Detecting Extreme Temperature Events Using Gaussian Mixture Models

TL;DR

This study uses Gaussian Mixture Models to more accurately detect and analyze the increasing frequency of extreme temperature events under climate change, revealing regional differences and higher-than-expected event frequencies.

Contribution

Introduces a multimodal GMM approach for detecting temperature extremes, improving accuracy over traditional unimodal methods and providing detailed regional analysis.

Findings

Extreme temperature events are becoming more frequent globally.

Future 10-year extreme events will occur up to 13.6 times more often under 3.0°C warming.

Tropical regions will experience nearly twice-weekly extreme temperature events.

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 1985-2014 conditions will occur 13.6 times more frequently in the future under 3.0{\deg}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 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.