Articles | Volume 16, issue 19
https://doi.org/10.5194/gmd-16-5601-2023
© 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-16-5601-2023
© 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
| 
10 Oct 2023
Model description paper |
| 10 Oct 2023
| 10 Oct 2023
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Nathan J. Mayne
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Thomas Bendall
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Ian A. Boutle
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Alex Brown
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Iva Kavčič
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
James Kent
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Krisztian Kohary
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
James Manners
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Thomas Melvin
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Enrico Olivier
Research Software Engineering, University of Exeter, Exeter, EX4 4QE, UK
Lokesh K. Ragta
Department of Information Technology, University of Leicester, University Road, Leicester, LE1 7RH, UK
Ben Shipway
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Jon Wakelin
Department of Information Technology, University of Leicester, University Road, Leicester, LE1 7RH, UK
Nigel Wood
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Mohamed Zerroukat
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Related authors
Danny McCulloch, Denis E. Sergeev, Nathan Mayne, Matthew Bate, James Manners, Ian Boutle, Benjamin Drummond, and Kristzian Kohary
Geosci. Model Dev., 16, 621–657, https://doi.org/10.5194/gmd-16-621-2023, https://doi.org/10.5194/gmd-16-621-2023, 2023
Short summary
Short summary
We present results from the Met Office Unified Model (UM) to study the dry Martian climate. We describe our model set-up conditions and run two scenarios, with radiatively active/inactive dust. We compare both scenarios to results from an existing Mars climate model, the planetary climate model. We find good agreement in winds and air temperatures, but dust amounts differ between models. This study highlights the importance of using the UM for future Mars research.
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
Short summary
Short summary
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.
Martin Richard Willett, Melissa Brooks, Andrew Bushell, Paul Earnshaw, Samantha Smith, Lorenzo Tomassini, Martin Best, Ian Boutle, Jennifer Brooke, John M. Edwards, Kalli Furtado, Catherine Hardacre, Andrew J. Hartley, Alan Hewitt, Ben Johnson, Adrian Lock, Andy Malcolm, Jane Mulcahy, Eike Müller, Heather Rumbold, Gabriel G. Rooney, Alistair Sellar, Masashi Ujiie, Annelize van Niekerk, Andy Wiltshire, and Michael Whitall
EGUsphere, https://doi.org/10.5194/egusphere-2025-1829, https://doi.org/10.5194/egusphere-2025-1829, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application. We describe a recent iteration of these configurations, GA8GL9, which includes improvements to the represenation of convection and other physical processes. GA8GL9 is used for operational weather prediction in the UK and forms the basis for the next GA and GL configuration.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Anthony Jones, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, Noah Asch, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2024-3376, https://doi.org/10.5194/egusphere-2024-3376, 2024
Short summary
Short summary
We study aerosol-fog interactions near Paris using a weather and climate model with high spatial resolution. We show that our model can simulate fog lifecycle effectively. We find that the fog droplet number concentrations, the amount of liquid water in the fog, and the vertical structure of the fog are highly sensitive to the parameterization that simulates droplet formation and growth. The changes we propose could improve fog forecasts significantly without increasing computational costs.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2024-3397, https://doi.org/10.5194/egusphere-2024-3397, 2024
Short summary
Short summary
We study the lifecycle of fog events in Europe using a weather and climate model. By incorporating droplet formation and growth driven by radiative cooling, our model better simulates the total liquid water in foggy atmospheric columns. We show that both adiabatic and radiative cooling play significant, often equally important roles in driving droplet formation and growth. We discuss strategies to address droplet number overpredictions, by improving model physics and addressing model artifacts.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
Short summary
Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023, https://doi.org/10.5194/gmd-16-1265-2023, 2023
Short summary
Short summary
This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Danny McCulloch, Denis E. Sergeev, Nathan Mayne, Matthew Bate, James Manners, Ian Boutle, Benjamin Drummond, and Kristzian Kohary
Geosci. Model Dev., 16, 621–657, https://doi.org/10.5194/gmd-16-621-2023, https://doi.org/10.5194/gmd-16-621-2023, 2023
Short summary
Short summary
We present results from the Met Office Unified Model (UM) to study the dry Martian climate. We describe our model set-up conditions and run two scenarios, with radiatively active/inactive dust. We compare both scenarios to results from an existing Mars climate model, the planetary climate model. We find good agreement in winds and air temperatures, but dust amounts differ between models. This study highlights the importance of using the UM for future Mars research.
