model
A preindustrial model simulation experiment (PI-Control EXP) is conducted to isolate the anthropogenic forcing and then justify the impact of AMO/PDO in controlling the variability of the WSTF/ESTF. The PI-Control EXP is based on the Community Earth System Model, version 1.06 (CESM; Hurrell et al. 2013) of NCAR. It starts from a standard PI-Control EXP that is based on the Community Climate System Model, version 4, and has been run for 863 yr. We further run the CESM model with the same configuration for another 600 yr and take the last 200 yr of output for analysis. The spatial resolution of the PI-Control EXP is 1° × 1° with 60 vertical levels. Based on the PI-Control EXP, we are able to discuss the AMO and PDO impact on the WSTF and ESTF without disturbance from anthropogenic forcing.
c. Methods
Following Kobashi et al. (2006), the STFs are defined as the maximum meridional gradient of potential density at 125-m depth between 18° and 30°N (Fig. 1). In the present study, we mainly investigate the STF changes in May, when the front has the greatest atmospheric effects (Xu et al. 2022). Specifically, the ESTF strength is defined as the area-averaged (180°–150°W, 24°–30°N) meridional density gradient at 125-m depth, while the ESTF position is calculated from the averaged position where the meridional density gradient at 125-m depth is larger than 1.1 × 10−6 kg m−4 within 180°–150°W, 24°–30°N.
Mean geographical position of the STFs from (a) Argo observations, (b) Ishii, (c) IAP, (d) SODA, and (e) PI-Control EXP. Centers of the three STFs (black circles) are identified as the maximum meridional density gradient at 125-m depth in May (color shading; 10−6 kg m−4) based on the ensemble mean field of (a)–(d). To compare the east–west asymmetric multidecadal variability of the STFs, the present study refers to the NSTF and SSTF as WSTF (solid-black-outlined rectangle). As a comparison, the general location of the ESTF is shown in the dashed-black-outlined rectangle.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
In the present study, the STMW is characterized by a layer of PV < 2.5 × 10−10 m−1 s−1, with potential density between 25.0 and 25.5 kg m−3 (left panels of Fig. 2). The CMW is defined as a layer of PV < 2.0 × 10−10 m−1 s−1, with potential density between 25.7 and 26.4 kg m−3 (Suga et al. 2004, 2008; Oka and Qiu 2012; right panels of Fig. 2). The mixed layer depth (MLD) is defined as where the potential density is 0.03 kg m−3 greater than that at the 10-m depth (Xu et al. 2012b).
The climatological distribution of the (left) STMW and (right) CMW, represented by their thickness (color shading; m) from (a),(b) Argo observations; (c),(d) Ishii; (e),(f) IAPl; (g),(h) SODA; and (i),(j) PI-Control EXP. The general locations of the WSTF and ESTF are superimposed as solid- and dashed-black-outlined rectangles, respectively, as illustrated in Fig. 1. Note the different colorbars for the STMW and CMW.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
3. Results
a. The zonally asymmetric variations of STMW and CMW
To illustrate the mean geographical position of the STFs, Fig. 1 presents the meridional potential density gradient at 125-m depth. According to the ensemble mean of the IPRC Argo, Ishii, IAP, and SODA results (Figs. 1a–d), we mark the maximum meridional density gradient in black dots as the central location of the three STFs. The central location of STFs (black dots in Figs. 1a–d) agrees well with the classic picture (Kobashi et al. 2006) and is well represented in the two oceanic objective analysis products (Ishii and IAP; Figs. 1b,c) and the SODA reanalysis data (Fig. 1d). The ESTF is found in the central Pacific around 26°N from 180° to 150°W; the NSTF (SSTF) is shown around 22°–25°N (19°–21°N) between 130°E and 180°, tilting slightly to the north as it extends to the east. As we defined in the introduction, the NSTF and SSTF are collectively referred to as WSTF (black rectangle in Figs. 1 and 2). While the ESTF (black dashed rectangle in Figs. 1 and 2) is much weaker and narrower in the meridional direction than the WSTF, its climate effects are as important as the WSTF’s, such as the strong impacts on the deep atmospheric convection and precipitation northwest of Hawaii (Xie et al. 2011).
