Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-1997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Apr 2023
Development and technical paper |
| 12 Apr 2023
| 12 Apr 2023
A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME
Elena Fillola
CORRESPONDING AUTHOR
Department of Engineering Mathematics, University of Bristol, Bristol, UK
Raul Santos-Rodriguez
Department of Engineering Mathematics, University of Bristol, Bristol, UK
Alistair Manning
Hadley Centre, Met Office, Exeter, UK
Simon O'Doherty
School of Chemistry, University of Bristol, Bristol, UK
Matt Rigby
School of Chemistry, University of Bristol, Bristol, UK
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Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
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Peter Bergamaschi, Arjo Segers, Dominik Brunner, Jean-Matthieu Haussaire, Stephan Henne, Michel Ramonet, Tim Arnold, Tobias Biermann, Huilin Chen, Sebastien Conil, Marc Delmotte, Grant Forster, Arnoud Frumau, Dagmar Kubistin, Xin Lan, Markus Leuenberger, Matthias Lindauer, Morgan Lopez, Giovanni Manca, Jennifer Müller-Williams, Simon O'Doherty, Bert Scheeren, Martin Steinbacher, Pamela Trisolino, Gabriela Vítková, and Camille Yver Kwok
Atmos. Chem. Phys., 22, 13243–13268, https://doi.org/10.5194/acp-22-13243-2022, https://doi.org/10.5194/acp-22-13243-2022, 2022
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Angharad C. Stell, Michael Bertolacci, Andrew Zammit-Mangion, Matthew Rigby, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Xin Lan, Manfredi Manizza, Jens Mühle, Simon O'Doherty, Ronald G. Prinn, Ray F. Weiss, Dickon Young, and Anita L. Ganesan
Atmos. Chem. Phys., 22, 12945–12960, https://doi.org/10.5194/acp-22-12945-2022, https://doi.org/10.5194/acp-22-12945-2022, 2022
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Luke M. Western, Alison L. Redington, Alistair J. Manning, Cathy M. Trudinger, Lei Hu, Stephan Henne, Xuekun Fang, Lambert J. M. Kuijpers, Christina Theodoridi, David S. Godwin, Jgor Arduini, Bronwyn Dunse, Andreas Engel, Paul J. Fraser, Christina M. Harth, Paul B. Krummel, Michela Maione, Jens Mühle, Simon O'Doherty, Hyeri Park, Sunyoung Park, Stefan Reimann, Peter K. Salameh, Daniel Say, Roland Schmidt, Tanja Schuck, Carolina Siso, Kieran M. Stanley, Isaac Vimont, Martin K. Vollmer, Dickon Young, Ronald G. Prinn, Ray F. Weiss, Stephen A. Montzka, and Matthew Rigby
Atmos. Chem. Phys., 22, 9601–9616, https://doi.org/10.5194/acp-22-9601-2022, https://doi.org/10.5194/acp-22-9601-2022, 2022
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Guus J. M. Velders, John S. Daniel, Stephen A. Montzka, Isaac Vimont, Matthew Rigby, Paul B. Krummel, Jens Muhle, Simon O'Doherty, Ronald G. Prinn, Ray F. Weiss, and Dickon Young
Atmos. Chem. Phys., 22, 6087–6101, https://doi.org/10.5194/acp-22-6087-2022, https://doi.org/10.5194/acp-22-6087-2022, 2022
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The emissions of hydrofluorocarbons (HFCs) have increased significantly in the past as a result of the phasing out of ozone-depleting substances. Observations indicate that HFCs are used much less in certain refrigeration applications than previously projected. Current policies are projected to reduce emissions and the surface temperature contribution of HFCs from 0.28–0.44 °C to 0.14–0.31 °C in 2100. The Kigali Amendment is projected to reduce the contributions further to 0.04 °C in 2100.
Haklim Choi, Mi-Kyung Park, Paul J. Fraser, Hyeri Park, Sohyeon Geum, Jens Mühle, Jooil Kim, Ian Porter, Peter K. Salameh, Christina M. Harth, Bronwyn L. Dunse, Paul B. Krummel, Ray F. Weiss, Simon O'Doherty, Dickon Young, and Sunyoung Park
Atmos. Chem. Phys., 22, 5157–5173, https://doi.org/10.5194/acp-22-5157-2022, https://doi.org/10.5194/acp-22-5157-2022, 2022
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We observed 12-year continuous CH3Br pollution signals at Gosan and estimated anthropogenic CH3Br emissions in eastern China. The analysis revealed a significant discrepancy between top-down estimates and the bottom-up emissions from the fumigation usage reported to the United Nations Environment Programme, likely due to unreported or inaccurately reported fumigation usage. This result provides information to monitor international compliance with the Montreal Protocol.
Alice E. Ramsden, Anita L. Ganesan, Luke M. Western, Matthew Rigby, Alistair J. Manning, Amy Foulds, James L. France, Patrick Barker, Peter Levy, Daniel Say, Adam Wisher, Tim Arnold, Chris Rennick, Kieran M. Stanley, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 22, 3911–3929, https://doi.org/10.5194/acp-22-3911-2022, https://doi.org/10.5194/acp-22-3911-2022, 2022
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Quantifying methane emissions from different sources is a key focus of current research. We present a method for estimating sectoral methane emissions that uses ethane as a tracer for fossil fuel methane. By incorporating variable ethane : methane emission ratios into this model, we produce emissions estimates with improved uncertainty characterisation. This method will be particularly useful for studying methane emissions in areas with complex distributions of sources.
Eric Saboya, Giulia Zazzeri, Heather Graven, Alistair J. Manning, and Sylvia Englund Michel
Atmos. Chem. Phys., 22, 3595–3613, https://doi.org/10.5194/acp-22-3595-2022, https://doi.org/10.5194/acp-22-3595-2022, 2022
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Continuous measurements of atmospheric methane concentrations and its carbon-13 isotope have been made in central London since early 2018. These measurements were used to evaluate methane emissions reported in global and UK-specific emission inventories for the London area. Compared to atmospheric methane measurements from March 2018 to October 2020, both inventories are under-reporting natural gas leakage for the London area.
Jens Mühle, Lambert J. M. Kuijpers, Kieran M. Stanley, Matthew Rigby, Luke M. Western, Jooil Kim, Sunyoung Park, Christina M. Harth, Paul B. Krummel, Paul J. Fraser, Simon O'Doherty, Peter K. Salameh, Roland Schmidt, Dickon Young, Ronald G. Prinn, Ray H. J. Wang, and Ray F. Weiss
Atmos. Chem. Phys., 22, 3371–3378, https://doi.org/10.5194/acp-22-3371-2022, https://doi.org/10.5194/acp-22-3371-2022, 2022
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Emissions of the strong greenhouse gas perfluorocyclobutane (c-C4F8) into the atmosphere have been increasing sharply since the early 2000s. These c-C4F8 emissions are highly correlated with the amount of hydrochlorofluorocarbon-22 produced to synthesize polytetrafluoroethylene (known for its non-stick properties) and related chemicals. From this process, c-C4F8 by-product is vented to the atmosphere. Avoiding these unnecessary c-C4F8 emissions could reduce the climate impact of this industry.
Dominique Rust, Ioannis Katharopoulos, Martin K. Vollmer, Stephan Henne, Simon O'Doherty, Daniel Say, Lukas Emmenegger, Renato Zenobi, and Stefan Reimann
Atmos. Chem. Phys., 22, 2447–2466, https://doi.org/10.5194/acp-22-2447-2022, https://doi.org/10.5194/acp-22-2447-2022, 2022
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Artificial halocarbons contribute to ozone layer depletion and to global warming. We measured the atmospheric concentrations of halocarbons at the Beromünster tower, modelled the Swiss emissions, and compared the results to the internationally reported Swiss emissions inventory. For most of the halocarbons, we found good agreement, whereas one refrigerant might be overestimated in the inventory. In addition, we present first emission estimates of the newest types of halocarbons.
Andrew Zammit-Mangion, Michael Bertolacci, Jenny Fisher, Ann Stavert, Matthew Rigby, Yi Cao, and Noel Cressie
Geosci. Model Dev., 15, 45–73, https://doi.org/10.5194/gmd-15-45-2022, https://doi.org/10.5194/gmd-15-45-2022, 2022
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We present a framework for estimating the sources and sinks (flux) of carbon dioxide from satellite data. The framework is statistical and yields measures of uncertainty alongside all estimates of flux and other parameters in the underlying model. It also allows us to generate other insights, such as the size of errors and biases in the data. The primary aim of this research was to develop a fully statistical flux inversion framework for use by atmospheric scientists.
Jan C. Minx, William F. Lamb, Robbie M. Andrew, Josep G. Canadell, Monica Crippa, Niklas Döbbeling, Piers M. Forster, Diego Guizzardi, Jos Olivier, Glen P. Peters, Julia Pongratz, Andy Reisinger, Matthew Rigby, Marielle Saunois, Steven J. Smith, Efisio Solazzo, and Hanqin Tian
Earth Syst. Sci. Data, 13, 5213–5252, https://doi.org/10.5194/essd-13-5213-2021, https://doi.org/10.5194/essd-13-5213-2021, 2021
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We provide a synthetic dataset on anthropogenic greenhouse gas (GHG) emissions for 1970–2018 with a fast-track extension to 2019. We show that GHG emissions continued to rise across all gases and sectors. Annual average GHG emissions growth slowed, but absolute decadal increases have never been higher in human history. We identify a number of data gaps and data quality issues in global inventories and highlight their importance for monitoring progress towards international climate goals.
Mark F. Lunt, Alistair J. Manning, Grant Allen, Tim Arnold, Stéphane J.-B. Bauguitte, Hartmut Boesch, Anita L. Ganesan, Aoife Grant, Carole Helfter, Eiko Nemitz, Simon J. O'Doherty, Paul I. Palmer, Joseph R. Pitt, Chris Rennick, Daniel Say, Kieran M. Stanley, Ann R. Stavert, Dickon Young, and Matt Rigby
Atmos. Chem. Phys., 21, 16257–16276, https://doi.org/10.5194/acp-21-16257-2021, https://doi.org/10.5194/acp-21-16257-2021, 2021
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We present an evaluation of the UK's methane emissions between 2013 and 2020 using a network of tall tower measurement sites. We find emissions that are consistent in both magnitude and trend with the UK's reported emissions, with a declining trend driven by a decrease in emissions from England. The impact of various components of the modelling set-up on these findings are explored through a number of sensitivity studies.
Alistair J. Manning, Alison L. Redington, Daniel Say, Simon O'Doherty, Dickon Young, Peter G. Simmonds, Martin K. Vollmer, Jens Mühle, Jgor Arduini, Gerard Spain, Adam Wisher, Michela Maione, Tanja J. Schuck, Kieran Stanley, Stefan Reimann, Andreas Engel, Paul B. Krummel, Paul J. Fraser, Christina M. Harth, Peter K. Salameh, Ray F. Weiss, Ray Gluckman, Peter N. Brown, John D. Watterson, and Tim Arnold
Atmos. Chem. Phys., 21, 12739–12755, https://doi.org/10.5194/acp-21-12739-2021, https://doi.org/10.5194/acp-21-12739-2021, 2021
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This paper estimates UK emissions of important greenhouse gases (hydrofluorocarbons (HFCs)) using high-quality atmospheric observations and atmospheric modelling. We compare these estimates with those submitted by the UK to the United Nations. We conclude that global concentrations of these gases are still increasing. Our estimates for the UK are 73 % of those reported and that the UK emissions are now falling, demonstrating an impact of UK government policy.
Ruth E. Hill-Pearce, Aimee Hillier, Eric Mussell Webber, Kanokrat Charoenpornpukdee, Simon O'Doherty, Joachim Mohn, Christoph Zellweger, David R. Worton, and Paul J. Brewer
Atmos. Meas. Tech., 14, 5447–5458, https://doi.org/10.5194/amt-14-5447-2021, https://doi.org/10.5194/amt-14-5447-2021, 2021
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There is currently a need for gas reference materials with well-characterised delta values for monitoring N2O amount fractions. We present work towards the preparation of gas reference materials for calibration of in-field monitoring equipment, which target the WMO-GAW data quality objectives for comparability of amount fraction and demonstrate the stability of δ15Nα, δ15Nβ and δ18O values with pressure and effects of cylinder passivation.
Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout, Francesco N. Tubiello, Peter Bergamaschi, Dominik Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Oreggioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Hanqin Tian, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, https://doi.org/10.5194/essd-13-2307-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK. The data integrate recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.
Daniel Say, Alistair J. Manning, Luke M. Western, Dickon Young, Adam Wisher, Matthew Rigby, Stefan Reimann, Martin K. Vollmer, Michela Maione, Jgor Arduini, Paul B. Krummel, Jens Mühle, Christina M. Harth, Brendan Evans, Ray F. Weiss, Ronald G. Prinn, and Simon O'Doherty
Atmos. Chem. Phys., 21, 2149–2164, https://doi.org/10.5194/acp-21-2149-2021, https://doi.org/10.5194/acp-21-2149-2021, 2021
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Perfluorocarbons (PFCs) are potent greenhouse gases with exceedingly long lifetimes. We used atmospheric measurements from a global monitoring network to track the accumulation of these gases in the atmosphere. In the case of the two most abundant PFCs, recent measurements indicate that global emissions are increasing. In Europe, we used a model to estimate regional PFC emissions. Our results show that there was no significant decline in northwest European PFC emissions between 2010 and 2019.
Angharad C. Stell, Luke M. Western, Tomás Sherwen, and Matthew Rigby
Atmos. Chem. Phys., 21, 1717–1736, https://doi.org/10.5194/acp-21-1717-2021, https://doi.org/10.5194/acp-21-1717-2021, 2021
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Although it is the second-most important greenhouse gas, our understanding of the atmospheric-methane budget is limited. The uncertainty highlights the need for new tools to investigate sources and sinks. Here, we use a Gaussian process emulator to efficiently approximate the response of atmospheric-methane observations to changes in the most uncertain emission or loss processes. With this new method, we rigorously quantify the sensitivity of atmospheric observations to budget uncertainties.
Rachel L. Tunnicliffe, Anita L. Ganesan, Robert J. Parker, Hartmut Boesch, Nicola Gedney, Benjamin Poulter, Zhen Zhang, Jošt V. Lavrič, David Walter, Matthew Rigby, Stephan Henne, Dickon Young, and Simon O'Doherty
Atmos. Chem. Phys., 20, 13041–13067, https://doi.org/10.5194/acp-20-13041-2020, https://doi.org/10.5194/acp-20-13041-2020, 2020
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This study quantifies Brazil’s emissions of a potent atmospheric greenhouse gas, methane. This is in the field of atmospheric modelling and uses remotely sensed data and surface measurements of methane concentrations as well as an atmospheric transport model to interpret the data. Because of Brazil’s large emissions from wetlands, agriculture and biomass burning, these emissions affect global methane concentrations and thus are of global significance.
Guillaume Monteil, Grégoire Broquet, Marko Scholze, Matthew Lang, Ute Karstens, Christoph Gerbig, Frank-Thomas Koch, Naomi E. Smith, Rona L. Thompson, Ingrid T. Luijkx, Emily White, Antoon Meesters, Philippe Ciais, Anita L. Ganesan, Alistair Manning, Michael Mischurow, Wouter Peters, Philippe Peylin, Jerôme Tarniewicz, Matt Rigby, Christian Rödenbeck, Alex Vermeulen, and Evie M. Walton
Atmos. Chem. Phys., 20, 12063–12091, https://doi.org/10.5194/acp-20-12063-2020, https://doi.org/10.5194/acp-20-12063-2020, 2020
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The paper presents the first results from the EUROCOM project, a regional atmospheric inversion intercomparison exercise involving six European research groups. It aims to produce an estimate of the net carbon flux between the European terrestrial ecosystems and the atmosphere for the period 2006–2015, based on constraints provided by observed CO2 concentrations and using inverse modelling techniques. The use of six different models enables us to investigate the robustness of the results.
Cited articles
Bergamaschi, P., Karstens, U., Manning, A. J., Saunois, M., Tsuruta, A., Berchet, A., Vermeulen, A. T., Arnold, T., Janssens-Maenhout, G., Hammer, S., Levin, I., Schmidt, M., Ramonet, M., Lopez, M., Lavric, J., Aalto, T., Chen, H., Feist, D. G., Gerbig, C., Haszpra, L., Hermansen, O., Manca, G., Moncrieff, J., Meinhardt, F., Necki, J., Galkowski, M., O'Doherty, S., Paramonova, N., Scheeren, H. A., Steinbacher, M., and Dlugokencky, E.: Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations, Atmos. Chem. Phys., 18, 901–920, https://doi.org/10.5194/acp-18-901-2018, 2018. a
Brown, P., Cardenas, L., Choudrie, S., Jones, L., Karagianni, E., MacCarthy,
J., Passant, N., Richmond, B., Smith, H., Thistlethwaite, G., Thomson, A.,
Turtle, L., and Wakeling, D.: UK Greenhouse Gas Inventory, 1990 to 2018:
Annual Report for Submission under the Framework Convention on Climate
Change, Tech. Rep., Department for Business, Energy & Industrial Strategy, 978-0-9933975-6-1, https://naei.beis.gov.uk/reports/reports?report_id=998 (last access: 28 March 2023),
2020. a
Butz, A., Galli, A., Hasekamp, O., Landgraf, J., Tol, P., and Aben, I.: TROPOMI
aboard Sentinel-5 Precursor: Prospective performance of CH4 retrievals for
aerosol and cirrus loaded atmospheres, Remote Sens. Environ., 120,
267–276, https://doi.org/10.1016/j.rse.2011.05.030, 2012. a
Cartwright, L., Zammit-Mangion, A., and Deutscher, N. M.: Emulation of greenhouse-gas sensitivities using variational autoencoders, Environmetrics, 34, e2754, https://doi.org/10.1002/env.2754, 2023. a, b
Chicco, D., Warrens, M. J., and Jurman, G.: The coefficient of determination
R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in
regression analysis evaluation, PeerJ Computer Science, 7, e623,
https://doi.org/10.7717/peerj-cs.623, 2021. a
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo, E.,
Monforti-Ferrario, F., Olivier, J., and Vignati, E.: EDGAR v6.0 Greenhouse
Gas Emissions, European Commission, Joint Research Centre (JRC) [data set],
https://data.jrc.ec.europa.eu/dataset/97a67d67-c62e-4826-b873-9d972c4f670b (last access: 1 March 2023), 2021. a
Cullen, M. J. P.: The unified forecast/climate model, Meteorol. Mag.,
122, 81–94, 1993. a
Fasoli, B., Lin, J. C., Bowling, D. R., Mitchell, L., and Mendoza, D.: Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model's R interface (STILT-R version 2), Geosci. Model Dev., 11, 2813–2824, https://doi.org/10.5194/gmd-11-2813-2018, 2018. a, b
Fillola, E.: Sample dataset for “A machine learning emulator for Lagrangian
particle dispersion model footprints”, Zenodo [data set], https://doi.org/10.5281/zenodo.7254330,
2022a. a
Fillola, E.: elenafillo/LPDM-emulation-trees: LPDM-emulation-trees v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7254667, 2022b. a
Francom, D., Sansó, B., Bulaevskaya, V., Lucas, D., and Simpson, M.: Inferring
atmospheric release characteristics in a large computer experiment using
Bayesian adaptive splines, J. Am. Stat. Assoc.,
114, 1450–1465, https://doi.org/10.1080/01621459.2018.1562933, 2019. a, b
Friedman, J. H.: Greedy function approximation: A gradient boosting machine,
Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001. a, b
Friedman, J. H.: Stochastic gradient boosting, Comput. Stat. Data
