Simulated coupled SST-SIC patterns identified through CCA between the corresponding AWI-ESM-2.1 "historical" annual detrended anomalies from 1850-2014. Left column: The SST (˚C) pattern (a) of the first pair, explaining 22% of variance and the SIC (%) structure (e), explaining 10% of variance. Their associated time series (c) with SIC (red line), SST (black line) are plotted with an 11yr running mean and have a correlation coefficient of 0.84. Their correlation with the simulated AMOC Index, defined as the time series of annual-mean anomaly of the maximum volume transport streamfunction at 26.5˚N (Sv) (blue line) is 0.48 (95% significance level). Right column: The SST (˚C) pattern of the second pair (b), explaining 14% of variance and the SIC (%) structure (f), explaining 2% of variance. Their associated time series (d), with SIC (red line), SST (black line), have a correlation coefficient of 0.65(95% significance level). https://doi.org/10.1371/journal.pone.0290437.g005

Simulated coupled SST-SIC patterns identified through CCA between the corresponding AWI-ESM-2.1 "historical" annual detrended anomalies from 1850-2014. Left column: The SST (˚C) pattern (a) of the first pair, explaining 22% of variance and the SIC (%) structure (e), explaining 10% of variance. Their associated time series (c) with SIC (red line), SST (black line) are plotted with an 11yr running mean and have a correlation coefficient of 0.84. Their correlation with the simulated AMOC Index, defined as the time series of annual-mean anomaly of the maximum volume transport streamfunction at 26.5˚N (Sv) (blue line) is 0.48 (95% significance level). Right column: The SST (˚C) pattern of the second pair (b), explaining 14% of variance and the SIC (%) structure (f), explaining 2% of variance. Their associated time series (d), with SIC (red line), SST (black line), have a correlation coefficient of 0.65(95% significance level). https://doi.org/10.1371/journal.pone.0290437.g005

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Due to its involvement in numerous feedbacks, sea ice plays a crucial role not only for polar climate but also at global scale. We analyse state-of-the-art observed, reconstructed, and modelled sea-ice concentration (SIC) together with sea surface temperature (SST) to disentangle the influence of different forcing factors on the variability of thes...

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Context 1
... verify results obtained from reconstructed Arctic SIC data (Fig 2) by performing a CCA of detrended annual average anomalies of Atlantic SST and Arctic SIC derived from the historical simulation over the period 1850-2014. The spatial structure of SST anomalies relating to 1 st coupled Atlantic SST-Arctic SIC pair (Fig 5A) features pronouncedly positive anomalies north-east of Greenland as well as negative anomalies over the subpolar gyre and across most of the South Atlantic. The simulated SST pattern explains ~22% of the total variance shows a dipole of anomalies, with positive loadings over most of the North Atlantic and negative values ...
Context 2
... obvious exception are negative anomalies over over most of the central-north Atlantic and the positive loadings over the western coast of Africa and parts of the Southern Ocean. The associated SIC pattern (Fig 5E) explains around a tenth of the total (Fig 2C), plotted from 55˚N-90˚N (a) and from 50˚S-90˚S (b), over the 1854-2015 period. The associated statistical significance in hatched areas exceeds 95%. ...
Context 3
... in this field and can be described by negative anomalies over most of the Arctic which is in very good agreement with observations ( Fig 2C). Temporal evolution of the two structures ( Fig 5C) is significantly correlated (r = 0.46, 95% significance level) with the AMOC index derived from the same simulation. ...
Context 4
... is similar to that of the NAO-linked observed Arctic SIC pattern (Fig 2F), although the percentage of variance explained is significantly lower. The time series of the two spatial patterns (Fig 5D) have a correlation coefficient of 0.56 (95% significance level) and are dominated by inter-annual variability. ...

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