Summary
This comprehensive narrative review synthesises statistical, dynamical, and modelling approaches for understanding extreme temperature events in North America and their association with large-scale meteorological patterns. The authors find that whilst climate models capture broad properties of heat waves and cold air outbreaks, systematic biases exist: models overestimate warm wave frequency and underestimate cold outbreak frequency, and underrepresent the influence of low-frequency atmospheric modes. The paper identifies critical knowledge gaps regarding large-scale meteorological pattern life cycles and calls for enhanced model assessment to better project future temperature extremes.
UK applicability
Whilst this review focuses on North American temperature extremes, the statistical and dynamical methods reviewed are applicable to UK climate science and extreme weather assessment. The identified model biases in representing low-frequency atmospheric modes and cold air outbreaks may be relevant to understanding UK winter weather variability and extreme events.
Key measures
Model performance in simulating warm wave frequency, cold air outbreak frequency, low-frequency atmospheric mode influence, large-scale meteorological pattern properties, and temperature extreme projections
Outcomes reported
The study reviewed statistical methods, dynamical mechanisms, and climate modelling approaches for understanding extreme temperature events in North America and their linkage to large-scale meteorological patterns. The review identified systematic biases in climate models and gaps in understanding large-scale meteorological pattern life cycles.
Topic tags
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