Climate change in the Congo basin: evaluating coupled models - Creese, A (2018)
The convective region of the Congo Basin is a vital component of both the tropical and global circulation, but despite its importance, the climate system is the least studied of any African region. This is due in part to the severe scarcity of recent observational weather data, which in turn has consequences for the use of climate models for providing reliable future projections. Models are usually tested on their ability to reproduce the historical climate, but this approach is unsuitable where observations are lacking. To circumvent this issue, this thesis develops an alternative methodology of model evaluation which does not rely on observational data. This regionally-focussed, process-based approach determines the credibility of multiple state-of-the-art coupled model (CMIP5) simulations of rainfall by assessing the representation of physical processes known to be important for rainfall. The models diverge vastly in the historical period over the Congo Basin, with mean climatological rainfall differing by a factor of five across models in some months. In all seasons, the amount of rainfall in the basin is positively correlated (p<0.05) with low-level moisture flux convergence over the basin and low-level moisture flux into the basin across certain boundaries, which with targeted observational data could be used to provide one constraint on the spectrum of model rainfall. In the September-November (SON) wet season, model wetness in the west Congo Basin relates to the strength of the tropical Atlantic SST bias, casting doubt on models which are extremely wet. In the east, model wetness is related to the strength and structure of the African Easterly Jets. There is also uncertainty amongst models as to the direction and magnitude of future rainfall change. A process-based assessment of future change in SON finds that in some areas, spurious climatologies in the historical period result in distinct future rainfall change signals, casting doubt on the plausibility of those changes. This thesis finds that a process-based approach to climate model evaluation can be used to help determine model credibility in the historical period in the absence of observational data, and is a promising method for constraining uncertainty in the future climate signal.