Articles | Volume 8, issue 8
https://doi.org/10.5194/gmd-8-2587-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-2587-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
14 Aug 2015
Model description paper |
| 14 Aug 2015
Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c
Earth System Science Department, University of California, Irvine, California, USA
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Clara Orbe, Alison Ming, Gabriel Chiodo, Michael Prather, Mohamadou Diallo, Qi Tang, Andreas Chrysanthou, Hiroaki Naoe, Xin Zhou, Irina Thaler, Dillon Elsbury, Ewa Bednarz, Jonathon S. Wright, Aaron Match, Shingo Watanabe, James Anstey, Tobias Kerzenmacher, Stefan Versick, Marion Marchand, Feng Li, and James Keeble
EGUsphere, https://doi.org/10.5194/egusphere-2025-2761, https://doi.org/10.5194/egusphere-2025-2761, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The quasi-biennial oscillation (QBO) is the main source of wind fluctuations in the tropical stratosphere, which can couple to surface climate. However, models do a poor job of simulating the QBO in the lower stratosphere, for reasons that remain unclear. One possibility is that models do not completely represent how ozone influences the QBO-associated wind variations. Here we propose a multi-model framework for assessing how ozone influences the QBO in recent past and future climates.
Calum P. Wilson and Michael J. Prather
Atmos. Meas. Tech., 18, 1757–1769, https://doi.org/10.5194/amt-18-1757-2025, https://doi.org/10.5194/amt-18-1757-2025, 2025
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We evaluated how well we can infer air pollutant levels (ozone, carbon monoxide, and nitrogen oxides) between air quality stations throughout South Korea, finding good representation in most densely measured cities in spite of intense small-scale emission hotspots. Comparing observed air quality with gridded model output is desirable, and so we created gridded datasets over South Korea using air quality station measurements, which agreed with airborne measurements around Seoul.
Maryam Ramezani Ziarani, Miriam Sinnhuber, Thomas Reddmann, Bernd Funke, Stefan Bender, and Michael Prather
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-227, https://doi.org/10.5194/gmd-2024-227, 2025
Preprint under review for GMD
Short summary
Short summary
Our study aims to present a new method for incorporating top-down solar forcing into stratospheric ozone relying on linearized ozone scheme. The addition of geomagnetic forcing led to significant ozone losses in the polar upper stratosphere of both hemispheres due to the catalytic cycles involving NOy. In addition to the particle precipitation effect, accounting for solar UV variability in the ICON-ART model leads to the changes in ozone in the tropical stratosphere.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael J. Prather, Alexander T. Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Christopher J. Smith, Steven T. Turnock, Duncan Watson-Parris, and Paul J. Young
EGUsphere, https://doi.org/10.5194/egusphere-2024-2528, https://doi.org/10.5194/egusphere-2024-2528, 2024
Short summary
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The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. In this paper, we review its contribution to AR6, and the wider understanding of the role of these species in climate and climate change. We identify remaining challenges concluding with recommendations aimed to improve the utility and uptake of climate model data to address the role of short-lived climate forcers in the Earth system.
Jinbo Xie, Qi Tang, Michael Prather, Jadwiga Richter, and Shixuan Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1927, https://doi.org/10.5194/egusphere-2024-1927, 2024
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Analysis of the interaction between the climate and ozone in the stratosphere is complicated by the in-ability climate model in simulating the Quasi-Biennial Oscillation (QBO) – an important climate mode in the stratosphere. We use a set of model simulation that realistically simulate QBO and a novel ozone diagnostic tool to separate the temperature and circulation-driven QBO impact. These are important for diagnosing model-model differences in the QBO-ozone responses for climate projections.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
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Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Hsiang-He Lee, Qi Tang, and Michael Prather
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-203, https://doi.org/10.5194/gmd-2023-203, 2024
Revised manuscript not accepted
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The E3SM Chemistry diagnostics package (ChemDyg) is a software tool, which is designed for the global climate model (E3SM) chemistry development. ChemDyg generates several diagnostic plots and tables for model-to-model and model-to-observation comparison, including 2-dimentional contour mapping plots, diurnal and annual cycle, time-series plots, and comprehensive processing tables. This paper is to introduce the details of each diagnostics set and its required input data formats in ChemDyg.
Michael J. Prather, Hao Guo, and Xin Zhu
Earth Syst. Sci. Data, 15, 3299–3349, https://doi.org/10.5194/essd-15-3299-2023, https://doi.org/10.5194/essd-15-3299-2023, 2023
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The Atmospheric Tomography Mission (ATom) measured the chemical composition in air parcels from 0–12 km altitude on 2 km horizontal by 80 m vertical scales for four seasons, resolving most scales of chemical heterogeneity. ATom is one of the first missions designed to calculate the chemical evolution of each parcel, providing semi-global diurnal budgets for ozone and methane. Observations covered the remote troposphere: Pacific and Atlantic Ocean basins, Southern Ocean, Arctic basin, Antarctica.