The spatial pattern of the horizontal density gradient in IPRC Argo (Fig. 1a) is patchy as a result of the active eddy activities (Qiu 1999) that are well captured by the Argo data, but we could still identify the three STFs. The meridional density gradients in Ishii, IAP, and SODA data are much smoother than that in IPRC Argo since the eddy activities cannot be well represented by these products (Wu et al. 2021). (Note that the current study does not involve eddy effects when we investigate the multidecadal variability of the STFs based on the Ishii, IAP, and SODA data in the following.) Consistent with the observational and reanalysis results (Figs. 1a–d), the PI-Control EXP simulates a slanted STF (Fig. 1e) from the western North Pacific (20°N, 130°E) to the north of Hawaii (25°N, 160°W). However, due to the relatively coarse resolution of the PI-Control EXP, the model can barely capture the observed structure of the WSTF that splits into two distinct fronts (i.e., the NSTF and SSTF). The simulated WSTF continues along a northeast-slanted path (24°–28°N, 180°–160°W), confirming the ESTF observed north of Hawaii.
Figure 2 presents the mean thickness of STMW (left panels) and CMW (right panels) based on IPRC Argo, Ishii, IAP, SODA, and the PI-Control EXP. The spatial distribution is generally consistent with the classic picture (Suga et al. 2004; Oka and Suga 2005; Oka and Qiu 2012). The STMW is distributed in the northwestern subtropical gyre over 130°E–180°, 25°–35°N. The main body of CMW is found in the central region of the northern subtropical gyre over 160°E–150°W, 30°–40°N. Even though the IPRC Argo, Ishii, and IAP are all based on observations, apparent differences of the mode water thickness still exist among these products (Figs. 2a–f). Significant differences have also been found in the global ocean heat content analyses among these products (e.g., Boyer et al. 2016; Liang et al. 2021). Those differences may be generally attributed to the different mapping methods, in which smoothing is performed over different length scales. For example, the smoothing length scale for IAP is 20° within 0–700 m (Cheng et al. 2016), while that for the IPRC Argo is only 2°–3° (http://apdrc.soest.hawaii.edu/projects/Argo/data/Documentation/gridded-var.pdf). For larger smoothing length scales, small-scale ocean features such as eddies are smoothed out more thoroughly. The STMW and CMW are much thicker in IAP and Ishii with larger spatial smoothing length scales (9°–20°) and lower eddy signals, comparing with that in IPRC Argo of smaller spatial smoothing length scale (2°–3°) and higher eddy signals. Besides, the eddy-permitting SODA results (Figs. 2g,h) also display a thicker mode water layer because of the weak eddy dissipation (Nishikawa et al. 2010; Xu et al. 2014). In the non-eddy-resolving PI-Control EXP results (Figs. 2i,j), the STMW and CMW is thickest and extends much farther southward (especially for the CMW).
Previous studies (Kobashi et al. 2006; Xu et al. 2012b; Wu et al. 2022; Xu et al. 2022) have related the mode water thickness to the STCC/STF formation. The thick mode water layer causes the upper pycnocline to steeply rise, forming a density front (i.e., STF) and a surface-intensified STCC by thermal wind (Fig. 1b in Xu et al. 2022). From Fig. 2, it can be readily seen that the WSTF is anchored at the southern edge of the thick STMW, while the ESTF is anchored by the thick CMW, suggesting a close relationship between STMW (CMW) and WSTF (ESTF; Sugimoto et al. 2012; Kobashi et al. 2021). Although the PI-Control EXP cannot perfectly simulate the fine-scale structure of the STFs, it could well represent the general location of the STFs and mode waters (Figs. 1, 2) as well as the dynamical role of mode waters in the STF multidecadal variability as shown in the following section.
