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Coupled decline in ocean pH and carbonate saturation during the Palaeocene–Eocene Thermal Maximum

Nature Geoscience volume 17, pages 1299–1305 (2024)Cite this article

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Abstract

The Palaeocene–Eocene Thermal Maximum, a climate event 56 million years ago, was characterized by rapid carbon release and extensive ocean acidification. However, our understanding of acidification and the evolution of ocean saturation states continues to be hindered by considerable uncertainties, primarily stemming from the limited availability of proxy data. Under such conditions, data assimilation allows for an internally consistent assessment of atmospheric CO2 changes, ocean acidification and carbonate saturation state during this period. Here, we present a reconstruction of the Palaeocene–Eocene Thermal Maximum carbon cycle perturbation by assimilating seafloor sediment CaCO3 and sea surface temperature proxy data with simulations from an Earth system model, which includes a comprehensive carbonate system. Our reconstructions indicate a substantial increase in atmospheric CO2 from 890 ppm (95% credible interval: 680–1,170 ppm) to 1,980 ppm (1,680–2,280 ppm), coupled with a notable decline in pH (0.46 units, ranging from 0.31 to 0.63 units) and surface-water calcite saturation state, decreasing from 10.2 (7.5–12.8) in the pre-event period to 3.8 (2.8–5.1) during the thermal maximum. Carbonate undersaturation intensified substantially in high-latitude surface waters during the Palaeocene–Eocene Thermal Maximum, paralleling the current decline in Arctic aragonite saturation driven by anthropogenic CO2 emissions.

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Fig. 1: Palaeo-locations of SST proxies and CaCO3 datasets for the PETM.
Fig. 2: Data assimilation reconstruction for the PETM.
Fig. 3: Sensitivity tests.
Fig. 4: Reconstruction maps and proxy data for the PETM.

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Data availability

All relevant data are included in the text and Supplementary Information. Detailed proxy data are provided in Supplementary Table 3. The posterior climate fields are available on Zenodo (https://doi.org/10.5281/zenodo.13779050)120.

Code availability

The data assimilation code used in this study is available as the Python Jupyter Notebook package ‘DeepDA’ on GitHub (https://github.com/mingsongli/DeepDA) and Zenodo (https://doi.org/10.5281/zenodo.13777776)121. The Bayesian forward models BAYSPAR (https://github.com/jesstierney/BAYSPAR), BAYFOX (https://github.com/mingsongli/bayfox) and BAYMAGPY (https://github.com/mingsongli/baymagpy) are publicly available on GitHub. The code for the version of the ‘muffin’ release of the cGENIE Earth system model used in this article is tagged as 0.9.56, and is accessible at https://doi.org/10.5281/zenodo.13839281 (ref. 122). Configuration files for the specific experiments presented in the article can be found in the directory: genie-userconfigs/PUBS/published/Li_et_al.NatGeo.2024. Details of the experiments, plus the command line needed to run each one, are given in the readme.txt file in that directory. All other configuration files and boundary conditions are provided as part of the code release. A manual detailing code installation, basic model configuration, tutorials covering various aspects of model configuration, experimental design and output, plus the processing of results, are available at https://doi.org/10.5281/zenodo.1407657 (ref. 123).

References

  1. Ridgwell, A. & Schmidt, D. N. Past constraints on the vulnerability of marine calcifiers to massive carbon dioxide release. Nat. Geosci. 3, 196–200 (2010).

    CAS  Google Scholar 

  2. Tierney, J. E. et al. Past climates inform our future. Science 370, eaay3701 (2020).

    CAS  Google Scholar 

  3. Hönisch, B. et al. The geological record of ocean acidification. Science 335, 1058–1063 (2012).

    Google Scholar 

  4. Rae, J. W. B. in Boron Isotopes: The Fifth Element (eds Marschall, H. & Foster, G.) 107–143 (Springer, 2018).

  5. Hönisch, B. et al. Toward a Cenozoic history of atmospheric CO2. Science 382, eadi5177 (2023).

    Google Scholar 

  6. Gibbs, S. J. et al. Ocean warming, not acidification, controlled coccolithophore response during past greenhouse climate change. Geology 44, 59–62 (2016).

    Google Scholar 

  7. Doney, S. C., Busch, D. S., Cooley, S. R. & Kroeker, K. J. The impacts of ocean acidification on marine ecosystems and reliant human communities. Annu. Rev. Environ. Resour. 45, 83–112 (2020).

    Google Scholar 

  8. Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681–686 (2005).

    CAS  Google Scholar 

  9. Thomas, E. in Large Ecosystem Perturbations: Causes and Consequences (eds Monechi, S. et al.) 1–23 (Geological Society of America, 2007).

  10. IPCC. Climate Change 2021: The Physical Science Basis—Working Group I contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 1–2391 (Cambridge Univ. Press, 2021).

  11. Ridgwell, A. A Mid Mesozoic Revolution in the regulation of ocean chemistry. Mar. Geol. 217, 339–357 (2005).

    CAS  Google Scholar 

  12. Kirtland Turner, S., Hull, P. M., Kump, L. R. & Ridgwell, A. A probabilistic assessment of the rapidity of PETM onset. Nat. Commun. 8, 353 (2017).

