Jeffrey Nicholas Clark

Regional atmospheric trace gas inverse modelling frameworks depend on accurately simulating mole fractions, which result from transporting surface fluxes within a domain and carrying mole fractions into the region from its boundaries. With the aim of improving the computational efficiency of inverse modelling pipelines, recent machine learning approaches have successfully emulated Lagrangian Particle Dispersion Model (LPDM) footprints (source-receptor relationships) at substantially reduced computational cost, compared to the numerical model simulations. However, the estimation of the background component of the simulated mole fractions, an important contributor to overall mole fraction variability, has received comparatively little attention. In this study, we build on the GATES model (Graph-Neural-Network Atmospheric Transport Emulation System, Fillola et al. (2026b)), which emulates LPDM outputs, and present GATES-Background 0.2.0, a complementary emulator for background mole fraction contributions to observations across a regional domain driven by meteorological data and large-scale background estimates from a global methane reanalysis. GATES-Background is trained and evaluated to emulate background concentrations for GOSAT observations over South America and demonstrates strong agreement with reference calculations generated from the propagation of boundary conditions to the measurement location. The emulator generally captures daily variability, seasonal structure, and regional gradients in background mole fraction contributions, with errors that are relatively small and spatially localized, compared to the variability in the simulated background values (root-mean-square errors of 5.3–7.4 ppb, depending on season, compared to GOSAT XCH4 retrieval error on the order of 13 ppb and estimated variability due to boundary conditions within this region of approximately 15–20 ppb). The proposed approach reduces computational cost by several orders of magnitude relative to physics-based background calculations, enabling scalable and near–real-time application. When combined with GATES or other LPDM footprint emulators, this work will enable efficient machine learning–based approximation of both components of the forward model used in regional LPDM-based greenhouse gas inversion systems.

Regional atmospheric trace gas inverse modelling frameworks depend on accurately simulating mole fractions, which result from transporting surface fluxes within a domain and carrying mole fractions into the region from its boundaries. With the aim of improving the computational efficiency of inverse modelling pipelines, recent machine learning approaches have successfully emulated Lagrangian Particle Dispersion Model (LPDM) footprints (source-receptor relationships) at substantially reduced computational cost, compared to the numerical model simulations. However, the estimation of the background component of the simulated mole fractions, an important contributor to overall mole fraction variability, has received comparatively little attention. In this study, we build on the GATES model (Graph-Neural-Network Atmospheric Transport Emulation System, Fillola et al. (2026b)), which emulates LPDM outputs, and present GATES-Background 0.2.0, a complementary emulator for background mole fraction contributions to observations across a regional domain driven by meteorological data and large-scale background estimates from a global methane reanalysis. GATES-Background is trained and evaluated to emulate background concentrations for GOSAT observations over South America and demonstrates strong agreement with reference calculations generated from the propagation of boundary conditions to the measurement location. The emulator generally captures daily variability, seasonal structure, and regional gradients in background mole fraction contributions, with errors that are relatively small and spatially localized, compared to the variability in the simulated background values (root-mean-square errors of 5.3–7.4 ppb, depending on season, compared to GOSAT XCH4 retrieval error on the order of 13 ppb and estimated variability due to boundary conditions within this region of approximately 15–20 ppb). The proposed approach reduces computational cost by several orders of magnitude relative to physics-based background calculations, enabling scalable and near–real-time application. When combined with GATES or other LPDM footprint emulators, this work will enable efficient machine learning–based approximation of both components of the forward model used in regional LPDM-based greenhouse gas inversion systems.

Regional atmospheric trace gas inverse modelling frameworks depend on accurately simulating mole fractions, which result from transporting surface fluxes within a domain and carrying mole fractions into the region from its boundaries. With the aim of improving the computational efficiency of inverse modelling pipelines, recent machine learning approaches have successfully emulated Lagrangian Particle Dispersion Model (LPDM) footprints (source-receptor relationships) at substantially reduced computational cost, compared to the numerical model simulations. However, the estimation of the background component of the simulated mole fractions, an important contributor to overall mole fraction variability, has received comparatively little attention. In this study, we build on the GATES model (Graph-Neural-Network Atmospheric Transport Emulation System, Fillola et al. (2026b)), which emulates LPDM outputs, and present GATES-Background 0.2.0, a complementary emulator for background mole fraction contributions to observations across a regional domain driven by meteorological data and large-scale background estimates from a global methane reanalysis. GATES-Background is trained and evaluated to emulate background concentrations for GOSAT observations over South America and demonstrates strong agreement with reference calculations generated from the propagation of boundary conditions to the measurement location. The emulator generally captures daily variability, seasonal structure, and regional gradients in background mole fraction contributions, with errors that are relatively small and spatially localized, compared to the variability in the simulated background values (root-mean-square errors of 5.3–7.4 ppb, depending on season, compared to GOSAT XCH4 retrieval error on the order of 13 ppb and estimated variability due to boundary conditions within this region of approximately 15–20 ppb). The proposed approach reduces computational cost by several orders of magnitude relative to physics-based background calculations, enabling scalable and near–real-time application. When combined with GATES or other LPDM footprint emulators, this work will enable efficient machine learning–based approximation of both components of the forward model used in regional LPDM-based greenhouse gas inversion systems.

