Detection and attribution of changes in extreme temperatures at regional scale
Abstract
This paper improves an extreme-value-theory-based detection and attribution method and then applies it to four types of extreme temperatures, annual minimum daily minimum (TNn) and maximum (TXn) and annual maximum daily minimum (TNx) and maximum (TXx), using the HadEX2 observation and the CMIP5 multimodel simulation datasets of the period 1951–2010 at 17 subcontinent regions. The methodology is an analog of the fingerprinting method adapted to extremes using the generalized extreme value (GEV) distribution. The signals are estimated as the time-dependent location parameters of GEV distributions fitted to extremes simulated by multimodel ensembles under anthropogenic (ANT), natural (NAT), or combined anthropogenic and natural (ALL) external forcings. The observed extremes are modeled by GEV distributions whose location parameters incorporate the signals as covariates. A coordinate descent algorithm improves both computational efficiency and accuracy in comparison to the existing method, facilitating detection of multiple signals simultaneously. An overall goodness-of-fit test was performed at the regional level. The ANT signal was separated from the NAT signal in four to six regions. In these analyses, the waiting times of the 1951–55 20-yr return level in the 2006–10 climate for the temperature of the coldest night and day were found to have increased to over 20 yr; the corresponding waiting times for the warmest night and day were found to have dropped below 20 yr in a majority of the regions.
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There is robust evidence confirming the role of human-induced climate change in driving increases in extreme heat.
“and there is robust evidence confirming the role of human-induced climate change in driving them (Wehner et al 2016, Pattanayak et al 2017, Wang et al 2017, Dileepkumar et al 2018, 2021, van Oldenborgh et al 2018, Seong et al 2021).”
From Attribution of 2022 early-spring heatwave in India and Pakistan to climate change: lessons in assessing vulnerability and preparedness in reducing impacts (2023) · cites this paper as Wang et al 2017
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- Authors
- Wang, Z., Jiang, Y., Wan, H., Yan, J., & Zhang, X.
- Affiliations
- Shenzhen University (CN)
- University of Connecticut (US)
- Environment and Climate Change Canada (CA)
- Corresponding author
- Yan, Jun
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2017-09
- Volume
- 30
- Issue
- 17
- Pages
- 7035-7047
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W2615635051
- Cited by
- 32
- References
- 39
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 7 · 2022: 3 · 2021: 9 · 2020: 2 · 2019: 3 · 2018: 2
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Climate (publishedVersion)
- Funders
- Shenzhen University (17QNFC61)
- Directorate for Mathematical and Physical Sciences (1521730)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- generalized extreme value distribution, anthropogenic forcing, natural climate variability, return levels, daily maximum and minimum temperature, coordinate descent, regional climate change
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