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Asymmetry of Salinity Variability in the Tropical Pacific during Interdecadal Pacific Oscillation Phases


doi: 10.1007/s00376-022-2284-y

  • It has been recognized that salinity variability in the tropical Pacific is closely related to the Interdecadal Pacific Oscillation (IPO). Here, we use model simulations from 1900 to 2017 to illustrate obvious asymmetries of salinity variability in the tropical Pacific during positive and negative IPO phases. The amplitude of salinity variability in the tropical Pacific during positive IPO phases is larger than that during negative IPO phases, with a more westward shift of a large Sea Surface Salinity (SSS) anomaly along the equator. Salinity budget analyses show that the asymmetry of salinity variability during positive and negative IPO phases is dominated by the difference in the surface forcing associated with the freshwater flux [FWF, precipitation (P) minus evaporation (E)], with a contribution of 40%–50% near the dateline on the equator. Moreover, the relationships between the salinity variability and its budget terms also show differences in their lead-lag correlations during positive and negative IPO phases. These differences in salinity variability during different IPO phases produce asymmetric effects on seawater density which can reduce or enhance upper-ocean stratification. Therefore, the salinity effects may modulate the intensity of El Niño-Southern Oscillation (ENSO), resulting in an enhanced (reduced) El Niño but a reduced (enhanced) La Niña during positive (negative) IPO phases by 1.6°C psu−1 (1.3°C psu−1), respectively. It is suggested that the asymmetry of salinity variability may be related to the recent change in ENSO amplitude associated with the IPO, which can help elucidate ENSO diversity.
    摘要: 现有研究表明,热带太平洋的盐度变化与太平洋年代际振荡(IPO)密切相关。本文使用1900年至2017年的模式模拟(LICOM3)结果,阐述了热带太平洋在正和负IPO相位期间盐度变化的不对称特性及机制。分析发现热带太平洋在正IPO相位盐度变化强度大于负IPO期间,且正IPO相位的海平面盐度异常沿赤道越向西扩展且增强越大。盐度收支平衡分析表明,在正和负IPO相位期间,盐度变化的不对称性,主要表现为与淡水通量(FWF,降水(P)减去蒸发(E))相关的海表强迫差异,淡水通量在赤道日期变更线附近对盐度异常的贡献为40%-50%。此外,盐度变化与其收支项之间的关系还表现为它们在正和负IPO相位的超前-滞后相关性的差异。因此,不同IPO相位盐度变化的差异对海水密度具有不对称效应,从而减弱或增强了上层海洋层结。因此,盐度效应可能会对ENSO的强度起到调节作用,导致在正(负)IPO相位的El Niño增强(减弱),La Niña减弱(增强)。盐度变化的不对称性可能与近期ENSO振幅的变化有关,这项研究结果有助于阐明ENSO多样性。
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  • Figure 1.  Interdecadal components of anomalies for (a) SSS and (b) SST along the equator during 1906–2011. Units: °C for SST and psu for SSS.

    Figure 2.  The horizontal distributions of SSS standard deviation (psu) during (a) negative and (b) positive IPO phases in the tropical Pacific, and (c) their difference (positive minus negative IPO phases).

    Figure 3.  The horizontal distributions of (a, b) standard deviations of SSS tendency and budget terms for (d, e) surface forcing, (g, h) surface advection, (j, k) subsurface effect during negative and positive IPO phases, and (c, f, i, and l) their differences (positive minus negative IPO). The red contours represent the contribution rate of the budget terms to SSS tendency. Units: 10–6 psu month–1.

    Figure 4.  The horizontal distributions of SSS anomalies (psu) in the tropical Pacific during (a) negative and (b) positive IPO phases, respectively.

    Figure 5.  The horizontal distributions of (a, b) the SSS tendency anomaly and the anomalies of budget terms for (c, d) surface forcing, (e, f) surface advection, and (g, h) subsurface effect in the tropical Pacific during the negative and positive IPO phases, respectively. Units: 10–6 psu month–1.

    Figure 6.  Lag/lead correlation coefficients between SSS budget terms and SSS tendency averaged over the key region (160°–180°E, 5°S–5°N) during (a) negative and (b) positive IPO phases. The red dashed lines represent values statistically significant at the 0.05 level using a student-t test.

    Figure 7.  The vertical distributions of (a, b) Density (Tinter , Sinter), (c, d) Density (Tclim, Sinter), and (e, f) Density (Tinter, Sclim) anomalies during the negative and positive IPO phases along the equator of the Pacific. Units: g cm–3. The straight line represents the dateline.

    Figure 8.  The horizontal distributions of MLD anomalies (m) in the tropical Pacific for (a, b) MLD (Tinter, Sinter), (c, d) MLD (Tclim, Sinter), and (e, f) MLD (Tinter, Sclim) during negative and positive IPO phases, respectively. Units: m.

    Figure 9.  Scatterplot of the MLD anomaly and corresponding SSS anomaly averaged in the key area (160°–180°E, 5°S–5°N) during the (a) negative and (b) positive IPO phases. The red line represents the linear regression. Units: m for MLD; psu for SSS.

    Figure 10.  Lead/lag correlation coefficients between the Niño-3.4 SST and SSS variability averaged over the key area (160°––180°E, 5°S–5°N) during the negative and positive IPO phases. Red dotted lines represent the values statistically significant at the .05 level based on a student-t test.

    Figure 11.  The spatial distributions of correlation coefficients between SSS variability and Niño-3.4 SST index during (a) negative and (b) positive IPO phases.

    Figure 12.  Scatterplot of the monthly Niño-3.4 SST index and corresponding SSS anomaly averaged in the key area (160°–180°E, 5°S–5°N) during (a) negative and (b) positive IPO phases. The red line represents the linear regression. Units are °C for SST and psu for SSS.