    Google Scholar 

  13. Li, M. et al. Astrochronology of the Paleocene–Eocene Thermal Maximum on the Atlantic Coastal Plain. Nat. Commun. 13, 5618 (2022).

    CAS  Google Scholar 

  14. Gutjahr, M. et al. Very large release of mostly volcanic carbon during the Palaeocene–Eocene Thermal Maximum. Nature 548, 573–577 (2017).

    CAS  Google Scholar 

  15. Hantsoo, K. G., Kump, L. R., Haupt, B. J. & Bralower, T. J. Tracking the Paleocene–Eocene Thermal Maximum in the North Atlantic: a shelf-to-basin analysis with a regional ocean model. Paleoceanogr. Paleoclimatol. 33, 1324–1338 (2018).

    Google Scholar 

  16. Haynes, L. L. & Hönisch, B. The seawater carbon inventory at the Paleocene–Eocene Thermal Maximum. Proc. Natl Acad. Sci. USA 117, 24088–24095 (2020).

    CAS  Google Scholar 

  17. Hakim, G. J. et al. The last millennium climate reanalysis project: framework and first results. J. Geophys. Res. Atmos. 121, 6745–6764 (2016).

    Google Scholar 

  18. Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239–244 (2021).

    CAS  Google Scholar 

  19. Tierney, J. E. et al. Spatial patterns of climate change across the Paleocene–Eocene Thermal Maximum. Proc. Natl Acad. Sci. USA 119, e2205326119 (2022).

    CAS  Google Scholar 

  20. Ridgwell, A., Zondervan, I., Hargreaves, J. C., Bijma, J. & Lenton, T. M. Significant long-term increase of fossil fuel CO2 uptake from reduced marine calcification. Biogeosci. Discuss. 3, 1763–1780 (2006).

    Google Scholar 

  21. Ridgwell, A. & Hargreaves, J. C. Regulation of atmospheric CO2 by deep-sea sediments in an Earth system model. Glob. Biogeochem. Cycles 21, GB2008 (2007).

    Google Scholar 

  22. Panchuk, K., Ridgwell, A. & Kump, L. R. Sedimentary response to Paleocene–Eocene Thermal Maximum carbon release: a model-data comparison. Geology 36, 315–318 (2008).

    CAS  Google Scholar 

  23. Cui, Y. et al. Slow release of fossil carbon during the Palaeocene–Eocene Thermal Maximum. Nat. Geosci. 4, 481–485 (2011).

    CAS  Google Scholar 

  24. Greene, S. E. et al. Early Cenozoic decoupling of climate and carbonate compensation depth trends. Paleoceanogr. Paleoclimatol. 34, 930–945 (2019).

    CAS  Google Scholar 

  25. Bralower, T. J. et al. Evidence for shelf acidification during the onset of the Paleocene–Eocene Thermal Maximum. Paleoceanogr. Paleoclimatol. 33, 1408–1426 (2018).

    Google Scholar 

  26. Zhu, J., Poulsen, C. J. & Tierney, J. E. Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. Sci. Adv. 5, eaax1874 (2019).

    CAS  Google Scholar 

  27. Hollis, C. J. et al. The DeepMIP contribution to PMIP4: methodologies for selection, compilation and analysis of latest Paleocene and early Eocene climate proxy data, incorporating version 0.1 of the DeepMIP database. Geosci. Model Dev. 12, 3149–3206 (2019).

    CAS  Google Scholar 

  28. Tierney, J. E. & Tingley, M. P. A Bayesian, spatially-varying calibration model for the TEX86 proxy. Geochim. Cosmochim. Acta 127, 83–106 (2014).

    CAS  Google Scholar 

  29. Malevich, S. B., Vetter, L. & Tierney, J. E. Global core-top calibration of δ18O in planktic foraminifera to sea-surface temperature. Paleoceanogr. Paleoclimatol. 34, 1292–1315 (2019).

    Google Scholar 

  30. 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).

    Google Scholar 

  31. Tardif, R. et al. Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling. Clim 15, 1251–1273 (2019).

    Google Scholar 

  32. Whitaker, J. S. & Hamill, T. M. Ensemble data assimilation without perturbed observations. Mon. Weather Rev. 130, 1913–1924 (2002).

    Google Scholar 

  33. Anagnostou, E. et al. Proxy evidence for state-dependence of climate sensitivity in the Eocene greenhouse. Nat. Commun. 11, 4436 (2020).

    CAS  Google Scholar 

  34. Spero, H. J., Bijma, J., Lea, D. W. & Bemis, B. E. Effect of seawater carbonate concentration on foraminiferal carbon and oxygen isotopes. Nature 390, 497–500 (1997).

    CAS  Google Scholar 

  35. Zeebe, R. E. An expression for the overall oxygen isotope fractionation between the sum of dissolved inorganic carbon and water. Geochem. Geophys. Geosyst. 8, Q09002 (2007).

