What might we learn from climate forecasts?
What might we learn from climate forecasts?
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DOI：
10.1073/pnas.012580599
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发表时间：
2002-02-19
影响因子：
11.1
通讯作者：
Smith, LA
中科院分区：
文献类型：
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作者：
Smith, LA
大多数气候模型都是大型动力系统，在大型计算机上涉及一百万（或更多）变量。考虑到它们是非线性的，并不完美，我们能从它们那里学到什么关于地球气候的知识呢？我们如何确定他们输出的哪些方面可能是有用的，哪些是噪音？我们应该如何分配资源，使它们“更好”，估计真正的社会和经济利益的变量，并量化它们目前有多好？正如“混乱”阻碍了准确的天气预报，模型误差也阻碍了对定义气候的分布的准确预报，从而产生了第二种不确定性。我们能在不确定性估计中估计不确定性吗？对这些问题进行了讨论。最终，所有的不确定性都是在给定的建模范式中量化的;我们的预测永远不需要反映物理系统中的不确定性。
Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them "better," estimating variables of true social and economic interest, and quantifying how good they are at the moment? Just as "chaos" prevents accurate weather forecasts, so model error precludes accurate forecasts of the distributions that define climate, yielding uncertainty of the second kind. Can we estimate the uncertainty in our uncertainty estimates? These questions are discussed. Ultimately, all uncertainty is quantified within a given modeling paradigm; our forecasts need never reflect the uncertainty in a physical system.