Atmospheric drivers of rainfall events in the Republic of Djibouti

Abstract

AbstractThe Republic of Djibouti is a small country in an arid context coupled with a high variability of rainfall that generates flash floods causing severe damage to the population and infrastructure. The mechanisms controlling extreme rainfall events in this part of the Horn of Africa remain poorly understood. In this study, we document the atmospheric circulation patterns associated with such events. To that end, we use rain‐gauge data (a network of 36 stations on the period 2013–2020), satellite‐based rainfall estimates (CHIRPS, IMERG, MSWEP and RFE) and atmospheric reanalyses (ERA5), all at the daily timescale, over their common period 2001–2020. A multivariate Agglomerative Hierarchical Clustering of rainy days in Djibouti (≥10% of grid‐points exceeding 1 mm·day−1, according to all four satellite products) reveal four clusters, which differentiate from each other by the intensity and spatial extent of rainfall. These clusters show a nonhomogeneous seasonal distribution, occurring mainly in the March–April–May (MAM) and July–August–September (JAS) seasons, and more rarely in October–November–December (OND). The atmospheric circulation anomalies associated with the clusters are quite similar and highly season‐dependent. In MAM most clusters display an anomalous trough over the Red Sea, from 700 to 200 hPa. In JAS, an anomalous low over the southern Red Sea drives a thicker than normal monsoon flow at 700 hPa, while upper northerlies prevail at 200 hPa. In OND, most rainy events result from moisture advection from the Western Indian Ocean, favoured by positive phases of the Indian Ocean Dipole. Some highly unusual atmospheric circulation patterns (e.g., associated with tropical cyclones) can also result in intense rainfall events in Djibouti. These findings provide new insights on the physical causes of extreme events in the Horn of Africa, with applications to improved early warning and skill evaluation of climate models for climate change projections.