    Google Scholar 

  36. Harper, D. T. et al. The magnitude of surface ocean acidification and carbon release during Eocene Thermal Maximum 2 (ETM-2) and the Paleocene–Eocene Thermal Maximum (PETM). Paleoceanogr. Paleoclimatol. 35, e2019PA003699 (2020).

    Google Scholar 

  37. Penman, D. E., Hönisch, B., Zeebe, R. E., Thomas, E. & Zachos, J. C. Rapid and sustained surface ocean acidification during the Paleocene–Eocene Thermal Maximum. Paleoceanography 29, 357–369 (2014).

    Google Scholar 

  38. Babila, T. L. et al. Capturing the global signature of surface ocean acidification during the Palaeocene–Eocene Thermal Maximum. Philos. Trans. R. Soc. A 376, 20170072 (2018).

    Google Scholar 

  39. Kakihana, H., Kotaka, M., Satoh, S., Nomura, M. & Okamoto, M. Fundamental studies on the ion-exchange separation of boron isotopes. Bull. Chem. Soc. Jpn 50, 158–163 (1977).

    CAS  Google Scholar 

  40. Klochko, K., Kaufman, A. J., Yao, W., Byrne, R. H. & Tossell, J. A. Experimental measurement of boron isotope fractionation in seawater. Earth Planet. Sci. Lett. 248, 276–285 (2006).

    CAS  Google Scholar 

  41. John, C. M. et al. North American continental margin records of the Paleocene-Eocene thermal maximum: implications for global carbon and hydrological cycling. Paleoceanography 23, PA2217 (2008).

    Google Scholar 

  42. Zachos, J. C. et al. Rapid acidification of the ocean during the Paleocene–Eocene thermal maximum. Science 308, 1611–1615 (2005).

    CAS  Google Scholar 

  43. Penman, D. E. et al. An abyssal carbonate compensation depth overshoot in the aftermath of the Palaeocene–Eocene Thermal Maximum. Nat. Geosci. 9, 575–580 (2016).

    CAS  Google Scholar 

  44. Dunkley Jones, T. et al. Dynamics of sediment flux to a bathyal continental margin section through the Paleocene–Eocene Thermal Maximum. Clim. Past 14, 1035–1049 (2018).

    Google Scholar 

  45. Cui, Y., Li, M., van Soelen, E. E., Peterse, F. & Kürschner, W. M. Massive and rapid predominantly volcanic CO2 emission during the end-Permian mass extinction. Proc. Natl Acad. Sci. USA 118, e2014701118 (2021).

    CAS  Google Scholar 

  46. 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).

    CAS  Google Scholar 

  47. Ma, D., Gregor, L. & Gruber, N. Four decades of trends and drivers of global surface ocean acidification. Glob. Biogeochem. Cycles 37, e2023GB007765 (2023).

    CAS  Google Scholar 

  48. Yamamoto-Kawai, M., McLaughlin, F. A., Carmack, E. C., Nishino, S. & Shimada, K. Aragonite undersaturation in the Arctic Ocean: effects of ocean acidification and sea ice melt. Science 326, 1098–1100 (2009).

    CAS  Google Scholar 

  49. Edwards, N. R. & Marsh, R. Uncertainties due to transport-parameter sensitivity in an efficient 3-D ocean-climate model. Clim. Dyn. 24, 415–433 (2005).

    Google Scholar 

  50. Ridgwell, A. Interpreting transient carbonate compensation depth changes by marine sediment core modeling. Paleoceanography 22, PA4102 (2007).

    Google Scholar 

  51. Colbourn, G., Ridgwell, A. & Lenton, T. The rock geochemical model (RokGeM) v0.9. Geosci. Model Dev. 6, 1543–1573 (2013).

    Google Scholar 

  52. Berner, R. A. GEOCARB III: a revised model of atmospheric CO2 over Phanerozoic time. Am. J. Sci. 301, 182–204 (2001).

    CAS  Google Scholar 

  53. Brady, P. V. The effect of silicate weathering on global temperature and atmospheric CO2. J. Geophys. Res. Solid Earth 96, 18101–18106 (1991).

    CAS  Google Scholar 

  54. Lunt, D. J. et al. DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data. Clim 17, 203–227 (2021).

    Google Scholar 

  55. IPCC. Climate Change 2007—the Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC (ed. Solomon, S.) (Cambridge Univ. Press, 2007).

  56. Zeebe, R. E. & Tyrrell, T. History of carbonate ion concentration over the last 100 million years II: revised calculations and new data. Geochim. Cosmochim. Acta 257, 373–392 (2019).

    CAS  Google Scholar 

  57. McKay, M. D., Beckman, R. J. & Conover, W. J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245 (1979).

    Google Scholar 

  58. Tripati, A. & Elderfield, H. Deep-sea temperature and circulation changes at the Paleocene–Eocene Thermal Maximum. Science 308, 1894–1898 (2005).

    CAS  Google Scholar 

  59. Barnet, J. S. K. et al. Coupled evolution of temperature and carbonate chemistry during the Paleocene–Eocene; new trace element records from the low latitude Indian Ocean. Earth Planet. Sci. Lett. 545, 116414 (2020).

    CAS  Google Scholar 

  60. Zachos, J. C. et al. A transient rise in tropical sea surface temperature during the Paleocene–Eocene Thermal Maximum. Science 302, 1551–1554 (2003).

