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- Title
The Effect of a Short Observational Record on the Statistics of Temperature Extremes.
- Authors
Zeder, Joel; Sippel, Sebastian; Pasche, Olivier C.; Engelke, Sebastian; Fischer, Erich M.
- Abstract
In June 2021, the Pacific Northwest experienced a heatwave that broke all previous records. Estimated return levels based on observations up to the year before the event suggested that reaching such high temperatures is not possible in today's climate. We here assess the suitability of the prevalent statistical approach by analyzing extreme temperature events in climate model large ensemble and synthetic extreme value data. We demonstrate that the method is subject to biases, as high return levels are generally underestimated and, correspondingly, the return period of low‐likelihood heatwave events is overestimated, if the underlying extreme value distribution is derived from a short historical record. These biases have even increased in recent decades due to the emergence of a pronounced climate change signal. Furthermore, if the analysis is triggered by an extreme event, the implicit selection bias affects the likelihood assessment depending on whether the event is included in the modeling. Plain Language Summary: In June 2021, the Pacific Northwest experienced a record‐breaking heatwave event. Based on historical data, the scientific community has applied statistical models to understand how likely this event was to occur. However, due to the record‐shattering nature of this particular heatwave, the model suggested that reaching such high temperatures should not have been possible. In this study, we evaluate the accuracy of these statistical models in describing the occurrence probability of extreme events. We find that the current models tend to underestimate the occurrence probability and that the bias has become more pronounced in recent years due to climate change. Finally, we assess how the way extreme events are included in the model can also affect the accuracy of estimates. Key Points: Standard return period estimates of temperature extremes are systematically overestimated in short records under non‐stationary conditionsThe small‐sample bias in maximum likelihood estimates is found both for extremes in climate model data and in synthetic data experimentsFuture analysis should account for the statistical implications of the selection bias if the analysis is triggered by an extreme event
- Subjects
DISTRIBUTION (Probability theory); HEAT waves (Meteorology); CLIMATE extremes; ATMOSPHERIC models; EXTREME value theory
- Publication
Geophysical Research Letters, 2023, Vol 50, Issue 16, p1
- ISSN
0094-8276
- Publication type
Article
- DOI
10.1029/2023GL104090