Ian Boutle, Wayne Angevine, Jian-Wen Bao, Thierry Bergot, Ritthik Bhattacharya, Andreas Bott, Leo Ducongé, Richard Forbes, Tobias Goecke, Evelyn Grell, Adrian Hill, Adele L. Igel, Innocent Kudzotsa, Christine Lac, Bjorn Maronga, Sami Romakkaniemi, Juerg Schmidli, Johannes Schwenkel, Gert-Jan Steeneveld, and Benoît Vié
Atmos. Chem. Phys., 22, 319–333, https://doi.org/10.5194/acp-22-319-2022, https://doi.org/10.5194/acp-22-319-2022, 2022
Short summary
Short summary
Fog forecasting is one of the biggest problems for numerical weather prediction. By comparing many models used for fog forecasting with others used for fog research, we hoped to help guide forecast improvements. We show some key processes that, if improved, will help improve fog forecasting, such as how water is deposited on the ground. We also showed that research models were not themselves a suitable baseline for comparison, and we discuss what future observations are required to improve them.
Rachel E. Hawker, Annette K. Miltenberger, Jill S. Johnson, Jonathan M. Wilkinson, Adrian A. Hill, Ben J. Shipway, Paul R. Field, Benjamin J. Murray, and Ken S. Carslaw
Atmos. Chem. Phys., 21, 17315–17343, https://doi.org/10.5194/acp-21-17315-2021, https://doi.org/10.5194/acp-21-17315-2021, 2021
Short summary
Short summary
We find that ice-nucleating particles (INPs), aerosols that can initiate the freezing of cloud droplets, cause substantial changes to the properties of radiatively important convectively generated anvil cirrus. The number concentration of INPs had a large effect on ice crystal number concentration while the INP temperature dependence controlled ice crystal size and cloud fraction. The results indicate information on INP number and source is necessary for the representation of cloud glaciation.
Craig Poku, Andrew N. Ross, Adrian A. Hill, Alan M. Blyth, and Ben Shipway
Atmos. Chem. Phys., 21, 7271–7292, https://doi.org/10.5194/acp-21-7271-2021, https://doi.org/10.5194/acp-21-7271-2021, 2021
Short summary
Short summary
We present a new aerosol activation scheme suitable for modelling both fog and convective clouds. Most current activation schemes are designed for convective clouds, and we demonstrate that using them to model fog can negatively impact its life cycle. Our scheme has been used to model an observed fog case in the UK, where we demonstrate that a more physically based representation of aerosol activation is required to capture the transition to a deeper layer – more in line with observations.
Rachel E. Hawker, Annette K. Miltenberger, Jonathan M. Wilkinson, Adrian A. Hill, Ben J. Shipway, Zhiqiang Cui, Richard J. Cotton, Ken S. Carslaw, Paul R. Field, and Benjamin J. Murray
Atmos. Chem. Phys., 21, 5439–5461, https://doi.org/10.5194/acp-21-5439-2021, https://doi.org/10.5194/acp-21-5439-2021, 2021
Short summary
Short summary
The impact of aerosols on clouds is a large source of uncertainty for future climate projections. Our results show that the radiative properties of a complex convective cloud field in the Saharan outflow region are sensitive to the temperature dependence of ice-nucleating particle concentrations. This means that differences in the aerosol source or composition, for the same aerosol size distribution, can cause differences in the outgoing radiation from regions dominated by tropical convection.
Mike Bush, Tom Allen, Caroline Bain, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Humphrey Lean, Adrian Lock, James Manners, Marion Mittermaier, Cyril Morcrette, Rachel North, Jon Petch, Chris Short, Simon Vosper, David Walters, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, https://doi.org/10.5194/gmd-13-1999-2020, 2020
Short summary
Short summary
In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against.
Thomas J. Fauchez, Martin Turbet, Eric T. Wolf, Ian Boutle, Michael J. Way, Anthony D. Del Genio, Nathan J. Mayne, Konstantinos Tsigaridis, Ravi K. Kopparapu, Jun Yang, Francois Forget, Avi Mandell, and Shawn D. Domagal Goldman
Geosci. Model Dev., 13, 707–716, https://doi.org/10.5194/gmd-13-707-2020, https://doi.org/10.5194/gmd-13-707-2020, 2020
Short summary
Short summary
Atmospheric characterization of rocky exoplanets orbiting within the habitable zone of nearby M dwarf stars is around the corner with the James Webb Space Telescope (JWST), expected to be launch in 2021.
Global climate models (GCMs) are powerful tools to model exoplanet atmospheres and to predict their habitability. However, intrinsic differences between the models can lead to various predictions. This paper presents an experiment protocol to evaluate these differences.