    CAS  Google Scholar 

  61. Tripati, A. K. & Elderfield, H. Abrupt hydrographic changes in the equatorial Pacific and subtropical Atlantic from foraminiferal Mg/Ca indicate greenhouse origin for the thermal maximum at the Paleocene–Eocene Boundary. Geochem. Geophys. Geosyst. 5, Q02006 (2004).

    Google Scholar 

  62. Elling, F. J. et al. Archaeal lipid biomarker constraints on the Paleocene–Eocene carbon isotope excursion. Nat. Commun. 10, 4519 (2019).

    Google Scholar 

  63. Sluijs, A. et al. Environmental precursors to rapid light carbon injection at the Palaeocene/Eocene boundary. Nature 450, 1218–1221 (2007).

    CAS  Google Scholar 

  64. Schoon, P. L., Heilmann-Clausen, C., Schultz, B. P., Sinninghe Damsté, J. S. & Schouten, S. Warming and environmental changes in the eastern North Sea Basin during the Palaeocene–Eocene Thermal Maximum as revealed by biomarker lipids. Org. Geochem. 78, 79–88 (2015).

    CAS  Google Scholar 

  65. Stokke, E. W., Jones, M. T., Tierney, J. E., Svensen, H. H. & Whiteside, J. H. Temperature changes across the Paleocene–Eocene Thermal Maximum—a new high-resolution TEX86 temperature record from the Eastern North Sea Basin. Earth Planet. Sci. Lett. 544, 116388 (2020).

    CAS  Google Scholar 

  66. Sluijs, A. et al. Warming, euxinia and sea level rise during the Paleocene–Eocene Thermal Maximum on the Gulf Coastal Plain: implications for ocean oxygenation and nutrient cycling. Clim.Past 10, 1421–1439 (2014).

    Google Scholar 

  67. Smith, V. et al. Life and death in the Chicxulub impact crater: a record of the Paleocene–Eocene Thermal Maximum. Clim. Past 16, 1889–1899 (2020).

    Google Scholar 

  68. Hollis, C. J. et al. Early Paleogene temperature history of the Southwest Pacific Ocean: reconciling proxies and models. Earth Planet. Sci. Lett. 349–350, 53–66 (2012).

    Google Scholar 

  69. Sluijs, A. et al. Southern ocean warming, sea level and hydrological change during the Paleocene–Eocene thermal maximum. Clim. Past 7, 47–61 (2011).

    Google Scholar 

  70. Frieling, J. et al. Tropical Atlantic climate and ecosystem regime shifts during the Paleocene–Eocene Thermal Maximum. Clim. Past 14, 39–55 (2018).

    Google Scholar 

  71. Frieling, J. et al. Paleocene–Eocene warming and biotic response in the epicontinental West Siberian Sea. Geology 42, 767–770 (2014).

    Google Scholar 

  72. Zachos, J. C. et al. Extreme warming of mid-latitude coastal ocean during the Paleocene–Eocene Thermal Maximum: inferences from TEX86 and isotope data. Geology 34, 737–740 (2006).

    Google Scholar 

  73. Babila, T. L. Boron/calcium in planktonic foraminifera: proxy development and application to the Paleocene–Eocene boundary (Citeseer, 2014).

  74. Bornemann, A. et al. Persistent environmental change after the Paleocene–Eocene Thermal Maximum in the eastern North Atlantic. Earth Planet. Sci. Lett. 394, 70–81 (2014).

    CAS  Google Scholar 

  75. Sluijs, A. et al. Late Paleocene–early Eocene Arctic Ocean sea surface temperatures: reassessing biomarker paleothermometry at Lomonosov Ridge. Clim. Past 16, 2381–2400 (2020).

    Google Scholar 

  76. Kozdon, R. et al. Enhanced poleward flux of atmospheric moisture to the Weddell Sea region (ODP Site 690) during the Paleocene–Eocene Thermal Maximum. Paleoceanogr. Paleoclimatol. 35, e2019PA003811 (2020).

    Google Scholar 

  77. Si, W. & Aubry, M. P. Vital effects and ecologic adaptation of photosymbiont-bearing planktonic foraminifera during the Paleocene–Eocene Thermal Maximum: implications for paleoclimate. Paleoceanogr. Paleoclimatol. 33, 112–125 (2018).

    Google Scholar 

  78. Frieling, J. et al. Extreme warmth and heat-stressed plankton in the tropics during the Paleocene–Eocene Thermal Maximum. Sci. Adv. 3, e1600891 (2017).

    Google Scholar 

  79. Kozdon, R. et al. In situ δ18O and Mg/Ca analyses of diagenetic and planktic foraminiferal calcite preserved in a deep‐sea record of the Paleocene–Eocene Thermal Maximum. Paleoceanography 28, 517–528 (2013).

    Google Scholar 

  80. Aze, T. et al. Extreme warming of tropical waters during the Paleocene–Eocene Thermal Maximum. Geology 42, 739–742 (2014).

