Data-driven upper bounds and event attribution for unprecedented heatwaves
The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provide...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Weather and Climate Extremes |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212094725000015 |
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| author | Mark D. Risser Likun Zhang Michael F. Wehner |
| author_facet | Mark D. Risser Likun Zhang Michael F. Wehner |
| author_sort | Mark D. Risser |
| collection | DOAJ |
| description | The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed yet unprecedented events that exceed a priori upper bound estimates, so-called “impossible” temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the “impossibility” of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements. |
| format | Article |
| id | doaj-art-c2a6db1818ec4822a9108654c99eeb8a |
| institution | Kabale University |
| issn | 2212-0947 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Weather and Climate Extremes |
| spelling | doaj-art-c2a6db1818ec4822a9108654c99eeb8a2025-02-02T05:27:07ZengElsevierWeather and Climate Extremes2212-09472025-03-0147100743Data-driven upper bounds and event attribution for unprecedented heatwavesMark D. Risser0Likun Zhang1Michael F. Wehner2Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, United States of America; Corresponding author.Department of Statistics, University of Missouri, Columbia, MO, 65211, United States of AmericaApplied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, United States of AmericaThe last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed yet unprecedented events that exceed a priori upper bound estimates, so-called “impossible” temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the “impossibility” of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements.http://www.sciencedirect.com/science/article/pii/S2212094725000015Extreme temperatureUpper boundsUncertainty quantificationSpatial statisticsExtreme value analysisBayesian analysis |
| spellingShingle | Mark D. Risser Likun Zhang Michael F. Wehner Data-driven upper bounds and event attribution for unprecedented heatwaves Weather and Climate Extremes Extreme temperature Upper bounds Uncertainty quantification Spatial statistics Extreme value analysis Bayesian analysis |
| title | Data-driven upper bounds and event attribution for unprecedented heatwaves |
| title_full | Data-driven upper bounds and event attribution for unprecedented heatwaves |
| title_fullStr | Data-driven upper bounds and event attribution for unprecedented heatwaves |
| title_full_unstemmed | Data-driven upper bounds and event attribution for unprecedented heatwaves |
| title_short | Data-driven upper bounds and event attribution for unprecedented heatwaves |
| title_sort | data driven upper bounds and event attribution for unprecedented heatwaves |
| topic | Extreme temperature Upper bounds Uncertainty quantification Spatial statistics Extreme value analysis Bayesian analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2212094725000015 |
| work_keys_str_mv | AT markdrisser datadrivenupperboundsandeventattributionforunprecedentedheatwaves AT likunzhang datadrivenupperboundsandeventattributionforunprecedentedheatwaves AT michaelfwehner datadrivenupperboundsandeventattributionforunprecedentedheatwaves |