Verification of operational Niño3.4 SST forecasts produced in South Africa since the 2015 El Niño event

Abstract

The production of operational seasonal forecasts in South Africa began in the early 1990s, as South African modellers published numerous papers describing the research and development supporting these forecast systems. While this effort focused largely on seasonal rainfall and temperature predictability over southern Africa, work has also gone into predictions of global sea-surface temperatures (SSTs), including predictions for the central Pacific Ocean, and particularly the ENSO-related Niño3.4 region. Here we present verification statistics of archived real-time Niño3.4 SST forecasts from multi-model forecasting systems developed respectively at the Council for Scientific and Industrial Research and at the University of Pretoria, both based in South Africa. These forecasting systems used forecasts produced by fully-coupled ocean-atmosphere models administered in the USA, and also by statistical models developed locally. Archived Niño3.4 SST forecast data are available continuously from 2015. The verification presented here covers a 9-year period beginning with forecasts for the 2015/16 El Niño event and ending with the 2023/24 El Niño event. In general, Niño3.4 forecast skill is limited during the boreal spring months and optimized during the boreal winter period when forecast variance is also largest. During boreal winter, probabilistic forecasts are able to discriminate between the El Niño, neutral and La Niña ENSO phases. Predictability of El Niño events is found to be highest of the three phases, with the lowest predictability for ENSO-neutral. Moreover, probability forecasts for El Niño and La Niña events are found to be mostly under-confident for high probability forecasts, and probabilities for neutral events are overestimated. A potential improvement in the probabilistic forecasts may be achieved by designing the climatological frequencies of the three forecast ENSO categories to match the observational definition based on ± 0.5 °C cutoffs.