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Detecting Extreme Temperature Events Using Gaussian Mixture Models

Pacal, Aytac and Hassler, Birgit and Weigel, Katja and Kurnaz, Levent and Wehner, M.F. and Eyring, Veronika (2023) Detecting Extreme Temperature Events Using Gaussian Mixture Models. Journal of Geophysical Research: Atmospheres, 128, e2023JD038906. Wiley. doi: 10.1029/2023JD038906. ISSN 2169-897X.

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Official URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JD038906


Extreme temperature events have traditionally been detected assuming a unimodal distribution of temperature data. We found that surface temperature data can be described more accurately with a multimodal rather than a unimodal distribution. Here, we applied Gaussian Mixture Models (GMM) to daily near-surface maximum air temperature data from the historical and future Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for 46 land regions defined by the Intergovernmental Panel on Climate Change. Using the multimodal distribution, we found that temperature extremes, defined based on daily data in the warmest mode of the GMM distributions, are getting more frequent in all regions. Globally, a 10-year extreme temperature event relative to 1985-2014 conditions will occur 13.6 times more frequently in the future under 3.0C of global warming levels (GWL). The frequency increase can be even higher in tropical regions, such that 10-year extreme temperature events will occur almost twice a week. Additionally, we analyzed the change in future temperature distributions under different GWL and found that the hot temperatures are increasing faster than cold temperatures in low latitudes, while the cold temperatures are increasing faster than the hot temperatures in high latitudes. The smallest changes in temperature distribution can be found in tropical regions, where the annual temperature range is small. Our method captures the differences in geographical regions and shows that the frequency of extreme events will be even higher than reported in previous studies.

Item URL in elib:https://elib.dlr.de/197541/
Document Type:Article
Title:Detecting Extreme Temperature Events Using Gaussian Mixture Models
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pacal, AytacDLR, IPAhttps://orcid.org/0000-0002-1324-2389143011457
Hassler, BirgitDLR, IPAhttps://orcid.org/0000-0003-2724-709XUNSPECIFIED
Kurnaz, LeventBoğaziçi University, Istanbul, TurkeyUNSPECIFIEDUNSPECIFIED
Wehner, M.F.Lawrence Berkely National Lab., Berkely, CA, USAUNSPECIFIEDUNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Date:22 September 2023
Journal or Publication Title:Journal of Geophysical Research: Atmospheres
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:e2023JD038906
Keywords:extreme events, Gaussian mixture models, daily maximum temperatures, return periods, bimodal distributions, multimodal distributions
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Pacal, Aytac
Deposited On:26 Sep 2023 08:24
Last Modified:26 Sep 2023 08:24

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