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Manuscript received: 07 October 2022
Manuscript revised: 19 December 2022
Manuscript accepted: 06 January 2023
通讯作者: 陈斌, bchen63@163.com
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Asymmetry of Salinity Variability in the Tropical Pacific during Interdecadal Pacific Oscillation Phases

    Corresponding author: Rong-Hua ZHANG, rzhang@nuist.edu.cn
  • 1. College of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 3. Laoshan Laboratory, Qingdao 266237, China
  • 4. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing 100029, China
  • 5. Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China

Abstract: It has been recognized that salinity variability in the tropical Pacific is closely related to the Interdecadal Pacific Oscillation (IPO). Here, we use model simulations from 1900 to 2017 to illustrate obvious asymmetries of salinity variability in the tropical Pacific during positive and negative IPO phases. The amplitude of salinity variability in the tropical Pacific during positive IPO phases is larger than that during negative IPO phases, with a more westward shift of a large Sea Surface Salinity (SSS) anomaly along the equator. Salinity budget analyses show that the asymmetry of salinity variability during positive and negative IPO phases is dominated by the difference in the surface forcing associated with the freshwater flux [FWF, precipitation (P) minus evaporation (E)], with a contribution of 40%–50% near the dateline on the equator. Moreover, the relationships between the salinity variability and its budget terms also show differences in their lead-lag correlations during positive and negative IPO phases. These differences in salinity variability during different IPO phases produce asymmetric effects on seawater density which can reduce or enhance upper-ocean stratification. Therefore, the salinity effects may modulate the intensity of El Niño-Southern Oscillation (ENSO), resulting in an enhanced (reduced) El Niño but a reduced (enhanced) La Niña during positive (negative) IPO phases by 1.6°C psu−1 (1.3°C psu−1), respectively. It is suggested that the asymmetry of salinity variability may be related to the recent change in ENSO amplitude associated with the IPO, which can help elucidate ENSO diversity.

摘要: 现有研究表明,热带太平洋的盐度变化与太平洋年代际振荡(IPO)密切相关。本文使用1900年至2017年的模式模拟(LICOM3)结果,阐述了热带太平洋在正和负IPO相位期间盐度变化的不对称特性及机制。分析发现热带太平洋在正IPO相位盐度变化强度大于负IPO期间,且正IPO相位的海平面盐度异常沿赤道越向西扩展且增强越大。盐度收支平衡分析表明,在正和负IPO相位期间,盐度变化的不对称性,主要表现为与淡水通量(FWF,降水(P)减去蒸发(E))相关的海表强迫差异,淡水通量在赤道日期变更线附近对盐度异常的贡献为40%-50%。此外,盐度变化与其收支项之间的关系还表现为它们在正和负IPO相位的超前-滞后相关性的差异。因此,不同IPO相位盐度变化的差异对海水密度具有不对称效应,从而减弱或增强了上层海洋层结。因此,盐度效应可能会对ENSO的强度起到调节作用,导致在正(负)IPO相位的El Niño增强(减弱),La Niña减弱(增强)。盐度变化的不对称性可能与近期ENSO振幅的变化有关,这项研究结果有助于阐明ENSO多样性。

    • El Niño-Southern Oscillation (ENSO) has undergone significant decadal changes as indicated in the sea surface temperature (SST) field in the tropical Pacific (e.g., Zhang et al., 1998), including its intensity, period, asymmetry of its El Niño-La Niña phases, etc. (e.g., An and Wang, 2000; Ye and Hsieh, 2006). Significant progress has been made in understanding the potential processes and mechanisms for decadal changes in ENSO (Timmermann and Jin, 2002). Various factors have been identified that can be responsible for producing SST asymmetries during ENSO evolution in the tropical Pacific, including variations in the mean climate state (Ault et al., 2013), the effects of interdecadal climate variability, the interrelationship changes between interannual anomaly fields, and so on.

      In ENSO-related interdecadal variability studies, the El Niño-La Niña asymmetry has been a recent focus due to its effects on the mean state, which can affect ENSO properties. For instance, strong, long-lasting, and frequently occurring El Niño events can make an accumulated warming contribution to the mean SST state in the tropical Pacific, having residual effects on the mean climate state (e.g., An and Wang, 2000; Timmermann, 2003; Yeh and Kirtman, 2004, 2004; Choi et al., 2013; Ogata et al., 2013; Wittenberg et al., 2014; Okumura et al., 2017). In turn, variations of the mean climate state can alter ENSO features (An and Jin, 2004). Thus, the effects of the El Niño-La Niña asymmetry-related phenomena on the mean SST state have been used to explain changes in ENSO properties (Zhang et al., 1998; Minobe and Mantua, 1999). Moreover, the causes for the asymmetry of SST evolution associated with the El Niño-La Niña events have been extensively investigated (Timmermann and Jin, 2002; Schopf and Burgman, 2006; An, 2009). Through heat budget analyses, for example, it has been illustrated that several specific processes can induce the El Niño-La Niña asymmetry of ENSO cycles, such as warming effects on both El Niño and La Niña through nonlinear dynamical heating (An et al., 2005), asymmetric negative feedback due to oceanic tropical instability waves (Jochum and Murtugudde, 2004; Zhang and Busalacchi, 2008), the thermal-barrier effect associated with salinity effects (Wang and Xu, 2018), the westerly wind burst and its interaction with ENSO (Chen et al., 2015), and the biological-physical feedback process (Zhang et al., 2015, 2020).

