Ponti, L., Gutierrez, A. P., Ruti, P. M., & Dell’Aquila, A. (2014). Fine-scale ecological and economic assessment of climate change on olive in the Mediterranean Basin reveals winners and losers. Proc. Natl. Acad. Sci. U. S. A., 111(15), 5598–5603.
Abstract: The Mediterranean Basin is a climate and biodiversity hot spot, and climate change threatens agro-ecosystems such as olive, an ancient drought-tolerant crop of considerable ecological and socioeconomic importance. Climate change will impact the interactions of olive and the obligate olive fruit fly (Bactrocera oleae), and alter the economics of olive culture across the Basin. We estimate the effects of climate change on the dynamics and interaction of olive and the fly using physiologically based demographic models in a geographic information system context as driven by daily climate change scenario weather. A regional climate model that includes fine-scale representation of the effects of topography and the influence of the Mediterranean Sea on regional climate was used to scale the global climate data. The system model for olive/olive fly was used as the production function in our economic analysis, replacing the commonly used production-damage control function. Climate warming will affect olive yield and fly infestation levels across the Basin, resulting in economic winners and losers at the local and regional scales. At the local scale, profitability of small olive farms in many marginal areas of Europe and elsewhere in the Basin will decrease, leading to increased abandonment. These marginal farms are critical to conserving soil, maintaining biodiversity, and reducing fire risk in these areas. Our fine-scale bioeconomic approach provides a realistic prototype for assessing climate change impacts in other Mediterranean agro-ecosystems facing extant and new invasive pests.
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Calanca, P., & Semenov, M. A. (2013). Local-scale climate scenarios for impact studies and risk assessments: integration of early 21st century ENSEMBLES projections into the ELPIS database. Theor. Appl. Climatol., 113(3-4), 445–455.
Abstract: We present the integration of early 21st century climate projections for Europe based on simulations carried out within the EU-FP6 ENSEMBLES project with the LARS-WG stochastic weather generator. The aim was to upgrade ELPIS, a repository of local-scale climate scenarios for use in impact studies and risk assessments that already included global projections from the CMIP3 ensemble and regional scenarios for Japan. To obtain a more reliable simulation of daily rainfall and extremes, changes in wet and dry series derived from daily ENSEMBLES outputs were taken into account. Kernel average smoothers were used to reduce noise arising from sampling artefacts. Examples of risk analyses based on 25-km climate projections from the ENSEMBLES ensemble of regional climate models illustrate the possibilities offered by the updated version of ELPIS. The results stress the importance of tailored information for local-scale impact assessments at the European level.
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Refsgaard, J. C., Madsen, H., Andréassian, V., Arnbjerg-Nielsen, K., Davidson, T. A., Drews, M., et al. (2014). A framework for testing the ability of models to project climate change and its impacts. Clim. Change, 122(1-2), 271–282.
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.
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Bai, H., Tao, F., Xiao, D., Liu, F., & Zhang, H. (2016). Attribution of yield change for rice-wheat rotation system in China to climate change, cultivars and agronomic management in the past three decades. Clim. Change, 135(3-4), 539–553.
Abstract: Using the detailed field experiment data from 1981 to 2009 at four representative agro-meteorological experiment stations in China, along with the Agricultural Production System Simulator (APSIM) rice-wheat model, we evaluated the impact of sowing/transplanting date on phenology and yield of rice-wheat rotation system (RWRS). We also disentangled the contributions of climate change, modern cultivars, sowing/transplanting density and fertilization management, as well as changes in each climate variables, to yield change in RWRS, in the past three decades. We found that change in sowing/transplanting date did not significantly affect rice and wheat yield in RWRS, although alleviated the negative impact of climate change to some extent. From 1981 to 2009, climate change jointly caused rice and wheat yield change by -17.4 to 1.5 %, of which increase in temperature reduced yield by 0.0-5.8 % and decrease in solar radiation reduced it by 1.5-8.7 %. Cultivars renewal, modern sowing/transplanting density and fertilization management contributed to yield change by 14.4-27.2, -4.7- -0.1 and 2.3-22.2 %, respectively. Our findings highlight that modern cultivars and agronomic management compensated the negative impacts of climate change and played key roles in yield increase in the past three decades.
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Wallach, D., Mearns, L. O., Ruane, A. C., Rötter, R. P., & Asseng, S. (2016). Lessons from climate modeling on the design and use of ensembles for crop modeling. Clim. Change, 139(3-4), 551–564.
Abstract: Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.
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