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The expanding Indo-Pacific freshwater pool and changing freshwater pathway in the South Indian Ocean

Nature Climate Change (2026)Cite this article

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Abstract

Understanding ocean freshwater variability is key to assessing the global water cycle and climate change, but changes in freshwater storage and transport remain unclear. Here we show that the South Indian Ocean—a vital conduit for interocean exchange—has experienced the strongest freshening in the Southern Hemisphere since the 1960s. This freshening drives a southward expansion of the Indo-Pacific freshwater pool into the South Indian Ocean, where freshwater has increased by 6.5 ± 0.5% per decade, as indicated by the shrinking 35.3 psu isohaline. Strengthened Indonesian Throughflow and intensified Subtropical Gyre inflow are the primary causes. In the upper ~200 m, freshening follows a new subtropical pathway rather than the usual tropical route. These changes arise from wind shifts linked to the Hadley cell’s poleward expansion and a stronger Indonesian Throughflow, both driven by warm-pool warming. Ongoing warming will further expand the freshwater pool and broaden the subtropical pathway, affecting climate, interocean exchange and marine ecosystems.

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Fig. 1: Salinity, current and evaporation–precipitation climatology and salinity trend from eight datasets.
Fig. 2: Linear trend of the RFWC and horizontal freshwater transport for the 1960–2022 period.
Fig. 3: Variations in wind and current patterns and their relationship HC expansion.
Fig. 4: Trends of SSH, SST and surface wind stress.
Fig. 5: Trends of salinity and ocean current.
Fig. 6: Trends of barrier-layer thickness, mixed-layer depth, buoyancy frequency and ocean heat content.

Data availability

IAP global ocean salinity and ocean geostrophic current 0.5° gridded dataset are available via http://www.ocean.iap.ac.cn/pages/dataService/dataService.html. EN.4.2.2 is available via https://www.metoffice.gov.uk/hadobs/en4/download-en4-2-2.html. NCEI is available via https://www.ncei.noaa.gov/access/global-ocean-heat-content/. WOA23 is available via https://www.ncei.noaa.gov/access/world-ocean-atlas-2023/. MOAA is available via https://www.jamstec.go.jp/argo_research/dataset/moaagpv/moaa_en.html. ORAS5 is available via https://www.ecmwf.int/en/forecasts/dataset/ocean-reanalysis-system-5. SODA3.12.2 is available via https://www2.atmos.umd.edu/~ocean/index_files/soda3.12.2_mn_download_b.htm. GOFS 3.1 is available via https://www.hycom.org/dataserver/gofs-3pt1/reanalysis. GLORYS is available via https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/description. JRA55 is available via https://rda.ucar.edu/datasets/d628001/. JRA55-do is available via https://climate.mri-jma.go.jp/pub/ocean/JRA55-do/. ERA5 is available via https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels-monthly-means?tab=overview. CCMP is available via https://www.remss.com/measurements/ccmp/. NCEP1 is available via https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. NCEP2 is available via https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. HadISST1 is available via https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. ERSST V5 is available via https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html. COBE-SST2 is available via https://www.data.jma.go.jp/tcc/tcc/products/elnino/cobesst2_doc.html. SST from CMIT6 is available via https://cds.climate.copernicus.eu/datasets/projections-cmip6?tab=download. CESM-LE is available via https://www.cesm.ucar.edu/community-projects/lens/data-sets. WP pacemaker ensemble is available via https://www.cesm.ucar.edu/working-groups/climate/simulations/cesm1-indian-ocean-pacemaker. TPO pacemaker ensemble is available via https://www.cesm.ucar.edu/working-groups/climate/simulations/cesm1-pacific-pacemaker.

Code availability

Codes for the main results are available via Zenodo at https://zenodo.org/records/17877372 (ref. 97).

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Acknowledgements

G.C. is supported by National Key Research and Development Program of China (grant no. 2023YFF0805300), NSFC (grant nos 42476199 and 42476022), the GuangDong Basic and Applied Basic Research Foundation (grant no. 2024B1515040024), the Open Research Cruise (grant no. NORC2022-10+NORC2022-303) supported by NSFC Shiptime Sharing Projects 42149910 and Youth Innovation Promotion Association CAS (Y2021093). W.H. is supported by the National Science Foundation awards NSF-OCE 2242193 and NSF-AGS 1935279. The CESM1 experiments were run on Cheyenne, the high-performance computing resources (https://doi.org/10.5065/D6RX99HX) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S. Department of Energy's Office of Biological & Environmental Research (BER) under Lawrence Livermore National Lab subaward DE-AC52-07NA27344, Lawrence Berkeley National Lab subaward DE-AC02-05CH11231, and Pacific Northwest National Lab subaward DE-AC05-76RL01830. This work was also supported by the National Science Foundation (NSF) National Center for Atmospheric Research, which is a major facility sponsored by NSF under Cooperative Agreement No. 1852977.

Author information

Authors and Affiliations

  1. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China

    Gengxin Chen & Lei Zhang

  2. Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA

    Gengxin Chen & Weiqing Han

  3. National Center for Atmospheric Research, Boulder, CO, USA

    Aixue Hu, Gerald A. Meehl & Nan Rosenbloom

  4. Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA

    Arnold L. Gordon

  5. Texas A&M University–Corpus Christi, Corpus Christi, TX, USA

    Toshiaki Shinoda

  6. Department of Earth and Planetary Science, Graduate School of Science, University of Tokyo, Tokyo, Japan

    Yukio Masumoto

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  1. Gengxin Chen
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Contributions

G.C. and W.H. designed the study and wrote the paper. G.C. conducted the analysis and produced the figures. A.H., G.A.M., N.R. and L.Z. carried out the numerical model experiments. A.H., A.L.G., T.S., L.Z. and Y.M. contributed to the refinement of the paper.

