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The quandary of detecting the signature of climate change in Antarctica

Nature Climate Change volume 13, pages 1082–1088 (2023)Cite this article

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Abstract

Global warming driven by human activities is expected to be accentuated in polar regions compared with the global average, an effect called polar amplification. Yet, for Antarctica, the amplitude of warming is still poorly constrained due to short weather observations and the large decadal climate variability. Using a compilation of 78 ice core records, we provide a high-resolution reconstruction of temperatures over the past 1,000 years for seven regions of Antarctica and direct evidence of Antarctic polar amplification at regional and continental scales. We also show that the amplitude of both natural and forced variability is not captured by the CMIP5 and six model ensemble members, which could be explained in part by the Southern Annular Mode. This shows that failing to consider the feedback loops causing polar amplification could lead to an underestimation of the magnitude of anthropogenic warming and its consequences in Antarctica.

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Fig. 1: Quantification of the impact of climate change in Antarctica when a large amount of natural variability competes with the warming signal.
Fig. 2: Identifying the signature of climate change in Antarctica in the context of the past 1,000 years.
Fig. 3: The current climate change in Antarctica in the context of the past 1,000 years.
Fig. 4: Decadal variability recorded as a function of the observed warming trend.

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Data availability

The data used in this manuscript are available from the iso2k database31 and can be retrieved from https://www.ncei.noaa.gov/access/paleo-search/study/29593. The datasets (Temperature T2m and Total Precipitation) from ERA5 are readily available from the European Centre for Medium-Range Weather Forecasts servers at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. We used CMIP model outputs (Temperature fields), which are available at https://esgf-node.ipsl.upmc.fr/projects/cmip6-ipsl/.

Code availability

The toolbox to calculate the persistence can be retrieved from https://fr.mathworks.com/matlabcentral/fileexchange/95768-attractor-local-dimension-and-local-persistence-computation.

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Acknowledgements

The investigations leading to these results have received funding from the DFG project CLIMAIC (M.C.). The SPACE ERC and GLACIAL LEGACY ERC projects have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 716092 and no. 772852 for R.H.). The authors acknowledge fruitful discussions with T. Laepple, J. A. Caccavo, A. Orsi and C. Agosta. This manuscript was realized in the framework of the PAGES working group CVAS.

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Authors and Affiliations

  1. Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, IPSL, Gif-sur-Yvette, France

    Mathieu Casado, Davide Faranda & Amaelle Landais

  2. Alfred-Wegener-Institut, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany

    Raphaël Hébert

  3. London Mathematical Laboratory, London, UK

    Davide Faranda

  4. LMD/IPSL, Ecole Normale Superieure, PSL Research University, Paris, France

    Davide Faranda

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  1. Mathieu Casado
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  4. Amaelle Landais
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Contributions

M.C. conceived the study and carried out the numerical surrogate data experiments. M.C. and D.F. applied the dynamical system theory to the system. M.C. and R.H. conceived the statistical and spectral approaches. M.C. led the redaction of the manuscript with the help of R.H. All the authors discussed the results and contributed to the manuscript.

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Correspondence to Mathieu Casado.

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Extended data

Extended Data Fig. 1 Impact of window length on trend estimates.

Iso2k stack for all of Antarctica (black), CPS reconstruction from Stenni et al,29, and for an ensemble of 50 surrogate data simulated for realistic conditions in Antarctica (decadal natural variability of 0.6 ‰2.yr, a beta of 0.6, and an amplitude of the anthropogenic warming of 0.1 ‰/dec starting 50 years prior to the end of the window over which the trend is calculated, see Supplementary Sections S2 and S3, dark green line), as well as confidence interval for an average of 50 cores calculated from 10 000 iterations (green shading).

Extended Data Fig. 2 Simulation of the trend impact on the persistence on surrogate data without any natural variability.

change of persistence (Θ, top), δ18O (middle), and 40-year running trend (bottom) as observed in the Iso2k stack (grey individual datapoints, black, 40-year block average) and for 4 different hypotheses on the intensity of the climatic change induced trend (from 0.05°C/dec to 0.3°C/dec, Δδ18O ≈ 0.5 × ΔT).

Extended Data Fig. 3 Simulation of the trend impact on the persistence on surrogate data with natural variability.

change of persistence (Θ, top), δ18O (middle), and 40-year running trend (bottom) as observed in the Iso2k stack (grey individual datapoints, black, 40-year block average) and for 4 different hypotheses on the intensity of the climatic change induced trend (from 0.05°C/dec to 0.3°C/dec, Δδ18O ≈ 0.5 × ΔT) with natural variability with a β of 0.6 and an average power at the decadal scale (10 - 40 years band) of 0.52 ‰2/y.

Extended Data Fig. 4 Simulation of the natural variability and trend impact on the persistence on surrogate data.

change of persistence (Θ, top), δ18O (middle), and 40-year running trend (bottom) as observed in the Iso2k stack (grey individual datapoints, black, 40-year block average) and for 4 different hypotheses on the intensity of the power of the natural variability with a β of 0.6 and average powers at the decadal scale (10 - 40 years band) ranging from 0.3 to 2.2 °C2/yr. The climate change induced trend is set to 0.2°C/dec (Δδ18O ≈ 0.5 × ΔT).

Extended Data Fig. 5 Probability to obtain a given number of decades where Θ out of the range of values observed in the last 1000 years.

Monte-Carlo analysis of the probability to obtain a given number of times when θ is above the confidence interval presented in Fig. 2 (1 std on the whole time series) for a period of a 100 years using 10 000 iterations on the dataset.

Extended Data Fig. 6 Regional last 1000 years reconstructions.

a) Antarctic Peninsula, b) Weddell Coast, c) Dronning Maud Land Coast, d) West Antarctica, e) map of the different regions, the black dots represent the station locations, f) East Antarctic Plateau, g) All of Antarctica, h) Victoria Land, and i) Indian Coast. For each panel, from top to bottom, time series of the persistence metric (Θ, y), isotopic composition anomaly stack for all ice cores in Antarctica (in δ18O units ‰), compared to the previous stack realised by Stenni et al.29 (purple: CPS stack, yellow, unweighted stack, ‰), trend on 40-year running windows ending on the given year with confidence intervals, and number of records covering a given time period,. For Θ and δ18O, light curves are the original data with annual resolution, thick curves are 10-year block averages. Note that the block averages and the trend estimates do not take into account datapoints after 2008 when the number of available cores drop below 10. For the top panel, the horizontal solid line represents the average value, while the horizontal dashed line is the average value +1 std.

Extended Data Fig. 7 Spectral constraint of the isotope-temperature calibration.

Power spectral density of the reconstructed temperature for different values of Δδ18O/ΔT between 1920 and 1990 compared to the spectrum of the ERA5 reanalysis outputs for the core location between 1951 and 2020 (the non-overlapping windows are set up to obtain the same length of time series with the largest number of cores; the results are insensitive to the window choice). The dip in ERA5 for timescales around 15 years is also found in the weather station observations and could be link to the SAM influence but was not taken into account for the sake of the calibration.

Extended Data Table 1 Summary of regional isotope to temperature calibration
Full size table

Supplementary information

Supplementary Information

Supplementary Sections 1–11, Figs. 1–9, Tables 1–7 and references 1–52.

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Casado, M., Hébert, R., Faranda, D. et al. The quandary of detecting the signature of climate change in Antarctica. Nat. Clim. Chang. 13, 1082–1088 (2023). https://doi.org/10.1038/s41558-023-01791-5

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