Deep-AeroGP: deep kernel learning for projecting the regional climate response to anthropogenic aerosol emission changes

We present a deep-kernel Gaussian process emulator (Deep-AeroGP) for predicting the climate response of surface temperature and precipitation to aerosol emission changes at high spatial and temporal resolution. Aerosols play a critical role in the climate system at both global and regional scales. Anthropogenic aerosol forcing has masked approximately 0.4 °C of global warming since the beginning of the industrial era¹, and recent reductions in aerosol emissions have been linked to an acceleration of global mean temperature increase². Because aerosol emissions are spatially heterogeneous and short-lived, changes in their magnitude and geographical distribution can drive pronounced regional and rapid climate responses, including shifts in precipitation patterns and monsoon intensity and timing^3,4. Modelling these regional responses is critical for evaluating the climate consequences of air quality and environmental policy decisions; however, exploring a wide range of regional aerosol emission scenarios is computationally prohibitive with fully coupled Earth system models (ESMs). Machine-learning emulators enable the rapid exploration of large ensembles of emission scenarios, facilitating scenario development, and impact assessment. Deep-AeroGP, which builds on the recently published AeroGP⁵ , combines the flexibility of deep neural networks with the probabilistic framework of Gaussian processes, using a neural network as a feature extractor such that the kernel is learned from the data rather than fixed a priori. This approach allows the emulator to capture both large-scale and regional patterns of aerosol-driven climate variability while providing uncertainty estimates. We demonstrate the accuracy and usefulness of Deep-AeroGP in policy-relevant studies by investigating the nonlinearity of the climate response to multiple regional aerosol emission perturbations. 

 

1. Forster, P. & Storelvmo, T. The Earth’s energy budget, climate feedbacks, and climate sensitivity. In Working Group 1 contribution to the IPCC 6th Assessment Report (eds Masson-Delmotte, V. et al.) Ch. 7 (Cambridge University Press, 2021). 

2. Samset, B.H., Wilcox, L.J., Allen, R.J. et al. East Asian aerosol cleanup has likely contributed to the recent acceleration in global warming. Commun Earth Environ 6, 543 (2025). https://doi.org/10.1038/s43247-025-02527-3 

3. López-Romero, J. M., Montávez, J. P., Jerez, S., Lorente-Plazas, R., Palacios-Peña, L., and Jiménez-Guerrero, P.: Precipitation response to aerosol–radiation and aerosol–cloud interactions in regional climate simulations over Europe, Atmos. Chem. Phys., 21, 415–430, https://doi.org/10.5194/acp-21-415-2021, 2021. 

4. Wilcox, L. J., Liu, Z., Samset, B. H., Hawkins, E., Lund, M. T., Nordling, K., Undorf, S., Bollasina, M., Ekman, A. M. L., Krishnan, S., Merikanto, J., and Turner, A. G.: Accelerated increases in global and Asian summer monsoon precipitation from future aerosol reductions, Atmos. Chem. Phys., 20, 11955–11977, https://doi.org/10.5194/acp-20-11955-2020, 2020. 

5. Dewey, M., Hansson, H.-C., Watson-Parris, D., Samset, B. H., Wilcox, L. J., Lewinschal, A., et al. (2025). AeroGP: Machine learning how aerosols impact regional climate. Journal of Geophysical Research: Machine Learning and Computation, 2, e2025JH000741. https://doi.org/10.1029/2025JH000741