Determining if particular extreme hot or cold spells were caused by climate change could be made easier by a new mathematical method published in the journal Physical Review Letters and co-authored by Professor Valerio Lucarini, Professor of Statistical Mechanics and Director of the Centre for the Mathematics of Planet Earth.
The statistical method, developed by physicists at the University of Reading and Uppsala University in Sweden, looks at the characteristics, or ‘fingerprints’, of a specific extreme weather event of interest, like a heatwave, in order to ascertain whether it can be attributed to natural climate variability of the climate or is a unique product of global warming.
The method also allows predictions to be made about how likely extreme climate events will be in the future.
Read the full press release here.
Galfi, V. M. and Lucarini, V. (2021) Fingerprinting heatwaves and cold spells and assessing their response to climate change using large deviation theory. Physical Review Letters, 127 (5). DOI: 10.1103/PhysRevLett.127.058701
Extreme events provide relevant insights into the dynamics of climate and their understanding is key for mitigating the impact of climate variability and climate change. By applying large deviation theory to a state-of-the-art Earth system model, we define the climatology of persistent heatwaves and cold spells in key target geographical regions by estimating the rate functions for the surface temperature, and we assess the impact of increasing CO2 concentration on such persistent anomalies. Hence, we can better quantify the increasing hazard due to heatwaves in a warmer climate. We show that two 2010 high impact events—summer Russian heatwave and winter Dzud in Mongolia—are associated with atmospheric patterns that are exceptional compared to the typical ones but typical compared to the climatology of extremes. Their dynamics is encoded in the natural variability of the climate. Finally, we propose and test an approximate formula for the return times of large and persistent temperature fluctuations from easily accessible statistical properties.