      Pronounced interdecadal climate variability has been observed over the Pacific Ocean, a decadal basin scale mode known as the Interdecadal Pacific Oscillation/Pacific Decadal Oscillation (IPO/PDO; e.g., Trenberth and Shea, 1987; Gu and Philander, 1997; Mantua et al., 1997; Power et al., 1999; Cobb et al., 2003). For example, an ENSO-like interdecadal shift occurs every 20 to 30 years, with one striking feature that is characterized by an asymmetry in the character of El Niño-La Niña events on interdecadal scales, displaying differences in the amplitude and period of ENSO events during positive and negative IPO phases. It has been further demonstrated that the IPO is linked with the ENSO-related interannual climate variations in the tropical Pacific (Deser et al., 2004; D’Arrigo et al., 2005; An, 2009; Li et al., 2011; Emile-Geay et al., 2013). Several studies have also proposed that the IPO can modulate ENSO, leading to obvious variations in its spatial pattern and amplitude on decadal and longer term time scales (Dettinger et al., 1998; An and Bong, 2016). For example, when an El Niño event overlaps with a positive IPO phase, its warm SST anomalies become stronger, with a residual warming effect on SST. Such overlapping effects of interdecadal climate variability on ENSO evolution produce asymmetric SST conditions in the tropical Pacific. Consequently, the positive IPO phase is associated with an enhanced frequency and amplitude of El Niño events, while the negative IPO phase tends to be more favorable for the development of La Niña events (Verdon and Franks, 2006; Ogata et al., 2013; Zhang et al., 2022b). Such clear interactions between processes on interannual and interdecadal time scales produce asymmetric effects on the SST evolution.

      At present, however, the underlying mechanism for the interdecadal climate variability in modulating the diversity of ENSO events exhibits substantial ambiguity. The decadal or long-term variations in ENSO and its relationships with the asymmetric effects of IPO phases have become a controversial issue (Cai et al., 2015; Zhang et al., 2022a). In addition, these previous studies have mainly focused on temperature fields and thermal forcing in the tropical Pacific; other factors have not been adequately considered. Both diagnostic and modeling results have demonstrated that interannual salinity variability in the central-western equatorial Pacific can affect the mixed layer depth (MLD) and the barrier layer thickness (BLT) through the mixing and entrainment of seawater, which acts to enhance/reduce the temperature variability of the upper ocean, thus exerting a positive feedback on the intensity and frequency of ENSO events (Zhang and Busalacchi, 2009; Maes et al., 2005; Zhang et al., 2015; Zhi et al., 2019; Gao et al., 2020).

      It is observed that ocean salinity variability also exhibits lower frequency variations independent of ENSO timescales (usually termed “decadal” or “interdecadal”), which is related to the IPO (Hare, 1996; Latif, 1998; Overland et al., 1999), showing abrupt shifts in the mid-1920s, mid-1940s, mid-1970s, late-1980s, and mid-1990s during the phase transitions of the IPO/PDO (Mantua and Hare, 2002). For example, an anomalous pattern of Sea Surface Salinity (SSS) appeared from 1997 to 2004, coincident with a transitional period in the late 1990s, when strong freshening prevailed in the southwestern basin, and moderate salinization occurred in the western equatorial Pacific (Du et al., 2015). While the salinity variations at three sites in the North Pacific were found to be consistent with the spatial pattern of interannual variability of North Pacific precipitation, they were also subjected to a shift in the mid-1970s (Overland et al., 1999). The SSS observed at a station near Hawaii seemed to have undergone freshening during 1991–97, followed by a signal reversal in 1998, which may be related to the IPO/PDO phase shift in the mid-1990s (Lukas, 2001). Due to much richer data availability in recent years, it became evident that PDO-like variability of SSS abruptly shifted in the mid-1970s, 1989/1990, and mid-1990s. These events have been investigated in several regions, including the western Pacific, the South Pacific convergence zone, and the equatorial cold tongue (Delcroix et al., 2007). Additionally, Zhang et al. (2022b) found that during the period from 1950 to 2018, two interdecadal phase transitions of warm/fresh and cool/salty conditions are evident, one in the late 1970s, and the other in the late 1990s. They further showed that the ocean salinity presents obviously decadal or interdecadal variabilities.

      However, salinity variability and its modulating effect on ENSO have rarely been considered for different IPO phases. In particular, the contribution of the salinity effect to the asymmetry of the SST decadal variation was seldom studied from the perspective of different ocean stratifications between different IPO phases. Understanding the physical mechanisms responsible for the connection between ENSO and salinity-related ocean processes in the equatorial Pacific is essential to addressing the underlying climate dynamics in the tropical Pacific, which is the main focus of this paper.

      The remainder of this paper is organized as follows. The data and methods are presented in section 2. In section 3.1, the SSS variability in the tropical Pacific during different IPO phases is identified and analyzed. The spatial distribution and mechanism for the upper-ocean salinity effect during positive and negative IPO phases are investigated in section 3.2. Based on the asymmetry of salinity variability associated with the IPO, the relationship between the SSS variability and ENSO is examined in section 3.3. Finally, conclusions and discussions are provided in section 4.

    2.   Data and Methodology
    • This study adopts the simulation produced by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), the Chinese Academy of Sciences Climate System Ocean Model (LICOM), which has developed since the late 1980s (Zhang and Liang, 1989; Zeng et al., 1991; Zhang and Endoh, 1992; Liu et al., 2012). The latest version (LICOM3) uses a finite-volume dynamical core and has 360 and 218 grid numbers for the zonal and meridional directions and 30 vertical levels, respectively. The zonal resolution is a uniform 1°, while the meridional resolution is 0.5° between 10°S and 10° N, and changes from 0.5° to 1° between 10° and 20°S/N, and 1° poleward of 20°S/N. Vertically, the uppermost level is at depth of 5 m using the η coordinate. In the upper 150 meters, the layer thickness is set to be 10 m, while 15 levels span the depths of 150–5600 m, with the thickness gradually increasing with increasing depth. The orthogonal curvilinear coordinate has been introduced in LICOM3. The tripolar grid is used with the North Pole split into two poles on the land in the Northern Hemisphere at (65°N, 65°E) and (65°N, 115°W), respectively; for more details see Lin et al. (2020).

