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- Title
Nonstationarity in High and Low‐Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme‐Value Methods.
- Authors
Nerantzaki, Sofia D.; Papalexiou, Simon Michael; Rajulapati, Chandra Rupa; Clark, Martyn P.
- Abstract
We merge classical extreme value methods to extract high (high temperatures (HT)) and low (low temperatures (LT)) temperatures and form time series having at least one extreme value per year. Observed daily maximum and minimum temperature records are used from 4,797 quality‐controlled, global, surface stations over 1970–2019. We assess changes in the magnitude and frequency of extreme temperatures by introducing and applying novel methods that exploit the definition of stationarity. Analysis shows significant increasing (40.6% of the stations) and decreasing (41.1%) trends in the frequency of high and LT, respectively, and increasing trends in both high‐ and low‐temperature values (35.6% and 49.7%). Globally, HT and LT frequencies are increasing and decreasing, respectively, by 0.9% and 1.1% per year, relative to the expected frequencies under the assumption of stationarity. The global mean annual HT and LT magnitudes are increasing by 0.004 and 0.016°C/year compared to the expected ones under stationarity. The results indicate that the assumption of stationarity fails to explain the observed changes. The proposed methods are an alternative approach to classical extreme value methods and a useful tool to reveal changes in extremes in the era of earth‐system change. Plain Language Summary: We present a method which determines the extreme temperature series by combining the advantages of classical extreme value methods. In this, the series of extremes have at least one extreme value per year. We apply the approach to 4,797 stations of high and low daily temperatures globally and estimate their trends during 1970–2019. We find that during the last 50 years, high and, especially, low temperatures (LT) have become significantly warmer. Fewer low‐temperature events occur with time, while the number of high‐temperature events is increasing. The results indicate that extreme high and LT are non‐stationary. The presented method has potential applications in the statistical modeling of extreme values. Key Points: We merge Annual Maxima (AM) and Peaks Over Threshold (POT) to extract extreme high and low‐temperature time series from a global observational data setNonstationarity is observed in the frequency and magnitude of extreme temperatures, particularly at low temperatures, which are becoming warmerWe compare trends in extreme temperatures extracted by our method and those obtained by AM and POT and examine further applications
- Subjects
EXTREME value theory; TIME series analysis; HIGH temperatures; TREND analysis; STATISTICAL models
- Publication
Earth's Future, 2023, Vol 11, Issue 11, p1
- ISSN
2328-4277
- Publication type
Article
- DOI
10.1029/2023EF003506