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Salinity interdecadal variability in the western equatorial Pacific and its effects during 1950–2018

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Abstract

Ocean reanalysis products are used to examine salinity variability and its relationships with temperature in the western equatorial Pacific during 1950–2018. An ensemble empirical mode decomposition (EEMD) method is adopted to separate salinity and temperature signals at different time scales; a focus is placed on interdecadal component in this study. Pronounced interdecadal variations in salinity are seen in the region, which exhibits persistent and transitional phases in association with temperature. A surface freshening is accompanied by a surface warming during the 1980s and 1990s, but saltening and cooling in the 2000s, with interdecadal shifts occurring around in the late 1970s, late 1990s, and during 2016–2018, respectively. Determined by anomaly signs of temperature and salinity, their combined effects can be density-compensated or density-uncompensated, correspondingly acting to produce density variability that is suppressed or enhanced, respectively. The temperature and salinity effects are phase- and depth-dependent. In the subsurface layers at 200 m, where salinity and temperature anomalies tend to be nearly of the same sign during interdecadal evolution, their effects are mostly density-compensated. The situation is more complicated in the surface layer, where variations in sea surface salinity (SSS) and sea surface temperature (SST) exhibit different signs during interdecadal evolution. SST and SSS tend to be of opposite sign during the persistent phases with their effects being density-uncompensated; but they can be of the same sign during the transitional periods and the corresponding changes in SST and SSS undergo density-compensated relationships. Examples are given for the relationships among these fields which exhibit phase differences in sign transitions in the late 1990s; salinity effects are seen to cause a delay in phase transition of density anomalies. Furthermore, the relative contributions to interdecadal variabilities of density and stratification are quantified. The consequences of interdecadal salinity variability are also discussed in terms of their effects on local SST.

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Acknowledgements

The author wishes to thank the two anonymous reviewers for their numerous comments that helped improve the original manuscript significantly. The author would like to thank Dr. Zhaohua Wu for providing helps in using the EEMD method and the related Fortran code. Zhang is supported by the National Natural Science Foundation of China (NSFC; Grant No. 42030410) and the Startup Foundation for Introducing Talent of NUIST; Gao is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (Grant Nos. XDA19060102, XDB42000000) and the NSFC (Grant No. 42176032); Wang is supported by the Strategic Priority Research Program of the CAS (Grant No. XDB40000000); Zhi and Wang are supported by the Financially supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao) (No. 2022QNLM010301-3 and 2022QNLM010301-4).

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Authors and Affiliations

  1. School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Rong-Hua Zhang

  2. Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China

    Rong-Hua Zhang, Guanghui Zhou, Chuan Gao & Hongna Wang

  3. University of Chinese Academy of Sciences, Beijing, 10029, China

    Rong-Hua Zhang, Guanghui Zhou & Chuan Gao

  4. Laboratory for Ocean and Climate Dynamics, Pilot National Laboratory for Marine Science and Technology, Qingdao, 266237, China

    Rong-Hua Zhang, Chuan Gao & Hongna Wang

  5. School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Hai Zhi

  6. National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, 100081, China

    Licheng Feng

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  1. Rong-Hua Zhang
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Appendix 1

Appendix 1

This appendix presents some additional figures in support of arguments in the main texts (see Figs. 15, 16, 17, 1819 and 20).

Fig. 15
figure 15

Horizontal distribution of a climatological mean SST field and of b the standard deviation (STD) for SST interannual fields in the tropical Pacific, which is calculated from the reanalysis data during the period 1942–2018. The unit is °C

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Fig. 16
figure 16

a Scatter plot showing the relationship between SSTA and SSSA in the western equatorial Pacific (the region A as indicated in Fig. 1) and the corresponding correlation coefficient is − 0.50; b the same as in (a) but for subsurface temperature and salinity anomalies at depth of 200 m, and the corresponding correlation coefficient is 0.87

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Fig. 17
figure 17

a The relationship between density and temperature interdecadal anomalies at the sea surface in the region A with their correlation coefficient of − 0.84; b the same as in (a) but for density and salinity interdecadal anomalies with their correlation coefficient of 0.73; c the same as in (a) but for density and temperature interdecadal anomalies at depth of 200 m with their correlation coefficient of − 0.99; d the same as in (c) but for density and salinity interdecadal anomalies with their correlation coefficient of − 0.80

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Fig. 18
figure 18

The ratio of the standard deviation for interdecadal density anomalies; three calculations are performed to quantify individual contributions of temperature and salinity interdecadal fields to density (see the text for detail), ρ(Tinterde, Sinterde) is obtained using both interdecadal temperature and salinity fields; ρ(Tinterde, Sclim) is obtained using interdecadal temperature field and climatological salinity field; ρ(Tclim, Sinterde) is obtained using climatological temperature field and interdecadal salinity field, respectively: a ρ(Tinterde, Sclim)/ρ(Tinterde, Sinterde) at the surface; b ρ(Tinterde, Sclim)/ρ(Tinterde, Sinterde) at depth of 200 m; c ρ(Tclim, Sinterde)/ρ(Tinterde, Sinterde) at the surface; d ρ(Tclim, Sinterde)/ρ(Tinterde, Sinterde) at depth of 200 m

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Fig. 19
figure 19

The ratio of the standard deviations for SST (a) and SSS (b) interdecadal variabilities relative to those of total SST and SSS interannual variabilities

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Fig. 20
figure 20

The lagged correlation calculated among interdecadal anomalies of SSD, SST and SSS in the western equatorial Pacific (the region A as indicated in Fig. 1). Interdecadal anomalies of SSD lead SSS, with their maximum correlation coefficient being achieved (0.8) when the SSS is lagging the SSD by 2 years. Also, interdecadal anomalies of SSD lag SST, with their maximum correlation coefficient being achieved (− 0.83) when the SST is leading the SSD by 2 years; interdecadal anomalies of SST lead SSS, with their maximum correlation coefficient being achieved (− 0.5) when the SSS is lagging the SST by about 4 years

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Zhang, RH., Zhou, G., Zhi, H. et al. Salinity interdecadal variability in the western equatorial Pacific and its effects during 1950–2018. Clim Dyn 60, 1963–1985 (2023). https://doi.org/10.1007/s00382-022-06417-8

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