Projection of Future Heat Waves in the United States. Part I: Selecting a Climate Model Subset

Shiva, Javad Shafiei; Chandler, D. G.

doi:10.3390/atmos11060587

Cited by 4 publications

(2 citation statements)

References 77 publications

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“…For this study, we used the TOPSIS method to investigate how heat wave hazard is spatially distributed across urban areas, based on all the individual heat wave components. This selection was mainly according to the ability of the TOPSIS method to rank the domain pixels based on all defined components of a heat wave, thereby removing any need for independent hazard components [78,84]. In addition, this selection is in line with previous studies on heat waves using TOPSIS multi-criteria decision-making [94][95][96].…”

Section: Multi-criteria Decision-makingmentioning

confidence: 80%

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Mapping Heat Wave Hazard in Urban Areas: A Novel Multi-Criteria Decision Making Approach

2022

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Global population is experiencing more frequent, longer, and more severe heat waves due to global warming and urbanization. Episodic heat waves increase mortality and morbidity rates and demands for water and energy. Urban managers typically assess heat wave risk based on heat wave hazard, population exposure, and vulnerability, with a general assumption of spatial uniformity of heat wave hazard. We present a novel analysis that demonstrates an approach to determine the spatial distribution of a set of heat wave properties and hazard. The analysis is based on the Livneh dataset at a 1/16-degree resolution from 1950 to 2009 in Maricopa County, Arizona, USA. We then focused on neighborhoods with the most frequent, severe, earlier, and extended periods of heat wave occurrences. On average, the first heat wave occurs 40 days earlier in the eastern part of the county; the northeast part of this region experiences 12 days further extreme hot days and 30 days longer heat wave season than other regions of the area. Then, we applied a multi-criteria decision-making (MCDM) tool (TOPSIS) to evaluate the total hazard posed by heat wave components. We found that the northern and central parts of the metropolitan area are subject to the greatest heat wave hazard and that individual heat wave hazard components did not necessarily indicate heat hazard. This approach is intended to support local government planning for heat wave adaptation and mitigation strategies, where cooling centers, heat emergency water distribution networks, and electrical energy delivery can be targeted based on current and projected local heat wave characteristics.

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Section: Multi-criteria Decision-makingmentioning

confidence: 80%

“…In addition to the traditional heat wave components, including frequency, intensity, duration, and timing [48], the presented analysis determines eight heat wave components based on the available climatological data. This approach provides clear definitions for each of the main heat wave components [49,78,79].…”

Section: Heat Wave Definition and Componentsmentioning

confidence: 99%

Mapping Heat Wave Hazard in Urban Areas: A Novel Multi-Criteria Decision Making Approach

2022

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Changes in characterising extremes

Hay

Williams²

2023

Science of Weather, Climate and Ocean Extremes

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Seasonal forecasts of Eurasian summer heat wave frequency

Zhang

Jia

Qian

2022

Environ. Res. Commun.

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Heat wave events usually cause disastrous consequences on human life, economy, environment, and ecosystem. However, current climate models usually perform poorly in forecasting heat wave events. In the current work, we identified that the leading mode of the summer (June-July-August) heat wave frequency (HWF) over the Eurasian continent (HWF_EC) is a continental-scale pattern. Two machine learning (ML) models are constructed and used to perform seasonal forecast experiments for the summer HWF_EC. The potential predictive sources for the HWF_EC are chosen from the fields related to the lower boundary conditions of the atmosphere, i.e., the sea surface temperature, snow cover, soil moisture and sea ice. The specific regions and months of these lower boundary condition fields selected to construct the potential predictors are those that are persistently and significantly correlated with the variation in the HWF_EC preceding the summer. The ML forecasting models are trained with data from the period 1980–2009 and then used to perform real seasonal forecasts for the summer HWF_EC for 2010–2019. The results show that the ML forecasting models have reasonably good skills in predicting the HWF_EC over high HWF regions. The two ML models show obviously better skill in the forecasting experiments than a traditional linear regression model, suggesting that the ML models may provide an additional and useful tool for forecasting the summer HWF_EC.

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Projection of Future Heat Waves in the United States. Part I: Selecting a Climate Model Subset

Cited by 4 publications

References 77 publications

Mapping Heat Wave Hazard in Urban Areas: A Novel Multi-Criteria Decision Making Approach

Mapping Heat Wave Hazard in Urban Areas: A Novel Multi-Criteria Decision Making Approach

Changes in characterising extremes

Seasonal forecasts of Eurasian summer heat wave frequency

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