Corresponding authors

Correspondence to Gengxin Chen or Weiqing Han.

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The authors declare no competing interests.

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Nature Climate Change thanks Noir Purba and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Salinity, current and current trend.

(a) The climatological salinity (color; unit: psu) and current (vector; scale: 0.1 m s−1) in the upper 200 m from the blended dataset for the 1960-2022 period. (b) Same as (a) but for 200-600 m. (c) Same as (a) but for the current trend (scale: 10−2m s−1/10 yr). Figure created with MATLAB version 9.11.0.

Extended Data Fig. 2 Trends of Evaporation–Precipitation and Salinity in the upper 200 m.

(a)The linear trend map from the blended salinity dataset (see Methods) in the global Southern Hemisphere’s subtropical oceans for the 1960-2022 period averaged in the upper 200 m. Unit: 10−2 psu/30 yr. Color contours show trend values exceed 95% significance and white areas are below 95% significance. (b) The linear trend map of evaporation-precipitation for the 1960-2022 period from the blended data (see Method). The black line labels 15°S. Unit:10−5 kg/m2/s/30 yr. Stippled areas denote the trend values are statistically significant at the 95% confidence level. Figure created with MATLAB version 9.11.0.

Extended Data Fig. 3 Salinity variation in the South Indian Ocean (SIO).

(a) The salinity differences between the recent 30 years (1990-2019) and the first 30 years (1960-1989). Unit: 10−2 psu. (b) The salinity trend for a zonal-vertical (x-z) section averaged for 30-20°S latitudes of the SIO from the blended dataset. Unit: 10−2 psu/30 yr. Figure created with MATLAB version 9.11.0.

Extended Data Fig. 4 Horizontal freshwater transport (FWH) along sections.

The meridional FWH averaged at 100-120°E along (a) 15°S and (b) 35°S. (c) The zonal FWH (westward) averaged at 24-26°E in the upper 200 m. Unit :104 m3/10 yr. Figure created with MATLAB version 9.11.0.

Extended Data Fig. 5 The RFWC differences on isopycnal surfaces between the last 30 years (1990-2019) and the first 30 years (1960-1989).

(a-f) The differences on isopycnal surfaces of sigma=24.2, 25, 25.4, 26.2, 26.6, and 27σ0, respectively (unit: 10−4m3). σ0 is the potential density of seawater referenced to the surface, minus 1000 kg/m3. (g-i) The differences along sections 15°S, 20°S, and 30°S (unit: 10−4m3). Contours indicate the climatological depth (unit: m) of the corresponding isopycnals. Figure created with MATLAB version 9.11.0.

Extended Data Fig. 6 Southward expansion of ocean circulation, the Hadley Cell (HC), and wind stress.

(a) The climatological thermocline depth (represented by the depth of 20 °C isotherm) during 1960-1989 (black contours) and 1990-2019 (red contours). SCTR: Seychelles-Chagos Thermocline Ridge; STG: Subtropical Gyre. (b) Yearly variation of the HC edge. (c) Yearly variation of zonal wind transition position of the counterclockwise wind stress in the subtropical SIO (refer to the sketched white circle in Fig. 3a in Main text). Figure created with MATLAB version 9.11.0.

Extended Data Fig. 7 Trends of SST and surface wind stress.

(a) Trends of SST from ERSSTV5 and wind stress from JRA55-do during 1960-2019. (b) Same as (a) but for HadiSST1 and NCEP1 wind. (c) Same as (a) but for COBE-SST v2 and EAR5 wind. (d) Trends of wind stress from JRA55. Unit for SST trend is oC/30 yr. Shading areas denote the trend values are statistically significant at the 95% confidence level. The vector scale for trend of wind stress is 5×10−3N m2/30 yr. Figure created with MATLAB version 9.11.0.

Extended Data Fig. 8 Trends of wind, salinity, current and sea surface temperature (SST).

Trends of (a) wind stress, (b) salinity in the upper 600 m and current in the upper 200 m from the 40-member ensemble mean of CESM1 (CESM1-LE) during 1960-2019, measuring the effects of external forcing. (c-d) Same as (a-b) but from the 10-member ensemble mean of experiment WP, measuring the effects of external forcing and Indo-Pacific warm pool SST forcing. (e) Indo-Pacific warm-pool SST anomaly (curves) and trend (straight lines) from the blended SST (black) and 24-member ensembles of CMIP6 (red). (f) Same as (e) but for tropical eastern Pacific Ocean SST. Time series of interannual IPO index and its trend are also shown in this subplot (blue). (g-h) Trends for SST and wind from the 10-member ensemble mean of experiment WP–CESM1-LE measuring the effects of Indo-Pacific warm pool SST forcing, and TPO–CESM1-LE measuring the effects of tropical east Pacific SST forcing during 1920-2019. The vector scales for (a, c, g, h) and (b, d) are 5×10−3N m2/10 y and 0.5 m s−1/10 y, respectively. Units for salinity trend in (b, d), and SST trend in (g, h) are 10−2psu/30 yr and oC/30 yr, respectively. Shading areas and stippled areas denote the trend values are statistically significant at the 95% confidence level. Figure created with MATLAB version 9.11.0.

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Chen, G., Han, W., Hu, A. et al. The expanding Indo-Pacific freshwater pool and changing freshwater pathway in the South Indian Ocean. Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-025-02553-1

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