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Abstract In situ observations and output from a numerical model are utilized to examine three dust outbreaks that occurred in the northwestern Sonoran Desert. Via analysis of these events, it is shown that trapped waves generated in the lee of an upwind mountain range produced high surface wind speeds along the desert floor and the observed dust storms. Based on analysis of observational and model output, general characteristics of dust outbreaks generated by trapped waves are suggested, including dust-layer depths and concentrations that are dependent upon wave phase and height above the surface, emission and transport associated with the presence of a low-level jet, and wave-generated high wind speeds and thus emission that occurs far downwind of the wave source. Trapped lee waves are ubiquitous in Earth’s atmosphere and thus it is likely that the meteorological aspects of the dust storms examined here are also relevant to understanding dust in other regions. These dust outbreaks occurred near the Salton Sea, an endorheic inland body of water that is rapidly drying due to changes in water-use management. As such, these findings are also relevant in terms of understanding how future changes in size of the Salton Sea will impact dust storms and air quality there. Significance Statement Dust storms are ubiquitous in Earth’s atmosphere, yet the physical processes underlying dust emission and subsequent transport are not always understood, in part due to the wide variety of meteorological processes that can generate high winds and dust. Here we use in situ measurements and numerical modeling to demonstrate that vertically trapped atmospheric waves generated by air flowing over a mountain are one such mechanism that can produce dust storms. We suggest several features of these dust outbreaks that are specific to their production by trapped waves. As the study area is a region undergoing rapid environmental change, these results are relevant in terms of predicting future dust there. 

Abstract Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. 

Temperature is widely known to influence the spatio-temporal dynamics of vector-borne disease transmission, particularly as temperatures vary across critical thermal thresholds. When temperature conditions exhibit such ‘transcritical variation’, abrupt spatial or temporal discontinuities may result, generating sharp geographical or seasonal boundaries in transmission. Here, we develop a spatio-temporal machine learning algorithm to examine the implications of transcritical variation for West Nile virus (WNV) transmission in the Los Angeles metropolitan area (LA). Analysing a large vector and WNV surveillance dataset spanning 2006–2016, we found that mean temperatures in the previous month strongly predicted the probability of WNV presence in pools of Culex quinquefasciatus mosquitoes, forming distinctive inhibitory (10.0–21.0°C) and favourable (22.7–30.2°C) mean temperature ranges that bound a narrow 1.7°C transitional zone (21–22.7°C). Temperatures during the most intense months of WNV transmission (August/September) were more strongly associated with infection probability in Cx. quinquefasciatus pools in coastal LA, where temperature variation more frequently traversed the narrow transitional temperature range compared to warmer inland locations. This contributed to a pronounced expansion in the geographical distribution of human cases near the coast during warmer-than-average periods. Our findings suggest that transcritical variation may influence the sensitivity of transmission to climate warming, and that especially vulnerable locations may occur where present climatic fluctuations traverse critical temperature thresholds.