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Contribution of changes in atmospheric circulation patterns to extreme temperature trends

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.

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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.

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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.

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Authors and Affiliations

Authors

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.

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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.

ad, 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. fj, 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
Extended Data Table 2 Quantitative partitioning of temperature extreme trends for select SOM analyses

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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

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