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Data availability
The ERA-Interim reanalysis is publicly available from ECMWF (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim). The AMIP FACTS simulations are publicly available from the National Oceanic and Atmospheric Administration (https://www.esrl.noaa.gov/psd/repository/alias/factsdocs).
References
Shepherd, T. G. Effects of a warming Arctic. Science 353, 989–990 (2016).
Mori, M., Kosaka, Y., Watanabe, M., Nakamura, H. & Kimoto, M. A reconciled estimate of the influence of Arctic sea-ice loss on recent Eurasian cooling. Nat. Clim. Change 9, 123–129 (2019).
Bretherton, C. S., Smith, C. & Wallace, J. M. An intercomparison of methods for finding coupled patterns in climate data. J. Clim. 5, 541–560 (1992).
Wallace, J. M., Smith, C. & Bretherton, C. S. Singular value decomposition of wintertime sea surface temperature and 500-mb height anomalies. J. Clim. 5, 561–576 (1992).
Screen, J. & Blackport, R. Is sea-ice-driven Eurasian cooling too weak in models? Nat. Clim. Change 9, 934–936 (2019).
Acknowledgements
The authors acknowledge Masato Mori and Hisashi Nakamura for useful feedback. G.Z. and T.G.S. were supported by the ERC advanced grant 339390. P.C. was supported by an Imperial College Research Fellowship and NERC grant NE/T006250/1.
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G.Z. conceived the study and performed the analyses. All the authors contributed to interpreting the results and writing the manuscript.
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Extended data
Extended Data Fig. 1 The singular vectors.
The pair of singular vectors composing the first co-varying surface temperature mode between the a) ERA-Interim and b) AMIP simulations via the maximum covariance analysis proposed in Mori et al.2. As in Fig. 1, the vectors are scaled to correspond to unit standard deviation in the expansion coefficients.
Extended Data Fig. 2 The robustness of the co-varying mode to model differences.
Homogeneous (top) and heterogeneous (bottom) regression maps of sea level pressure in the AMIP simulations obtained by separately performing the maximum covariance analysis for each individual model and using all the available ensemble members: 17 members are used for AM3, 12 for GEOS-5, 20 for CAM4, and 50 for all other models. Stippling shows statistical significance at the 5% level as in Fig. 1.
Extended Data Fig. 3 The potential confounding role of the SSTs.
Heterogeneous map of the SSTs associated to the co-varying mode in the AMIP simulations. Stippling denotes statistical significance at the 5% level. The potential of these SST anomalies, such as the ENSO- like pattern in the tropical Pacific, to force the circulation signals associated to the co-varying mode in Fig. 1f is discussed in the Supplementary Information (Supplementary Figure 2).
Supplementary information
Supplementary Information
Methodology, Supplementary Discussion and Figs. 1 and 2.
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Zappa, G., Ceppi, P. & Shepherd, T.G. Eurasian cooling in response to Arctic sea-ice loss is not proved by maximum covariance analysis. Nat. Clim. Chang. 11, 106–108 (2021). https://doi.org/10.1038/s41558-020-00982-8
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DOI: https://doi.org/10.1038/s41558-020-00982-8
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