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The state of the El Niño–Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts. [ABSTRACT FROM AUTHOR]

One of the primary sources of predictability for seasonal hydroclimate forecasts are sea surface temperatures (SSTs) in the tropical Pacific, including El Niño–Southern Oscillation. Multiyear La Niña events in particular may be both predictable at long lead times and favor drought in the bimodal rainfall regions of East Africa. However, SST patterns in the tropical Pacific and adjacent ocean basins often differ substantially between first- and second-year La Niñas, which can change how these events affect regional climate. Here, we demonstrate that multiyear La Niña events favor drought in the Horn of Africa in three consecutive seasons [October–December (OND), March–May (MAM), OND]. But they do not tend to increase the probability of a fourth season of drought owing to the sea surface temperatures and associated atmospheric teleconnections in the MAM long rains season following second-year La Niña events. First-year La Niñas tend to have both greater subsidence over the Horn of Africa, associated with warmer waters in the west Pacific that enhance the Walker circulation, and greater cross-continental moisture transport, associated with a warm tropical Atlantic, as compared to second-year La Niñas. Both the increased subsidence and enhanced cross-continental moisture transport favors drought in the Horn of Africa. Our results provide a physical understanding of the sources and limitations of predictability for using multiyear La Niña forecasts to predict drought in the Horn of Africa. [ABSTRACT FROM AUTHOR]

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA), Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP); however, these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we evaluate the quality of a 30+ year time series of merged active–passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM) over East Africa. Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we find substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies are well correlated ( R > 0.5) with modeled soil moisture, and in some regions, NDVI. We use pixel-wise correlation analysis and qualitative comparisons of seasonal maps and time series to show that remotely sensed soil moisture can inform remote drought monitoring that has traditionally relied on rainfall and NDVI in moderately vegetated regions. [ABSTRACT FROM AUTHOR]