Publication Date:
2024-02-12
Description:
〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉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 3.0°C 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.〈/p〉
Description:
Plain Language Summary: Extreme temperature events are unusual weather conditions with exceptionally low or high temperatures. Traditionally, the temperature range was determined by assuming a single distribution, which describes the frequency of temperatures at a given climate using their mean and variability. This single distribution was then used to detect extreme weather events. In this study, we found that temperature data from reanalyses and climate models can be more accurately described using a mixture of multiple Gaussian distributions. We used the information from this mixture of Gaussians to determine the cold and hot extremes of the distributions. We analyzed their change in a future climate and found that hot temperature extremes are getting more frequent in all analyzed regions at a rate that is even higher than found in previous studies. For example, a global 10‐year event will occur 13.6 times more frequently under 3.0°C of global warming. Furthermore, our results show that the temperatures of hot days will increase faster than the temperature of cold days in equatorial regions, while the opposite will occur in polar regions. Extreme hot temperatures will be the new normal in highly populated regions such as the Mediterranean basin.〈/p〉
Description:
Key Points:
〈list list-type="bullet"〉
〈list-item〉
〈p xml:lang="en"〉Extreme temperature events are detected with Gaussian Mixture Models to follow a multimodal rather than a unimodal distribution〈/p〉〈/list-item〉
〈list-item〉
〈p xml:lang="en"〉10‐year temperature extremes will occur 13.6 times more frequently under 3.0°C future warming〈/p〉〈/list-item〉
〈list-item〉
〈p xml:lang="en"〉Colder days are getting warmer faster than hotter days in high latitudes, whereas it is the opposite for many regions in low latitudes〈/p〉〈/list-item〉
〈/list〉
〈/p〉
Description:
European Research Council
http://dx.doi.org/10.13039/501100000781
Description:
https://github.com/EyringMLClimateGroup/pacal23jgr_GaussianMixtureModels_Extremes
Description:
https://doi.org/10.5281/zenodo.3401363
Keywords:
ddc:551.5
;
extreme events
;
Gaussian mixture models
;
daily maximum temperatures
;
return periods
;
bimodal distributions
;
multimodal distributions
Language:
English
Type:
doc-type:article
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