Persistent shift of the Arctic polar vortex towards the Eurasian continent in recent decades

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1 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE3136 Persistent shift of the Arctic polar vortex towards the Eurasian continent in recent decades Jiankai Zhang 1, Wenshou Tian 1 *, Martyn P. Chipperfield 2, Fei Xie 3, Jinlong Huang 1 1 Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China. 2. School of Earth and Environment, University of Leeds, Leeds, UK 3. College of Global Change and Earth System Science, Beijing Normal University, Beijing, China *Corresponding author. wstian@lzu.edu.cn NATURE CLIMATE CHANGE Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

2 Methods: Data sets. The ERA-Interim data used here has a horizontal resolution of 1 (latitude) 1.25 (longitude) and it has 17 isentropic levels and 37 pressure levels. The NASA-MERRA data set is also used to analyze the polar vortex edge and temperature. We have vertically interpolated the MERRA data fields onto the isentropic coordinates of the ERA-Interim data. The sea-ice cover (SIC) and SST are derived from the monthly Sea Ice and Sea Surface Temperature dataset from the Hadley Centre in which sea ice is retrieved from various sources of digitized sea ice charts and passive microwave imagery Polar vortex analysis. The vortex edge is defined as the location of the largest Ertel potential vorticity (EPV) gradient, with an additional constraint of close proximity to a strong westerly jet, according to the method of Nash et al. 2. The monthly mean EPV and vortex edge are not calculated when the polar vortex breaks up or if the shape of vortex is not well defined, usually when the duration of major sudden stratospheric warming (SSW) events occurring in this month lasts more than 15 days. Such major SSW events occurred in February during 1987, 2006, 2009 and The daily mean vortex edge is also not calculated during the major SSW events. In addition, we defined the location of the maximum value of the PV averaged between the layers K as the vortex centre Composite analysis. The composites during the high and low SIC years are 2

3 calculated when the detrended and 6-month (September-October-November- December-January-February) mean SIC over the BKS (70-90 N, 0-90 E) is greater and less than 1 and 1 standard deviation, respectively. The composited differences for a given field are calculated by averaging the detrended monthly mean field during the low SIC years minus that during the high SIC years. The two-tailed Student s t-test is used to calculate the statistical significance of the composite differences Model and simulations. The Whole Atmosphere Community Climate Model, version 3 (WACCM3) has 66 vertical levels extending from the ground to 145 km, with a vertical resolution of km near the tropopause region. The time-slice simulations presented in this paper were performed at a resolution of 1.9 (latitude) 2.5 (longitude), with interactive chemistry disabled and the QBO turned off. The control experiment (R1) was performed with a seasonal cycle of SIC and SST based on the decadal mean of the Hadley Centre SIC and SST in the 1980s. In experiment R2, the SST is the same as in R1, except that the SIC forcing represents the decadal mean of the SIC during the 2000s when the sea-ice is reduced. Simulations R1 and R2 use the same GHGs emissions and the same climatology of ozone for the period The experiments R1 and R2 were run for 25 years with the first 5 years excluded for model spin-up ; the remaining 20 years of output is used for the analysis. Two additional experiments (R3 and R4) were performed with 6 ensemble members. Each ensemble run was run for 15 years with the first 5 years excluded for model spin-up ; the remaining 10 years of model data from each ensemble run is used for 3

