This thesis presents an informatics-based approach to dissect observed or simulated meteorological data by the synoptic-scale weather conditions, and the local-scale geographical characteristics of the data locality relative to the direction of synoptic alr-flow. The approach can be used to investigate climate variability. or to assess model performance, and is applied in two contrasting study areas; Cumbria, northwest England, for 400" rainfall gauges, and South Georgia, sub-Antarctica, for a single temperature record. For Cumbria, the method investigates patterns in the frequencies and characteristics of precipitating weather types for six distinct sub-regions defined using GIS techniques; background-coastal, windward-lowland, windward-upland, leeward-upland, leeward-lowland and secondary-upland. From 1961-2007, the total winter rainfall associated with south-westerly and westerly weather types Increased, particularly In upland regions. These increases result from an increased frequency of these weather types, and for westerlies, a change In the weather type characteristics towards higher rainfall rates. This change is linked to higher mean temperatures and wind speeds associated with the positive trend In the North Atlantic Oscillation Index. Additionally, the performance of the Weather Research and Forecasting model is evaluated for westerly weather types for different conditions of temperature, wind speed, and relative humidity. The model under-predicts the daily rainfall rate in all sub-regions except the secondary-upland, where the model over-predicts, suggesting an incorrect representation of atmospheric processes. For South Georgia, analyses from 1920 to 2009 reveal how climatic changes between periods of glacier advance, glacier retreat, and the recent decade, are composed of alterations in the temperature and frequency of regional air-masses. The conditional method summarises large datasets In order to provide detailed insight into the relationship between local and synoptic-scale atmospheric processes. The method Is cost-effective, user-friendly, and portable between study areas of different sizes and data availability. As such, the scope for further applications is considerable.

Land/atmosphere feedback processes play a significant role in determining climate forcing on monthly to decadal timescales. Considerable uncertainty however exists in land surface model representation of these processes. This investigation represents an innovative approach to understanding key land surface processes in African savannahs in the framework of the UK‘s most important land surface model – the Joint UK Land Environment Simulator (JULES). Findings from an investigation into the carbon balance of Africa for a 25-year period from 1982 to 2006 inclusive show that JULES estimated Africa to behave as a carbon sink for most of the 1980‘s and 1990‘s punctuated by three periods as a carbon source, which coincided with the three strongest El Niño events of the period. From 2002 until 2006 the continent was also estimated to be a source of carbon. Overall, the JULES simulation suggests a weakening of the African terrestrial carbon sink during this period primarily caused by hot and dry conditions in savannahs. Applying the model further, land surface temperature (LST) displayed large uncertainty with respect to savannah field measurements from Kruger National Park, South Africa, and JULES systematically underestimated LST with respect to Earth Observation data continent-wide. The postulation was that a reduction in the uncertainty of surface-to-atmosphere heat and water fluxes could be achieved by constraining JULES simulations with satellite-derived LST using an Ensemble Kalman Filter. Findings show statistically significant reductions in root mean square errors with data assimilation than without; for heat flux simulations when compared with Eddy Covariance measurements, and surface soil moisture when compared with derivations from microwave scatterometers. The improved representation of LST was applied to map daily fuel moisture content – one of the most important wildfire determinants - over the mixed tree/grass landscapes of Africa, whereby values were strongly correlated with field measurements acquired from three savannah locations.

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