Abstract
AbstractMidlatitude extreme precipitation events are caused by well-understood meteorological drivers, such as vertical instability and low pressure systems. In principle, dynamical weather and climate models behave in the same way, although perhaps with the sensitivities to the drivers varying between models. Unlike parameterized convection models (PCMs), convection-permitting models (CPMs) are able to realistically capture subdaily extreme precipitation. CPMs are computationally expensive; being able to diagnose the occurrence of subdaily extreme precipitation from large-scale drivers, with sufficient skill, would allow effective targeting of CPM downscaling simulations. Here the regression relationships are quantified between the occurrence of extreme hourly precipitation events and vertical stability and circulation predictors in southern United Kingdom 1.5-km CPM and 12-km PCM present- and future-climate simulations. Overall, the large-scale predictors demonstrate skill in predicting the occurrence of extreme hourly events in both the 1.5- and 12-km simulations. For the present-climate simulations, extreme occurrences in the 12-km model are less sensitive to vertical stability than in the 1.5-km model, consistent with understanding the limitations of cumulus parameterization. In the future-climate simulations, the regression relationship is more similar between the two models, which may be understood from changes to the large-scale circulation patterns and land surface climate. Overall, regression analysis offers a promising avenue for targeting CPM simulations. The authors also outline which events would be missed by adopting such a targeted approach.
Highlights
Extreme precipitation events from subhourly to multiday time scales are driven by specific weather processes and drivers
In Receiver operating characteristic (ROC), we aim to find forecast thresholds that maximize the number of extreme events that we capture [the true positive rate (TPR)] with the least computer time spent on modeling times when no extremes occur [the false positive rate (FPR)]
The most efficient detection threshold is one that maximizes the margin between the TPR and FPR, meaning that one gets the maximum number of true predictions with the least number of false alarms
Summary
Extreme precipitation events from subhourly to multiday time scales are driven by specific weather processes and drivers. Despite the improved realism in representing extreme precipitation in CPMs, their computational costs are high; as a consequence, their use is limited to specific regions Such limited-area simulations are often termed dynamical downscaling as they are driven by lowerresolution reanalysis and GCM data. The predictors for extreme hourly precipitation should be consistent with what is known a priori, namely the importance of vertical instability and synoptic weather conditions They are diagnosed from the driving simulation to predict events in the downscaling CPM simulation. Selective CPM simulations are almost certain to miss some events [false negatives (FNs)] as it is impossible for our large-scale predictors to represent all drivers for extreme hourly precipitation. A discussion of the results and the main conclusions are presented in section 6, where we outline the significance of these results in the context of strategies and cost efficiencies for CPM dynamical downscaling
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