Twenty-five winters of unexpected Eurasian cooling unlikely due to Arctic sea-ice loss

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1 SUPPLEMENTARY INFORMATION DOI: /NGEO2820 Twenty-five winters of unexpected Eurasian cooling unlikely due to Arctic sea-ice loss Kelly E. McCusker 1,2, John C. Fyfe 2 & Michael Sigmond 2 1 School of Earth and Ocean Sciences, University of Victoria, Victoria, BC V8P 5C2, Canada. 2 Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, V8W 2Y2, Canada This document contains the following supplementary information: (i) Differences to earlier work (ii) Sensitivity of the AGCM results to SST adjustment (iii) Limitations of the AGCM model setup (iv) Supplementary Figures S1-9 (v) Results from NCAR CESM1 30-member ensemble (Fig. S10) (vi) Supplementary Figure S11 (i) Differences to earlier work As discussed in the main text, sampling uncertainty may help to reconcile our results with some previous modeling work. However in addition, there are other experimental and analysis differences that may play a role. In the case of Kug et al. (2015) 1, they found that intra-seasonal to interannual changes in surface air temperature (SAT) over the BKS are inversely correlated to SAT over East Asia. They show that by increasing SAT over the Barents-Kara Sea (BKS) in model experiments, East Asian SAT decreases. We tested this connection in our AOGCM simulations and found that there is no contradiction between our results, which explore the multi-decadal connection between BKS ice concentration and CEUR SAT, and the Kug et al. (2015) study. We find an ensemble-mean correlation coefficient between wintertime monthly SAT over the BKS and East Asian regions of ± 0.16 with 46 of the 50 correlation coefficients showing statistical significance at the 95% level, consistent with Kug et al In contrast, the ensemble mean correlation coefficient between wintertime monthly BKS sea ice concentration and East Asian SAT is 0.12 ± 0.12, with just 5 of 50 correlation coefficients satisfying statistical significance at 95%. On the multidecadal timescale, we find no significant relationship in AOGCM-simulated epoch differences in SAT over the East Asian and BKS regions (r = -0.14, p = 0.33), nor between East Asian SAT and BKS ice concentration (r = 0.08, p = 0.57). Thus, although BKS SAT is linked to East Asian SAT, the underlying BKS sea ice does 1 NATURE GEOSCIENCE Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

2 not drive it. As in the main text, differences are computed across the 50-member ensemble and are between and For this analysis we use East Asian and BKS regions as defined in Kug et al. (2015). Mori et al. (2014) 2 study the connection between observed BKS sea ice and SAT over central Eurasia (CEUR) and find a statistically significant CEUR cooling due to BKS ice loss with a 100-member ensemble. We study the connection between observed BKS sea ice loss and CEUR SAT and do not find a statistically significant CEUR cooling with a 120-member ensemble. The difference in these findings could be due to the particular nature of the boundary forcing pattern, which in the case of Mori et al. is a composite on low and high September BKS ice concentration from HadISST 3. Our observed sea ice concentration boundary pattern is a difference in epoch averages from NSIDC 4. These patterns are similar (r = 0.91 north of 40 o N; Supplementary Fig. 6a-b), however subtle differences, such as the greater amount of ice loss in the BKS in the composite, could explain the differing results to observed boundary forcing. That said, we additionally use five other epoch difference forcing patterns that range from very little BKS ice loss to BKS ice loss comparable to the composite pattern (Supplementary Fig. 6c) and do not find a significant CEUR cooling in any case. (ii) Sensitivity of the AGCM response to SST adjustment In order to determine the importance of perturbing local Arctic sea surface temperature (SST) where sea ice decreased between the control period ( ) and perturbation period ( ), we performed a set of sensitivity simulations wherein those local Arctic SSTs were also held fixed at the climatology instead of set to the climatology. We compared this to a simulation with sea ice concentration and SST where sea ice decreased by 10% in absolute terms. Both simulations had perturbed sea ice thickness as well. The average change in SAT poleward of 70 N due just to SST perturbation is much smaller than the total SAT anomaly and also not distinguishable from the range of responses to the five separate AGCM boundary forcings (Supplementary Fig. 11), suggesting that perturbing local SSTs are of secondary importance to the decrease in sea ice concentration itself. (iii) Limitations of the AGCM model setup Our AGCM simulations are designed to isolate the effect of Arctic sea ice loss (and those SSTs that are directly attributable to that ice loss 5 ). This methodology is comparable to that of previous modeling work in which the links between sea ice and Eurasian surface air temperature are investigated (e.g. refs 13-16, 21, 22 in the main text). However, the methodology, because it does not include ocean coupling, does not account for potential ocean feedbacks or changes in air-sea 2