The multidecadal variability of the STMW (CMW) is mainly controlled by the AMO (PDO). We regress the 7-yr low-pass-filtered surface wind stress and SST fields upon the similarly filtered normalized PDO and AMO index (Fig. 3). We find that the AMO induces a strong easterly (southerly) wind anomaly on the eastern (western) part of the STMW formation region (marked as a black dashed rectangle in Fig. 3a). Either the easterly wind anomaly or the southerly wind anomaly can cause an anomalous Ekman transport of warm water, with anomalous warm SST found in the whole STMW formation region (Fig. 3a). The warm advection increases the vertical stratification in the STMW formation region and finally reduces the STMW volume (Fig. 4a; note the phase of AMO index is reversed here). By contrast, the PDO-induced westerly wind and cold SST anomalies are mainly confined in the central Pacific Ocean (30°–40°N, 170°E–150°W), where the CMW forms and exists (Fig. 3b). The westerly wind anomaly in the central eastern Pacific during the PDO positive phase enhances the surface heat loss and drives a southward Ekman transport of cold water (Fig. 3b), which would decrease the vertical stratification in the CMW formation region and increase the CMW volume (Fig. 4b).
(a) AMO- and (b) PDO-induced surface wind stress (vectors; N m−2) and sea surface temperature (color shading; °C) anomalies. Here, the 7-yr low-pass-filtered wind stress and sea surface temperature from 1948 to 2012 are regressed upon the similarly filtered normalized AMO [in (a)] and PDO [in (b)] indices. Gray dots indicate significance at the 95% confidence level in terms of Student’s t test. The dashed-black-outlined rectangles in (a) and (b) denote the STMW and CMW formation region, according to Oka et al. (2019) and Oka and Qiu (2012), respectively.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
(left) Time series of the normalized STMW volume anomaly (integrated close to its formation region, i.e., north of 28°N; black dashed lines) and the AMO index (red solid lines); (right) time series of the normalized CMW volume anomaly (black dashed lines) and the PDO index (red solid lines) based on (a),(b) Ishii; (c),(d) IAP; (e),(f) SODA; and (g),(h) PI-Control EXP. Note that the y axis for the AMO index in the left panels is reversed to better display. Thin lines indicate the original results, and thick lines are smoothed by a 7-yr low-pass filter. The STMW volume is defined as the area-integrated STMW thickness between 130° and 175°E and between 28° and 35°N, and the CMW volume is defined as the area-integrated CMW thickness between 150°E and 130°W and between 25° and 40°N.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
Furthermore, we conduct the time series of the anomalous STMW and CMW volume based on Ishii, IAP, SODA, and PI-Control EXP (black dashed lines in Fig. 4), with the AMO index (red solid in left panels of Fig. 4) and PDO index (red solid in left panels of Fig. 4) superimposed. Both the STMW and CMW volume exhibits clear multidecadal variability. The STMW volume fluctuates coherently with the AMO (Fig. 4a; r = −0.67 for Ishii data, r = −0.79 for IAP, r = −0.74 for SODA, and r = 0.71 for PI-Control EXP; all pass the 95% confidence level; note that the y axis for the AMO index is reversed in the left panels of Fig. 4 for a better display). It indicates that the STMW volume decreases during the AMO positive phase and increases during the AMO negative phase, which is consistent with the results from Wu et al. (2020b). Here, the STMW volume in Fig. 4a is integrated close to its formation region (i.e., north of 28°N) and is thus synchronous with the AMO index. The southern STMW volume (integrated south of 26°N just underneath the WSTF) lags the AMO index by ∼5 yr (Fig. 5 in WU22). While the STMW formed west of 160°E is mostly trapped locally, the STMW formed east of 160°E could carry the AMO-induced temperature anomaly, propagate along the recirculation gyre, and take ∼5 yr to reach the WSTF region (Fig. 7 in WU22; refer to WU22 for more details).
