Article | Published:

Long-lead predictions of eastern United States hot days from Pacific sea surface temperatures

Nature Geoscience volume 9, pages 389394 (2016) | Download Citation

Abstract

Seasonal forecast models exhibit only modest skill in predicting extreme summer temperatures across the eastern US. Anomalies in sea surface temperature and monthly-resolution rainfall have, however, been correlated with hot days in the US, and seasonal persistence of these anomalies suggests potential for long-lead predictability. Here we present a clustering analysis of daily maximum summer temperatures from US weather stations between 1982–2015 and identify a region spanning most of the eastern US where hot weather events tend to occur synchronously. We then show that an evolving pattern of sea surface temperature anomalies, termed the Pacific Extreme Pattern, provides for skillful prediction of hot weather within this region as much as 50 days in advance. Skill is demonstrated using out-of-sample predictions between 1950 and 2015. Rainfall deficits over the eastern US are also associated with the occurrence of the Pacific Extreme Pattern and are demonstrated to offer complementary skill in predicting high temperatures. The Pacific Extreme Pattern appears to provide a cohesive framework for improving seasonal prediction of summer precipitation deficits and high temperature anomalies in the eastern US.

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References

  1. 1.

    & US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat. Hazards 67, 387–410 (2013).

  2. 2.

    et al. Was there a basis for anticipating the 2010 Russian heat wave? Geophys. Res. Lett. 38, L06702 (2011).

  3. 3.

    & Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).

  4. 4.

    & Heat stress and public health: a critical review. Annu. Rev. Public Health 29, 41–55 (2008).

  5. 5.

    & More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).

  6. 6.

    & A stitch in time: improving public health early warning systems for extreme weather events. Epidemiol. Rev. 27, 115–121 (2005).

  7. 7.

    , , , & The ability of a multi-model seasonal forecasting ensemble to forecast the frequency of warm, cold and wet extremes. Weath. Clim. Extremes 9, 68–77 (2015).

  8. 8.

    & Did we see the 2011 summer heat wave coming? Geophys. Res. Lett. 39, L09708 (2012).

  9. 9.

    & Hot days induced by precipitation deficits at the global scale. Proc. Natl Acad. Sci. USA 109, 12398–12403 (2012).

  10. 10.

    Anatomy of Great Plains protracted heat waves (especially the 1980 US summer drought). Mon. Weath. Rev. 110, 824–838 (1982).

  11. 11.

    Spring and summer 1988 drought over the contiguous United States—Causes and prediction. J. Clim. 4, 54–65 (1991).

  12. 12.

    & A diagnostic comparison of the 1980 and 1988 US summer heat wave-droughts. J. Clim. 8, 1658–1675 (1995).

  13. 13.

    et al. Extraordinary heat during the 1930s US Dust Bowl and associated large-scale conditions. Clim. Dynam. 46, 413–426 (2016).

  14. 14.

    , , , & An overview of the global historical climatology network-daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).

  15. 15.

    , & Heat-stress-related mortality in five cities in Southern Ontario: 1980–1996. Int. J. Biometeorol. 44, 190–197 (2000).

  16. 16.

    , & The relationship of drought frequency and duration to time scales. In Proc. 8th Conf. Appl. Climatol. Vol. 17, 179–183 (American Meteorological Society Boston, 1993).

  17. 17.

    , & The theory of signal detectability. Trans. IRE Prof. Group Inf. Theory 4, 171–212 (1954).

  18. 18.

    , , & The extreme dependency score: a non-vanishing measure for forecasts of rare events. Meteorol. Appl. 15, 41–50 (2008).

  19. 19.

    & Evaluation of IRI’s seasonal climate forecasts for the extreme 15% tails. Weath. Forecast. 26, 545–554 (2011).

  20. 20.

    , & Understanding the persistence of sea surface temperature anomalies in midlatitudes. J. Clim. 16, 57–72 (2003).

  21. 21.

    , , , & Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 1. Measurement and sampling uncertainties. J. Geophys. Res. 116 (2011).

  22. 22.

    , & Amplification of the North American Dust Bowl drought through human-induced land degradation. Proc. Natl Acad. Sci. USA 106, 4997–5001 (2009).

  23. 23.

    , , & Effects of subseasonal basic state changes on Rossby wave propagation during northern summer. J. Geophys. Res. 116, D24102 (2011).

  24. 24.

    et al. The atmospheric bridge: the influence of ENSO teleconnections on air-sea interaction over the global oceans. J. Clim. 15, 2205–2231 (2002).

  25. 25.

    , & Long-lead seasonal temperature and precipitation prediction using tropical Pacific SST consolidation forecasts. J. Clim. 17, 3398–3414 (2004).

  26. 26.

    , , & Spatiotemporal variability of ENSO and SST teleconnections to summer drought over the United States during the twentieth century. J. Clim. 13, 4244–4255 (2000).

  27. 27.

    & Systematic comparison of ENSO teleconnection patterns between models and observations. J. Clim. 25, 425–446 (2012).

  28. 28.

    & Cross-equatorial response to middle-latitude forcing in a zonally varying basic state. J. Atmos. Sci. 39, 722–733 (1982).

  29. 29.

    & The impact of the annual cycle on the North Pacific/North American response to remote low-frequency forcing. J. Atmos. Sci. 55, 1336–1353 (1998).

  30. 30.

    & Stochastic climate models, Part II: application to sea-surface temperature anomalies and thermocline variability. Tellus 29, 289–305 (1977).

  31. 31.

    & A formulation of a wave-activity flux for stationary Rossby waves on a zonally varying basic flow. Geophys. Res. Lett. 24, 2985–2988 (1997).

  32. 32.

    , , , & Probability of US heat waves affected by a subseasonal planetary wave pattern. Nature Geosci. 6, 1056–1061 (2013).

  33. 33.

    CPC Monthly & Seasonal Forecast Archive (National Weather Service Climate Prediction Center, accessed 3 December 2015);

  34. 34.

    , & The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Clim. Dynam. 35, 841–858 (2010).

  35. 35.

    et al. Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature. J. Geophys. Res. 109, D24115 (2004).

  36. 36.

    , , , & An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).

  37. 37.

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

  38. 38.

    , & Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables OAFlux Project Technical Report OA-2008-01 (Woods Hole Oceanographic Institution, 2008).

  39. 39.

    The distribution of the flora in the alpine zone. New Phytol. 11, 37–50 (1912).

  40. 40.

    Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666 (2010).

  41. 41.

    Algorithm 772: STRIPACK: Delaunay triangulation and Voronoi diagram on the surface of a sphere. ACM Trans. Math. Softw. 23, 416–434 (1997).

  42. 42.

    Eddy fluxes of conserved quantities by small-amplitude waves. J. Atmos. Sci. 36, 1699–1704 (1979).

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Acknowledgements

The authors acknowledge funding from the NSF GRFP, NASA NESSF, NCAR ASP, and NSF grant 1304309, and thank B. Farrell, C. Wunsch, C. Bitz, C. Deser, D. Schrag, D. Battisti, J. Mitrovica, M. Cane, P. Hassanzadeh and Z. Kuang for their valuable comments.

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Affiliations

  1. National Center for Atmospheric Research, Boulder, Colorado 80305, USA

    • K. A. McKinnon
  2. Department of Atmospheric Sciences, University of Washington, Seattle, Washington 98195, USA

    • A. Rhines
  3. Department of Meteorology, Pennsylvania State University, Pennsylvania 16802, USA

    • M. P. Tingley
  4. Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts 02138, USA

    • P. Huybers

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Contributions

The authors contributed equally in designing the study and contributing analysis tools, K.A.M. and A.R. analysed data, and K.A.M. led the writing.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to K. A. McKinnon.

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https://doi.org/10.1038/ngeo2687

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