Assessing climate data for studies of climate change impacts: A case study of wheat cropping in New South Wales

Abstract

Wheat yields for New South Wales, Australia simulated by the APSIM agricultural model are used as a case study to investigate how the suitability of climate datasets for input into complex impact models can be assessed. Climate forcing datasets for APSIM are generated from the output of multiple climate models using three different types of method using unprocessed climate output, statistically correcting climate model output and perturbing observations of the recent climate with simulated future climate changes.

The application of multiple processing methods to multiple climate model simulations generates a range of different climate datasets. Simulated impact changes are sensitive to differences in future changes in the climate between these datasets. In addition, simulated impact changes can be sensitive to differences in the representation of the recent climate. These arise because different climate models have different errors in their simulations of the real climate and different processing methods correct these to different extents.

The thesis takes an impact-based approach to assessing different climate datasets. It compares wheat yields for a recent time period from APSIM simulations forced with datasets derived from climate models with yields from APSIM simulations forced with climate observations and addresses the question:

To what extent can impact-based assessment be used to evaluate climate forcing datasets for a complex crop model?

While the impact-based approach reveals those combinations of climate models and processing methods that result in large errors in simulated recent yields, there is no strong overall relationship between errors in simulated recent yields and future changes in yields. The approach is therefore not useful in determining which climate models and processing methods give the most plausible simulated future changes in yields. This thesis therefore concludes that, to sample uncertainty, impact studies should ideally apply multiple processing methods to many climate models.