Background: Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known abo...

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Opportunities and challenges for personal heat exposure research

Estimating and projecting the effect of cold waves on mortality in 209 US cities

Impacts modelers and stakeholders use publicly available data sets of downscaled climate projections to assess and design infrastructure for changes in future rainfall extremes. If differences across data sets exist, infrastructure resilience decisions could change depending on which single data set is used. We assess changes in U.S. rainfall extremes from 2044–2099 compared with 1951–2005 based on 227 projections under RCP4.5 and RCP8.5 from five widely used data sets. We show there are large differences in the change magnitude and its spatial structure between data sets. At the continental scale, the data sets show different increases, with high-end extremes (e.g. 100-year event) generally increasing more (between 10% and 50%) than low-end extremes (e.g. 5-year). These differences largely contribute to the overall uncertainty for small average recurrence intervals (ARIs) extremes (2to 10-year), while uncertainties due to short record length dominate large ARIs (25to 100-year). The results indicate that robust infrastructure planning should consider these uncertainties to enable resilient infrastructure under climate change. Plain Language Summary Observed extreme rainfall magnitudes have increased since 1950, and climate model projections indicate that these increases will continue throughout the 21st century in many areas in the US. Adapting and designing infrastructure for climate change requires future extreme rainfall projections at high resolutions. Global climate model (GCM) output resolution is generally much coarser, and methods have been developed to create relevant climate information at regional scales. Multiple open data sets exist that provide downscaled projections of future rainfall extremes. We analyze changes in future daily extremes using five widely used data sets in climate change impacts assessments and decision making. We found large differences between data sets in how much and where extreme events will intensify by the end of the 21st century. This and other sources of uncertainty need to be considered when designing resilient infrastructure.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019GL086797

Uncertainties in Future U.S. Extreme Precipitation from Downscaled Climate Projections

The U.S. Midwest is an area that has been plagued by heavy and persistent precipitation leading to frequent flood events. The improved understanding of the types of weather conditions and settings associated with heavy precipitation can provide basic information to improve our preparation for and response to these events. Here we identify five weather types from daily 500-hPa geopotential height using the k-means cluster analysis. Consistent with their distinct large-scale atmospheric patterns, these weather types exert different effects on precipitation in the Midwest. Weather type 1 (WT1) features a zonally-aligned wave train propagating from the North Pacific to North America. Overall, WT2 is characterized by a wave train pattern with high (low) pressure in the western (eastern) United States. WT3 features a unique pattern with a high pressure system over the continental United States except for the northwestern United States, similar to the La-Nina forced responses. WT4 is characterized by a wave train moving from the Pacific Northwest to the North Atlantic with a strong ridge over the western United States, while WT5 features a positive geopotential height anomaly originating from the Arctic, probably influenced by the Arctic Amplification. Because of the strong moisture transport, strengthened low-level jet stream and wavy upper-level polar jet stream located in the western United States, among the five weather types WT1 exerts the strongest impacts on precipitation, accounting for up to 40% of the total precipitation over the Midwest, followed by WT5. Moreover, we detect a significant upward trend in the number of WT1 and WT5 events for 1948–2017 and their persistency, suggesting a rising risk of heavy and long-lasting precipitation across the Midwest. Overall, the weather types during summer and winter are consistent with those obtained from the analysis of the entire year, although the weather types during winter have a larger magnitude in the geopotential height anomaly. WT1 accounts for the largest contribution to total precipitation in the Midwest during summer and winter.

On the weather types that shape the precipitation patterns across the U.S. Midwest

This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs and indicated user needs for the gridding and processing of model output.

/pdf/the-vulnerability-impacts-adaptation-and-climate-services-10laucf6jh.pdf

The Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB V1.0) Contribution to CMIP6

https://hal.archives-ouvertes.fr/hal-01375615/document

Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0)

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections.

/pdf/evaluating-the-appropriateness-of-downscaled-climate-2mamjc4s1k.pdf

Ben Koziol

Papers

Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0)

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella.