论文标题
检测和归因于气候和复杂系统的变化：基础，绿色功能和非线性指纹
Detecting and Attributing Change in Climate and Complex Systems: Foundations, Green's Functions, and Nonlinear Fingerprints
论文作者
论文摘要
检测和归因（D＆A）研究是气候科学的基石，为政策决策提供了重要的证据。他们的目标是通过最佳指纹方法（OFM）将观察到的气候变化模式与人为和天然驱动器联系起来。我们表明，非平衡系统的响应理论为OFM提供了物理和动力学基础，包括用于归因的因果关系的概念。我们的框架阐明了该方法的假设，优势和潜在的弱点。我们使用我们的理论来执行D＆A，以用于在能量平衡模型和低分辨率耦合气候模型上执行的原型气候变化实验。我们还解释了退化指纹识别的基础，该指纹识别提供了临界点的预警指标。最后，我们将OFM扩展到非线性响应制度。我们的分析表明，OFM在受时间依赖性强迫影响的不同随机系统中具有广泛的适用性，与生态系统，定量社会科学和金融等潜在相关。
Detection and attribution (D&A) studies are cornerstones of climate science, providing crucial evidence for policy decisions. Their goal is to link observed climate change patterns to anthropogenic and natural drivers via the optimal fingerprinting method (OFM). We show that response theory for nonequilibrium systems offers the physical and dynamical basis for OFM, including the concept of causality used for attribution. Our framework clarifies the method's assumptions, advantages, and potential weaknesses. We use our theory to perform D&A for prototypical climate change experiments performed on an energy balance model and on a low-resolution coupled climate model. We also explain the underpinnings of degenerate fingerprinting, which offers early warning indicators for tipping points. Finally, we extend the OFM to the nonlinear response regime. Our analysis shows that OFM has broad applicability across diverse stochastic systems influenced by time-dependent forcings, with potential relevance to ecosystems, quantitative social sciences, and finance, among others.