Michael J. Prather, Lucien Froidevaux, and Nathaniel J. Livesey
Atmos. Chem. Phys., 23, 843–849, https://doi.org/10.5194/acp-23-843-2023, https://doi.org/10.5194/acp-23-843-2023, 2023
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From satellite data for nitrous oxide (N2O), ozone and temperature, we calculate the monthly loss of N2O and find it is increasing faster than expected, resulting in a shorter lifetime, which reduces the impact of anthropogenic emissions. We identify the cause as enhanced vertical lofting of high-N2O air into the tropical middle stratosphere, where it is destroyed photochemically. Because global warming is due in part to N2O, this finding presents a new negative climate-chemistry feedback.
Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven C. Wofsy
Atmos. Chem. Phys., 23, 99–117, https://doi.org/10.5194/acp-23-99-2023, https://doi.org/10.5194/acp-23-99-2023, 2023
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We have prepared a unique and unusual result from the recent ATom aircraft mission: a measurement-based derivation of the production and loss rates of ozone and methane over the ocean basins. These are the key products of chemistry models used in assessments but have thus far lacked observational metrics. It also shows the scales of variability of atmospheric chemical rates and provides a major challenge to the atmospheric models.
Michael J. Prather
Earth Syst. Dynam., 13, 703–709, https://doi.org/10.5194/esd-13-703-2022, https://doi.org/10.5194/esd-13-703-2022, 2022
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Atmospheric CO2 fluctuations point to changes in fossil fuel emissions plus natural and perturbed variations in the natural carbon cycle. One unstudied source of variability is the stratosphere, where the influx of aged CO2-depleted air can cause surface fluctuations. Using modeling and, separately, scaling the observed N2O variability, I find that stratosphere-driven surface variability in CO2 is not a significant uncertainty (at most 10 % of the observed interannual variability).
Daniel J. Ruiz and Michael J. Prather
Atmos. Chem. Phys., 22, 2079–2093, https://doi.org/10.5194/acp-22-2079-2022, https://doi.org/10.5194/acp-22-2079-2022, 2022
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The stratosphere is an important source of tropospheric ozone, which affects climate, chemistry, and air quality, but is extremely difficult to quantify given the large production and loss terms in the troposphere. Here, we use other gases that are well observed and quantified as a reference to test our simulations of ozone transport in the atmosphere. This allows us to better constrain the stratospheric source of ozone and also offers guidance to improve future simulations of ozone transport.
Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven Wofsy
Atmos. Chem. Phys., 21, 13729–13746, https://doi.org/10.5194/acp-21-13729-2021, https://doi.org/10.5194/acp-21-13729-2021, 2021
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The NASA Atmospheric Tomography (ATom) mission built a climatology of the chemical composition of tropospheric air parcels throughout the middle of the Pacific and Atlantic oceans. The level of detail allows us to reconstruct the photochemical budgets of O3 and CH4 over these vast, remote regions. We find that most of the chemical heterogeneity is captured at the resolution used in current global chemistry models and that the majority of reactivity occurs in the
hottest20 % of parcels.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, https://doi.org/10.5194/gmd-14-1219-2021, 2021
Samuel R. Hall, Kirk Ullmann, Michael J. Prather, Clare M. Flynn, Lee T. Murray, Arlene M. Fiore, Gustavo Correa, Sarah A. Strode, Stephen D. Steenrod, Jean-Francois Lamarque, Jonathan Guth, Béatrice Josse, Johannes Flemming, Vincent Huijnen, N. Luke Abraham, and Alex T. Archibald
Atmos. Chem. Phys., 18, 16809–16828, https://doi.org/10.5194/acp-18-16809-2018, https://doi.org/10.5194/acp-18-16809-2018, 2018
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Photolysis (J rates) initiates and drives atmospheric chemistry, and Js are perturbed by factors of 2 by clouds. The NASA Atmospheric Tomography (ATom) Mission provides the first comprehensive observations on how clouds perturb Js through the remote Pacific and Atlantic basins. We compare these cloud-perturbation J statistics with those from nine global chemistry models. While basic patterns agree, there is a large spread across models, and all lack some basic features of the observations.
Sarah A. Strode, Junhua Liu, Leslie Lait, Róisín Commane, Bruce Daube, Steven Wofsy, Austin Conaty, Paul Newman, and Michael Prather
Atmos. Chem. Phys., 18, 10955–10971, https://doi.org/10.5194/acp-18-10955-2018, https://doi.org/10.5194/acp-18-10955-2018, 2018
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The GEOS-5 atmospheric model provided forecasts for the Atmospheric Tomography Mission (ATom). GEOS-5 shows skill in simulating the carbon monoxide (CO) measured in ATom-1. African fires contribute to high CO over the tropical Atlantic, but non-fire sources are the main contributors elsewhere. ATom aims to provide a chemical climatology, so we consider whether ATom-1 occurred during a typical summer month. Satellite observations suggest ATom-1 occurred in a clean but not exceptional month.
