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Reply to: Concerns of assuming linearity in the reconstruction of thermal maxima

Nature volume 607, pages E15–E18 (2022)Cite this article

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The Original Article was published on 27 July 2022

The seasonal-to-annual mean transformation (SAT) proposed by Bova et al.1, offers a possible solution to the apparent discrepancy between proxy records showing long-term cooling2,3, and models, which show long-term warming across the Holocene known as the ‘Holocene temperature conundrum’4,5. Model–data inconsistencies and conflicting proxy records are particularly prominent in the mid- to low latitudes, and have been variably attributed to seasonal biases in proxy temperature reconstructions6,7,8,9, model deficiencies10,11 or both. Bova et al.1 have suggested that proxy seasonal biases are the primary source of the conundrum. Although it is widely acknowledged that seasonal biases complicate palaeoclimate data interpretations, Laepple et al.12 question whether SAT is a robust solution to this problem, challenging the validity of the foundational assumptions of SAT and thereby arguing that the consistency with model results is fortuitous.

The clear impact of seasonality on marine proxies of surface temperature, which compose 30% (ref. 2) and 80% (ref. 3), respectively, of the proxy records included in prior global stacks, has been discussed previously8,9,13,14,15. These authors show a systematic divergence in Holocene sea surface temperature (SST) trends between alkenone and Globigerinoides ruber magnesium (Mg)/calcium (Ca) proxy reconstructions. In regions, such as the eastern equatorial Pacific, for example, alkenone and G. ruber Mg/Ca SST estimates measured on adjacent sediment cores show opposite Holocene SST trends, making it impossible for both proxies to reflect mean annual sea surface conditions. We therefore assert that any proposed resolution of the Holocene temperature conundrum must also come to terms with this second conundrum—the observed discrepancies among the proxy records themselves. Notably, the SAT method proposed and implemented in our recent Article resolves both.

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Fig. 1: Impact of nonlinearities on Western Pacific Warm Pool SSTs.
Fig. 2: PMIP global versus tropical (40° S–40° N) mean annual temperature (area weighted) change from 6 ka to 0 ka for PMIP2 (13 models), PMIP3 (15 models) and PMIP4 (15 models).

Data availability

The datasets used in this study are available in the NOAA Database, World Data Service for Paleoclimatology at https://www.ncdc.noaa.gov/paleo/study/31752.

Code availability

A MATLAB code that implements the SAT method and the analysis presented in Fig. 1 is available on GitHub at https://github.com/sambova/SAT.

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Acknowledgements

Funding for this research was provided by NSF grants OCE-1834208 and OCE-1810681, the NSF-sponsored US Science Support Program for IODP, the Institute of Earth, Ocean, and Atmospheric Sciences at Rutgers University, the Chinese NSF 41630527, the School of Geography, Nanjing Normal University, and the USIEF-Fulbright Program.

Author information

Author notes

  1. Shital P. Godad

    Present address: Department of Geosciences, National Taiwan University, Taipei, Taiwan

Authors and Affiliations

  1. Department of Geological Sciences, San Diego State University, San Diego, CA, USA

    Samantha Bova

  2. Department of Marine and Coastal Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA

    Yair Rosenthal & Shital P. Godad

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

    Yair Rosenthal

  4. Atmospheric Science Program, Department of Geography, The Ohio State University, Columbus, OH, USA

    Zhengyu Liu

  5. School of Geography, Nanjing Normal University, Nanjing, China

    Mi Yan & Cheng Zeng

  6. Open Studio for Ocean-Climate-Isotope Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao, China

    Mi Yan & Cheng Zeng

  7. Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA

    Anthony J. Broccoli

  8. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Weipeng Zheng

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Contributions

S.B., Y.R., Z.L., M.Y., A.J.B., S.P.G. and C.Z. contributed to conception of the presented ideas. S.B. wrote the first manuscript draft. All authors provided review and editing. Three authors not on the original paper were added to the author list. C.Z. provided additional analysis of model results. A.J.B. provided critical feedback and discussion. W.Z. provided the analysis of the PMIP global versus tropical mean annual temperature shown in Fig. 2.

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Correspondence to Samantha Bova.

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Bova, S., Rosenthal, Y., Liu, Z. et al. Reply to: Concerns of assuming linearity in the reconstruction of thermal maxima. Nature 607, E15–E18 (2022). https://doi.org/10.1038/s41586-022-04832-9

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