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ABSTRACT
Global climate and weather models are a key tool for the prediction of future crop productivity, but they all rely on parameterizations of atmospheric convection, which often produce significant biases in rainfall characteristics over the tropics. The authors evaluate the impact of these biases by driving the General Large Area Model for annual crops (GLAM) with regional-scale atmospheric simulations of one cropping season over West Africa at different resolutions, with and without a parameterization of convection, and compare these with a GLAM run driven by observations. The parameterization of convection produces too light and frequent rainfall throughout the domain, as compared with the short, localized, high-intensity events in the observations and in the convection-permitting runs. Persistent light rain increases surface evaporation, and much heavier rainfall is required to trigger planting. Planting is therefore delayed in the runs with parameterized convection and occurs at a seasonally cooler time, altering the environmental conditions experienced by the crops. Even at high resolutions, runs driven by parameterized convection underpredict the small-scale variability in yields produced by realistic rainfall patterns. Correcting the distribution of rainfall frequencies and intensities before use in crop models will improve the process-based representation of the crop life cycle, increasing confidence in the predictions of crop yield. The rainfall biases described here are a common feature of parameterizations of convection, and therefore the crop-model errors described are likely to occur when using any global weather or climate model, thus remaining hidden when using climate-model intercomparisons to evaluate uncertainty.
1. Introduction
Agricultural systems are inherently vulnerable to climate variability. This is particularly true in Africa where a large fraction of crop production is rainfed (Cooper et al. 2008), making climate a key driver of food security (Gregory et al. 2005). Climate change is predicted to further increase the vulnerability and uncertainty of crop production in Africa, with most studies showing a negative impact, although the magnitude of this impact varies significantly among studies (Challinor et al. 2007; Schlenker and Lobell 2010; Thornton et al. 2011; Knox et al. 2012).
To predict climate-change impacts on crops it is essential to correctly capture the complex and dynamic relationship between crops and climate. Yields can be affected by changes in temperature, water vapor, radiation, and rainfall (Schlenker and...