Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Extreme Indian summer monsoon states stifled Bay of Bengal productivity across the last deglaciation

Nature Geoscience volume 18, pages 443–449 (2025)Cite this article

Subjects

Abstract

Indian summer monsoon (ISM) hydrology fuels biogeochemical cycling across South Asia and the Indian Ocean, exerting a first-order control on food security in Earth’s most densely populated areas. Although the ISM is projected to intensify under continued greenhouse forcing, substantial uncertainty surrounds anticipating its impacts on future Indian Ocean stratification and primary production—processes key to the health of already-declining fisheries in the region. Here we present century-scale records of ISM runoff variability and marine biogeochemical impacts in the Bay of Bengal (BoB) since the Last Glacial Maximum (∼21 thousand years ago (ka)). These records reveal extreme monsoon states relative to modern strength, with weakest ISM intensity during Heinrich Stadial 1 (∼17.5–15.5 ka) and strongest during the early Holocene (∼10.5–9.5 ka). Counterintuitively, we find that BoB productivity collapsed during both extreme states of peak monsoon excess and deficits—both due to upper-ocean stratification. Our findings point to the possibility of future declines in BoB primary productivity under a strengthening and more variable ISM regime.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Site locations and ISM rainfall and post-monsoon salinity.
Fig. 2: Palaeoceanographic records from the BoB and Indian palaeomonsoon reconstructions since the Last Glacial Maximum.
Fig. 3: Mechanisms of upper-ocean stratification in the BoB and impacts on primary production inferred from foraminiferal geochemistry.
Fig. 4: Past, present and future density of surface waters in the northern BoB.

Similar content being viewed by others

Data availability

All datasets produced in this study are available via Zenodo90 at https://doi.org/10.5281/zenodo.14994416 (ref. 90). The map in Fig. 1 was generated using open-source Python software (Code availability statement).

Code availability

Open-sourced Python code was used to make the figures, perform the analyses and all calculations including the following modules and their required dependencies: matplotlib91, pandas92, NumPy93, xarray94, cartopy95, SciPy96, Pangeo, seaborn and seawater. All codes generated for this Article (including data) are available via GitHub at https://github.com/planktic/paleoISMthirumalai2025.

References

  1. Hossain, M. S., Sarker, S., Sharifuzzaman, S. M. & Chowdhury, S. R. Primary productivity connects hilsa fishery in the Bay of Bengal. Sci. Rep. 10, 5659 (2020).

    Article  CAS  Google Scholar 

  2. Lam, V. W. Y. & Pauly, D. Status of fisheries in 13 Asian large marine ecosystems. Deep Sea Res. Part II 163, 57–64 (2019).

    Article  Google Scholar 

  3. State of World Fisheries and Aquaculture: Sustainability in Action (FAO, 2020).

  4. Sooraj, K. P., Terray, P. & Mujumdar, M. Global warming and the weakening of the Asian summer monsoon circulation: assessments from the CMIP5 models. Clim. Dyn. 45, 233–252 (2015).

    Article  Google Scholar 

  5. Katzenberger, A., Schewe, J., Pongratz, J. & Levermann, A. Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models. Earth Syst. Dyn. 12, 367–386 (2021).

    Article  Google Scholar 

  6. Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).

    Article  CAS  Google Scholar 

  7. Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

    Article  Google Scholar 

  8. Prasanna Kumar, S. et al. Are eddies nature’s trigger to enhance biological productivity in the Bay of Bengal. Geophys. Res. Lett. 31 (2004).

  9. Narvekar, J. & Kumar, S. P. Seasonal variability of the mixed layer in the central Bay of Bengal and associated changes in nutrients and chlorophyll. Deep Sea Res. Part I 53, 820–835 (2006).

    Article  CAS  Google Scholar 

  10. Singh, A., Gandhi, N., Ramesh, R. & Prakash, S. Role of cyclonic eddy in enhancing primary and new production in the Bay of Bengal. J. Sea Res. 97, 5–13 (2013).

    Article  Google Scholar 

  11. Vinayachandran, P. N. Observations of barrier layer formation in the Bay of Bengal during summer monsoon. J. Geophys. Res. 107, 8018–8019 (2002).

    Article  Google Scholar 

  12. Singh, A., Jani, R. A. & Ramesh, R. Spatiotemporal variations of the δ18O–salinity relation in the northern Indian Ocean. Deep Sea Res. Part I 57, 1422–1431 (2010).

    Article  CAS  Google Scholar 

  13. Clemens, S. C., Kuhnt, W., LeVay, L. J. & Scientists, I. O. D. P. E. International Ocean Discovery Program Expedition 353 Preliminary Report: Indian Monsoon Rainfall (International Ocean Discovery Program, 2015).

  14. Thirumalai, K. & Clemens, S. Monsoon reconstructions using bulk and individual foraminiferal analyses in marine sediments offshore India. Curr. Sci. 119, 328–334 (2020).

    Article  CAS  Google Scholar 

  15. Clemens, S. C. et al. Remote and local drivers of Pleistocene South Asian summer monsoon precipitation: a test for future predictions. Sci. Adv. 7, eabg3848 (2021).

    Article  Google Scholar 

  16. Guptha, M. V. S., Curry, W. B., Ittekkot, V. & Muralinath, A. S. Seasonal variation in the flux of planktic Foraminifera; sediment trap results from the Bay of Bengal, northern Indian Ocean. J. Foraminifer. Res. 27, 5–19 (1997).

    Article  Google Scholar 

  17. Thirumalai, K., Quinn, T. M. & Marino, G. Constraining past seawater δ18O and temperature records developed from foraminiferal geochemistry. Paleoceanogr. Paleoclimatol. 31, 1409–1422 (2016).

    Article  Google Scholar 

  18. Gray, W. R. & Evans, D. Nonthermal influences on Mg/Ca in planktonic foraminifera: a review of culture studies and application to the Last Glacial Maximum. Paleoceanogr. Paleoclimatol. 34, 306–315 (2019).

    Article  Google Scholar 

  19. Ripperger, S., Schiebel, R., Rehkämper, M. & Halliday, A. N. Cd/Ca ratios of in situ collected planktonic foraminiferal tests. Paleoceanography https://doi.org/10.1029/2007PA001524 (2008).

  20. Boyle, E. A., Labeyrie, L. D. & Duplessly, J.-C. Calcitic foraminiferal data confirmed by cadmium in aragonitic Hoeglundina: application to the Last Glacial Maximum in the northern Indian Ocean. Paleoceanography 10, 881–900 (1995).

    Article  Google Scholar 

  21. Lear, C. H. et al. Breathing more deeply: deep ocean carbon storage during the mid-Pleistocene climate transition. Geology 44, 1035–1038 (2016).

    Article  CAS  Google Scholar 

  22. Jaccard, S. L. et al. Subarctic Pacific evidence for a glacial deepening of the oceanic respired carbon pool. Earth Planet. Sci. Lett. 277, 156–165 (2009).

    Article  CAS  Google Scholar 

  23. Pattan, J. N. et al. Coupling between suboxic condition in sediments of the western Bay of Bengal and southwest monsoon intensification: a geochemical study. Chem. Geol. 343, 55–66 (2013).

    Article  CAS  Google Scholar 

  24. Lakhani, K. Q., Lynch-Stieglitz, J. & Monteagudo, M. M. Constraining calcification habitat using oxygen isotope measurements in tropical planktonic foraminiferal tests from surface sediments. Mar. Micropaleontol. 170, 102074 (2022).

    Article  Google Scholar 

  25. Behara, A. & Vinayachandran, P. N. An OGCM study of the impact of rain and river water forcing on the Bay of Bengal. J. Geophys. Res. C 121, 2425–2446 (2016).

    Article  Google Scholar 

  26. Liu, Z. et al. Transient simulation of last deglaciation with a new mechanism for Bolling-Allerod warming. Science 325, 310–314 (2009).

    Article  CAS  Google Scholar 

  27. Kudrass, H. R., Hofmann, A., Doose, H., Emeis, K.-C. & Erlenkeuser, H. Modulation and amplification of climatic changes in the Northern Hemisphere by the Indian summer monsoon during the past 80 k.y. Geology 29, 63–65 (2001).

    Article  Google Scholar 

  28. Zorzi, C. et al. When eastern India oscillated between desert versus savannah‐dominated vegetation. Geophys. Res. Lett. 49, e2022GL099417 (2022).