On the other hand, the CMW volume is closely associated with the PDO (Fig. 4b; r = 0.63 for Ishii, r = 0.70 for IAP, r = 0.78 for SODA, and r = 0.62 for PI-Control EXP; all pass the 95% confidence level). The CMW volume increases during the PDO positive phase and decreases during the PDO negative phase. We further use the multidata ensembled time series of the PDO and the CMW volume (Fig. 4) for a lead–lag correlation analysis (Fig. 5a). The time series from Ishii, IAP, SODA, and PI-Control EXP are joined together as an ensemble. The correlation when the PDO leads CMW by 1–3 yr reaches the maximum (r = 0.70; pass the 95% confidence level). Whereas the AMO-induced anomalies reach the WSTF region by ∼5 yr (Wu et al. 2022), the PDO-induced CMW anomalies modulate the ESTF region in 1–3 yr. This result may be because of the different distances between the PDO loading area (30°–35°N) and the ESTF location (25°–30°N) versus between the AMO loading area (35°–40°N) and the WSTF location (20°–22°N) (Figs. 1–3).
A lead–lag correlation (a) between CMW volume and PDO and (b) between CMW volume and ESTF strength. Here, the time series from Ishii, IAP, SODA, and PI-Control EXP are all joined together as an ensemble. The blue dash–dotted lines indicate the 95% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
While the multidecadal variability of STMW is mainly controlled by AMO, the decadal variability of STMW is dominated by PDO and the resultant decadal variability of the Kuroshio Extension system (Oka et al. 2015, 2019). We briefly compare the decadal and multidecadal components of the AMO-induced STMW and PDO-induced CMW variability (Tables 1, 2) based on the standard deviation of the low-pass (>20 yr for multidecadal scale) and bandpass (7–20 yr for decadal scale) filtered time series. On average, the multidecadal variability of the STMW accounts for 56% ± 16% of the total variance, as compared with 18% ± 7% for the decadal component (Table 1); the multidecadal variability of the CMW accounts for 55% ± 15% of the total variance, as compared with 20% ± 8% for the decadal component (Table 2). Here, the uncertainties are estimated as the standard deviation of the four datasets (Ishii, IAP, SODA, and PI-Control EXP). Note that in the present study we mainly analyze the multidecadal variability of STMW and CMW. As aforementioned, WU22 has demonstrated the relationship between WSTF and AMO via the multidecadal variability of the STMW (please refer to WU22 for more details). In the next section, we mainly focus on the relationship between PDO and ESTF via the CMW variability on multidecadal time scales.
Decadal and multidecadal components of the AMO-induced STMW variability.
Decadal and multidecadal components of the PDO-induced CMW variability.
b. Mechanism for the multidecadal variability of the ESTF
A lead–lag correlation analysis of Fig. 5 illustrates the close relationship among the PDO, the CMW volume, and the ESTF intensity. We get lag-4 (0.69), lag-3 (0.70), lag-2 (0.70), lag-1 (0.70), and lag-0 (0.69) correlations (lag-x means that CMW lags PDO by x years) between the CMW volume and the PDO as well as lag-4 (0.63), lag-3 (0.67), lag-2 (0.71), lag-1 (0.73), and lag-0 (0.73) correlations (lag-x means that ESTF lag CMW by x years) between the ESTF strength and the CMW volume. All of these correlation coefficients have passed the 95% confidence level. The correlation when the PDO leads CMW by 1–3 yr reaches the maximum (Fig. 5a), while the CMW volume leads the ESTF strength by 0–1 yr reaches the maximum (Fig. 5b), quasi synchronously on multidecadal time scales. Accordingly, we perform a 2-yr lagged composite analysis for the CMW volume (Fig. 6) and ESTF strength (Fig. 7) relative to the PDO index, with ensemble mean of the Ishii, IAP, and SODA. Hereinafter, we take the PDO index > 0.5 and < −0.5 as the positive and negative phase, respectively, in the following analysis.