      The LICOM is coupled with the Community Ice Code version 4 (CICE4), i.e., the ocean-ice coupled model. Based on the dynamic framework on the longitude-latitude grids used in the previous model version, Yu et al. (2018) first established a dynamic framework applicable to a general horizontal orthogonal curvilinear coordinate, with a tripolar grid introduced into the LICOM3. More detailed configurations of LICOM3 can be found in Lin et al. (2020).

      A simulated monthly oceanic dataset covering 1900–2017 is used in this study, and the LICOM3 is forced by atmospheric data from the European Centre for Medium-Range Weather Forecast 20th century reanalysis (ERA-20C) (Poli et al., 2016). The monthly ocean data used in this study include sea temperature, sea salinity, ocean freshwater flux, and the related salinity budget terms, which are all resampled to a 1° × 1° resolution. Notably, if the seasonal cycles and global warming trend are removed, the decadal variability is derived by applying a 13-year low-pass filter to the processed dataset, while the interannual variability is obtained through a high-pass filter.

    • In this study, the MLD is used to represent upper-ocean mixing processes. The MLD is defined as the depth at which the density has changed by an absolute difference of Δρ from the density at the 10 m beneath the ocean surface, where Δρ is the value equivalent to a temperature decrease of 0.2°C (Kara et al., 2000). Here, the isothermal layer depth is defined as the depth at which the temperature has changed by an absolute temperature difference of ΔT = 0.2°C from the temperature at 10 m beneath the ocean surface.

      To compare the relative contributions of salinity and temperature to MLD change, we utilize the diagnostic method proposed in previous studies (Zhang et al., 2012; Zheng et al., 2014). A field, denoted as $ F(T,S) $, is calculated based on the temperature and salinity fields. $ F({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{\mathrm{S}}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ is denoted as the anomaly field attributed to the interannual variations of temperature and salinity, while $ F({T}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ represents the anomaly field attributed to the interannual variations of salinity and climatological temperature, and $ F({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}) $ denotes the anomaly field attributed to the interannual variations of temperature and climatological salinity.

      The IPO index is calculated based on the difference between the SST anomaly (SSTA) averaged over the central equatorial Pacific and the average of the SSTA in the Northwest and Southwest Pacific (Henley et al., 2015). The tripole index (TPI) is an easily calculated, non-EOF-based index used for tracking the decadal SST variability associated with the IPO. Based on the global monthly SST data, the TPI is calculated as follows.

      where $ {\mathrm{S}\mathrm{S}\mathrm{T}\mathrm{A}}_{i} $ indicates the SSTA averaged over regions bounded by box 1 (25°–45°N, 140°E–145°W), box 2 (10°S –10°N, 170°E–90°W) and box 3 (50°–15°S, 150°E–160°W). Afterward, a 13-year low-pass filter is applied to obtain the filtered TPI.

    3.   Results
    • To address the difference in salinity variability between the positive and negative IPO phases, the spatiotemporal features of the salinity variability and related processes during the positive and negative IPO phases from 1900 to 2017 are investigated through composite analyses. Here, positive (negative) IPO phases are selected based on the TPI index (figure omitted), which corresponds to the periods of 1925–46 and 1979–2002 (1907–24, 1947–78, and 2003–11), which resemble the classifications of the positive and negative IPO phases by Dong et al. (2018) and Zhang et al. (2022b).

    • In the ocean, salinity and temperature variations are closely related. The SSS and corresponding SST interdecadal variabilities along the equator are shown in Fig. 1. The temperature and salinity evolutions exhibit pronounced interdecadal signals in the upper ocean of the equatorial Pacific. The SSS interdecadal evolution is observed to undergo persistent and transitional periods corresponding to the positive and negative IPO phases. For example, over the period 1906 to 2011, warm and fresh surface conditions persisted during 1925–46 and 1979–2002, whereas cold and salty surface conditions were seen during 1906–24, 1947–78, and 2003–11. Correspondingly, several interdecadal phase transitions are evident. Transitions from cold and salty conditions to warm and fresh conditions occurred in the 1900s, 1940s, and 2000s, and conversely, transitions from warm and fresh conditions to cold and salty conditions occurred in the 1920s, late 1980s, and 2010s.

      Figure 1.  Interdecadal components of anomalies for (a) SSS and (b) SST along the equator during 1906–2011. Units: °C for SST and psu for SSS.

      Based on the composition of the positive and negative IPO phases during 1906–2011, Fig. 2 shows the standard deviations of SSS variability in the tropical Pacific during positive and negative IPO phases and their differences. It is revealed that during both IPO phases, the region with large SSS variability (>0.5 psu) is concentrated in the central-western tropical Pacific, with areas of large values centered in the regions west of the dateline and the eastern tropical Pacific close to the American continent (Figs. 2ab). However, there are differences in the intensity and location of large SSS variability between negative and positive IPO phases. During positive IPO phases, the center of large SSS variability is farther eastward than that during negative IPO phases. Except for a few regions, the intensity of large SSS variability during positive IPO phases is stronger than that in negative phases, especially in the central equatorial Pacific (160°E–160°W), where the difference in SSS variability between negative and positive IPO phases reaches up to 0.3 psu (Fig. 2c). Thus, the salinity variability during positive IPO phases is larger, and the region with large SSS variability is located further eastward than that during negative IPO phases.

      Figure 2.  The horizontal distributions of SSS standard deviation (psu) during (a) negative and (b) positive IPO phases in the tropical Pacific, and (c) their difference (positive minus negative IPO phases).

