Articles | Volume 16, issue 4
https://doi.org/10.5194/gmd-16-1427-2023
© 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-16-1427-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
02 Mar 2023
Model description paper |
| 02 Mar 2023
| 02 Mar 2023
Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions
Edward C. Chan
CORRESPONDING AUTHOR
Institute for Advanced Sustainability Studies, Potsdam, Germany
Joana Leitão
Institute for Advanced Sustainability Studies, Potsdam, Germany
Andreas Kerschbaumer
Senatsverwaltung für Umwelt, Mobilität, Verbraucher- und
Klimaschutz, Berlin, Germany
Timothy M. Butler
Institute for Advanced Sustainability Studies, Potsdam, Germany
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Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
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.
Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021, https://doi.org/10.5194/gmd-14-1171-2021, 2021
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An atmospheric chemistry model has been implemented in the microscale PALM model system 6.0. This article provides a detailed description of the model, its structure, input requirements, various features and limitations. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code; however, users can also easily implement other mechanisms. A case study is presented to demonstrate the application of the new chemistry model in the urban environment.
Ioannis Kioutsioukis, Christian Hogrefe, Paul A. Makar, Ummugulsun Alyuz, Jessy O. Bash, Roberto Bellasio, Roberto Bianconi, Tim Buttler, Olivia E. Clifton, Philippe Cheung, Alma Hodzic, Richard Kranenburg, Aurelia Lupascu, Kester Momoh, Juan Luis Perez-Camaño, John Pleim, Young-Hee Ryu, Robero San Jose, Donna Schwede, Ranjeet Sokhi, and Stefano Galmarini
EGUsphere, https://doi.org/10.5194/egusphere-2025-1091, https://doi.org/10.5194/egusphere-2025-1091, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Deposition is a key in air quality modelling. An evaluation of the AQMEII4 models is performed prior to analysing the different deposition schemes in relation to the LULC used. Such analysis is unprecedented. Among the results, LULC masks have to be harmonised and up-to-date information used in place of outdated and too course masks. Alternatively LULC masks should be evaluated and intercom pared when multiple model results are analysed.
Paul A. Makar, Philip Cheung, Christian Hogrefe, Ayodeji Akingunola, Ummugulsum Alyuz, Jesse O. Bash, Michael D. Bell, Roberto Bellasio, Roberto Bianconi, Tim Butler, Hazel Cathcart, Olivia E. Clifton, Alma Hodzic, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Jason A. Lynch, Kester Momoh, Juan L. Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Thomas Scheuschner, Mark W. Shephard, Ranjeet S. Sokhi, and Stefano Galmarini
Atmos. Chem. Phys., 25, 3049–3107, https://doi.org/10.5194/acp-25-3049-2025, https://doi.org/10.5194/acp-25-3049-2025, 2025
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The large range of sulfur and nitrogen deposition estimates from air quality models results in a large range of predicted impacts. We used models and deposition diagnostics to identify the processes controlling atmospheric sulfur and nitrogen deposition variability. Controlling factors included the uptake of gases and aerosols by hydrometeors, aerosol inorganic chemistry, particle dry deposition, ammonia bidirectional fluxes, gas deposition via plant cuticles and soil, and land use data.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Diego Guizzardi, Monica Crippa, Tim Butler, Terry Keating, Rosa Wu, Jacek W. Kamiński, 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, Rachel Hoesly, Marilena Muntean, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Annie Duhamel, Tabish Ansari, Kristen Foley, Guannan Geng, Yifei Chen, and Qiang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-601, https://doi.org/10.5194/essd-2024-601, 2025
Preprint under review for ESSD
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The global air pollution emission mosaic HTAP_v3.1 is the state-of-the-art database for addressing the evolution of a set of policy-relevant air pollutants over the past 2 decades. The inventory is made by the harmonization and blending of seven regional inventories, gapfilled using the most recent release of EDGAR (EDGARv8). By incorporating the best available local information, the HTAP_v3.1 mosaic inventory can be used for policy-relevant studies at both regional and global levels.
