Elston, D.A., Rivington, M., Topp, C., Muhammed, S., Potts, J.M., Butler, A., Kettle, H., Baggaley, N.J., Rees, R.M. and Matthews, R.
Climate Research 66, 1-12.
Climate change impact assessments; Stratification; Crop models; Simulated yields; Uncertainty; Variance components
||The range of uncertainties inherent in climate models can only be portrayed by provision
of multiple climate projections. Unfortunately, such provision poses a challenge to model-based
impact studies, since driving the relevant impact models using weather data from large numbers of
climate projections may not be computationally feasible. Hence, it is important to investigate how to
draw sub-samples of climate projections in a manner that reduces the subsequent computational
burden. We describe a stratification-based protocol for sub-sampling climate projections to drive crop
models with strata based on changes in mean temperature and changes in relative mean rainfall. As
an example of the protocol's utility, simulated weather for each selected climate projection was used to
drive 3 contrasting process-based models of plant-environment interactions to predict yields of spring
barley, managed grassland, and short-rotation coppice. Many of the questions about potential impact
that we wish to answer are related to variation in predicted yields. Variance components analyses of
predicted yields for each of 2 time periods (2040s and 2080s) indicated that, after allowing for
variability between grid squares, between 16 and 61% of the remaining variance in annual yields was
uncertainty due to climate projections, the corresponding range for mean yields over 9 yr being from
63 to 93%. We found that our stratification procedure enhanced the precision in the estimate of the
variance component due to climate projection, enabling reductions of up to 20% in the number of
climate projections required to achieve equivalent precision compared to simple random sampling.