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.

  • Article
  • Published:

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

Nature Geoscience volume 9, pages 389–394 (2016)Cite this article

Subjects

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.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Hot days and heat events are defined from station measurements of daily maximum summer temperature.
Figure 2: Precipitation deficits and the Pacific Extreme Pattern skillfully predict hot days at long-lead times.
Figure 3: SST anomalies, geopotential height anomalies and wave activity fluxes associated with eastern US hot days.

Similar content being viewed by others

References

  1. Smith, A. B. & Katz, R. W. US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat. Hazards 67, 387–410 (2013).

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Pepler, A. S., Díaz, L. B., Prodhomme, C., Doblas-Reyes, F. J. & Kumar, A. 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).

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An overview of the global historical climatology network-daily database. J. Atmos. Ocean. Technol. 29, 897–910 (2012).

    Article  Google Scholar 

  15. Smoyer, K. E., Rainham, D. G. & Hewko, J. N. Heat-stress-related mortality in five cities in Southern Ontario: 1980–1996. Int. J. Biometeorol. 44, 190–197 (2000).

    Article  Google Scholar 

  16. McKee, T. B., Doesken, N. J. & Kleist, J. 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).

    Google Scholar 

  17. Peterson, W. W., Birdsall, T. G. & Fox, W. C. The theory of signal detectability. Trans. IRE Prof. Group Inf. Theory 4, 171–212 (1954).

    Article  Google Scholar 

  18. Stephenson, D., Casati, B., Ferro, C. & Wilson, C. The extreme dependency score: a non-vanishing measure for forecasts of rare events. Meteorol. Appl. 15, 41–50 (2008).

    Article  Google Scholar 

  19. Barnston, A. G. & Mason, S. J. Evaluation of IRI’s seasonal climate forecasts for the extreme 15% tails. Weath. Forecast. 26, 545–554 (2011).

    Article  Google Scholar 

  20. Deser, C., Alexander, M. A. & Timlin, M. S. Understanding the persistence of sea surface temperature anomalies in midlatitudes. J. Clim. 16, 57–72 (2003).

    Article  Google Scholar 

  21. Kennedy, J., Rayner, N., Smith, R., Parker, D. & Saunby, M. 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. Cook, B. I., Miller, R. L. & Seager, R. Amplification of the North American Dust Bowl drought through human-induced land degradation. Proc. Natl Acad. Sci. USA 106, 4997–5001 (2009).

    Article  Google Scholar 

  23. Yun, K.-S., Kim, S.-Y., Ha, K.-J. & Watanabe, M. Effects of subseasonal basic state changes on Rossby wave propagation during northern summer. J. Geophys. Res. 116, D24102 (2011).

    Google Scholar 

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

    Article  Google Scholar 

  25. Higgins, R., Kim, H. & Unger, D. Long-lead seasonal temperature and precipitation prediction using tropical Pacific SST consolidation forecasts. J. Clim. 17, 3398–3414 (2004).

    Article  Google Scholar 

  26. Rajagopalan, B., Cook, E., Lall, U. & Ray, B. K. Spatiotemporal variability of ENSO and SST teleconnections to summer drought over the United States during the twentieth century. J. Clim. 13, 4244–4255 (2000).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Newman, M. & Sardeshmukh, P. D. 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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Teng, H., Branstator, G., Wang, H., Meehl, G. A. & Washington, W. M. Probability of US heat waves affected by a subseasonal planetary wave pattern. Nature Geosci. 6, 1056–1061 (2013).

    Article  Google Scholar 

  33. CPC Monthly & Seasonal Forecast Archive (National Weather Service Climate Prediction Center, accessed 3 December 2015); http://www.cpc.ncep.noaa.gov/products/archives/long_lead/llarc.ind.php

  34. Hofstra, N., New, M. & McSweeney, C. 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).

    Article  Google Scholar 

  35. Simmons, A. 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).

    Article  Google Scholar 

  36. Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Jin, X., Yu, L. & Weller, R. A. 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. Jaccard, P. The distribution of the flora in the alpine zone. New Phytol. 11, 37–50 (1912).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

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.

Author information

Authors and 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

Authors
  1. K. A. McKinnon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Rhines
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. P. Tingley
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Huybers
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to K. A. McKinnon.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 20650 kb)

Supplementary Movies

Supplementary Movie 1 (MOV 4770 kb)

Supplementary Movies

Supplementary Movie 2 (MOV 5953 kb)

Supplementary Movies

Supplementary Movie 3 (MOV 5456 kb)

Supplementary Movies

Supplementary Movie 4 (MOV 4512 kb)

Supplementary Information

Supplementary Information (TXT 239 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McKinnon, K., Rhines, A., Tingley, M. et al. Long-lead predictions of eastern United States hot days from Pacific sea surface temperatures. Nature Geosci 9, 389–394 (2016). https://doi.org/10.1038/ngeo2687

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ngeo2687

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