Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Contribution of changes in atmospheric circulation patterns to extreme temperature trends

Nature volume 522, pages 465–469 (2015)Cite this article

Subjects

Abstract

Surface weather conditions are closely governed by the large-scale circulation of the Earth’s atmosphere. Recent increases in the occurrence of some extreme weather phenomena1,2 have led to multiple mechanistic hypotheses linking changes in atmospheric circulation to increasing probability of extreme events3,4,5. However, observed evidence of long-term change in atmospheric circulation remains inconclusive6,7,8. Here we identify statistically significant trends in the occurrence of atmospheric circulation patterns, which partially explain observed trends in surface temperature extremes over seven mid-latitude regions of the Northern Hemisphere. Using self-organizing map cluster analysis9,10,11,12, we detect robust circulation pattern trends in a subset of these regions during both the satellite observation era (1979–2013) and the recent period of rapid Arctic sea-ice decline (1990–2013). Particularly substantial influences include the contribution of increasing trends in anticyclonic circulations to summer and autumn hot extremes over portions of Eurasia and North America, and the contribution of increasing trends in northerly flow to winter cold extremes over central Asia. Our results indicate that although a substantial portion of the observed change in extreme temperature occurrence has resulted from regional- and global-scale thermodynamic changes, the risk of extreme temperatures over some regions has also been altered by recent changes in the frequency, persistence and maximum duration of regional circulation patterns.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Trends in mid-atmospheric geopotential heights.
Figure 2: Trends in surface temperature extremes and atmospheric circulation patterns.
Figure 3: Trends in circulation patterns and hot extremes over Europe.
Figure 4: Circulation pattern and thermal extreme trends for selected regions.

Similar content being viewed by others

References

  1. Field, C. B. et al. (eds) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (Cambridge Univ. Press, 2012)

    Book  Google Scholar 

  2. Donat, M. G. et al. Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. J. Geophys. Res. 118, 2098–2118 (2013)

    Google Scholar 

  3. Francis, J. A. & Vavrus, S. J. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys. Res. Lett. 39, L06801 (2012)

    Article  ADS  Google Scholar 

  4. Liu, J., Curry, J. A., Wang, H., Song, M. & Horton, R. M. Impact of declining Arctic sea ice on winter snowfall. Proc. Natl Acad. Sci. USA 109, 4074–4079 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Petoukhov, V., Rahmstorf, S., Petri, S. & Schellnhuber, H. J. Quasiresonant amplification of planetary waves and recent Northern Hemisphere weather extremes. Proc. Natl Acad. Sci. USA 110, 5336–5341 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Screen, J. A. & Simmonds, I. Caution needed when linking weather extremes to amplified planetary waves. Proc. Natl Acad. Sci. USA 110, E2327 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Barnes, E. A., Dunn-Sigouin, E., Masato, G. & Woolings, T. Exploring recent trends in Northern Hemisphere blocking. Geophys. Res. Lett. 41, 638–644 (2014)

    Article  ADS  Google Scholar 

  8. Screen, J. & Simmonds, I. Exploring links between Arctic amplification and mid-latitude weather. Geophys. Res. Lett. 40, 959–964 (2013)

    Article  ADS  Google Scholar 

  9. Kohonen, T. Self-Organizing Maps 501 (Springer, 2001)

    Book  MATH  Google Scholar 

  10. Cassano, J. J., Uotila, P., Lynch, A. H. & Cassano, E. N. Predicted changes in synoptic forcing of net precipitation in large Arctic river basins during the 21st century. J. Geophys. Res. 112, G04S49 (2007)

    Article  ADS  Google Scholar 

  11. Johnson, N. C., Feldstein, S. B. & Tremblay, B. The continuum of Northern Hemisphere teleconnection patterns and a description of the NAO shift with the use of self-organizing maps. J. Clim. 21, 6354–6371 (2008)

    Article  ADS  Google Scholar 

  12. Lee, S. & Feldstein, S. B. Detecting ozone- and greenhouse gas-driven wind trends with observational data. Science 339, 563–567 (2013)

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Diffenbaugh, N. S. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 137–141 (IPCC, Cambridge Univ. Press, 2014)

    Google Scholar 

  14. Field, C. B. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 1–32 (IPCC, Cambridge Univ. Press, 2014)

    Book  Google Scholar 

  15. Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014)

    Article  ADS  CAS  Google Scholar 

  16. Deser, C., Phillips, A. S., Alexander, M. A. & Smoliak, B. V. Projecting North American climate over the next 50 years: uncertainty due to internal variability. J. Clim. 27, 2271–2296 (2014)

