Scale-interaction of convection and its role in key African meteorological processes using novel observations and model simulations.

Across Africa, many regions are vulnerable to climate change and variability, yet large gaps remain in our understanding of regional climate systems, particularly due to a lack of observational data over many areas of the continent (James et al., 2018). Projections of future change are generally provided by low resolution global climate models, many of which fail to accurately reproduce present-day climatology.

For example, over southern Africa, some climate models overestimate precipitation rates by as much as 300% (Munday and Washington, 2018) and over East Africa, biases in the rainfall annual cycle result from failures to represent the mean state of flow over the Indian Ocean (Hirons and Turner, 2018). Such inaccuracies cast doubt over the ability of models to predict how these regional climates will change in the future.

My research aims to explore the role of convection in meteorological processes using novel models and observations alongside evaluating the added value of convective-scale models. There is scope to analyse the differences between the representation of regional circulations across model, reanalysis and observational datasets to infer the effect of model resolution and convection parameterisation on the simulation of the climate system.

Such model evaluation projects are vital to interrogate modelled trends of climate change, such as the anticipated shortening of the rainy season over southern Africa, by giving an indication of the processes that underlie signals of change (Dunning et al., 2018). Alongside this, these high-resolution datasets present an opportunity to explore the role of convection in regional climate dynamics, which may contribute to a better understanding of the processes controlling current climate variability.

Such developments are of significant socioeconomic importance, as water, energy and food security are often tightly coupled to regional climate (Conway et al., 2015).