Understanding a high resolution regional climate model's ability in simulating tropical East Africa climate variability and change

Doctoral Thesis


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University of Cape Town

The main aim of this thesis is to investigate the potential benefits of increasing resolution in regional climate models in the simulation of climate variability and change over East Africa. This study is based on two high resolution regional climate simulations with a horizontal resolution of 50km and 10km, respectively. These represent present day climate and a projection of future climate change over East Africa. The regional climate model (RCM) used here is HIRHAM5, which is driven by the global circulation model (ECHAM5). Downscaled ECHAM5 output is used to drive the 50km HIRHAM5 simulation for the period 1950-2100, and output from this simulation is used to drive the 10km simulation for three time slices: 1980-1999, representative for present-day climate and two time slices for near future (2046-2065) and far future (2080- 2099), respectively. HIRHAM5 is evaluated with respect to the observed mean climatologies of rainfall, surface temperature and surface winds over East Africa, and representations of the observed annual cycles and inter-annual variability of rainfall and surface temperature. This study utilizes reanalysis and observational datasets: a hindcast of HIRHAM5 forced with ERA Interim, as well as two observation datasets for temperature and rainfall. Since reanalyses aim to make "best use" of all available observations by making a physically consistent representation continuous in time and space, and since there is a paucity of observations over many parts of Africa, the ERAI reanalysis is also used as a best estimate for model evaluation. Additionally, for evaluation of the bimodal nature of East Africa's rainfall, especially over Tanzania, three stations run by the Tanzania Meteorological Agency were used. The model data used in th is evaluation ranges from 1980 to 2006 iv HIRHAM5 demonstrates reasonable skill in the reproduction of observed patterns of mean climatology of rainfall, surface temperature and winds over East Africa. Moreover, the patterns of annual cycles of rainfall and surface temperature in the bimodal nature of East Africa are well represented. Furthermore, the model showed reasonable skill in the representation of the inter- annual variability and ENSO signals as suggested by the observation. Despite these strengths, HIRHAM5 shows some shortcomings. One weakness of the model is the simulation of the magnitude of a given variable over a specific region. For example, HIRHAM5 driven by ERAI underestimates rainfall and overestimates surface temperature over the entire domain of East Africa. The higher resolution HIRHAM5 (10km resolution) overestimates rainfall over high ground. The model bias could be due in part to the inadequacy of the observation networks in East Africa, represented in this thesis by the CRU and FEWS datasets. However, these two datasets draw on some different sources and neither do they have the same resolution. FEWS is a high resolution data (0.1 o ) gridded satellite-derived precipitation estimate covering the entire African continent while CRU datasets is a relatively low resolution (0.5 o ) dataset based on rain gauge monthly precipitation only; in addition , near surface temperature is also available. As no reliable wind observations exist, wind data was taken from the ERA-Interim reanalysis. The different observational datasets do not agree particularly well, which impedes evaluating the quality of the HIRHAM5 simulations, in particular the high resolution one. So while the higher resolution HIRHAM5 appears to be generally reliable, caution must be exercised in formulating conclusions from the results, especially over high ground and remote areas without adequate observation data. Under these constraints, the results suggest HIRHAM5 may be useful for assessing climate variability and change over East Africa. A weakness of the analysis presented here is that only one combination of GCM and RCM could be investigated in depth due to computer and time constraints. Therefore the results presented here, if used in application for climate change adaptation, should be considered in conjunction with a broader suite of data, such from the CORDEX programme. This has potential to increase the reliability of information about climate variability and change at a regional to local level necessary for impact assessment.

Includes bibliographical references