    Article  ADS  Google Scholar 

  17. Cohen, J. et al. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 7, 627–637 (2014)

    Article  ADS  CAS  Google Scholar 

  18. Palmer, T. Record-breaking winters and global climate change. Science 344, 803–804 (2014)

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Wallace, J. M., Held, I. M., Thompson, D. W. J., Trenberth, K. E. & Walsh, J. E. Global warming and winter weather. Science 343, 729–730 (2014)

    Article  ADS  CAS  PubMed  Google Scholar 

  20. Screen, J. A. Arctic amplification decreases temperature variance in northern mid- to high-latitudes. Nature Clim. Change 4, 577–582 (2014)

    Article  ADS  Google Scholar 

  21. Hartmann, D. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 159–254 (IPCC, Cambridge Univ. Press, 2013)

    Google Scholar 

  22. Simmonds, I. Comparing and contrasting the behaviour of Arctic and Antarctic sea ice over the 35-year period 1979–2013. Ann. Glaciol. 56, 18–28 (2015)

    Article  ADS  Google Scholar 

  23. Diffenbaugh, N. S. & Ashfaq, M. Intensification of hot extremes in the United States. Geophys. Res. Lett. 37, L15701 (2010)

    Article  ADS  Google Scholar 

  24. Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C. & Vila-Guerau de Arellano, J. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014)

    Article  ADS  CAS  Google Scholar 

  25. Diffenbaugh, N. S., Pal, J. S., Trapp, R. J. & Giorgi, F. Fine-scale processes regulate the response of extreme events to global climate change. Proc. Natl Acad. Sci. USA 102, 15774–15778 (2005)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Inoue, J., Hori, M. E. & Takaya, K. The role of Barents Sea ice in the wintertime cyclone track and emergence of a warm-Artic cold-Siberian Anomaly. J. Clim. 25, 2561–2568 (2012)

    Article  ADS  Google Scholar 

  27. 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, 869–873 (2014)

    Article  ADS  CAS  Google Scholar 

  28. Cohen, J., Furtado, J., Barlow, J. M., Alexeev, V. & Cherry, J. Arctic warming, increasing fall snow cover and widespread boreal winter cooling. Environ. Res. Lett. 7, 014007 (2012)

    Article  ADS  Google Scholar 

  29. Screen, J. A., Deser, C., Simmonds, I. & Tomas, R. Atmospheric impacts of Arctic sea-ice loss, 1979–2009: Separating forced change from atmospheric internal variability. Clim. Dyn. 43, 333–344 (2014)

    Article  Google Scholar 

  30. Screen, J. A., Simmonds, I., Deser, C. & Tomas, R. The atmospheric response to three decades of observed Arctic sea ice loss. J. Clim. 26, 1230–1248 (2013)

    Article  ADS  Google Scholar 

  31. Screen, J. A. & Simmonds, I. Amplified mid-latitude planetary waves favour particular regional weather extremes. Nature Clim. Change 4, 704–709 (2014)

    Article  ADS  Google Scholar 

  32. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996)

    Article  ADS  Google Scholar 

  33. Kanamitsu, M. et al. NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Am. Meteorol. Soc. 83, 1631–1643 (2002)

    Article  ADS  Google Scholar 

  34. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011)

    Article  ADS  Google Scholar 

  35. Driouech, F., Déqué, M. & Sánchez-Gómez, E. Weather regimes–Moroccan precipitation link in a regional climate change simulation. Glob. Plan. Ch. 72, 1–10 (2010)

    Article  ADS  Google Scholar 

  36. Cattiaux, J., Douville, H. & Peings, P. European temperatures in CMIP5: origins of present-day biases and future uncertainties. Clim. Dyn. 41, 2889–2907 (2013)

    Article  Google Scholar 

  37. Zwiers, F. W. & von Storch, H. Taking serial correlation into account in tests of the mean. J. Clim. 8, 336–351 (1995)

    Article  ADS  Google Scholar 

  38. Anderson, T. W. & Darling, D. A. Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann. Math. Stat. 23, 193–212 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  39. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc., B 57, 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  40. Box, G. E. P. & Cox, D. R. An analysis of transformations. J. R. Stat. Soc. B 26, 211–243 (1964)

    MATH  Google Scholar 

  41. Lehmann, E. & Romano, J. P. Generalizations of the familywise error rate. Ann. Stat. 33, 1138–1154 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  42. Skific, N., Francis, J. A. & Cassano, J. J. Attribution of projected changes in atmospheric moisture transport in the Arctic: A self-organizing map perspective. J. Clim. 22, 4135–4153 (2009)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

