The prediction of climate variability and change requires the use of a range of simulation models. Multiple climate model simulations are needed to sample the inherent uncertainties in seasonal to centennial prediction. Because climate models are computationally expensive, there is a tradeoff between complexity, spatial resolution, simulation length, and ensemble size. The methods used to assess climate impacts are examined in the context of this trade-off. An emphasis on complexity allows simulation of coupled mechanisms, such as the carbon cycle and feedbacks between agricultural land management and climate. In addition to improving skill, greater spatial resolution increases relevance to regional planning. Greater ensemble size improves the sampling of probabilities. Research from major international projects is used to show the importance of synergistic research efforts.
The primary climate impact examined is crop yield, although many of the issues discussed are relevant to hydrology and health modeling. Methods used to bridge the scale gap between climate and crop models are reviewed. Recent advances include large-area crop modeling, quantification of uncertainty in crop yield, and fully integrated crop-climate modeling. The implications of trends in computer power, including supercomputers, are also discussed.
Institute for Climate and Atmospheric Science, School of Earth and Environment, The University of Leeds, Leeds, United Kingdom
Department of Meteorology, Walker Institute, University of Reading, Reading, United Kingdom
Department of Geography, University of Liverpool, Liverpool, United Kingdom
Department of Agriculture, Walker Institute, University of Reading, Reading, United Kingdom
UK-Japan Climate Collaboration, Earth Simulator Center, Yokohama, Japan, and National Centre for Atmospheric Science, Reading, United Kingdom.