Christian Hogrefe, Stefano Galmarini, Paul A. Makar, Ioannis Kioutsioukis, Olivia E. Clifton, Ummugulsum Alyuz, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Tim Butler, Philip Cheung, Alma Hodzic, Richard Kranenburg, Aurelia Lupascu, Kester Momoh, Juan Luis Perez-Camanyo, Jonathan E. Pleim, Young-Hee Ryu, Roberto San Jose, Martijn Schaap, Donna B. Schwede, and Ranjeet Sokhi
EGUsphere, https://doi.org/10.5194/egusphere-2025-225, https://doi.org/10.5194/egusphere-2025-225, 2025
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Performed under the umbrella of the fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4), this study applies AQMEII4 diagnostic tools to better characterize how dry deposition removes pollutants from the atmosphere in regional-scale models. The results also strongly suggest that improvement and harmonization of the representation of land use in these models would serve the community in their future development efforts.
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
EGUsphere, https://doi.org/10.5194/egusphere-2024-3702, https://doi.org/10.5194/egusphere-2024-3702, 2024
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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 increases in anthropogenic emission are the primary driver of ozone increases both in the free troposphere and at the surface.
Tabish Ansari, Aditya Nalam, Aurelia Lupaşcu, Carsten Hinz, Simon Grasse, and Tim Butler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3752, https://doi.org/10.5194/egusphere-2024-3752, 2024
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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.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Stephen R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christophe Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-126, https://doi.org/10.5194/gmd-2024-126, 2024
Revised manuscript accepted for GMD
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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 set up are discussed, and the official recommendations for the project are presented.
Aditya Nalam, Aura Lupascu, Tabish Ansari, and Timothy Butler
EGUsphere, https://doi.org/10.5194/egusphere-2024-432, https://doi.org/10.5194/egusphere-2024-432, 2024
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Tropospheric O3 molecules are labelled with the identity of their precursor source in CAM-Chem to quantify the contribution from various emission sources to the tropospheric O3 burden (TOB) and its trends. With an equatorward shift, anthropogenic NOx emissions become significantly more efficient at producing O3 and play a major role in driving TOB trends. This is due to larger convection at the tropics effectively lifting O3 and its precursors to the free troposphere where O3 lifetime is longer.
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.
Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022, https://doi.org/10.5194/gmd-15-8913-2022, 2022
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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.
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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Ground-level ozone is an important air pollutant that affects human health, ecosystems, and climate. Ozone is not emitted directly but rather formed in the atmosphere through chemical reactions involving two distinct precursors. Our results provide detailed information about the origin of ozone in Germany during two peak ozone events that took place in 2015 and 2018, thus improving our understanding of ground-level ozone.
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.
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.
Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021, https://doi.org/10.5194/gmd-14-1171-2021, 2021
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An atmospheric chemistry model has been implemented in the microscale PALM model system 6.0. This article provides a detailed description of the model, its structure, input requirements, various features and limitations. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code; however, users can also easily implement other mechanisms. A case study is presented to demonstrate the application of the new chemistry model in the urban environment.
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.
Noelia Otero, Henning W. Rust, and Tim Butler
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-691, https://doi.org/10.5194/acp-2020-691, 2020
Revised manuscript not accepted
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Surface ozone concentrations are strongly correlated with temperature in summertime. Using long-term measurements, we investigate changes in the observed relationship between ozone and temperature over Germany. We propose a new statistical approach based on Generalized Additive Models (GAMs) to describe ozone production rates as a function of nitrogen oxides (NOx) and temperature. Our results suggest that NOx reductions alone can not explain the changes in the temperature dependence of ozone.
