Abstract: Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.

Abstract: This review examines relevant impact assessments identified by a literature search from 1991to date. A bibliographic search was applied to the CAB Abstracts database with a given searchstring. Resultant papers were checked for relevance, based on expert judgment. This yielded 91 papers, which were subjected to further analysis. Firstly, publication intensity over time and distribution by geographic location and cereal crop were examined. Next, for a given crop, the assessments and their outcomes were grouped by type and number of the change variables considered – that is, effects of climate change only, elevated CO 2 and technological progress(improved breeds, management). Finally, separately for individual countries/subregions and Europe as a whole, we examined whether and to what extent study results have changed over time, for example become more positive/negative. Based on our sample, we found that publication intensity increased exponentially during thelast 4 years, the majority of studies are Europe-wide, but some concentrated on a few countries(Italy, Spain and UK), whereby studies on wheat are clearly most popular. Taking the factor of technological progress into account has an overruling influence on results. Finally, over time, projected yield impacts have become more negative. This is in line with finding from global analyses, as reflected by the most recent comparison of agricultural impact chapters, of the 4thand 5th Assessment Reports of Intergovernmental Panel on Climate Change, Working Group II.In the future, there is particular need to consider impacts under various incremental and transformational adaptation measures in more depth (e.g. their interconnections across scales)and with more breadth (e.g. anticipated new breeds). Follow-up reviews should also examine how projected impacts are changing with the new climate scenario data sets (CMIP5) and with improved impact models and assessment approaches.

Abstract: To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.