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Abstract Global climate change has been shown to cause longer, more intense, and frequent heatwaves, of which anthropogenic stressors concentrated in urban areas are a critical contributor. In this study, we investigate the causal interactions during heatwaves across 520 urban sites in the U.S. combining complex network and causal analysis. The presence of regional mediators is manifest in the constructed causal networks, together with long-range teleconnections. More importantly, megacities, such as New York City and Chicago, are causally connected with most of other cities and mediate the structure of urban networks during heatwaves. We also identified a significantly positive correlation between the causality strength and the total populations in megacities. These findings corroborate the contribution of human activities e.g., anthropogenic emissions of greenhouse gases or waste heat, to urban heatwaves. The emergence of teleconnections and supernodes are informative for the prediction and adaptation to heatwaves under global climate change.
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Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data
Abstract Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.
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- Award ID(s):
- 2230772
- PAR ID:
- 10409571
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 7
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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