Work by D.E.H., D.S., D.L.S. and N.S.D. was supported by NSF CAREER Award 0955283, DOE Integrated Assessment Research Program Grant No. DE-SC005171DE-SC005171, and a G.J. Lieberman Fellowship to D.S. Contributions from N.C.J. were supported by NOAA’s Climate Program Office’s Modeling, Analysis, Predictions, and Projections program award NA14OAR4310189. B.R. acknowledges support from the US Air Force Office of Scientific Research (FA9550-13-1-0043), the US National Science Foundation (DMS-0906392, DMS-CMG-1025465, AGS-1003823, DMS-1106642, and DMS-CAREER-1352656), the Defense Advanced Research Projects Agency (DARPA YFA N66001-111-4131), and the UPS Foundation (SMC-DBNKY). We thank B. Santer, J. Cattiaux, D. Touma, and J. S. Mankin for discussions that improved the manuscript. Computational resources for data processing and analysis were provided by the Center for Computational Earth and Environmental Science in the School of Earth, Energy, and Environmental Sciences at Stanford University.

Author information

Authors and Affiliations

  1. Department of Earth System Science, Stanford University, Stanford, 94305, California, USA

    Daniel E. Horton, Deepti Singh, Daniel L. Swain, Bala Rajaratnam & Noah S. Diffenbaugh

  2. Woods Institute for the Environment, Stanford University, Stanford, 94305, California, USA

    Daniel E. Horton, Bala Rajaratnam & Noah S. Diffenbaugh

  3. International Pacific Research Center, University of Hawaii at Manoa, Honolulu, 96822, Hawaii, USA

    Nathaniel C. Johnson

  4. Scripps Institution of Oceanography, University of California San Diego, La Jolla, 92093, California, USA

    Nathaniel C. Johnson

  5. Cooperative Institute for Climate Science, Princeton University, Princeton, 08540, New Jersey, USA

    Nathaniel C. Johnson

  6. Department of Statistics, Stanford University, Stanford, 94305, California, USA

    Bala Rajaratnam

Authors
  1. Daniel E. Horton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nathaniel C. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deepti Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel L. Swain
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bala Rajaratnam
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Noah S. Diffenbaugh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.E.H. conceived the study. D.E.H., N.C.J., D.S., D.L.S. and N.S.D. designed the analysis and co-wrote the manuscript. D.E.H., N.C.J. and D.S. provided analysis tools. D.E.H. performed the analysis. B.R. provided and described the multiple hypothesis testing and transformation analysis.

Corresponding author

Correspondence to Daniel E. Horton.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 2-, 4- and 8-node SOM analyses.

SOM-derived mid-atmospheric summer (JJA) circulation patterns (500 hPa geopotential height anomalies) over Europe using 2- (a), 4- (b) and 8-node (c) analyses. White boxed values show pattern frequencies in the top left and SOM node numbers in the top right. Time series of SOM circulation pattern occurrence (black (d yr−1)), persistence (blue (d event−1)) and maximum duration (red (d event−1)). The slope of the trend line (yr−1) and P values (in parentheses) are colour coded, with the values from 1979 to 2013 (solid trend line) displayed above those from 1990 to 2013 (dashed trend line). Statistically significant trends (5% significance level; Methods) are shown by bold fonts in the scatter plots. Geopotential height fields are sourced from the NCEP-DOE-R2 reanalysis33.

Extended Data Figure 2 16-node SOM analysis.

SOM-derived mid-atmospheric summer (JJA) circulation patterns (500 hPa geopotential height anomalies) over Europe derived from a 16-node analysis. White boxed values show pattern frequencies in the top left and SOM node numbers in the top right. Time series of SOM circulation pattern occurrence (black (d yr−1)), persistence (blue (d event−1)) and maximum duration (red (d event−1)). The slope of the trend line (yr−1) and P values (in parentheses) are colour coded, with the values from 1979 to 2013 (solid trend line) displayed above those from 1990 to 2013 (dashed trend line). Statistically significant trends (5% significance level; Methods) are shown by bold fonts in the scatter plots. Geopotential height fields are sourced from the NCEP-DOE-R2 reanalysis33.

