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Weaker Atlantic overturning circulation increases the vulnerability of northern Amazon forests

Nature Geoscience volume 17, pages 1284–1290 (2024)Cite this article

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Abstract

The Atlantic meridional overturning circulation (AMOC) and the Amazon forest are viewed as connected tipping elements in a warming climate system. If global warming exceeds a critical threshold, the AMOC may slow down substantially, changing atmospheric circulation and leading to Amazonia becoming drier in the north and wetter in the south. Yet, the impact of an AMOC slowdown on Amazon vegetation is still not well constrained. Here we use pollen and microcharcoal data from a marine sediment core to assess changes in Amazon vegetation from 25,000 to 12,500 years ago. Additionally, we model vegetation responses to an AMOC slowdown under both glacial and pre-industrial conditions. During a past AMOC slowdown (Heinrich Stadial 1–18,000 to 14,800 years ago), pollen data evidence a decline in cold- and moist-affinity elements, coupled with a rise in seasonal tropical vegetation. This pattern is consistent with the decline in suitability of northern Amazon moist forests in a model with an imposed 50% AMOC weakening under glacial conditions. Our modelling results suggest similar changes for a comparable AMOC slowdown under pre-industrial conditions. Combined with current disturbances such as deforestation and wildfires elsewhere in the basin, an AMOC slowdown may exert a systemic impact on the Amazon forest.

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Fig. 1: Study region and representativity of the marine pollen spectra.
Fig. 2: Amazonian and equatorial Atlantic environmental changes assessed through data from marine sediment core GeoB16224-1.
Fig. 3: Anomalies in the suitability of tropical moist forests over South America.

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Data availability

The new data shown herein are available within the supplementary material and through Pangaea (pollen data: https://doi.org/10.1594/PANGAEA.968664, charcoal data: https://doi.org/10.1594/PANGAEA.968665).

Code availability

The methodology used in the MaxEnt algorithm90 is detailed in the manuscript, including the parameters used for running the algorithm. The analysis is based on published methods90,91,92, allowing reproducibility of results. The CCSM3 source code is disseminated via the Earth System Grid (ESG). Detailed information on how to access the code can be found at https://www2.cesm.ucar.edu/models/ccsm3.0. Any further requests for materials can be addressed to T.K.A. or C.M.C.

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Acknowledgements

This work was funded by the State of São Paulo Research Foundation–FAPESP project PPTEAM (grant 2018/15123-4). T.K.A. acknowledges the financial from FAPESP (grants 2019/19948-0 and 2021/13129-8). C.M.C. acknowledges the financial support from CNPq (grant 312458/2020-7). C.M.C. thanks the CLAMBIO consortium and the BiodivERsA 2019-2020 Joint COFUND Call on ‘Biodiversity and Climate Change’. CLAMBIO is partially funded by FAPESP (2019/24349-9). Logistic and technical assistance was provided by the captain and crew of the RV MS Merian. M.C.C. acknowledges the financial support from FAPESP (grant 2019/25179-0). D.J.B. acknowledges the financial support from FAPESP (grants 2019/24977-0 and 2022/06640-1). N.B. acknowledges funding by the Volkswagen foundation and the European Union’s Horizon Europe research and innovation programme under grant agreement number 101137601 (ClimTip contribution number 17). We thank M. Georget on laboratory assistance during the microcharcoal analysis. Thanks are also due to the Herbarium staff of the Field Museum of Natural History in Chicago for allowing pollen sampling, which improved our taxonomical identifications.

Author information

Authors and Affiliations

  1. Institute of Geosciences, University of São Paulo, São Paulo, Brazil

    T. K. Akabane, D. J. Bertassoli Jr, M. C. Campos & P. E. De Oliveira

  2. School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, Brazil

    C. M. Chiessi

  3. Department of Physics, Federal University of Santa Catarina, Florianópolis, Brazil

    M. Hirota

  4. Faculté des Sciences Appliquées, UIZ-Ibn Zohr University of Agadir, Agadir, Morocco

    I. Bouimetarhan

  5. MARUM–Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany

    M. Prange & S. Mulitza

  6. Department of Earth and Planetary Sciences, Geological Institute, ETH Zurich, Zurich, Switzerland

    C. Häggi

  7. Intercantonal Laboratory, Schaffhausen, Switzerland

    C. Häggi

  8. Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands

    A. Staal

  9. AWI–Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

    G. Lohmann

  10. Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany

    N. Boers

  11. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    N. Boers

  12. Department of Mathematics and Global Systems Institute, University of Exeter, Exeter, UK

    N. Boers

  13. UMR 5805 EPOC, Université de Bordeaux, CNRS, Bordeaux INP, Pessac, France

    A. L. Daniau

  14. Departamento de Biologia Vegetal, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil

    R. S. Oliveira

  15. Institute of Geosciences, University of Campinas, Campinas, Brazil

    M. C. Campos

  16. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

    X. Shi

  17. Keller Science Action Center, The Field Museum of Natural History, Chicago, IL, USA

    P. E. De Oliveira

Authors
  1. T. K. Akabane
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  2. C. M. Chiessi
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  3. M. Hirota
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  4. I. Bouimetarhan
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  5. M. Prange
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  6. S. Mulitza
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  7. D. J. Bertassoli Jr
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  8. C. Häggi
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  12. A. L. Daniau
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  16. P. E. De Oliveira
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Contributions

T.K.A. carried out the palynological analysis and the niche distribution modelling; contributed to the design and discussion and led the writing of the manuscript. C.M.C. conceived the project, provided a substantial contribution to the design and discussions and co-wrote the paper. P.E.D.O. assisted with and provided resources for the palynological analysis and contributed to the interpretation of the data. M.H. contributed to the design and co-wrote the paper. I.B. performed preliminary palynological analysis. M.P. and G.L. provided the climate models. A.L.D. assisted with the microcharcoal analysis. D.J.B. contributed to the interpretations. M.H., I.B., M.P., G.L., D.J.B., C.H., M.C.C., A.S., N.B., R.S.O., A.L.D., X.S. and S.M. contributed to the discussion and critical revision of the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to T. K. Akabane.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Marcelo Cohen, Alan Wanamaker and Xianfeng Wang for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Age model of marine sediment core GeoB16224-1.

The age model is based on fifteen calibrated 14 C ages obtained on planktonic foraminifera60. The calibration was performed with the Marine20 calibration curve62. A tie point was added at 710 cm core depth with age 46,830 ± 470 years BP from the correlation of X-ray fluorescence data of core GeoB16224-1 and the El Condor stalagmite ELC-B stable oxygen isotope record (tie point)22,31. The age model was produced using the ‘BACON’ R package61. Dashed horizontal line indicates the depositional hiatus placed at 57 cm core depth.

Extended Data Fig. 2 Multivariate analysis of marine and continental Amazonian pollen signals.

(a) Map with the location of all the samples used in the Correspondence analysis (CA). Marine core top (red squares), GeoB16224-1 downcore data (violet circles); modern fluvial (asterisk)24, and pollen rain (crosses)25 data (Supplementary Table 3). (b) CA after removing aquatic, mangrove (Rhizophora and Avicennia), and interpreted coastal taxa, such as Cyperaceae and Amaranthus. (c) CA including mangrove and potentially coastal vegetation taxa. For both CA, we removed singletons and taxa with less than 5 % abundance, Cecropia and aquatic taxa.

Extended Data Fig. 3 Pollen diagram from core GeoB16224-1, and core-top samples GeoB16212-3, GeoB16217-2, and GeoB16223-1.

The values are given in relative abundance calculated after excluding taxa attributed to coastal vegetation (*) (Supplementary Table 4). CONISS analysis is based on the Bray-Curtis distance of square root-transformed pollen relative abundances, excluding coastal vegetation and taxa with less than 2% occurrence in the total sum of samples. Data from marine core GeoB16224-1 spans the interval from ca. 25,000 to 5,000 yr BP. Pollen curves exaggeration: 10×. Blue bands delimit the Heinrich stadials 1 (subdivided in HS1a and HS1b) and 2 (HS2). Dashed lines represent main pollen zones according to the CONISS analysis.

Extended Data Fig. 4 Predicted suitability of tropical moist forest (TMF) over South America for different scenarios.

(a) Pre-industrial (PI); (b) pre-industrial with an Atlantic meridional overturning circulation (AMOC) slowdown (PI.hos); (c) Last Glacial Maximum (LGM); (d) LGM with an AMOC slowdown, simulating Heinrich stadial 1 (LGM.hos). AMOC flow rate: (a) 18 Sv, (b) 9 Sv, (c) 12 Sv, and (d) 6 Sv. Arrows in (c) and (d) indicate potential migration routes either by the continuous (continuous arrows) or stepwise (dashed arrows) connection of forests or savannas that allowed biotic exchange and gene flow. Time-slice averaged pollen records are indicated by circles (continental records) and diamonds (marine records) with colours representing the interpreted dominant vegetation at each site. The brown star in panels (c) and (d) indicate open vegetation or absence of vegetation cover based on sedimentary proxies. A list of the depicted records can be found in Supplementary Table 2. The value of 10th percentile training presence logistic threshold, 0.33, represents the threshold in which values equal to or higher are considered to represent suitable habitats.

Extended Data Fig. 5 Environmental data for the last 25,000 years in the region.

(a) Atmospheric CO2 concentrations (ppm)93. (b) Reflectance from a marine sediment record from Cariaco basin. Inverted Y axis, with lower (higher) values associated to more (less) precipitation in the northern South America – northern (southern) position of the Intertropical Convergence Zone19. (c-i) GeoB16224-1 data: (c) Rainforests percentage. (d) Correspondence analysis (CA) axis 1 – positive values are driven by warm-affinity taxa. (e) Benthic foraminiferal stable carbon isotopes (δ13C)36. (f) Aboreal pollen (%). (g) Plan-wax δD n-C29-3122 –more negative values indicate higher precipitation. (h) CA axis 2 – positive values are driven mostly by dry-affinity taxa; (i) Seasonal vegetation index.

Supplementary information

Supplementary Information (download PDF )

Supplementary discussion.

Supplementary Tables 1–5 (download XLSX )

Raw pollen data and analytical metadata.

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Akabane, T.K., Chiessi, C.M., Hirota, M. et al. Weaker Atlantic overturning circulation increases the vulnerability of northern Amazon forests. Nat. Geosci. 17, 1284–1290 (2024). https://doi.org/10.1038/s41561-024-01578-z

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