Detecting and Attributing Health Burdens to Climate Change.
ABSTRACT: BACKGROUND:Detection and attribution of health impacts caused by climate change uses formal methods to determine a) whether the occurrence of adverse health outcomes has changed, and b) the extent to which that change could be attributed to climate change. There have been limited efforts to undertake detection and attribution analyses in health. OBJECTIVE:Our goal was to show a range of approaches for conducting detection and attribution analyses. RESULTS:Case studies for heatwaves, Lyme disease in Canada, and Vibrio emergence in northern Europe highlight evidence that climate change is adversely affecting human health. Changes in rates and geographic distribution of adverse health outcomes were detected, and, in each instance, a proportion of the observed changes could, in our judgment, be attributed to changes in weather patterns associated with climate change. CONCLUSIONS:The results of detection and attribution studies can inform evidence-based risk management to reduce current, and plan for future, changes in health risks associated with climate change. Gaining a better understanding of the size, timing, and distribution of the climate change burden of disease and injury requires reliable long-term data sets, more knowledge about the factors that confound and modify the effects of climate on health, and refinement of analytic techniques for detection and attribution. At the same time, significant advances are possible in the absence of complete data and statistical certainty: there is a place for well-informed judgments, based on understanding of underlying processes and matching of patterns of health, climate, and other determinants of human well-being. https://doi.org/10.1289/EHP1509.
Project description:As the frequency and intensity of extreme events such as droughts, heatwaves and floods have increased over recent decades, more extreme biological responses are being reported, and there is widespread interest in attributing such responses to anthropogenic climate change. However, the formal detection and attribution of biological responses to climate change is associated with many challenges. We illustrate these challenges with data from the Elbe River floodplain, Germany. Using community turnover and stability indices, we show that responses in plant, carabid and mollusc communities are detectable following extreme events. Community composition and species dominance changed following the extreme flood and summer heatwave of 2002/2003 (all taxa); the 2006 flood and heatwave (molluscs); and after the recurring floods and heatwave of 2010 and the 2013 flood (plants). Nevertheless, our ability to attribute these responses to anthropogenic climate change is limited by high natural variability in climate and biological data; lack of long-term data and replication, and the effects of multiple events. Without better understanding of the mechanisms behind change and the interactions, feedbacks and potentially lagged responses, multiple-driver attribution is unlikely. We discuss whether formal detection and/or attribution is necessary and suggest ways in which understanding of biological responses to extreme events could progress.
Project description:Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm's core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones.
Project description:The responses of hydrological processes to climate change and anthropogenic influence have received significant attention over the past few decades. Several Budyko-based methods have been widely used to attribute hydrological variations and identify the extent of variation due to climate change and human activities. However, the accuracy of various methods has rarely been compared. This study employed four Budyko-based methods, namely the total differential method, complementary method, extrapolation method, and decomposition method, to attribute the changes in actual evapotranspiration in 13 basins in China's Loess Plateau. We compared their performances and analysed factors that contribute to the differences in attribution results yielded by the various methods. The results showed that the total differential, complementary, and decomposition methods presented similar estimates of the contributions of climate change and human activities. However, the extrapolation method showed a large deviation in the contribution of human activities. The error of the extrapolation method was the largest, followed by that of the two-stage total differential method. The complementary method and decomposition method exhibited negligible errors.
Project description:Heatwaves with severe impacts have increased and projected to become more frequent under warming climate in India. Concurrent day and nighttime heatwaves can exacerbate human discomfort causing high morbidity and mortality; however, their changes in the observed and projected climate remain unrecognized. Here using observations and model simulations from climate of 20th century plus (C20C+) detection and attribution (D&A) and coupled model intercomparison project 5 (CMIP5) projects, we show that 1 and 3-day concurrent hot day and hot night (CHDHN) events have significantly increased during the observed climate in India. Our results show that the anthropogenic emissions contribute considerably to the increase of 1 and 3-day CHDHN events in India. The frequency of 3-day CHDHN events is projected to increase 12-fold of the current level by the end of 21st century and 4-fold by the mid 21st century under the high emission pathway of RCP 8.5. The increase in 3-day CHDHN events can be limited to only 2-fold by the end of 21st century under low emission scenario of RCP 2.6. One and 3-day CHDHN events are projected to increase by 4, 6, and 8 folds of the current level in India under the 1.5, 2, and 3?°C warming worlds, respectively. Restricting global mean temperature below 1.5° from the pre-industrial level can substantially reduce the risk of 1 and 3-day CHDHN events and associated implications in India.
Project description:Psychological research has revealed that people attribute mental states to groups such as companies, especially to those groups that are highly entitative. Moreover, attributing a mind to a group results in the decreased attribution of mind to individual group members. Recent research has demonstrated that the minds of others are perceived in two dimensions-agency and experience. The present study investigated the possibility that this two-dimensional structure exists in mind attribution to groups, and group entitativity has different patterns of relations with these dimensions. A vignette experiment revealed that highly entitative groups were attributed both agency and experience to greater degrees compared to non-entitative groups, while group entitativity reduced only the attribution of agency to the individual group members. Individual members were attributed an equivalent amount of experience regardless of group entitativity. Mind attribution to individual members showed an unpredicted third factor of other-recognition, which was positively related to group entitativity. The implications of mind attribution to moral issues were discussed.