Extended Data Figure 3 Geopotential height trends and thermal dilation adjustment.

a–d, Northern Hemisphere polar projections of 1979–2013 seasonal trends in 500 hPa geopotential heights (same as Fig. 1, reproduced here for convenience). e, Area-weighted trends in seasonal geopotential heights over the Northern Hemisphere and regional SOM domains. f–j, Trends in raw and detrended geopotential height SOM pattern occurrence (OCC), persistence (PER) and maximum duration (DUR) in units of d yr−1 yr−1 for domains and seasons highlighted in the main text. The magnitudes of the (removed) seasonal Northern Hemisphere trends can be found in e. Grid cells highlighted in grey contain trends significant at the 5% level (Methods). SOM circulation patterns are abbreviated as follows: A, anticyclonic; C, cyclonic; and combinations of the two represent dipole patterns and west–east configurations. Geopotential height fields are sourced from the NCEP-DOE-R2 reanalysis33.

Extended Data Figure 4 1979–2013 (satellite era) robust atmospheric circulation pattern trends.

Time series of circulation pattern occurrence (black (d yr−1)), persistence (blue (d event−1)) and maximum duration (red (d event−1)) from the NCEP-DOE-R2 reanalysis33: a, summer over Europe; b, summer over western Asia; c, summer over eastern North America; d, autumn over eastern Asia; e, autumn over western Asia; f, autumn over central North America; g, autumn over eastern North America; and h, spring over Europe. Statistically significant trends ((yr−1); 5% significance level; Methods) are identified by bold font in the scatter plots.

Extended Data Figure 5 1990–2013 (ice era) robust atmospheric circulation pattern trends.

Time series of circulation pattern occurrence (black (d yr−1)), persistence (blue (d event−1)) and maximum duration (red (d event−1)) from the NCEP-DOE-R2 reanalysis33: a, winter over western Asia; b, winter over central Asia; c, summer over western Asia; d, summer over eastern North America; e, autumn over western Asia; and f, autumn over eastern North America. Statistically significant trends ((yr−1); 5% significance level; Methods) are identified by bold font in the scatter plots.

Extended Data Figure 6 1979–2013 (satellite era) Northern Hemisphere extreme temperature occurrence trends.

Satellite-era extreme temperature trends (d yr−1 yr−1) for winter cold (a) and hot (b) occurrences; spring cold (c) and hot (d) occurrences; summer cold (e) and hot (f) occurrences; and autumn cold (g) and hot (h) occurrences. Trends are calculated from the NCEP-DOE-R2 reanalysis 2-m daily maximum/minimum temperatures33. Grid cells with statistically significant trends (5% significance level; Methods) are stippled.

Extended Data Figure 7 1990–2013 (ice era) Northern Hemisphere extreme temperature occurrence trends.

Ice-era extreme temperature trends (d yr−1 yr−1) for winter cold (a) and hot (b) occurrences; spring cold (c) and hot (d) occurrences; summer cold (e) and hot (f) occurrences; and autumn cold (g) and hot (h) occurrences. Trends are calculated from the NCEP-DOE-R2 reanalysis 2-m daily maximum/minimum temperatures33. Grid cells with statistically significant trends (5% significance level; Methods) are stippled.

Extended Data Figure 8 Sensitivity of pattern similarity to number of SOM nodes.

To determine an adequate number of SOM nodes, we follow a modified version of the methodology introduced by ref. 12, wherein the mean pattern correlation of all daily geopotential height anomaly fields and their matching SOM node patterns are computed for a suite of different SOM node counts (3, 4, 5, 6, 7 and 8), for all regions and all seasons (black dots). We also compute the maximum/minimum pattern correlation of daily geopotential height anomaly fields with their matching SOM node pattern (red dots) and the maximum/minimum SOM-pattern-to-SOM-pattern correlation (blue triangles). The goal is to select an adequate number of nodes such that: (1) the mean pattern correlation of all daily geopotential height anomaly fields is relatively large; (2) the minimum pattern correlation of daily geopotential height anomaly fields is relatively large; and (3) the maximum SOM-pattern-to-SOM-pattern correlation is relatively small. Similar to ref. 12, we find that four SOM nodes are generally sufficient to capture the different modes of atmospheric variability, but small enough that SOM patterns depict distinct circulations. Geopotential height anomaly fields are sourced from the NCEP-DOE-R2 reanalysis33.

Extended Data Table 1 Significant reanalysis circulation pattern trends and summary of multiple hypothesis testing
Full size table
Extended Data Table 2 Quantitative partitioning of temperature extreme trends for select SOM analyses
Full size table

Related audio

PowerPoint slides

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Horton, D., Johnson, N., Singh, D. et al. Contribution of changes in atmospheric circulation patterns to extreme temperature trends. Nature 522, 465–469 (2015). https://doi.org/10.1038/nature14550

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature14550

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing