Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations

Li, Jingmin; Righi, Mattia; Hendricks, Johannes; Beer, Christof G.; Burkhardt, Ulrike; Schmidt, Anja

doi:https://doi.org/10.5194/acp-24-12727-2024

Articles | Volume 24, issue 22

https://doi.org/10.5194/acp-24-12727-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

The Modular Earth Submodel System (MESSy) (ACP/GMD inter-journal...

https://doi.org/10.5194/acp-24-12727-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 22

Research article

|

15 Nov 2024

Research article |

| 15 Nov 2024

Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations

Jingmin Li, Mattia Righi, Johannes Hendricks, Christof G. Beer, Ulrike Burkhardt, and Anja Schmidt

Supplement

https://doi.org/10.5194/acp-24-12727-2024-supplement

Data sets

Data used in "Revealing dominant patterns of aerosols regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations" (Li et al., Atmos. Chem. Phys. 2024) Jingmin Li https://doi.org/10.5281/zenodo.8398024

Model simulation data used in “The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)” (Righi et al., Earth Syst. Dynam., 2023) Mattia Righi https://doi.org/10.5281/zenodo.8134336

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Short summary

Aiming to understand underlying patterns and trends in aerosols, we characterize the spatial patterns and long-term evolution of lower tropospheric aerosols by clustering multiple aerosol properties from preindustrial times to the year 2050 under three Shared
Socioeconomic Pathway scenarios. The results provide a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios.