Dynamical seasonal prediction of Southern African summer precipitation

Abstract

Prediction skills for southern African (16˚-33˚E, 22˚-35˚S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982-2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. It is shown that these scores for forecasts of December-February precipitation initialized on October 1 st are significant at 95%confidence level. On a local scale, the prediction skills in the northwestern and central parts of southern Africa are higher than those in northeastern South Africa. El Niño/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is over-confident in the model. Also, the Benguela Niño, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern IndianOceans and ENSO Modoki may provide additional sources of predictability. When prediction skills are evaluated for the whole wet season from October to the following April, it is found that precipitation anomalies in December-February are most predictable. The present study presents promising results for seasonal prediction of precipitation anomalies in the extratropics, where seasonal forecast are considered a difficult task.3

A model study of regional air‐sea interaction in the austral summer precipitation over southern Africa

“…The austral summer precipitation over southern Africa and its variations have been modeled using both the global models [ Joubert and Hewitson , ; Mason and Joubert , ; Landman et al ., ; Landman and Beraki , ; Yuan et al ., ; Beraki et al ., ] and regional models [ Joubert et al ., ; Hansingo and Reason , ; Tadross et al ., ; Kagtuke et al ., ; MacKellar et al ., ; Crétat et al ., ; Sylla et al ., ; Ratnam et al ., ; Ratna et al ., ; Pohl et al ., ]. Few studies [ Landman and Beraki , ; Beraki et al ., ] indicate that using fully coupled global models improves the spatial and temporal precipitation over southern Africa compared to using the two‐tier approach of specifying observed/forecast SSTs to the atmospheric models.…”

Seasonal rainfall predictability over the Lake Kariba catchment area

“…For no-skill forecasts the area would be ≤0.5 and for perfect discrimination the ROC score is 1.0. When there is consistency between the predicted probabilities of the extreme rainfall categories and the observed relative frequencies of the observed rainfall being assigned to these categories, then the downscaled hindcasts are considered reliable (Yuan et al, 2014). We also performed some verification on deterministic downscaled hindcasts.…”

Improvements to the WRF Seasonal Hindcasts over South Africa by Bias Correcting the Driving SINTEX-F2v CGCM Fields

“…However, the SINTEX-F2v forecast precipitation could well capture the east-west gradient in precipitation over South Africa similar to what is observed in the verifying datasets. The earlier version, SINTEX-F1, also had a similar wet bias in precipitation forecasts over South Africa (Yuan et al 2014). Similar to SINTEX-F2v, the ensemble mean WRFsint (Fig.…”

“…However, Landman et al (2012) found that the CGCM has improved skill in forecasting seasonal precipitation over southern Africa compared to the two-tier approach of forcing the atmosphere-only general circulation models (GCMs) by predictive sea surface temperature (SST). Yuan et al (2014) found skill in forecasting precipitation over South Africa in the retrospective forecasts of a CGCM called Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F1; Luo et al 2005). However, the skill in the forecast precipitation was largely confined to the central and western parts of South Africa that receive relatively less precipitation during the season.…”