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Wallach, D., & Rivington, M. (2015). Identification and quantification of differences between models (Vol. 6).
Abstract: A major goal of crop model inter-comparison is model improvement, and an important intermediate step toward that goal is understanding in some detail how models differ, and the consequences of those differences. This report is intended as a first attempt at describing possible techniques for relating differences between model outputs to specific aspects of the models. No Label
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Rivington, M., & Wallach, D. (2015). Quantified Evidence of Error Propagation (Vol. 6).
Abstract: Error propagation within models is an issue that requires a structured approach involving the testing of individual equations and evaluation of the consequences of error creation from imperfect equation and model structure on estimates of interest made by a model. This report briefly covers some of the key issues in error propagation and sets out several concepts, across a range of complexity, that may be used to organise an investigation into error propagation. No Label
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Rivington, M., & Wallach, D. (2015). Information to support input data quality and model improvement (Vol. 6).
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
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Semenov, M. (2015). Local-scale climate scenarios based on ensembles of global/regional climate models for regional applications in Europe (Vol. 3).
Abstract: Local-scale climate scenarios based on ensembles of global/regional climate models for regional applications in Europe is a deliverable for WP4 ‘Scenario development and impact uncertainty evaluation’. We developed the integration of 21st century climate projections for Europe based on simulations carried out within the EU-ENSEMBLES and CMIP3 projects with the LARS-WG stochastic weather generator. The aim was to update ELPIS, a repository of local-scale climate scenarios, for use in impact assessment studies in Europe. No Label
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Pasqui, M. (2015). Evaluation of future diurnal variability and projected changes in extremes of precipitation and temperature and their impacts on crop production over regional case studies (e.g. Agroscenari case studies) (Vol. 6).
Abstract: The daily weather of the four decades were used as input to EPIC simulation model to test the effects on crop yield, crop evapotranspiration, number of days with water and nitrogen stress in the silage maize -Italian ryegrass irrigated cropping systems in the Oristanese case study area.The monthly DTR (diurnal temperature range) pattern predicted for the FC (future climate, 2020-2030) indicates that spring and summer months are the most sensitive to DTR increase. The increase ryegrass yield simulated by EPIC under FC was interpreted as the positive effects on increased temperature on the winter-spring grass growth rates. The decreased production of maize was attributed to a shortening of the crop cycle, which reduced the intercepted radiation. The simulations run to assess the pure effect of DTR shift indicated almost no effects on crop yield but significant effects on crop evapotranspiration, whose increase observed under FC was largely associated to DTR, particularly in maize. The stochastic generation of daily weather with WXGEN indicates a sufficient accuracy for average DTR patterns and the central part of the daily DTR distribution, while the range of absolute values increased substantially, in relation to the increased probability of extremes in one century vs one decade.(Abstract supplied by the publisher) No Label
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