使用带控制的动态模式分解在单个气候实现中分离强迫和内部气候变率

中文摘要 AI 辅助

我们表明，通过将外部强迫视为线性随机系统中的动态驱动因素，可将单个气候实现分解为强迫和内部成分，这一想法基于回溯吸引子理论。这样做解决了气候科学中的一个核心方法挑战，对气候预测以及强迫响应的检测和归因有直接影响，即从单个观测记录中分离强迫气候响应和内部变率。统计方法多样，从基于大集合训练的方法到处理单个实现的技术。后者常依赖线性框架，如线性逆模型（LIMs）和线性回归，前者忽略强迫预测因子，后者忽略气候系统动力学。我们引入PullbackDMDc方法，基于非自治动力系统理论和带控制的动态模式分解（DMDc），结合回溯吸引子估计，将单个气候实现分解为空间模式及其相关的强迫和内部成分，给出潜在动力学的物理解释。我们通过将其应用于再分析和四个地球系统模型（ESM）大集合的近地表气温和海平面气压，说明了PullbackDMDc在ESM评估中的效用。PullbackDMDc估计强迫响应的技能匹配或超过既定基线，并针对基于模型的地面真值识别最佳强迫预测因子。其内部变率成分表明，ESM定性地捕捉了年际和年代际模式，同时彼此之间以及与观测存在系统差异。PullbackDMDc凭借其对强迫响应的有效估计和新颖的分解，成为单实现气候分析和ESM评估的实用工具。

英文摘要

We show that a single climate realization can be decomposed into forced and internal components by treating external forcing as a dynamical driver within a linear stochastic system, an idea grounded in pullback attractor theory. In doing so, we address a central methodological challenge in climate science with direct implications for climate projection and the detection and attribution of the forced response, disentangling the forced climate response from internal variability in a single observed record. Statistical methods range from approaches trained on large ensembles to techniques operating on single realizations. The latter often rely on linear frameworks such as linear inverse models (LIMs) and linear regression. LIMs ignore forcing predictors, whereas linear regression omits climate system dynamics. Here we introduce PullbackDMDc, a method grounded in non-autonomous dynamical systems theory and dynamic mode decomposition with control (DMDc), incorporating pullback attractor estimation to decompose a single climate realization into spatial modes and their associated forced and internal components, yielding a physically interpretable picture of the underlying dynamics. We illustrate the utility of PullbackDMDc for Earth System Model (ESM) evaluation by applying it to near-surface air temperature and sea-level pressure from reanalysis and four ESM large ensembles. PullbackDMDc estimates the forced response with skill matching or exceeding established baselines and identifies optimal forcing predictors against model-based ground truth. Its internal variability components reveal that ESMs qualitatively capture interannual and decadal modes while exhibiting systematic differences relative to each other and to observations. Skillful forced response estimation and a novel decomposition position PullbackDMDc as a practical tool for single-realization climate analysis and ESM evaluation.

发表机构

• Image Processing Laboratory (IPL), Universitat de Val\`encia, Spain + These authors contributed equally to the manuscript

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