Abstract: Data quality is a key factor in determining the quality of model estimates and hence a models’ overall utility. Good models run with poor quality explanatory variables and parameters will produce meaningless estimates. Many models are now well developed and have been shown to perform well where and when good quality data is available. Hence a major limitation now to further use of models in new locations and applications is likely to be the availability of good quality data. Improvements in the quality of data may be seen as the starting point of further model improvement, in that better data itself will lead to more accurate model estimates (i.e. through better calibration), and it will facilitate reduction of model residual error by enabling refinements to model equations. This report sets out why data quality is important as well as the basis for additional investment in improving data quality. No Label

Abstract: The purpose of this report is to propose a general procedure for sensitivity analysis when used to evaluate system sensitivity to climate change, including uncertainty information. While sensitivity analysis has been largely used to evaluate how uncertainties in inputs or parameters propagate through the model and manifest themselves in uncertainties in model outputs, there is much less experience with sensitivity analysis as a tool for studying how sensitive a system is to changes in inputs. This report should help make clear the differences between these two uses of sensitivity analysis, and provide guidance as to the procedure for using sensitivity analysis for evaluating system sensitivity to climate change. No Label

Abstract: In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate between the sources of uncertainty in climate models and how these lead to errors in estimating the past climate and biases in future projections, and how these affect crop model estimates. This paper investigates the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed, original (50•50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 Regional Climate Model (RCM). Original and bias corrected downscaled weather data were evaluated against the observed data. The comparisons made between the crop models were in the light of lessons learned from this data evaluation. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop models estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite differences in the weather data, giving a situation of ‘right results for the wrong reasons’. This was likely due to compensating errors in the input weather data and non-linearity in crop models processes, making interpretation of results problematic. Overall, bias correction downscaling improved the quality of simulated outputs. Understanding how biases in climate data manifest themselves in crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections. The results indicate implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles. No Label