Two distinct influences of Arctic warming on cold winters over North America and East Asia

Journal name:
Nature Geoscience
Year published:
Published online

Arctic warming has sparked a growing interest because of its possible impacts on mid-latitude climate1, 2, 3, 4, 5. A number of unusually harsh cold winters have occurred in many parts of East Asia and North America in the past few years2, 6, 7, and observational and modelling studies have suggested that atmospheric variability linked to Arctic warming might have played a central role1, 3, 4, 8, 9, 10, 11. Here we identify two distinct influences of Arctic warming which may lead to cold winters over East Asia or North America, based on observational analyses and extensive climate model results. We find that severe winters across East Asia are associated with anomalous warmth in the Barents–Kara Sea region, whereas severe winters over North America are related to anomalous warmth in the East Siberian–Chukchi Sea region. Each regional warming over the Arctic Ocean is accompanied by the local development of an anomalous anticyclone and the downstream development of a mid-latitude trough. The resulting northerly flow of cold air provides favourable conditions for severe winters in East Asia or North America. These links between Arctic and mid-latitude weather are also robustly found in idealized climate model experiments and CMIP5 multi-model simulations. We suggest that our results may help improve seasonal prediction of winter weather and extreme events in these regions.

At a glance


  1. SAT trends and Arctic temperature (ART) indices.
    Figure 1: SAT trends and Arctic temperature (ART) indices.

    a,b, The linear trend in surface air temperature during December–February for the periods 1979/1980–1997/1998 (a) and 1997/1998–2013/2014 (b) from the observed data32. Green boxes denote the region for ART indices in b. c, Time series of seasonal-mean ART1 and ART2 during December–February for the period 1979/1980–2013/2014. DT denotes the de-trended state.

  2. Relationships between Arctic temperature and SAT over the NH extratropics.
    Figure 2: Relationships between Arctic temperature and SAT over the NH extratropics.

    a,b, Correlation coefficients of SAT anomalies with respect to de-trended monthly ART1 (a) and ART2 indices (b) during December–February for the period 1979/1980–2013/2014 from the reanalysis data. Shading denotes significant values at the 95% confidence level based on a Students t-test. c,d, Lead–lag regression coefficients of a moving 31-day-mean SAT over East Asia (80°–130° E, 35°–50° N) with respect to the normalized ART1 index (c), and over North America (80°–120° W, 40°–55° N) with respect to the normalized ART2 index (d). Correlation coefficients that are statistically significant at the 95% confidence level are indicated with filled circles.

  3. Atmospheric circulation anomalies linked to Arctic temperature.
    Figure 3: Atmospheric circulation anomalies linked to Arctic temperature.

    Linear regression of sea-level pressure (Pa) (a,b) and 300hPa geopotential height (m) (c,d) with respect to de-trended monthly ART1 (a,c) and ART2 indices (b,d) during December–February for the period of 1979/1980–2013/2014. Shading denotes significant values at 95% confidence level based on a Students t-test.

  4. Modelling support on the relationships between Arctic temperature and SAT over the NH extratropics.
    Figure 4: Modelling support on the relationships between Arctic temperature and SAT over the NH extratropics.

    a,b, SAT anomalies regressed on de-trended monthly ART1 (a) and ART2 indices (b) during December–February for the period 1979/1980–2012/13 from observation (contour) and CM2.1 model experiments (shaded). The pattern correlation coefficients between the observation and the model experiments over 30°–90° N are denoted in the upper right side of the figure. c,d, Regression coefficients of SAT over the East Asia region on the ART1 index (c) and SAT over the North America region on the ART2 index (d) during December–February in the 39 CMIP5 simulations. Red and orange bars show the coefficients from the observation and the CM2.1 model experiments, respectively. Green bars show the coefficients from the CMIP5 models, and blue bars denote the multi-model ensemble mean. The scale bars represent a range of 95% confidence levels from internal variability using a Monte Carlo approach.


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Author information


  1. School of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), 37673 Pohang, Korea

    • Jong-Seong Kug,
    • Yeon-Soo Jang &
    • Seung-Ki Min
  2. Department of Oceanography, Chonnam National University, 61186 Gwangju, Korea

    • Jee-Hoon Jeong
  3. Korea Polar Research Institute, 21990 Incheon, Korea

    • Baek-Min Kim
  4. Met Office Hadley Centre, Exeter EX1 3PB, UK

    • Chris K. Folland
  5. Department of Earth Sciences, University of Gothenburg, 405 30 Gothenburg, Sweden

    • Chris K. Folland
  6. School of Earth and Environmental Sciences, Seoul National University, 00826 Seoul, Korea

    • Seok-Woo Son


J.-S.K. and J.-H.J. designed the research, conducted analyses, and wrote the majority of the manuscript content. B.-M.K., C.K.F., S.-K.M. and S.-W.S. conducted the analysis and report-writing tasks. Y.-S.J. conducted analyses, numerical experiments and prepared figures. All the authors discussed the study results and reviewed the manuscript.

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The authors declare no competing financial interests.

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