Abstract
Analyses of observed non-Gaussian daily minimum and maximum temperature probability distribution functions (PDFs) in the Southwest US highlight the importance of variance and warm tail length in determining future heat wave probability. Even if no PDF shape change occurs with climate change, locations with shorter warm tails and/or smaller variance will see a greater increase in heat wave probability, defined as exceedances above the historical 95th percentile threshold, than will long tailed/larger variance distributions. Projections from ten downscaled CMIP5 models show important geospatial differences in the amount of warming expected for a location. However, changes in heat wave probability do not directly follow changes in background warming. Projected changes in heat wave probability are largely explained by a rigid shift of the daily temperature distribution. In some locations where there is more warming, future heat wave probability is buffered somewhat by longer warm tails. In other parts of the Southwest where there is less warming, heat wave probability is relatively enhanced because of shorter tailed PDFs. Effects of PDF shape changes are generally small by comparison to those from a rigid shift, and fall within the range of uncertainty among models in the amount of warming expected by the end of the century.
Similar content being viewed by others
References
Azzalini A (2005a) The skew-normal distribution and related multivariate families. Scand J Stat 32:159–188. doi:10.1111/j.1467-9469.2005.00426.x
Azzalini A (2005b) A very brief introduction to the skew-normal distribution. http://azzalini.stat.unipd.it/SN/Intro/. Accessed 11 2016
Azzalini A (2014) The R package ‘sn’: the skew-normal and skew-t distributions (version 1.1–2). http://azzalini.stat.unipd.it/SN
Azzalini A, Capitanio A (1999) Statistical applications of the multivariate skew normal distribution. J R Stat Soc Ser B 61:579–602. doi:10.1111/1467-9868.00194
California Department of Water Resources (2015) Perspectives and guidance for climate change analysis. California Department of Water Resources (DWR) Climate Change Technical Advisory Group (CCTAG). http://www.water.ca.gov/climatechange/docs/2015/Perspectives_Guidance_Climate_Change_Analysis.pdf. Accessed 11 2016
Cavanaugh NR, Shen SP (2014) Northern hemisphere climatology and trends of statistical moments documented from GHCN-daily surface air temperature station data from 1950 to 2010. J Clim 27:5396–5410
Clemesha RES, Gershunov A, Iacobellis SF, Williams AP, Cayan DR (2016) The northward march of summer low cloudiness along the California coast. Geophys Res Lett 43:1287–1295. doi:10.1002/2015GL067081
Diffenbaugh NS, Pal JS, Giorgi F, Gao X (2007) Heat stress intensification in the Mediterranean climate change hotspot. Geophys Res Lett 34:L11706. doi:10.1029/2007GL030000
Gershunov A, Guirguis K (2012) California heat waves in the present and future. Geophys Res Lett. doi:10.1029/2012GL05297
Guirguis K, Gershunov A, Tardy A, Basu R (2014) The impact of recent heat waves on human health in California. J Appl Meteorol Clim 53:3–19
Guirguis K, Gershunov A, Cayan DR (2015) Interannual variability in associations between seasonal climate, weather, and extremes: wintertime temperature over the Southwestern United States. Environ Res Lett. doi:10.1088/1748-9326/10/12/124023
Hidalgo HG, Dettinger MD, Cyan DR (2008) Downscaling with constructed analogues: daily precipitation and temperature fields over the United States. California Energy Commission report CEC-500-2007-123, pp 82
IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the intergovernmental panel on climate change. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (eds) Cambridge University Press, Cambridge
Kodra E, Ganguly AR (2014) Asymmetry of projected increases in extreme temperature distributions. Sci Rep 4:5884. doi:10.1038/srep05884
Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change AR4 models using equidistant quantile matching. J Geophys Res 115:D10101. doi:10.1029/2009JD012882
Livneh B, Rosenberg EA, Lin C, Nijssen B, Mishra V, Andreadis KM, Maurer EP, Lettenmaier DP (2013) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. J Clim 26:9384–9392
Loikith PC, Lintner BR, Kim J, Lee H, Neelin JD, Waliser DE (2013) Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis. Geophys Res Lett 40:3710–3714. doi:10.1002/grl.50688
Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States. J Clim 15:3237–3251
National Climatic Date Center (2009) Data documentation for data set 3200 (DSI-3200): surface land daily cooperative summary of the day, report, Asheville, NC. http://www1.ncdc.noaa.gov/pub/data/documentlibrary/tddoc/td3200.pdf. Accessed 11 2016
Perron M, Sura P (2013) Climatology of non-Gaussian atmospheric statistics. J Clim 26:1063–1083. doi:10.1175/JCLI-D-11-00504.1
Pierce DW, Cayan DR, Thrasher BL (2014) Statistical downscaling using localized constructed analogs (LOCA). J Hydrometeorol 15(6):2558–2585
Pierce DW, Cayan DR, Maurer EP, Abatzoglou JT, Hegewisch KC (2015) Improved bias correction techniquesx for hydrological simulations of climate change. J Hydrometeorol 16:2421–2442. doi:10.1175/JHM-D-14-0236.1
Poumadère M, Mays C, Le Mer S, Blong R (2005) The 2003 heat wave in France: dangerous climate change here and now. Risk Anal 25:1483–1494. doi:10.1111/j.1539-6924.2005.00694.x
Ruff TW, Neelin JD (2012) Long tails in regional surface temperature probability distributions with implications for extremes under global warming. Geophys Res Lett 39:L04704. doi:10.1029/2011GL050610
Schär C, Vidale PL, Lüthi D, Frei C, Häberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability for European summer heat waves. Nature 427:332–336
Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J Geophys Res Atmos 118:1716–1733. doi:10.1002/jgrd.50203
Simolo C, Brunetti M, Maugeri M, Nanni T, Speranza A (2010) Understanding climate change-induced variations in daily temperature distributions over Italy. J Geophys Res 115:D22110. doi:10.1029/2010JD014088
Simolo C, Brunetti M, Maugeri M, Nanni T (2011) Evolution of extreme temperatures in a warming climate. Geophys Res Lett 38:L16701. doi:10.1029/2011GL048437
Stefanova L, Sura P, Griffin M (2013) Quantifying the non-Gaussianity of wintertime daily maximum and minimum temperatures in the southeast. J Clim 26(3):838–850
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498
Van den Dool HM (1994) Searching for analogues, how long must we wait? Tellus A 46(3):314–324
Weaver SJ, Kumar A, Chen M (2014) Recent increases in extreme temperature occurrence over land. Geophys Res Lett 41:4669–4675. doi:10.1002/2014GL060300
Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Chang 2:851–870. doi:10.1002/wcc.147
Acknowledgements
This work was supported by DOI via the Southwest Climate Science Center on a grant titled: “Natural variability in the changing climate: Interaction of interannual, decadal, and century timescales with daily weather”, by the National Science Foundation via Grant #F12078-2013-005, and by NOAA via the RISA program through the California and Nevada Applications Program. The observational temperature dataset is freely and publicly available from the University of Washington (ftp://ftp.hydro.washington.edu/pub/blivneh/CONUS/). The LOCA downscaled GCM data are publically available from the USGS Center for Integrated Data Analytics. We thank Mary Tyree for data retrieval and handling. We additionally thank three anonymous reviewers for providing helpful comments during the evaluation of this paper.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Guirguis, K., Gershunov, A., Cayan, D.R. et al. Heat wave probability in the changing climate of the Southwest US. Clim Dyn 50, 3853–3864 (2018). https://doi.org/10.1007/s00382-017-3850-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-017-3850-3