3 interaction outside the Arctic. Any changes in surface flux due to changing overlying air masses therefore do not change SST, which could be important in regions bordering the Arctic over which air that was anomalously warmed over areas of sea ice loss is advected. Recent work has shown that the inclusion of ocean coupling can expand the influence of future Arctic sea ice loss globally 6 and amplify the pattern of Northern Hemisphere SAT and circulation response 7. However, given that we did not find any influence of historical Arctic sea ice loss on multi-decadal changes in central Eurasian (CEUR) SAT in our AOGCM simulations, we are confident that the impact of ocean coupling is secondary if not negligible. (iv) Supplementary Figures S1-9 Supplementary Figure 1 AOGCM seasonal cycle of changes in boreal winter Arctic sea ice area. Seasonal cycle of the difference between the climatological Arctic sea ice area and climatological Arctic sea ice area for the mean of the AOGCM simulations (grey curve) and the ensemble 95% confidence interval (grey shading). The observed seasonal cycle difference calculated from NSIDC 4 is shown in red. 3

4 Supplementary Figure 2 Distribution of CEUR winter SAT anomalies sampled from a long preindustrial control simulation. The histogram is made up of 49 epoch differences to mirror the AOGCM and AGCM distributions in the main text (Fig. 2). The box at the top shows the histogram mean (center line), 95% confidence interval on the mean (inner box), and 95% interval of the histogram (outer box). The red vertical line shows the observed change from to computed from GISTEMP 8. 4

5 Supplementary Figure 3 Cumulative probability density of raw and adjusted AOGCM changes in boreal winter CEUR SAT. Cumulative histogram and kernel density estimation of the change in winter CEUR SAT from to in the 50 AOGCM simulations (grey). The red vertical line shows the observed change 8, which lies outside the range of raw AOGCM differences, or at p = 0.06 in the raw kernel density estimate (grey curve). AOGCM CEUR differences are adjusted to account for a general warm bias in the response of CanESM2 to historical forcings 9 by removing each simulation s Northern Hemisphere average temperature difference from its CEUR temperature difference, and then adding back the observed Northern Hemisphere temperature difference to the distribution (black curve). The observed CEUR temperature change (red) has p = 0.13 in the context of the adjusted AOGCM distribution. 5

6 Supplementary Figure 4 Winter sea ice concentration boundary conditions. Maps of the difference between the climatological sea ice concentration boundary condition and sea ice concentration climatological boundary condition that is prescribed in each of the six sets of control-perturbation AGCM simulations. OBS is the observed sea ice concentration from NSIDC 4, and E1-E5 are taken from the AOGCM simulations. 6

7 Supplementary Figure 5 AGCM boreal winter CEUR SAT. Histograms and kernel density estimates of winter CEUR SAT in each pair of AGCM simulations, forced with observed boundary conditions (NSIDC 4 ), and with AOGCM boundary conditions (E1-E5). CEUR temperature from the control simulations, forced with climatological boundary conditions, are shown in light grey, and perturbation simulations, forced with Arctic boundary conditions are shown in blue. Each distribution is shown relative to the respective control mean. Simulations are subsampled into 11-year averages to match the sampling uncertainty of AOGCM anomalies, so that each histogram is made up of 10 elements. The boxes at the top show the respective histogram mean (center line), 95% confidence interval on the mean (inner box), and 95% interval of the histogram (outer box). 7

8 Supplementary Figure 6 Comparison of September BKS composite boundary condition to decadal average AGCM boundary condition. a. Sea ice concentration (SIC) difference in SON for the 10 lowest BKS September sea ice concentrations minus the 10 highest BKS September sea ice concentrations from HadISST 3. Panel a is an estimate of the boundary forcing in the AGCM experiments of Mori et al b. Sea ice concentration difference in SON between and averages from NSIDC 4. Panel b is the observed AGCM boundary forcing used in this study. c. SON sea ice concentration boundary forcing averaged in the BKS for our six AGCM simulation pairs (McCusker AGCM and NSIDC), compared to the BKS sea ice concentration composite difference as used in Mori et al

9 Supplementary Figure 7 Lead-lag correlations of ΔZ with CEUR SAT. a, inter-seasonally in the 20 th Century Reanalysis (20CR) dataset 10 and b. across multi-decadal changes in the AOGCM ensemble. a. Correlations were computed between 3-month running means of ΔZ and DJF CEUR SAT in de-trended 20CR ( ). b, is as a. but showing correlations between changes in the 3- month running mean between and in AOGCM. Correlations significant at 95% are indicated with square markers. 9