      Furthermore, a salinity budget analysis is performed to explain the asymmetry of SSS variability between negative and positive IPO phases. Figure 3 displays the standard deviations of the SSS tendency and its budget terms, as well as the contribution of each term to the SSS tendency during negative and positive IPO phases. In general, the large-value areas of SSS tendency as measured by the standard deviation are mainly concentrated in the tropical eastern and western Pacific and the South Pacific convergence zone; also, there are large values sporadically along the equator during both IPO phases (Figs. 3ab). Note that in the tropical central-eastern Pacific, the SSS tendency during positive IPO phases is stronger than that during negative IPO phases (Fig. 3c). During positive IPO phases, the surface forcing contributes the most to the SSS tendency, which explains 40%–50% of the SSS tendency; the surface forcing also explains 30%–40% of the SSS contributions during negative IPO phases (Figs. 3de). The surface advection term contributes 20%–35% to the SSS tendency during both IPO phases, and there is no evident difference in its spatial distributions (Figs. 3gh). However, the intensity of the surface advection variability is significantly stronger during positive phases than in negative phases (Fig. 3i). Additionally, the subsurface effect contributes 20%–40% to the SSS tendency, and the area where the contribution reaches up to 40% is concentrated in the equatorial region of 5°S–5°N (Figs. 3jk). Moreover, the intensity of the subsurface effect variability during positive IPO phases is stronger (weaker) in the tropical western (eastern) Pacific compared to negative IPO phases. Consequently, the SSS tendency during positive IPO phases is generally stronger in the tropical central Pacific than that during negative IPO phases, and all corresponding SSS budget terms favor the asymmetry of SSS variability in the tropical central-western Pacific during positive IPO phases.

      Figure 3.  The horizontal distributions of (a, b) standard deviations of SSS tendency and budget terms for (d, e) surface forcing, (g, h) surface advection, (j, k) subsurface effect during negative and positive IPO phases, and (c, f, i, and l) their differences (positive minus negative IPO). The red contours represent the contribution rate of the budget terms to SSS tendency. Units: 10–6 psu month–1.

      The aforementioned analyses have revealed that surface forcing is the main contributor to the SSS tendency during both IPO phases. Moreover, the surface forcing-induced asymmetry between positive and negative phases in the tropical central Pacific also plays a dominant role in SSS tendency. Additionally, during positive IPO phases, the large-value region of SSS tendency shows an obvious eastward extension, mainly caused by the surface forcing, followed by the surface advection.

      In brief, the salinity variability presents asymmetry in its intensity between positive and negative IPO phases, which is explained by the differences in salinity budget terms. During positive IPO phases, the large-value region of SSS variability presents an eastward extension, which is caused by both surface forcing and surface advection. This feature indicates that during positive IPO phases, the eastward movement owing to stronger surface advection in the strong FWF region contributes to the asymmetry of amplitude of the SSS variation. Previous studies have pointed out that due to variation of salinity-related FWF across the Pacific basin, the redistribution of the salinity anomaly causes a basin-scale adjustment, with its low-frequency part favoring the regulation of the dry-wet precipitation conditions related to the PDO (Lukas, 2001; Westra et al., 2015; Dong et al., 2018). The proposed distribution characteristics of tropical Pacific precipitation during the IPO phases have also indirectly verified the conclusion of interdecadal variation characteristics of the SSS found above.

      Note that the intensity of SSS variability differs between positive and negative IPO phases, and the SSS anomalies also show opposite features in terms of spatial distribution patterns between positive and negative phases (Fig. 4). As seen in Fig. 4a, during negative IPO phases, positive SSS anomalies appear in most of the tropical Pacific, with the large-value region being located south of the equator. In contrast, during positive IPO phases, negative SSS anomalies are mainly found in the central tropical Pacific, approximately within the range of 20°S–10°N, 160°E–120°W (Fig. 4b). It indicates that the SSS variability is enhanced due to the positive anomaly found in the central tropical Pacific during negative IPO phases, while it is reduced due to the negative anomaly appearing in the same region during positive IPO phases. In addition, the SSS anomaly also shows an obvious asymmetry between positive and negative IPO phases, with a negative interannual anomaly during positive IPO phases and a positive interannual anomaly during negative phases, with a maximum difference as large as 0.4 psu in the central equatorial Pacific.

      Figure 4.  The horizontal distributions of SSS anomalies (psu) in the tropical Pacific during (a) negative and (b) positive IPO phases, respectively.

      By using the SSS budget analysis to explain the asymmetry of the SSS anomaly, the anomalous SSS tendency and its budget terms during negative and positive IPO phases are shown in Fig. 5. It is revealed that the spatial patterns of SSS tendency are basically consistent with those of the SSS anomaly. That is, an obvious asymmetry evidenced by opposite distribution patterns in the central equatorial Pacific is also found between positive and negative phases, along with the salinity budget terms. During the negative IPO phases, the positive SSS tendency anomaly in the central equatorial Pacific corresponds to a positive surface forcing anomaly, negative surface advection anomaly, and positive subsurface effect anomaly (Figs. 5c, e, and g). Similarly, during the positive IPO phase, the negative SSS tendency anomaly in the central equatorial Pacific corresponds to the negative surface forcing anomaly, positive surface advection anomaly, and negative subsurface effect anomaly (Figs. 5d, f, and h). This means that during negative and positive IPO phases, the SSS tendency anomaly results from the combined effects of surface forcing, surface advection, and subsurface contributions, with its characteristics and intensity primarily depending on the surface forcing in the central tropical Pacific. It can be concluded that the FWF-related surface forcing determines the asymmetry of the SSS tendency anomaly between positive and negative IPO phases, and the other two budget terms (surface advection and subsurface effects) play a secondary role in reducing or enhancing the SSS tendency anomaly.

      Figure 5.  The horizontal distributions of (a, b) the SSS tendency anomaly and the anomalies of budget terms for (c, d) surface forcing, (e, f) surface advection, and (g, h) subsurface effect in the tropical Pacific during the negative and positive IPO phases, respectively. Units: 10–6 psu month–1.

