Decadal and multidecadal natural variability of African rainfall

Study region: Africa Study focus: African rainfall shows significant year-to-year natural fluctuations that in part are linked to teleconnections associated with modes of variability in the Atlantic, Pacific and Indian oceans. A better understanding of African rainfall variability and potential drivers would help to better prepare African societies for anticipated droughts and floods by taking early precautionary action. Here we are presenting the first continent-wide analysis of African rainfall variability on a month-by-month and country-by-country basis. We have calculated Pearson r values for smoothed monthly rainfall data of 49 African countries over the period 1901-2017 which we compared to six potential climatic drivers of natural variability, namely AMO, NAO, ENSO (El Nino Southern Oscillation), Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD) and solar activity changes. We allowed time lags of up to 11 months for each potential driver (66 months for solar activity). New hydrological insights for the region: The dynamic temporal-spatial evolution of the seasonal Pearson correlations was mapped out across the continent, tracking the gradual or abrupt expansion, displacement and subsequent waning of the various effects over the course of the year. Relationships are complicated by characteristic time lags, non-stationary correlations and occasional phase shifts. Our empirical results may help to further improve short- to midterm rainfall prognoses in Africa and provide important calibration data for the further improvement of climate models.

Automated summary

This study conducted a continent-wide analysis of African rainfall variability on a month-by-month and country-by-country basis, examining the impacts of multiple potential climatic drivers on rainfall. The results show complex relationships between African rainfall and climate change drivers, which can help improve short- to midterm rainfall forecasts in Africa and provide important calibration data for further improvements in climate models.