David Walters, Anthony J. Baran, Ian Boutle, Malcolm Brooks, Paul Earnshaw, John Edwards, Kalli Furtado, Peter Hill, Adrian Lock, James Manners, Cyril Morcrette, Jane Mulcahy, Claudio Sanchez, Chris Smith, Rachel Stratton, Warren Tennant, Lorenzo Tomassini, Kwinten Van Weverberg, Simon Vosper, Martin Willett, Jo Browse, Andrew Bushell, Kenneth Carslaw, Mohit Dalvi, Richard Essery, Nicola Gedney, Steven Hardiman, Ben Johnson, Colin Johnson, Andy Jones, Colin Jones, Graham Mann, Sean Milton, Heather Rumbold, Alistair Sellar, Masashi Ujiie, Michael Whitall, Keith Williams, and Mohamed Zerroukat
Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, https://doi.org/10.5194/gmd-12-1909-2019, 2019
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application. We describe a recent iteration of these configurations, GA7/GL7, which includes new aerosol and snow schemes and addresses the four critical errors identified in GA6. GA7/GL7 will underpin the UK's contributions to CMIP6, and hence their documentation is important.
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
Short summary
Short summary
The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Gary Lloyd, Thomas W. Choularton, Keith N. Bower, Martin W. Gallagher, Jonathan Crosier, Sebastian O'Shea, Steven J. Abel, Stuart Fox, Richard Cotton, and Ian A. Boutle
Atmos. Chem. Phys., 18, 17191–17206, https://doi.org/10.5194/acp-18-17191-2018, https://doi.org/10.5194/acp-18-17191-2018, 2018
Short summary
Short summary
The work deals with cold weather outbreaks at high latitudes that often bring severe weather such as heavy snow, lightning and high winds but are poorly forecast by weather models. Here we made measurements of these events and the clouds associated with them using a research aircraft. We found that the properties of these clouds were often very different to what the models predicted, and these results can potentially be used to bring significant improvement to the forecasting of these events.
Robin G. Stevens, Katharina Loewe, Christopher Dearden, Antonios Dimitrelos, Anna Possner, Gesa K. Eirund, Tomi Raatikainen, Adrian A. Hill, Benjamin J. Shipway, Jonathan Wilkinson, Sami Romakkaniemi, Juha Tonttila, Ari Laaksonen, Hannele Korhonen, Paul Connolly, Ulrike Lohmann, Corinna Hoose, Annica M. L. Ekman, Ken S. Carslaw, and Paul R. Field
Atmos. Chem. Phys., 18, 11041–11071, https://doi.org/10.5194/acp-18-11041-2018, https://doi.org/10.5194/acp-18-11041-2018, 2018
Short summary
Short summary
We perform a model intercomparison of summertime high Arctic clouds. Observed concentrations of aerosol particles necessary for cloud formation fell to extremely low values, coincident with a transition from cloudy to nearly cloud-free conditions. Previous analyses have suggested that at these low concentrations, the radiative properties of the clouds are determined primarily by these particle concentrations. The model results strongly support this hypothesis.
Annette K. Miltenberger, Paul R. Field, Adrian A. Hill, Ben J. Shipway, and Jonathan M. Wilkinson
Atmos. Chem. Phys., 18, 10593–10613, https://doi.org/10.5194/acp-18-10593-2018, https://doi.org/10.5194/acp-18-10593-2018, 2018
Ian Boutle, Jeremy Price, Innocent Kudzotsa, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 18, 7827–7840, https://doi.org/10.5194/acp-18-7827-2018, https://doi.org/10.5194/acp-18-7827-2018, 2018
Short summary
Short summary
Aerosol processes are a key mechanism in the development of fog. Poor representation of aerosol–fog interaction can result in large biases in fog forecasts, such as surface temperatures which are too high and fog which is too deep and long lived. A relatively simple representation of aerosol–fog interaction can actually lead to significant improvements in forecasting. Aerosol–fog interaction can have a large effect on the climate system but is poorly represented in climate models.
Daniel T. McCoy, Paul R. Field, Anja Schmidt, Daniel P. Grosvenor, Frida A.-M. Bender, Ben J. Shipway, Adrian A. Hill, Jonathan M. Wilkinson, and Gregory S. Elsaesser
Atmos. Chem. Phys., 18, 5821–5846, https://doi.org/10.5194/acp-18-5821-2018, https://doi.org/10.5194/acp-18-5821-2018, 2018
Short summary
Short summary
Here we use a combination of global convection-permitting models, satellite observations and the Holuhraun volcanic eruption to demonstrate that aerosol enhances the cloud liquid content and brightness of midlatitude cyclones. This is important because the strength of anthropogenic radiative forcing is uncertain, leading to uncertainty in the climate sensitivity consistent with observed temperature record.
