A warming-induced reduction in snow fraction amplifies rainfall extremes.

The intensity of extreme precipitation events is projected to increase in a warmer climate1-5, posing a great challenge to water sustainability in natural and built environments. Of particular importance are rainfall (liquid precipitation) extremes owing to their instantaneous triggering of runoff and association with floods6, landslides7-9 and soil erosion10,11. However, so far, the body of literature on intensification of precipitation extremes has not examined the extremes of precipitation phase separately, namely liquid versus solid precipitation. Here we show that the increase in rainfall extremes in high-elevation regions of the Northern Hemisphere is amplified, averaging 15 per cent per degree Celsius of warming-double the rate expected from increases in atmospheric water vapour. We utilize both a climate reanalysis dataset and future model projections to show that the amplified increase is due to a warming-induced shift from snow to rain. Furthermore, we demonstrate that intermodel uncertainty in projections of rainfall extremes can be appreciably explained by changes in snow-rain partitioning (coefficient of determination 0.47). Our findings pinpoint high-altitude regions as 'hotspots' that are vulnerable to future risk of extreme-rainfall-related hazards, thereby requiring robust climate adaptation plans to alleviate potential risk. Moreover, our results offer a pathway towards reducing model uncertainty in projections of rainfall extremes.

Moral Distress in Nurses Providing Direct Patient Care at an Academic Medical Center.

BACKGROUND: Moral distress is the psychological response to knowing the appropriate action but not being able to act due to constraints. Previous authors reported moral distress among nurses, especially those that work in critical care units. AIMS: The aims of this study were: (1) to examine the level of moral distress among nurses who work at an academic health system, (2) to compare the level of moral distress in nurses who work across specialty units at an academic health system, (3) to compare moral distress by the demographic characteristics of nurses and work experience variables, and (4) to identify demographic characteristics and type of clinical setting that may predict which nurses are at high risk for moral distress. METHODS: A cross-sectional survey design was used with staff nurses who work on inpatient units and ambulatory units at an academic medical center. The moral distress scale-revised (MDS-R) was used to assess the intensity and frequency of moral distress. RESULTS: The overall mean MDS-R score in this project was low at 94.97 with mean scores in the low to moderate range (44.57 to 134.58). Nurses who work in critical care, perioperative services, and procedure areas had the highest mean MDS-R scores. There have been no previous reports of higher scores for nurses working in perioperative and procedure areas. There was weak positive correlation between MDS-R scores and years of experience (Rho = .17, p = .003) but no correlation between age (Rho = .02, p = .78) or education (Rho = .05, p = .802) and moral distress. LINKING EVIDENCE TO ACTION: Three variables were found useful in predicting moral distress: the type of unit and responses to two qualitative questions related to quitting their job. Identification of these variables allows organizations to focus their interventions.

A meta-analysis of brief alcohol interventions for adolescents and young adults: variability in effects across alcohol measures.

BACKGROUND: Brief alcohol interventions are one approach for reducing drinking among youth, but may vary in effectiveness depending on the type of alcohol assessments used to measure effects. OBJECTIVES: To conduct a meta-analysis that examined the effectiveness of brief alcohol interventions for adolescents and young adults, with particular emphasis on exploring variability in effects across outcome measurement characteristics. METHOD: Eligible studies were those using an experimental or quasi-experimental design to examine the effects of a brief alcohol intervention on a post-intervention alcohol use measure for youth aged 11-30. A comprehensive literature review identified 190 unique samples that were included in the meta-analysis. Taking a Bayesian approach, we used random-effects multilevel models to estimate the average effect and model variability across outcome measurement types. RESULTS: Brief alcohol interventions led to significant reductions in self-reported alcohol use among adolescents (g = 0.25, 95% credible interval [CrI 0.13, 0.37]) and young adults (g = 0.15, 95% CrI [0.12, 0.18]). These results were consistent across outcomes with varying reference periods, but varied across outcome construct type and assessment instruments. Among adolescents, effects were larger when measured using the Timeline Followback; among young adults, effects were smaller when measured using the Alcohol Use Disorders Identification Test. CONCLUSION: The strength of the beneficial effects of brief alcohol interventions on youth's alcohol use may vary depending upon the outcome measure utilized. Nevertheless, significant effects were observed across measures. Although effects were modest in size, they were clinically significant and show promise for interrupting problematic alcohol use trajectories among youth.

Anthropogenic aerosols mask increases in US rainfall by greenhouse gases.

A comprehensive understanding of human-induced changes to rainfall is essential for water resource management and infrastructure design. However, at regional scales, existing detection and attribution studies are rarely able to conclusively identify human influence on precipitation. Here we show that anthropogenic aerosol and greenhouse gas (GHG) emissions are the primary drivers of precipitation change over the United States. GHG emissions increase mean and extreme precipitation from rain gauge measurements across all seasons, while the decadal-scale effect of global aerosol emissions decreases precipitation. Local aerosol emissions further offset GHG increases in the winter and spring but enhance rainfall during the summer and fall. Our results show that the conflicting literature on historical precipitation trends can be explained by offsetting aerosol and greenhouse gas signals. At the scale of the United States, individual climate models reproduce observed changes but cannot confidently determine whether a given anthropogenic agent has increased or decreased rainfall.

Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels.

A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP's analytical tractability, robustness, and natural inclusion of uncertainty quantification. Unfortunately, the use of exact GPs is prohibitively expensive for large datasets due to their unfavorable numerical complexity of [Formula: see text] in computation and [Formula: see text] in storage. All existing methods addressing this issue utilize some form of approximation-usually considering subsets of the full dataset or finding representative pseudo-points that render the covariance matrix well-structured and sparse. These approximate methods can lead to inaccuracies in function approximations and often limit the user's flexibility in designing expressive kernels. Instead of inducing sparsity via data-point geometry and structure, we propose to take advantage of naturally-occurring sparsity by allowing the kernel to discover-instead of induce-sparse structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel definitions that provide enough flexibility to learn and encode not only non-zero but also zero covariances. This principle of ultra-flexible, compactly-supported, and non-stationary kernels, combined with HPC and constrained optimization, lets us scale exact GPs well beyond 5 million data points.