Towards a New Generation of Impulse‐Response Functions for Integrated Earth System Understanding and Climate Change Attribution

Abstract 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.