Abstract
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 (IPCC). 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
1980-2010 conditions will occur 15 times more frequently in the future
under 3.0oC 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 analysed 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.