Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors

Drivers of interannual variability of the East African ‘Long Rains’


The East African Long Rains season is unusual in that its year‐to‐year rainfall variability is mostly insensitive to the main modes of interannual tropical SST variability (ENSO, Indian Ocean dipole). Various alternative drivers of interannual variability have been described previously but remain poorly understood. Here we present an analysis of three important drivers: regional Indian Ocean SST, seasonal amplitude of the Madden‐Julian oscillation (MJO) and phase of the quasi‐biennial oscillation (QBO). Reanalyses and instrumental datasets are in close agreement about rainfall interannual variability across the region as a whole, which represents 30‐50% of the total variance. Sub‐regional structure of the remaining variance is far more uncertain and is not considered here. We use modern reanalyses to understand how the proposed drivers affect March‐April mean. Common to all three drivers is their ability to modify the large‐scale subsidence over the East African region during boreal spring. SST in the western Indian Ocean achieves this via anomalous boundary layer heating of the lower troposphere. The MJO modifies subsidence over the region through anomalous ascent and descent. Rainfall over East Africa responds to this MJO forcing in a uni‐directional way, allowing seasonal rectification and interannual modulation by seasonal MJO amplitude. Understanding the QBO’s influence is complicated by the limited number of cycles over the reanalysis period. Each driver individually has a modest effect on the Long Rains, but added together they explain 30‐60% of the variance of yearly rainfall variability that affects the region as a whole. This constitutes 13‐25% of the total interannual precipitation variance, depending on dataset. The mechanisms we discuss suggest priorities for model development to improve model variability over East Africa. The metrics developed here lend themselves for easy evaluation of the remote drivers in models and other datasets.