Climate data is complex to understand and observe with variables covering the 3D surface of the globe and changing over time. Consequently, it is difficult to convey rich climate information through static 2D images. To address this, we designed and built interactive 3D Earth models using the Unity game engine to visualise data from the Tropospheric Monitoring Instrument (TROPOMI) satellite. These virtual world models aim to help researchers share climate insights more effectively and make them accessible to the public. The immersive environment presents the use of a number of 3D Earth objects. The first one displays the ‘XCH4’ variable (column-averaged dry-air mole fraction of methane in ppb) for an entire month, allowing the user to cycle through months via a controller. The Earth spins on its axis, automatically displaying methane concentrations, while the user can manually adjust the view to inspect regions of interest. A second Earth object features an automatic animation displaying the density of data points collected by the TROPOMI satellite as days progress. We also render auxiliary reference globes without thematic overlays and include a 2D static plot of atmospheric Methane (ppb) for 2023 for comparison. The entire layout is optimised for immersive systems, specifically where the user is positioned centrally within a 360-degree display ring, such as the Reality Emulator at the University of Bristol, a VR-enabled ‘CAVE’ system. Audience feedback thus far has been highly positive: the immersive 3D visualisation gave participants a clearer view of methane concentrations across the Earth and deepened their interest in the planet’s atmosphere. It also sparked curiosity about the factors affecting atmospheric composition, prompting many questions about methane sources and satellite monitoring. This setup demonstrates the potential of virtual reality in communicating high-dimensional earth science data.

The growing availability of satellite-based methane observations provides new opportunities to improve estimates of surface emissions. Inverse modelling frameworks commonly rely on Lagrangian Particle Dispersion Models (LPDMs) to simulate atmospheric transport and derive source–receptor relationships (“footprints”), but these approaches are computationally expensive and struggle to scale to the rapidly increasing volume of satellite data.Previously, we introduced GATES (Graph-Neural-Network Atmospheric Transport Emulation System), a machine learning (ML) based emulator capable of reproducing LPDM footprint sensitivities three orders of magnitude faster than the underlying physics-based model, and demonstrated its application to infer methane emissions over South America. While such footprints capture the local contribution from surface fluxes, observed methane concentrations are often dominated by the background mole fraction associated with large-scale atmospheric transport entering the domain. Despite its importance, this background component has received comparatively little attention in ML-based transport emulation.Here, we present a machine learning emulator for background methane mole fractions, designed to reproduce the contribution from outside the modelled domain to observed concentrations using meteorological and atmospheric state information. By combining this background emulator with the existing GATES footprint emulator, we construct a fully ML-driven pipeline capable of predicting total methane concentrations without requiring explicit LPDM simulations. We demonstrate that this framework reproduces key spatial and temporal characteristics of LPDM-based background estimates over South America, including seasonal structure, daily variability, and regional patterns, as well as its performance within inversions to estimate Brazil’s methane emissions.We further assess the scalability of the approach by applying the footprint emulator to regions outside the original training domain. While the model performs well when trained and evaluated within the same region, performance degrades when applied to unseen domains with different meteorological regimes. These results indicate that atmospheric transport learning is strongly domain-specific, highlighting both the potential and the limitations of transfer learning, and underscoring the need for region-specific training data when extending the approach to global emulation.This work demonstrates the feasibility of a fully ML-driven atmospheric transport and background modelling framework for methane inversion, offering the next steps towards computationally efficient, satellite-based emissions monitoring.

Surface methane emissions can be estimated from atmospheric observations using inverse modelling systems, which often rely on Lagrangian Particle Dispersion Models (LPDMs) to simulate how the gas is transported through the atmosphere using meteorological fields. However, LPDM-based techniques struggle to scale to the size of modern satellite datasets, as one LPDM run is needed for each observation, taking on the order of 10 CPU-minutes to complete. Previously, we introduced the Machine Learning model GATES (Graph-Neural-Network Atmospheric Transport Emulation System), which can replicate LPDM outputs 1000x faster than the physics-based model, and demonstrated its application to infer emissions over South America. Training GATES over other world regions and comparing cross-regional performance shows that the learnt transport is domain-specific, consistent with the strong heterogeneity in wind patterns and topography across continents. In this presentation, we discuss transfer learning techniques and characterisation of regional differences in wind patterns, topography, data availability and the shape and magnitude of LPDM outputs, to increase transfer learning performance. This work builds capabilities towards efficient, global methane emissions emulation.

Atmospheric observation-based “inverse” greenhouse gas flux estimates are increasingly important to evaluate national inventories. A dramatic improvement in “top-down” flux inference is expected in the coming years due to the rapidly growing number of measurements from space. However, many well-established inverse modelling techniques face significant computational challenges scaling to modern satellite datasets, particularly those that rely on Lagrangian Particle Dispersion Models (LPDM) to simulate atmospheric transport. Here, we introduce GATES (Graph-Neural-Network Atmospheric Transport Emulation System), a data-driven LPDM emulator which outputs source-receptor relationships (“footprints”) using only meteorology and surface data as inputs, approximately 1000× times faster than an LPDM. We demonstrate GATES's skill in estimating footprints over South America and integrate it into an emissions estimation pipeline, evaluating Brazil's methane emissions using GOSAT (Greenhouse Gases Observing SATellite) observations for 2016 and 2018 and finding emissions that are consistent in space and time with the physics-driven estimate. This work highlights the potential of machine learning-based emulators like GATES to overcome a key bottleneck in large-scale, satellite-based inverse modeling, accelerating greenhouse gas emissions estimation and enabling timely, improved evaluations of national GHG inventories.