Simulations by the atmospheric model intercomparison project of atmospheric circulation over southern Africa

Joubert, A. M.

doi:10.1002/(sici)1097-0088(199709)17:11<1129::aid-joc167>3.0.co;2-7

Cited by 17 publications

(4 citation statements)

References 43 publications

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“…1d), but the simulated amount is about twice as large as the observed, because the simulated moisture fluxes to the subcontinent in the lower troposphere are much stronger and extend farther to the west compared to the reanalysis data. Similar wet biases have been reported in many general circulation and regional models (e.g., Joubert 1997;Ratnam et al 2011;Crétat et al 2012). To exclude the model biases in the climatology, predicted anomalies are verified against the observations after removing the monthly climatology in each dataset (Kirtman et al 1997).…”

Section: Verification Data and Methodssupporting

confidence: 60%

Dynamical seasonal prediction of Southern African summer precipitation

et al. 2013

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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

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Section: Verification Data and Methodssupporting

confidence: 60%

Dynamical seasonal prediction of Southern African summer precipitation

et al. 2013

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“…It found that convective rainfall was poorly simulated by all GCMs, and performance appeared to be seasonally dependent (Joubert, 1997). To investigate the integrity of GCM results specifically for the Kalahari, the four GCMs described above were compared to the CRU observed data set with temperature being the most consistently modelled parameter yielding high regression relationships for all GCMs (Fig.…”

Section: Gcm Accuracymentioning

confidence: 99%

Challenges of calculating dunefield mobility over the 21st century

2004

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“…Since general circulation models (GCMs) are based on the physical laws governing the processes within the climate system, with improved and thorough representation of the real physics, there is room for further improvement in forecast skill. GCMs have demonstrated skill in simulating large-scale atmospheric features in the southern African region (Joubert, 1997), but are not equally skilful at much smaller spatial scales. The perceived failure of climate models on the local scale can be attributed in part to the coarse horizontal resolution whereby the structure of the earth's surface is not adequately described in the models.…”

Section: Introductionmentioning

confidence: 99%

Performance of recalibration systems for GCM forecasts for southern Africa

Shongwe

Landman

Mason

2006

Intl Journal of Climatology

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Two regression-based methods that recalibrate the ECHAM4.5 general circulation model (GCM) output during austral summer have been developed for southern Africa, and their performance assessed over a 12-year retroactive period 1989/90-2000/01. A linear statistical model linking near-global sea-surface temperatures (SSTs) to regional rainfall has also been developed. The recalibration technique is model output statistics (MOS) using principal components regression (PCR) and canonical correlation analysis (CCA) to statistically link archived records of the GCM to regional rainfall over much of Africa, south of the equator. The predictability of anomalously dry and wet conditions over each rainfall region during December-February (DJF) using the linear statistical model and MOS models has been quantitatively evaluated. The MOS technique outperforms the raw-GCM ensembles and the linear statistical model. Neither the PCR-MOS nor the CCA-MOS models show clear superiority over the other, probably because the two methods are closely related. The need to recalibrate GCM predictions at regional scales to improve their skill at smaller spatial scales is further demonstrated in this paper.

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Simulations by the atmospheric model intercomparison project of atmospheric circulation over southern Africa

Cited by 17 publications

References 43 publications

Dynamical seasonal prediction of Southern African summer precipitation

Dynamical seasonal prediction of Southern African summer precipitation

Challenges of calculating dunefield mobility over the 21st century

Performance of recalibration systems for GCM forecasts for southern Africa

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