Articles | Volume 23, issue 6
https://doi.org/10.5194/acp-23-3609-2023
© 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/acp-23-3609-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
24 Mar 2023
Technical note |
| 24 Mar 2023
| 24 Mar 2023
Technical note: Unsupervised classification of ozone profiles in UKESM1
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, USA
Department of Mathematical Sciences, Georgia Southern University, Statesboro, USA
Daniel C. Jones
British Antarctic Survey, NERC, UKRI, Cambridge, UK
Yan Wu
Department of Mathematical Sciences, Georgia Southern University, Statesboro, USA
James Keeble
Department of Chemistry, University of Cambridge, Cambridge, UK
National Centre for Atmospheric Science (NCAS), University of Cambridge, Cambridge, UK
Alexander T. Archibald
Department of Chemistry, University of Cambridge, Cambridge, UK
National Centre for Atmospheric Science (NCAS), University of Cambridge, Cambridge, UK
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Sean Davis, William Ball, Yue Jia, Gabriel Chiodo, Justin Alsing, James Keeble, Hideharu Akiyoshi, Carlo Arosio, Ewa Bednarz, Andreas Chrysanthou, Melanie Coldewey-Egbers, Robert Damadeo, Sandip Dhomse, Mohamadou Diallo, Simone Dietmuller, Roland Eichinger, Stacey Frith, Birgit Hassler, Michaela Hegglin, Daan Hubert, Patrick Jöckel, Béatrice Josse, Natalya Kramarova, Diego Loyola, Eliane Maillard Barras, Marion Marchand, Olaf Morgenstern, David Plummer, Robert Portmann, Karen Rosenlof, Alexei Rozanov, Viktoria Sofieva, Johannes Staehelin, Timofei Sukhodolov, Kleareti Tourpali, Ronald Van der A, H. J. Ray Wang, Krzysztof Wargan, Shingo Watanabe, Mark Weber, Jeannette Wild, Yousuke Yamashita, and Jerry Ziemke
EGUsphere, https://doi.org/10.5194/egusphere-2026-532, https://doi.org/10.5194/egusphere-2026-532, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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This study investigates how tropical ozone levels have changed since 2000 in chemistry climate models and satellite observations to determine how well they agree with one another, and to see if current trends can help predict future levels. At some, satellite records disagree significantly on the magnitude of ozone changes. The study shows a connection between recent ozone trends and future ozone levels, suggesting that satellite measurements could help constrain future ozone changes.
Megan A. J. Brown, Nicola J. Warwick, Nathan Luke Abraham, Paul T. Griffiths, Steve T. Rumbold, Gerd A. Folberth, Fiona M. O'Connor, Hannah Bryant, and Alex T. Archibald
Geosci. Model Dev., 19, 1537–1557, https://doi.org/10.5194/gmd-19-1537-2026, https://doi.org/10.5194/gmd-19-1537-2026, 2026
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Hydrogen (H2) is an indirect greenhouse gas by increasing methane (CH4) lifetime. Interaction between H2 and CH4 is important for hydrogen’s global warming potential (GWP). Global models do not represent this interaction well; H2 or CH4 are prescribed at the surface. We implement an interactive H2 scheme into a global model coupled with interactive CH4. We simulate scenarios demonstrating its capability, improving model performance and more accurately representing H2-CH4 interaction.
Clara Orbe, Alison Ming, Gabriel Chiodo, Michael Prather, Mohamadou Diallo, Qi Tang, Andreas Chrysanthou, Hiroaki Naoe, Xin Zhou, Irina Thaler, Dillon Elsbury, Ewa Bednarz, Jonathon S. Wright, Aaron Match, Shingo Watanabe, James Anstey, Tobias Kerzenmacher, Stefan Versick, Marion Marchand, Feng Li, and James Keeble
Geosci. Model Dev., 19, 773–794, https://doi.org/10.5194/gmd-19-773-2026, https://doi.org/10.5194/gmd-19-773-2026, 2026
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The quasi-biennial oscillation (QBO) is the main source of wind fluctuations in the tropical stratosphere, which can couple to surface climate. However, models do a poor job of simulating the QBO in the lower stratosphere, for reasons that remain unclear. One possibility is that models do not completely represent how ozone influences the QBO-associated wind variations. Here we propose a multi-model framework for assessing how ozone influences the QBO in recent past and future climates.
William J. Collins, John S. Daniel, Martyn P. Chipperfield, Martin Cussac, Makoto Deushi, Gregory Faluvegi, Paul Griffiths, Øivind Hodnebrog, Larry W. Horowitz, James Keeble, Douglas Kinnison, Vaishali Naik, Fiona M. O'Connor, Drew Shindell, Simone Tilmes, Kostas Tsigaridis, Zihao Wang, and James Weber
EGUsphere, https://doi.org/10.5194/egusphere-2025-6033, https://doi.org/10.5194/egusphere-2025-6033, 2026
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Ozone depleting substances (ODSs) are also greenhouse gases that cause global warming. However, their destruction of ozone contributes a global cooling. We have used results from climate models that include atmospheric chemistry and found that the cooling effect of the ozone depletion diagnosed in the models was larger than that calculated using a standard method. We find that some ODSs have a net cooling effect whereas for others the warming effect is significantly reduced.
Xingpei Ye, Lin Zhang, Xiaolin Wang, Ni Lu, Sebastian Hickman, Guo Luo, and Alex T. Archibald
EGUsphere, https://doi.org/10.5194/egusphere-2026-74, https://doi.org/10.5194/egusphere-2026-74, 2026
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This study investigates how meteorology influences long-term surface ozone trends and pollution events across three major regions in eastern China using an explainable machine learning framework. The results show physically interpretable yet model-dependent ozone-meteorology relationships, highlighting both the potential and the limitations of explainable machine learning for process understanding in atmospheric chemistry.