    Article  Google Scholar 

  29. Saraswat, R., Lea, D. W., Nigam, R., Mackensen, A. & Naik, D. K. Deglaciation in the tropical Indian Ocean driven by interplay between the regional monsoon and global teleconnections. Earth Planet. Sci. Lett. 375, 166–175 (2013).

    Article  CAS  Google Scholar 

  30. Kumar, P. K. & Ramesh, R. Revisiting reconstructed Indian monsoon rainfall variations during the last ~25ka from planktonic foraminiferal δ18O from the Eastern Arabian Sea. Quat. Int. 443, 29–38 (2017).

    Article  Google Scholar 

  31. Sijinkumar, A. V. et al. δ18O and salinity variability from the Last Glacial Maximum to recent in the Bay of Bengal and Andaman Sea. Quat. Sci. Rev. 135, 79–91 (2016).

    Article  Google Scholar 

  32. Weldeab, S. et al. Impact of Indian Ocean surface temperature gradient reversals on the Indian summer monsoon. Earth Planet. Sci. Lett. 578, 117327 (2022).

    Article  CAS  Google Scholar 

  33. Phillips, S. C., Johnson, J. E., Giosan, L. & Rose, K. Monsoon-influenced variation in productivity and lithogenic sediment flux since 110 ka in the offshore Mahanadi Basin, northern Bay of Bengal. Mar. Pet. Geol. 58, 502–525 (2014).

    Article  CAS  Google Scholar 

  34. Zhou, X. et al. Dynamics of primary productivity in the northeastern Bay of Bengal over the last 26000 years. Climate 16, 1969–1986 (2020).

    Google Scholar 

  35. Sijinkumar, A. V., Nath, B. N. & Clemens, S. North Atlantic climatic changes reflected in the Late Quaternary foraminiferal abundance record of the Andaman Sea, north-eastern Indian Ocean. Palaeogeogr. Palaeoclimatol. Palaeoecol. 446, 11–18 (2016).

    Article  Google Scholar 

  36. Ma, R. et al. North Indian Ocean circulation since the last deglaciation as inferred from new elemental ratio records for benthic foraminifera Hoeglundina elegans. Paleoceanogr. Paleoclimatol. 35, e2019PA003801 (2020).

    Article  Google Scholar 

  37. Sarma, V. V. S. S. et al. Effects of freshwater stratification on nutrients, dissolved oxygen, and phytoplankton in the Bay of Bengal. Oceanography 29, 222–231 (2016).

    Article  Google Scholar 

  38. Girishkumar, M. S., Ravichandran, M. & Pant, V. Observed chlorophyll-a bloom in the southern Bay of Bengal during winter 2006–2007. Int. J. Remote Sens. 33, 1264–1275 (2012).

    Article  Google Scholar 

  39. Mukherjee, A., Shankar, D., Chatterjee, A. & Vinayachandran, P. N. Numerical simulation of the observed near-surface East India Coastal Current on the continental slope. Clim. Dyn. 50, 3949–3980 (2018).

    Article  Google Scholar 

  40. Spero, H. J., Lerche, I. & Williams, D. F. Opening the carbon isotope ‘vital effect’ black box, 2, quantitative model for interpreting foraminiferal carbon isotope data. Paleoceanography 6, 639–655 (1991).

    Article  Google Scholar 

  41. Shroyer, E. et al. Bay of Bengal intraseasonal oscillations and the 2018 monsoon onset. Bull. Am. Meteorolog. Soc. 102, E1936–E1951 (2018).

    Article  Google Scholar 

  42. Madhu, N. V. et al. Lack of seasonality in phytoplankton standing stock (chlorophyll a) and production in the western Bay of Bengal. Cont. Shelf Res. 26, 1868–1883 (2006).

    Article  Google Scholar 

  43. Gray, W. R. et al. The effects of temperature, salinity, and the carbonate system on Mg/Ca in Globigerinoides ruber (white): a global sediment trap calibration. Earth Planet. Sci. Lett. 482, 607–620 (2018).

    Article  CAS  Google Scholar 

  44. Lambeck, K., Rouby, H., Purcell, A., Sun, Y. & Sambridge, M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proc. Natl Acad. Sci. USA 111, 15296–15303 (2014).

