- 1ETH Zürich, Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, Zürich, Switzerland (erich.fischer@env.ethz.ch)
- 2Statistics Program, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
- 3Department of Earth, Atmospheric and Planetary Sciences, MIT, USA
- 4Institute for Meteorology, Leipzig University, Germany
Global mean temperatures are currently warming at a rate unprecedented in the observational record and most likely in millennia. Europe has experienced one of the highest warming rates globally in the last three decades. The high warming rate results from global greenhouse gas emissions and is regionally amplified by reduced aerosol emissions. Internal variability can both further amplify and damp the rate locally, for extended periods of time.
The exceptionally high forced warming rate is a crucial factor explaining the high frequency of record-breaking and record-shattering heat over land and oceans in recent decades. The ratio of occurrence of daily surface temperature records since 1950 (in ERA5 and the BEST gridded observational data set) relative to the theoretically expected occurrence in a stationary climate, is now about 3–3.5 for hot records globally.
To prepare for future record events, it is crucial to identify regions where the probability of breaking or shattering the local standing record is highest. Here, we use statistical tools to quantify the record probability conditional on the standing record level and identify hotspots of high record probability in the coming years. Our method is evaluated using several single-model initial condition large ensembles.
We demonstrate that the conditional probability of setting a new record is particularly high in years and regions where the standing record level is low relative to the forced response. This typically happens after periods of little to no warming, when a forced warming trend has been slowed down or muted by unforced internal variability. Ironically, it is thus often regions where recent trends were low that deserve particular attention with regard to preparing for record-shattering extremes.
We demonstrate that the biggest source of uncertainty relates to the separation of historical warming trends into the forced response and internal variability. We evaluate different methods to estimate the forced response and show that while the exact conditional probability is uncertain, the hot spot regions where the conditional record probability is high can be robustly identified.
How to cite: Fischer, E., Huser, R., de Vries, I., and Sippel, S.: Where is the probability of the next record-shattering extreme highest?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15063, https://doi.org/10.5194/egusphere-egu25-15063, 2025.
