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Reply to: Eurasian cooling in response to Arctic sea-ice loss is not proved by maximum covariance analysis

Nature Climate Change volume 11, pages 109–111 (2021)Cite this article

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A Publisher Correction to this article was published on 16 February 2021

The Original Article was published on 02 February 2021

This article has been updated

replying to Giuseppe Zappa et al. Nature Climate Change https://doi.org/10.1038/s41558-020-00982-8 (2021)

Zappa et al.1 challenged the interpretation of the results obtained based on singular value decomposition/maximum covariance analysis (SVD/MCA) in Mori et al.2. Zappa et al.1 concluded with the aid of a simple statistical model that the most plausible explanation of the results presented in Mori et al.2 is that the warm Arctic–cold Eurasian (WACE) mode predominantly forces sea-ice variability, rather than is forced by the latter. Here we clarify that their conclusion is drawn by implicitly assuming that the atmospheric general circulation model (AGCM) can perfectly reproduce the magnitude and structure of the WACE mode without errors or biases. Additionally, we supplement Mori et al.2 to dispel the readers’ concerns and potential misunderstanding.

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Fig. 1: Spatial patterns of paired singular vectors of the leading SVD/MCA mode.
Fig. 2: Estimates of forced WACE variance and its fraction.

Data availability

The monthly sea-surface temperature and sea-ice concentrations in the Hadley Centre Sea Ice and Sea Surface Temperature dataset are available from the Met Office website (https://www.metoffice.gov.uk/hadobs/hadisst/). The ERA-Interim reanalysis datasets are available from the European Centre for Medium-Range Weather Forecasts website (https://apps.ecmwf.int/datasets/). The FACTS simulations are available from https://www.esrl.noaa.gov/psd/repository/alias/factsdocs.

Change history

References

  1. Zappa, G., Shepherd, T. G. & Ceppi, P. Eurasian cooling in response to Arctic sea-ice loss is not proved by maximum covariance analysis. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00982-8 (2021).

  2. 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).

    Article  Google Scholar 

  3. Mori, M. et al. Reply to: is sea-ice-driven Eurasian cooling too weak in models? Nat. Clim. Change 9, 937–939 (2019).

    Article  Google Scholar 

  4. Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).

    Article  CAS  Google Scholar 

  5. Hoskins, B. & Woollings, T. Persistent extratropical regimes and climate extremes. Curr. Clim. Change Rep. 1, 115–124 (2015).

    Article  Google Scholar 

  6. 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).

    Article  Google Scholar 

  7. 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).

    Article  Google Scholar 

  8. Mori, M., Watanabe, M., Shiogama, H., Inoue, J. & Kimoto, M. Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades. Nat. Geosci. 7, 869–873 (2014).

    Article  CAS  Google Scholar 

  9. Palmer, T. N. A nonlinear dynamical perspective on climate prediction. J. Clim. 12, 575–591 (1999).

    Article  Google Scholar 

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Acknowledgements

We thank G. Zappa for giving us the opportunity to write this reply. We acknowledge the individuals and modelling groups who provided the FACTS dataset. This work is supported by Japan Ministry of Education, Culture, Sports, Science and Technology through the Integrated Research Program for Advancing Climate Models (TOUGOU, JPMXD0717935457), the Arctic Challenge for Sustainability (ArCS) Program (JPMXD1300000000) and ArCS II (JPMXD1420318865), and by the Japan Society and Technology Agency through KAKENHI Grant no. JP19H01964 and JP19H05703.

Author information

Authors and Affiliations

  1. Research Institute for Applied Mechanics, Kyushu University, Kasuga, Japan

    Masato Mori

  2. Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan

    Yu Kosaka & Hisashi Nakamura

  3. Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan

    Masahiro Watanabe & Masahide Kimoto

  4. Faculty of Sustainable Design, University of Toyama, Toyama, Japan

    Bunmei Taguchi

Authors
  1. Masato Mori
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  2. Yu Kosaka
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  3. Masahiro Watanabe
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  4. Bunmei Taguchi
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  5. Hisashi Nakamura
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  6. Masahide Kimoto
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Contributions

M.M. performed the analyses. The manuscript was written mainly by M.M., Y.K., B.T. and H.N. with discussion and feedback from all the authors.

Corresponding author

Correspondence to Masato Mori.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Hans Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Covarying components of winter-mean SAT and SLP anomalies between ERA-Interim and the multi-model ensemble mean of AMIP-FACTS.

a–d, Homogeneous and heterogeneous regression anomalies of detrended DJF-mean SAT (shading) and SLP (contours at 0.5 hPa intervals, dashed if negative). Anomalies in ERA-Interim (a, c) and the AMIP-FACTS ensemble mean (b, d) are regressed onto standardized ECERA (a, d) and \(\overline {EC} _{FACTS}\) (b, c). e-f, Detrended DJF-mean SAT (shading) and SLP (contours at 0.5 hPa intervals, dashed if negative) anomalies in ERA-Interim (e) and AMIP-FACTS ensemble mean (f) regressed onto the detrended DJF-mean BK SIC anomaly (sign reversed). Twelve members from each of the seven AGCMs (total of 84 members) were used in the multi-model ensemble mean of AMIP-FACTS (see Methods in M19 for detail). Hatching indicates the statistical confidence for SAT anomalies exceeding 95% based on a t-test.

Extended Data Fig. 2 Winter-mean SST anomalies associated with the forced-WACE mode in AMIP-FACTS.

Detrended DJF-mean SST anomalies regressed onto the \(\overline {EC} _{FACTS}\) (shown only where the statistical confidence exceeds 95% based on a t-test).

Supplementary information

Supplementary Information

Supplementary Texts 1 and 2, and references.

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Mori, M., Kosaka, Y., Watanabe, M. et al. Reply to: Eurasian cooling in response to Arctic sea-ice loss is not proved by maximum covariance analysis. Nat. Clim. Chang. 11, 109–111 (2021). https://doi.org/10.1038/s41558-020-00983-7

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