Articles | Volume 25, issue 21
https://doi.org/10.5194/acp-25-15033-2025
https://doi.org/10.5194/acp-25-15033-2025
Research article
 | 
07 Nov 2025
Research article |  | 07 Nov 2025

Emulating chemistry-climate dynamics with a linear inverse model

Eric J. Mei, Gregory J. Hakim, Max Taniguchi-King, Dominik Stiller, and Alexander J. Turner

Related authors

Stratospheric aerosol forcing for CMIP7 (part 1): Optical properties for pre-industrial, historical, and scenario simulations (version 2.2.1)
Thomas Jacques Aubry, Matthew Toohey, Sujan Khanal, Man Mei Chim, Magali Verkerk, Ben Johnson, Anja Schmidt, Mahesh Kovilakam, Michael Sigl, Zebedee Nicholls, Larry Thomason, Vaishali Naik, Landon Rieger, Dominik Stiller, Elisa Ziegler, and Isabel Smith
EGUsphere, https://doi.org/10.5194/egusphere-2025-4990,https://doi.org/10.5194/egusphere-2025-4990, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
State-wide California 2020 carbon dioxide budget estimated with OCO-2 and OCO-3 satellite data
Matthew S. Johnson, Sofia D. Hamilton, Seongeun Jeong, Yu Yan Cui, Dien Wu, Alex Turner, and Marc Fischer
Atmos. Chem. Phys., 25, 8475–8492, https://doi.org/10.5194/acp-25-8475-2025,https://doi.org/10.5194/acp-25-8475-2025, 2025
Short summary
Simulating out-of-sample atmospheric transport to enable flux inversions
Nikhil Dadheech and Alexander J. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3441,https://doi.org/10.5194/egusphere-2025-3441, 2025
Short summary
High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
Atmos. Chem. Phys., 25, 5159–5174, https://doi.org/10.5194/acp-25-5159-2025,https://doi.org/10.5194/acp-25-5159-2025, 2025
Short summary
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025,https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary

Cited articles

Alexander, M. A., Matrosova, L., Penland, C., Scott, J. D., and Chang, P.: Forecasting Pacific SSTs: Linear Inverse Model Predictions of the PDO, Journal of Climate, 21, 385–402, https://doi.org/10.1175/2007JCLI1849.1, 2008. a, b, c
Anderson, D. C., Duncan, B. N., Fiore, A. M., Baublitz, C. B., Follette-Cook, M. B., Nicely, J. M., and Wolfe, G. M.: Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers, Atmos. Chem. Phys., 21, 6481–6508, https://doi.org/10.5194/acp-21-6481-2021, 2021. a, b, c, d
Anderson, D. C., Follette-Cook, M. B., Strode, S. A., Nicely, J. M., Liu, J., Ivatt, P. D., and Duncan, B. N.: A machine learning methodology for the generation of a parameterization of the hydroxyl radical, Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, 2022. a
Anderson, D. C., Duncan, B. N., Nicely, J. M., Liu, J., Strode, S. A., and Follette-Cook, M. B.: Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy, Atmos. Chem. Phys., 23, 6319–6338, https://doi.org/10.5194/acp-23-6319-2023, 2023. a
Anderson, D. C., Duncan, B. N., Liu, J., Nicely, J. M., Strode, S. A., Follette-Cook, M. B., Souri, A. H., Ziemke, J. R., González-Abad, G., and Ayazpour, Z.: Trends and Interannual Variability of the Hydroxyl Radical in the Remote Tropics During Boreal Autumn Inferred From Satellite Proxy Data, Geophysical Research Letters, 51, e2024GL108531, https://doi.org/10.1029/2024GL108531, 2024. a, b
Download
Short summary
Chemistry-climate models are used to investigate how physical climate influences the composition of the atmosphere but are slow and expensive to run. We train a linear inverse model that can replicate the behavior of chemistry-climate models at low computational cost. It captures how large-scale climate features like El Niño affect atmospheric composition and can make accurate forecasts up to a year ahead. This model enables fast hypothesis testing and estimates of past atmospheric composition.
Share
Altmetrics
Final-revised paper
Preprint