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Zachary S. Kaufman aUniversity of California, Santa Cruz, Santa Cruz, California

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https://orcid.org/0000-0001-6734-915X
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Nicole Feldl aUniversity of California, Santa Cruz, Santa Cruz, California

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Wilbert Weijer bLos Alamos National Laboratory, Los Alamos, New Mexico

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Milena Veneziani bLos Alamos National Laboratory, Los Alamos, New Mexico

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Open access

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zachary S. Kaufman, zskaufma@ucsc.edu

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zachary S. Kaufman, zskaufma@ucsc.edu

Kaufman et al. (2020) overestimated the surface area of the study’s polynya mask region and underestimated the magnitude of the region’s surface heat fluxes. Specifically, an error in area weighting impacts Figs. 1a,b and 2a therein. Revised versions of these figures are shown below. The corrected polynya mask surface area (1.2 × 106 km2) is smaller than previously stated (3.9 × 106 km2). Within the mask, the corrected difference in ocean–atmosphere heat fluxes during polynya years (29.5 W m−2; see revised Fig. 1a) is larger than previously stated (12 W m−2). Last, there are small modifications to the shape of the corrected polynya heat loss (Fp) time series (see revised Fig. 2a), leading to quantitative differences in Granger causality between Fp and Southern Hemisphere climate variability. However, the spatial pattern of predicted anomalies is largely unaffected, as seen in revised Figs. 2b and 2c, and changes in their magnitude are small. Accordingly, the qualitative nature of the results remains unchanged, as does the discussion of the causal interactions.

Fig. 1.
Fig. 1.

Revised Figs. 1a and 1b and original Figs. 1c and 1d. The August–October (ASO) seasonal-average composite difference between polynya years and nonpolynya years for (a) total ocean–atmosphere surface heat flux, (b) upward turbulent heat flux, (c) surface temperature, and (d) near-surface zonal wind U, where red (positive) colors indicate an eastward wind anomaly, and blue (negative) colors indicate a westward wind anomaly. The solid black contour denotes the polynya mask. In (a)–(c), the dotted black contour represents the 15% ASO-average sea ice fraction contour. In (d), the dotted black contours represent the borders where ASO-average, climatological zonal wind shifts from positive (westerly) to negative (easterly). Stippling indicates the 95% statistical significance level for a Student’s t test that the means of polynya and nonpolynya years are different.

Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-22-0129.1

Fig. 2.
Fig. 2.

Revised Fig. 2. (a) August, September, and October (ASO) average times series of polynya heat loss (Fp; black line) and annual average surface temperature poleward of 55°S (Ts,hl; red line) for each year in the 127-yr simulation. Vertical blue lines denote polynya years. (b),(c) Granger causality between heat loss in the polynya mask region (Fp) and surface temperature (Ts). The causality test in each panel is designated by the predictor variable (left of arrow) and response variable (right of arrow). Shaded values are only shown where Granger causality is found. Predicted anomalies represent the change in the response variable that is predicted by a one standard deviation anomaly in the predictor variable. Solid and dotted black contours are shown as in revised Fig. 1.

Citation: Journal of Climate 35, 20; 10.1175/JCLI-D-22-0129.1

REFERENCE

Kaufman, Z. S., N. Feldl, W. Weijer, and M. Veneziani, 2020: Causal interactions between Southern Ocean polynyas and high-latitude atmosphere–ocean variability. J. Climate, 33, 48914905, https://doi.org/10.1175/JCLI-D-19-0525.1.

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  • Kaufman, Z. S., N. Feldl, W. Weijer, and M. Veneziani, 2020: Causal interactions between Southern Ocean polynyas and high-latitude atmosphere–ocean variability. J. Climate, 33, 48914905, https://doi.org/10.1175/JCLI-D-19-0525.1.

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  • Fig. 1.

    Revised Figs. 1a and 1b and original Figs. 1c and 1d. The August–October (ASO) seasonal-average composite difference between polynya years and nonpolynya years for (a) total ocean–atmosphere surface heat flux, (b) upward turbulent heat flux, (c) surface temperature, and (d) near-surface zonal wind U, where red (positive) colors indicate an eastward wind anomaly, and blue (negative) colors indicate a westward wind anomaly. The solid black contour denotes the polynya mask. In (a)–(c), the dotted black contour represents the 15% ASO-average sea ice fraction contour. In (d), the dotted black contours represent the borders where ASO-average, climatological zonal wind shifts from positive (westerly) to negative (easterly). Stippling indicates the 95% statistical significance level for a Student’s t test that the means of polynya and nonpolynya years are different.

  • Fig. 2.

    Revised Fig. 2. (a) August, September, and October (ASO) average times series of polynya heat loss (Fp; black line) and annual average surface temperature poleward of 55°S (Ts,hl; red line) for each year in the 127-yr simulation. Vertical blue lines denote polynya years. (b),(c) Granger causality between heat loss in the polynya mask region (Fp) and surface temperature (Ts). The causality test in each panel is designated by the predictor variable (left of arrow) and response variable (right of arrow). Shaded values are only shown where Granger causality is found. Predicted anomalies represent the change in the response variable that is predicted by a one standard deviation anomaly in the predictor variable. Solid and dotted black contours are shown as in revised Fig. 1.

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