4 the analysis. The control experiment (R3) was performed with the same seasonal varying SIC and SSTs in the 1980s as those in R1. In the perturbed run (R4), the SSTs and SIC are the same as those in R3, except that the sea-ice over the BKS in September, October, November, December, January and February are replaced with corresponding sea-ice climatologies in the 2000s. The experiments R3 and R4 are designed to analyze the influence of sea-ice loss over the BKS in autumn and winter on the vortex shift. To analyze the influence of Eurasian snow cover changes on the polar vortex shift, the experiment R5 was conducted. The SIC and SSTs of R5 are the same as those in R1, except that the surface albedo over the Eurasian continent ( N, E) during autumn and winter in R5 is 5% higher than that in R A linearized barotropic vorticity equation model (LBM) is used to investigate how the PV changes over Siberia associated with the vortex shift influence the Northern Hemisphere surface temperatures through Rossby wave trains. The LBM is based on the steady, forced barotropic vorticity equation: J( ψ, ψ f) α ψ K ( ψ) R =, where ψ is the stream function, f is the coriolis parameter, R is a forcing function, α of s -1 is the Rayleigh coefficient, K is equal to m 4 s -1, and 91 1 A B A B Jacobi function J( A, B) = ( ) 2 a cosφ λ φ φ λ with latitude φ, longitude λ 92 and earth s radius a. More details please see the paper of Branstator 3. 4

5 93 94 Supplementary Figure 1. Decadal mean of vortex edge (coloured contour lines) and 95 vortex centre (coloured dots) in the 1980s (black), 1990s (purple) and 2000s (green) 5

6 as well as the PV trends (colour-filled contours) for the period averaged between the isentropic layer K during (a-c) December, (d-f) January, (g-i) February and (j-l) their DJF mean derived from (left) ERA-Interim daily data, (middle) MERRA monthly data and (right) MERRA daily data. The differences over the dotted regions are statistically significant at the 90% confidence level according to the Student s t-test Supplementary Figure 2. Time series of (a) latitude and (b) longitude of the polar vortex centre derived from daily mean (line with triangles) MERRA data and (line with dots) ERA-Interim data. Solid line represents the corresponding linear trends which are statistically significant at the 90% confidence levels; whereas, the linear trends denoted by the dotted lines are not significant at the 90% confidence level. 6

7 Supplementary Figure 3. Difference in February mean PV (colour-filled contours) between ensemble runs R4 and R3 (see Methods) and February mean vortex edge in R3 (black line contour) and R4 (green line contour) averaged between the isentropic layers K. The differences over the dotted regions are statistically significant at the 90% confidence level according to the Student s t-test

8 Supplementary Figure 4. Composited differences in temperature at 925 hpa (a) between the low and high Barents-Kara SIC years derived from the ERA-Interim data and (b) between the WACCM simulations R2 and R1. (c) Height-latitude cross section of the temperature along 60 E (colour-filled contours), Brunt Väisälä frequency N 2 (line contours) as well as vertical and meridional component of Plumb 122 flux 4 on time scales of days (vectors), and (d) as in (c), but for the corresponding modelled differences between R2 and R1. The differences over the dotted regions are statistically significant at the 90% confidence level according to the Student s t-test. 8

9 Supplementary Figure 5. Differences in February mean geopotential height at (a) 50 hpa and (b) 850 hpa between ensemble runs R4 and R3 (see Methods). Height-longitude cross section along 60 N of geopotential height differences between ensemble runs R4 and R3 (colour-filled contours). The climatological mean of zonal wave-1 geopotential height anomalies along 60 N in R3 (contours lines) are also shown (solid and dashed lines represent positive and negative contours, respectively). The differences over the dotted regions are statistically significant at the 90% confidence level according to the Student s t-test

10 Supplementary Figure 6. Differences in geopotential height at (a) 850 hpa and (b) 50 hpa between the WACCM3 simulations R5 (see Methods) and R1. The differences over the dotted regions are statistically significant at the 90% confidence level according to the Student s t-test I. Analysis of the contribution of the vortex shift on Eurasian cooling: It is worth noting that the strengthened blockings over the upstream region of Siberia during the vortex shift events associated with the sea-ice loss may play a role in the Siberian cooling 5-6. Here we quantify the role of stratospheric impacts on the Siberian cooling as compared to the effects of the upstream blockings on the Siberian cooling. Supplementary Figure 7 shows that the Siberian geopotential height anomalies have significant correlations with the positive height anomalies at 500 hpa over the upstream region of Siberia two days ago and the negative height anomalies in the UTLS region ( hpa) two days ago. We further use the multiple regression method to quantify the contributions of stratospheric height anomalies and the tropospheric upstream height anomalies to the Siberian cooling. It is found that the upstream height anomalies could account for ~36% of the Siberian cooling, while the stratospheric height anomalies contribute to about 10%. It is worth noting that other processes such as the internal climate variability could also contribute to the Siberian cooling 7. On the other hand, Charlton et al. 8 confirmed that a high PV anomaly could lead to a significant cyclonic flow at the surface and noticeable low pressure anomalies (up to 3 hpa) beneath the PV anomaly. Their study provided evidence that 10

