McWilliams James C
Department of Atmospheric and Oceanic Sciences and Institute of Geophysics and Planetary Physics, University of California, Los Angeles, CA 90095-1565, USA.
Proc Natl Acad Sci U S A. 2007 May 22;104(21):8709-13. doi: 10.1073/pnas.0702971104. Epub 2007 May 14.
Atmospheric and oceanic computational simulation models often successfully depict chaotic space-time patterns, flow phenomena, dynamical balances, and equilibrium distributions that mimic nature. This success is accomplished through necessary but non-unique choices for discrete algorithms, parameterizations, and coupled contributing processes that introduce structural instability into the model. Therefore, we should expect a degree of irreducible imprecision in quantitative correspondences with nature, even with plausibly formulated models and careful calibration (tuning) to several empirical measures. Where precision is an issue (e.g., in a climate forecast), only simulation ensembles made across systematically designed model families allow an estimate of the level of relevant irreducible imprecision.
大气和海洋计算模拟模型常常能够成功描绘出模仿自然的混沌时空模式、流动现象、动力平衡以及平衡分布。这种成功是通过对离散算法、参数化以及引入模型结构不稳定性的耦合贡献过程做出必要但并非唯一的选择来实现的。因此,即便采用看似合理构建的模型并针对若干经验测度进行仔细校准(调整),我们也应预料到与自然的定量对应会存在一定程度的不可约不精确性。在精度至关重要的情况下(例如在气候预测中),只有通过系统设计的模型族进行的模拟集合才能估计相关不可约不精确性的水平。