Towards bridging the gap between climate change projections and maize producers in South Africa

Loading...
Thumbnail Image

Authors

Landman, Willem Adolf
Engelbrecht, Francois
Hewitson, Bruce
Malherbe, Johan B.
Van der Merwe, Jacobus

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

Abstract

Multi-decadal regional projections of future climate change are introduced into a linear statistical model in order to produce an ensemble of austral mid-summer maximum temperature simulations for southern Africa. The statistical model uses atmospheric thickness fields from a high-resolution (0.5° × 0.5°) reanalysis-forced simulation as predictors in order to develop a linear recalibration model which represents the relationship between atmospheric thickness fields and gridded maximum temperatures across the region. The regional climate model, the conformal-cubic atmospheric model (CCAM), projects maximum temperatures increases over southern Africa to be in the order of 4 °C under low mitigation towards the end of the century or even higher. The statistical recalibration model is able to replicate these increasing temperatures, and the atmospheric thickness–maximum temperature relationship is shown to be stable under future climate conditions. Since dry land crop yields are not explicitly simulated by climate models but are sensitive to maximum temperature extremes, the effect of projected maximum temperature change on dry land crops of the Witbank maize production district of South Africa, assuming other factors remain unchanged, is then assessed by employing a statistical approach similar to the one used for maximum temperature projections.

Description

Keywords

Conformal-cubic atmospheric model (CCAM), Skill, Frequency, Forecasting, Atmospheric thickness fields, Gridded maximum temperatures, Climate change projections

Sustainable Development Goals

Citation

Landman, W.A., Engelbrecht, F., Hewitson, B. et al. Towards bridging the gap between climate change projections and maize producers in South Africa. Theoretical and Applied Climatology (2018) 132: 1153-1163. https://doi.org/10.1007/s00704-017-2168-8.