11 PV increases over the Eurasian continent affect directly the low pressure and cooling over Eurasia. Using PV inversion method, they calculated that a 30 % PV increase in the lower stratosphere (the value is close to the PV difference between high and low SPV events) can cause a ~ 10 gpm decrease in geopotential height at 500 hpa (Fig.2 and Fig.6 of their paper), accounting for around % of the composited negative height anomalies over Eurasia associated with the vortex shift in February (Fig.4a), similar to our statistical results derived from the multiple regression model Supplementary Figure 7. Scatters of daily mean 500 hpa Siberian (45-60 N, E) geopotential height anomalies from -5 day to +1 day for 8 high SPV events (56 dots) and (a) the geopotential height anomalies at 500 hpa in the upstream region of Siberia (50-70 N, 5-40 E) 2 days before the occurrence of Siberian height anomalies, and (b) geopotential height anomalies in the UTLS ( hpa) over the SPV region (60-75 N, E) 2 days before the occurrence of Siberian height anomalies. Day 0 is the day with the maximum value of daily mean SPV index in February for 8 high SPV events. The blue lines represent the regressed line. The numbers in the upper-right corner of the panel represent the standardized regression coefficient and the symbol * represents that the coefficient is statistically significant at the 95% confidence level

12 Supplementary Figure 8. Correlation maps between February-mean PV averaged 182 over Siberia (60-75 N, E) and (a) February mean geopotential height at 50 hpa, 183 March mean geopotential height at (b) 300 hpa, (c) 500 hpa as well as (d) 850 hpa. (e) 184 The vorticity forcing used in the experiment of the linear barotropic model (LBM). (f) 185 Steady state responses of geopotential height at 300 hpa in the LBM simulation. The 186 correlation coefficients over the dotted regions are statistically significant at the 90% 187 confidence level according to the Student s t-test. Symbols + and - in (b), (c), (d) 188 and (f) represent the ridges and troughs of Rossby wave trains, respectively References: Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and 12

13 night marine air temperature since the late nineteenth century. J Geophys Res-Atmos 108 (2003). 2 Nash, E. R., Newman, P. A., Rosenfield, J. E. & Schoeberl, M. R. An objective determination of the polar vortex using Ertel's potential vorticity. J Geophys Res-Atmos 101, (1996). 3 Branstator, G. Horizontal energy propagation in a barotropic atmosphere with meridional and zonal structure. J Atmos Sci 40, (1983). 4 Plumb A. On the Three-Dimensional Propagation of Stationary Waves, J. Atmos. Sci. 42, (1985). 5 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(12), (2014). 6 Kug, J. S., Jeong, J. H., Jang, Y. S., Kim, B. M., Folland, C. K., Min, S. K., & Son, S. W. Two distinct influences of Arctic warming on cold winters over North America and East Asia. Nat Geosci 8, (2015). 7 Sun, L., Perlwitz, J., & Hoerling, M. What caused the recent Warm Arctic, Cold Continents trend pattern in winter temperatures? Geophys. Res. Lett. 43, (2016). 8 Charlton, A. J., O'neill, A., Berrisford, P., & Lahoz, W. A. Can the dynamical impact of the stratosphere on the troposphere be described by large scale adjustment to the stratospheric PV distribution? Quart J Roy Meteor Soc. 131(606), (2005). 13

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