Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change
<p>Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what de...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-07-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://ascmo.copernicus.org/articles/9/83/2023/ascmo-9-83-2023.pdf |
| _version_ | 1827901360522133504 |
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| author | J. Zeder E. M. Fischer |
| author_facet | J. Zeder E. M. Fischer |
| author_sort | J. Zeder |
| collection | DOAJ |
| description | <p>Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer “what if” questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost <span class="inline-formula">2</span> <span class="inline-formula"><sup>∘</sup></span>C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least <span class="inline-formula">2.9</span> <span class="inline-formula"><sup>∘</sup></span>C accounts for 60 %–80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.</p> |
| first_indexed | 2024-03-12T23:45:05Z |
| format | Article |
| id | doaj.art-09e8636499224502a86dc8356465aa0e |
| institution | Directory Open Access Journal |
| issn | 2364-3579 2364-3587 |
| language | English |
| last_indexed | 2024-03-12T23:45:05Z |
| publishDate | 2023-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Advances in Statistical Climatology, Meteorology and Oceanography |
| spelling | doaj.art-09e8636499224502a86dc8356465aa0e2023-07-14T10:09:18ZengCopernicus PublicationsAdvances in Statistical Climatology, Meteorology and Oceanography2364-35792364-35872023-07-0198310210.5194/ascmo-9-83-2023Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate changeJ. ZederE. M. Fischer<p>Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer “what if” questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost <span class="inline-formula">2</span> <span class="inline-formula"><sup>∘</sup></span>C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least <span class="inline-formula">2.9</span> <span class="inline-formula"><sup>∘</sup></span>C accounts for 60 %–80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.</p>https://ascmo.copernicus.org/articles/9/83/2023/ascmo-9-83-2023.pdf |
| spellingShingle | J. Zeder E. M. Fischer Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change Advances in Statistical Climatology, Meteorology and Oceanography |
| title | Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change |
| title_full | Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change |
| title_fullStr | Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change |
| title_full_unstemmed | Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change |
| title_short | Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change |
| title_sort | quantifying the statistical dependence of mid latitude heatwave intensity and likelihood on prevalent physical drivers and climate change |
| url | https://ascmo.copernicus.org/articles/9/83/2023/ascmo-9-83-2023.pdf |
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