Characterising and modelling time-varying rainfall extremes and their climatic drivers

Abstract

Extreme climate responses such as floods or droughts pose multi-dimensional hazards to critical infrastructure and the most vulnerable sectors of society; these hazards may increase under climate change. The extreme rainfall events driving these responses may arrive non-uniformly in time, clustering on intra- and inter-annual scales; yet the dependent relationship between events is often ignored. This thesis examines extreme daily rainfall within year clustering to determine whether changes in their temporal pattern are apparent in observational records. It then identifies the key atmospheric variables which drive event frequency and intensity, before testing hypotheses related to clustering. Extreme rainfall regions were developed from the station maxima of a comprehensive new set of 223 daily rainfall observations, spanning the period 1856-2009. The observations are contained within 14 regions which represent the distinctive seasonal clustering, orographic and atmospheric variations in UK extreme rainfall. Significant increases in annual maxima and associated return frequencies over the period 1961-2009 were observed from a Generalized Extreme Value (GEV) analysis. Increases in spring, autumn and winter maxima and their estimated return frequencies were also found. Estimates from summer maxima were variable across the country but indicated an increase in the highest intensity events. Extreme rainfall seasonal clustering and the dependence on sea surface temperatures (SST), air temperature range and the North Atlantic Oscillation (NAO) were represented in flexible GEV and Poisson parameter estimates using Vector Generalized Additive Models. There is a strong negative correlation with air temperature range, reflecting heightened event intensity and probability when the diurnal temperature range is at its lowest. Event frequency is positively correlated with SST for all regions; event magnitude is dependent on either SST or NAO with a north-south divide. While the timing of events has not changed substantially, event probability has increased - resulting in greater within-year clustering. Climate projections indicate increasing SST and decreasing temperature range; this extreme rainfall model corroborates projected increases in event intensity and frequency.