A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models
A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models
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DOI：
10.1002/qj.49712757202
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发表时间：
2001-01-01
影响因子：
8.9
通讯作者：
Palmer, TN
中科院分区：
文献类型：
--
作者：
Palmer, TN
天气和气候预测模式中的常规参数化方案通过确定性的整体公式来描述次网格尺度过程的影响，这些公式取决于当地可分辨的尺度变量和一些可调整的参数。尽管这些模型在天气和气候预测方面取得了毋庸置疑的成功，但从第一原理来看，使用这些公式是不可能的。使用低阶动力系统模型，和动力系统和湍流理论的基本结果，它表明，即使未解决的尺度只描述了系统的总方差的一小部分，忽略它们的变化，在某些情况下，导致气候的主要尺度的粗差。有人建议，在天气和气候预测模型中的一些剩余的错误可能有其起源在忽略的次网格尺度的变化，这种变化应参数化的非本地动态随机参数化方案。现有的计划的结果进行了描述，并可能占随机参数化误差对行星尺度运动的影响的机制进行了讨论。基于位涡诊断、奇异向量分析和一个简单的随机元胞自动机模型，提出了发展非局部随机动力参数化方案的建议。
wdConventional parametrization schemes in weather and climate prediction models describe the effects of subgrid-scale processes by deterministic bulk formulae which depend on local resolved-scale variables and a number of adjustable parameters. Despite the unquestionable success of such models for weather and climate prediction, it is impossible to justify the use of such formulae from first principles. Using low-order dynamical-systems models, and elementary results from dynamical-systems and turbulence theory, it is shown that even if unresolved scales only describe a small fraction of the total variance of the system, neglecting their variability can, in some circumstances, lead to gross errors in the climatology of the dominant scales. It is suggested that some of the remaining errors in weather and climate prediction models may have their origin in the neglect of subgrid-scale variability, and that such variability should be parametrized by non-local dynamically based stochastic parametrization schemes. Results from existing schemes are described, and mechanisms which might account for the impact of random parametrization error on planetary-scale motions are discussed. Proposals for the development of non-local stochastic-dynamic parametrization schemes are outlined, based on potential-vorticity diagnosis, singular-vector analysis and a simple stochastic cellular automaton model.