Articles | Volume 25, issue 21
https://doi.org/10.5194/acp-25-15033-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-15033-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Emulating chemistry-climate dynamics with a linear inverse model
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA 98195, USA
Gregory J. Hakim
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA 98195, USA
Max Taniguchi-King
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA 98195, USA
Department of Earth and Planetary Sciences, University of California, Berkeley, CA 94720, USA
Dominik Stiller
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA 98195, USA
Alexander J. Turner
Department of Atmospheric and Climate Science, University of Washington, Seattle, WA 98195, USA
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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).
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Climate forcings, such as solar radiation or anthropogenic greenhouse gases, are required to run global climate model simulations. Stratospheric aerosols, which mostly originate from large volcanic eruptions, are a key natural forcing. In this paper, we document the stratospheric aerosol forcing dataset that will feed the next generation (CMIP7) of climate models. Our dataset is very different from its predecessor (CMIP6), which might affect simulations of the 1850–2021 climate.
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
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Satellites, such as NASA's Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3, respectively), retrieve carbon dioxide (CO2) concentrations, which provide vital information for estimating surface CO2 emissions. Here, we investigate the ability of OCO-2/3 retrievals to constrain CO2 emissions for the state of California for the major emission sectors (i.e., fossil fuels, net ecosystem exchange, and wildfire).
Nikhil Dadheech and Alexander J. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3441, https://doi.org/10.5194/egusphere-2025-3441, 2025
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We developed a generalized emulator of atmospheric transport (FootNet v3) trained over the United States, enabling the emulation of both surface & column-averaged footprints at kilometer-scale resolution. We demonstrate that FootNet v3 generalizes to previously unseen regions and meteorological conditions, enabling accurate out-of-sample simulation of atmospheric transport. Flux inversion case studies show that FootNet matches or exceeds the performance of full-physics models in unseen regions.
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
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We developed an efficient GHG (greenhouse gas) flux inversion framework using a machine-learning emulator (FootNet) as a surrogate for an atmospheric transport model, resulting in a 650 × speedup. Paradoxically, the flux inversion using the ML (machine-learning) model outperforms the full-physics model in our case study. We attribute this to the ML model mitigating transport errors in the GHG flux inversion.
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
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Gemma K. O'Connor, Paul R. Holland, Eric J. Steig, Pierre Dutrieux, and Gregory J. Hakim
The Cryosphere, 17, 4399–4420, https://doi.org/10.5194/tc-17-4399-2023, https://doi.org/10.5194/tc-17-4399-2023, 2023
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Glaciers in West Antarctica are rapidly melting, but the causes are unknown due to limited observations. A leading hypothesis is that an unusually large wind event in the 1940s initiated the ocean-driven melting. Using proxy reconstructions (e.g., using ice cores) and climate model simulations, we find that wind events similar to the 1940s event are relatively common on millennial timescales, implying that ocean variability or climate trends are also necessary to explain the start of ice loss.
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, and Jianxiong Sheng
Atmos. Chem. Phys., 23, 3325–3346, https://doi.org/10.5194/acp-23-3325-2023, https://doi.org/10.5194/acp-23-3325-2023, 2023
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We combine satellite measurements with a novel downscaling method to map global methane emissions at 0.1°×0.1° resolution. These fine-scale emission estimates reveal unreported emission hotspots and shed light on the roles of agriculture, wetlands, and fossil fuels for regional methane budgets. The satellite-derived emissions point in particular to missing fossil fuel emissions in the Middle East and to a large emission underestimate in South Asia that appears to be tied to monsoon rainfall.
Johannes Gensheimer, Alexander J. Turner, Philipp Köhler, Christian Frankenberg, and Jia Chen
Biogeosciences, 19, 1777–1793, https://doi.org/10.5194/bg-19-1777-2022, https://doi.org/10.5194/bg-19-1777-2022, 2022
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We develop a convolutional neural network, named SIFnet, that increases the spatial resolution of SIF from TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations, together with a broad range of high-resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.
Helen L. Fitzmaurice, Alexander J. Turner, Jinsol Kim, Katherine Chan, Erin R. Delaria, Catherine Newman, Paul Wooldridge, and Ronald C. Cohen
Atmos. Chem. Phys., 22, 3891–3900, https://doi.org/10.5194/acp-22-3891-2022, https://doi.org/10.5194/acp-22-3891-2022, 2022
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On-road emissions are thought to vary widely from existing predictions, as the effects of the age of the vehicle fleet, the performance of emission control systems, and variations in speed are difficult to assess under ambient driving conditions. We present an observational approach to characterize on-road emissions and show that the method is consistent with other approaches to within ~ 3 %.
Alexander J. Turner, Philipp Köhler, Troy S. Magney, Christian Frankenberg, Inez Fung, and Ronald C. Cohen
Biogeosciences, 18, 6579–6588, https://doi.org/10.5194/bg-18-6579-2021, https://doi.org/10.5194/bg-18-6579-2021, 2021
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This work builds a high-resolution estimate (500 m) of gross primary productivity (GPP) over the US using satellite measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) between 2018 and 2020. We identify ecosystem-specific scaling factors for estimating gross primary productivity (GPP) from TROPOMI SIF. Extreme precipitation events drive four regional GPP anomalies that account for 28 % of year-to-year GPP differences across the US.
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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.
Chemistry-climate models are used to investigate how physical climate influences the composition...
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