Heat wave probability in the changing climate of the Southwest US
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.
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.
- 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
- 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
- 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 82Google Scholar
- 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, CambridgeGoogle Scholar
- 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