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SCIENCE CHINA Earth Sciences, Volume 62, Issue 5: 771-782(2019) https://doi.org/10.1007/s11430-018-9276-6

Relationship between sea surface salinity and ocean circulation and climate change

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  • ReceivedApr 5, 2018
  • AcceptedSep 25, 2018
  • PublishedJan 11, 2019

Abstract

Based on Argo sea surface salinity (SSS) and the related precipitation (P), evaporation (E), and sea surface height data sets, the climatological annual mean and low-frequency variability in SSS in the global ocean and their relationship with ocean circulation and climate change were analyzed. Meanwhile, together with previous studies, a brief retrospect and prospect of seawater salinity were given in this work. Freshwater flux (E-P) dominated the mean pattern of SSS, while the dynamics of ocean circulation modulated the spatial structure and low-frequency variability in SSS in most regions. Under global warming, the trend in SSS indicated the intensification of the global hydrological cycle, and featured a decreasing trend at low and high latitudes and an increasing trend in subtropical regions. In the most recent two decades, global warming has slowed down, which is called the “global warming hiatus”. The trend in SSS during this phase, which was different to that under global warming, mainly indicated the response of the ocean surface to the decadal and multi-decadal variability in the climate system, referring to the intensification of the Walker Circulation. The significant contrast of SSS trends between the western Pacific and the southeastern Indian Ocean suggested the importance of oceanic dynamics in the cross-basin interaction in recent decades. Ocean Rossby waves and the Indonesian Throughflow contributed to the freshening trend in SSS in the southeastern Indian Ocean, while the increasing trend in the southeastern Pacific and the decreasing trend in the northern Atlantic implied a long-term linear trend under global warming. In the future, higher resolution SSS data observed by satellites, together with Argo observations, will help to extend our knowledge on the dynamics of mesoscale eddies, regional oceanography, and climate change.


Funded by

the Chinese Academy of Sciences(Grant,No.,XDA19060501)

the State Oceanic Administration of China(Grant,No.,GASI-IPOV,AI-02)

and the National Natural Science Foundation of China(Grant,Nos.,41525019,41506019,&,41830538)


Acknowledgment

We thank Water Cycle Observation Mission (WCOM) group and PhD Student Qiwei SUN for their helps. Argo salinity data is available at (http://www.argo.ucsd.edu), EN4salinity data is obtained from (http://hadobs.metoffice.com/en4/index.html), CCMP wind data is provided by RSS (http://www.remss.com), sea surface height data is provided by AVISO (https://www.aviso.altimetry.fr), GPCP precipitation data is obtained from NASA/GSFC, evaporation data is provided by OAFlux (http://oaflux.whoi.edu), and ERA Interim sea level pressure data is provided by ECMWF (http://apps.ecmwf.int/datasets). This work was supported by the Chinese Academy of Sciences (Grant No. XDA19060501), the State Oceanic Administration of China (Grant No. GASI-IPOV AI-02), and the National Natural Science Foundation of China (Grant Nos. 41525019, 41506019 & 41830538).


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  • Figure 1

    Annual mean Argo sea surface salinity (SSS) (shaded, unit: psu) and evaporation minus precipitation (E-P, contour, unit: mm day−1).

  • Figure 2

    The interannual root mean square of the anomalies for Argo sea surface salinity (shaded, unit: psu) and evaporation minus precipitation (E-P, contour, unit: mm day−1), and the regional mean SSS (black curve) and E-P (blue curve) anomalies in the eastern equatorial Indian Ocean, western equatorial Pacific, eastern equatorial Pacific, northern Bay of Bengal, northwestern Atlantic, and western equatorial Atlantic. The anomalies are with respect to the climatology over the period from 2001 to 2017, and the long-term linear trend is subtracted. The bold curves in subgraphs 1–6 denote the 5-year running average. Note that in subgraph 6, the scale is half of the SSS anomaly in the region.

  • Figure 3

    Correlation of Argo SSS anomalies with precipitation (P), evaporation (E), and E-P. The dotted region passed the 95% confidence level according to a two-tailed Student’s t-test.

  • Figure 4

    Argo SSS linear trend (shaded, unit: psu) and the ratio of the long-term trend to the interannual variability (contour) for 2001–2017, and the average time series of the six maximum-value regions, namely the tropical southeast Indian Ocean, tropical northwestern Pacific, eastern equatorial Indian Ocean, southeastern Pacific, North Atlantic, and southwest Atlantic (unit: psu). The bold curves in subgraphs 1--6 denote the 5-year running average; and the gray lines superimposed in subgraph 1, 2, 4, and 5.

  • Figure 5

    Empirical orthogonal function (EOF) first mode of ERA-Interim sea surface pressure (unit: hPa) and cross-calibrated multiplatform wind field low frequency variation (unit: m s−1). (a) Spatial distribution; (b) time series. The seasonal cycles are removed and a 5-yearrunning average is applied.

  • Figure 6

    Time series of regional mean sea surface salinity in EN4 (unit: psu). (a) Northwestern Pacific and (b) southeastern Indian Ocean corresponding to regions 1 and 2 in Figure 4a, respectively. Bold curves denote the 5-year running average.

  • Figure 7

    (a) The linear trend in the AVISO sea surface height (SSH) anomaly during 1993–2016, (b) the time series of the regional mean SSH anomaly in the northwestern Pacific, and (c) the southeastern Indian Ocean (unit: cm). Rectangles in (a) denote the regions for (b) and (c), while bold curves in (b) and (c) denote the 5-year running average.

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