A 2-yr lagged composite of the CMW thickness (color shading; m) during the (a) positive and (b) negative phase of PDO, with ensemble mean of the Ishii, IAP, and SODA data. Notice that we take the PDO index > 0.5 and < −0.5 as the positive and negative phases, respectively, here. The black solid contour marks the 250-m CMW thickness in the PDO positive phase, and the white dashed contour denotes the 250-m CMW thickness in the PDO negative phase. The averaged center location of CMW for positive phase is marked with a black plus sign, and that for negative phase is marked with a white plus sign. Stippling indicates that the mean during the PDO positive phase differs significantly from that during the PDO negative phase at the 95% confidence level (we use a Student’s t test to reject the null hypothesis of no statistical significance difference between the means).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
A 2-yr lagged comparison of the ESTF strength between the (a) positive and (b) negative phase of the PDO, with ensemble mean of the Ishii, IAP, and SODA data. The ESTF strength is represented by the meridional density gradient at 125-m depth (color shading; 10−6 kg m−4). The black solid contour marks the density gradient of 1.0 × 10−6 kg m−4 in the PDO positive phase, and the white dashed contour denotes the density gradient of 1.0 × 10−6 kg m−4 for the PDO negative phase. The averaged center location of ESTF for the positive phase is marked with a black star, and that for negative phase is marked with a white star. Notice that we take the PDO index > 0.5 and < 0.5 as the positive and negative phases, respectively, here. Stippling indicates that the mean during the PDO positive phase differs significantly from that during the PDO negative phase at the 95% confidence level (we use a Student’s t test to reject the null hypothesis of no statistical significance difference between the means).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
The CMW is much thicker during the PDO positive phase (Fig. 6a) than that during the negative phase (Fig. 6b). The southern rim of the thick CMW shifts equatorward during the PDO positive phase (black solid contours in Fig. 6) in comparison with that during the negative phase (white dashed contours in Fig. 6). As revealed by previous studies, the strength of the ESTF is controlled by the CMW thickness (Sugimoto et al. 2012), while the position of the ESTF is anchored by the southern ridge of the CMW (Kobashi et al. 2006; Xu et al. 2012b). Indeed, we find that corresponding to the CMW changes during the PDO positive phase (Fig. 6a), the ESTF is stronger and its mean position shifts farther equatorward (Fig. 7a) and vice versa during the PDO negative phase (Figs. 6b, 7b). In addition, we construct the time evolution of the ESTF strength and position in Fig. 8, with all the time series normalized. It can be readily seen that the time evolution of the ESTF strength and position agrees well with the CMW changes. That is, the ESTF strengthens and moves southward because the CMW is thicker with its southern boundary extending more southward during the PDO positive phase, and the ESTF weakens and moves northward because the CMW is thinner with its southern boundary extending more northward during the PDO negative phase (Figs. 6–8).
Evolution of the anomalous ESTF (left) strength (red solid;) and (right) position (red solid) based on (a),(b) Ishii; (c),(d) IAP; (e),(f) SODA; and (g),(h) PI-Control EXP. The CMW volume is superimposed in black dashed lines (as in Fig. 4). Thin lines indicate the original results, and thick lines are smoothed by a 7-yr low-pass filter. Note that all time series are normalized by their standard deviations. The ESTF strength is defined as the area-averaged (180°150°W, 24°30°N) meridional density gradient at 125-m depth, and the ESTF position is calculated from the averaged position where the meridional density gradient at 125-m depth is larger than 1.1 × 10−6 kg m−4 within 180°–150°W, 24°–30°N (Fig. 6).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
The PDO controls the multidecadal variability of the CMW through the surface variability (Figs. 9, 10). Figure 9 compares the changes of surface buoyancy flux between the PDO positive and negative phase. During the PDO positive phase, the westerlies in the midlatitude central North Pacific are enhanced (Fig. 3b), which increases the surface heat loss centered around 30°–50°N, 150°E–150°W (Fig. 9a). Furthermore, the westerly wind anomaly induces a southward Ekman transport of surface dense water (Fig. 9b). The surface heat loss and the southward Ekman transport of dense water results in a large surface buoyancy loss (Fig. 9d) over the CMW formation region (black dashed rectangles). In comparison with the heat flux and Ekman buoyancy flux changes, the freshwater flux change is negligible in the CMW formation region (Fig. 9c). The large surface buoyancy loss reduces the surface stratification, deepens the mixed layer (Fig. 10c), and favors a thicker CMW in the PDO positive phase (Fig. 6) and vice versa for the PDO negative phase.
