Can the Excess Heat Factor Indicate Heatwave-Related Morbidity? A Case Study in Adelaide, South Australia

Hatvani-Kovacs, Gertrud; Belusko, Martin; Pockett, John; Boland, John

doi:10.1007/s10393-015-1085-5

Cited by 42 publications

(29 citation statements)

References 30 publications

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“…While there is no “universal” definition of heat waves, the use of some heat wave definitions is less suitable for certain applications and regions [ Perkins and Alexander , ]. In this study heat waves are defined based on the excess heat factor (EHF) [ Nairn and Fawcett , ] which has been shown to be a useful predictor of adverse human health effects in Australia [e.g., Scalley et al ., ; Hatvani‐Kovacs et al ., ].…”

Section: Methodsmentioning

confidence: 69%

Comparing Australian heat waves in the CMIP5 models through cluster analysis

et al. 2017

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Quantitative projections of climate extremes on local to regional scales are highly valuable for planners and decision makers and necessary for effective local climate change adaptation. However, in contrast to the model robustness of simulated extremes at the global scale, the robustness in simulating past and future extremes often diminishes over finer spatial scales. In this study we analyze heat waves simulated by state‐of‐the‐art global climate models over the Australian region. For the first time we present results explicitly detailing the model spread in simulated heat wave trends and climatology for this region for the recent past (1958–2005). As expected, large intermodel spread is observed at the local to regional scale for both heat wave trends and climatology. By analyzing multiple initial condition runs from individual models, we show that model internal variability strongly influences the spatial patterns of heat wave trends, while intermodel differences in heat wave climatology appear more influenced by model uncertainty. From a model evaluation perspective, cluster analysis is shown to be useful in characterizing robust spatial features of heat waves simulated by the models. In contrast to the multimodel mean, where uncorrelated spatial features tend to be averaged out, cluster composites preserve these features. Since previous examinations have tended to focus on the multimodel mean the extent of model spread may have been overlooked. Further examination of the processes that lead to model differences and biases is needed.

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Section: Methodsmentioning

confidence: 69%

Comparing Australian heat waves in the CMIP5 models through cluster analysis

et al. 2017

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“…These definitions are good predictors of extreme heat or cold events, but they may be imprecise when it comes to related morbidity and mortality, and also to impacts on other specific sectors. Recently, a few studies have analysed HWs by using the excess heat factor (EHF) and showed that this approach is superior to other definitions and it can be used as a better indicator for HW‐related mortality and morbidity compared to singular daily values of TX or TN (Langlois et al, ; Nairn and Fawcett, ; Scalley et al, ; Hatvani‐Kovacs et al, ). EHF quantifies the intensity of an HW event by factoring in the cumulative effect of high TX and TN in a 3‐day period contrasted with a prior 30‐day acclimatization period, in terms of temperature, and with the long‐term normal climatic conditions (Nairn and Fawcett, , ; Scalley et al, ; WMO and WHO, ; Rohini et al, ; Loughran et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Hatvani‐Kovacs et al . () indicated that EHF can predict 77% of excess morbidity during the most intense HW days and enables a more in‐depth analysis of the HWs severity. This approach is also analogously applicable to CWs by employing the excess cold factor (ECF) (Nairn and Fawcett, ; Wang et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…The acclimatization period used in the EHF formula considers all these physiological processes that may result in human body responses to HWs (Scalley et al, 2015). Hatvani-Kovacs et al (2016) indicated that EHF can predict 77% of excess morbidity during the most intense HW days and enables a more in-depth analysis of the HWs severity. This approach is also analogously applicable to CWs by employing the excess cold factor (ECF) (Nairn and Fawcett, 2013;Wang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Recent changes in heat waves and cold waves detected based on excess heat factor and excess cold factor in Romania

Piticar

Croitoru

Ciupertea

et al. 2017

Intl Journal of Climatology

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In this paper, we investigated changes in heat and cold waves in Romania over the period 1961–2015 by employing a new and superior approach. It consists in using excess heat factor to identify heat waves and excess cold factor to identify cold waves. Five indices were calculated and then analysed for both heat waves and cold waves resulting in a set of ten indices. Indices for heat waves were analysed for the extended summer season (May–September), whereas those for cold waves were assessed for the extended winter (November–March). The intensity threshold was set to be equal or above the 90th percentile for heat waves, and equal or below the 10th percentile for cold waves, while the duration threshold for both heat and cold waves was of at least three consecutive days. For a better comparison with other studies conducted worldwide, and to get more information from the data sets, the percentile thresholds for heat and cold waves identification were calculated based on three reference periods: 1961–1990, 1971–2000, and 1981–2010. Trends were calculated using ordinary least square method, whereas statistical significance was assessed by the t‐test. The main results indicated that changes are more substantial in the case of indices calculated based on excess heat factor compared to those based on excess cold factor, suggesting that the warming process is more reflected in heat waves rather than in cold waves. Thus, heat waves became more frequent, longer, and more intense, while cold waves became less frequent, but more intense. When the reference period for percentile threshold calculation was changed from the earliest to the most recent ones, the frequency of increasing and significant increasing trends decreased for some of the heat wave indices, while for the cold wave indices the significant downward trends increased.

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“…The heatwave service has combined high quality climate data and forecast temperatures in a manner that forecasts community impacts, improving the national dialogue with both the public and emergency services. Forecasts of Excess Heat Factor act as predictions of exposure (heatwave intensity) that predict human health impact, verified in multiple human health studies (Langlois et al 2013;Herbst et al 2014;Hatvani-Kovacs et al 2015;Scalley et al 2015;Nitschke et al 2016;Jegasothy et al 2017;Williams et al 2018).…”

Section: Heat and Heatwave Impact Servicesmentioning

confidence: 87%

A heatwave forecast service for Australia

Bettio¹,

Nairn²,

McGibbony³

et al. 2019

Proc. R. Soc. Vic.

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The Australian Bureau of Meteorology monitors, researches, predicts and communicates Australia's weather and climate. Australia's mean temperature has risen by over 1°C since 1910, leading to an increase in the frequency of extreme heat events. Extreme heat can profoundly impact human health, infrastructure and the environment. Research conducted at the Bureau and elsewhere shows that climate change is impacting the intensity and frequency of extreme heat events. One way that the Bureau has responded to this challenge is by providing a forecast service specifically targeted at identifying heatwaves. The heatwave service identifies areas expected to be impacted by three or more consecutive days of unusually high maximum and minimum temperatures on a national map. The service has been developed with clear impact-based categories of heatwave severity. This heatwave service is now available operationally on the Bureau's website during the heatwave season (nominally November to March) and is proving a valuable tool for engaging the community, including emergency services, with forecasts and warnings of extreme heat.

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Can the Excess Heat Factor Indicate Heatwave-Related Morbidity? A Case Study in Adelaide, South Australia

Cited by 42 publications

References 30 publications

Comparing Australian heat waves in the CMIP5 models through cluster analysis

Comparing Australian heat waves in the CMIP5 models through cluster analysis

Recent changes in heat waves and cold waves detected based on excess heat factor and excess cold factor in Romania

A heatwave forecast service for Australia

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