SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: /NGEO2517 Two distinct influences of Arctic warming on cold winters over North America and East Asia Jong-Seong Kug 1, Jee-Hoon Jeong 2*, Yeon-Soo Jang 1, Baek-Min Kim 3, Chris K. Folland 4,5, Seung-Ki Min 1, Seok-Woo Son 6 1 School of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea 2 Department of Oceanography, Chonnam National University, Gwangju, Korea 3 Korea Polar Research Institute, Incheon, Korea 4 Met Office Hadley Centre, UK 5 Department of Earth Sciences, Gothenburg University, Gothenburg, Sweden 6 School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea * To whom correspondence should be addressed. jjeehoon@jnu.ac.kr NATURE GEOSCIENCE 1

2 Further observational evidence The relationship between SAT and ART indices is clearly shown in other observational data. Figure S1 shows correlations between SAT and ART indices based on observations derived from interpolated HadCRUT4 data hybridized with the University of Alabama in Huntsville (UAH) satellite data ( Cowtan and Way, 2014) for the period 1979 to It is evident that the two ART1 and ART2 indices are closely related to cold winters over East Asia and North America, respectively. Also, the spatial patterns are quite similar to those in Figs. 2a-b, suggesting a robust relation with ART indices. In order to check the robustness and sensitivity of these statistical relationships to the choice of analysis period, long-term historical CRUTEM4 temperature data are also used for the period ( Jones et al., 2012). CRUTEM4 is based on surface station data. Since the data are available for only land regions, some island station data are also used to construct the ART indices. Nevertheless, the correlation patterns, shown in Fig. S2, are quite similar to the results from the recent reanalysis and observational data shown in Fig. 2 and Fig. S1. Thus ART1 is negatively correlated with SAT over the East Asia, and ART2 is negatively correlated with SAT over North America for the period Based on the strong relation between ART indices and mid-latitude climate, it is important for improving climate predictions to know what lead to the Arctic temperature increase. In order to find signals that preceded the ART indices, a lag composite analysis is used. Here the years for positive and negative DJF seasonal mean ART indices are selected based on the value of their standard deviations (values above and below one standard deviation, respectively), and composites of preceding sea-ice concentration (Oct-Nov) and SAT (Nov-Dec) calculated for

3 both positive and negative cases. Figures S3 and S4 show the composite differences that result from subtracting composites of the negative cases from composites of the positive cases. For ART1, there is a significant reduction of sea-ice over the Arctic during Oct-Nov (Fig. S3a), suggesting that sea-ice loss tends to lead the ART1 increase. This is followed by anomalous warm conditions over the Barents-Kara Sea region in late autumn to early winter (Nov-Dec) (Fig. S3b). Interestingly, however, significant sea-ice anomalies are not confined to the Barents-Kara Sea, but are located over broader regions. This implies that the anomalous warm conditions over the Barents-Kara Sea can be induced not only by the direct effect of sea-ice melting but also by other large scale patterns. Further investigations are needed concerning how and how much preceding sea-ice signals can explain Arctic temperature variability during the following winter. This question will be important for improving current seasonal predictions for the extratropical regions. The CM2.1 model simulations also show similar features to those observed (Figs S3c and S3d), but an anomalous warm condition in early winter appears over the whole Arctic. For the ART2, there are some significant preceding signals of sea-ice loss, but these are relatively weak compared to those for the ART1 (Fig. S4a). This result indicates that temperature variability over the ART2 region is more affected by factors additional to the local sea-ice boundary conditions. However, there are significant warming signals in late autumn to early winter (Nov-Dec) (Fig. S4b) which may be associated partly with the preceding sea-ice signals. Compared to the observations, the model simulations show more significant signals of preceding sea-ice loss during Oct-Nov and warming during Nov-Dec. Compared to sea-ice over Barents-Kara Sea, the role of sea-ice variations over the ART2 regions has had less attention so far. More modeling studies are needed to understand the observed complexity between sea-ice and SAT variations.

