Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-2187-2021
© Author(s) 2021. 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-14-2187-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
JlBox v1.1: a Julia-based multi-phase atmospheric chemistry box model
Department of Mathematics, ETH Zurich, Zurich, Switzerland
Department of Earth and Environmental Science, The University of Manchester, Manchester, UK
David Topping
CORRESPONDING AUTHOR
Department of Earth and Environmental Science, The University of Manchester, Manchester, UK
Related authors
Alexander Pietak, Langwen Huang, Luigi Fusco, Michael Sprenger, Sebastian Schemm, and Torsten Hoefler
EGUsphere, https://doi.org/10.5194/egusphere-2025-793, https://doi.org/10.5194/egusphere-2025-793, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
As meteorological models grow in complexity, the volume of output data increases, making compression increasingly desirable. However, no specialized methods currently exist for compressing data in the Lagrangian frame. To address this gap, we developed psit, a pipeline for the lossy compression of Lagrangian flow data. In most cases, psit achieves performance that is equivalent or superior to non specialized alternatives, with compression errors behaving similar to measurement inaccuracies.
Magdalena Pühl, Anke Roiger, Alina Fiehn, Alan M. Gorchov Negron, Eric A. Kort, Stefan Schwietzke, Ignacio Pisso, Amy Foulds, James Lee, James L. France, Anna E. Jones, Dave Lowry, Rebecca E. Fisher, Langwen Huang, Jacob Shaw, Prudence Bateson, Stephen Andrews, Stuart Young, Pamela Dominutti, Tom Lachlan-Cope, Alexandra Weiss, and Grant Allen
Atmos. Chem. Phys., 24, 1005–1024, https://doi.org/10.5194/acp-24-1005-2024, https://doi.org/10.5194/acp-24-1005-2024, 2024
Short summary
Short summary
In April–May 2019 we carried out an airborne field campaign in the southern North Sea with the aim of studying methane emissions of offshore gas installations. We determined methane emissions from elevated methane measured downstream of the sampled installations. We compare our measured methane emissions with estimated methane emissions from national and global annual inventories. As a result, we find inconsistencies of inventories and large discrepancies between measurements and inventories.
Amy Foulds, Grant Allen, Jacob T. Shaw, Prudence Bateson, Patrick A. Barker, Langwen Huang, Joseph R. Pitt, James D. Lee, Shona E. Wilde, Pamela Dominutti, Ruth M. Purvis, David Lowry, James L. France, Rebecca E. Fisher, Alina Fiehn, Magdalena Pühl, Stéphane J. B. Bauguitte, Stephen A. Conley, Mackenzie L. Smith, Tom Lachlan-Cope, Ignacio Pisso, and Stefan Schwietzke
Atmos. Chem. Phys., 22, 4303–4322, https://doi.org/10.5194/acp-22-4303-2022, https://doi.org/10.5194/acp-22-4303-2022, 2022
Short summary
Short summary
We measured CH4 emissions from 21 offshore oil and gas facilities in the Norwegian Sea in 2019. Measurements compared well with operator-reported emissions but were greatly underestimated when compared with a 2016 global fossil fuel inventory. This study demonstrates the need for up-to-date and accurate inventories for use in research and policy and the important benefits of best-practice reporting methods by operators. Airborne measurements are an effective tool to validate such inventories.
Shona E. Wilde, Pamela A. Dominutti, Grant Allen, Stephen J. Andrews, Prudence Bateson, Stephane J.-B. Bauguitte, Ralph R. Burton, Ioana Colfescu, James France, James R. Hopkins, Langwen Huang, Anna E. Jones, Tom Lachlan-Cope, James D. Lee, Alastair C. Lewis, Stephen D. Mobbs, Alexandra Weiss, Stuart Young, and Ruth M. Purvis
Atmos. Chem. Phys., 21, 3741–3762, https://doi.org/10.5194/acp-21-3741-2021, https://doi.org/10.5194/acp-21-3741-2021, 2021
Short summary
Short summary
We use airborne measurements to evaluate the speciation of volatile organic compound (VOC) emissions from offshore oil and gas (O&G) installations in the North Sea. The composition of emissions varied across regions associated with either gas, condensate or oil extraction, demonstrating that VOC emissions are not uniform across the whole O&G sector. We compare our results to VOC source profiles in the UK emissions inventory, showing these emissions are not currently fully characterized.
James L. France, Prudence Bateson, Pamela Dominutti, Grant Allen, Stephen Andrews, Stephane Bauguitte, Max Coleman, Tom Lachlan-Cope, Rebecca E. Fisher, Langwen Huang, Anna E. Jones, James Lee, David Lowry, Joseph Pitt, Ruth Purvis, John Pyle, Jacob Shaw, Nicola Warwick, Alexandra Weiss, Shona Wilde, Jonathan Witherstone, and Stuart Young
Atmos. Meas. Tech., 14, 71–88, https://doi.org/10.5194/amt-14-71-2021, https://doi.org/10.5194/amt-14-71-2021, 2021
Short summary
Short summary
Measuring emission rates of methane from installations is tricky, and it is even more so when those installations are located offshore. Here, we show the aircraft set-up and demonstrate an effective methodology for surveying emissions from UK and Dutch offshore oil and gas installations. We present example data collected from two campaigns to demonstrate the challenges and solutions encountered during these surveys.
Alexander Pietak, Langwen Huang, Luigi Fusco, Michael Sprenger, Sebastian Schemm, and Torsten Hoefler
EGUsphere, https://doi.org/10.5194/egusphere-2025-793, https://doi.org/10.5194/egusphere-2025-793, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
As meteorological models grow in complexity, the volume of output data increases, making compression increasingly desirable. However, no specialized methods currently exist for compressing data in the Lagrangian frame. To address this gap, we developed psit, a pipeline for the lossy compression of Lagrangian flow data. In most cases, psit achieves performance that is equivalent or superior to non specialized alternatives, with compression errors behaving similar to measurement inaccuracies.
Magdalena Pühl, Anke Roiger, Alina Fiehn, Alan M. Gorchov Negron, Eric A. Kort, Stefan Schwietzke, Ignacio Pisso, Amy Foulds, James Lee, James L. France, Anna E. Jones, Dave Lowry, Rebecca E. Fisher, Langwen Huang, Jacob Shaw, Prudence Bateson, Stephen Andrews, Stuart Young, Pamela Dominutti, Tom Lachlan-Cope, Alexandra Weiss, and Grant Allen
Atmos. Chem. Phys., 24, 1005–1024, https://doi.org/10.5194/acp-24-1005-2024, https://doi.org/10.5194/acp-24-1005-2024, 2024
Short summary
Short summary
In April–May 2019 we carried out an airborne field campaign in the southern North Sea with the aim of studying methane emissions of offshore gas installations. We determined methane emissions from elevated methane measured downstream of the sampled installations. We compare our measured methane emissions with estimated methane emissions from national and global annual inventories. As a result, we find inconsistencies of inventories and large discrepancies between measurements and inventories.
Matthias Karl, Liisa Pirjola, Tiia Grönholm, Mona Kurppa, Srinivasan Anand, Xiaole Zhang, Andreas Held, Rolf Sander, Miikka Dal Maso, David Topping, Shuai Jiang, Leena Kangas, and Jaakko Kukkonen
Geosci. Model Dev., 15, 3969–4026, https://doi.org/10.5194/gmd-15-3969-2022, https://doi.org/10.5194/gmd-15-3969-2022, 2022
Short summary
Short summary
The community aerosol dynamics model MAFOR includes several advanced features: coupling with an up-to-date chemistry mechanism for volatile organic compounds, a revised Brownian coagulation kernel that takes into account the fractal geometry of soot particles, a multitude of nucleation parameterizations, size-resolved partitioning of semi-volatile inorganics, and a hybrid method for the formation of secondary organic aerosols within the framework of condensation and evaporation.
Amy Foulds, Grant Allen, Jacob T. Shaw, Prudence Bateson, Patrick A. Barker, Langwen Huang, Joseph R. Pitt, James D. Lee, Shona E. Wilde, Pamela Dominutti, Ruth M. Purvis, David Lowry, James L. France, Rebecca E. Fisher, Alina Fiehn, Magdalena Pühl, Stéphane J. B. Bauguitte, Stephen A. Conley, Mackenzie L. Smith, Tom Lachlan-Cope, Ignacio Pisso, and Stefan Schwietzke
Atmos. Chem. Phys., 22, 4303–4322, https://doi.org/10.5194/acp-22-4303-2022, https://doi.org/10.5194/acp-22-4303-2022, 2022
Short summary
Short summary
We measured CH4 emissions from 21 offshore oil and gas facilities in the Norwegian Sea in 2019. Measurements compared well with operator-reported emissions but were greatly underestimated when compared with a 2016 global fossil fuel inventory. This study demonstrates the need for up-to-date and accurate inventories for use in research and policy and the important benefits of best-practice reporting methods by operators. Airborne measurements are an effective tool to validate such inventories.
Shona E. Wilde, Pamela A. Dominutti, Grant Allen, Stephen J. Andrews, Prudence Bateson, Stephane J.-B. Bauguitte, Ralph R. Burton, Ioana Colfescu, James France, James R. Hopkins, Langwen Huang, Anna E. Jones, Tom Lachlan-Cope, James D. Lee, Alastair C. Lewis, Stephen D. Mobbs, Alexandra Weiss, Stuart Young, and Ruth M. Purvis
Atmos. Chem. Phys., 21, 3741–3762, https://doi.org/10.5194/acp-21-3741-2021, https://doi.org/10.5194/acp-21-3741-2021, 2021
Short summary
Short summary
We use airborne measurements to evaluate the speciation of volatile organic compound (VOC) emissions from offshore oil and gas (O&G) installations in the North Sea. The composition of emissions varied across regions associated with either gas, condensate or oil extraction, demonstrating that VOC emissions are not uniform across the whole O&G sector. We compare our results to VOC source profiles in the UK emissions inventory, showing these emissions are not currently fully characterized.
Simon Patrick O'Meara, Shuxuan Xu, David Topping, M. Rami Alfarra, Gerard Capes, Douglas Lowe, Yunqi Shao, and Gordon McFiggans
Geosci. Model Dev., 14, 675–702, https://doi.org/10.5194/gmd-14-675-2021, https://doi.org/10.5194/gmd-14-675-2021, 2021
Short summary
Short summary
User-friendly and open-source software for simulating aerosol chambers is a valuable tool for research scientists in designing and analysing their experiments. This paper describes a new version of such software and will therefore provide a useful reference for those applying it. Central to the paper is an assessment of the software's accuracy through comparison against previously published simulations.
James L. France, Prudence Bateson, Pamela Dominutti, Grant Allen, Stephen Andrews, Stephane Bauguitte, Max Coleman, Tom Lachlan-Cope, Rebecca E. Fisher, Langwen Huang, Anna E. Jones, James Lee, David Lowry, Joseph Pitt, Ruth Purvis, John Pyle, Jacob Shaw, Nicola Warwick, Alexandra Weiss, Shona Wilde, Jonathan Witherstone, and Stuart Young
Atmos. Meas. Tech., 14, 71–88, https://doi.org/10.5194/amt-14-71-2021, https://doi.org/10.5194/amt-14-71-2021, 2021
Short summary
Short summary
Measuring emission rates of methane from installations is tricky, and it is even more so when those installations are located offshore. Here, we show the aircraft set-up and demonstrate an effective methodology for surveying emissions from UK and Dutch offshore oil and gas installations. We present example data collected from two campaigns to demonstrate the challenges and solutions encountered during these surveys.
Douglas Morrison, Ian Crawford, Nicholas Marsden, Michael Flynn, Katie Read, Luis Neves, Virginia Foot, Paul Kaye, Warren Stanley, Hugh Coe, David Topping, and Martin Gallagher
Atmos. Chem. Phys., 20, 14473–14490, https://doi.org/10.5194/acp-20-14473-2020, https://doi.org/10.5194/acp-20-14473-2020, 2020
Short summary
Short summary
We provide conservative estimates of the concentrations of bacteria within transatlantic dust clouds, originating from the African continent. We observe significant seasonal differences in the overall concentrations of particles but no seasonal variation in the ratio between bacteria and dust. With bacteria contributing to ice formation at warmer temperatures than dust, our observations should improve the accuracy of climate models.
Cited articles
Amundson, N. R., Caboussat, A., He, J. W., Martynenko, A. V., Savarin, V. B., Seinfeld, J. H., and Yoo, K. Y.: A new inorganic atmospheric aerosol phase equilibrium model (UHAERO), Atmos. Chem. Phys., 6, 975–992, https://doi.org/10.5194/acp-6-975-2006, 2006. a
Bilde, M., Barsanti, K., Booth, M., Cappa, C. D., Donahue, N. M., Emanuelsson, E. U., McFiggans, G., Krieger, U. K., Marcolli, C., Topping, D., Ziemann, P., Barley, M., Clegg, S., Dennis-Smither, B., Hallquist, M., Hallquist, Å. M., Khlystov, A., Kulmala, M., Mogensen, D., Percival, C. J., Pope, F., Reid, J. P., Ribeiro Da Silva, M. A., Rosenoern, T., Salo, K., Soonsin, V. P., Yli-Juuti, T., Prisle, N. L., Pagels, J., Rarey, J., Zardini, A. A., and Riipinen, I.: Saturation Vapor Pressures and Transition Enthalpies of Low-Volatility Organic Molecules of Atmospheric Relevance: From Dicarboxylic Acids to Complex Mixtures, Chem. Rev., 115, 4115–4156, https://doi.org/10.1021/cr5005502, 2015. a
Couvidat, F., Vivanco, M. G., and Bessagnet, B.: Simulating secondary organic aerosol from anthropogenic and biogenic precursors: comparison to outdoor chamber experiments, effect of oligomerization on SOA formation and reactive uptake of aldehydes, Atmos. Chem. Phys., 18, 15743–15766, https://doi.org/10.5194/acp-18-15743-2018, 2018. a, b, c
Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G. R.: The kinetic preprocessor KPP - A software environment for solving chemical kinetics, Computers and Chemical Engineering, 26, 1567–1579, https://doi.org/10.1016/S0098-1354(02)00128-X, 2002. a, b, c, d
Ehn, M., Thornton, J. A., Kleist, E., Sipilä, M., Junninen, H., Pullinen, I., Springer, M., Rubach, F., Tillmann, R., Lee, B., Lopez-Hilfiker, F., Andres, S., Acir, I. H., Rissanen, M., Jokinen, T., Schobesberger, S., Kangasluoma, J., Kontkanen, J., Nieminen, T., Kurtén, T., Nielsen, L. B., Jørgensen, S., Kjaergaard, H. G., Canagaratna, M., Maso, M. D., Berndt, T., Petäjä, T., Wahner, A., Kerminen, V. M., Kulmala, M., Worsnop, D. R., Wildt, J., and Mentel, T. F.: A large source of low-volatility secondary organic aerosol, Nature, 506, 476–479, https://doi.org/10.1038/nature13032, 2014. a
Hallquist, M., Wenger, J. C., Baltensperger, U., Rudich, Y., Simpson, D., Claeys, M., Dommen, J., Donahue, N. M., George, C., Goldstein, A. H., Hamilton, J. F., Herrmann, H., Hoffmann, T., Iinuma, Y., Jang, M., Jenkin, M. E., Jimenez, J. L., Kiendler-Scharr, A., Maenhaut, W., McFiggans, G., Mentel, T. F., Monod, A., Prévôt, A. S. H., Seinfeld, J. H., Surratt, J. D., Szmigielski, R., and Wildt, J.: The formation, properties and impact of secondary organic aerosol: current and emerging issues, Atmos. Chem. Phys., 9, 5155–5236, https://doi.org/10.5194/acp-9-5155-2009, 2009. a, b
Hosea, M. and Shampine, L.: Analysis and implementation of TR-BDF2, in: Method of Lines for Time-Dependent Problems, Appl. Numer. Math., 20, 21–37, https://doi.org/10.1016/0168-9274(95)00115-8, 1996. a
Huang, L.: KPP archived generated schema for APINENE v0.1, Zenodo, https://doi.org/10.5281/zenodo.4075632, 2020. a
Huang, L.: JlBox v1.1, Zenodo, https://doi.org/10.5281/zenodo.4519192, 2021a. a
Huang, L.: JlBox project Github repository, GitHub, https://github.com/huanglangwen/JlBox (last access: 8 February 2021), 2021b. a
Huang, L.: Files to reproduce JlBoxv1.1, v0.2, project Github repository, Zenodo, https://doi.org/10.5281/zenodo.4543713, 2021c. a
Innes, M.: Flux: Elegant machine learning with Julia, Journal of Open Source Software, 3, 602, https://doi.org/10.21105/joss.00602, 2018. a
Jacobson, M. Z.: Fundamentals of atmospheric modeling second edition, Cambridge University Press, second edn., Cambridge UK, https://doi.org/10.1017/CBO9781139165389, 2005. a
Jenkin, M. E., Saunders, S. M., and Pilling, M. J.: The tropospheric degradation of volatile organic compounds: A protocol for mechanism development, Atmos. Environ., 31, 81–104, https://doi.org/10.1016/S1352-2310(96)00105-7, 1997. a, b
Jenkin, M. E., Saunders, S. M., Derwent, R. G., and Pilling, M. J.: Development of a reduced speciated VOC degradation mechanism for use in ozone models, Atmos. Environ., 36, 4725–4734, https://doi.org/10.1016/S1352-2310(02)00563-0, 2002. a, b
Joback, K. G. and Reid, R. C.: Estimation of Pure-Component Properties from Group-Contributions, Chem Eng. Commun., 57, 233–243, https://doi.org/10.1080/00986448708960487, 1987. a, b
Kokkola, H., Kühn, T., Laakso, A., Bergman, T., Lehtinen, K. E. J., Mielonen, T., Arola, A., Stadtler, S., Korhonen, H., Ferrachat, S., Lohmann, U., Neubauer, D., Tegen, I., Siegenthaler-Le Drian, C., Schultz, M. G., Bey, I., Stier, P., Daskalakis, N., Heald, C. L., and Romakkaniemi, S.: SALSA2.0: The sectional aerosol module of the aerosol–chemistry–climate model ECHAM6.3.0-HAM2.3-MOZ1.0, Geosci. Model Dev., 11, 3833–3863, https://doi.org/10.5194/gmd-11-3833-2018, 2018. a, b, c
Korhonen, H., Lehtinen, K. E. J., and Kulmala, M.: Multicomponent aerosol dynamics model UHMA: model development and validation, Atmos. Chem. Phys., 4, 757–771, https://doi.org/10.5194/acp-4-757-2004, 2004. a
O'Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., and Hutchison, G. R.: Open Babel: An Open chemical toolbox, J. Cheminformatics, 3, 33, https://doi.org/10.1186/1758-2946-3-33, 2011. a
Perkel, J. M.: Julia: come for the syntax, stay for the speed, Nature Toolbox, 572, 141–142, https://doi.org/10.1038/d41586-019-02310-3, 2019.
a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat, P.: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Riemer, N. and Ault, A.: The Diversity and Complexity of Atmospheric Aerosol, Eos, 100, https://doi.org/10.1029/2019eo124333, 2019. a
Riemer, N., West, M., Zaveri, R. A., and Easter, R. C.: Simulating the evolution of soot mixing state with a particle-resolved aerosol model, J. Geophys. Res.-Atmos., 114, D09202, https://doi.org/10.1029/2008JD011073, 2009. a, b
Roldin, P., Eriksson, A. C., Nordin, E. Z., Hermansson, E., Mogensen, D., Rusanen, A., Boy, M., Swietlicki, E., Svenningsson, B., Zelenyuk, A., and Pagels, J.: Modelling non-equilibrium secondary organic aerosol formation and evaporation with the aerosol dynamics, gas- and particle-phase chemistry kinetic multilayer model ADCHAM, Atmos. Chem. Phys., 14, 7953–7993, https://doi.org/10.5194/acp-14-7953-2014, 2014. a
Shelley, P. and Topping, D.: loftytopping/UManSysProp_public: Base version, Zenodo, https://doi.org/10.5281/zenodo.4110145, 2021. a
Sherwen, T., Chance, R. J., Tinel, L., Ellis, D., Evans, M. J., and Carpenter, L. J.: A machine-learning-based global sea-surface iodide distribution, Earth Syst. Sci. Data, 11, 1239–1262, https://doi.org/10.5194/essd-11-1239-2019, 2019. a
Sommariva, R., Cox, S., Martin, C., Borońska, K., Young, J., Jimack, P. K., Pilling, M. J., Matthaios, V. N., Nelson, B. S., Newland, M. J., Panagi, M., Bloss, W. J., Monks, P. S., and Rickard, A. R.: AtChem (version 1), an open-source box model for the Master Chemical Mechanism, Geosci. Model Dev., 13, 169–183, https://doi.org/10.5194/gmd-13-169-2020, 2020. a, b, c
Topping, D.: PyBox base model archive, Zenodo, https://doi.org/10.5281/zenodo.1345005, 2021. a
Topping, D., Connolly, P., and Reid, J.: PyBox: An automated box-model generator for atmospheric chemistry and aerosol simulations., Journal of Open Source Software, 3, 755, https://doi.org/10.21105/joss.00755, 2018. a, b, c, d
Wanner, G. and Hairer, E.: Solving ordinary differential equations II, Springer, Berlin, Heidelberg, 1996. a
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, D13204, https://doi.org/10.1029/2007JD008782, 2008. a, b
Short summary
As our knowledge and understanding of atmospheric aerosol particle evolution and impact grows, designing community mechanistic models requires an ability to capture increasing chemical, physical and therefore numerical complexity. As the landscape of computing software and hardware evolves, it is important to profile the usefulness of emerging platforms in tackling this complexity. With this in mind we present JlBox v1.1, written in Julia.
As our knowledge and understanding of atmospheric aerosol particle evolution and impact grows,...