Abstract

Carbon dioxide (CO₂) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time-dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO₂ emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO₂. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time-dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios.

Key Points

• With a Green's Function approach, we emulate the linear component of the spatially resolved temperature response to CO₂ emissions

• We reproduce the temperature response well within multi-model uncertainty except in the Arctic and Southern Ocean

• This approach allows expedient quantification of the spatial and temporal temperature response to varying CO₂ emissions pathways

Plain Language Summary

Carbon dioxide (CO₂) emissions impact surface temperature. It is well established that the global mean temperature change is proportional to the cumulative emissions of CO₂. This has led to the creation of carbon budgets to reach temperature goals. We test this relationship at the spatio-temporal scale, quantifying a simple approach that estimates the local temperature response to CO₂ emissions alone. We use an approach built from the Climate Model Intercomparison Project Phase 6 (CMIP6) Earth System Models, based on the concept that an additional unit of CO₂ can be scaled for larger emissions and summed over time to estimate cumulative impacts. We evaluate this with additional CMIP6 experiments, showing that this approach captures the temperature response in most locations with lower accuracy in the Arctic and Southern Ocean. This type of approach may be useful to evaluate many policy scenarios and to better understand earth system processes that are represented in the models, as it takes around one second to quantify 90 years' worth of temperature change on a local computer, while Earth System Models can require weeks of runtime on supercomputers.