Trends in the potential spread of seasonal climate simulations over South Africa

2014 ◽  
Vol 35 (9) ◽  
pp. 2193-2209 ◽  
Author(s):  
Kamoru A. Lawal ◽  
Dáithí A. Stone ◽  
Tolu Aina ◽  
Cameron Rye ◽  
Babatunde J. Abiodun
Keyword(s):  
South Africa ◽  
Seasonal Climate ◽  
Climate Simulations

Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

2009 ◽  
Vol 29 (11) ◽  
pp. 1535-1549 ◽  
Author(s):  
Willem A. Landman ◽  
Mary-Jane Kgatuke ◽  
Maluta Mbedzi ◽  
Asmerom Beraki ◽  
Anna Bartman ◽  
...  
Keyword(s):  
South Africa ◽  
Performance Comparison ◽  
Climate Modelling ◽  
Seasonal Climate ◽  
Empirical Downscaling

scholarly journals Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa

2005 ◽  
Vol 360 (1463) ◽  
pp. 2183-2194 ◽  
Author(s):  
Sukaina Bharwani ◽  
Mike Bithell ◽  
Thomas E Downing ◽  
Mark New ◽  
Richard Washington ◽  
...  
Keyword(s):  
South Africa ◽  
Food Security ◽  
Climate Variability ◽  
Crop Yields ◽  
Smallholder Farmers ◽  
Limpopo Province ◽  
Annual Rainfall ◽  
Climate Conditions ◽  
Agent Based ◽  
Seasonal Climate

Seasonal climate outlooks provide one tool to help decision-makers allocate resources in anticipation of poor, fair or good seasons. The aim of the ‘Climate Outlooks and Agent-Based Simulation of Adaptation in South Africa’ project has been to investigate whether individuals, who adapt gradually to annual climate variability, are better equipped to respond to longer-term climate variability and change in a sustainable manner. Seasonal climate outlooks provide information on expected annual rainfall and thus can be used to adjust seasonal agricultural strategies to respond to expected climate conditions. A case study of smallholder farmers in a village in Vhembe district, Limpopo Province, South Africa has been used to examine how such climate outlooks might influence agricultural strategies and how this climate information can be improved to be more useful to farmers. Empirical field data has been collected using surveys, participatory approaches and computer-based knowledge elicitation tools to investigate the drivers of decision-making with a focus on the role of climate, market and livelihood needs. This data is used in an agent-based social simulation which incorporates household agents with varying adaptation options which result in differing impacts on crop yields and thus food security, as a result of using or ignoring the seasonal outlook. Key variables are the skill of the forecast, the social communication of the forecast and the range of available household and community-based risk coping strategies. This research provides a novel approach for exploring adaptation within the context of climate change.

scholarly journals Dynamical Seasonal Climate Prediction Using an Ocean–Atmosphere Coupled Climate Model Developed in Partnership between South Africa and the IRI

2014 ◽  
Vol 27 (4) ◽  
pp. 1719-1741 ◽  
Author(s):  
Asmerom F. Beraki ◽  
David G. DeWitt ◽  
Willem A. Landman ◽  
Cobus Olivier
Keyword(s):  
South Africa ◽  
El Niño ◽  
El Nino ◽  
Coupled Model ◽  
Climate Prediction ◽  
La Niña ◽  
Seasonal Climate ◽  
Seasonal Climate Prediction ◽  
La Nina ◽  
Ocean Atmosphere

Abstract The recent increase in availability of high-performance computing (HPC) resources in South Africa allowed the development of an ocean–atmosphere coupled general circulation model (OAGCM). The ECHAM4.5-South African Weather Service (SAWS) Modular Oceanic Model version 3 (MOM3-SA) is the first OAGCM to be developed in Africa for seasonal climate prediction. This model employs an initialization strategy that is different from previous versions of the model that coupled the same atmosphere and ocean models. Evaluation of hindcasts performed with the model revealed that the OAGCM is successful in capturing the development and maturity of El Niño and La Niña episodes up to 8 months ahead. A model intercomparison also indicated that the ECHAM4.5-MOM3-SA has skill levels for the Niño-3.4 region SST comparable with other coupled models administered by international centers. Further analysis of the coupled model revealed that La Niña events are more skillfully discriminated than El Niño events. However, as is typical for OAGCM, the model skill was generally found to decay faster during the spring barrier. The analysis also showed that the coupled model has useful skill up to several-months lead time when predicting the equatorial Indian Ocean dipole (IOD) during the period spanning between the middle of austral spring and the start of the summer seasons, which reaches its peak in November. The weakness of the model in other seasons was mainly caused by the western segment of the dipole, which eventually contaminates the dipole mode index (DMI). The model is also able to forecast the anomalous upper air circulations, particularly in the equatorial belt, and surface air temperature in the Southern African region as opposed to precipitation.

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