Evaluating the utility of dynamical downscaling in agricultural impacts projections
| Autor: | Joshua Elliott, Michael Glotter, Ian Foster, David McInerney, Elisabeth J. Moyer, Neil Best |
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| Rok vydání: | 2014 |
| Předmět: |
Crops
Agricultural Systematic error Conservation of Natural Resources Decision support system Climate Climate Change Yield (finance) Climate change Zea mays Food Supply Computer Simulation Probability Multidisciplinary Geography business.industry Reproducibility of Results Agriculture Carbon Dioxide Models Theoretical Climatology General Circulation Model North America Physical Sciences Environmental science Climate model business Algorithms Forecasting Downscaling |
| Zdroj: | Proceedings of the National Academy of Sciences. 111:8776-8781 |
| ISSN: | 1091-6490 0027-8424 |
| Popis: | Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios ( |
| Databáze: | OpenAIRE |
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