(a) Characteristics of OBP variability in the South Pacific
The standard deviation map of monthly OBP in the South Pacific from 2003 to 2016 is shown in Fig. 1a. It is obvious that the OBP shows strong variability in the southeastern Pacific. Such result is consistent with Piecuch et al. (2013) and Ponte and Piecuch (2014), which reported that large OBP anomalies variability observed in the Southern Ocean. Then, a simple index that measures the variability of the OBP in the southeastern Pacific (hereafter referred to as the OBP index, OBPI, Fig. 1b) was constructed by the OBP averaged in the (60°−45°S, 120°−80°W; black box in Fig. 1a). The largest amplitude of the OBPI is higher than 6 cm in 2015.
Figure 2 shows the empirical orthogonal function (EOF) analysis of monthly OBP anomalies in the South Pacific (70°−30°S, 150°E−60°W). The leading two EOF modes for the period 2003–2016 explain 47.7% and 11.7% of the total squared covariance, respectively. As shown in Fig. 2a, the positive center of OBP anomalies in the southeastern Pacific (70°−45°S, 140°−80°W) is captured by the spatial structure of EOF1, which is similar with the standard deviation map of monthly OBP (Fig. 1a). The principal component (PC) of EOF1 has a close relationship with the OBPI (R = 0.83, significant at a 99% confidence level) during the period 2003–2016. Therefore, OBPI can also be used to represent the temporal variation of the EOF1 (i.e. PC1). In contrast, the EOF2 represents a “sea-saw” OBP anomalies in the meridional direction, and the correlation coefficient between the OBPI and PC2 is only 0.03.
Figure 1c shows the climatological seasonal variation of the OBPI. It is obvious that the OBPI is active during austral spring (ASON) and autumn (MAMJ) but suppressed during austral summer (DJF). Moreover, the amplitudes of PC1 for both the positive and the negative phases are also enhanced from March to November (Fig. 2e), while the PC2 is active during austral summer and winter (Fig. 2f). Thus, the question that will be addressed in this study is—what facilitates energizing the OBP in the southeastern Pacific (Fig. 1a) during austral spring (ASON) and autumn (MAMJ)?
According to the classic theory of Gill and Niiler (1973), low frequency OBP variability is mainly driven by local redistribution of internal mass forced by winds. Regression maps of the OBPI with OBP (contours) and Ekman transports (vectors) during spring (ASON) and autumn (MAMJ) in the South Pacific for the period 2003–2016 are shown in Fig. 3a and 3b, respectively. One can see that OBP is significant positive in the southeastern Pacific consistent with convergence Ekman transports both during spring (ASON) and autumn (MAMJ), indicating the important role of surface winds in OBP variability. Figure 3c and 3d show the regression maps of the OBPI with SLP (contours) and winds at 10m (vectors) spring (ASON) and autumn (MAMJ) in the South Pacific for the period 2003–2016, respectively. Positive OBP anomalies in the southeastern Pacific is associated with high pressure and anti-cyclone on the sea level, which is benefit to ocean circulation convergence. Such strong OBP in this region is due to close potential vorticity (PV) contours (caused by ocean topography, white lines in Fig. 1a), which prevent OBP signal propagating westward (Gill and Niller, 1973). However, the low pressures consistent with cyclones over the South Pacific are not coincident during two seasons. There is a significant low pressure in west-southern Pacific (60°−40°S, 180°−130°W; Fig. 3c) during austral autumn (Fig. 3c), but in central-southern Pacific (40°−20°S, 160°−110°W; Fig. 3d) during austral spring (Fig. 3d). This result suggests that OBP variabilities in the South Pacific during different seasons are likely due to different atmosphere variabilities.
(b) mechanisms of OBP variability during austral spring and autumn
To further explore the influence of atmosphere on the OBP in the South Pacific, we compare with the three leading modes of atmospheric variability in the Southern Hemisphere. The canonical Southern Annular Mode (SAM) and Pacific South American (PSA) patterns are usually obtained from EOF analysis of the Southern Hemisphere monthly SLP anomalies (Mo, 2000, Qin et al., 2017). The first three leading EOF modes of the monthly SLP anomalies in the Southern Hemisphere poleward of 20°S (after removing the monthly mean global average SLP) for the period 2003–2016 are presented in Fig. 4 (the spatial structures only over the South Pacific are shown). EOF1, EOF2, and EOF3 account for 22.4%, 11.8%, and 8.9% of the SLP variability, respectively. The EOF1 pattern (Fig. 4a) is the SAM, which is a dominant mode of atmospheric variability in the Southern Hemisphere. The EOF2 and EOF3 patterns are respectively referred to as PSA1 and PSA2. Moreover, their phases are almost in quadrature, and display a zonal wavenumber-3 structure from the tropical Pacific to Argentina (Fig. 4b and 4c). Correlation coefficients between the OBPI and the time series of the three leading modes (referred to as the SAM index, PSA1 index and PSA2 index, respectively) are listed in Table 1. The OBPI has a high correlation with the PSA2 index during austral spring and autumn (R = 0.74 and 0.67, respectively, significant at the 99% confidence level), which is slightly greater than the correlation between the OBPI and the PSA1 index during austral spring (R = 0.46, significant at the 90% confidence level). In contrast, the correlation coefficients between the OBPI and SAM index is below 0.25 (not significant even at the 90% confidence level). The spatial structures of PSA1 and PSA2 show closed high pressure over the Ocean (Fig. 4b and 4c), which benefits to persistent convergence of Ekman transports. Conversely, the high pressure exist mainly at the south of 60°S in SAM (Fig. 4a), leading to ocean circulations toward the Antarctica continent. Thus, the OBP in the southeastern Pacific has the strongest relationship with PSA2, and it is also significantly correlated with PSA1 during austral spring.