    CAS  Google Scholar 

  81. Babila, T. L., Rosenthal, Y., Wright, J. D. & Miller, K. G. A continental shelf perspective of ocean acidification and temperature evolution during the Paleocene–Eocene Thermal Maximum. Geology 44, 275–278 (2016).

    CAS  Google Scholar 

  82. Hines, B. R. et al. Reduction of oceanic temperature gradients in the early Eocene Southwest Pacific Ocean. Palaeogeogr. Palaeoclimatol. Palaeoecol. 475, 41–54 (2017).

    Google Scholar 

  83. Huurdeman, E. P. et al. Rapid expansion of meso-megathermal rain forests into the southern high latitudes at the onset of the Paleocene–Eocene Thermal Maximum. Geology 49, 40–44 (2020).

    Google Scholar 

  84. Inglis, G. N. et al. Terrestrial methane cycle perturbations during the onset of the Paleocene–Eocene Thermal Maximum. Geology 49, 520–524 (2020).

    Google Scholar 

  85. Jin, S. et al. Tropical ocean temperatures and changes in terrigenous flux during the Paleocene–Eocene Thermal Maximum in southern Tibet. Glob. Planet. Change 230, 104289 (2023).

    Google Scholar 

  86. Kozdon, R., Kelly, D. C., Kita, N. T., Fournelle, J. H. & Valley, J. W. Planktonic foraminiferal oxygen isotope analysis by ion microprobe technique suggests warm tropical sea surface temperatures during the Early Paleogene. Paleoceanography 26, PA3206 (2011).

    Google Scholar 

  87. Van Andel, T. H. Mesozoic/cenozoic calcite compensation depth and the global distribution of calcareous sediments. Earth Planet. Sci. Lett. 26, 187–194 (1975).

    Google Scholar 

  88. Zeebe, R. E., Zachos, J. C. & Dickens, G. R. Carbon dioxide forcing alone insufficient to explain Palaeocene–Eocene Thermal Maximum warming. Nat. Geosci. 2, 576–580 (2009).

    CAS  Google Scholar 

  89. Kaya, M. Y. et al. The Eurasian epicontinental sea was an important carbon sink during the Palaeocene–Eocene Thermal Maximum. Commun. Earth Environ. 3, 124 (2022).

    Google Scholar 

  90. Self-Trail, J. M., Powars, D. S., Watkins, D. K. & Wandless, G. A. Calcareous nannofossil assemblage changes across the Paleocene–Eocene Thermal Maximum: evidence from a shelf setting. Mar. Micropaleontol. 92, 61–80 (2012).

    Google Scholar 

  91. Crouch, E. M. et al. The Apectodinium acme and terrestrial discharge during the Paleocene–Eocene thermal maximum: new palynological, geochemical and calcareous nannoplankton observations at Tawanui, New Zealand. Palaeogeogr. Palaeoclimatol. Palaeoecol. 194, 387–403 (2003).

    Google Scholar 

  92. Schmitz, B. et al. High-resolution iridium, δ13C, δ18O, foraminifera and nannofossil profiles across the latest Paleocene benthic extinction event at Zumaya, Spain. Palaeogeogr. Palaeoclimatol. Palaeoecol. 133, 49–68 (1997).

    Google Scholar 

  93. Bolle, M., Adatte, T., Keller, G., Von Salis, K. & Hunziker, J. Biostratigraphy, mineralogy and geochemistry of the Trabakua Pass and Ermua sections in Spain: Paleocene–Eocene transition. Eclogae Geol. Helv. 91, 1–25 (1998).

    CAS  Google Scholar 

  94. Wright, J. D. & Schaller, M. F. Evidence for a rapid release of carbon at the Paleocene–Eocene Thermal Maximum. Proc. Natl Acad. Sci. USA 110, 15908–15913 (2013).

    CAS  Google Scholar 

  95. Hollis, C. J. et al. The Paleocene–Eocene Thermal Maximum at DSDP Site 277, Campbell Plateau, southern Pacific Ocean. Clim. Past 11, 1009–1025 (2015).

    Google Scholar 

  96. Thomas, D. J., Bralower, T. J. & Zachos, J. C. New evidence for subtropical warming during the Late Paleocene thermal maximum: stable isotopes from Deep Sea Drilling Project Site 527, Walvis Ridge. Paleoceanography 14, 561–570 (1999).

    Google Scholar 

  97. Thomas, D. J. & Bralower, T. J. Sedimentary trace element constraints on the role of North Atlantic Igneous Province volcanism in late Paleocene–early Eocene environmental change. Mar. Geol. 217, 233–254 (2005).

    CAS  Google Scholar 

  98. Bralower, T. J. Evidence of surface water oligotrophy during the Paleocene–Eocene thermal maximum: nannofossil assemblage data from Ocean Drilling Program Site 690, Maud Rise, Weddell Sea. Paleoceanography 17, 1023 (2002).

    Google Scholar 

  99. Bralower, T. J. et al. High-resolution records of the late Paleocene thermal maximum and circum-Caribbean volcanism: is there a causal link? Geology 25, 963–966 (1997).

