Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8913-2022
© Author(s) 2022. 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-15-8913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
Forschungszentrum Jülich GmbH, Jülich Supercomputing Centre (JSC), Jülich, Germany
Institute of Geosciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Lukas H. Leufen
Forschungszentrum Jülich GmbH, Jülich Supercomputing Centre (JSC), Jülich, Germany
Institute of Geosciences, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
Aurelia Lupascu
Institute for Advanced Sustainability Studies, Potsdam, Germany
Tim Butler
Institute for Advanced Sustainability Studies, Potsdam, Germany
Martin G. Schultz
Forschungszentrum Jülich GmbH, Jülich Supercomputing Centre (JSC), Jülich, Germany
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Geosci. Model Dev., 14, 1553–1574, https://doi.org/10.5194/gmd-14-1553-2021, https://doi.org/10.5194/gmd-14-1553-2021, 2021
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MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using machine learning (ML). MLAir is adaptable to a wide range of ML use cases, focusing in particular on deep learning. The user has a free hand with the ML model itself and can select from different methods during preprocessing, training, and postprocessing. MLAir offers tools to track the experiment conduction, documents necessary ML parameters, and creates a variety of publication-ready plots.
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Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021, https://doi.org/10.5194/gmd-14-1-2021, 2021
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With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for daily aggregated near-surface ozone concentrations with a lead time of up to 4 d. We used measurement and reanalysis data from more than 300 German monitoring stations to train, fine tune, and test the model. We show that the model outperforms standard reference models like persistence models and demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d.
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We investigate the distribution and trends of surface ozone and its precursors over Ireland using advanced modelling to determine the drivers of ozone. Trajectory analysis is used to trace the origins of air masses, revealing the impact of transboundary pollution and atmospheric transport. The rising trend has been observed at urban sites over the past two decades, but without a similar trend at coastal sites. Coastal areas consistently show higher ozone levels than rural and urban areas.
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This study analyzes summertime ozone trends in East and Southeast Asia derived from a comprehensive observational database spanning from 1995 to 2019, incorporating aircraft observations, ozonesonde data, and measurements from 2500 surface sites. Multiple models are applied to attribute to changes in anthropogenic emissions and climate. The results highlight that increases in anthropogenic emissions are the primary driver of ozone increases both in the free troposphere and at the surface.
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
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Atmos. Chem. Phys., 25, 5287–5311, https://doi.org/10.5194/acp-25-5287-2025, https://doi.org/10.5194/acp-25-5287-2025, 2025
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-566, https://doi.org/10.5194/essd-2024-566, 2025
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-601, https://doi.org/10.5194/essd-2024-601, 2025
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Hantao Wang, Kazuyuki Miyazaki, Haitong Zhe Sun, Zhen Qu, Xiang Liu, Antje Inness, Martin Schultz, Sabine Schröder, Marc Serre, and J. Jason West
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Tabish Ansari, Aditya Nalam, Aurelia Lupaşcu, Carsten Hinz, Simon Grasse, and Tim Butler
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Surface ozone can travel far from its sources. In recent decades, emissions of ozone-forming gases have decreased in North America and Europe but risen in Asia, alongside rising global methane levels. Using advanced modeling, this study reveals that while local reductions in nitrogen oxides have lowered summer ozone, increases in natural and foreign sources offset these gains. Methane remains important, but its ozone impact has declined with reduced local emissions.
Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 23, 10713–10730, https://doi.org/10.5194/acp-23-10713-2023, https://doi.org/10.5194/acp-23-10713-2023, 2023
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New particle formation is an important source of atmospheric particles, exerting critical influences on global climate. Numerical models are vital tools to understanding atmospheric particle evolution, which, however, suffer from large biases in simulating particle numbers. Here we improve the model chemical processes governing particle sizes and compositions. The improved model reveals substantial contributions of newly formed particles to climate through effects on cloud condensation nuclei.