    Article  CAS  Google Scholar 

  45. Dutt, S. et al. Abrupt changes in Indian summer monsoon strength during 33,800 to 5500 years BP. Geophys. Res. Lett. 42, 5526–5532 (2015).

    Article  Google Scholar 

  46. Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2013).

    Article  Google Scholar 

  47. Balmaseda, M. A., Mogensen, K. & Weaver, A. T. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q. J. R. Meteorol. Soc. 139, 1132–1161 (2012).

    Article  Google Scholar 

  48. Contreras-Rosales, L. A. et al. Evolution of the Indian Summer Monsoon and terrestrial vegetation in the Bengal region during the past 18 ka. Quat. Sci. Rev. 102, 133–148 (2014).

    Article  Google Scholar 

  49. Sengupta, S., Parekh, A., Chakraborty, S., Ravi Kumar, K. & Bose, T. Vertical variation of oxygen isotope in Bay of Bengal and its relationships with water masses. J. Geophys. Res. Oceans 118, 6411–6424 (2013).

    Article  CAS  Google Scholar 

  50. Jian, J., Webster, P. J. & Hoyos, C. D. Large-scale controls on Ganges and Brahmaputra river discharge on intraseasonal and seasonal time-scales. Q. J. R. Meteorol. Soc. 135, 353–370 (2009).

    Article  Google Scholar 

  51. Shetye, S. R. et al. Hydrography and circulation in the western Bay of Bengal during the northeast monsoon. J. Geophys. Res. Oceans 101, 14011–14025 (1996).

    Article  Google Scholar 

  52. Durand, F., Papa, F., Rahman, A. & Bala, S. K. Impact of Ganges–Brahmaputra interannual discharge variations on Bay of Bengal salinity and temperature during 1992–1999 period. J. Earth Syst. Sci. 120, 859–872 (2011).

    Article  Google Scholar 

  53. Benshila, R. et al. The upper Bay of Bengal salinity structure in a high-resolution model. Ocean Modell. 74, 36–52 (2014).

    Article  Google Scholar 

  54. Dandapat, S., Gnanaseelan, C. & Parekh, A. Impact of excess and deficit river runoff on Bay of Bengal upper ocean characteristics using an ocean general circulation model. Deep Sea Res. Part II 172, 104714 (2020).

    Article  Google Scholar 

  55. Thirumalai, K. & Richey, J. N. Potential for paleosalinity reconstructions to provide information about AMOC variability. US Clivar Var. 14, 8–12 (2016).

    Google Scholar 

  56. Thirumalai, K. Salty seas sway global glacial cycles. Nature 617, 258–259 (2023).

    Article  CAS  Google Scholar 

  57. Kumar, A., Sanyal, P. & Agrawal, S. Spatial distribution of δ18O values of water in the Ganga river basin: insight into the hydrological processes. J. Hydrol. 571, 225–234 (2019).

    Article  CAS  Google Scholar 

  58. Pausata, F. S. R., Battisti, D. S., Nisancioglu, K. H. & Bitz, C. M. Chinese stalagmite δ18O controlled by changes in the Indian monsoon during a simulated Heinrich event. Nat. Geosci. 4, 474–480 (2011).

    Article  CAS  Google Scholar 

  59. Tharammal, T. et al. Orbitally driven evolution of Asian monsoon and stable water isotope ratios during the Holocene: isotope-enabled climate model simulations and proxy data comparisons. Quat. Sci. Rev. 252, 106743 (2021).

    Article  Google Scholar 

  60. Dasgupta, B., Ajay, A., Kumar, A., Thamban, M. & Sanyal, P. Isoscape of surface runoff in high mountain catchments: an alternate model for meteoric water characterization and its implications. J. Geophys. Res. Atmos. 126, e2020JD033950 (2021).

    Article  Google Scholar 

  61. Mahto, S. S. & Mishra, V. Does ERA‐5 outperform other reanalysis products for hydrologic applications in India. J. Geophys. Res. Atmos. 124, 9423–9441 (2019).

  62. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  63. Singh, A., Mohiuddin, A., Ramesh, R. & Raghav, S. Estimating the loss of Himalayan glaciers under global warming using the δ18O–salinity relation in the Bay of Bengal. Environ. Sci. Technol. Lett. 1, 249–253 (2014).

