是的,当然更好的输入数据* *会导致更好的模型精度,但前提是模型的概念化接近。气候模型的许多问题的误解,我想强调以下:a)大多数气候模型的隐含精密输出是荒谬的,所以需要很长,困难和怀疑看数据与现实的误差范围。b)缩小模型倾向于给精度高的印象。在大多数情况下,这是虚幻的。c)减半网格尺寸和你需要的4倍输入数据。在现实中通常没有那么多的高质量数据,即使有(原则上),通常没有人力能力处理它。d)减少网格的大小,你必须更加关注概念化。例如,地形学的影响在降雨,地面水达到比,和小气候因素变得越来越重要。以我的经验的工作在东非AOGCM输出,我发现小网格只突出了当地气候差异模型和以证据为基础的辅助数据。即使有较小的网格,该模型完全不能捕获meso-climatic影响大型湖泊,或陡峭的气候梯度的规模约100公里。 So the message is clear: global climatic models are great for general climatic trends, dubious for rainfall trends, and downright misleading for most small scale climate trend analysis. My worry is that one of leading categories of GCM users, water resources analysts, don't yet seem to have a clear grasp of GCM limitations. There are dangers in treating climatic computer models as a 'magical black box'. There are inherent dangers in gridding the available data realistically (a *major* source of error!). And whenever you choose a finer grid size, *always* pay very close attention to differences between hind-cast outputs and the actual spread of climatic data on the ground.