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

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

The Original Article was published on 02 February 2021

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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 ( The ERA-Interim reanalysis datasets are available from the European Centre for Medium-Range Weather Forecasts website ( The FACTS simulations are available from

Change history


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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




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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Competing interests

The authors declare no competing interests.

Additional information

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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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).

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