    Article  CAS  Google Scholar 

  64. Lambs, L., Balakrishna, K., Brunet, F. & Probst, J. L. Oxygen and hydrogen isotopic composition of major Indian rivers: a first global assessment. Hydrol. Process. 19, 3345–3355 (2005).

    Article  CAS  Google Scholar 

  65. Thirumalai, K., Singh, A., Ramesh, R. & Ramesh, R. A MATLAB™ code to perform weighted linear regression with (correlated or uncorrelated) errors in bivariate data. J. Geol. Soc. India 77, 377–380 (2011).

    Article  Google Scholar 

  66. Sarkar, A., Ramesh, R., Bhattacharya, S. K. & Rajagopalan, G. Oxygen isotope evidence for a stronger winter monsoon current during the last glaciation. Nature 343, 549–551 (1990).

    Article  Google Scholar 

  67. Blaauw, M. & Christen, J. A. Flexible paleoclimate age–depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474 (2011).

    Article  Google Scholar 

  68. Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).

    Article  CAS  Google Scholar 

  69. Raj, H., Bhushan, R., Muruganantham, M., Nambiar, R. & Dabhi, A. J. Marine reservoir age correction for the Andaman Basin. Radiocarbon 62, 1339–1347 (2020).

    Article  Google Scholar 

  70. Prabhakar, M. et al. Morphotypical and geochemical variations of planktic foraminiferal species in Siberian and central Arctic ocean core tops. J. Foraminifer. Res. 54, 1–19 (2024).

    Article  Google Scholar 

  71. Rosenthal, Y., Boyle, E. A. & Labeyrie, L. Last glacial maximum paleochemistry and deepwater circulation in the Southern Ocean: evidence from foraminiferal cadmium. Paleoceanography 12, 787–796 (1997).

    Article  Google Scholar 

  72. Barker, S., Greaves, M. & Elderfield, H. A study of cleaning procedures used for foraminiferal Mg/Ca paleothermometry. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2003GC000559 (2003).

  73. Rosenthal, Y., Field, M. P. & Sherrell, R. M. Precise determination of element/calcium ratios in calcareous samples using sector field inductively coupled plasma mass spectrometry. Anal. Chem. 71, 3248–3253 (1999).

    Article  CAS  Google Scholar 

  74. Fehrenbacher, J. S. et al. Individual foraminiferal analyses: a review of current and emerging geochemical techniques. J. Foraminifer. Res. 54, 312–331 (2024).

    Article  Google Scholar 

  75. Thirumalai, K., Partin, J. W., Jackson, C. S. & Quinn, T. M. Statistical constraints on El Niño Southern Oscillation reconstructions using individual foraminifera: a sensitivity analysis. Paleoceanography 28, 401–412 (2013).

    Article  Google Scholar 

  76. Thirumalai, K., Cohen, A. S. & Taylor, D. Hydrologic controls on individual ostracode stable isotopes in a desert lake: a modern baseline for Lake Turkana. Geochem. Geophys. Geosyst. 24, e2022GC010790 (2023).

    Article  CAS  Google Scholar 

  77. Jana, D., Torres, M., Evans, K., Jayan, A. K. & Thirumalai, K. Paired X‐ray micro CT scanning and individual foraminifera isotopic analysis reveal (de)coupled changes in carbonate preservation and temperature. Paleoceanogr. Paleoclimatol. 39, e2023PA004821 (2024).

  78. Tierney, J. E., Malevich, S. B., Gray, W., Vetter, L. & Thirumalai, K. Bayesian calibration of the Mg/Ca paleothermometer in planktic foraminifera. Paleoceanogr. Paleoclimatol. 34, 2005–2030 (2019).

    Article  Google Scholar 

  79. Holland, S. M. The stratigraphy of mass extinctions and recoveries. Annu. Rev. Earth Planet. Sci. 48, 75–97 (2020).

    Article  CAS  Google Scholar 

  80. Sarma, V. V. S. S. et al. Elevated acidification rates due to deposition of atmospheric pollutants in the coastal Bay of Bengal. Geophys. Res. Lett. 48, e2021GL095159 (2021).

    Article  CAS  Google Scholar 

  81. Bemis, B. E., Spero, H. J., Bijma, J. & Lea, D. W. Reevaluation of the oxygen isotopic composition of planktonic foraminifera: experimental results and revised paleotemperature equations. Paleoceanography 13, 150–160 (1998).