    Google Scholar 

  100. Quillévéré, F., Aubry, M.-P., Norris, R. D. & Berggren, W. A. Paleocene oceanography of the eastern subtropical Indian Ocean: an integrated magnetobiostratigraphic and stable isotope study of ODP Hole 761B (Wombat Plateau). Palaeogeogr. Palaeoclimatol. Palaeoecol. 184, 371–405 (2002).

    Google Scholar 

  101. Rudnicki, M. D., Wilson, P. A. & Anderson, W. T. Numerical models of diagenesis, sediment properties and pore fluid chemistry on a paleoceanographic transect: Blake Nose, Ocean Drilling Program Leg 171B. Paleoceanography 16, 563–575 (2001).

    Google Scholar 

  102. Colosimo, A. B., Bralower, T. & Zachos, J. C. in Proc. ODP Science Results Vol. 198 (eds Bralower, T. J. et al.) 98SR-112 (Texas A & M Univ., 2006).

  103. Leon-Rodriguez, L. & Dickens, G. R. Constraints on ocean acidification associated with rapid and massive carbon injections: the early Paleogene record at ocean drilling program site 1215, equatorial Pacific Ocean. Palaeogeogr. Palaeoclimatol. Palaeoecol. 298, 409–420 (2010).

    Google Scholar 

  104. Murphy, B., Lyle, M. & Lyle, A. O. in Proc. ODP Science Results Vol. 199 (eds Wilson, P. A. et al.) 199SR-215 (Texas A & M Univ., 2006).

  105. Mutterlose, J., Linnert, C. & Norris, R. Calcareous nannofossils from the Paleocene–Eocene Thermal Maximum of the equatorial Atlantic (ODP Site 1260B): evidence for tropical warming. Mar. Micropaleontol. 65, 13–31 (2007).

    Google Scholar 

  106. Barron, J. et al. in Proc. ODP Science Results Vol. 119 (eds Barron J. et al.) 229–288 (Texas A & M Univ., 1989).

  107. Ludden, J., Gradstein, F. & Shipboard Scientific Party. in Proc. ODP Science Results Vol. 123 (eds Ludden, J. N. et al.) 269–352 (Texas A & M Univ., 1990).

  108. Lu, G., Keller, G., Adatte, T., Ortiz, N. & Molina, E. Long-term (105) or short-term (103) δ13C excursion near the Palaeocene–Eocene transition: evidence from the Tethys. Terra Nova 8, 347–355 (1996).

    Google Scholar 

  109. Khozyem, H. et al. New geochemical constraints on the Paleocene–Eocene thermal maximum: Dababiya GSSP, Egypt. Palaeogeogr. Palaeoclimatol. Palaeoecol. 429, 117–135 (2015).

    Google Scholar 

  110. Gavrilov, Y., Shcherbinina, E., Golovanova, O. & Pokrovsky, B. in Climatic and Biotic Events of the Paleogene [CBEP 2009]. Extended Abstracts (eds Crouch, E. M. et al.) 67–70 (Wellington, 2009).

  111. Giusberti, L. et al. Mode and tempo of the Paleocene–Eocene thermal maximum in an expanded section from the Venetian pre-Alps. Geol. Soc. Am. Bull. 119, 391–412 (2007).

    CAS  Google Scholar 

  112. Speijer, R. & Wagner, T. in Catastrophic Events and Mass Extinctions: Impacts and Beyond (eds Koeberl, C. & MacLeod, K. G.) 533–549 (Geological Society of America, 2002).

  113. Gavrilov, Y. O., Shcherbinina, E. A. & Oberhiinsli, H. in Causes and Consequences of Globally Warm Climates in the Early Paleogene Vol. 369 (eds Wing, S. et al.) 147–168 (Geological Society of America, 2003).

  114. Slotnick, B. S. et al. Large-amplitude variations in carbon cycling and terrestrial weathering during the latest Paleocene and earliest Eocene: the record at Mead Stream, New Zealand. J. Geol. 120, 487–505 (2012).

    CAS  Google Scholar 

  115. Slotnick, B. S. et al. Early Paleogene variations in the calcite compensation depth: new constraints using old borehole sediments from across Ninetyeast Ridge, central Indian Ocean. Clim 11, 473–493 (2015).

    Google Scholar 

  116. Hancock, H. J. L., Dickens, G. R., Thomas, E. & Blake, K. L. Reappraisal of early Paleogene CCD curves: foraminiferal assemblages and stable carbon isotopes across the carbonate facies of Perth Abyssal Plain. Int. J. Earth Sci. 96, 925–946 (2006).

    Google Scholar 

  117. Killick, R. & Eckley, I. changepoint: an R package for changepoint analysis. J. Stat. Softw. 58, 1–19 (2014).

    Google Scholar 

  118. Zachos, J., Stott, L. D. & Lohmann, K. C. Evolution of Early Cenozoic marine temperatures. Paleoceanography 9, 353–387 (1994).

    Google Scholar 

  119. Zeebe, R. E. Seawater pH and isotopic paleotemperatures of Cretaceous oceans. Palaeogeogr. Palaeoclimatol. Palaeoecol. 170, 49–57 (2001).

    Google Scholar 

  120. Li, M. mingsongli/PETMcGENIE_DA: PETMcGENIE_DA Dataset—Li et al., 2024 Release. Zenodo https://doi.org/10.5281/zenodo.13779049 (2024).

  121. Li, M. mingsongli/deepDA: v1.0.0. Zenodo https://doi.org/10.5281/zenodo.13777775 (2024).

  122. Ridgwell, A. et al. derpycode/cgenie.muffin: v0.9.56. Zenodo https://doi.org/10.5281/zenodo.13839281 (2024).

  123. Ridgwell, A. et al. derpycode/muffindoc: v0.24-574-NSF. Zenodo https://doi.org/10.5281/zenodo.13377225 (2024).

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Acknowledgements

We thank F. Zhang (deceased), T. Bralower, O. Ajayi, X. Zhang, Q. Jiang and X. Chen for their help with the methodology. This research was funded by the National Key R&D Program of China 2022YFF0802900 to M.L., Heising-Simons Foundation, United States 2016-011 to L.R.K. and M.L., 2016-013 to A.R., 2016-015 to J.E.T., 2016-014 to G.J.H. and 2016-012 to C.J.P. A.R. acknowledges support from the National Science Foundation (grant no. OCE-2244897). This work is supported by High-Performance Computing Platform of Peking University and computing cluster Sterling of University of California at Riverside. This paper is a contribution to the ‘Deep-Time Digital Earth’ Big Science Program.

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Authors and Affiliations

  1. Key Laboratory of Orogenic Belts and Crustal Evolution, MOE, School of Earth and Space Sciences, Peking University, Beijing, China

    Mingsong Li & Haoxun Zhang

  2. Department of Geosciences, Pennsylvania State University, University Park, PA, USA

    Mingsong Li & Lee R. Kump

  3. State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing, China

    Mingsong Li

  4. Department of Earth and Planetary Sciences, University of California, Riverside, CA, USA

    Andy Ridgwell

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

    Jessica E. Tierney & Steven B. Malevich

  6. Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA

    Gregory J. Hakim & Robert Tardif

  7. Department of Earth Sciences, University of Oregon, Eugene, OR, USA

    Christopher J. Poulsen

  8. Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA

    Jiang Zhu

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  1. Mingsong Li
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Contributions

Study conceptualization was provided by M.L., L.R.K. and J.E.T. Methodology was provided by M.L., L.R.K., A.R., G.J.H., S.B.M., R.T. and J.Z. Investigation and visualization were provided by M.L. The original manuscript draft was produced by M.L. with major input from L.R.K., A.R. and J.E.T. All authors contributed to manuscript editing.

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Correspondence to Mingsong Li.

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Extended data

Extended Data Fig. 1 State-dependent climate sensitivity in cGENIE.

a, Relationship between atmospheric CO2 concentration (pCO2) and surface air temperature (SAT) in cGENIE under varying climate sensitivity scenarios (controlled by τ, as shown in b). Each black dot represents a steady-state pCO2 and SAT value achieved after 2 million years of open system simulations with different τ values and outgassing rates (from left to right: 0.6×, 0.8×, 1.0×, 1.2×, 1.4×, 1.6×, 1.8×, 2.0×, 2.2×, and 2.4× 7.24 × 1012 mol C yr−1). The cyan dots represent pCO2 and SAT values from Eocene CESM simulations, for CO2 levels of 1×, 3×, 6×, and 9× preindustrial atmospheric level (PAL)26. The slope of each green dot comparing to the leftmost cyan dot (1× PAL) is shown near the green dot. b, Relationship between τ and climate sensitivity in cGENIE (blue dots), alongside the slopes of each pCO2 scenario from CESM simulations (dashed horizontal lines, corresponding to cyan dots in a). c, A linear fit is presented to determine τ within cGENIE, with the regression line shown in solid red and the actual data points from CESM simulations depicted as cyan dots. The dashed black lines represent the 95% confidence intervals for the regression line, indicating the range within which the true relationship is expected to fall. d, Updated cGENIE model (solid lines) incorporating state-dependent climate sensitivity accurately reproduces SATs as observed in CESM simulations (cyan dots). In contrast, the earlier version of the cGENIE model, employing a fixed τ of 5.77, results in lower SATs at pCO2 values exceeding 1 × PAL, as shown by the dotted line in comparison to the cyan dots.

Extended Data Fig. 2 Determination of parameters used in cGENIE model.

a, Relationship between pCO2 and global mean alkalinity. This panel illustrates the correlation used to set initial conditions in our model, ensuring that a 20,000-year closed system run can replicate results analogous to a 2-million-year open system run. b, Historical changes in ion concentrations. Displayed here are past variations in ion concentrations ([Ca2+], [Mg2+], and [SO42−]) derived from fluid inclusion data56. The dots indicate the specific values employed in the PETM simulations.

Extended Data Fig. 3 Model comparisons: 20,000-year closed system run versus 2-million-year open system run.

The solid red lines represent results from a closed system run with prescribed initial ocean alkalinity, and the dashed blue lines correspond to the final 20,000 years of 2-million-year open system simulations. Top panels show results for a volcanic outgassing rate at 0.6×, reaching steady-state pCO2 of 362 ppm. Middle panels depict the 1.0× outgassing rate, stabilizing at a pCO2 of 869 ppm. Bottom panels present the 2.2× outgassing rate, culminating in a steady-state pCO2 of 1937 ppm.