Annette K. Miltenberger, Paul R. Field, Adrian A. Hill, Phil Rosenberg, Ben J. Shipway, Jonathan M. Wilkinson, Robert Scovell, and Alan M. Blyth
Atmos. Chem. Phys., 18, 3119–3145, https://doi.org/10.5194/acp-18-3119-2018, https://doi.org/10.5194/acp-18-3119-2018, 2018
Daniel P. Grosvenor, Paul R. Field, Adrian A. Hill, and Benjamin J. Shipway
Atmos. Chem. Phys., 17, 5155–5183, https://doi.org/10.5194/acp-17-5155-2017, https://doi.org/10.5194/acp-17-5155-2017, 2017
Short summary
Short summary
We used a weather model to simulate low-level layer clouds that lie off the coast of Chile and tested how they would be affected by airborne particulate matter (aerosols) according to the model. We found that as aerosols were increased, the clouds reflected more and more of the sun’s incoming energy due to the combined effects of the cloud droplets becoming smaller, the thickening of clouds, and increased areal coverage. However, the latter two effects were only important at low aerosol levels.
David Walters, Ian Boutle, Malcolm Brooks, Thomas Melvin, Rachel Stratton, Simon Vosper, Helen Wells, Keith Williams, Nigel Wood, Thomas Allen, Andrew Bushell, Dan Copsey, Paul Earnshaw, John Edwards, Markus Gross, Steven Hardiman, Chris Harris, Julian Heming, Nicholas Klingaman, Richard Levine, James Manners, Gill Martin, Sean Milton, Marion Mittermaier, Cyril Morcrette, Thomas Riddick, Malcolm Roberts, Claudio Sanchez, Paul Selwood, Alison Stirling, Chris Smith, Dan Suri, Warren Tennant, Pier Luigi Vidale, Jonathan Wilkinson, Martin Willett, Steve Woolnough, and Prince Xavier
Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, https://doi.org/10.5194/gmd-10-1487-2017, 2017
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
B. J. Shipway
Atmos. Chem. Phys., 15, 3803–3814, https://doi.org/10.5194/acp-15-3803-2015, https://doi.org/10.5194/acp-15-3803-2015, 2015
Short summary
Short summary
A new parametrization for cloud droplet nucleation is described. This revised approach makes use of a simple look-up table which is very efficient and computationally very cheap. Adopting this approach further allows for a more accurate treatment of the necessary approximations of supersaturation evolution and ultimately leads to a more accurate calculation of peak supersaturation and hence droplet nucleation.
D. N. Walters, K. D. Williams, I. A. Boutle, A. C. Bushell, J. M. Edwards, P. R. Field, A. P. Lock, C. J. Morcrette, R. A. Stratton, J. M. Wilkinson, M. R. Willett, N. Bellouin, A. Bodas-Salcedo, M. E. Brooks, D. Copsey, P. D. Earnshaw, S. C. Hardiman, C. M. Harris, R. C. Levine, C. MacLachlan, J. C. Manners, G. M. Martin, S. F. Milton, M. D. Palmer, M. J. Roberts, J. M. Rodríguez, W. J. Tennant, and P. L. Vidale
Geosci. Model Dev., 7, 361–386, https://doi.org/10.5194/gmd-7-361-2014, https://doi.org/10.5194/gmd-7-361-2014, 2014
Related subject area
Atmospheric sciences
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
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
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
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Implementation of a dry deposition module (DEPAC v3.11) in a large eddy simulation code (DALES v4.4)
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
Mitigating Hail Overforecasting in the 2-Moment Milbrandt-Yau Microphysics Scheme (v2.25.2_beta_04) in WRF (v4.5.1) by Incorporating the Graupel Spongy Wet Growth Process (MY2_GSWG v1.0)
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Short summary
We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary
Short summary
This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
Short summary
Short summary
Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Cited articles
Adams, S., Ford, R., Hambley, M., Hobson, J., Kavčič, I., Maynard, C.,
Melvin, T., Müller, E., Mullerworth, S., Porter, A., Rezny, M., Shipway, B.,
and Wong, R.: LFRic: Meeting the challenges of scalability and
performance portability in Weather and Climate models, J.
Parall. Distr. Com., 132, 383–396,
https://doi.org/10.1016/j.jpdc.2019.02.007, 2019. a, b, c, d, e, f, g, h, i, j
Amundsen, D. S., Mayne, N. J., Baraffe, I., Manners, J., Tremblin, P.,
Drummond, B., Smith, C., Acreman, D. M., and Homeier, D.: The UK Met
Office global circulation model with a sophisticated radiation scheme
applied to the hot Jupiter HD 209458b, Astron. Astrophys., 595, A36,
https://doi.org/10.1051/0004-6361/201629183, 2016. a, b
Balaji, V., Couvreux, F., Deshayes, J., Gautrais, J., Hourdin, F., and Rio, C.:
Are general circulation models obsolete?, P. Natl. Acad.
Sci. USA, 119, e2202075119, https://doi.org/10.1073/pnas.2202075119, 2022. a
Barnes, R.: Tidal locking of habitable exoplanets, Celest. Mech.
Dyn. Astr., 129, 509–536, https://doi.org/10.1007/s10569-017-9783-7, 2017. a
Bendall, T. M., Gibson, T. H., Shipton, J., Cotter, C. J., and Shipway, B.: A
compatible finite-element discretisation for the moist compressible Euler
equations, Q. J. Roy. Meteor. Soc., 146,
3187–3205, https://doi.org/10.1002/qj.3841, 2020. a
Bendall, T. M., Wood, N., Thuburn, J., and Cotter, C. J.: A solution to the
trilemma of the moist Charney–Phillips staggering, Q. J.
Roy. Meteor. Soc., 149, 262–276, https://doi.org/10.1002/qj.4406, 2022. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a, b
Boutle, I. A., Eyre, J. E. J., and Lock, A. P.: Seamless Stratocumulus
Simulation across the Turbulent Gray Zone, Mon. Weather Rev.,
142, 1655–1668, https://doi.org/10.1175/MWR-D-13-00229.1, 2014. a
Boutle, I. A., Mayne, N. J., Drummond, B., Manners, J., Goyal, J.,
Hugo Lambert, F., Acreman, D. M., and Earnshaw, P. D.: Exploring the climate
of Proxima B with the Met Office Unified Model, Astron.
Astrophys., 601, A120, https://doi.org/10.1051/0004-6361/201630020, 2017. a
Boutle, I. A., Joshi, M., Lambert, F. H., Mayne, N. J., Lyster, D., Manners,
J., Ridgway, R., and Kohary, K.: Mineral dust increases the habitability of
terrestrial planets but confounds biomarker detection, Nat. Commun.,
11, 2731, https://doi.org/10.1038/s41467-020-16543-8, 2020. a, b
Braam, M., Palmer, P. I., Decin, L., Ridgway, R. J., Zamyatina, M., Mayne,
N. J., Sergeev, D. E., and Abraham, N. L.: Lightning-induced chemistry on
tidally-locked Earth-like exoplanets, Mon. Not. R.
Astron. Soc., 517, 2383–2402, https://doi.org/10.1093/mnras/stac2722, 2022. a, b
Brown, A. R., Beare, R. J., Edwards, J. M., Lock, A. P., Keogh, S. J., Milton,
S. F., and Walters, D. N.: Upgrades to the Boundary-Layer Scheme in the
Met Office Numerical Weather Prediction Model, Bound.-Lay.
Meteorol., 128, 117–132, https://doi.org/10.1007/s10546-008-9275-0,
2008. a
Carone, L., Keppens, R., and Decin, L.: Connecting the dots – II. Phase
changes in the climate dynamics of tidally locked terrestrial exoplanets,
Mon. Not. R.
Astron. Soc., 453, 2413–2438,
https://doi.org/10.1093/mnras/stv1752, 2015. a
Carone, L., Keppens, R., and Decin, L.: Connecting the dots – III.
Nightside cooling and surface friction affect climates of tidally locked
terrestrial planets, Mon. Not. R. Astron. Soc., 461,
1981–2002, https://doi.org/10.1093/mnras/stw1265,
2016. a
Carone, L., Keppens, R., Decin, L., and Henning, T.: Stratosphere circulation
on tidally locked ExoEarths, Mon. Not. R. Astron. Soc., 473, 4672–4685, https://doi.org/10.1093/mnras/stx2732,
Publisher: Oxford Academic, 2018. a, b
Christie, D. A., Mayne, N. J., Lines, S., Parmentier, V., Manners, J., Boutle,
I., Drummond, B., Mikal-Evans, T., Sing, D. K., and Kohary, K.: The impact of
mixing treatments on cloud modelling in 3D simulations of hot Jupiters,
Mon. Not. R. Astron. Soc., 506, 4500–4515,
https://doi.org/10.1093/mnras/stab2027, 2021. a, b
Christie, D. A., Lee, E. K. H., Innes, H., Noti, P. A., Charnay, B., Fauchez,
T. J., Mayne, N. J., Deitrick, R., Ding, F., Greco, J. J., Hammond, M.,
Malsky, I., Mandell, A., Rauscher, E., Roman, M. T., Sergeev, D. E., Sohl,
L., Steinrueck, M. E., Turbet, M., Wolf, E. T., Zamyatina, M., and Carone,
L.: CAMEMBERT: A Mini-Neptunes General Circulation Model
Intercomparison, Protocol Version 1.0.A CUISINES Model
Intercomparison Project, Planet. Sci. J., 3, 261,
https://doi.org/10.3847/PSJ/ac9dfe, 2022. a, b
Cohen, M., Bollasina, M. A., Palmer, P. I., Sergeev, D. E., Boutle, I. A.,
Mayne, N. J., and Manners, J.: Longitudinally Asymmetric Stratospheric
Oscillation on a Tidally Locked Exoplanet, Astrophys. J.,
930, 152, https://doi.org/10.3847/1538-4357/ac625d, 2022. a, b
Cotter, C. and Shipton, J.: Mixed finite elements for numerical weather
prediction, J. Comput. Phys., 231, 7076–7091,
https://doi.org/10.1016/j.jcp.2012.05.020, 2012. a
Dressing, C. D. and Charbonneau, D.: The occurrence of potentially
habitable planets orbiting m dwarfs estimated from the full
Kepler dataset and an empirical measurement of the
detection sensitivity, Astrophys. J., 807, 45,
https://doi.org/10.1088/0004-637X/807/1/45, 2015. a
Drummond, B., Hébrard, E., Mayne, N. J., Venot, O., Ridgway, R. J., Changeat,
Q., Tsai, S.-M., Manners, J., Tremblin, P., Abraham, N. L., Sing, D., and
Kohary, K.: Implications of three-dimensional chemical transport in hot
Jupiter atmospheres: Results from a consistently coupled
chemistry-radiation-hydrodynamics model, Astron. Astrophys., 636, A68,
https://doi.org/10.1051/0004-6361/201937153, 2020. a
Eager-Nash, J. K., Reichelt, D. J., Mayne, N. J., Lambert, F. H., Sergeev,
D. E., Ridgway, R. J., Manners, J., Boutle, I. A., Lenton, T. M., and Kohary,
K.: Implications of different stellar spectra for the climate of tidally
locked Earth-like exoplanets, Astron. Astrophys., 639, A99,
https://doi.org/10.1051/0004-6361/202038089, 2020. a, b
Edson, A., Lee, S., Bannon, P., Kasting, J. F., and Pollard, D.: Atmospheric
circulations of terrestrial planets orbiting low-mass stars, Icarus, 212,
1–13, https://doi.org/10.1016/j.icarus.2010.11.023, 2011. a
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I:
Choosing a configuration for a large-scale model, Q. J.
Roy. Meteor. Soc., 122, 689–719, https://doi.org/10.1002/qj.49712253107,
1996. a
Fauchez, T. J., Turbet, M., Wolf, E. T., Boutle, I., Way, M. J., Del Genio, A. D., Mayne, N. J., Tsigaridis, K., Kopparapu, R. K., Yang, J., Forget, F., Mandell, A., and Domagal Goldman, S. D.: TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI): motivations and protocol version 1.0, Geosci. Model Dev., 13, 707–716, https://doi.org/10.5194/gmd-13-707-2020, 2020. a, b, c, d, e, f, g, h
Fauchez, T. J., Turbet, M., Sergeev, D. E., Mayne, N. J., Spiga, A., Sohl, L.,
Saxena, P., Deitrick, R., Gilli, G., Domagal-Goldman, S. D., Forget, F.,
Consentino, R., Barnes, R., Haqq-Misra, J., Way, M. J., Wolf, E. T., Olson,
S., Crouse, J. S., Janin, E., Bolmont, E., Leconte, J., Chaverot, G., Jaziri,
Y., Tsigaridis, K., Yang, J., Pidhorodetska, D., Kopparapu, R. K., Chen, H.,
Boutle, I. A., Lefevre, M., Charnay, B., Burnett, A., Cabra, J., and Bouldin,
N.: TRAPPIST Habitable Atmosphere Intercomparison (THAI) Workshop
Report, Planet. Sci. J., 2, 106, https://doi.org/10.3847/PSJ/abf4df,
2021. a, b, c, d
Fauchez, T. J., Villanueva, G. L., Sergeev, D. E., Turbet, M., Boutle, I. A.,
Tsigaridis, K., Way, M. J., Wolf, E. T., Domagal-Goldman, S. D., Forget, F.,
Haqq-Misra, J., Kopparapu, R. K., Manners, J., and Mayne, N. J.: The
TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). III.
Simulated Observables – the Return of the Spectrum, Planet.