    Article  Google Scholar 

  82. Mehta, S., Singh, A. & Thirumalai, K. Uncertainty in palaeosalinity estimates from foraminiferal geochemical records in the northern Indian Ocean. Palaeogeogr. Palaeoclimatol. Palaeoecol. 569, 110326 (2021).

    Article  Google Scholar 

  83. Spindler, M., Hemleben, C., Salomons, J. B. & Smit, L. P. Feeding behavior of some planktonic foraminifers in laboratory cultures. J. Foraminifer. Res. 14, 237–249 (1984).

    Article  Google Scholar 

  84. Lea, D. W. & Boyle, E. A. Barium in planktonic foraminifera. Geochim. Cosmochim. Acta 55, 3321–3331 (1991).

    Article  CAS  Google Scholar 

  85. Fehrenbacher, J. S. et al. Ba/Ca ratios in the non-spinose planktic foraminifer Neogloboquadrina dutertrei: evidence for an organic aggregate microhabitat. Geochim. Cosmochim. Acta 236, 361–372 (2018).

    Article  CAS  Google Scholar 

  86. Stainbank, S. et al. Controls on planktonic foraminifera apparent calcification depths for the northern equatorial Indian Ocean. PLoS ONE 14, e0222299 (2019).

    Article  CAS  Google Scholar 

  87. Marzin, C., Kallel, N., Kageyama, M., Duplessy, J.-C. & Braconnot, P. Glacial fluctuations of the Indian monsoon and their relationship with North Atlantic climate: new data and modelling experiments. Climate 9, 2135–2151 (2013).

    Google Scholar 

  88. Goodbred, S. L. Jr & Kuehl, S. A. Enormous Ganges–Brahmaputra sediment discharge during strengthened early Holocene monsoon. Geology 28, 1083–1086 (2000).

    Article  Google Scholar 

  89. Ramya Bala, P. et al. Paleovegetation dynamics in an alternative stable states landscape in the montane Western Ghats, India. Holocene 32, 297–307 (2021).

    Article  Google Scholar 

  90. Thirumalai, K. Indian summer monsoon runoff variability, foraminiferal biogeochemical changes, and radiocarbon ages since the Last Glacial Maximum from Site U1446, NW Bay of Bengal. Zenodo https://doi.org/10.5281/zenodo.14994416 (2025).

  91. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  92. McKinney, W. Data structures for statistical computing in Python. In Proc. 9th Python in Science Conf. 51–56 (SciPy, 2010).

  93. Oliphant, T. NumPy: A Guide to NumPy (Continuum Press, 2006).

  94. Hoyer, S. & J., H. xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. 5 (2017).

  95. Met, O. Cartopy: A Cartographic Python Library with a Matplotlib Interface. Github https://github.com/SciTools/cartopy (2015).

  96. Jones, E., Oliphant, T. & Peterson, P. SciPy: Open Source Scientific Tools for Python (ScienceOpen, 2001).

Download references

Acknowledgements

We acknowledge support from the International Ocean Discovery Program (IODP) and are grateful to the IODP Expedition 353 scientific party and JOIDES Resolution staff that enabled successful core sample recovery. We thank M. Glicksman and M. Lis for their assistance in sample preparation. This work was supported by National Science Foundation grants AGS–2103077 and OCE–2423147 to K.T.

Author information

Authors and Affiliations

  1. Department of Geosciences, University of Arizona, Tucson, AZ, USA

    K. Thirumalai, S. Conde, L. Vetter & M. Franks

  2. Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA

    S. C. Clemens

  3. Department of Marine and Coastal Sciences and Department of Earth and Planetary Science, Rutgers, State University of New Jersey, New Brunswick, NJ, USA

    Y. Rosenthal & K. Bu

  4. Environnements et Paléoenvironnements Océaniques et Continentaux (EPOC), University of Bordeaux, CNRS, EPHE, UMR 5805, Pessac, France

    S. Desprat

  5. EPHE, PSL University, Paris, France

    S. Desprat

  6. School of Earth and Sustainability, Northern Arizona University, Flagstaff, AZ, USA

    M. Erb

  7. Department of Geography, Ohio State University, Columbus, OH, USA

    J. Cheng, L. Li & Z. Liu

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

    J. Cheng

  9. Laboratory for Earth Surface Processes, Department of Geography and Institute of Ocean Research, Peking University, Beijing, China