Aurelia Lupaşcu and Tim Butler
Atmos. Chem. Phys., 19, 14535–14558, https://doi.org/10.5194/acp-19-14535-2019, https://doi.org/10.5194/acp-19-14535-2019, 2019
Noelia Otero, Jana Sillmann, Kathleen A. Mar, Henning W. Rust, Sverre Solberg, Camilla Andersson, Magnuz Engardt, Robert Bergström, Bertrand Bessagnet, Augustin Colette, Florian Couvidat, Cournelius Cuvelier, Svetlana Tsyro, Hilde Fagerli, Martijn Schaap, Astrid Manders, Mihaela Mircea, Gino Briganti, Andrea Cappelletti, Mario Adani, Massimo D'Isidoro, María-Teresa Pay, Mark Theobald, Marta G. Vivanco, Peter Wind, Narendra Ojha, Valentin Raffort, and Tim Butler
Atmos. Chem. Phys., 18, 12269–12288, https://doi.org/10.5194/acp-18-12269-2018, https://doi.org/10.5194/acp-18-12269-2018, 2018
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This paper evaluates the capability of air-quality models to capture the observed relationship between surface ozone concentrations and meteorology over Europe. The air-quality models tended to overestimate the influence of maximum temperature and surface solar radiation. None of the air-quality models captured the strength of the observed relationship between ozone and relative humidity appropriately, underestimating the effect of relative humidity, a key factor in the ozone removal processes.
Tim Butler, Aurelia Lupascu, Jane Coates, and Shuai Zhu
Geosci. Model Dev., 11, 2825–2840, https://doi.org/10.5194/gmd-11-2825-2018, https://doi.org/10.5194/gmd-11-2825-2018, 2018
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This paper describes a method for determining origin of tropospheric ozone simulated in a global chemistry–climate model. This technique can show which precursor compounds were responsible for simulated ozone, and where they were emitted. In this paper we describe our technique, compare and contrast it with several other similar techniques, and use it to calculate the contribution of several different NOx and VOC precursor categories to the tropospheric ozone burden.
Erika von Schneidemesser, Boris Bonn, Tim M. Butler, Christian Ehlers, Holger Gerwig, Hannele Hakola, Heidi Hellén, Andreas Kerschbaumer, Dieter Klemp, Claudia Kofahl, Jürgen Kura, Anja Lüdecke, Rainer Nothard, Axel Pietsch, Jörn Quedenau, Klaus Schäfer, James J. Schauer, Ashish Singh, Ana-Maria Villalobos, Matthias Wiegner, and Mark G. Lawrence
Atmos. Chem. Phys., 18, 8621–8645, https://doi.org/10.5194/acp-18-8621-2018, https://doi.org/10.5194/acp-18-8621-2018, 2018
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This paper provides an overview of the measurements done at an urban background site in Berlin from June-August of 2014. Results show that natural source contributions to ozone and particulate matter (PM) air pollutants are substantial. Large contributions of secondary aerosols formed in the atmosphere to PM10 concentrations were quantified. An analysis of the sources also identified contributions to PM from plant-based sources, vehicles, and a small contribution from wood burning.
Friderike Kuik, Andreas Kerschbaumer, Axel Lauer, Aurelia Lupascu, Erika von Schneidemesser, and Tim M. Butler
Atmos. Chem. Phys., 18, 8203–8225, https://doi.org/10.5194/acp-18-8203-2018, https://doi.org/10.5194/acp-18-8203-2018, 2018
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Modelled NOx concentrations are often underestimated compared to observations, and measurement studies show that reported NOx emissions in urban areas are often too low when the contribution from traffic is largest. This modelling study quantifies the underestimation of traffic NOx emissions in the Berlin–Brandenburg and finds that they are underestimated by ca. 50 % in the core urban area. More research is needed in order to more accurately understand real-world NOx emissions from traffic.
Heiko Bozem, Tim M. Butler, Mark G. Lawrence, Hartwig Harder, Monica Martinez, Dagmar Kubistin, Jos Lelieveld, and Horst Fischer
Atmos. Chem. Phys., 17, 10565–10582, https://doi.org/10.5194/acp-17-10565-2017, https://doi.org/10.5194/acp-17-10565-2017, 2017
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We present airborne measurements and model simulations in the tropics and mid-latitudes during GABRIEL and HOOVER, respectively. Based only on in situ data net ozone formation/destruction tendencies (NOPR) are calculated and compared to a 3-D chemistry transport model. The NOPR is positive in the continental boundary layer and the upper troposphere above 6 km. In the marine boundary layer and the middle troposphere ozone destruction prevails. Fresh convection shows strong net ozone formation.