Sci. J., 3, 213, https://doi.org/10.3847/PSJ/ac6cf1,
2022. a
Ge, H., Li, C., Zhang, X., and Lee, D.: A Global Nonhydrostatic
Atmospheric Model with a Mass- and Energy-conserving Vertically
Implicit Correction (VIC) Scheme, Astrophys. J., 898,
130, https://doi.org/10.3847/1538-4357/ab9ec7, 2020. a
Gillon, M., Triaud, A. H. M. J., Demory, B.-O., Jehin, E., Agol, E., Deck,
K. M., Lederer, S. M., de Wit, J., Burdanov, A., Ingalls, J. G., Bolmont, E.,
Leconte, J., Raymond, S. N., Selsis, F., Turbet, M., Barkaoui, K., Burgasser,
A., Burleigh, M. R., Carey, S. J., Chaushev, A., Copperwheat, C. M., Delrez,
L., Fernandes, C. S., Holdsworth, D. L., Kotze, E. J., Van Grootel, V.,
Almleaky, Y., Benkhaldoun, Z., Magain, P., and Queloz, D.: Seven temperate
terrestrial planets around the nearby ultracool dwarf star TRAPPIST-1,
Nature, 542, 456–460, https://doi.org/10.1038/nature21360, 2017. a
Gregory, D. and Rowntree, P. R.: A Mass Flux Convection Scheme with
Representation of Cloud Ensemble Characteristics and
Stability-Dependent Closure, Mon. Weather Rev., 118, 1483–1506,
https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2, iSBN: 0027-0644, 1990. a
Hammond, M. and Lewis, N. T.: The rotational and divergent components of
atmospheric circulation on tidally locked planets, P.
Natl. Acad. Sci. USA, 118, e2022705118,
https://doi.org/10.1073/pnas.2022705118, 2021. a
Haqq-Misra, J., Wolf, E. T., Joshi, M., Zhang, X., and Kopparapu, R. K.:
Demarcating Circulation Regimes of Synchronously Rotating
Terrestrial Planets within the Habitable Zone, Astrophys.
J., 852, 67, https://doi.org/10.3847/1538-4357/aa9f1f, 2018. a
Haqq-Misra, J., Wolf, E. T., Fauchez, T. J., Shields, A. L., and Kopparapu,
R. K.: The Sparse Atmospheric Model Sampling Analysis (SAMOSA)
Intercomparison: Motivations and Protocol Version 1.0: A CUISINES
Model Intercomparison Project, Planet. Sci. J., 3, 260,
https://doi.org/10.3847/PSJ/ac9479, 2022. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río,
J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy,
T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array
programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020a. a
Harris, L., Zhou, L., Lin, S., Chen, J., Chen, X., Gao, K., Morin, M., Rees,
S., Sun, Y., Tong, M., Xiang, B., Bender, M., Benson, R., Cheng, K., Clark,
S., Elbert, O. D., Hazelton, A., Huff, J. J., Kaltenbaugh, A., Liang, Z.,
Marchok, T., Shin, H. H., and Stern, W.: GFDL SHiELD: A Unified
System for Weather-to-Seasonal Prediction, J. Adv.
Model. Earth Sy., 12, e2020MS002223, https://doi.org/10.1029/2020MS002223,
2020b. a
Heng, K. and Vogt, S. S.: Gliese 581g as a scaled-up version of Earth:
atmospheric circulation simulations, Mon. Not. R. Astron. Soc., 415, 2145–2157,
https://doi.org/10.1111/j.1365-2966.2011.18853.x, 2011. a, b
Hunter, J. D.: Matplotlib: A 2D Graphics Environment, Comput.
Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Joshi, M. M., Elvidge, A. D., Wordsworth, R., and Sergeev, D.: Earth’s
Polar Night Boundary Layer as an Analog for Dark Side
Inversions on Synchronously Rotating Terrestrial Exoplanets,
Astrophys. J., 892, L33, https://doi.org/10.3847/2041-8213/ab7fb3, 2020. a
Komacek, T. D. and Abbot, D. S.: The Atmospheric Circulation and Climate
of Terrestrial Planets Orbiting Sun-like and M Dwarf Stars over
a Broad Range of Planetary Parameters, Astrophys. J.,
871, 245, https://doi.org/10.3847/1538-4357/aafb33, 2019. a
Komacek, T. D., Gao, P., Thorngren, D. P., May, E. M., and Tan, X.: The
Effect of Interior Heat Flux on the Atmospheric Circulation of
Hot and Ultra-hot Jupiters, Astrophys. J. Lett., 941,
L40, https://doi.org/10.3847/2041-8213/aca975, 2022. a, b
Kopparapu, R. K., Wolf, E. T., Arney, G., Batalha, N. E., Haqq-Misra, J.,
Grimm, S. L., and Heng, K.: Habitable Moist Atmospheres on Terrestrial
Planets near the Inner Edge of the Habitable Zone around M
Dwarfs, Astrophys. J., 845, 5, https://doi.org/10.3847/1538-4357/aa7cf9,
2017. a
Kopparla, P., Deitrick, R., Heng, K., Mendonça, J. M., and Hammond, M.:
General Circulation Model Errors Are Variable across Exoclimate
Parameter Spaces, Astrophys. J., 923, 39,
https://doi.org/10.3847/1538-4357/ac2d27,
2021. a
Lawrence, B. N., Rezny, M., Budich, R., Bauer, P., Behrens, J., Carter, M., Deconinck, W., Ford, R., Maynard, C., Mullerworth, S., Osuna, C., Porter, A., Serradell, K., Valcke, S., Wedi, N., and Wilson, S.: Crossing the chasm: how to develop weather and climate models for next generation computers?, Geosci. Model Dev., 11, 1799–1821, https://doi.org/10.5194/gmd-11-1799-2018, 2018. a
Leconte, J., Forget, F., Charnay, B., Wordsworth, R., Selsis, F., Millour, E.,
and Spiga, A.: 3D climate modeling of close-in land planets: Circulation
patterns, climate moist bistability, and habitability, Astron.