Table 1
Correlation coefficients between the OBPI and PCs (obtained by SLP over Southern Hemisphere; Fig. 4) during MAMJ and ASON. * and ** represent the significant at 90% and 95% confident level, respectively.
Correlation
|
the SAM index
|
the PSA1 index
|
the PSA2 index
|
MAMJ OBPI
|
-0.21
|
-0.02
|
0.74**
|
ASON OBPI
|
-0.22
|
0.46*
|
0.67**
|
Figure 5 shows partial regressions of the OBPI on OBP anomalies, Ekman transport, SLP and winds at 10m in South Pacific during austral spring (ASON) and autumn (MAMJ) with the PSA2 index removed. Compared with Fig. 3, it is shown that partial regressions of positive OBP anomalies and Ekman transports are remarkably reduced in the southeastern Pacific and the triple pattern of SLP becomes indistinct after the PSA2 index removed from the OBPI. Thus, it can be concluded that the PSA2 (the spatial structure of PC3, Fig. 4c) plays an important action role in the OBP variability in the southeastern Pacific. However, the PSA2 only explain approximately 45% of the OBP variability, and the residual regression of SLP during austral spring (ASON, Fig. 5d) is close to the spatial structure of PSA2 (Fig. 4b), which indicates other factors in affecting the OBP variability in the southeastern Pacific.
Previous studies found that the energy of ENSO can be transmitted from the low latitudes to the mid and high latitudes of the Southern Hemisphere by PSA wave trains, allowing the influence of ENSO to extend from the tropics to the extratropics (e.g. Mo, 2000, Yu et al., 2015). As shown in Fig. 6a, the Niño 3.4 index during austral summer shows highest correlation (significant at the 95% confidence level) with the MAMJ-averaged PSA2 index (blue line) when ENSO leads the PSA2 around 3 months, but the correlation between the Niño 3.4 index and PSA1 index is extremely low (read line). Although both PSA indices have significant correlation with the Niño 3.4 index during austral spring (Fig. 6b), the correlation coefficient of the Niño 3.4 index with the PSA1 index is higher than that with the PSA2 index. Furthermore, the correlation coefficient between the D(-1)JF-averaged Niño 3.4 index and the MAMJ-averaged OBPI is 0.46, and the correlation coefficient between the Niño 3.4 index and OBPI during austral spring (ASON) is 0.74, both of which are significant at 95% confidence level.
Figure 7 show the regression maps of SST anomalies with the MAMJ-averaged and ASON-averaged OBPI in the tropical and South Pacific for the period 2003–2016. The SST anomalies associated with the MAMJ-averaged OBPI are dominated by negative anomalies in the western tropical Pacific and positive anomalies in the eastern tropical Pacific during austral summer (Fig. 7a). Similar result can be seen in Fig. 7d, which represents the regression map of SST anomalies with the OBPI during austral spring (ASON). In contrast, the regression of SST anomalies does not show “ENSO-like” pattern during austral summer (D-1JF) associated with the ASON-averaged OBPI (Fig. 7b) and during austral autumn (MAMJ) associated with the MAMJ-averaged OBPI (Fig. 7c). Therefore, these results verify the influence of ENSO on OBP variability during austral spring (ASON) and autumn (MAMJ) in the southeastern Pacific.
To further illustrate the independent relationship of ENSO and PSAs with OBP variability, partial correlation is carried out. The partial correlation between the PSA indices and OBPI during austral spring (ASON) and autumn (MAMJ) are listed in Table 2 with the Niño 3.4 index removed. Note that the D(-1)JF-averaged Niño 3.4 index is removed during austral autumn (MAMJ). Additionally, the correlation coefficients of the OBPI with PSA1 and PSA2 indices almost unchanged with ENSO removed during austral autumn (MAMJ). It is evident that only the PSA2 index remains significant correlation with the OBPI during austral spring (ASON) and autumn (MAMJ). However, correlation coefficient between the PSA1 index and OBPI declined and non-significant after the Niño 3.4 index removed during austral spring (ASON), indicating that the PSA1 (the spatial structure of PC2, Fig. 4c) links ENSO to the OBP variability in the southeastern Pacific.
