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Regional but not global temperature variability underestimated by climate models at supradecadal timescales

Nature Geoscience volume 16, pages 958–966 (2023)Cite this article

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Abstract

Knowledge of the characteristics of natural climate variability is vital when assessing the range of plausible future climate trajectories in the next decades to centuries. The reliable detection of climate fluctuations on multidecadal to centennial timescales depends on proxy reconstructions and model simulations, as the instrumental record extends back only a few decades in most parts of the world. Systematic comparisons between model-simulated and proxy-based inferences of natural variability, however, often seem contradictory. Locally, simulated temperature variability is consistently smaller on multidecadal and longer timescales than is indicated by proxy-based reconstructions, implying that climate models or proxy interpretations might have deficiencies. In contrast, at global scales, studies found agreement between simulated and proxy reconstructed temperature variations. Here we review the evidence regarding the scale of natural temperature variability during recent millennia. We identify systematic reconstruction deficiencies that may contribute to differing local and global model–proxy agreement but conclude that they are probably insufficient to resolve such discrepancies. Instead, we argue that regional climate variations persisted for longer timescales than climate models simulating past climate states are able to reproduce. This would imply an underestimation of the regional variability on multidecadal and longer timescales and would bias climate projections and attribution studies. Thus, efforts are needed to improve the simulation of natural variability in climate models accompanied by further refining proxy-based inferences of variability.

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Fig. 1: Examples of local and global surface temperature variability in models and reconstructions over the 1,000–1,800 CE time span.
Fig. 2: Model–data (dis)agreement on Holocene temperature variability in the literature.
Fig. 3: Local versus global temperature variability and its implied spatial scale.
Fig. 4: Slow variability increases the magnitude of extreme events and decreases their return time in a conceptual time series.

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Data availability

The PAGES 2k palaeotemperature records (PAGES 2k v.2.0.0) are available at www.ncdc.noaa.gov/paleo/study/21171. The ensemble of global temperature reconstructions based on the PAGES2k16 data are available through the World Data Service (NOAA) Palaeoclimatology at https://www.ncdc.noaa.gov/paleo/study/26872 and via Figshare at https://doi.org/10.6084/m9.figshare.c.4507043. The pollen-based reconstructions are available via PANGEA at https://doi.pangaea.de/10.1594/PANGAEA.930512. The marine proxy data are available via PANGEA at https://doi.org/10.1594/PANGAEA.899489. The CMIP5 millennium simulations are available through the Earth System Grid Federation portal at https://esgf-data.dkrz.de. Source data are provided with this paper.

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Acknowledgements

This study was undertaken by members of CVAS and 2k Network, working groups of the Past Global Changes (PAGES) Global Research association. This is a contribution to the SPACE ERC, STACY and PALMOD projects. The SPACE ERC project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 716092). STACY has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 395588486). This work has also been supported by the German Federal Ministry of Education and Research (BMBF), through the PalMod project (subprojects 01LP1926B (O.B.), 01LP1926D (M.C.) and 01LP1926C (B.E., P.S. and N.W.)) from the Research for Sustainability initiative (FONA). B.E. is supported by the Heinrich Böll Foundation. E.M.-C. was supported by the PARAMOUR project, funded by the Fonds de la Recherche Scientifique–FNRS and the FWO under the Excellence of Science (EOS) programme (grant no. O0100718F, EOS ID no. 30454083). A.H. was supported by a Legacy Grant from the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage. B.M. was supported by LINKA20102 and the Spanish Ministry of Science and Innovation project CEX2018‐000794‐S. The work originated from discussions at the CVAS working group of PAGES at a workshop at the Internationales Wissenschaftsforum Heidelberg, which was funded by a Hengstberger Prize. We thank N. Beech, C. Brierley, F. Gonzalez-Rouco and M. MacPartland for comments on earlier drafts of the manuscript. This manuscript uses data provided by the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP and PMIP. We thank the research groups for producing and kindly making their model outputs, measurements and palaeoclimate reconstructions available to us. Editorial assistance, in the form of language editing and correction, was provided by XpertScientific Editing and Consulting Services. We acknowledge support by the Open Access Publication Funds of Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung.

Author information

Author notes

  1. B. Ellerhoff

    Present address: Department Greenhouse gas emission verification, Deutscher Wetterdienst, Offenbach, Germany

Authors and Affiliations

  1. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany

    T. Laepple & R. Hébert

  2. MARUM–Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Bremen, Germany

    T. Laepple

  3. Department of Geosciences, University of Tübingen, Tübingen, Germany

    E. Ziegler, N. Weitzel, B. Ellerhoff & K. Rehfeld

  4. Department of Physics, University of Tübingen, Tübingen, Germany

    E. Ziegler, B. Ellerhoff & K. Rehfeld

  5. Institut für Naturwissenschaften, Geographie und Technik, Pädagogische Hochschule Heidelberg, Heidelberg, Germany

    P. Schoch

  6. Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain

    B. Martrat

  7. Institute for Coastal Systems—Analysis and Modelling, Helmholtz-Zentrum Hereon, Geesthacht, Germany

    O. Bothe

  8. Barcelona Supercomputing Center (BSC), Barcelona, Spain

    E. Moreno-Chamarro

  9. Section Meteorology, Institute of Geosciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

    M. Chevalier

  10. Institute of Earth Surface Dynamics, Geopolis, University of Lausanne, Lausanne, Switzerland

    M. Chevalier

  11. ARC Centre of Excellence in Australian Biodiversity and Heritage (CABAH), Canberra, Australian Capital Territory, Australia