Michael J. Prather, Clare M. Flynn, Xin Zhu, Stephen D. Steenrod, Sarah A. Strode, Arlene M. Fiore, Gustavo Correa, Lee T. Murray, and Jean-Francois Lamarque
Atmos. Meas. Tech., 11, 2653–2668, https://doi.org/10.5194/amt-11-2653-2018, https://doi.org/10.5194/amt-11-2653-2018, 2018
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A new protocol for merging in situ atmospheric chemistry measurements with 3-D models is developed. This technique can identify the most reactive air parcels in terms of tropospheric production/loss of O3 & CH4. This approach highlights differences in 6 global chemistry models even with composition specified. Thus in situ measurements from, e.g., NASA's ATom mission can be used to develop a chemical climatology of, not only the key species, but also the rates of key reactions in each air parcel.
Ruth M. Doherty, Clara Orbe, Guang Zeng, David A. Plummer, Michael J. Prather, Oliver Wild, Meiyun Lin, Drew T. Shindell, and Ian A. Mackenzie
Atmos. Chem. Phys., 17, 14219–14237, https://doi.org/10.5194/acp-17-14219-2017, https://doi.org/10.5194/acp-17-14219-2017, 2017
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We investigate how climate change impacts global air pollution transport. To study transport changes, we use a carbon monoxide (CO) tracer species emitted from global sources. We find robust and consistent changes in CO-tracer distributions in climate change simulations performed by four chemistry–climate models in different seasons. We highlight the importance of the co-location of emission source regions and controlling transport processes in determining future pollution transport.
Michael J. Prather, Xin Zhu, Clare M. Flynn, Sarah A. Strode, Jose M. Rodriguez, Stephen D. Steenrod, Junhua Liu, Jean-Francois Lamarque, Arlene M. Fiore, Larry W. Horowitz, Jingqiu Mao, Lee T. Murray, Drew T. Shindell, and Steven C. Wofsy
Atmos. Chem. Phys., 17, 9081–9102, https://doi.org/10.5194/acp-17-9081-2017, https://doi.org/10.5194/acp-17-9081-2017, 2017
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We present a new approach for comparing atmospheric chemistry models with measurements based on what these models are used to do, i.e., calculate changes in ozone and methane, prime greenhouse gases. This method anticipates a new type of measurements from the NASA Atmospheric Tomography (ATom) mission. In comparing the mixture of species within air parcels, we focus on those responsible for key chemical changes and weight these parcels by their chemical reactivity.
James Hansen, Makiko Sato, Pushker Kharecha, Karina von Schuckmann, David J. Beerling, Junji Cao, Shaun Marcott, Valerie Masson-Delmotte, Michael J. Prather, Eelco J. Rohling, Jeremy Shakun, Pete Smith, Andrew Lacis, Gary Russell, and Reto Ruedy
Earth Syst. Dynam., 8, 577–616, https://doi.org/10.5194/esd-8-577-2017, https://doi.org/10.5194/esd-8-577-2017, 2017
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Global temperature now exceeds +1.25 °C relative to 1880–1920, similar to warmth of the Eemian period. Keeping warming less than 1.5 °C or CO2 below 350 ppm now requires extraction of CO2 from the air. If rapid phaseout of fossil fuel emissions begins soon, most extraction can be via improved agricultural and forestry practices. In contrast, continued high emissions places a burden on young people of massive technological CO2 extraction with large risks, high costs and uncertain feasibility.
Juno Hsu, Michael J. Prather, Philip Cameron-Smith, Alex Veidenbaum, and Alex Nicolau
Geosci. Model Dev., 10, 2525–2545, https://doi.org/10.5194/gmd-10-2525-2017, https://doi.org/10.5194/gmd-10-2525-2017, 2017
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Solar-J is a high-fidelity solar radiative transfer Fortran 90 code. It has been developed for consistently calculating both the photolysis rates of important chemical species and the heating rates of the atmosphere and the Earth's surface. Its spectral range spans from 177 nm to 12 microns. It can be easily dropped in as a module in global climate–chemistry models.
Gunnar Myhre, Wenche Aas, Ribu Cherian, William Collins, Greg Faluvegi, Mark Flanner, Piers Forster, Øivind Hodnebrog, Zbigniew Klimont, Marianne T. Lund, Johannes Mülmenstädt, Cathrine Lund Myhre, Dirk Olivié, Michael Prather, Johannes Quaas, Bjørn H. Samset, Jordan L. Schnell, Michael Schulz, Drew Shindell, Ragnhild B. Skeie, Toshihiko Takemura, and Svetlana Tsyro
Atmos. Chem. Phys., 17, 2709–2720, https://doi.org/10.5194/acp-17-2709-2017, https://doi.org/10.5194/acp-17-2709-2017, 2017
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Over the past decades, the geographical distribution of emissions of substances that alter the atmospheric energy balance has changed due to economic growth and pollution regulations. Here, we show the resulting changes to aerosol and ozone abundances and their radiative forcing using recently updated emission data for the period 1990–2015, as simulated by seven global atmospheric composition models. The global mean radiative forcing is more strongly positive than reported in IPCC AR5.
