Yan Liu, Changqing Song, Sijing Ye, Jiaying Lv, Peichao Gao
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引用次数: 0
Abstract
As global warming intensifies, extreme heat events, especially those occurring simultaneously or sequentially in multiple regions, are becoming more frequent. This highlights the growing need to analyze heat stress from the perspectives of human health and spatiotemporal correlations. Wet-Bulb Globe Temperature (WBGT) is a well-established heat stress indicator closely linked to human health. However, its reliance on specialized measurements and resource-intensive computations limits its widespread use, particularly for researchers without an earth sciences background. To address this, we adopted a simplified WBGT (sWBGT), which effectively simulates human cooling through sweating, to generate a global 2° resolution dataset of daily maximum sWBGT from 1940 to 2022. This dataset fills a critical gap in long-term, global-scale heat stress data. Additionally, we employed climate network methods to innovatively explore teleconnections of extreme heat events, providing a tool to reveal their spatiotemporal relationships and supporting the development of effective health protection strategies.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.