      Moreover, the time evolution of SSS variability and its relationship with the oceanic fields involving physical quantities are further analyzed during negative and positive IPO phases. The results of this analysis are shown in Fig. 6. The correlations between the SSS budget and SSS tendency terms, averaged in the key area (160°–180°E, 5°S–5°N), display a lead/lag relationship of approximately 5–10 months during both phases. For example, during negative IPO phases, a negative correlation is seen so that the surface forcing and subsurface effects on variation lead the SSS tendency by 10 months, while a positive correlation is also found when surface advection variation leads the SSS tendency by 10 months (Fig. 6a). During positive IPO phases, the SSS budget terms show a negative correlation relative to the SSS tendency, with the surface forcing and the subsurface effect leading the SSS tendency by 10 months and the surface advection leading the SSS tendency by 5 months (Fig. 6b). In addition, the main differences are the amplitude value of in-phase correlation and the correlation between the surface advection and the SSS tendency. For instance, a positive correlation is found when the surface advection variation leads the SSS tendency by 10 months, and the maximum correlation occurs at zero lag during negative IPO phases. In contrast, during positive IPO phases, there is a positive correlation when the surface advection variation leads the SSS tendency by 5 months, but the maximum positive correlation occurs at a time lag of 10 months.

      Figure 6.  Lag/lead correlation coefficients between SSS budget terms and SSS tendency averaged over the key region (160°–180°E, 5°S–5°N) during (a) negative and (b) positive IPO phases. The red dashed lines represent values statistically significant at the 0.05 level using a student-t test.

      A striking difference is seen between positive and negative IPO phases when the surface advection tends to have an opposite effect on the SSS tendency. Since salinity variability is determined and controlled by salinity budget terms (surface forcing, surface advection, and subsurface forcing terms), which are dependent on different physical processes, the related forcing and process variabilities are phase- and regionally dependent. Examples are given for the relationships among these fields, which exhibit phase differences during negative and positive IPO phases in the western equatorial Pacific, and the relationship of salinity anomalies with physical processes is observed to cause changes. For instance, during negative IPO phases, the surface advection anomaly exerts a positive contribution to the SSS anomaly, while during positive IPO phases, it contributes negatively. Although the correlation of the SSS tendency with surface forcing is the strongest among the SSS budget terms, the correlation between the surface advection and the SSS tendency may be related to the asymmetry of SSS anomalies between positive and negative IPO phases. This factor is speculated to be one of the reasons for the salinity asymmetry between positive and negative IPO phases.

    • The formation and maintenance of upper-ocean stratification result from both oceanic and atmospheric forcing and resulting feedback onto the SST (e.g., the thermal-barrier effect). Physically, ocean temperature and salinity anomalies determine the ocean stratification changes by affecting the ocean density and MLD. Moreover, the upper-ocean vertical mixing and entrainment processes act first to alter the upper-ocean heat content, and then modulate ENSO phenomena (Zhang et al., 2020). By comparing the relationships of the density distribution with the upper-ocean salinity variation during positive and negative IPO phases, it is found that the salinity effect dominates the asymmetry of upper-ocean stratification variability in the western equatorial Pacific during different IPO phases.

      Figure 7 shows the vertical density distribution averaged over the zone of 5°S–5°N along the equatorial Pacific, aiming to illustrate the effects of temperature and salinity on the distribution of sea-water density. It can be seen that the distributions of vertical density anomalies during positive and negative IPO phases are obviously different. For example, during negative IPO phases, in the upper layer of the central equatorial Pacific, the positive $ D({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ anomaly appears at depths of 0–100 m, leading to a weakened stratification of the upper ocean in the central equatorial Pacific (Fig. 7a), while during positive IPO phases, the negative density anomaly appears at the depths of 0–50 m, leading to a strengthened stratification of the upper ocean in the central equatorial Pacific (Fig. 7b). This indicates that the variability of upper-ocean density in the central equatorial Pacific exhibits an obvious asymmetry between different IPO phases.

      Figure 7.  The vertical distributions of (a, b) Density (Tinter , Sinter), (c, d) Density (Tclim, Sinter), and (e, f) Density (Tinter, Sclim) anomalies during the negative and positive IPO phases along the equator of the Pacific. Units: g cm–3. The straight line represents the dateline.

      The influences of salinity and temperature variabilities on the density variability between different IPO phases are further addressed. During negative IPO phases, the large-value region with positive density anomalies is mainly located west of the dateline corresponding to $ D({T}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $, while the positive $ D({T}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ anomaly is mainly located in the central equatorial Pacific, resembling $ D({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{\mathrm{S}}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ (Fig. 7c). By comparison, $ D({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}) $ shows a positive anomaly west of the dateline in the equatorial Pacific, while negative anomalies occur east of the dateline in the equatorial Pacific during negative IPO phases (Fig. 7e). Meanwhile, during positive IPO phases, $ D({T}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ shows a negative anomaly mainly located over the central equatorial Pacific (Fig. 7d), similar to $ D({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $. In comparison, the negative anomaly of $ D({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}) $ is mainly located in the eastern equatorial Pacific during negative IPO phases (Fig. 7f). It can be concluded that the density variability of the upper ocean in the equatorial Pacific caused by salinity variability is mainly found in the central and western equatorial Pacific, presenting an obvious asymmetry between different IPO phases.

