Spatially Resolved Temperature Response Functions to CO2 Emissions
Name
Geophysical Research Letters - 2024 - Freese - Spatially Resolved Temperature Response Functions to CO2 Emissions.pdf
Description
Published version
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6.66 MB
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Author(s) • • •
Freese, Lyssa M
Giani, Paolo
Fiore, Arlene M
Selin, Noelle E
Date Issued
August 7, 2024
Journal
Geophysical Research Letters
Publisher
American Geophysical Union
Citation
Freese, L. M., Giani, P., Fiore, A. M., & Selin, N. E. (2024). Spatially resolved temperature response functions to CO2 emissions. Geophysical Research Letters, 51, e2024GL108788.
Version
Final published version
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.
Description
Article relates to: Winkler, A. J., & Sierra, C. A. (2025). Towards a new generation of impulse-response functions for integrated Earth system understanding and climate change attribution. Geophysical Research Letters, 52, e2024GL112295. https://doi.org/10.1029/2024GL112295
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
MIT Institute for Data, Systems, and Society
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DOI of Published Version
10.1029/2024gl108788