Lookup NU author(s): Dr Steven Chan,
Dr Stephen Blenkinsop,
Professor Hayley Fowler
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2018 American Meteorological Society. Midlatitude 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.
Author(s): Chan SC, Kendon EJ, Roberts N, Blenkinsop S, Fowler HJ
Publication type: Article
Publication status: Published
Journal: Journal of Climate
Online publication date: 14/02/2018
Acceptance date: 30/11/2017
Date deposited: 23/04/2018
ISSN (print): 0894-8755
ISSN (electronic): 1520-0442
Publisher: American Meteorological Society
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