William J. Collins, Jean-François Lamarque, Michael Schulz, Olivier Boucher, Veronika Eyring, Michaela I. Hegglin, Amanda Maycock, Gunnar Myhre, Michael Prather, Drew Shindell, and Steven J. Smith
Geosci. Model Dev., 10, 585–607, https://doi.org/10.5194/gmd-10-585-2017, https://doi.org/10.5194/gmd-10-585-2017, 2017
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We have designed a set of climate model experiments called the Aerosol Chemistry Model Intercomparison Project (AerChemMIP). These are designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases in the climate models that are used to simulate past and future climate. We hope that many climate modelling centres will choose to run these experiments to help understand the contribution of aerosols and chemistry to climate change.
Bojan Sič, Laaziz El Amraoui, Andrea Piacentini, Virginie Marécal, Emanuele Emili, Daniel Cariolle, Michael Prather, and Jean-Luc Attié
Atmos. Meas. Tech., 9, 5535–5554, https://doi.org/10.5194/amt-9-5535-2016, https://doi.org/10.5194/amt-9-5535-2016, 2016
J. L. Schnell, M. J. Prather, B. Josse, V. Naik, L. W. Horowitz, P. Cameron-Smith, D. Bergmann, G. Zeng, D. A. Plummer, K. Sudo, T. Nagashima, D. T. Shindell, G. Faluvegi, and S. A. Strode
Atmos. Chem. Phys., 15, 10581–10596, https://doi.org/10.5194/acp-15-10581-2015, https://doi.org/10.5194/acp-15-10581-2015, 2015
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We test global chemistry--climate models in their ability to simulate present-day surface ozone. Models are tested against observed hourly ozone from 4217 stations in North America and Europe that are averaged over 1°x1° grid cells. Using novel metrics, we find most models match the shape but not the amplitude of regional summertime diurnal and annual cycles and match the pattern but not the magnitude of summer ozone enhancement. Most also match the observed distribution of extreme episode sizes
J. L. Schnell, C. D. Holmes, A. Jangam, and M. J. Prather
Atmos. Chem. Phys., 14, 7721–7739, https://doi.org/10.5194/acp-14-7721-2014, https://doi.org/10.5194/acp-14-7721-2014, 2014
C. D. Holmes, M. J. Prather, and G. C. M. Vinken
Atmos. Chem. Phys., 14, 6801–6812, https://doi.org/10.5194/acp-14-6801-2014, https://doi.org/10.5194/acp-14-6801-2014, 2014
P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh
Geosci. Model Dev., 7, 105–145, https://doi.org/10.5194/gmd-7-105-2014, https://doi.org/10.5194/gmd-7-105-2014, 2014
V. Naik, A. Voulgarakis, A. M. Fiore, L. W. Horowitz, J.-F. Lamarque, M. Lin, M. J. Prather, P. J. Young, D. Bergmann, P. J. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, T. P. C. van Noije, D. A. Plummer, M. Righi, S. T. Rumbold, R. Skeie, D. T. Shindell, D. S. Stevenson, S. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, https://doi.org/10.5194/acp-13-5277-2013, 2013
A. Voulgarakis, V. Naik, J.-F. Lamarque, D. T. Shindell, P. J. Young, M. J. Prather, O. Wild, R. D. Field, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, L. W. Horowitz, B. Josse, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, D. S. Stevenson, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, https://doi.org/10.5194/acp-13-2563-2013, 2013
C. D. Holmes, M. J. Prather, O. A. Søvde, and G. Myhre
Atmos. Chem. Phys., 13, 285–302, https://doi.org/10.5194/acp-13-285-2013, https://doi.org/10.5194/acp-13-285-2013, 2013
Ø. Hodnebrog, T. K. Berntsen, O. Dessens, M. Gauss, V. Grewe, I. S. A. Isaksen, B. Koffi, G. Myhre, D. Olivié, M. J. Prather, F. Stordal, S. Szopa, Q. Tang, P. van Velthoven, and J. E. Williams
Atmos. Chem. Phys., 12, 12211–12225, https://doi.org/10.5194/acp-12-12211-2012, https://doi.org/10.5194/acp-12-12211-2012, 2012
Clara Orbe, Alison Ming, Gabriel Chiodo, Michael Prather, Mohamadou Diallo, Qi Tang, Andreas Chrysanthou, Hiroaki Naoe, Xin Zhou, Irina Thaler, Dillon Elsbury, Ewa Bednarz, Jonathon S. Wright, Aaron Match, Shingo Watanabe, James Anstey, Tobias Kerzenmacher, Stefan Versick, Marion Marchand, Feng Li, and James Keeble
EGUsphere, https://doi.org/10.5194/egusphere-2025-2761, https://doi.org/10.5194/egusphere-2025-2761, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The quasi-biennial oscillation (QBO) is the main source of wind fluctuations in the tropical stratosphere, which can couple to surface climate. However, models do a poor job of simulating the QBO in the lower stratosphere, for reasons that remain unclear. One possibility is that models do not completely represent how ozone influences the QBO-associated wind variations. Here we propose a multi-model framework for assessing how ozone influences the QBO in recent past and future climates.