4 As shown in Fig. 3, there are significant upstream signals in the circulation patterns related to the ART indices. The statistical results suggest that regional Arctic warming and their downstream teleconnection patterns could be influenced by such upstream disturbances. In particular, as Sato et al. (2014) and Govekar et al. (2014) suggested, large-scale atmospheric variability over North Atlantic could lead to a downstream teleconnection pattern similar to ART-related variability. To check how much the circulation patterns related to the ART indices are sensitive to the upstream signals, we calculated partial correlation (regression) coefficients of SAT (SLP) with respect to de-trended ART1 after removing the North Atlantic variability (defined by area-averaged SLP over 10W-50W, 35N-50N). Furthermore, we carried out the same analysis for ART2 after removing North Pacific variability (defined by area-averaged SLP over 140W-180, 20N-40N). Figure S5 shows temperature and atmospheric circulation anomalies linked to the ART indices after their respective downstream signals are removed. It is clear that the partial correlations and regressions are quite similar to their counterparts in Figs 2 and 3. This shows that the original downstream teleconnection patterns related to the ART indices calculated in this paper remain robust when upstream signals over the Atlantic and Pacific regions do not exist. More results from CM2.1 model experiments In the experiments with prescribed high-latitude SST, the model makes a reasonable simulation of the observed circulation patterns associated with ART1 and ART2 indices. Figure S6 shows regression maps using the ART indices similar to the observed counterparts shown in Fig 3. For ART1, positive SLP anomalies develop from the coastal regions of the Barents-Kara Seas to the central Eurasian continent. In the upper troposphere, anticyclonic anomalies are located over the Ural Mountains with cyclonic anomalies in the downstream

5 region, consistent with the observed features shown in Fig. 3. For ART2, positive SLP develops over Alaska and western Canada. Cyclonic flow also develops in the downstream region, consistent with Figs. 3b and d. Further support from CMIP5 Analyses The relationship between ART indices and extratropical climate is investigated in each of 39 CMIP5 models. Some details of each model are listed in Table S1. Figure S7 shows the correlation between the modelled ART1 index and modelled SAT in each model. Though the correlation pattern differs slightly between models, it is evident that most models simulate the negative correlation with SAT over East Asia, consistent with Fig. 4c. Furthermore, it is striking that every CMIP5 model simulates a negative SAT correlation with its ART2 index over North America, as shown in Fig. S8. As well as the SAT pattern, the CMIP5 models tend to simulate the observed circulation patterns associated with ART1 and ART2 indices. It is evident that the patterns shown in Fig. S9 are rather similar to those in Fig. 3, strongly supporting the observational arguments. For ART1, strong positive SLP anomalies develop near the Ural Mountains. Anomalous anticyclonic flow also appears in at upper levels near coastal regions of the Kara-Barents Seas and near the Ural Mountains while downstream cyclonic flow develops over East Asia. For ART2, anomalous anticyclonic flow develops over Alaska and the far western part of Canada. In the upper troposphere, the anomalous anticyclone flow is very strong and coincident with the low-level anomalous anticyclone and anomalous cyclonic flow established in the downstream region. These similarities in circulation patterns between the

6 observational and model results indicate that our deductions about the physical process for these Arctic-to-extratropical connections are robust. Supplementary References 1. Cowtan K., Way R. G. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q.J. Roy. Met Soc. 2014, 140(683): Jones P. D., Lister D. H., Osborn T. J., Harpham C., Salmon M., Morice C. P. Hemispheric and large-scale land-surface air temperature variations: An extensive revision and an update to J. Geophys. Res. 2012, 117(D5): D Sato K., Inoue J., Watanabe M. Influence of the Gulf Stream on the Barents Sea ice retreat and Eurasian coldness during early winter. Environ. Res. Lett. 2014, 9(8): Simmonds I., Govekar P. What are the physical links between Arctic sea ice loss and Eurasian winter climate? Environ. Res. Lett. 2014, 9(10):