The heat-equivalent buoyancy flux changes (color shading; W m−2) between the PDO positive and negative phase (i.e., positive minus negative). Here, the positive value indicates ocean buoyancy loss. Shown are (a) the neat surface heat flux change, (b) the Ekman buoyancy flux change, (c) the freshwater flux change, and (d) the total heat-equivalent buoyancy flux [(a) + (b) + (c)] change. The surface wind stress change (vectors; N m−2) is superimposed in (b). The CMW formation location is inside the dashed-black-outlined rectangles.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
A comparison of the MLD (color shading; m) between (a) the positive and (b) negative phase of PDO, and (c) their difference [i.e., (a) minus (b)]. The black solid contour in (a) marks the 120-m MLD contour for the northern deep mixed layer pool (where CMW originates) in the PDO positive phase, and the white dashed contour in (a) and (b) denotes that in the PDO negative phase. The center location of the CMW for the positive or negative phases is marked as a black or white plus sign, respectively, in (c), and the center location of the ESTF for the positive or negative phase is also marked as a black or white star, respectively, as is shown in Figs. 5 and 6, respectively. The CMW formation location is inside the dashed-black-outlined rectangle in (c).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
To better understand the above process, we compare the mean MLD pattern between the PDO positive and negative phase (Figs. 10a,b). We mark the 120-m MLD contour of the northern deep mixed layer pool (where CMW originates; Oka and Qiu 2012) in black solid line for the PDO positive phase and white dashed line for the PDO negative phase in Figs. 10a and 10b. The eastern deep mixed layer water in the central Pacific (160°E–160°W) is the major source of the CMW that could spread into the southern subtropical gyre (Oka and Qiu 2012) and modulate the ESTF variability (Sugimoto et al. 2012; Xu et al. 2022). Overall, the eastern deep mixed layer changes most significantly (Fig. 10c): it deepens during the PDO positive phase, which could increase the CMW formation (Fig. 11) and the CMW volume (Ladd and Thompson 2002), and it shallows during the PDO negative phase, which could decrease the CMW formation and the CMW volume.
(a) The composite subduction rate (color shading; 10−7 m s−1; positive subduction and negative obduction) for the PDO positive phase, (b) the difference between the PDO positive and negative phase (i.e., positive minus negative), and (c) the lateral induction changes between the PDO positive and negative phase (i.e., positive minus negative). The composite 100-m MLD for the PDO positive phase is plotted as the black solid line in (a)–(c) to mark the MLD front. The outcrop lines of 25.7σθ (the southern one) and 26.4σθ (the northern one), bounding the formation region of CMW, are superimposed as blue dashed (PDO positive phase) and red dotted (PDO negative phase) lines. The stippling in (b) denotes MLD changes (Fig. 10c) larger than 15 m.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0299.1
The subduction rate between the positive and negative phase of PDO (Fig. 11) is further analyzed to see how the subduction process affects the mode water volumes. Generally, the subduction of water into the permanent thermocline (red shading in Fig. 11a) is mainly concentrated on the MLD front region. By contrast, obduction (blue shading in Fig. 11a) appears in the upstream of the deep mixed layers. The CMW are mainly subducted into the main thermocline from the eastern MLD front in the central Pacific near 180°–150°W, bounding by the 25.7σθ and 26.4σθ outcrop lines (blue dashed lines in Fig. 11a). The eastern deep mixed layer in the central Pacific (165°E–165°W) deepens most significantly during the PDO positive phase (Fig. 10c; >15 m marked as black dots in Fig. 11b). It sharpens the eastern MLD front and increases the subduction rate of CMW (Fig. 11b), which further increases the CMW volume. [Here, the subduction rate changes are mainly caused by the lateral induction term (Fig. 11c), while the changes of MLD and Ekman pumping terms are negligible (not shown).]