      The relationships of the MLD variability with the salinity and temperature variabilities are further investigated. Similarly to the density, $ \mathrm{M}\mathrm{L}\mathrm{D}({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ is divided into $ \mathrm{M}\mathrm{L}\mathrm{D}({T}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ and $ \mathrm{M}\mathrm{L}\mathrm{D}({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}) $, respectively. It can be seen that the $ \mathrm{M}\mathrm{L}\mathrm{D}({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ distribution varies under different IPO phases in the tropical Pacific. Positive MLD anomalies appear in the western-central equatorial Pacific during negative IPO phases, corresponding to a thicker MLD. In contrast, negative anomalies appear during positive IPO phases, corresponding to thinner MLD (Figs. 8ab). Compared with the distribution characteristics of $ \mathrm{M}\mathrm{L}\mathrm{D}({T}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}},{S}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}) $ during different phases (Figs. 8ef), the MLD anomalies in the tropical Pacific are mainly caused by $ \mathrm{M}\mathrm{L}\mathrm{D}({T}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ (Figs. 8cd). This suggests that the MLD anomaly distribution shows an obvious asymmetry during different IPO phases. Moreover, the MLD distribution characteristics are closely related to the salinity variability in the central-western Pacific, which is the main factor influencing the MLD variability. However, the temperature variation causing the MLD anomalies only plays a weak complementary or even counteracting role. Accordingly, the relationship between the salinity variability and MLD variability is further quantified. According to the distribution characteristics of $ \mathrm{M}\mathrm{L}\mathrm{D}({T}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}},{S}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}}) $ and the corresponding salinity variability during different IPO phases, a key area (160°–180°E, 5°S–5°N) is selected for quantitative estimation of the relationship between the SSS variability and MLD variability during different IPO phases.

      Figure 8.  The horizontal distributions of MLD anomalies (m) in the tropical Pacific for (a, b) MLD (Tinter, Sinter), (c, d) MLD (Tclim, Sinter), and (e, f) MLD (Tinter, Sclim) during negative and positive IPO phases, respectively. Units: m.

      As shown in Fig. 9, although the SSS and MLD show an obvious asymmetry that presents different distribution characteristics between negative and positive phases, the proportionality between the salinity anomaly in the mixed layer and the MLD anomaly exists in both IPO phases. The fresher (saltier) surface layer in the tropical Pacific results in a strengthened (weakened) stratification and a shallower (deeper) mixed layer. The slope of the linear relationship between the SSS anomaly and MLD anomaly in the key area of the tropical Pacific differs during different IPO phases. For example, a salinity change of 1 psu corresponds to a change of 16 m (20 m) in MLD during negative (positive) IPO phases. Corresponding to the salinity anomalies, the MLD anomalies in the non-passing IPO phase are not only asymmetrical in their spatial distribution characteristics but also different in their intensity, as evidenced by the slope of their relationship. This means that the effects of salinity and temperature on the ocean density and stratification are reflected as the density compensation would indicate. As for the anomalous signals of temperature and salinity, their combined effects can be density-compensated or density-uncompensated, resulting in the suppression or enhancement of density variability, respectively. These effects are related to the phase and region of salinity variability (Zhang et al., 2022b). The temperature and salinity jointly determine ocean density, which is an important ocean property. The effects of salinity and temperature on density anomaly differ according to their relative contributions to the density. For example, when the salinity anomaly has a signal opposite in sign to the temperature anomaly, their combined effect results in a larger density change (density-uncompensated effect). On the contrary, when temperature and salinity anomalies have the same sign, their combined effects on the density tend to offset one another (density compensated effect), resulting in a suppressed density change and a reduced magnitude of the density anomaly.

      Figure 9.  Scatterplot of the MLD anomaly and corresponding SSS anomaly averaged in the key area (160°–180°E, 5°S–5°N) during the (a) negative and (b) positive IPO phases. The red line represents the linear regression. Units: m for MLD; psu for SSS.

    • Previous studies have pointed out that the salinity anomalies in the tropical Pacific can modulate the ENSO by influencing ocean density and stratification (Maes et al., 2005; Zhang et al., 2012; Zhu et al., 2014). The salinity variability in the tropical Pacific reveals a difference in its spatiotemporal structures between positive and negative IPO phases, which acts to induce the anomalous thermal-barrier effect and influence the thermal and dynamic structure in the upper ocean. Analysis reveals that during different IPO phases, an asymmetrical relationship can be found between the salinity variabilities and MLD due to the asymmetry of salinity variability. Based on this finding, the relationship between SSS variability and ENSO is further analyzed during different IPO phases to explore the influence of the asymmetry due to this relationship.

      First, the lead-lag relationships between the Niño-3.4 SST index and the salinity variability in the key area (160°–180°E, 5°S–5°N) during negative and positive phases are investigated. Figure 10 shows that the SSS variability negatively correlates with the Niño-3.4 index during both IPO phases, with the correlation coefficients between −0.4 and −0.6. Note that the relationships show some differences during positive and negative phases. For instance, the absolute values of correlation coefficients during positive IPO phases are higher than those during negative IPO phases. Interestingly, no obvious lead/lag relationships can be found between the SSS variability and Niño-3.4 index. In contrast, significant relationships can be found during negative IPO phases when the variation of SSS leads the Niño-3.4 index by 4–6 months. The nature of this covariation implies that the relationships between SSSA and Niño-3.4 SSTA during different IPO phases also exhibit asymmetry. As shown in a previous study (((Maes et al., 2006), a salinity anomaly can alter the thermodynamic relationship with ocean temperature by affecting ocean stratification. From the asymmetric relationship, one may infer that this thermodynamic relationship has changed owing to the difference in salinity variability between the positive and negative IPO phases, confirming the results of Zhang et al. (2022b).

      Figure 10.  Lead/lag correlation coefficients between the Niño-3.4 SST and SSS variability averaged over the key area (160°––180°E, 5°S–5°N) during the negative and positive IPO phases. Red dotted lines represent the values statistically significant at the .05 level based on a student-t test.

      The correlations during different IPO phases also show anisotropy in their spatial distributions. The relationships between the SSS variability and ENSO evolution in the key area are shown in Fig. 11, with negative correlations found in the tropical central Pacific (140°E–140°W, 10°S–10°N) during both IPO phases. However, the location and intensity of large-correlation regions differ between positive and negative IPO phases, as the correlation coefficients observed during positive IPO phases are stronger than those during negative IPO phases, along with their location extending further westward.

      Figure 11.  The spatial distributions of correlation coefficients between SSS variability and Niño-3.4 SST index during (a) negative and (b) positive IPO phases.

