EGU23-12070
https://doi.org/10.5194/egusphere-egu23-12070
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Quantifying statistical associations among persistent events: Interval coincidence analysis between Northern hemisphere heatwaves and different types of circulation anomalies

Reik Donner1,2, Dominik Diedrich1,2,3, Sven Praast1,2,3, and Giorgia Di Capua1,2
Reik Donner et al.
  • 1Magdeburg-Stendal University of Applied Sciences, Magdeburg, Germany (reik.donner@h2.de)
  • 2Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 3Otto von Guericke University of Magdeburg, Magdeburg, Germany

Statistical associations between variables of interest are commonly assessed by applying similarity measures like Pearson correlation to corresponding observational time series. Most traditional measures focus on continuous variables and their associated complete variability, while there is a vast amount of practical examples where only times with specific conditions (e.g. extreme events) are of interest. For the latter cases, concepts like event synchronization strength or event coincidence rates have been introduced as proper similarity measures, and have proven their broad applicability across many areas of research. However, recent work has shown that such event based similarity measures may have conceptual as well as practical limitations when studying co-occurrence statistics between temporally clustered or extended events that do not meet the common assumption of serially uncorrelated point processes.

In this work, we introduce and discuss a straightforward extension of event coincidence analysis (ECA) to studying statistical associations between sequences of persistent events, which we tentatively call interval coincidence analysis (InCA). Here, each event of interest corresponds to a well-defined time interval, and the discrete counts of event co-occurrences in ECA are replaced by the fractions of time during which event intervals in two sequences mutually overlap. A statistical significance test for the obtained interval coincidence rates is realized by block bootstrapping event and non-event intervals, retaining the event duration and waiting time distributions of the persistent events in both sequences.

We demonstrate the practical potentials of InCA, as well as its similarities and differences with ECA, for a specific case study on atmospheric dynamics. Specifically, we apply both methods to studying the likelihood of co-occurrences between boreal summer (June to August) heatwaves in different parts of the Northern hemisphere and hemispheric anomalies of the atmospheric circulation, such as a jet stream pattern exhibiting two distinct wind bands known as double-jet. Our analysis reveals large-scale regions of markedly elevated likelihood of co-occurrences over Northern Europe, Central to Eastern Siberia, Northeastern Canada as well as the Middle East, Eastern China, the Southwestern and Northeastern United States and Northwest Africa, indicating a particular vulnerability of those regions to the presence of double-jet patterns.

How to cite: Donner, R., Diedrich, D., Praast, S., and Di Capua, G.: Quantifying statistical associations among persistent events: Interval coincidence analysis between Northern hemisphere heatwaves and different types of circulation anomalies, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12070, https://doi.org/10.5194/egusphere-egu23-12070, 2023.