10 Supplementary Figure 8 AOGCM interannual relationship of boreal winter ΔZ and winter CEUR SAT. The linear regression slopes for and correlation coefficients between interannual variations in CEUR SAT and ΔZ for each AOGCM realization (grey). The regressions are computed on the de-trended anomaly timeseries from 1979 to The observed value is shown as a red vertical line. 10

11 Supplementary Figure 9 Temperature and circulation regressions on ΔZ (defined at 500 hpa) and BKS sea ice concentration. a, Regression across 11

12 the AOGCM ensemble of changes in winter temperature at 500 hpa (T; shading; o C) and geopotential height at 500 hpa (Z; contours; m) on ΔZ changes. b, is as a, but showing regressions on BKS ice concentration changes. c, d, and e, f are as in a and b but showing regressions of T (shading; o C) and Z (contours; m) at 700 and 850 hpa respectively. g, h. are as a and b but showing regressions of sea level pressure (SLP; contours; hpa) and SAT (shading; o C). The SLP contour interval is 0.4 hpa beginning with +/-0.2 hpa. Z contour interval is +/- 2 m. Zero contours are omitted. All changes are computed between and averages. 12

13 (v) Results from NCAR CESM1 30-member ensemble (Fig S9) To investigate whether our AOGCM results are model dependent, we additionally consider a 30-member ensemble of historical simulations from NCAR s Community Earth System Model version 1 (CESM; ref. 11) in which each simulation is forced with historical well-mixed greenhouse gases and short-lived gases and aerosols and initialized from slightly perturbed initial conditions. The results from this large ensemble are in agreement with those of CanESM2 (compare Fig. S10 to Figs 3 and 4 in the main text). The relationship of the change in circulation index to the change in CEUR SAT between and averages (r = -0.60, p < 0.001) is statistically indistinguishable from that obtained using the CanESM2 large ensemble (r = -0.77, p < ). Similarly, regression maps computed using the CESM ensemble (Fig. S10b-e) are similar to those using the CanESM2 ensemble (Fig. 4 in the main text). We conclude that our findings are reasonably model independent. Supplementary Figure 10 Scatter of CESM boreal winter ΔZ and CEUR SAT changes, and maps of regressions on ΔZ and BKS ice changes. As Figures 3 and 4 in the main text, but for the 30-member NCAR Community Earth System Model simulations. 13

14 (vi) Supplementary Figure S11 Supplementary Figure 11 Polar surface air temperature from AGCM sensitivity simulations. Seasonal cycle of the change in SAT averaged poleward of 70 o N between a 120-year AGCM simulation with boundary conditions (BC) and one with BCs except for Arctic sea ice concentration and sea ice thickness (blue), and between a simulation with BCs except for Arctic sea ice concentation, sea ice thickness, and SST where Arctic sea ice concentration reduced by 10% in absolute terms (red). Grey shading shows the range of SAT change in the five AGCM experiments with variable boundary conditions taken from the AOGCM. The BCs for the SST sensitivity experiments are the average of the five variable boundary conditions taken from the AOGCM. 14

15 References 1. Kug, J.-S. et al. Two distinct influences of Arctic warming on cold winters over North America and East Asia. Nat. Geosci. 8, (2015). 2. 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, (2014). 3. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003). 4. Comiso, J. C. Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/ISSMIS. Version 2. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. (2000, updated 2015). 5. Screen, J. A., Simmonds, I., Deser, C., & Tomas, R. The Atmospheric Response to Three Decades of Observed Arctic Sea Ice Loss. J. Clim. 26 (4): (2013). 6. Deser, C., Tomas, R.A. & Sun, L. The role of ocean atmosphere coupling in the zonal-mean atmospheric response to Arctic sea ice loss. J. Clim. 28, (2015). 7. Deser, C., Sun, L., Tomas, R. A. & Screen, J. Does ocean coupling matter for the northern extratropical response to projected Arctic sea ice loss? Geophys. Res. Lett. 43, 2016GL (2016). 8. Hansen, J., Ruedy, R., Sato, M. & Lo, K. Global surface temperature change. Rev. Geophys. 48, RG4004 (2010). 9. Flato, G., et al. 2013: Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 10. Compo, G. P. et al. The twentieth century reanalysis project. Q. J. R. Meteorol. Soc. 137, 1 28 (2011). 11. Kay, J. et al. The Community Earth System Model (CESM) large ensemble project: A com- munity resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society 96, (2015). 15