Calum P. Wilson and Michael J. Prather
Atmos. Meas. Tech., 18, 1757–1769, https://doi.org/10.5194/amt-18-1757-2025, https://doi.org/10.5194/amt-18-1757-2025, 2025
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We evaluated how well we can infer air pollutant levels (ozone, carbon monoxide, and nitrogen oxides) between air quality stations throughout South Korea, finding good representation in most densely measured cities in spite of intense small-scale emission hotspots. Comparing observed air quality with gridded model output is desirable, and so we created gridded datasets over South Korea using air quality station measurements, which agreed with airborne measurements around Seoul.
Maryam Ramezani Ziarani, Miriam Sinnhuber, Thomas Reddmann, Bernd Funke, Stefan Bender, and Michael Prather
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-227, https://doi.org/10.5194/gmd-2024-227, 2025
Preprint under review for GMD
Short summary
Short summary
Our study aims to present a new method for incorporating top-down solar forcing into stratospheric ozone relying on linearized ozone scheme. The addition of geomagnetic forcing led to significant ozone losses in the polar upper stratosphere of both hemispheres due to the catalytic cycles involving NOy. In addition to the particle precipitation effect, accounting for solar UV variability in the ICON-ART model leads to the changes in ozone in the tropical stratosphere.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael J. Prather, Alexander T. Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Christopher J. Smith, Steven T. Turnock, Duncan Watson-Parris, and Paul J. Young
EGUsphere, https://doi.org/10.5194/egusphere-2024-2528, https://doi.org/10.5194/egusphere-2024-2528, 2024
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The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. In this paper, we review its contribution to AR6, and the wider understanding of the role of these species in climate and climate change. We identify remaining challenges concluding with recommendations aimed to improve the utility and uptake of climate model data to address the role of short-lived climate forcers in the Earth system.
Jinbo Xie, Qi Tang, Michael Prather, Jadwiga Richter, and Shixuan Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1927, https://doi.org/10.5194/egusphere-2024-1927, 2024
Short summary
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Analysis of the interaction between the climate and ozone in the stratosphere is complicated by the in-ability climate model in simulating the Quasi-Biennial Oscillation (QBO) – an important climate mode in the stratosphere. We use a set of model simulation that realistically simulate QBO and a novel ozone diagnostic tool to separate the temperature and circulation-driven QBO impact. These are important for diagnosing model-model differences in the QBO-ozone responses for climate projections.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
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Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Hsiang-He Lee, Qi Tang, and Michael Prather
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-203, https://doi.org/10.5194/gmd-2023-203, 2024
Revised manuscript not accepted
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The E3SM Chemistry diagnostics package (ChemDyg) is a software tool, which is designed for the global climate model (E3SM) chemistry development. ChemDyg generates several diagnostic plots and tables for model-to-model and model-to-observation comparison, including 2-dimentional contour mapping plots, diurnal and annual cycle, time-series plots, and comprehensive processing tables. This paper is to introduce the details of each diagnostics set and its required input data formats in ChemDyg.
Michael J. Prather, Hao Guo, and Xin Zhu
Earth Syst. Sci. Data, 15, 3299–3349, https://doi.org/10.5194/essd-15-3299-2023, https://doi.org/10.5194/essd-15-3299-2023, 2023
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The Atmospheric Tomography Mission (ATom) measured the chemical composition in air parcels from 0–12 km altitude on 2 km horizontal by 80 m vertical scales for four seasons, resolving most scales of chemical heterogeneity. ATom is one of the first missions designed to calculate the chemical evolution of each parcel, providing semi-global diurnal budgets for ozone and methane. Observations covered the remote troposphere: Pacific and Atlantic Ocean basins, Southern Ocean, Arctic basin, Antarctica.
Michael J. Prather, Lucien Froidevaux, and Nathaniel J. Livesey
Atmos. Chem. Phys., 23, 843–849, https://doi.org/10.5194/acp-23-843-2023, https://doi.org/10.5194/acp-23-843-2023, 2023
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From satellite data for nitrous oxide (N2O), ozone and temperature, we calculate the monthly loss of N2O and find it is increasing faster than expected, resulting in a shorter lifetime, which reduces the impact of anthropogenic emissions. We identify the cause as enhanced vertical lofting of high-N2O air into the tropical middle stratosphere, where it is destroyed photochemically. Because global warming is due in part to N2O, this finding presents a new negative climate-chemistry feedback.
Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven C. Wofsy
Atmos. Chem. Phys., 23, 99–117, https://doi.org/10.5194/acp-23-99-2023, https://doi.org/10.5194/acp-23-99-2023, 2023
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We have prepared a unique and unusual result from the recent ATom aircraft mission: a measurement-based derivation of the production and loss rates of ozone and methane over the ocean basins. These are the key products of chemistry models used in assessments but have thus far lacked observational metrics. It also shows the scales of variability of atmospheric chemical rates and provides a major challenge to the atmospheric models.
Michael J. Prather
Earth Syst. Dynam., 13, 703–709, https://doi.org/10.5194/esd-13-703-2022, https://doi.org/10.5194/esd-13-703-2022, 2022
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Atmospheric CO2 fluctuations point to changes in fossil fuel emissions plus natural and perturbed variations in the natural carbon cycle. One unstudied source of variability is the stratosphere, where the influx of aged CO2-depleted air can cause surface fluctuations. Using modeling and, separately, scaling the observed N2O variability, I find that stratosphere-driven surface variability in CO2 is not a significant uncertainty (at most 10 % of the observed interannual variability).
Daniel J. Ruiz and Michael J. Prather
Atmos. Chem. Phys., 22, 2079–2093, https://doi.org/10.5194/acp-22-2079-2022, https://doi.org/10.5194/acp-22-2079-2022, 2022
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The stratosphere is an important source of tropospheric ozone, which affects climate, chemistry, and air quality, but is extremely difficult to quantify given the large production and loss terms in the troposphere. Here, we use other gases that are well observed and quantified as a reference to test our simulations of ozone transport in the atmosphere. This allows us to better constrain the stratospheric source of ozone and also offers guidance to improve future simulations of ozone transport.
Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven Wofsy
Atmos. Chem. Phys., 21, 13729–13746, https://doi.org/10.5194/acp-21-13729-2021, https://doi.org/10.5194/acp-21-13729-2021, 2021
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The NASA Atmospheric Tomography (ATom) mission built a climatology of the chemical composition of tropospheric air parcels throughout the middle of the Pacific and Atlantic oceans. The level of detail allows us to reconstruct the photochemical budgets of O3 and CH4 over these vast, remote regions. We find that most of the chemical heterogeneity is captured at the resolution used in current global chemistry models and that the majority of reactivity occurs in the
hottest20 % of parcels.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, https://doi.org/10.5194/gmd-14-1219-2021, 2021
Samuel R. Hall, Kirk Ullmann, Michael J. Prather, Clare M. Flynn, Lee T. Murray, Arlene M. Fiore, Gustavo Correa, Sarah A. Strode, Stephen D. Steenrod, Jean-Francois Lamarque, Jonathan Guth, Béatrice Josse, Johannes Flemming, Vincent Huijnen, N. Luke Abraham, and Alex T. Archibald
Atmos. Chem. Phys., 18, 16809–16828, https://doi.org/10.5194/acp-18-16809-2018, https://doi.org/10.5194/acp-18-16809-2018, 2018
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Photolysis (J rates) initiates and drives atmospheric chemistry, and Js are perturbed by factors of 2 by clouds. The NASA Atmospheric Tomography (ATom) Mission provides the first comprehensive observations on how clouds perturb Js through the remote Pacific and Atlantic basins. We compare these cloud-perturbation J statistics with those from nine global chemistry models. While basic patterns agree, there is a large spread across models, and all lack some basic features of the observations.
Sarah A. Strode, Junhua Liu, Leslie Lait, Róisín Commane, Bruce Daube, Steven Wofsy, Austin Conaty, Paul Newman, and Michael Prather
Atmos. Chem. Phys., 18, 10955–10971, https://doi.org/10.5194/acp-18-10955-2018, https://doi.org/10.5194/acp-18-10955-2018, 2018
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The GEOS-5 atmospheric model provided forecasts for the Atmospheric Tomography Mission (ATom). GEOS-5 shows skill in simulating the carbon monoxide (CO) measured in ATom-1. African fires contribute to high CO over the tropical Atlantic, but non-fire sources are the main contributors elsewhere. ATom aims to provide a chemical climatology, so we consider whether ATom-1 occurred during a typical summer month. Satellite observations suggest ATom-1 occurred in a clean but not exceptional month.
Michael J. Prather, Clare M. Flynn, Xin Zhu, Stephen D. Steenrod, Sarah A. Strode, Arlene M. Fiore, Gustavo Correa, Lee T. Murray, and Jean-Francois Lamarque
Atmos. Meas. Tech., 11, 2653–2668, https://doi.org/10.5194/amt-11-2653-2018, https://doi.org/10.5194/amt-11-2653-2018, 2018
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A new protocol for merging in situ atmospheric chemistry measurements with 3-D models is developed. This technique can identify the most reactive air parcels in terms of tropospheric production/loss of O3 & CH4. This approach highlights differences in 6 global chemistry models even with composition specified. Thus in situ measurements from, e.g., NASA's ATom mission can be used to develop a chemical climatology of, not only the key species, but also the rates of key reactions in each air parcel.
