Spatial variability of cold waves over Southern Africa and their potential physical mechanisms

Abstract Despite significant evidence of global warming, cold waves still happen occasionally and are of great concern in regions such as Southern Africa as they are associated with negative impacts such as loss of lives, destruction of infrastructure, loss of crops and livestock and high peak demand of electricity consumption. In this study, we examine the cold wave characteristics over the southern African region for the extended austral winter (May to September) over the 1979–2021 period, using minimum temperature from the European Centre for Medium-Range Weather Forecasts and Berkeley Earth Surface Temperature reanalysis products. Cold waves are calculated using the excess cold factor. Principal component analysis was performed on the cold waves to extract their dominant modes. Linear regression, Spearman rank correlation as well as lagged correlations were employed to find the links between the cold wave characteristics and different climate drives. Results from the study reveal that the lowest nighttime temperatures are found in interior South Africa and Lesotho. There is a decreasing trend in terms of the number of cold wave events over southern Africa, especially north of latitude 15˚S. Local and remote seasonal sea surface temperatures (SST) have the potential to change the likelihood of cold wave characteristics over Southern Africa. Also, seasonal mean circulation results contain seasonal mean ridge/trough structures, which suggests the role of these synoptic features and cold fronts in modulating cold waves over Southern Africa. A future study is needed to analyze the impact of these climate drivers at daily and intraseasonal timescales. There is moderate to high lead correlation between El Ninõ Southern Oscillation (ENSO) and some of the cold wave characteristics, hence ENSO, SST surrounding Southern Africa are good indicators for predicting cold waves over the region. Findings from this study can be useful in improving weather and climate forecasting over the region. Also, these results can support local authorities and communities at risk in developing early actions for tackling cold waves.