An., 38, 367–378, https://doi.org/10.1016/s0167-9473(01)00065-2, 2002. a
Ganesan, A. L., Rigby, M., Zammit-Mangion, A., Manning, A. J., Prinn, R. G., Fraser, P. J., Harth, C. M., Kim, K.-R., Krummel, P. B., Li, S., Mühle, J., O'Doherty, S. J., Park, S., Salameh, P. K., Steele, L. P., and Weiss, R. F.: Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods, Atmos. Chem. Phys., 14, 3855–3864, https://doi.org/10.5194/acp-14-3855-2014, 2014. a
Gunawardena, N., Pallotta, G., Simpson, M., and Lucas, D. D.: Machine learning
emulation of spatial deposition from a multi-physics ensemble of weather and
atmospheric transport models, Atmosphere, 12, 953,
https://doi.org/10.3390/atmos12080953, 2021. a, b
Harvey, N. J., Huntley, N., Dacre, H. F., Goldstein, M., Thomson, D., and Webster, H.: Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters, Nat. Hazards Earth Syst. Sci., 18, 41–63, https://doi.org/10.5194/nhess-18-41-2018, 2018. a
Hodson, T. O.: Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev., 15, 5481–5487, https://doi.org/10.5194/gmd-15-5481-2022, 2022. a
Ivatt, P. D. and Evans, M. J.: Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees, Atmos. Chem. Phys., 20, 8063–8082, https://doi.org/10.5194/acp-20-8063-2020, 2020. a, b
Jones, A., Thomson, D., Hort, M., and Devenish, B.: The U.K. Met Office's
next-generation atmospheric dispersion model, NAME III, Air Pollution
Modeling and Its Application XVII, 580–589, ISBN 978-0-387-68854-1,
https://doi.org/10.1007/978-0-387-68854-1_62, 2007. a
Kaminski, T., Heimann, M., and Giering, R.: A coarse grid three-dimensional
global inverse model of the atmospheric transport: 1. Adjoint model and
Jacobian matrix, J. Geophys. Res.-Atmos., 104,
18535–18553, https://doi.org/10.1029/1999jd900147, 1999. a
Lucas, D. D., Simpson, M., Cameron-Smith, P., and Baskett, R. L.: Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant, Atmos. Chem. Phys., 17, 13521–13543, https://doi.org/10.5194/acp-17-13521-2017, 2017. a, b
Lunt, M. F., Rigby, M., Ganesan, A. L., and Manning, A. J.: Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo, Geosci. Model Dev., 9, 3213–3229, https://doi.org/10.5194/gmd-9-3213-2016, 2016. a, b
Lunt, M. F., Manning, A. J., Allen, G., Arnold, T., Bauguitte, S. J.-B., Boesch, H., Ganesan, A. L., Grant, A., Helfter, C., Nemitz, E., O'Doherty, S. J., Palmer, P. I., Pitt, J. R., Rennick, C., Say, D., Stanley, K. M., Stavert, A. R., Young, D., and Rigby, M.: Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020), Atmos. Chem. Phys., 21, 16257–16276, https://doi.org/10.5194/acp-21-16257-2021, 2021. a, b, c, d, e, f, g
Manning, A., Redington, A., O'Doherty, S., Say, D., Young, D., Arnold, T.,
Rennick, C., Rigby, M., Wisher, A., and Simmonds, P.: Long-Term Atmospheric
Measurement and Interpretation of Radiatively Active Trace Gases – Detailed
Report (September 2019 to August 2020), Tech. Rep., Department for Business,
Energy & Industrial Strategy, https://assets.publishing.service.gov.uk/government (last access 1 June 2022), 2020. a
Mendil, M., Leirens, S., Armand, P., and Duchenne, C.: Hazardous atmospheric
dispersion in urban areas: A Deep Learning approach for emergency pollution
forecast, Environ. Modell. Softw., 152, 105387,
https://doi.org/10.1016/j.envsoft.2022.105387, 2022. a
Met Office: Operational Numerical Weather Prediction (NWP) Output from
the UK Variable (UKV) Resolution Part of the Met Office Unified Model (UM),
NCAS British Atmospheric Data Centre [data set],
http://catalogue.ceda.ac.uk/uuid/292da1ccfebd650f6d123e53270016a8 (last access: 1 March 2022), 2013a. a, b
Met Office: Operational Numerical Weather Prediction (NWP) Output from
the North Atlantic European (NAE) Part of the Met Office Unified Model (UM),
NCAS British Atmospheric Data Centre [data set],
http://catalogue.ceda.ac.uk/uuid/220f1c04ffe39af29233b78c2cf2699a
(last access: 1 March 2022), 2013b. a, b
Molnar, C.: Global Model-Agnostic Methods: Permutation Feature Importance,
Christoph Molnar,
https://christophm.github.io/interpretable-ml-book/feature-importance.html, last access: 1 July 2022. a
O'Doherty, S., Say, D., Stanley, K., Spain, G., Arnold, T., Rennick, C., Young,
D., Stavert, A., Grant, A., Ganesan, A., Pitt, J., Wisher, A., Wenger, A.,
and Garrard, N.: UK DECC (Deriving Emissions linked to Climate Change)
Network, Centre for Environmental Data Analysis [data set],
https://catalogue.ceda.ac.uk/uuid/f5b38d1654d84b03ba79060746541e4f (last access: 1 March 2022), 2020. a, b
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a
Peters, W., Miller, J. B., Whitaker, J., Denning, A. S., Hirsch, A., Krol,
M. C., Zupanski, D., Bruhwiler, L., and Tans, P. P.: An ensemble data
assimilation system to estimate CO2 surface fluxes from atmospheric trace gas
observations, J. Geophys. Res., 110, D24304,
https://doi.org/10.1029/2005jd006157, 2005. a
Pisso, I., Sollum, E., Grythe, H., Kristiansen, N. I., Cassiani, M., Eckhardt, S., Arnold, D., Morton, D., Thompson, R. L., Groot Zwaaftink, C. D., Evangeliou, N., Sodemann, H., Haimberger, L., Henne, S., Brunner, D., Burkhart, J. F., Fouilloux, A., Brioude, J., Philipp, A., Seibert, P., and Stohl, A.: The Lagrangian particle dispersion model FLEXPART version 10.4, Geosci. Model Dev., 12, 4955–4997, https://doi.org/10.5194/gmd-12-4955-2019, 2019. a
Prinn, R. G., Weiss, R. F., Arduini, J., Arnold, T., DeWitt, H. L., Fraser, P. J., Ganesan, A. L., Gasore, J., Harth, C. M., Hermansen, O., Kim, J., Krummel, P. B., Li, S., Loh, Z. M., Lunder, C. R., Maione, M., Manning, A. J., Miller, B. R., Mitrevski, B., Mühle, J., O'Doherty, S., Park, S., Reimann, S., Rigby, M., Saito, T., Salameh, P. K., Schmidt, R., Simmonds, P. G., Steele, L. P., Vollmer, M. K., Wang, R. H., Yao, B., Yokouchi, Y., Young, D., and Zhou, L.: History of chemically and radiatively important atmospheric gases from the Advanced Global Atmospheric Gases Experiment (AGAGE), Earth Syst. Sci. Data, 10, 985–1018, https://doi.org/10.5194/essd-10-985-2018, 2018. a
Prinn, R. G., Weiss, R. F., Arduini, J., Arnold, T., Fraser, P. J., Ganesan,
A. L., Gasore, J., Harth, C. M., Hermansen, O., Kim, J., Krummel, P. B., Li,
S., Loh, Z. M., Lunder, C. R., Maione, M., Manning, A. J., Miller, B. R.,
Mitrevski, B., Mühle, J., O'Doherty, S., Park, S., Reimann, S., Rigby, M.,
Salameh, P. K., Schmidt, R., Simmonds, P., Steele, L. P., Vollmer, M. K.,
Wang, R. H., and Young, D.: The ALE/GAGE/AGAGE Data Base [data set],
http://agage.mit.edu/data, last access: 1 March 2022. a, b
Rigby, M., Park, S., Saito, T., Western, L. M., Redington, A. L., Fang, X.,
Henne, S., Manning, A. J., Prinn, R. G., Dutton, G. S., Fraser, P. J.,