We find that the southern boundary of the northern deep mixed layer pool has a significant meridional shift (black-solid and white-dashed contours in Figs. 10a and 10b): much more southward during the PDO positive phase and much more northward during the PDO negative phase. Meanwhile, because of the enhanced surface buoyancy loss (Fig. 9d), the outcropping zone of the CMW (bounding by the 25.7σθ and 26.4σθ outcrop lines) shifts southward during the PDO positive phase (blue dashed contours of Fig. 11c) and vice versa for the PDO negative phase (red dotted contours of Fig. 11c). When the deep mixed layer pool and the CMW outcropping zone migrate southward during the PDO positive phase, the formation region and thus the main location of the CMW shift southward correspondingly, and when the deep mixed layer pool and the CMW outcropping zone migrate northward during the PDO negative phase, the formation region and thus the main location of the CMW shift northward correspondingly (Fig. 6; black and white plus sign in Fig. 10c), which further controls the ESTF position (Fig. 7; black and white stars in Fig. 10c).
In this section, we demonstrate the connection among the PDO, CMW, and ESTF on multidecadal time scales. During the PDO positive phase, the enhanced westerly wind increases the surface buoyancy loss over the central midlatitude North Pacific. There, the mixed layer deepens, and the southern boundary of the deep mixed layer pool moves equatorward. It favors the CMW subduction, increases its volume, and extends its southern rim southward. Subsequently, the ESTF strengthens and shifts southward and vice versa for the PDO negative phase. In summary, the ESTF variations are mainly related to the CMW changes and further dominated by the PDO on multidecadal time scales.
4. Summary and discussion
We have systematically demonstrated the east–west asymmetric variability of the STFs on multidecadal time scales. WU22 linked the WSTF changes to AMO via the STMW variability. In the present study, we show that the multidecadal variability of the ESTF is controlled by the PDO-induced CMW changes. During the PDO positive phase, the enhanced westerlies in the midlatitude central North Pacific increases surface buoyancy loss and deepens the mixed layer in the CMW formation region. The deep mixed layer pool and the outcropping zone of CMW extend farther southward, together with the deepened MLD, acting to enlarge the CMW volume and push the southern boundary of the CMW southward. Subsequently, the ESTF strengthens and shifts southward. That is, the multidecadal variations of ESTF are mainly related to the CMW changes, which are dominated by the PDO.
Combined with WU22, we first show the zonally asymmetric variability of the WSTF and ESTF on multidecadal time scales. WU22 found that the AMO-induced surface changes (35°–40°N; Fig. 3a) are stored in the STMW and reach the northern flank of the WSTF (centered near 20°–22°N) in about 5 yr through the background thermocline circulation. The current study finds that the PDO-induced surface changes (Fig. 3b) are stored in the CMW and modulate the ESTF with a lag of 1–3 yr, quasi synchronously on multidecadal time scales. This may be due to the different distance between the PDO loading area (the ESTF location) and the AMO loading area (WSTF location; Fig. 3). We note that our analysis has uncertainties due to the quality of the currently available dataset (i.e., Ishii, IAP, and SODA used in the present study) and the model bias of the PI-Control EXP. Long-term observations are still needed to further demonstrate the characteristic and mechanism of this phenomenon.
Based on the study carried out by Xu et al. (2022), the STFs show zonally asymmetric variations from 1980 to 2018. Here, we further reveal that during this period (from 1980 to 2018), the PDO has a phase transition from a positive to a negative phase (Fig. 4b), while the AMO transits from a negative to positive phase (Fig. 4a). The weakening and northward shifting of the CMW and ESTF from 1980 to 2018 is mainly due to the negative-transiting PDO, whereas the weakening and southward shifting of the WSTF is mainly influenced by the positive-transiting AMO (WU22). The zonally asymmetric low-frequency variability of the WSTF and ESTF would influence the atmospheric deep convection (Wang et al. 2019; Chen et al. 2019, 2020) in the subtropical frontal region. Changes in the STF-related baroclinicity would also modulate the local eddy activities that further impact the biogeochemical cycles (Qiu 1999; Jing et al. 2021).
Ladd and Thompson (2002) have shown that a positive state of the PDO is associated with increased formation of the CMW layers based on an isopycnal model. Sugimoto et al. (2012) have found that the low-frequency variations of the ESTF intensity is associated with the CMW intensity by using an eddy-resolving ocean general circulation model (OGCM). The present study further indicates that the multidecadal variability of the ESTF, CMW, and PDO is closely related from an observational viewpoint. While previous studies have only implied that the strength of ESTF is associated with the CMW volume change (Sugimoto et al. 2012) that are further related to surface forcing changes (i.e., Ladd and Thompson 2002), here we first show that the position of the ESTF is also modulated by the PDO via the CMW variability. The anomalous PDO-induced surface buoyancy flux meridionally shifts the southern boundary of the winter deep mixed layer pool in the KOE region, as well as the outcropping zone of CMW, which alters the southern rim of the thick CMW and thus the ESTF location.
The present study mainly focuses on multidecadal variations of the CMW and ESTF with a time scale of 50+ yr (Fig. 8). We find that the CMW and the ESTF show not only strength but also position changes (Figs. 6, 7). On the other hand, Sugimoto et al. (2012) investigated the interdecadal variation of the ESTF intensity with a time scale of about 20 yr based on an eddy-resolving OGCM. They indicated that the ESTF only shows intensity change but without position shift, and the interdecadal ESTF variation lags the surface forcing and the outcropped CMW changes by ∼3 yr. Here, we find a 1–3-yr time lag between the PDO index and the CMW volume, while we find a 0–1-yr lag between the CMW formation and the ESTF variability. It may be associated with the different dataset and time scales examined by Sugimoto et al. (2012) and the present study. PDO fluctuations were most energetic in two general periodicities, about 20 and 50+ yr (Newman et al. 2016). It would be an interesting topic to compare effects of the interdecadal (i.e., 20 yr) and multidecadal (i.e., 50+ yr) variations of PDO on the CMW and ESTF variability in the future, using coupled long-term numerical model simulations.
Acknowledgments.
Baolan Wu is supported by the Natural Science Foundation of China (42206030) and China Postdoctoral Science Foundation (BX2021086). Lixiao Xu is supported by the Natural Science Foundation of China (42276207 and 41876006) and the Fundamental Research Funds for the Central Universities (202241005). This study benefited from discussion with Prof. Xiaopei Lin. Three anonymous reviewers provided useful and constructive comments in revising the paper.
Data availability statement.
Data related to this paper can be downloaded from the following sources: IPRC Argo data (http://apdrc.soest.hawaii.edu/projects/argo/), Ishii data (version 7.3) (https://climate.mri-jma.go.jp/pub/), IAP data (http://159.226.119.60/cheng/), SODA data (version 2.2.4) (https://climatedataguide.ucar.edu/climate-data/soda-simple-ocean-data-assimilation), NCEP data https://www.esrl.noaa.gov/), the PDO index (https://www.ncdc.noaa.gov/teleconnections/pdo/data.csv), and the AMO index (https://climatedataguide.ucar.edu/climate-data/atlantic-multi-decadal-oscillation-AMO-index-station-based). The data of the PI-Control model experiment are available at the Ocean and Atmosphere Data Center of Ocean University of China (http://coadc.ouc.edu.cn/download/).
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