      The above analyses indicate that due to the asymmetry of salinity variability during different IPO phases, the in-phase and out-of-phase variations between SSS and ENSO can be caused by the influence of upper-ocean density stratification. The linear relationship between SSS anomaly and ENSO intensity is further quantified through linear regression analysis, as shown in Fig. 12. It is seen that an SSS change of 1 psu during negative (positive) IPO phases corresponds to the ENSO intensity change of 1.3°C (1.6°C).

      Figure 12.  Scatterplot of the monthly Niño-3.4 SST index and corresponding SSS anomaly averaged in the key area (160°–180°E, 5°S–5°N) during (a) negative and (b) positive IPO phases. The red line represents the linear regression. Units are °C for SST and psu for SSS.

    4.   Conclusions and discussion
    • A large gap exists in our understanding of the decadal and interdecadal variability of ocean salinity due to the lack of long-term observations over the ocean basin. However, recent significant improvements in ocean modeling allow for much more realistic simulations of ocean physics and its variations. In this paper, the LASG/IAP LICOM3 is used to depict the interdecadal variability in the Pacific. Using the complete model output fields and employing various methods to analyze the salinity and its budget terms, we have investigated the interdecadal variabilities of oceanic salinity-related parameters in the tropical Pacific and have addressed their relationships to multi-scale and multi-physical processes in the Pacific.

      Based on LICOM3 simulations during 1900–2017, variabilities of salinity and related physics are explored during positive and negative IPO phases. It is found that the asymmetry of salinity variability is closely tied to the IPO in the tropical Pacific, presenting obvious differences which can alter SST over the tropical Pacific. Additionally, the asymmetry of the SSS variability during positive and negative IPO phases is analyzed in terms of the differences in salinity budget terms, including surface forcing caused by the FWF, which contributes up to 40%–50% of the total forcing near the dateline. Moreover, the relationships of salinity variability with salinity budget terms also indicate differences in lead-lag correlations during positive and negative IPO phases. These differences in the asymmetry of salinity variability between positive and negative IPO phases act to modify temperature and salinity effects on density; the altered density variability can be reduced or enhanced, which changes the stratification in the upper ocean. In this way, the salinity anomaly can modulate ENSO intensity, with enhancements (reductions) to El Niño and reductions (enhancements) to La Niña of 1.6°C psu−1 (1.3°C psu−1) during positive (negative) IPO phases. We propose that the modulations of ENSO events may be associated with the salinity asymmetry, which is related to the recent variation in ENSO amplitude (Zhang et al., 2022b). This study can help explain the ENSO diversity and improve its predictability.

      In short, although the nature of the mechanism responsible for salinity variability indicates differences during positive and negative IPO phases, current research supports the premise that large-scale FWF forcing dominates the asymmetry of salinity variability in the tropical Pacific. However, wind anomalies can change the oceanic advection by adjusting the position and intensity of the seawater density, resulting in the western Pacific salinity variations. It is worth noting that there are uncertainties in simulating the location and intensity variations of the salinity in current ocean models. For example, through multi-model and observational analyses, Lin et al. (2018) found that the positive (negative) SST anomaly in the eastern equatorial Pacific is much stronger during the positive (negative) PDO phase than during the negative (positive) phase. Interestingly, the models cannot reasonably reproduce this difference, which indicates that the models have some difficulty in simulating the decadal variability of oceanic physical quantities. Additionally, the conclusions in this paper not only depend on model performance but are also sensitive to the selected analysis periods of the positive and negative IPO phases. In previous analyses, different data and indices were used to represent and analyze the periods of positive and negative IPO phases, which led to several differences in the periods associated with the persistence and transition of IPO phases (Xu and Hu, 2018). For example, regarding the transition of IPO phases around 1980, the dates for IPO phase transitions exhibit several differences; some appear in 1979, while others appear in 1982. This discrepancy may result in different analyses of the physical fields related to IPO phases, thus affecting the outcomes to some extent.

      In addition, covariances exist among the salinity budget terms. For example, salinity tendencies tend to have nonlinear residuals, including surface advection, surface forcing, and subsurface forcing. Thus, the salinity tendency is not completely conserved. At the same time, the model simulations can also affect the salinity budget analyses, such as the u, v, and w fields of the ocean circulation, etc. The analysis of salinity tendency, based on LICOM3 simulations, indicates that the deviations show several regional and model dependencies.

      Processes acting on different timescales interact with each other in the tropical Pacific, and the combined impacts lead to the diversity, variability, and complexity of ENSO, as well as its prediction uncertainty (Gao et al., 2022). The relationship between the interannual variability of ENSO and the IPO is still a subject of much debate, with some studies arguing that the IPO is a residual pattern resulting from the spatial asymmetries of ENSO and the skewness in ENSO statistics (Meehl et al., 2014). Conversely, other studies suggest that decadal variations of the mean climate state in the tropical Pacific are forced by separate mechanisms, which may influence the amplitude, frequency, and teleconnections of ENSO (Power et al., 1999; Meehl and Hu, 2006; Meehl et al., 2010, 2014). The relationships between ENSO and the interdecadal variability of various parameters, including the possible modulating effects of salinity on the interdecadal variability on ENSO in the tropical Pacific, should be addressed in more detail.

      Acknowledgements. This research is supported by the National Natural Science Foundation of China (NSFC; Grant No. 42030410), the Laoshan Laboratory (Grant No. LSKJ202202403), and the National Key Research and Development Program on Monitoring, Early Warning and Prevention of Major Natural Disaster (Grant Nos. 2019YFC1510004, 2020YFA0608902). LIN is supported by the NSFC (Grant No. 41976026); ZHANG is additionally supported by the Startup Foundation for Introducing Talent of NUIST. We thank the Nanjing Hurricane Translation for reviewing the English language quality of this paper.

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