Augustin Colette, Camilla Andersson, Astrid Manders, Kathleen Mar, Mihaela Mircea, Maria-Teresa Pay, Valentin Raffort, Svetlana Tsyro, Cornelius Cuvelier, Mario Adani, Bertrand Bessagnet, Robert Bergström, Gino Briganti, Tim Butler, Andrea Cappelletti, Florian Couvidat, Massimo D'Isidoro, Thierno Doumbia, Hilde Fagerli, Claire Granier, Chris Heyes, Zig Klimont, Narendra Ojha, Noelia Otero, Martijn Schaap, Katarina Sindelarova, Annemiek I. Stegehuis, Yelva Roustan, Robert Vautard, Erik van Meijgaard, Marta Garcia Vivanco, and Peter Wind
Geosci. Model Dev., 10, 3255–3276, https://doi.org/10.5194/gmd-10-3255-2017, https://doi.org/10.5194/gmd-10-3255-2017, 2017
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The EURODELTA-Trends numerical experiment has been designed to assess the capability of chemistry-transport models to capture the evolution of surface air quality over the 1990–2010 period in Europe. It also includes sensitivity experiments in order to analyse the relative contribution of (i) emission changes, (ii) meteorological variability, and (iii) boundary conditions to air quality trends. The article is a detailed presentation of the experiment design and participating models.
Andrea Mues, Maheswar Rupakheti, Christoph Münkel, Axel Lauer, Heiko Bozem, Peter Hoor, Tim Butler, and Mark G. Lawrence
Atmos. Chem. Phys., 17, 8157–8176, https://doi.org/10.5194/acp-17-8157-2017, https://doi.org/10.5194/acp-17-8157-2017, 2017
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Ceilometer measurements taken in the Kathmandu Valley, Nepal, were used to study the temporal and spatial evolution of the mixing layer height in the valley. This provides important information on the vertical structure of the atmosphere and can thus also help to understand the mixing of air pollutants (e.g. black carbon) in the valley. The seasonal and diurnal cycles of the mixing layer were found to be highly dependent on meteorology and mainly anticorrelated to black carbon concentrations.
Friderike Kuik, Axel Lauer, Galina Churkina, Hugo A. C. Denier van der Gon, Daniel Fenner, Kathleen A. Mar, and Tim M. Butler
Geosci. Model Dev., 9, 4339–4363, https://doi.org/10.5194/gmd-9-4339-2016, https://doi.org/10.5194/gmd-9-4339-2016, 2016
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The study evaluates the performance of a setup of the Weather Research and Forecasting model with chemistry and aerosols (WRF–Chem) for the Berlin–Brandenburg region of Germany. Its sensitivity to updating urban input parameters based on structural data for Berlin is tested, specifying land use classes on a sub-grid scale, downscaling the original emissions to a resolution of ca. 1 km by 1 km for Berlin based on proxy data and model resolution.
Carolina Cavazos Guerra, Axel Lauer, Andreas B. Herber, Tim M. Butler, Annette Rinke, and Klaus Dethloff
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-942, https://doi.org/10.5194/acp-2016-942, 2016
Revised manuscript has not been submitted
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Accurate description of the Arctic atmosphere is a challenge for the modelling comunity. We evaluate the performance of the Weather Research and Forecast model (WRF) in the Eurasian Arctic and analyse the implications of data to initialise the model and a land surface scheme. The results show that biases can be related to the quality of data used and in the case of black carbon concentrations, to emission data. More long term measurements are need for model Validation in the area.
Kathleen A. Mar, Narendra Ojha, Andrea Pozzer, and Tim M. Butler
Geosci. Model Dev., 9, 3699–3728, https://doi.org/10.5194/gmd-9-3699-2016, https://doi.org/10.5194/gmd-9-3699-2016, 2016
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Ground-level ozone is an air pollutant with adverse effects on human and ecosystem health and is also a climate forcer with a significant warming effect. This paper presents the setup and evaluation of a model for ozone air quality over Europe. Within the model evaluation, we compare the use of two commonly used photochemical schemes, and we conclude that uncertainties in the representation of chemistry are important to consider when using air quality models for policy applications.
