Non-Linearity in Climate Change Impact Assessments

Abstract

Crop growth simulation models have been used to predict the consequences of climate change for cereal growth and yield (Adams et al., 1990). Two significant uncertainties in impact assessments result from the use of possible future climate scenarios as inputs to crop models. First, although based on the same physical principles, General Circulation Models (GCMs) provide individual estimates of the global climate and thus GCMs predict a variety of expected climate changes. We question the rationale of formulating average or 'composite' climate change scenarios when used in combi- nation with the many non-linear responses of crops to their environment. Secondly, uncertainty also arises from the way in which weather scenarios are constructed from GCMs. Future climatic scenarios, derived from GCMs, have described changes in mean weather (Kenny, Harrison & Parry, 1993) but non-linear crop models require explicit incorporation of changes in climatic variability to assess the risks to agricul- tural production from climate change. Accordingly, we cou- pled a wheat crop simulation model (AFRCWHEAT2) with a stochastic weather generator (LARS-WG) and report that modelled changes in temperature variability may have more profound effect on simulated grain yield than changes in its mean value.