Impulse-response functions (IRFs) are mathematical functions that represent the response of the coupled carbon-climate system to different trajectories of fossil-fuel emissions and land-use. They help understand the time-scales of the Earth system response to perturbations and have played a prominent role in climate policy. However, there are limitations due to assumptions of linearity and time/state-invariance in obtaining IRFs. Recent research has brought new mathematical and computational techniques to address non-linearity and to obtain spatially explicit state-aware IRFs to tackle a new set of questions in carbon-climate science. One path forward is to integrate the IRF framework with data-driven methods, such as machine learning, to learn these state dependencies in a hybrid approach. Specifically, we present a prototype of a state-aware carbon-climate system emulator. Additionally, we discuss how to compare IRFs with observed radiocarbon data and how to apply the convolution of IRFs in climate change attribution studies.

中文翻译：

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迈向新一代脉冲响应函数，用于综合地球系统理解和气候变化归因


脉冲响应函数 （IRF） 是数学函数，表示耦合碳气候系统对化石燃料排放和土地利用的不同轨迹的响应。它们有助于了解地球系统对扰动的响应的时间尺度，并在气候政策中发挥了重要作用。然而，由于线性和时间/状态不变性的假设，在获得 IRF 时存在局限性。最近的研究带来了新的数学和计算技术来解决非线性问题，并获得空间显式状态感知 IRF，以解决碳气候科学中的一系列新问题。一种前进的途径是将 IRF 框架与数据驱动方法（如机器学习）集成，以混合方法学习这些状态依赖关系。具体来说，我们提出了一个状态感知碳气候系统仿真器的原型。此外，我们还讨论了如何将 IRF 与观察到的放射性碳数据进行比较，以及如何在气候变化归因研究中应用 IRF 的卷积。