|Title||Heat wave probability in the changing climate of the Southwest US|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Guirguis K., Gershunov A, Cayan DR, Pierce DW|
|Type of Article||Article|
|Keywords||analogs; california; CMIP5; conterminous united-states; hydrologically based dataset; indexes; land-surface fluxes; long; Meteorology & Atmospheric Sciences; skew-normal-distribution; temperature|
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.
|Short Title||Clim. Dyn.|
Daily summer temperature probability distributions in the Southwest US are non-Gaussian and asymmetric with shapes that vary considerably across the region. This analysis shows how geospatial differences in the shape of the historical temperature probability distribution function (PDF) has important implications for future climate change.