    L. P. Zhou

  10. STAR Institute, Babeș-Bolyai University, Cluj-Napoca, Romania

    L. Giosan

  11. Research Institute of the University of Bucharest (ICUB), Bucharest, Romania

    L. Giosan

  12. Physical Research Laboratory, Navrangpura, Ahmedabad, India

    A. Singh

  13. Civil Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, India

    V. Mishra

Authors
  1. K. Thirumalai
    View author publications

    Search author on:PubMed Google Scholar

  2. S. C. Clemens
    View author publications

    Search author on:PubMed Google Scholar

  3. Y. Rosenthal
    View author publications

    Search author on:PubMed Google Scholar

  4. S. Conde
    View author publications

    Search author on:PubMed Google Scholar

  5. K. Bu
    View author publications

    Search author on:PubMed Google Scholar

  6. S. Desprat
    View author publications

    Search author on:PubMed Google Scholar

  7. M. Erb
    View author publications

    Search author on:PubMed Google Scholar

  8. L. Vetter
    View author publications

    Search author on:PubMed Google Scholar

  9. M. Franks
    View author publications

    Search author on:PubMed Google Scholar

  10. J. Cheng
    View author publications

    Search author on:PubMed Google Scholar

  11. L. Li
    View author publications

    Search author on:PubMed Google Scholar

  12. Z. Liu
    View author publications

    Search author on:PubMed Google Scholar

  13. L. P. Zhou
    View author publications

    Search author on:PubMed Google Scholar

  14. L. Giosan
    View author publications

    Search author on:PubMed Google Scholar

  15. A. Singh
    View author publications

    Search author on:PubMed Google Scholar

  16. V. Mishra
    View author publications

    Search author on:PubMed Google Scholar

Contributions

K.T., S.C.C. and Y.R. conceived the study and experimental design. K.T., S.C.C., Y.R., K.B., S.C., M.F. and L.V. generated measurements for the datasets presented in this Article. S.C.C., S.D., L.P.Z., and L.G. assisted K.T. in generating an age model for Site U1446. M.E., J.C., L.L. and Z.L. assisted K.T. in retrieving and interpreting the climate model output. A.S. and V.M. assisted K.T. in addressing productivity, ocean and monsoon dynamics in the Bay of Bengal. K.T. generated all the figures and wrote the paper with input from all authors. All authors interpreted the results and extensively revised the paper.

Corresponding author

Correspondence to K. Thirumalai.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Ajoy Bhaumik, Kelly Gibson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Observed ISM Runoff and Site U1446 seawater δ18O Seasonality.

(a) Monthly-averaged ERA-5 runoff for the Ganges-Brahmaputra-Meghna (GBM) drainage basin (80–99.5°E, 18–29°N) and (b) observed12 and calculated17 δ18Osw at Site U1446, offshore Mahanadi Basin, northwestern Bay of Bengal. Observed δ18Osw (yellow stars) values were subsampled from a previous compilation12 where the error bars represent the standard deviation of available measurements in that month (1σ whiskers based on a minimum of 5 observations; data unavailable for January, June, August, October, and December). SSS-derived δ18Osw (purple squares) values were calculated from ORA-S4 reanalysis salinity (n = 480 months) using an EICC-specific equation14. Seasonality in δ18Osw via GBM runoff (pink triangles) as input was calculated from a theoretical fractionation model (see Methods). Note that minima in δ18Osw occurs during September–to–October, lagging peak runoff values by ~1 month, and that available δ18Osw values in the EICC region during September are even lower than modeled values. All markers in the above plot depict median values as central measure.

Extended Data Fig. 2 Relationship between estimated mean-annual and October δ18Osw, and the overall seasonal range at Site U1446.