Project description:The health burden from heatwaves is expected to increase with rising global mean temperatures and more extreme heat events over the coming decades. Health-related effects from extreme heat are more common in elderly populations. The population of Europe is rapidly aging, which will increase the health effects of future temperatures. In this study, we estimate the magnitude of adaptation needed to lower vulnerability to heat in order to prevent an increase in heat-related deaths in the 2050s; this is the Adaptive Risk Reduction (ARR) needed. Temperature projections under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 from 18 climate models were coupled with gridded population data and exposure-response relationships from a European multi-city study on heat-related mortality. In the 2050s, the ARR for the general population is 53.5%, based on temperature projections under RCP 4.5. For the population above 65 years in Southern Europe, the ARR is projected to be 45.9% in a future with an unchanged climate and 74.7% with climate change under RCP 4.5. The ARRs were higher under RCP 8.5. Whichever emission scenario is followed or population projection assumed, Europe will need to adapt to a great degree to maintain heat-related mortality at present levels, which are themselves unacceptably high, posing an even greater challenge.
Project description:Heatwaves are divided between moderate, more common heatwaves and rare "high-mortality" heatwaves that have extremely large health effects per day, which we define as heatwaves with a 20% or higher increase in mortality risk. Better projections of the expected frequency of and exposure to these separate types of heatwaves could help communities optimize heat mitigation and response plans and gauge the potential benefits of limiting climate change. Whether a heatwave is high-mortality or moderate could depend on multiple heatwave characteristics, including intensity, length, and timing. We created heatwave classification models using a heatwave training dataset created using recent (1987-2005) health and weather data from 82 large US urban communities. We built twenty potential classification models and used Monte Carlo cross-validations to evaluate these models. We ultimately identified several models that can adequately classify high-mortality heatwaves. These models can be used to project future trends in high-mortality heatwaves under different scenarios of a changing future (e.g., climate change, population change). Further, these models are novel in the way they allow exploration of different scenarios of adaptation to heat, as they include, as predictive variables, heatwave characteristics that are measured relative to a community's temperature distribution, allowing different adaptation scenarios to be explored by selecting alternative community temperature distributions. The three selected models have been placed on GitHub for use by other researchers, and we use them in a companion paper to project trends in high-mortality heatwaves under different climate, population, and adaptation scenarios.
Project description:Canada is expected to see an increase in fire risk under future climate projections. Large fires, such as that near Fort McMurray, Alberta in 2016, can be devastating to the communities affected. Understanding the role of human emissions in the occurrence of such extreme fire events can lend insight into how these events might change in the future. An event attribution framework is used to quantify the influence of anthropogenic forcings on extreme fire risk in the current climate of a western Canada region. Fourteen metrics from the Canadian Forest Fire Danger Rating System are used to define the extreme fire seasons. For the majority of these metrics and during the current decade, the combined effect of anthropogenic and natural forcing is estimated to have made extreme fire risk events in the region 1.5 to 6 times as likely compared to a climate that would have been with natural forcings alone.
Project description:The distributions of bird species have changed over the past 50 years in China. To evaluate whether the changes can be attributed to the changing climate, we analyzed the distributions of 20 subspecies of resident birds in relation to climate change. Long-term records of bird distributions, gray relational analysis, fuzzy-set classification techniques, and attribution methods were used. Among the 20 subspecies of resident birds, the northern limits of over half of the subspecies have shifted northward since the 1960s, and most changes have been related to the thermal index. Driven by climate change over the past 50 years, the suitable range and latitude or longitude of the distribution centers of certain birds have exhibited increased fluctuations. The northern boundaries of over half of the subspecies have shifted northward compared with those in the 1960s. The consistency between the observed and predicted changes in the range limits was quite high for some subspecies. The changes in the northern boundaries or the latitudes of the centers of distribution of nearly half of the subspecies can be attributed to climate change. The results suggest that climate change has affected the distributions of particular birds. The method used to attribute changes in bird distributions to climate change may also be effective for other animals.
Project description:A combination of climate events (e.g., low precipitation and high temperatures) may cause a significant impact on the ecosystem and society, although individual events involved may not be severe extremes themselves. Analyzing historical changes in concurrent climate extremes is critical to preparing for and mitigating the negative effects of climatic change and variability. This study focuses on the changes in concurrences of heatwaves and meteorological droughts from 1960 to 2010. Despite an apparent hiatus in rising temperature and no significant trend in droughts, we show a substantial increase in concurrent droughts and heatwaves across most parts of the United States, and a statistically significant shift in the distribution of concurrent extremes. Although commonly used trend analysis methods do not show any trend in concurrent droughts and heatwaves, a unique statistical approach discussed in this study exhibits a statistically significant change in the distribution of the data.