Monica Crippa, Diego Guizzardi, Tim Butler, Terry Keating, Rosa Wu, Jacek Kaminski, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Harrison Suchyta, Marilena Muntean, Efisio Solazzo, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Fabio Monforti-Ferrario, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Tabish Ansari, and Kristen Foley
Earth Syst. Sci. Data, 15, 2667–2694, https://doi.org/10.5194/essd-15-2667-2023, https://doi.org/10.5194/essd-15-2667-2023, 2023
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This study responds to the global and regional atmospheric modelling community's need for a mosaic of air pollutant emissions with global coverage, long time series, spatially distributed data at a high time resolution, and a high sectoral resolution in order to enhance the understanding of transboundary air pollution. The mosaic approach to integrating official regional emission inventories with a global inventory based on a consistent methodology ensures policy-relevant results.
Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler
Geosci. Model Dev., 16, 1427–1444, https://doi.org/10.5194/gmd-16-1427-2023, https://doi.org/10.5194/gmd-16-1427-2023, 2023
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Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
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Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Johana Romero-Alvarez, Aurelia Lupaşcu, Douglas Lowe, Alba Badia, Scott Archer-Nicholls, Steve Dorling, Claire E. Reeves, and Tim Butler
Atmos. Chem. Phys., 22, 13797–13815, https://doi.org/10.5194/acp-22-13797-2022, https://doi.org/10.5194/acp-22-13797-2022, 2022
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As ozone can be transported across countries, efficient air quality management and regulatory policies rely on the assessment of local ozone production vs. transport. In our study, we investigate the origin of surface ozone in the UK and the contribution of the different source regions to regulatory ozone metrics. It is shown that emission controls would be necessary over western Europe to improve health-related metrics and over larger areas to reduce impacts on ecosystems.
Aurelia Lupaşcu, Noelia Otero, Andrea Minkos, and Tim Butler
Atmos. Chem. Phys., 22, 11675–11699, https://doi.org/10.5194/acp-22-11675-2022, https://doi.org/10.5194/acp-22-11675-2022, 2022
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Swantje Preuschmann, Tanja Blome, Knut Görl, Fiona Köhnke, Bettina Steuri, Juliane El Zohbi, Diana Rechid, Martin Schultz, Jianing Sun, and Daniela Jacob
Adv. Sci. Res., 19, 51–71, https://doi.org/10.5194/asr-19-51-2022, https://doi.org/10.5194/asr-19-51-2022, 2022
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The main aspect of the paper is to obtain transferable principles for the development of digital knowledge transfer products. As such products are still unstandardised, the authors explored challenges and approaches for product developments. The authors report what they see as useful principles for developing digital knowledge transfer products, by describing the experience of developing the Net-Zero-2050 Web-Atlas and the "Bodenkohlenstoff-App".
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
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Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Noelia Otero, Oscar E. Jurado, Tim Butler, and Henning W. Rust
Atmos. Chem. Phys., 22, 1905–1919, https://doi.org/10.5194/acp-22-1905-2022, https://doi.org/10.5194/acp-22-1905-2022, 2022
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Surface ozone and temperature are strongly dependent and their extremes might be exacerbated by underlying climatological drivers, such as atmospheric blocking. Using an observational data set, we measure the dependence structure between ozone and temperature under the influence of atmospheric blocking. Blocks enhanced the probability of occurrence of compound ozone and temperature extremes over northwestern and central Europe, leading to greater health risks.
Stefano Galmarini, Paul Makar, Olivia E. Clifton, Christian Hogrefe, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Tim Butler, Jason Ducker, Johannes Flemming, Alma Hodzic, Christopher D. Holmes, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Juan Luis Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Sam Silva, and Ralf Wolke
Atmos. Chem. Phys., 21, 15663–15697, https://doi.org/10.5194/acp-21-15663-2021, https://doi.org/10.5194/acp-21-15663-2021, 2021
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This technical note presents the research protocols for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This initiative has three goals: (i) to define the state of wet and dry deposition in regional models, (ii) to evaluate how dry deposition influences air concentration and flux predictions, and (iii) to identify the causes for prediction differences. The evaluation compares LULC-specific dry deposition and effective conductances and fluxes.