7 Supplementary Table S1 Summary of CMIP5 models used. The 39 CGCM models in the historical simulation and group names given in the table below are analyzed. Model Name Modeling Center (or Group) Integration Period BCC-CSM1.1 Beijing Climate Center, China Meteorological Administration JAN1850 ~ DEC2012 BNU-ESM College of Global Change and Earth System Science, Beijing Normal University CanESM2 Canadian Centre for Climate Modelling and Analysis CanCM4 JAN1961 ~ DEC2005 CCSM4 National Center for Atmospheric Research CESM1(BGC) CESM1(CAM5) CESM1(FASTCHEM) CESM1(WACCM) Community Earth System Model Contributors CMCC-CESM CMCC-CM Centro Euro-Mediterraneo per I Cambiamenti Climatici CNRM-CM5 Centre National de Recherches Météorologiques / Centre Européen de Recherche et Formation Avancée en Calcul Scientifique CSIRO-Mk3.6.0 Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence FIO-ESM The First Institute of Oceanography, SOA, China GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H GISS-E2-H-CC GISS-E2-R GISS-E2-R-CC HadGEM2-AO HadCM3 HadGEM2-CC HadGEM2-ES NOAA Geophysical Fluid Dynamics Laboratory NASA Goddard Institute for Space Studies National Institute of Meteorological Research/Korea Meteorological Administration Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais) JAN1860 ~ DEC2005 JAN1861 ~ DEC2005 JAN1861 ~ DEC2005 JAN1850 ~ DEC2010 JAN1850 ~ DEC2010 JAN1860 ~ DEC2005 DEC1859 ~ DEC2005 DEC1859 ~ NOV2005 DEC1859 ~ NOV2005 INM-CM4 Institute for Numerical Mathematics IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC-ESM MIROC-ESM-CHEM MIROC4h MIROC5 MPI-ESM-MR MPI-ESM-LR MPI-ESM-P Institut Pierre-Simon Laplace Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) JAN1950 ~ DEC2005 JAN1850 ~ DEC2012 MRI-CGCM3 Meteorological Research Institute NorESM1-M NorESM1-ME Norwegian Climate Centre

8 Supplementary Figure S1 Correlations between Arctic temperature and SAT over the NH extratropics in other observational datasets. As Fig. 2a,b except for the data from HadCRUT4 combined with UAH lower tropospheric temperatures (Cowtan and Way, 2014). The confidence levels of correlation coefficients based on a Student s t-test are denoted below the legend bar.

9 Supplementary Figure S2 Correlation between Arctic temperature and SAT over the NH extratropics in long-term observations. Correlation coefficients between ART indices and observed SAT anomalies during December-February for the period 1890/ /14 from the CRUTEM4 dataset (Jones et al., 2012). The confidence levels of correlation coefficients based on a Student s t-test are denoted below the legend bar.

Supplementary Figure S3 Preceding sea ice and SAT signals linked to ART1. [Left Panels] Differences in the composites of sea ice concentration during the preceding Oct.-Nov.

10 Supplementary Figure S3 Preceding sea ice and SAT signals linked to ART1. [Left Panels] Differences in the composites of sea ice concentration during the preceding Oct.-Nov. and [Right Panels] SAT during Nov.-Dec. between the positive and negative cases of (a,b) the observed winter mean (DJF) ART1 index and (c,d) similarly for the CM2.1 simulation. The blue and red circles represent positive and negative differences, respectively, and their depths of the color denote confidence levels of the differences based on a Student s t-test. The gray shading indicates the climatological distribution of sea ice concentration.

11 Supplementary Figure S4 Preceding sea ice and SAT signals linked to ART2. As Fig S3 but for ART2 index

12 Supplementary Figure S5 SAT and atmospheric circulation anomalies linked to Arctic temperature independent of the upstream signals. (a,b) Partial correlation coefficients of SAT anomalies with respect to de-trended monthly ART1 (a) and ART2 indices (b) after removing the Atlantic signal (defined by area-averaged SLP over 10W-50W, 35N-50N) and the Pacific signal (defined by area-averaged SLP over 140W-180, 20N-40N), respectively. Partial regression of (c, d) sea level pressure [Pa], and (e,f) 300 hpa geopotential height [m] with respect to de-trended ART1 [left panels] and ART2 [right panels] indices.

13 Supplementary Figure S6 Modelled atmospheric circulation anomalies linked to Arctic temperature. As Fig. 3, but here for data from the mean of the CM2.1 model simulations. The regression is calculated for the period 1979/ /13.

14 Supplementary Figure S7 Correlation between ART1 and SAT in CMIP5 simulations. Correlation coefficients between ART1 and SAT anomalies during December-February from the CMIP5 models.

15 Supplementary Figure S8 Correlation between ART2 and SAT in CMIP5 simulations. As Fig.S6 but for ART2 index

16 Supplementary Figure S9 Atmospheric circulation anomalies linked to Arctic temperature in the CMIP5 models. As Fig. 3 but here for data from the CMIP5 models. Linear regressions from the 39 models are averaged.

Supplement of Insignificant effect of climate change on winter haze pollution in Beijing

Supplement of Insignificant effect of climate change on winter haze pollution in Beijing Supplement of Atmos. Chem. Phys., 18, 17489 17496, 2018 https://doi.org/10.5194/acp-18-17489-2018-supplement Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.