Ruth M. Doherty, Clara Orbe, Guang Zeng, David A. Plummer, Michael J. Prather, Oliver Wild, Meiyun Lin, Drew T. Shindell, and Ian A. Mackenzie
Atmos. Chem. Phys., 17, 14219–14237, https://doi.org/10.5194/acp-17-14219-2017, https://doi.org/10.5194/acp-17-14219-2017, 2017
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We investigate how climate change impacts global air pollution transport. To study transport changes, we use a carbon monoxide (CO) tracer species emitted from global sources. We find robust and consistent changes in CO-tracer distributions in climate change simulations performed by four chemistry–climate models in different seasons. We highlight the importance of the co-location of emission source regions and controlling transport processes in determining future pollution transport.
Michael J. Prather, Xin Zhu, Clare M. Flynn, Sarah A. Strode, Jose M. Rodriguez, Stephen D. Steenrod, Junhua Liu, Jean-Francois Lamarque, Arlene M. Fiore, Larry W. Horowitz, Jingqiu Mao, Lee T. Murray, Drew T. Shindell, and Steven C. Wofsy
Atmos. Chem. Phys., 17, 9081–9102, https://doi.org/10.5194/acp-17-9081-2017, https://doi.org/10.5194/acp-17-9081-2017, 2017
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We present a new approach for comparing atmospheric chemistry models with measurements based on what these models are used to do, i.e., calculate changes in ozone and methane, prime greenhouse gases. This method anticipates a new type of measurements from the NASA Atmospheric Tomography (ATom) mission. In comparing the mixture of species within air parcels, we focus on those responsible for key chemical changes and weight these parcels by their chemical reactivity.
James Hansen, Makiko Sato, Pushker Kharecha, Karina von Schuckmann, David J. Beerling, Junji Cao, Shaun Marcott, Valerie Masson-Delmotte, Michael J. Prather, Eelco J. Rohling, Jeremy Shakun, Pete Smith, Andrew Lacis, Gary Russell, and Reto Ruedy
Earth Syst. Dynam., 8, 577–616, https://doi.org/10.5194/esd-8-577-2017, https://doi.org/10.5194/esd-8-577-2017, 2017
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Global temperature now exceeds +1.25 °C relative to 1880–1920, similar to warmth of the Eemian period. Keeping warming less than 1.5 °C or CO2 below 350 ppm now requires extraction of CO2 from the air. If rapid phaseout of fossil fuel emissions begins soon, most extraction can be via improved agricultural and forestry practices. In contrast, continued high emissions places a burden on young people of massive technological CO2 extraction with large risks, high costs and uncertain feasibility.
Juno Hsu, Michael J. Prather, Philip Cameron-Smith, Alex Veidenbaum, and Alex Nicolau
Geosci. Model Dev., 10, 2525–2545, https://doi.org/10.5194/gmd-10-2525-2017, https://doi.org/10.5194/gmd-10-2525-2017, 2017
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Solar-J is a high-fidelity solar radiative transfer Fortran 90 code. It has been developed for consistently calculating both the photolysis rates of important chemical species and the heating rates of the atmosphere and the Earth's surface. Its spectral range spans from 177 nm to 12 microns. It can be easily dropped in as a module in global climate–chemistry models.
Gunnar Myhre, Wenche Aas, Ribu Cherian, William Collins, Greg Faluvegi, Mark Flanner, Piers Forster, Øivind Hodnebrog, Zbigniew Klimont, Marianne T. Lund, Johannes Mülmenstädt, Cathrine Lund Myhre, Dirk Olivié, Michael Prather, Johannes Quaas, Bjørn H. Samset, Jordan L. Schnell, Michael Schulz, Drew Shindell, Ragnhild B. Skeie, Toshihiko Takemura, and Svetlana Tsyro
Atmos. Chem. Phys., 17, 2709–2720, https://doi.org/10.5194/acp-17-2709-2017, https://doi.org/10.5194/acp-17-2709-2017, 2017
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Over the past decades, the geographical distribution of emissions of substances that alter the atmospheric energy balance has changed due to economic growth and pollution regulations. Here, we show the resulting changes to aerosol and ozone abundances and their radiative forcing using recently updated emission data for the period 1990–2015, as simulated by seven global atmospheric composition models. The global mean radiative forcing is more strongly positive than reported in IPCC AR5.
William J. Collins, Jean-François Lamarque, Michael Schulz, Olivier Boucher, Veronika Eyring, Michaela I. Hegglin, Amanda Maycock, Gunnar Myhre, Michael Prather, Drew Shindell, and Steven J. Smith
Geosci. Model Dev., 10, 585–607, https://doi.org/10.5194/gmd-10-585-2017, https://doi.org/10.5194/gmd-10-585-2017, 2017
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We have designed a set of climate model experiments called the Aerosol Chemistry Model Intercomparison Project (AerChemMIP). These are designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases in the climate models that are used to simulate past and future climate. We hope that many climate modelling centres will choose to run these experiments to help understand the contribution of aerosols and chemistry to climate change.
