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Guirguis, K, Gershunov A, Cayan DR, Pierce DW.  2018.  Heat wave probability in the changing climate of the Southwest US. Climate Dynamics. 50:3853-3864.   10.1007/s00382-017-3850-3   AbstractWebsite

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.

Gershunov, A, Cayan DR.  2003.  Heavy daily precipitation frequency over the contiguous United States: Sources of climatic variability and seasonal predictability. Journal of Climate. 16:2752-2765.   10.1175/1520-0442(2003)016<2752:hdpfot>;2   AbstractWebsite

By matching large-scale patterns in climate fields with patterns in observed station precipitation, this work explores seasonal predictability of precipitation in the contiguous United States for all seasons. Although it is shown that total seasonal precipitation and frequencies of less-than-extreme daily precipitation events can be predicted with much higher skill, the focus of this study is on frequencies of daily precipitation above the seasonal 90th percentile (P90), a variable whose skillful prediction is more challenging. Frequency of heavy daily precipitation is shown to respond to ENSO as well as to non-ENSO interannual and interdecadal variability in the North Pacific. Specification skill achieved by a statistical model based on contemporaneous SST forcing with and without an explicit dynamical atmosphere is compared and contrasted. Statistical models relating the SST forcing patterns directly to observed station precipitation are shown to perform consistently better in all seasons than hybrid (dynamical-statistical) models where the SST forcing is first translated to atmospheric circulation via three separate general circulation models and the dynamically computed circulation anomalies are statistically related to observed precipitation. Skill is summarized for all seasons, but in detail for January-February-March, when it is shown that predictable patterns are spatially robust regardless of the approach used. Predictably, much of the skill is due to ENSO. While the U. S. average skill is modest, regional skill levels can be quite high. It is also found that non-ENSO-related skill is significant, especially for the extreme Southwest and that this is due mostly to non-ENSO interannual and decadal variability in the North Pacific SST forcing. Although useful specification skill is achieved by both approaches, hybrid predictability is not pursued further in this effort. Rather, prognostic analysis is carried out with the purely statistical approach to analyze P90 predictability based on antecedent SST forcing. Skill at various lead times is investigated and it is shown that significant regional skill can be achieved at lead times of several months even in the absence of strong ENSO forcing.

Ben Ari, T, Gershunov A, Gage KL, Snall T, Ettestad P, Kausrud KL, Stenseth NC.  2008.  Human plague in the USA: the importance of regional and local climate. Biology Letters. 4:737-740.   10.1098/rsbl.2008.0363   AbstractWebsite

A 56-year time series of human plague cases (Yersinia pestis) in the western United States was used to explore the effects of climatic patterns on plague levels. We found that the Pacific Decadal Oscillation (PDO), together with previous plague levels and above-normal temperatures, explained much of the plague variability. We propose that the PDO's impact on plague is conveyed via its effect on precipitation and temperature and the effect of precipitation and temperature on plague hosts and vectors: warmer and wetter climate leading to increased plague activity and thus an increased number of human cases. Our analysis furthermore provides insights into the consistency of plague mechanisms at larger scales.