Guidelines for Constructing Climate Scenarios
نویسنده
چکیده
Scientists and others from academia, government, and the private sector increasingly are using climate model outputs in research and decision support. For the most recent assessment report of the Intergovernmental Panel on Climate Change, 18 global modeling centers contributed outputs from hundreds of simulations, coordinated through the Coupled Model Intercomparison Project Phase 3 (CMIP3), to the archive at the Program for Climate Model Diagnostics and Intercomparison (PCMDI; http://pcmdi3.llnl.gov) [Meehl et al., 2007]. Many users of climate model outputs prefer downscaled data—i.e., data at higher spatial resolution— to direct global climate model (GCM) outputs; downscaling can be statistical [e.g., Maurer et al., 2007] or dynamical [e.g., Mearns et al., 2009]. More than 800 users have obtained downscaled CMIP3 results from one such Web site alone (see http:// gdo -dcp .ucllnl .org/ downscaled _cmip3 _projections/, described by Maurer et al. [2007]). A common request from those applying any of these outputs—whether to conduct impact research or to support adaptation planning—is guidance on how to select, treat, and combine the vast amount of climate model output into useful climate scenarios. A scenario is a postulated sequence of events, whether of human development, climate, etc. Specifically, two questions are often asked: (1) How best can scientists understand and characterize uncertainty? (2) What are some key considerations when selecting and combining climate model outputs to generate scenarios? Addressing these questions in the context of recent research leads to some possible guidelines for creating and applying climate scenarios [see also Knutti et al., 2010]. At this juncture, with a new generation of global and regional climate projections becoming available, such guidelines may prove useful to researchers and policy makers. Understanding and Characterizing Uncertainty
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