The satellite altimetry data for SLA from 1993 through 2020 are obtained from Copernicus Marine Environment Monitoring Service (CMEMS). The altimeter measurements from multiple satellites are processed by the Data Unification and Altimeter Combination System (DUACS) and is distributed by CMEMS (Taburet et al., 2019). The data are available on a regular grid with a spatial resolution of 0.25°, and the monthly mean of sea level anomalies are computed with respect to 1993–2012 period. We have used the indices that represent the dominant Pacific climate drivers such as ENSO, IPO, and PDO (https://psl.noaa.gov/gcos_wgsp/Timeseries).

Two different filtering methods are applied to extract interannual and interdecadal variability in the SLA such as Lanczos band pass filter (Duchon, 1979) and EEMD (Huang et al., 1998). Study also examines the SLA composites for El Niño and La Niña events in the total SLA, interannual and interdecadal SLA derived from EEMD. The composites are formed considering 12 months spanning the developing phase of ENSO (from June) to decaying phase (to May). The 12-month means are considered for each ENSO event and during 1993–2020; there is a total of five El Niño events and four La Niña events.

The EEMD approach is the most essential part of the analysis in this study. It is an advanced version of the basic EMD method introduced to decompose the signals into finite number of intrinsic mode functions (IMFs) of different periods and a residual as a representative of long-term non-linear trend in the data (Huang et al., 1998). In EMD, the first IMF is estimated by fitting a cubic spline to the local maximum and the minimum envelops identified in the data. The second IMF is obtained after removing first IMF from the data, and it is repeated until no significant oscillations in the signal (residual mode). However, in EEMD, each IMF is derived as a mean of ensembles of corresponding IMF obtained by EMD in each realization of signal with the addition of varying Gaussian white noise (Huang and Wu, 2008). The ensemble averaging largely reduces the noise and significantly improves the mode separation compared with EMD (Wu and Huang, 2009; Wu et al., 2009). Many researchers have successfully applied EEMD in scientific literature to separate the variability associated with the signal into intrinsic modes corresponding to different periods (Jeong et al., 2022). Particularly, in this study, the application of EEMD is mainly intended to extract interannual and interdecadal variations (IDVs) in SLA and the linear trends associated with residual. In addition, a band pass filter is also used to extract variations in climate indices at interannual, interdecadal, and decadal timescales to support our analysis.

The SLA data used in this study are relatively short in distinguishing natural and anthropogenic signals in SLR (Ablain et al., 2015; Nicholls et al., 2021). The linear trend estimated over 1993–2020 is broadly a trend of decadal variability. We initially computed the SLR over the tropical Pacific Ocean for the period of 1993–2012 ( Figure 1A ) and the current period 1993–2020 ( Figure 1B ) after the removal of GMSL trend (GMSL value is based on the altimeter data; see Table 1 ). In comparison, an overall weakening of SLR over the western Pacific and an enhancement in SLR over the east Pacific is noticed in Figure 1B . Quantitatively, parts of NWP showing SLR estimates as high as 8–10 mm year⁻¹ in Figure 1A are significantly reduced to 1–2 mm year⁻¹ in Figure 1B . Also noted that, the appearance of positive SLR anomaly over the east Pacific little away from the west coast of America ( Figure 1C ) is an indication of rising sea levels over the east Pacific in consistent with earlier studies (Hamlington et al., 2020).

Furthermore, we attempt to understand the differences in SLR by analyzing the SLA variations at interannual, interdecadal timescales by applying EEMD technique. The IMF modes derived from EEMD contain frequency information, where the combination of 5^th and 6^th IMF modes represents interannual variations (IAVs) with oscillating periods (2–9 years), 7^th and 8^th IMF modes represent IDVs with oscillating periods (10–20 years), and the last mode (9^th mode in this data set) represents the residual. The residual mode indicates the low-frequency variations in SLA after the removal of variations associated with all frequencies including IAV and IDV modes. The residual trend patterns in Figures 1D, E are strikingly different from Figures 1A, B , with significant weakening of SLR during 1993–2012 ( Figure 1D ) and the strengthening during 1993–2020 ( Figure 1E ) over the entire tropical Pacific. The observed difference in trends in SLA between two periods ( Figure 1C ) over the Northwest Pacific is largely reduced in Figure 1F . This suggests that the trend estimated from residual modes is a better estimate of long-term trend compared with trends estimated from total SLA due to strong influences from IAV and IDV (Cazenave et al., 2014).

We further examined the trend pattern by selectively removing the IAV (IMF 5 and IMF 6) and IDV (IMF 7 and IMF 8) from the total SLA. Therefore, the SLR estimates in Figures 2A, B do not include the variations in SLA at interannual timescales, while Figures 2C, D do not include the variations in SLA at interdecadal timescales. Due to the removal of IAV, the trend in SLA considerably decreased for the period 1993–2012 ( Figure 2A ) compared with Figure 1A ; however, no significant changes in trends for the period 1993–2020 ( Figures 1B , 2B ). Similarly, in the absence of IDV of SLA, there are no significant differences in SLR over NWP between Figures 1D , 2C , whereas significant rise in SLR in Figure 2D compared with Figure 1B . The trend pattern in Figure 2D appears a close match with residual trend pattern ( Figure 1E ). This indicates that the length of the data 1993–2020 is longer enough to substantially reduce the IAV influence on trend estimates, whereas the influence of IDVs played a significant role in the reduction of SLR during 1993–2020 (Lyu et al., 2017). This is consistent with trend estimates shown in Table 1 for IDV, which shows a significant negative trend of ≈ −1.73 mm year^-1 over the NWP region during the period of 1993–2020.

The SLA variations over NWP are largely dominated by the interannual fluctuations from ENSO in response to El Niño and La Niña (Guo et al., 2022), which can be seen in Figures 3A, B . The SLA time series shows positive SLA persistent over longer duration during La Niña and adjoining neutral years than a sharp dip in SLA during El Niño ( Figure 3A ). The residual mode overlaid on the SLA time series clearly indicates a rising trend. A close match between interannual SLA mode ( Figure 3B ) and the observed SLA ( Figure 3A ) suggests dominant interannual fluctuations as mentioned earlier. The SLA over NWP exhibits strong negative correlations with ENSO (−0.87 at lag-0), IPO (−0.84 at lag-0) while the slightly weak correlation with PDO (−0.64 at lag-3 while −0.58 at lag-0), which are statistically significant ( Table 2 ). The bar diagram in Figure 4 pictorially represents the negative correlation between the NWP SLA with the ENSO (Nino34 index) as well as with the PDO (PDO index). The correlations shown in Table 2 are not lag-0 correlations; these are maximum correlations with lag and are negatively correlated. Here, positive lag indicates a leading climate mode index with respect to SLA over NWP. Despite the low amplitude of variations, both interdecadal and the detrended residual (decadal) variations ( Figure 3B ) significantly influences the long-term trend estimated. For example, the observed decreasing trend in interdecadal mode as discussed in section 2.1 is a result of change in phase of IDVs from positive during 1994–2012 to negative during 2013–2020. Similarly, the negative amplitude of decadal variations noted from the year 2015 (detrended residual, Figure 3B ) also have reinforced the recent decrease in SLA trend (Sun et al., 2017).

In addition, we have estimated the lag correlations between interannual and interdecadal modes of SLA over NWP with the corresponding frequencies (after bandpass filtering) of climate indices. The filtering of climate indices is based on the power spectrum of SLA over the NWP region ( Figure 5 ) along with power spectrum for different modes of SLA derived from EEMD. The spectral power of SLA at interannual timescales (< 10 years) is significantly higher than at interdecadal (> 10 years, less than ≈ 20 years) and decadal timescales > 20 years (residual, not shown). The correlations computed between the SLA modes and the bandpass-filtered indices of ENSO, PDO, and IPO show interannual correlations peaks at lag-0 with ENSO (−0.81), IPO (−0.80), and PDO (−0.71), respectively, while the interdecadal correlations peaks at negative (≈ 1 year or more) lags considering the oscillatory and the low-frequency nature of variations at interdecadal timescales ( Table 2 ). The interdecadal correlations at lag-0 are week such as −0.61, −0.45, and −0.43, respectively, with ENSO, PDO, and IPO. In contrast, the negative lag correlations are slightly strong with ENSO (−0.72 at lag-14), PDO (−0.64 at lag-21), and IPO (−0.69 at lag-23), respectively, as shown in Table 2 .

The east–west asymmetry in SLR in the tropical Pacific is largely attributed to the ENSO, that is, the occurrence of El Niño and La Niña events. During 1993–2020, there are a total of five El Niño events such as 1994–1995, 1997–1998, 2002–2003, 2009–2010, 2015–2016, and four La Niña year events 1995–1996, 1998–2000, 2007–2008, 2010–2012 occurred. Out of these events, there are two La Niña events spanning over 2 years, that is, 1998–2000 and 2010–2012. Currently, this year is a likely extension of La Niña conditions from 2021 to 2023, that is, a triple La Niña event likely to form, and it will be more common in a warming environment as experts suggested. Figure 6 shows the mean composite maps of SLA during El Niño, and La Niña and the pattern of SLA during La Niña ( Figure 6D ) closely resemble the observed trend pattern in SLA ( Figure 1 ). Although both El Niño and La Niña patterns look similar but opposite ( Figures 6A, D ), slightly higher magnitude of positive SLA is observed during La Niña to the west of 150^°E between 20^°S and 20^°N compared with magnitude of negative anomalies during El Niño. Furthermore, a slight westward shift of negative SLA across the equator toward the central Pacific in La Niña compared with El Niño. The frequency of occurrence and the duration of these events certainly impact the variations in SLA, specifically over western Pacific.

We further examine El Niño and La Niña mean composite maps of SLA derived from interannual and interdecadal modes of SLA from EEMD. The interannual SLA pattern ( Figures 6B, E ) shows a significant reduction in the magnitude of anomalies in both El Niño and La Niña; however, both the patterns look very much like the total SLA pattens shown in Figures 6A, D . However, the interdecadal mean patterns during El Niño and La Niña show notable differences ( Figures 6C, F ). El Niño pattern shows negative SLA over the central Pacific while positive anomalies on either side of it in contrast to the interannual and the total SLA mean patterns. It resembles a central Pacific El Niño–type response in SLA and is consistent with earlier studies suggesting the role of decadal anomalies in modulating the frequency of central Pacific El Niño (Di Lorenzo et al., 2010; Sullivan et al., 2016). However, the mean pattern of SLA during La Niña ( Figure 6F ) is in phase with the interannual, and the total SLA mean patterns, although considerably weak magnitude. Therefore, the occurrence of La Niña may partly influence the estimated trends in the interdecadal SLA due to its in-phase connection. In general, La Niña induces positive SLA over the west Pacific, the non-occurrence of La Niña after 2012 until 2020, might have contributed for the observed decreasing trend in interdecadal SLA. The present analysis suggests the complexity of influences on west pacific SLA by interannual and IDVs in the limited data sets and reiterates the dominant role of IAVs arising from ENSO. Especially, the more frequent and long-duration La Niña events, as suggested in literature, will pose a severe threat to the coastal regions in the west Pacific.