    A. Herbert

  12. School of Culture, History and Language, The Australian National University, Canberra, Australian Capital Territory, Australia

    A. Herbert

Authors
  1. T. Laepple
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  2. E. Ziegler
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  3. N. Weitzel
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  4. R. Hébert
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  5. B. Ellerhoff
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  6. P. Schoch
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  7. B. Martrat
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  8. O. Bothe
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  9. E. Moreno-Chamarro
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  10. M. Chevalier
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  11. A. Herbert
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  12. K. Rehfeld
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Contributions

T.L. led the synthesis and overall analysis; E.Z. coordinated the work. K.R. and T.L. initiated the project through the workshop. R.H. led the writing of the introductory paragraph. E.Z. led the writing on the remainder of the introduction regarding the conflicting evidence in the literature; E.Z., B.M. and P.S. led the literature review; A.H. and all authors contributed to it; B.E. and R.H. produced Fig. 1, P.S. produced Fig. 2. N.W. and T.L. led the writing of the section on reconstruction deficiencies; N.W., E.M.-C. & T.L. led the writing of the section on the consequences for the spatial structure; R.H & T.L. produced Fig. 3. E.Z and B.E. led the writing of the section on the implications for climate projections and attribution efforts; B.E. produced Fig. 4. R.H. led the writing of the concluding section. O.B. provided a critical check of the current literature O.B., N.W., B.E, R.H., E.Z., T.L., K.R., B.M., M.C., reviewed each section in detail. All authors reviewed the manuscript.

Corresponding author

Correspondence to T. Laepple.

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The authors declare no competing interests.

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Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor(s): James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Spectrum of mean local simulated and reconstructed temperature variability.

As in Fig. 1d of the main text but for the mean local temperature spectrum from CMIP5/CMIP6 simulations and from PAGES2k temperature reconstructions (see Methods and Ref. 19). This shows that local variability reconstructed from instrumentally calibrated annually resolved records displays a similar model-data variability mismatch on supra-decadal time-scales as the example reconstruction (Cariaco) shown in Fig. 1d, or other marine or terrestrial records4,14.

Source data

Extended Data Fig. 2 Overview of model-data (dis)agreement in Holocene temperature variability in the literature with explicit references.

As in Fig. 2 of the main text, model-data agreement is grouped according to temporal (x-axis) and spatial scale (y-axis). Each symbol represents a specific study (refs. 4,8,9,10,11,12,13,14,16,19,20,21,22,23,24,25,26,27,28,2997) and the color-code indicates strength of (dis)agreement. Multiple occurrences in one box can happen when differing results are reported that is depending on reconstruction method or proxy type. Such cases are highlighted with a black border. The number at the bottom right of each box is the number of distinct studies in this box. Dashing of a box indicates only one or two studies for this spatio-temporal scale. Further details can be found in the Methods section.

Extended Data Fig. 3 Local precipitation and local temperature variability show a different temporal scaling.

Local mean spectral estimates from CMIP5/6 precipitation (dark blue) and temperature (brown). Across all models, local precipitation variability shows a flatter (more white) scaling than local temperature variability. This implies that the mismatch between simulated and reconstructed local supra-decadal variability would increase, if the proxies would represent a mix of precipitation and temperature (calibrated to temperature units), as the difference in scaling between proxies and simulated precipitation is even larger than between proxies and simulated temperature.

Source data

Extended Data Table 1 CMIP5/CMIP6 model experiments. The model experiments were used in Figs. 1 and 3 (refs. 99,100,101,102,103,104,105,106,107,108,109)
Full size table

Supplementary information

Supplementary Table 1

Full results of the meta-analysis on which Fig. 2 and Extended Data Fig. 2 are based together with statements from the original papers that were the basis for assigning them a level of (dis)agreement.

Source data

Source Data Fig. 1

Raw data (.csv files) for each curve in Fig. 1.

Source Data Fig. 2

Text file with the data relating to Fig. 2.

Source Data Fig. 3

Raw data (.csv files) for each curve in Fig. 3.

Source Data Extended Data Fig. 1

Raw data (.csv files) for each curve in Extended Data Fig. 1.

Source Data Extended Data Fig. 3

Raw data (.csv files) for each curve in Extended Data Fig. 3.

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Laepple, T., Ziegler, E., Weitzel, N. et al. Regional but not global temperature variability underestimated by climate models at supradecadal timescales. Nat. Geosci. 16, 958–966 (2023). https://doi.org/10.1038/s41561-023-01299-9

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