Mark D. Risser

Attributable Human-Induced Changes in the Likelihood and Magnitude of the Observed Extreme Precipitation during Hurricane Harvey

Author(s): Risser, MD; Wehner, MF | Abstract: ©2017. American Geophysical Union. All Rights Reserved. Record rainfall amounts were recorded during Hurricane Harvey in the Houston, Texas, area, leading to widespread flooding. We analyze observed precipitation from the Global Historical Climatology Network with a covariate-based extreme value statistical analysis, accounting for both the external influence of global warming and the internal influence of El Nino–Southern Oscillation. We find that human-induced climate change likely increased the chances of the observed precipitation accumulations during Hurricane Harvey in the most affected areas of Houston by a factor of at least 3.5. Further, precipitation accumulations in these areas were likely increased by at least 18.8% (best estimate of 37.7%), which is larger than the 6–7% associated with an attributable warming of 1°C in the Gulf of Mexico and Clausius-Clapeyron scaling. In a Granger causality sense, these statements provide lower bounds on the impact of climate change and motivate further attribution studies using dynamical climate models.

Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence

In recent years, the climate change research community has become highly interested in describing the anthropogenic influence on extreme weather events, commonly termed “event attribution.” Limitations in the observational record and in computational resources motivate the use of uncoupled, atmosphere/land-only climate models with prescribed ocean conditions run over a short period, leading up to and including an event of interest. In this approach, large ensembles of high-resolution simulations can be generated under factual observed conditions and counterfactual conditions that might have been observed in the absence of human interference; these can be used to estimate the change in probability of the given event due to anthropogenic influence. However, using a prescribed ocean state ignores the possibility that estimates of attributable risk might be a function of the ocean state. Thus, the uncertainty in attributable risk is likely underestimated, implying an over-confidence in anthropogenic influence. In this work, we estimate the year-to-year variability in calculations of the anthropogenic contribution to extreme weather based on large ensembles of atmospheric model simulations. Our results both quantify the magnitude of year-to-year variability and categorize the degree to which conclusions of attributable risk are qualitatively affected. The methodology is illustrated by exploring extreme temperature and precipitation events for the northwest coast of South America and northern-central Siberia; we also provides results for regions around the globe. While it remains preferable to perform a full multi-year analysis, the results presented here can serve as an indication of where and when attribution researchers should be concerned about the use of atmosphere-only simulations.

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

ABSTRACT 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 ( = 0.25, 95% credible interval [CrI 0.13, 0.37]) and young adults ( = 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.

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.

Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events

ABSTRACT The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses output from global climate models to make simultaneous attribution statements about whether and how greenhouse gas emissions have contributed to extreme weather across the globe. However, in conducting a large number of simultaneous hypothesis tests, the WRAF is prone to identifying false “discoveries.” A common technique for addressing this multiple testing problem is to adjust the procedure in a way that controls the proportion of true null hypotheses that are incorrectly rejected, or the false discovery rate (FDR). Unfortunately, generic FDR procedures suffer from low power when the hypotheses are dependent, and techniques designed to account for dependence are sensitive to misspecification of the underlying statistical model. In this article, we develop a Bayesian decision-theoretical approach for dependent multiple testing and a nonparametric hierarchical statistical model that flexibly controls false discovery and is robust to model misspecification. We illustrate the robustness of our procedure to model error with a simulation study, using a framework that accounts for generic spatial dependence and allows the practitioner to flexibly specify the decision criteria. Finally, we apply our procedure to several seasonal forecasts and discuss implementation for the WRAF workflow. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.