Scatterplot of SSS-derived mean-annual δ18Osw (using a region-specific equation on reanalysis SSS data14) with (a) October δ18Osw (blue triangles) and (b) its seasonal range (blue circles; maximum minus minimum values in a year) at Site U1446. Lines of best fit were built using (solely) the SSS-derived δ18Osw values on both plots (dashed blue lines), incorporating bivariate uncertainty in the parameters (slope, intercept, and correlation coefficients are provided at the bottom65), where 1σ errorbars are based on average error propagation for δ18Osw inversion (based on 225 downcore points), including analytical and sampling error17. The resultant covariability indicates that October δ18Osw minima drive yearly-averaged δ18Osw values, which are in turn, highly correlated with the seasonal range of δ18Osw at Site U1446; 1σ errorbars for the seasonal range were derived from bootstrap resampling (n = 480). This relationship thereby allows us to estimate past seasonality based on our mean-annual δ18Osw reconstruction. For (b) we also plot available observations of δ18Osw near the Mahanadi Basin (black square) and reconstructed δ18O*sw for the late Holocene (red square), early Holocene (green square), and Heinrich Stadial 1 (purple square). Note that these reconstructed points of mean-annual δ18Osw are plotted on the line of best fit to estimate changes in seasonality during that interval. For example, our δ18Osw record indicates a value of ~0.55 ‰ (VSMOW) for HS1, which yields a seasonal range of ~0.7 ‰ (VSMOW) — a ~ 45% decrease relative to the late Holocene seasonal range of ~1.45 ‰.

Extended Data Fig. 3 Age model for upper 7 m of IODP Site U1446 splice used in this study.

Posterior calibrated ages (diamonds are weighted-means) and uncertainty distributions (envelope) derived from age-modeling uncertainty software, BACON67. Analytical 14C errors on individual ages are depicted using ±1σ uncertainty reported from AMS measurements (although most are smaller than the plotted symbols). Radiocarbon measurements were performed using samples containing pristine specimens of upper-ocean planktic foraminiferal species and corrected using the Marine13 curve with a marine reservoir age ΔR correction of ± 40 years69.

Extended Data Fig. 4 Water column properties and foraminiferal calcification depth habitat at Site U1446.

Average seasonal water column (a) temperature, (b) salinity, and (c) calculated δ18Oc profiles at Site U1446 from the World Ocean Atlas Database, alongside median IFA-δ18Oc for different species and their estimated depth of calcification ranges, based on the intersection of measured median δ18Oc values and variability within the calculated δ18Oc seasonal depth profile. δ18Oc was calculated from temperature and salinity using the low-light equation81, and local surface-ocean14 and subsurface49 δ18Osw-salinity relationships for 0–40 m and 40–100 m respectively. JFM: January–March (winter); AMJ: April–June (pre-monsoon season); JAS: July–September (monsoon), OND: October–December (post-monsoon season).

Extended Data Fig. 5 TraCE21ka simulation output from 21ka to present.

Surface winds (a) and precipitation (b) during June-July-August-September (JJAS) over the core monsoon zone of India, with October surface-ocean salinity (c) from the grid point most proximal to Site U1446. HS1 = Heinrich Stadial 1; EH = Early Holocene; LH = Late Holocene (corresponding to the IFA timeslices).

Extended Data Fig. 6 TraCE21ka Late Holocene (LH) mean-state and anomalies relative to Heinrich Stadial 1 (HS1) and the early Holocene (EH).

Mean June–September surface winds (a), and precipitation (b) and October sea-surface salinity (c) for the LH, alongside anomalies of these variables relative to HS1 (d–f), and the EH (g–i).

Extended Data Fig. 7 Simulated climate anomalies for Heinrich Stadial 1 (HS1; 17.5–16.5 ka) and the early Holocene (EH; 10.5–9.5 ka) relative to the Last Glacial Maximum (21–20 ka) and the earliest Holocene (11.7–11.0 ka), respectively.

Surface winds (a, e), precipitation (b, f), sea-surface salinity (c, g) and sea-surface temperature (d, h) during June-July-August-September (JJAS). Proxy sites discussed in the text are depicted as follows: Mawmluh Cave speleothem δ18O (green triangle), SO188-KL342 (purple circle), SK237-GC04 (brown circle), Site U1446 (this study; yellow star).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thirumalai, K., Clemens, S.C., Rosenthal, Y. et al. Extreme Indian summer monsoon states stifled Bay of Bengal productivity across the last deglaciation. Nat. Geosci. 18, 443–449 (2025). https://doi.org/10.1038/s41561-025-01684-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41561-025-01684-6

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing