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| Titel |
The influence of synoptic airflow on UK daily precipitation extremes: observed spatio-temporal relationships |
| VerfasserIn |
D. Maraun, T. J. Osborn, H. W. Rust |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250027195
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| Zusammenfassung |
| Extreme precipitation is one of the major natural hazards in the
United Kingdom, and knowledge about its present and future occurrence
is needed with a high resolution to assess its impact on local scales
and to implement adaptation measures accordingly. Climate projections
of regional climate models (RCMs) provide resonable accuracy on scales
well above their typical resolution (25km-50km length scales). On grid
scales their output might be considerably biased, and they do not
provide information about subgrid processes. Thus it is important to
identify and understand stable relationships between processes on
large scales and extremes on local scales to downscale climate model
output to the desired resolution.
Against this background, we investigated the relationship between the
large scale atmospheric circulation and local extremes of daily
precipitation. We developed a vector generalised additive model that
uses synoptic scale airflow indices on extreme days to predict the
extreme value distribution of monthly precipitation maxima at 689 rain
gauges covering the UK. We found distinct spatial patterns of
relationships between the predictors airflow strength, direction and
vorticity on the one hand, and extreme precipitation on the other
hand. We also found that depending on region and season, the relative
importance of these predictors changes. For instance in winter,
extreme precipitation in north Scotland is dominated by airflow
strength, whereas in summer, East Anglian extreme precipitation is
dominated by the airflow direction. Furthermore, we investigated the
temporal variability of extreme precipitation for ten different UK
regions, and studied its predictability by air flow indices from
monthly to decadal time scales. |
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