Extended Data Fig. 4 Evaluation statistics for the pre-PETM.

This figure illustrates the improvement in model accuracy post-data assimilation, indicated by a decrease in the root-mean-square-error (r.m.s.e.) and an increase in the coefficient of efficiency (CE) between observed data and model predictions. Data-model comparisons are visualized through density plots (blue for data-prior comparison and orange for data-posterior comparison), with crosses marking the respective mean values.

Extended Data Fig. 5 Covariance maps.

This figure shows the relationships between the CaCO3 field at IODP Site U1409 (blue circles) and global site fields of sea surface temperature (SST, a), pH (b), and CaCO3 (c).

Extended Data Fig. 6 Sensitivity tests for the PETM.

This figure displays the reconstructed changes during the PETM in sea surface temperature (ΔSST, a), atmospheric CO2 concentration (ΔpCO2, b), surface pH (ΔpH, c), seafloor weight % CaCO3 (ΔCaCO3, d), surface calcite saturation state (ΔΩcalcite, e), and surface aragonite saturation state (ΔΩaragonite, f) across five different scenarios. The ‘Σg’ indicates reconstructions using cGENIE modeled pH, salinity, and saturation for proxy forward models. ‘no CaCO3’ excludes CaCO3 data assimilation. ‘CaCO3’ represents reconstructions assimilating only CaCO3 data. ‘ΣfixECS’ refers to reconstructions using the originally climate sensitivity in terms of radiative forcing set at 4 W/m2. ‘Σgsp’ features reconstructions using the same model ensemble as the prior for both the pre-PETM and PETM intervals. Gray areas represent density plots for the prior, while orange areas represent density plots for the posterior. Dashed lines represent the medians and 50% quantiles from the corresponding experiments, with accompanying text denoting the medians.

Extended Data Fig. 7 Data assimilation reconstruction in the ‘no CaCO3’ scenario for the PETM.

This figure presents the data assimilation reconstructions of SST (a), pCO2 (b), surface pH (c), CaCO3 (d), surface calcite saturation state (Ωcalcite, e), and surface aragonite saturation state (Ωaragonite, f) for both the pre-PETM and PETM intervals, represented by filled lines. These are contrasted with corresponding priors, depicted by unfiled lines. Color coding is as follows: blue for pre-PETM and orange for PETM. The analyses are based on a total of 10,000 realizations.

Extended Data Fig. 8 Data assimilation reconstruction in the ‘CaCO3’ scenario for the PETM.

This figure presents the data assimilation reconstructions of SST (a), pCO2 (b), surface pH (c), CaCO3 (d), surface calcite saturation state (Ωcalcite, e), and surface aragonite saturation state (Ωaragonite, f) for both the pre-PETM and PETM intervals, represented by filled lines. These are contrasted with corresponding priors, depicted by unfiled lines. Color coding is as follows: blue for pre-PETM and orange for PETM. The analyses are based on a total of 10,000 realizations.

Extended Data Fig. 9 Data assimilation reconstruction in the ‘ΣfixECS’ scenario for the PETM.

This figure presents the data assimilation reconstructions of SST (a), pCO2 (b), surface pH (c), CaCO3 (d), surface calcite saturation state (Ωcalcite, e), and surface aragonite saturation state (Ωaragonite, f) for both the pre-PETM and PETM intervals, represented by filled lines. These are contrasted with corresponding priors, depicted by unfiled lines. Color coding is as follows: blue for pre-PETM and orange for PETM. The analyses are based on a total of 10,000 realizations.

Extended Data Fig. 10 Data assimilation reconstruction in the ‘Σg’ scenario for the PETM.

This figure presents the increase in SST during the PETM (a), the change in weight % CaCO3 (pre-PETM minus PETM, b), and ocean topography in cGENIE (c). Some cell grids show an increase in weight % CaCO3 (blue grids) during the PETM, including two in Siberian and Paleo-Tethys located in shallow water settings with a water depth of less than ~300 m.

Supplementary information

Supplementary Information

Supplementary Discussion.

Supplementary Tables 1–14

Proxy variance scaling and assimilation evaluation. These tables provide a comprehensive overview of proxy variance scaling evaluation, assimilation statistics, proxy data and posterior distributions for all scenarios analysed in the study. Supplementary Tables 1 and 2 present evaluation statistics, showing that scaling TEX86, δ18O and Mg/Ca variances by 2× yields optimal performance, while scaling CaCO3 variance by 0.75× is optimal. Supplementary Table 3 contains the full table of proxy data used for assimilation, and Supplementary Tables 4–14 summarize the posterior distributions for all analysed scenarios.

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Li, M., Kump, L.R., Ridgwell, A. et al. Coupled decline in ocean pH and carbonate saturation during the Palaeocene–Eocene Thermal Maximum. Nat. Geosci. 17, 1299–1305 (2024). https://doi.org/10.1038/s41561-024-01579-y

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