Edward C. Chan and Timothy M. Butler
Geosci. Model Dev., 14, 4555–4572, https://doi.org/10.5194/gmd-14-4555-2021, https://doi.org/10.5194/gmd-14-4555-2021, 2021
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A large-eddy simulation based chemical transport model is implemented for an idealized street canyon. The dynamics of the model are evaluated using stationary measurements. A transient model run is also conducted over a 24 h period, where variations of pollutant concentrations indicate dependence on emissions, background concentrations, and solar state. Comparison stationary model runs show changes in flow structure concentrations.
Clara Betancourt, Timo Stomberg, Ribana Roscher, Martin G. Schultz, and Scarlet Stadtler
Earth Syst. Sci. Data, 13, 3013–3033, https://doi.org/10.5194/essd-13-3013-2021, https://doi.org/10.5194/essd-13-3013-2021, 2021
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With the AQ-Bench dataset, we contribute to shared data usage and machine learning methods in the field of environmental science. The AQ-Bench dataset contains air quality data and metadata from more than 5500 air quality observation stations all over the world. The dataset offers a low-threshold entrance to machine learning on a real-world environmental dataset. AQ-Bench thus provides a blueprint for environmental benchmark datasets.
Elena Macdonald, Noelia Otero, and Tim Butler
Atmos. Chem. Phys., 21, 4007–4023, https://doi.org/10.5194/acp-21-4007-2021, https://doi.org/10.5194/acp-21-4007-2021, 2021
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NO2 limit values are still regularly exceeded in many European cities despite decreasing emissions. Measurements of NOx concentrations from stations across Europe were systematically analysed to assess long-term changes observed in urban areas. We compared trends in concentration increments to trends in total and traffic emissions to find potential discrepancies. The results can help in evaluating inaccuracies in emission inventories and in improving spatial imbalances in data availability.
Lukas Hubert Leufen, Felix Kleinert, and Martin G. Schultz
Geosci. Model Dev., 14, 1553–1574, https://doi.org/10.5194/gmd-14-1553-2021, https://doi.org/10.5194/gmd-14-1553-2021, 2021
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MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using machine learning (ML). MLAir is adaptable to a wide range of ML use cases, focusing in particular on deep learning. The user has a free hand with the ML model itself and can select from different methods during preprocessing, training, and postprocessing. MLAir offers tools to track the experiment conduction, documents necessary ML parameters, and creates a variety of publication-ready plots.
Felix Kleinert, Lukas H. Leufen, and Martin G. Schultz
Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021, https://doi.org/10.5194/gmd-14-1-2021, 2021
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With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for daily aggregated near-surface ozone concentrations with a lead time of up to 4 d. We used measurement and reanalysis data from more than 300 German monitoring stations to train, fine tune, and test the model. We show that the model outperforms standard reference models like persistence models and demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d.
Tim Butler, Aurelia Lupascu, and Aditya Nalam
Atmos. Chem. Phys., 20, 10707–10731, https://doi.org/10.5194/acp-20-10707-2020, https://doi.org/10.5194/acp-20-10707-2020, 2020
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Ground-level ozone (O3) is not directly emitted; it is formed chemically in the atmosphere. Some ground-level O3 is transported from the stratosphere, but most O3 is produced from reactive precursors that are emitted by both natural and anthropogenic sources. We present the results of a novel source apportionment method for ground-level O3. Our results are consistent with previous work and also provide new insights. In particular, we highlight the roles of methane and international shipping.
Cited articles
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Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
We examine the effects of spatially aggregated upstream information as input for a deep learning...