HR: 0800h
AN: GC11A-0123    [Abstracts]
TI: Extreme Rainfall Events Over Southern Africa: Assessment of a Climate Model to Reproduce Daily Extremes
AU: * Williams, C
EM: C.J.R.Williams@reading.ac.uk
AF: Walker Institute for Climate System Research, NCAS-Climate, Department of Meteorology, University of Reading, Reading, RG1 3GG, United Kingdom
AU: Kniveton, D
EM: D.R.Kniveton@sussex.ac.uk
AF: University of Sussex, Chichester Building 1, Department of Geography, University of Sussex, Brighton, BN1 9RH, United Kingdom
AU: Layberry, R
EM: russell.layberry@ouce.ox.ac.uk
AF: Oxford University Centre for the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, United Kingdom
AB: It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable extreme events, due to a number of factors including extensive poverty, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of a state-of-the-art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of SST anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the UK Meteorological Office Hadley Centre's climate model's domain size are firstly presented. Then simulations of current climate from the model, operating in both regional and global mode, are compared to the MIRA dataset at daily timescales. Thirdly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. Finally, the results from the idealised SST experiments are briefly presented, suggesting associations between rainfall extremes and both local and remote SST anomalies.
DE: 1600 GLOBAL CHANGE
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1626 Global climate models (3337, 4928)
DE: 1637 Regional climate change
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting