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In recent decades, changes in precipitation, temperature, and air circulation patterns have led to increases in the occurrences of extreme weather events. These events can have devastating effects on communities causing destruction to property and croplands, as well as negative impacts on public health. As changes in the climate are projected to continue throughout the remainder of the twenty-first century, the ability for a community to plan for extreme weather events is essential to its survival. In this paper, we introduce a new index for examining the potential impacts of climate extremes on community resilience throughout the conterminous United States at the county level. We use an established disaster resilience index (baseline resilience indicators for communities) together with a revised version of the U.S. climate extremes index to create a combined measure of climate resilience-the climate extremes resilience index (CERI). To demonstrate the index, we test it on the 2021 Pacific Northwest heat wave, a 1000-yr weather event made 150 times as likely by climate change. To promote the use of the index, we also introduce a Google Earth Engine web app to calculate and map the CERI for the CONUS. By developing a web application for calculating the CERI, we expand the use of climate-resilience indices beyond theoretical applications. We anticipate that this tool and the CERI could be useful for policy makers to plan for climate-related disasters, as well as help the public with understanding and visualizing the impacts of extreme climatic events. [ABSTRACT FROM AUTHOR]

Background The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico. Methods To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models. Results We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases. Conclusion The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico. [ABSTRACT FROM AUTHOR]

Intense precipitation events (IPE; 99th percentile) in the southeastern United States from 1950 to 2016 were analysed temporally, spatially, and synoptically. The study area was partitioned into latitudinal and physiographic regions to identify subregions that experienced significant changes in IPE frequency or intensity. Furthermore, the spatial synoptic classification (SSC) was used to ascertain what surface weather types are associated with IPEs. Additionally, in conjunction with the SSC, surface forcing mechanisms for the 30 most extreme subregional IPEs were studied to uncover the surface synoptic conditions responsible for IPEs. Results revealed that IPEs increased in frequency and intensity on an annual basis for the southeastern United States. Seasonal results indicated that IPE frequency only increased in the fall. Subregional results reveal that latitudinally, IPEs became more common in the northern latitudes of the study area, while physiographically, significant increases in IPE frequency were most pronounced in areas inland from the Atlantic Coastal Plain. An increase in the annual number of IPEs associated with moist tropical (MT) days was identified across the study area, but was more prevalent in the central and north central latitudinal regions, and areas inland from the Atlantic Coastal Plain outside of the Appalachian Mountains. This MT increase was possibly caused by more common northwards and inland intrusion of these types of IPEs. While moist moderate (MM) and transitional (TR) days were most commonly associated with IPEs, these weather types did not have significant trends. The surface forcing mechanisms most commonly associated with the strongest IPEs were tropical events, followed by stationary fronts and concentric low‐pressure systems. Within this research, intense precipitation events in the southeastern United States are temporally, spatially, and synoptically analysed. Significant increases in the strength and frequency of these events have been identified across the entire region, as well as over a variety of physiographic and latitudinal subregions. Additionally, a significant increase in the number of moist tropical weather types associated with these events has been identified. [ABSTRACT FROM AUTHOR]

Maximum and minimum daily temperatures from the second half of the twentieth century are examined using a high resolution dataset of 833 grid cells across the state of Florida. A bivariate extreme value analysis point process approach is used to model characteristics including the frequency, magnitude, duration, and timing of periods or heat waves during which both daily maximum and minimum temperatures exceed their respective 90th percentile thresholds. The temperature dataset is combined with indices of the El Niño-Southern Oscillation (ENSO) and the Atlantic multi-decadal oscillation (AMO) to explore the influence of these oscillations on heat wave characteristics in Florida. In order to investigate the influence of a time varying signal (ENSO and AMO) on heat waves the signals are introduced into non-stationary models as covariates in the location and log-transformed scale parameters. The improvements to the model obtained by introducing covariates are examined using the deviance statistic whereby the difference in negative log-likelihood values between two models is tested for significance using a Chi squared distribution. Significant improvements in the non-stationary models with ENSO and AMO covariates indicate spatially varying impacts in the frequency, magnitude, and duration of heat waves. In particular, the warm phase of the AMO brings heat waves earlier in the summertime while also increasing their magnitude, frequency, and duration. [ABSTRACT FROM AUTHOR]

A survey was conducted to evaluate the perceptions of undergraduate students enrolled in an introductory atmospheric processes course towards various climate change related topics, such as the validity of, and the scientific consensus surrounding climate change. Further questions sought information regarding student knowledge of climate change, such as when they were first exposed to the science, and their methods of seeking out information regarding climate change. This study additionally seeks to identify sectors of the human and natural world that students feel climate change negatively affects. Results indicate that students desire information about mitigation strategies, regardless of whether they believe humans are the leading cause of climate change. Additionally, students must be made more aware of the non-natural impacts of climate change (infrastructure, economy, etc.). These findings can help instructors enrich course content to include topics relevant to student concerns regarding climate change, particularly its adverse impacts, and mitigation strategies. [ABSTRACT FROM AUTHOR]