Jane Coates, Kathleen A. Mar, Narendra Ojha, and Tim M. Butler
Atmos. Chem. Phys., 16, 11601–11615, https://doi.org/10.5194/acp-16-11601-2016, https://doi.org/10.5194/acp-16-11601-2016, 2016
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This modelling study reproduced the non-linear relationship of ozone, NOx and temperature using various chemical mechanisms previously determined from observational studies. Under urban conditions, faster reaction rates rather than increased isoprene emissions led to a slightly greater increase of ozone with temperature using different NOx conditions. This study also shows that the increase of ozone with temperature is more sensitive to atmospheric mixing than the choice of chemical mechanism.
Boris Bonn, Erika von Schneidemesser, Dorota Andrich, Jörn Quedenau, Holger Gerwig, Anja Lüdecke, Jürgen Kura, Axel Pietsch, Christian Ehlers, Dieter Klemp, Claudia Kofahl, Rainer Nothard, Andreas Kerschbaumer, Wolfgang Junkermann, Rüdiger Grote, Tobias Pohl, Konradin Weber, Birgit Lode, Philipp Schönberger, Galina Churkina, Tim M. Butler, and Mark G. Lawrence
Atmos. Chem. Phys., 16, 7785–7811, https://doi.org/10.5194/acp-16-7785-2016, https://doi.org/10.5194/acp-16-7785-2016, 2016
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The distribution of air pollutants (gases and particles) have been investigated in different environments in Potsdam, Germany. Remarkable variations of the pollutants have been observed for distances of tens of meters by bicycles, vans and aircraft. Vegetated areas caused reductions depending on the pollutants, the vegetation type and dimensions. Our measurements show the pollutants to be of predominantly local origin, resulting in a huge challenge for common models to resolve.
J. Coates and T. M. Butler
Atmos. Chem. Phys., 15, 8795–8808, https://doi.org/10.5194/acp-15-8795-2015, https://doi.org/10.5194/acp-15-8795-2015, 2015
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We show that simplified chemical mechanisms break down VOC into smaller sized degradation products on the first day faster than the near-explicit MCM chemical mechanism which would lead to an underprediction of ozone levels downwind of VOC emissions, and an underestimation of the VOC contribution to tropospheric background ozone when using simplified chemical mechanisms in regional or global modelling studies.
C. Liu, S. Beirle, T. Butler, P. Hoor, C. Frankenberg, P. Jöckel, M. Penning de Vries, U. Platt, A. Pozzer, M. G. Lawrence, J. Lelieveld, H. Tost, and T. Wagner
Atmos. Chem. Phys., 14, 1717–1732, https://doi.org/10.5194/acp-14-1717-2014, https://doi.org/10.5194/acp-14-1717-2014, 2014
Z. S. Stock, M. R. Russo, T. M. Butler, A. T. Archibald, M. G. Lawrence, P. J. Telford, N. L. Abraham, and J. A. Pyle
Atmos. Chem. Phys., 13, 12215–12231, https://doi.org/10.5194/acp-13-12215-2013, https://doi.org/10.5194/acp-13-12215-2013, 2013
S. M. Burrows, P. J. Rayner, T. Butler, and M. G. Lawrence
Atmos. Chem. Phys., 13, 5473–5488, https://doi.org/10.5194/acp-13-5473-2013, https://doi.org/10.5194/acp-13-5473-2013, 2013
Related subject area
Climate and Earth system modeling
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
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Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
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Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
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CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
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Short summary
Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written in the Python 3 scripting language, which adopts a generalized treatment for activity data using traffic information of varying levels of detail introduced in a systematic and consistent manner, with the ability to maximize reusability. Thus, Yeti has been conceived and implemented with a high degree of data and process symmetry, allowing scalable and flexible execution while affording ease of use.
Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written...