Bojan Sič, Laaziz El Amraoui, Andrea Piacentini, Virginie Marécal, Emanuele Emili, Daniel Cariolle, Michael Prather, and Jean-Luc Attié
Atmos. Meas. Tech., 9, 5535–5554, https://doi.org/10.5194/amt-9-5535-2016, https://doi.org/10.5194/amt-9-5535-2016, 2016
J. L. Schnell, M. J. Prather, B. Josse, V. Naik, L. W. Horowitz, P. Cameron-Smith, D. Bergmann, G. Zeng, D. A. Plummer, K. Sudo, T. Nagashima, D. T. Shindell, G. Faluvegi, and S. A. Strode
Atmos. Chem. Phys., 15, 10581–10596, https://doi.org/10.5194/acp-15-10581-2015, https://doi.org/10.5194/acp-15-10581-2015, 2015
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We test global chemistry--climate models in their ability to simulate present-day surface ozone. Models are tested against observed hourly ozone from 4217 stations in North America and Europe that are averaged over 1°x1° grid cells. Using novel metrics, we find most models match the shape but not the amplitude of regional summertime diurnal and annual cycles and match the pattern but not the magnitude of summer ozone enhancement. Most also match the observed distribution of extreme episode sizes
J. L. Schnell, C. D. Holmes, A. Jangam, and M. J. Prather
Atmos. Chem. Phys., 14, 7721–7739, https://doi.org/10.5194/acp-14-7721-2014, https://doi.org/10.5194/acp-14-7721-2014, 2014
C. D. Holmes, M. J. Prather, and G. C. M. Vinken
Atmos. Chem. Phys., 14, 6801–6812, https://doi.org/10.5194/acp-14-6801-2014, https://doi.org/10.5194/acp-14-6801-2014, 2014
P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh
Geosci. Model Dev., 7, 105–145, https://doi.org/10.5194/gmd-7-105-2014, https://doi.org/10.5194/gmd-7-105-2014, 2014
V. Naik, A. Voulgarakis, A. M. Fiore, L. W. Horowitz, J.-F. Lamarque, M. Lin, M. J. Prather, P. J. Young, D. Bergmann, P. J. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, T. P. C. van Noije, D. A. Plummer, M. Righi, S. T. Rumbold, R. Skeie, D. T. Shindell, D. S. Stevenson, S. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, https://doi.org/10.5194/acp-13-5277-2013, 2013
A. Voulgarakis, V. Naik, J.-F. Lamarque, D. T. Shindell, P. J. Young, M. J. Prather, O. Wild, R. D. Field, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, L. W. Horowitz, B. Josse, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, D. S. Stevenson, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, https://doi.org/10.5194/acp-13-2563-2013, 2013
C. D. Holmes, M. J. Prather, O. A. Søvde, and G. Myhre
Atmos. Chem. Phys., 13, 285–302, https://doi.org/10.5194/acp-13-285-2013, https://doi.org/10.5194/acp-13-285-2013, 2013
Ø. Hodnebrog, T. K. Berntsen, O. Dessens, M. Gauss, V. Grewe, I. S. A. Isaksen, B. Koffi, G. Myhre, D. Olivié, M. J. Prather, F. Stordal, S. Szopa, Q. Tang, P. van Velthoven, and J. E. Williams
Atmos. Chem. Phys., 12, 12211–12225, https://doi.org/10.5194/acp-12-12211-2012, https://doi.org/10.5194/acp-12-12211-2012, 2012
Related subject area
Atmospheric sciences
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
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Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
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SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
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Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
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SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
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It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
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Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
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Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
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This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph 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 Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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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.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
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.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Leon Geers, Ruud Janssen, Gudrun Thorkelsdottir, Jordi Vilà-Guerau de Arellano, and Martijn Schaap
EGUsphere, https://doi.org/10.5194/egusphere-2025-426, https://doi.org/10.5194/egusphere-2025-426, 2025
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High-resolution data on reactive nitrogen deposition are needed to inform cost-effective policies. Here, we describe the implementation of a dry deposition module into a large eddy simulation code. With this model, we are able to represent the turbulent exchange of tracers at the hectometer resolution. The model calculates the dispersion and deposition of NOx and NH3 in great spatial detail, clearly showing the influence of local land use patterns.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
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Short summary
A new approach for modeling photolysis rates (J values) in atmospheres with fractional cloud cover has been developed and is implemented as Cloud-J – a multi-scattering eight-stream radiative transfer model for solar radiation based on Fast-J. Using observations of the vertical correlation of cloud layers, Cloud-J provides a practical and accurate method for modeling atmospheric chemistry, which can be extended to solar heating rates.
A new approach for modeling photolysis rates (J values) in atmospheres with fractional cloud...
