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Toward understanding predictability of climate: a linear stochastic modeling approach

机译:理解气候的可预测性:线性随机建模方法

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摘要

This dissertation discusses the predictability of the atmosphere-ocean climate system on interannual and decadal timescales. We investigate the extent to which the atmospheric internal variability (weather noise) can cause climate prediction to lose skill; and we also look for the oceanic processes that contribute to the climate predictability via interaction with the atmosphere.First, we develop a framework for assessing the predictability of a linear stochastic system. Based on the information of deterministic dynamics and noise forcing, various predictability measures are defined and new predictability-analysis tools are introduced. For the sake of computational efficiency, we also discuss the formulation of a low-order model within the context of four reduction methods: modal, EOF, most predictable pattern, and balanced truncation.Subsequently, predictabilities of two specific physical systems are investigated within such framework. The first is a mixed layer model of SST with focus on the effect of oceanic advection.Analytical solution of a one-dimensional model shows that even though advection can give rise to a pair of low-frequency normal modes, no enhancement in the predictability is found in terms of domain averaged error variance. However, a Predictable Component Analysis (PrCA) shows that advection can play a role in redistributing the predictable variance. This analytical result is further tested in a more realistic two-dimensional North Atlantic model with observed mean currents. The second is a linear coupled model of tropical Atlantic atmosphere-ocean system. Eigen-analysis reveals that the system has two types of coupled modes: a decadal meridional mode and an interannual equatorial mode. The meridional mode, which manifests itself as a dipole pattern in SST, is controlled by thermodynamic feedback between wind, latent heat flux, and SST, and modified by ocean heat transport. The equatorial mode, which manifests itself as an SST anomaly in the eastern equatorial basin, is dominated by dynamic feedback between wind, thermocline, upwelling, and SST. The relative strength of thermodynamic vs dynamic feedbacks determines the behavior of the coupled system, and enables the tropical Atlantic variability to be more predictable than the passive-ocean scenario.
机译:本文讨论了年际和年代际尺度上大气海洋气候系统的可预测性。我们调查了大气内部变化(天气噪声)在多大程度上会使气候预测失去技能。首先,我们开发了一个评估线性随机系统可预测性的框架。基于确定性动力学和噪声强迫的信息,定义了各种可预测性措施,并引入了新的可预测性分析工具。为了提高计算效率,我们还讨论了四种降低方法(模态,EOF,最可预测的模式和平衡的截断)下的低阶模型的形成,随后,研究了两种特定物理系统的可预测性框架。第一个是SST的混合层模型,重点是海洋对流的影响。一维模型的解析解表明,即使对流可以产生一对低频正态模式,但可预测性没有增强根据域平均误差方差找到。但是,可预测成分分析(PrCA)显示,对流可以在重新分配可预测方差中发挥作用。在具有观测平均电流的更逼真的二维北大西洋模型中进一步测试了此分析结果。第二个是热带大西洋大气-海洋系统的线性耦合模型。本征分析表明,该系统具有两种耦合模式:年代际子午模式和年际赤道模式。子午模式在SST中表现为偶极子模式,由风,潜热通量和SST之间的热力学反馈控制,并通过海洋热传输进行修改。赤道模式在赤道东部盆地表现为海温异常,主要由风,温跃层,上升流和海温之间的动态反馈控制。热力学与动态反馈的相对强度决定了耦合系统的行为,并使热带大西洋的变率比被动海洋的情景更可预测。

著录项

  • 作者

    Wang Faming;

  • 作者单位
  • 年度 2004
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  • 原文格式 PDF
  • 正文语种 en_US
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