Table 2
is the same as Table 1, but with D(-1)JF ENSO and concurrent ENSO removed.
With D(-1)JF ENSO removed
|
the SAM index
|
the PSA1 index
|
the PSA2 index
|
MAMJ OBPI
|
-0.09
|
0.09
|
0.66**
|
ASON OBPI
|
-0.22
|
0.50**
|
0.69**
|
With concurrent ENSO removed
|
the SAM index
|
the PSA1 index
|
the PSA2 index
|
MAMJ OBPI
|
-0.07
|
-0.03
|
0.73**
|
ASON OBPI
|
-0.12
|
-0.20
|
0.50**
|
Partial regression maps of the OBPI on OBP anomalies, Ekman transport, SLP and winds at 10m during austral spring (MAMJ) are shown in Fig. 8a and 8c with the D(-1)JF ENSO removed. Compared with Fig. 3, the OBP and SLP anomalies (especially the positive centers) are weaker in the southeastern Pacific. Similar conclusions are yield out by comparing the OBPI during austral autumn (ASON) with the result that the coincident ENSO removed (Fig. 8b and 8d). Moreover, the partial regressions on OBP anomalies, Ekman transport, SLP and winds are negligible after removing PSA2 and ENSO (not shown). Therefore, it is our conclusion that the energetic OBP variabilities in the southeastern Pacific are dominated by PSA2 and ENSO during austral spring (ASON) and autumn (MAMJ)
(c) OBP variability in PCOM
To further examine the mechanism of PSA2 and ENSO related processes on OBP variability, the Pressure Coordinate Ocean Model (PCOM) is used in this study. PCOM is a mass conservation (non-Boussinesq approximation) ocean model, which can be used to directly simulate the OBP. See Huang et al. (2001) and Zhang et al. (2014) for more detailed descriptions about the model. In this study, a spin-up run in PCOM with 60 pressure layers and the horizontal resolution of 1°×1°, was performed for 600 years from a static state under repeating climatological monthly mean atmospheric forcing, including fresh water flux, surface heat flux, surface wind and SLP. To examine the contributions of wind and sea level pressure forcing to the OBP variability, two experiments are carried out from 1990 to 2018 restarting from the spin-up run. The control run (Exp.1) is forced by daily atmospheric forcing during 1990–2018. Exp. 2 is the same as the control run, except excluding wind forcing. As shown in Fig. 9a, the pattern of OBP variance in the southeastern Pacific is quite similar to observations (Fig. 1a), especially in 60°−45°S, 120°−80°W (black box in Fig. 1a), which indicates that the PCOM can reproduce interannual variability of OBP quite well. Without wind forcing, the variance of OBP is quite weak, except along the coasts and Mid-Ridges (Fig. 9b), where the non-static response to sea level pressure forcing cannot be neglected. Thus, the control run in PCOM is used for further studies.
The PCOM OBPI is calculated by the PCOM OBP averaged in the (60°−45°S, 120°−80°W; black box in Fig. 1a). The correlation coefficient between the monthly observed OBPI (red line, also shown in Fig. 1b) and PCOM OBPI (blue line) is 0.66 (significant at 99% confidence level, Fig. 10a). As shown in Fig. 10b, the OBPI in PCOM also active during austral spring (ASON) and autumn (MAMJ). The EOF1 calculated by PCOM OBP in the South Pacific (Fig. 10c) is also similar with that in GRACE (Fig. 2a), which is captured by positive OBP anomalies in the southeastern Pacific. In addition, the PC1 (Fig. 10d) has high correlation with that in observation (R = 0.93, significant at 99% confidence level).
Regression maps of the PCOM OBPI with OBP anomalies in PCOM, Ekman transports, SLP and winds at 10m during austral spring (ASON) and autumn (MAMJ) in the South Pacific for the period 2003–2016 are shown in Fig. 11. The convergence Ekman transports are forced by high pressure and anti-cyclone, leading to positive OBP anomalies in the southeastern Pacific. Such result is the same with observations (Fig. 3). Furthermore, PSA2 has close relationship with the PCOM OBPI (exceed 0.60, listed in Table 3), and the significant correlation decrease slightly after ENSO removed. Therefore, it is confirmed that the interannual OBP variability in the southeastern Pacific is attribute to ENSO and PSA2 during austral spring (ASON) and autumn (MAMJ).
Table 3
Correlation coefficients of the PCOM OBPI with ENSO and PSA2 during MAMJ and ASON. * and ** represent the significant at 90% and 95% confident level, respectively.
Correlation
|
the PSA2 index
|
Niño 3.4 index
|
the PSA2 index with ENSO removed
|
MAMJ OBPI
|
0.83**
|
0.56** D(-1)JF
|
0.72**
|
ASON OBPI
|
0.60**
|
0.82** ASON
|
0.45*
|