1Support information

2Uncertainty in the winter tropospheric3

response to Arctic Sea ice loss: the role of4stratospheric polar vortex internal variability

56Lantao Sun*

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Department of Atmospheric Science, Colorado State University,

8Fort Collins, CO

9Clara Deser and Isla Simpson

10National Center for Atmospheric Research Climate & Global Dynamics,

11Boulder, CO

12Michael Sigmond

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Canadian Centre for Climate Modelling and Analysis, Victoria, British

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Columbia, Canada.

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Figure S1. (a) Seasonal maps of 1979-2020 linear trend in sea ice concentration (%) basedon merged Hadley-Optimum Interpolation (OI) SIC (Had-OI; Hurrell et al. 2008) data
updated through 2020. Summer, autumn, winter and spring seasons are defined to be theaverage of July-September (JAS), October-December (OND), January-March (JFM) and
April-June (AMJ), respectively. (b) Observed Arctic sea ice area loss (106 km2) during
1979-2020; note the inverted y-axis scale.

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Figure S2. OND atmospheric response to future Arctic sea ice loss for: (a) ΔfutpdSST;
ΔfutfutSST and (c) their difference. Two rows indicate (from top to bottom): SAT (shading;oC) and precipitation (shading; mm day-1). Stippling indicates the 90% statisticalsignificance based on a two-sided student’s t-test and false discovery rate (FDR; Wilks
2016).

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Figure S3. Correlation of geopotential height response with U10 across the 1000
bootstrapped samples of 100-member averages. Rows 1-4 show the correlation of 10 hPa
DJF response and the 500-hPa, 850-hPa and 1000-hPa response in JFM, respectively.

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Figure S4. Regression analysis of JFM response onto U10 in ΔpastpdSST across the 1000
bootstrapped samples of 100-member averages. Rows 1-3 show the SLP (shading; hPa),SAT (shading; oC) and precipitation (shading; mm day-1) responses, respectively. Panels a,b and c show the Mean-2σ, Mean+2σ and their difference, respectively. The SLP responses(contours; interval of 0.5 hPa) are overlaid on the SAT and precipitation panels. Red andblue contours denote positive and negative values, respectively. Zero contour line has beenomitted. Stippling indicates the 90% statistical significance based on a two-sided student’s
t-test and false discovery rate.

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Figure S5. As in Fig. S4 but for the regression analysis of JFM response onto U10 inΔfutfutSST across the 1000 bootstrapped samples.

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Figure S6. Correlation of JFM (from top to bottom) SLP, SAT and precipitation responses
with U10 across the 1000 bootstrapped samples for (from left to right): ΔpastpiSST;
ΔpastpdSST; ΔfutpdSST and ΔfutfutSST.

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Figure S7. Regression analysis of eddy heat flux response onto U10 across the 1000
bootstrapped samples of 100-member averages for (from top to bottom): ΔpastpdSST;
ΔfutpdSST and ΔfutfutSST. Panels a, b and c show the Mean-2σ, Mean+2σ and their difference,respectively. Stippling indicates the 90% statistical significance based on a two-sided
student’s t-test and false discovery rate. The climatologies (contours; interval of 15 K m s1) are overlaid on the panels a and b.

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Figure S8. Regression analysis of NDJ zonal wave-1 response at 300 hPa onto U10 inacross the 1000 bootstrapped samples for (from top to bottom): ΔpastpdSST; ΔfutpdSST andΔfutfutSST. Panels a, b and c show the Mean-2σ, Mean+2σ and their difference, respectively.
The climatologies (contours; interval of 50 m) are overlaid on the panels a, b and c. solidand dashed contours denote positive and negative values, respectively. Zero contour line
has been omitted.

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Figure S9. As in Fig. S8, but for the regression analysis of NDJ zonal wave-2 response at
300 hPa onto U10 in across the 1000 bootstrapped samples.

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Figure S10. Time series of zonal-mean zonal wind at 10 hPa and 60oN based on the NCEPNCAR reanalysis (solid black curve), ERA-40 (dashed blue curve) and ERA-Interim(dashed red curve) datasets.

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Figure S11. (Top) Arctic sea ice area time series in November-March based on merged
Hadley-Optimum Interpolation (OI) sea ice concentration (Had-OI; Hurrell et al. 2008)(black curve) and its linear trend (red dashed line) during 1971-2020. (Bottom) NovemberMarch averaged Arctic SIC loss (%) in the PAMIP past sea ice loss protocol (left) andobserved linear change during 1971-2020. The numbers on the parentheses indicate thecorresponding Arctic sea ice area loss (106 km2).

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Figure S12. Regression analysis of 60oN zonal-mean zonal wind response in ΔpastpiSST
onto U10 (defined as DJF zonal-mean zonal wind at 10 hPa and 60oN) across the 1000
bootstrapped samples of single ensemble members. Panels a, b and c show the Mean-2σ,Mean+2σ and their difference, respectively. Panel d shows the correlation of the zonalmean zonal wind response with U10 across the 1000 bootstrapped samples.

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Figure S13. Standard deviation of JFM response in ΔpastpiSST across the 1000 bootstrappedsamples of 100-member averages for (from top to bottom): SLP (shading; hPa), SAT(shading; oC) and precipitation (shading; mm day-1). Column 1-3 show the total standard
deviation (𝜎!"!#$ ), standard deviation after removing the component that is linearly related
to U10 (𝜎!%"&"'&()%) ; similar to Simpson et al. 2018), and the standard deviation that islinearly related to U10 (𝜎'!%#!"'&()%) = #𝜎!"!#$ * − 𝜎!%"&"'&()%) * ), respectively.

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REFERENES

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Hurrell, James W., James J. Hack, Dennis Shea, Julie M. Caron, James Rosinski (2008), A

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New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community

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Atmosphere Model. J. Climate, 21, 5145–5153. doi: 0.1175/2008JCLI2292.1.

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Simpson, I. R., Hitchcock, P., Seager, R., Wu, Y., & Callaghan, P. (2018). The Downward

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Influence of Uncertainty in the Northern Hemisphere Stratospheric Polar Vortex Response114

to Climate Change, Journal of Climate, 31(16), 6371-6391. Retrieved Jul 9, 2021, from115

https://journals.ametsoc.org/view/journals/clim/31/16/jcli-d-18-0041.1.xml

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