Browsing by Author "Pinto, Izidine S de Sousa"

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    Future changes in extreme events in Mozambique as simulated using the PRECIS regional climate modeling system
    (2011) Pinto, Izidine S de Sousa; Tadross, Mark; Hewitson, Bruce
    Future climate change is generally believed to lead to an increase in climate variability and inthe frequency and intensity of extreme events. Mozambique is well known for its occurrenceof severe weather and extreme climate events such as floods, tropical cyclones and droughts.Such events have serious impacts on the livelihoods of most people who often rely on subsistence agriculture.This dissertation explores possible changes in extremes in temperature and precipitation over Mozambique, based on high-resolution (25 km) simulations of the regional climate model system PRECIS (HadRM3P), forced by the ECHAM4 global mode.
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    Future changes in extreme rainfall events and circulation patterns over southern Africa
    (2015) Pinto, Izidine S de Sousa; Lennard, Chris; Tadross, Mark; Hewitson, Bruce
    Changes in precipitation extremes are projected by many global climate models as a response to greenhouse gas increases, and such changes will have significant environmental and social impacts. These impacts are a function of exposure and vulnerability. Hence there is critical need to understand the nature of weather and climate extremes. Results from an ensemble of regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project are used to investigate projected changes in extreme precipitation characteristics over southern Africa for the middle (2036-2065) and late century (2069-2098) under the representative concentration pathway 4.5 (RCP4.5) and 8.5 (RCP8.5). Two approaches are followed to identify and analyze extreme precipitation events. First, indices for extreme events, which capture moderate extreme events, are calculated on the basis of model data and are compared with indices from two observational gridded datasets at annual basis. The second approach is based on extreme value theory. Here, the Generalized Extreme Value distribution (GEV) is fitted to annual maxima precipitation by a L-moments method. The 20-year return values are analyzed for present and future climate conditions. The physical drivers of the projected change are evaluated by examining the models ability to simulate circulation patterns over the regions with the aid of Self-Organizing Maps (SOM).