Marta Abalos, Thomas Birner, Andreas Chrysanthou, Sean Davis, Alvaro de la Cámara, Sandip Dhomse, Hella Garny, Michaela I. Hegglin, Daan Hubert, Oksana Ivaniha, James Keeble, Marianna Linz, Daniele Minganti, Jessica Neu, David Plummer, Laura Saunders, Kasturi Shah, Gabriele Stiller, Kleareti Tourpali, Darryn Waugh, Nathan Luke Abraham, Hideharu Akiyoshi, Martyn P. Chipperfield, Patrick Jöckel, Béatrice Josse, Olaf Morgenstern, Timofei Sukhodolov, Shingo Watanabe, and Yousuke Yamashita
EGUsphere, https://doi.org/10.5194/egusphere-2025-6549, https://doi.org/10.5194/egusphere-2025-6549, 2026
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Chemistry-climate models are widely used to understand stratospheric ozone and its interactions with climate. We evaluate the most recent generations of models against modern observations. We find that important long-standing errors remain, and some have increased in recent models. Transported too fast in the stratosphere, and the strong winter circulation around the polar region lasts too long. These results highlight where models must improve to better assess past and future changes.
Jingyu Wang, Gabriel Chiodo, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, Sandro Vattioni, and Timofei Sukhodolov
Atmos. Chem. Phys., 25, 17819–17844, https://doi.org/10.5194/acp-25-17819-2025, https://doi.org/10.5194/acp-25-17819-2025, 2025
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We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then examined the relations between ozone response and changes in temperature and circulation to identify the drivers of ozone change. The climate feedback of ozone is investigated through offline calculations and by comparing models with and without interactive chemistry. We find that ozone–climate interactions are important for Earth system models and thus should be considered in future model development.
Lorrie S. D. Jacob, Benedict E. H. Harvey, Chiara Giorio, and Alexander T. Archibald
EGUsphere, https://doi.org/10.5194/egusphere-2025-5103, https://doi.org/10.5194/egusphere-2025-5103, 2025
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We develop an approach to determine the uncertainty in reactions involved in the breakdown of dimethyl sulfide and methanethiol in the atmosphere, and run simulations spanning temperate, tropical, and polar conditions to quantify the impacts of this uncertainty on the concentration of these sulfur species and their products. We extend this analysis to identify key reactions that drive the uncertainty and highlight areas for further experimental and theoretical work.
Gunnar Myhre, Øivind Hodnebrog, Srinath Krishnan, Maria Sand, Marit Sandstad, Ragnhild B. Skeie, Lieven Clarisse, Bruno Franco, Dylan B. Millet, Kelley C. Wells, Alexander Archibald, Hannah N. Bryant, Alex T. Chaudhri, David S. Stevenson, Didier Hauglustaine, Michael Prather, J. Christopher Kaiser, Dirk J. L. Olivie, Michael Schulz, Oliver Wild, Ye Wang, Thérèse Salameh, Jason E. Williams, Philippe Le Sager, Fabien Paulot, Kostas Tsigaridis, and Haley E. Plaas
EGUsphere, https://doi.org/10.5194/egusphere-2025-3057, https://doi.org/10.5194/egusphere-2025-3057, 2025
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Volatile organic compounds (VOCs) affect air quality and climate, but their behavior in the atmosphere is still uncertain. We launched a global research effort to compare how different models represent these compounds and to improve their accuracy. By analyzing model results alongside observations and satellite data, we aim to better understand the atmospheric composition of these compounds.
Xu-Cheng He, Nathan Luke Abraham, Han Ding, Maria R. Russo, Daniel P. Grosvenor, Yao Ge, Xuemei Wang, Anthony C. Jones, Pedro Campuzano-Jost, Benjamin Nault, Agnieszka Kupc, Donald Blake, Jose L. Jimenez, Christina J. Williamson, Kenneth S. Carslaw, James Weber, Alexander T. Archibald, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2025-3700, https://doi.org/10.5194/egusphere-2025-3700, 2025
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Aerosols affect clouds and climate. However, current climate models still struggle to simulate them accurately. We used aircraft data from a global mission to evaluate how well the UK Earth System Model represents aerosols and their precursors. Our results show that the model misses key formation processes in clean ocean regions, suggesting that future improvements should focus on better representing how aerosols form naturally in the atmosphere.
William J. Collins, Fiona M. O'Connor, Rachael E. Byrom, Øivind Hodnebrog, Patrick Jöckel, Mariano Mertens, Gunnar Myhre, Matthias Nützel, Dirk Olivié, Ragnhild Bieltvedt Skeie, Laura Stecher, Larry W. Horowitz, Vaishali Naik, Gregory Faluvegi, Ulas Im, Lee T. Murray, Drew Shindell, Kostas Tsigaridis, Nathan Luke Abraham, and James Keeble
Atmos. Chem. Phys., 25, 9031–9060, https://doi.org/10.5194/acp-25-9031-2025, https://doi.org/10.5194/acp-25-9031-2025, 2025
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We used 7 climate models that include atmospheric chemistry and find that in a scenario with weak controls on air quality, the warming effects (over 2015 to 2050) of decreases in ozone-depleting substances and increases in air quality pollutants are approximately equal and would make ozone the second highest contributor to warming over this period. We find that for stratospheric ozone recovery, the standard measure of climate effects underestimates a more comprehensive measure.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael Prather, Alex Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Bjørn H. Samset, Chris Smith, Steven Turnock, Duncan Watson-Parris, and Paul J. Young
Atmos. Chem. Phys., 25, 8289–8328, https://doi.org/10.5194/acp-25-8289-2025, https://doi.org/10.5194/acp-25-8289-2025, 2025
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The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. We review its contribution to AR6 (Sixth Assessment Report of the Intergovernmental Panel on Climate Change) and the wider understanding of the role of these species in climate and climate change. We identify challenges and provide recommendations to improve the utility and uptake of climate model data, detailed summary tables of CMIP6 models, experiments, and emergent diagnostics.
Alex T. Archibald, Bablu Sinha, Maria R. Russo, Emily Matthews, Freya A. Squires, N. Luke Abraham, Stephane J.-B. Bauguitte, Thomas J. Bannan, Thomas G. Bell, David Berry, Lucy J. Carpenter, Hugh Coe, Andrew Coward, Peter Edwards, Daniel Feltham, Dwayne Heard, Jim Hopkins, James Keeble, Elizabeth C. Kent, Brian A. King, Isobel R. Lawrence, James Lee, Claire R. Macintosh, Alex Megann, Bengamin I. Moat, Katie Read, Chris Reed, Malcolm J. Roberts, Reinhard Schiemann, David Schroeder, Timothy J. Smyth, Loren Temple, Navaneeth Thamban, Lisa Whalley, Simon Williams, Huihui Wu, and Mingxi Yang
Earth Syst. Sci. Data, 17, 135–164, https://doi.org/10.5194/essd-17-135-2025, https://doi.org/10.5194/essd-17-135-2025, 2025
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Here, we present an overview of the data generated as part of the North Atlantic Climate System Integrated Study (ACSIS) programme that are available through dedicated repositories at the Centre for Environmental Data Analysis (CEDA; www.ceda.ac.uk) and the British Oceanographic Data Centre (BODC; bodc.ac.uk). The datasets described here cover the North Atlantic Ocean, the atmosphere above (it including its composition), and Arctic sea ice.
Lorrie Simone Denise Jacob, Chiara Giorio, and Alexander Thomas Archibald
Atmos. Chem. Phys., 24, 3329–3347, https://doi.org/10.5194/acp-24-3329-2024, https://doi.org/10.5194/acp-24-3329-2024, 2024
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Recent studies on DMS have provided new challenges to our mechanistic understanding. Here we synthesise a number of recent studies to further develop and extend a state-of-the-art mechanism. Our new mechanism is shown to outperform all existing mechanisms when compared over a wide set of conditions. The development of an improved DMS mechanism will help lead the way to better the understanding the climate impacts of DMS emissions in past, present, and future atmospheric conditions.
Lea Poropat, Dani Jones, Simon D. A. Thomas, and Céline Heuzé
Ocean Sci., 20, 201–215, https://doi.org/10.5194/os-20-201-2024, https://doi.org/10.5194/os-20-201-2024, 2024
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In this study we use a machine learning method called a Gaussian mixture model to divide part of the ocean (northwestern European seas and part of the Atlantic Ocean) into regions based on satellite observations of sea level. This helps us study each of these regions separately and learn more about what causes sea level changes there. We find that the ocean is first divided based on bathymetry and then based on other features such as water masses and typical atmospheric conditions.
Yusuf A. Bhatti, Laura E. Revell, Alex J. Schuddeboom, Adrian J. McDonald, Alex T. Archibald, Jonny Williams, Abhijith U. Venugopal, Catherine Hardacre, and Erik Behrens
Atmos. Chem. Phys., 23, 15181–15196, https://doi.org/10.5194/acp-23-15181-2023, https://doi.org/10.5194/acp-23-15181-2023, 2023
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Aerosols are a large source of uncertainty over the Southern Ocean. A dominant source of sulfate aerosol in this region is dimethyl sulfide (DMS), which is poorly simulated by climate models. We show the sensitivity of simulated atmospheric DMS to the choice of oceanic DMS data set and emission scheme. We show that oceanic DMS has twice the influence on atmospheric DMS than the emission scheme. Simulating DMS more accurately in climate models will help to constrain aerosol uncertainty.
Ben A. Cala, Scott Archer-Nicholls, James Weber, N. Luke Abraham, Paul T. Griffiths, Lorrie Jacob, Y. Matthew Shin, Laura E. Revell, Matthew Woodhouse, and Alexander T. Archibald
Atmos. Chem. Phys., 23, 14735–14760, https://doi.org/10.5194/acp-23-14735-2023, https://doi.org/10.5194/acp-23-14735-2023, 2023
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Dimethyl sulfide (DMS) is an important trace gas emitted from the ocean recognised as setting the sulfate aerosol background, but its oxidation is complex. As a result representation in chemistry-climate models is greatly simplified. We develop and compare a new mechanism to existing mechanisms via a series of global and box model experiments. Our studies show our updated DMS scheme is a significant improvement but significant variance exists between mechanisms.
Nicola J. Warwick, Alex T. Archibald, Paul T. Griffiths, James Keeble, Fiona M. O'Connor, John A. Pyle, and Keith P. Shine
Atmos. Chem. Phys., 23, 13451–13467, https://doi.org/10.5194/acp-23-13451-2023, https://doi.org/10.5194/acp-23-13451-2023, 2023
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A chemistry–climate model has been used to explore the atmospheric response to changes in emissions of hydrogen and other species associated with a shift from fossil fuel to hydrogen use. Leakage of hydrogen results in indirect global warming, offsetting greenhouse gas emission reductions from reduced fossil fuel use. To maximise the benefit of hydrogen as an energy source, hydrogen leakage and emissions of methane, carbon monoxide and nitrogen oxides should be minimised.
Marina Friedel, Gabriel Chiodo, Timofei Sukhodolov, James Keeble, Thomas Peter, Svenja Seeber, Andrea Stenke, Hideharu Akiyoshi, Eugene Rozanov, David Plummer, Patrick Jöckel, Guang Zeng, Olaf Morgenstern, and Béatrice Josse
Atmos. Chem. Phys., 23, 10235–10254, https://doi.org/10.5194/acp-23-10235-2023, https://doi.org/10.5194/acp-23-10235-2023, 2023
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Previously, it has been suggested that springtime Arctic ozone depletion might worsen in the coming decades due to climate change, which might counteract the effect of reduced ozone-depleting substances. Here, we show with different chemistry–climate models that springtime Arctic ozone depletion will likely decrease in the future. Further, we explain why models show a large spread in the projected development of Arctic ozone depletion and use the model spread to constrain future projections.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
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Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Dani C. Jones, Maike Sonnewald, Shenjie Zhou, Ute Hausmann, Andrew J. S. Meijers, Isabella Rosso, Lars Boehme, Michael P. Meredith, and Alberto C. Naveira Garabato
Ocean Sci., 19, 857–885, https://doi.org/10.5194/os-19-857-2023, https://doi.org/10.5194/os-19-857-2023, 2023
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Machine learning is transforming oceanography. For example, unsupervised classification approaches help researchers identify underappreciated structures in ocean data, helping to generate new hypotheses. In this work, we use a type of unsupervised classification to identify structures in the temperature and salinity structure of the Weddell Gyre, which is an important region for global ocean circulation and for climate. We use our method to generate new ideas about mixing in the Weddell Gyre.
Maria Rosa Russo, Brian John Kerridge, Nathan Luke Abraham, James Keeble, Barry Graham Latter, Richard Siddans, James Weber, Paul Thomas Griffiths, John Adrian Pyle, and Alexander Thomas Archibald
Atmos. Chem. Phys., 23, 6169–6196, https://doi.org/10.5194/acp-23-6169-2023, https://doi.org/10.5194/acp-23-6169-2023, 2023
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Tropospheric ozone is an important component of the Earth system as it can affect both climate and air quality. In this work we use observed tropospheric ozone derived from satellite observations and compare it to tropospheric ozone from model simulations. Our aim is to investigate recent changes (2005–2018) in tropospheric ozone in the North Atlantic region and to understand what factors are driving such changes.
Scott Archer-Nicholls, Rachel Allen, Nathan L. Abraham, Paul T. Griffiths, and Alex T. Archibald
Atmos. Chem. Phys., 23, 5801–5813, https://doi.org/10.5194/acp-23-5801-2023, https://doi.org/10.5194/acp-23-5801-2023, 2023
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The nitrate radical is a major oxidant at nighttime, but much less is known about it than about the other oxidants ozone and OH. We use Earth system model calculations to show how the nitrate radical has changed in abundance from 1850–2014 and to 2100 under a range of different climate and emission scenarios. Depending on the emissions and climate scenario, significant increases are projected with implications for the oxidation of volatile organic compounds and the formation of fine aerosol.
Ernesto Reyes-Villegas, Douglas Lowe, Jill S. Johnson, Kenneth S. Carslaw, Eoghan Darbyshire, Michael Flynn, James D. Allan, Hugh Coe, Ying Chen, Oliver Wild, Scott Archer-Nicholls, Alex Archibald, Siddhartha Singh, Manish Shrivastava, Rahul A. Zaveri, Vikas Singh, Gufran Beig, Ranjeet Sokhi, and Gordon McFiggans
Atmos. Chem. Phys., 23, 5763–5782, https://doi.org/10.5194/acp-23-5763-2023, https://doi.org/10.5194/acp-23-5763-2023, 2023
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Organic aerosols (OAs), their sources and their processes remain poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem, can be a useful tool to describe primary OA (POA) production and aging. However, the main disadvantage is its complexity. We used a Gaussian process simulator to reproduce model results and to estimate the sources of model uncertainty. We do this by comparing the outputs with OA observations made at Delhi, India, in 2018.
Flossie Brown, Gerd A. Folberth, Stephen Sitch, Susanne Bauer, Marijn Bauters, Pascal Boeckx, Alexander W. Cheesman, Makoto Deushi, Inês Dos Santos Vieira, Corinne Galy-Lacaux, James Haywood, James Keeble, Lina M. Mercado, Fiona M. O'Connor, Naga Oshima, Kostas Tsigaridis, and Hans Verbeeck
Atmos. Chem. Phys., 22, 12331–12352, https://doi.org/10.5194/acp-22-12331-2022, https://doi.org/10.5194/acp-22-12331-2022, 2022
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Surface ozone can decrease plant productivity and impair human health. In this study, we evaluate the change in surface ozone due to climate change over South America and Africa using Earth system models. We find that if the climate were to change according to the worst-case scenario used here, models predict that forested areas in biomass burning locations and urban populations will be at increasing risk of ozone exposure, but other areas will experience a climate benefit.
Rachael N. C. Sanders, Daniel C. Jones, Simon A. Josey, Bablu Sinha, and Gael Forget
Ocean Sci., 18, 953–978, https://doi.org/10.5194/os-18-953-2022, https://doi.org/10.5194/os-18-953-2022, 2022
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In 2015, record low temperatures were observed in the North Atlantic. Using an ocean model, we show that surface heat loss in December 2013 caused 75 % of the initial cooling before this "cold blob" was trapped below the surface. The following summer, the cold blob re-emerged due to a strong temperature difference between the surface ocean and below, driving vertical diffusion of heat. Lower than average surface warming then led to the coldest temperature anomalies in August 2015.
Le Yuan, Olalekan A. M. Popoola, Christina Hood, David Carruthers, Roderic L. Jones, Haitong Zhe Sun, Huan Liu, Qiang Zhang, and Alexander T. Archibald
Atmos. Chem. Phys., 22, 8617–8637, https://doi.org/10.5194/acp-22-8617-2022, https://doi.org/10.5194/acp-22-8617-2022, 2022
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Emission estimates represent a major source of uncertainty in air quality modelling. We developed a novel approach to improve emission estimates from existing inventories using air quality models and routine in situ observations. Using this approach, we derived improved estimates of NOx emissions from the transport sector in Beijing in 2016. This approach has great potential in deriving timely updates of emissions for other pollutants, particularly in regions undergoing rapid emission changes.
Simon D. A. Thomas, Daniel C. Jones, Anita Faul, Erik Mackie, and Etienne Pauthenet
Ocean Sci., 17, 1545–1562, https://doi.org/10.5194/os-17-1545-2021, https://doi.org/10.5194/os-17-1545-2021, 2021
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We propose a probabilistic method and a new inter-class comparison metric for highlighting fronts in the Southern Ocean. We compare it with an image processing method that provides a more localised view of fronts that effectively highlights sharp jets. These two complementary approaches offer two views of Southern Ocean structure: the probabilistic method highlights boundaries between coherent thermohaline structures across the entire Southern Ocean, whereas edge detection highlights local jets.
James Weber, Scott Archer-Nicholls, Nathan Luke Abraham, Youngsub M. Shin, Thomas J. Bannan, Carl J. Percival, Asan Bacak, Paulo Artaxo, Michael Jenkin, M. Anwar H. Khan, Dudley E. Shallcross, Rebecca H. Schwantes, Jonathan Williams, and Alex T. Archibald
Geosci. Model Dev., 14, 5239–5268, https://doi.org/10.5194/gmd-14-5239-2021, https://doi.org/10.5194/gmd-14-5239-2021, 2021
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The new mechanism CRI-Strat 2 features state-of-the-art isoprene chemistry not previously available in UKCA and improves UKCA's ability to reproduce observed concentrations of isoprene, monoterpenes, and OH in tropical regions. The enhanced ability to model isoprene, the most widely emitted non-methane volatile organic compound (VOC), will allow understanding of how isoprene and other biogenic VOCs affect atmospheric composition and, through biosphere–atmosphere feedbacks, climate change.
John Staunton-Sykes, Thomas J. Aubry, Youngsub M. Shin, James Weber, Lauren R. Marshall, Nathan Luke Abraham, Alex Archibald, and Anja Schmidt
Atmos. Chem. Phys., 21, 9009–9029, https://doi.org/10.5194/acp-21-9009-2021, https://doi.org/10.5194/acp-21-9009-2021, 2021
Rachel Furner, Peter Haynes, Dave Munday, Brooks Paige, Daniel C. Jones, and Emily Shuckburgh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-132, https://doi.org/10.5194/gmd-2021-132, 2021
Revised manuscript not accepted
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Traditional weather & climate models are built from physics-based equations, while data-driven models are built from patterns found in datasets using Machine Learning or statistics. There is growing interest in using data-driven models for weather & climate prediction, but confidence in their use depends on understanding the patterns they're finding. We look at this with a simple regression model of ocean temperature and see the patterns found by the regression model are similar to the physics.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
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Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Paul T. Griffiths, Lee T. Murray, Guang Zeng, Youngsub Matthew Shin, N. Luke Abraham, Alexander T. Archibald, Makoto Deushi, Louisa K. Emmons, Ian E. Galbally, Birgit Hassler, Larry W. Horowitz, James Keeble, Jane Liu, Omid Moeini, Vaishali Naik, Fiona M. O'Connor, Naga Oshima, David Tarasick, Simone Tilmes, Steven T. Turnock, Oliver Wild, Paul J. Young, and Prodromos Zanis
Atmos. Chem. Phys., 21, 4187–4218, https://doi.org/10.5194/acp-21-4187-2021, https://doi.org/10.5194/acp-21-4187-2021, 2021
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We analyse the CMIP6 Historical and future simulations for tropospheric ozone, a species which is important for many aspects of atmospheric chemistry. We show that the current generation of models agrees well with observations, being particularly successful in capturing trends in surface ozone and its vertical distribution in the troposphere. We analyse the factors that control ozone and show that they evolve over the period of the CMIP6 experiments.
Peter Sherman, Meng Gao, Shaojie Song, Alex T. Archibald, Nathan Luke Abraham, Jean-François Lamarque, Drew Shindell, Gregory Faluvegi, and Michael B. McElroy
Atmos. Chem. Phys., 21, 3593–3605, https://doi.org/10.5194/acp-21-3593-2021, https://doi.org/10.5194/acp-21-3593-2021, 2021
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The aims here are to assess the role of aerosols in India's monsoon precipitation and to determine the relative contributions from Chinese and Indian emissions using CMIP6 models. We find that increased sulfur emissions reduce precipitation, which is primarily dynamically driven due to spatial shifts in convection over the region. A significant increase in precipitation (up to ~ 20 %) is found only when both Indian and Chinese sulfate emissions are regulated.
Cited articles
Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone, T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie, T. A., Robinson, N. H., and Signell, R. P.: Cloud-Native Repositories for Big Scientific Data, Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/MCSE.2021.3059437, 2021. a
Allen, R. J., Sherwood, S. C., Norris, J. R., and Zender, C. S.: Recent Northern Hemisphere tropical expansion primarily driven by black carbon and tropospheric ozone, Nature, 485, 350–354, 2012. a
Archibald, A., Neu, J., Elshorbany, Y., Cooper, O., Young, P., Akiyoshi, H., Cox, R., Coyle, M., Derwent, R., Deushi, M., Finco, A., Frost, G. J., Galbally, I. E., Gerosa, G., Granier, C., Griffiths, P. T., Hossaini, R., Hu, L., Jöckel, P., Josse, B., Lin, M. Y., Mertens, M., Morgenstern, O., Naja, M., Naik, V., Oltmans, S., Plummer, D. A., Revell, L. E., Saiz-Lopez, A., Saxena, P., Shin, Y. M., Shahid, I., Shallcross, D., Tilmes, S., Trickl, T., Wallington, T. J., Wang, T., Worden, H. M., and Zeng, G.: Tropospheric Ozone Assessment ReportA critical review of changes in the tropospheric ozone burden and budget from 1850 to 2100, Elementa: Science of the Anthropocene, 8, 2325–1026, https://doi.org/10.1525/elementa.2020.034, 2020a. a, b
Archibald, A. T., Turnock, S. T., Griffiths, P. T., Cox, T., Derwent, R. G., Knote, C., and Shin, M.: On the changes in surface ozone over the twenty-first century: sensitivity to changes in surface temperature and chemical mechanisms, Philos. T. Roy. Soc. A, 378, 20190329, https://doi.org/10.1098/rsta.2019.0329, 2020b. a, b
Banerjee, A., Maycock, A. C., Archibald, A. T., Abraham, N. L., Telford, P., Braesicke, P., and Pyle, J. A.: Drivers of changes in stratospheric and tropospheric ozone between year 2000 and 2100, Atmos. Chem. Phys., 16, 2727–2746, https://doi.org/10.5194/acp-16-2727-2016, 2016. a
Bates, D. R. and Nicolet, M.: The photochemistry of atmospheric water vapor, J. Geophys. Res., 55, 301–327, 1950. a
Boehme, L. and Rosso, I.: Classifying Oceanographic Structures in the Amundsen Sea, Antarctica, Geophys. Res. Lett., 48, e2020GL089412, https://doi.org/10.1029/2020GL089412, 2021. a
Boleti, E., Hueglin, C., Grange, S. K., Prévôt, A. S. H., and Takahama, S.: Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach, Atmos. Chem. Phys., 20, 9051–9066, https://doi.org/10.5194/acp-20-9051-2020, 2020. a
Busecke, J., Ritschel, M., Maroon, E., Nicholas, T., and readthedocs-assistant: jbusecke/xMIP: v0.7.1,
Zenodo [data set], https://doi.org/10.5281/zenodo.7519179, 2023. a
Chameides, W. L., Fehsenfeld, F., Rodgers, M. O., Cardelino, C., Martinez, J., Parrish, D., Lonneman, W., Lawson, D. R., Rasmussen, R. A., Zimmerman, P., Greenberg, J., Mlddleton, P., and Wang, T.: Ozone precursor relationships in the ambient atmosphere, J. Geophys. Res.-Atmos., 97, 6037–6055, 1992. a
Chapman, S.: XXXV. On ozone and atomic oxygen in the upper atmosphere, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 10, 369–383, 1930. a
Cicerone, R. J., Stolarski, R. S., and Walters, S.: Stratospheric ozone destruction by man-made chlorofluoromethanes, Science, 185, 1165–1167, 1974. a
Crutzen, P. J.: The influence of nitrogen oxides on the atmospheric ozone content, Q. J. Roy. Meteor. Soc., 96, 320–325, 1970. a
Desbruyères, D., Chafik, L., and Maze, G.: A shift in the ocean circulation has warmed the subpolar North Atlantic Ocean since 2016, Communications Earth & Environment, 2, 48, https://doi.org/10.1038/s43247-021-00120-y, 2021. a
Diab, R., Thompson, A., Mari, K., Ramsay, L., and Coetzee, G.: Tropospheric ozone climatology over Irene, South Africa, from 1990 to 1994 and 1998 to 2002, J. Geophys. Res.-Atmos., 109, D20301, https://doi.org/10.1029/2004JD004793, 2004. a, b
Dunn, R. J., Aldred, F., Gobron, N., et al.: Global climate, B. Am. Meteorol. Soc., 103, S11–S142, 2022. a
Eyring, V., Arblaster, J. M., Cionni, I., Sedláček, J., Perlwitz, J., Young, P. J., Bekki, S., Bergmann, D., Cameron-Smith, P., Collins, W. J., Faluvegi, G., Gottschaldt, K.-D., Horowitz, L. W., Kinnison, D. E., Lamarque, J.-F., Marsh, D. R., Saint-Martin, D., Shindell, D. T., Sudo, K., Szopa, S., and Watanabe, S.: Long-term ozone changes and associated climate impacts in CMIP5 simulations, J. Geophys. Res.-Atmos., 118, 5029–5060, 2013. a
Fahrin, F. and Jones, D.: UKESM1_Ozone_clustering: UKESM1 Seasonal Ozone Profiles Clustering, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7662179, 2023. a
Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., and Kuhlbrodt, T.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp126,
Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6333, 2019a. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., and Kuhlbrodt, T.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6405, 2019b. a
Griffiths, P. T., Murray, L. T., Zeng, G., Shin, Y. M., Abraham, N. L., Archibald, A. T., Deushi, M., Emmons, L. K., Galbally, I. E., Hassler, B., Horowitz, L. W., Keeble, J., Liu, J., Moeini, O., Naik, V., O'Connor, F. M., Oshima, N., Tarasick, D., Tilmes, S., Turnock, S. T., Wild, O., Young, P. J., and Zanis, P.: Tropospheric ozone in CMIP6 simulations, Atmos. Chem. Phys., 21, 4187–4218, https://doi.org/10.5194/acp-21-4187-2021, 2021. a, b, c, d, e, f
Haigh, J. and Pyle, J.: Ozone perturbation experiments in a two-dimensional circulation model, Q. J. Roy. Meteor. Soc., 108, 551–574, 1982. a
Houghton, I. A. and Wilson, J. D.: El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles, J. Geophys. Res.-Oceans, 125, e2019JC015947, https://doi.org/10.1029/2019JC015947, 2020. a
Hu, Y., Huang, H., and Zhou, C.: Widening and weakening of the Hadley circulation under global warming, Sci. Bull., 63, 640–644, 2018. a
Jaadi, Z.: A step by step explanation of Principal Component Analysis, Towards Data Science, 1–9, https://builtin.com/data-science/step-step-explanation-principal-component-analysis (last access: 5 November 2021), 2019. a
Jensen, A. A., Thompson, A. M., and Schmidlin, F.: Classification of Ascension Island and Natal ozonesondes using self-organizing maps, J. Geophys. Res.-Atmos., 117, D04302, https://doi.org/10.1029/2011JD016573, 2012. a
Johnston, H.: Reduction of stratospheric ozone by nitrogen oxide catalysts from supersonic transport exhaust, Science, 173, 517–522, 1971. a
Jonsson, A., De Grandpre, J., Fomichev, V., McConnell, J., and Beagley, S.: Doubled CO2-induced cooling in the middle atmosphere: Photochemical analysis of the ozone radiative feedback, J. Geophys. Res.-Atmos., 109, D24103, https://doi.org/10.1029/2004JD005093, 2004. a
Kaiser, B. E., Saenz, J. A., Sonnewald, M., and Livescu, D.: Automated identification of dominant physical processes, Eng. Appl. Artif. Intel., 116, 105496, https://doi.org/10.1016/j.engappai.2022.105496, 2022. a
Keeble, J., Bednarz, E. M., Banerjee, A., Abraham, N. L., Harris, N. R. P., Maycock, A. C., and Pyle, J. A.: Diagnosing the radiative and chemical contributions to future changes in tropical column ozone with the UM-UKCA chemistry–climate model, Atmos. Chem. Phys., 17, 13801–13818, https://doi.org/10.5194/acp-17-13801-2017, 2017. a
Keeble, J., Hassler, B., Banerjee, A., Checa-Garcia, R., Chiodo, G., Davis, S., Eyring, V., Griffiths, P. T., Morgenstern, O., Nowack, P., Zeng, G., Zhang, J., Bodeker, G., Burrows, S., Cameron-Smith, P., Cugnet, D., Danek, C., Deushi, M., Horowitz, L. W., Kubin, A., Li, L., Lohmann, G., Michou, M., Mills, M. J., Nabat, P., Olivié, D., Park, S., Seland, Ø., Stoll, J., Wieners, K.-H., and Wu, T.: Evaluating stratospheric ozone and water vapour changes in CMIP6 models from 1850 to 2100, Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, 2021. a, b, c, d, e, f
Kohonen, T.: Self-organizing maps, vol. 30, Springer Science & Business Media, ISBN 978-3-540-67921-9, ISSN 0720-678X, 2012. a
Laban, T. L., van Zyl, P. G., Beukes, J. P., Vakkari, V., Jaars, K., Borduas-Dedekind, N., Josipovic, M., Thompson, A. M., Kulmala, M., and Laakso, L.: Seasonal influences on surface ozone variability in continental South Africa and implications for air quality, Atmos. Chem. Phys., 18, 15491–15514, https://doi.org/10.5194/acp-18-15491-2018, 2018. a, b, c
Li, Y. and Thompson, D. W.: The signature of the stratospheric Brewer–Dobson circulation in tropospheric clouds, J. Geophys. Res.-Atmos., 118, 3486–3494, 2013. a
Lu, X., Zhang, L., Zhao, Y., Jacob, D. J., Hu, Y., Hu, L., Gao, M., Liu, X., Petropavlovskikh, I., McClure-Begley, A., and Querel, R.: Surface and tropospheric ozone trends in the Southern Hemisphere since 1990: possible linkages to poleward expansion of the Hadley circulation, Sci. Bull., 64, 400–409, 2019. a
McLachlan, G. J. and Basford, K. E.: Mixture models: Inference and applications to clustering, vol. 38, M. Dekker, New York, https://ui.adsabs.harvard.edu/abs/1988mmia.book.....M (last access: 5 November 2021), 1988. a
Meul, S., Dameris, M., Langematz, U., Abalichin, J., Kerschbaumer, A., Kubin, A., and Oberländer-Hayn, S.: Impact of rising greenhouse gas concentrations on future tropical ozone and UV exposure, Geophys. Res. Lett., 43, 2919–2927, 2016. a
Meul, S., Langematz, U., Kröger, P., Oberländer-Hayn, S., and Jöckel, P.: Future changes in the stratosphere-to-troposphere ozone mass flux and the contribution from climate change and ozone recovery, Atmos. Chem. Phys., 18, 7721–7738, https://doi.org/10.5194/acp-18-7721-2018, 2018. a
Molina, M. J. and Rowland, F. S.: Stratospheric sink for chlorofluoromethanes: chlorine atom-catalysed destruction of ozone, Nature, 249, 810–812, 1974. a
Monks, P. S., Granier, C., Fuzzi, S., et al.: Atmospheric composition change–global and regional air quality, Atmos. Environ., 43, 5268–5350, https://doi.org/10.1016/j.atmosenv.2009.08.021, 2009. a, b, c
Monks, P. S., Archibald, A. T., Colette, A., Cooper, O., Coyle, M., Derwent, R., Fowler, D., Granier, C., Law, K. S., Mills, G. E., Stevenson, D. S., Tarasova, O., Thouret, V., von Schneidemesser, E., Sommariva, R., Wild, O., and Williams, M. L.: Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer, Atmos. Chem. Phys., 15, 8889–8973, https://doi.org/10.5194/acp-15-8889-2015, 2015. a, b, c, d, e, f, g, h
Oehrlein, J., Chiodo, G., and Polvani, L. M.: The effect of interactive ozone chemistry on weak and strong stratospheric polar vortex events, Atmos. Chem. Phys., 20, 10531–10544, https://doi.org/10.5194/acp-20-10531-2020, 2020. a
Rosso, I., Mazloff, M. R., Talley, L. D., Purkey, S. G., Freeman, N. M., and Maze, G.: Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hot Spot, J. Geophys. Res.-Oceans, 125, e2019JC015877, https://doi.org/10.1029/2019JC015877, 2020. a, b
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Seviour, W. J., Butchart, N., and Hardiman, S. C.: The Brewer–Dobson circulation inferred from ERA-Interim, Q. J. Roy. Meteor. Soc., 138, 878–888, 2012. a
Sonnewald, M., Dutkiewicz, S., Hill, C., and Forget, G.: Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces, Science Advances, 6, 1–12, https://doi.org/10.1126/sciadv.aay4740, 2020. a
Stauffer, R. M., Thompson, A. M., and Young, G. S.: Tropospheric ozonesonde profiles at long-term US monitoring sites: 1. A climatology based on self-organizing maps, J. Geophys. Res.-Atmos., 121, 1320–1339, 2016. a
Stauffer, R. M., Thompson, A. M., and Witte, J. C.: Characterizing global ozonesonde profile variability from surface to the UT/LS with a clustering technique and MERRA-2 reanalysis, J. Geophys. Res.-Atmos., 123, 6213–6229, 2018. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical,
Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6113, 2019. a
Thompson, A. M., Witte, J. C., McPeters, R. D., Oltmans, S. J., Schmidlin, F. J., Logan, J. A., Fujiwara, M., Kirchhoff, V. W., Posny, F., Coetzee, G. J., Hoegger, B., Kawakami, S., Ogawa, T., Johnson, B. J., Vömel, H., and Labow, G.: Southern hemisphere additional Ozonesondes (SHADOZ) 1998–2000 tropical ozone climatology 1. Comparison with Total ozone mapping spectrometer (TOMS) and ground-based measurements, J. Geophys. Res.-Atmos., 108, 8238, https://doi.org/10.1029/2001JD000967, 2003. a
Turnock, S. T., Allen, R. J., Andrews, M., Bauer, S. E., Deushi, M., Emmons, L., Good, P., Horowitz, L., John, J. G., Michou, M., Nabat, P., Naik, V., Neubauer, D., O'Connor, F. M., Olivié, D., Oshima, N., Schulz, M., Sellar, A., Shim, S., Takemura, T., Tilmes, S., Tsigaridis, K., Wu, T., and Zhang, J.: Historical and future changes in air pollutants from CMIP6 models, Atmos. Chem. Phys., 20, 14547–14579, https://doi.org/10.5194/acp-20-14547-2020, 2020. a
Wargan, K., Weir, B., Manney, G. L., Cohn, S. E., and Livesey, N. J.: The anomalous 2019 Antarctic ozone hole in the GEOS Constituent Data Assimilation System with MLS observations, J. Geophys. Res.-Atmos., 125, e2020JD033335, https://doi.org/10.1029/2020JD033335, 2020. a
Weber, M., Dikty, S., Burrows, J. P., Garny, H., Dameris, M., Kubin, A., Abalichin, J., and Langematz, U.: The Brewer-Dobson circulation and total ozone from seasonal to decadal time scales, Atmos. Chem. Phys., 11, 11221–11235, https://doi.org/10.5194/acp-11-11221-2011, 2011. a
Young, P. J., Archibald, A. T., Bowman, K. W., Lamarque, J.-F., Naik, V., Stevenson, D. S., Tilmes, S., Voulgarakis, A., Wild, O., Bergmann, D., Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R. M., Eyring, V., Faluvegi, G., Horowitz, L. W., Josse, B., Lee, Y. H., MacKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Skeie, R. B., Shindell, D. T., Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Pre-industrial to end 21st century projections of tropospheric ozone from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), Atmos. Chem. Phys., 13, 2063–2090, https://doi.org/10.5194/acp-13-2063-2013, 2013.
a
Zanis, P., Akritidis, D., Turnock, S., Naik, V., Szopa, S., Georgoulias, A. K., Bauer, S. E., Deushi, M., Horowitz, L. W., Keeble, J., Le Sager, P., O'Connor, F. M., Oshima, N., Tsigaridis, K., and van Noije, T.: Climate change penalty and benefit on surface ozone: a global perspective based on CMIP6 earth system models, Environ. Res. Lett., 17, 024014, https://doi.org/10.1088/1748-9326/ac4a34, 2022. a
Short summary
We use a machine learning technique called Gaussian mixture modeling (GMM) to classify vertical ozone profiles into groups based on how the ozone concentration changes with pressure. Even though the GMM algorithm was not provided with spatial information, the classes are geographically coherent. We also detect signatures of tropical broadening in UKESM1 future climate scenarios. GMM may be useful for understanding ozone structures in modeled and observed datasets.
We use a machine learning technique called Gaussian mixture modeling (GMM) to classify vertical...
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