Ganesan, A. L., Hall, B. D., Harth, C. M., Kim, J., Kim, K.-R., Krummel,
P. B., Lee, T., Li, S., Liang, Q., Lunt, M. F., Montzka, S. A., Mühle, J.,
O’Doherty, S., Park, M.-K., Reimann, S., Salameh, P. K., Simmonds, P.,
Tunnicliffe, R. L., Weiss, R. F., Yokouchi, Y., and Young, D.: Increase in
CFC-11 emissions from eastern China based on atmospheric observations,
Nature, 569, 546–550, https://doi.org/10.1038/s41586-019-1193-4, 2019. a, b
Rigby, M., Tunnicliffe, R., Western, L., Chawner, H., Ganesan, A., Ramsden, A.,
Jones, G., Young, D., Ward, R., Stell, A., Nickless, A., and Pitt, J.:
ACRG-Bristol/acrg: ACRG v0.2.0 (v0.2.0), Zenodo [code],
https://doi.org/10.5281/zenodo.6834888, 2022. a
Roten, D., Wu, D., Fasoli, B., Oda, T., and Lin, J. C.: An interpolation method
to reduce the computational time in the stochastic Lagrangian particle
dispersion modeling of spatially dense XCO2 retrievals, Earth and Space
Science, 8, e2020EA001343, https://doi.org/10.1029/2020ea001343, 2021. a
Sayegh, A., Tate, J. E., and Ropkins, K.: Understanding how roadside
concentrations of NOX are influenced by the background levels, traffic
density, and meteorological conditions using boosted regression trees,
Atmos. Environ., 127, 163–175,
https://doi.org/10.1016/j.atmosenv.2015.12.024, 2016.
a
Stanley, K. M., Grant, A., O'Doherty, S., Young, D., Manning, A. J., Stavert, A. R., Spain, T. G., Salameh, P. K., Harth, C. M., Simmonds, P. G., Sturges, W. T., Oram, D. E., and Derwent, R. G.: Greenhouse gas measurements from a UK network of tall towers: technical description and first results, Atmos. Meas. Tech., 11, 1437–1458, https://doi.org/10.5194/amt-11-1437-2018, 2018. a
Taylor, T. E., O'Dell, C. W., Crisp, D., Kuze, A., Lindqvist, H., Wennberg, P. O., Chatterjee, A., Gunson, M., Eldering, A., Fisher, B., Kiel, M., Nelson, R. R., Merrelli, A., Osterman, G., Chevallier, F., Palmer, P. I., Feng, L., Deutscher, N. M., Dubey, M. K., Feist, D. G., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Liu, C., De Mazière, M., Morino, I., Notholt, J., Oh, Y.-S., Ohyama, H., Pollard, D. F., Rettinger, M., Schneider, M., Roehl, C. M., Sha, M. K., Shiomi, K., Strong, K., Sussmann, R., Té, Y., Velazco, V. A., Vrekoussis, M., Warneke, T., and Wunch, D.: An 11-year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm, Earth Syst. Sci. Data, 14, 325–360, https://doi.org/10.5194/essd-14-325-2022, 2022. a
Western, L. M., Sha, Z., Rigby, M., Ganesan, A. L., Manning, A. J., Stanley, K. M., O'Doherty, S. J., Young, D., and Rougier, J.: Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields, Geosci. Model Dev., 13, 2095–2107, https://doi.org/10.5194/gmd-13-2095-2020, 2020. a
Western, L. M., Ramsden, A. E., Ganesan, A. L., Boesch, H., Parker, R. J.,
Scarpelli, T. R., Tunnicliffe, R. L., and Rigby, M.: Estimates of North
African methane emissions from 2010 to 2017 using GOSAT observations,
Environ. Sci. Tech. Lett., 8, 626–632,
https://doi.org/10.1021/acs.estlett.1c00327, 2021. a, b
Zammit-Mangion, A., Bertolacci, M., Fisher, J., Stavert, A., Rigby, M., Cao, Y., and Cressie, N.: WOMBAT v1.0: a fully Bayesian global flux-inversion framework, Geosci. Model Dev., 15, 45–73, https://doi.org/10.5194/gmd-15-45-2022, 2022. a
Short summary
Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas...