Astrophys., 554, A69, https://doi.org/10.1051/0004-6361/201321042,
2013. a
Lee, E. K. H., Parmentier, V., Hammond, M., Grimm, S. L., Kitzmann, D., Tan,
X., Tsai, S.-M., and Pierrehumbert, R. T.: Simulating gas giant exoplanet
atmospheres with Exo-FMS: Comparing semi-grey, picket fence and
correlated-k radiative-transfer schemes, Mon. Not. R. Astron. Soc., 506, 2695–2711, https://doi.org/10.1093/mnras/stab1851, 2021. a, b, c
Li, C. and Chen, X.: Simulating Nonhydrostatic Atmospheres on Planets
(SNAP): Formulation, Validation, and Application to the Jovian
Atmosphere, Astrophys. J. Suppl. S., 240, 37,
https://doi.org/10.3847/1538-4365/aafdaa,
2019. a
Lines, S., Mayne, N. J., Boutle, I. A., Manners, J., Lee, G. K. H., Helling,
C., Drummond, B., Amundsen, D. S., Goyal, J., Acreman, D. M., Tremblin, P.,
and Kerslake, M.: Simulating the cloudy atmospheres of HD 209458 b and HD
189733 b with the 3D Met Office Unified Model, Astron.
Astrophys., 615, A97, https://doi.org/10.1051/0004-6361/201732278,
2018. a
Lines, S., Mayne, N. J., Manners, J., Boutle, I. A., Drummond, B., Mikal-Evans,
T., Kohary, K., and Sing, D. K.: Overcast on Osiris: 3D
radiative-hydrodynamical simulations of a cloudy hot Jupiter using the
parametrized, phase-equilibrium cloud formation code EddySed, Mon. Not. R. Astron. Soc., 488, 1332–1355,
https://doi.org/10.1093/mnras/stz1788, 2019. a, b
Little, B.: geovista, Zenodo [code], https://doi.org/10.5281/zenodo.7608302, 2023. a
Lock, A. P.: The Numerical Representation of Entrainment in
Parameterizations of Boundary Layer Turbulent Mixing, Mon.
Weather Rev., 129, 1148–1163,
https://doi.org/10.1175/1520-0493(2001)129<1148:TNROEI>2.0.CO;2, 2001. a
Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.: A
New Boundary Layer Mixing Scheme. Part I: Scheme
Description and Single-Column Model Tests, Mon. Weather Rev.,
128, 3187–3199, https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2,
2000. a
Manners, J., Edwards, J. M., Hill, P., and Thelen, J.-C.: SOCRATES (Suite
Of Community RAdiative Transfer codes based on Edwards and
Slingo) Technical Guide, 1–125,
https://code.metoffice.gov.uk/trac/socrates (last access: 4 September 2023),
2022. a
Maynard, C., Melvin, T., and Müller, E. H.: Multigrid preconditioners for the
mixed finite element dynamical core of the LFRic atmospheric model,
Q. J. Roy. Meteor. Soc., 146, 3917–3936,
https://doi.org/10.1002/qj.3880,
2020. a, b, c
Mayne, N. J., Baraffe, I., Acreman, D. M., Smith, C., Browning, M. K.,
Amundsen, D. S., Wood, N., Thuburn, J., and Jackson, D. R.: The unified
model, a fully-compressible, non-hydrostatic, deep atmosphere global
circulation model, applied to hot Jupiters, Astron. Astrophys., 561,
A1, https://doi.org/10.1051/0004-6361/201322174, 2014a. a, b, c
Mayne, N. J., Baraffe, I., Acreman, D. M., Smith, C., Wood, N., Amundsen, D. S., Thuburn, J., and Jackson, D. R.: Using the UM dynamical cores to reproduce idealised 3-D flows, Geosci. Model Dev., 7, 3059–3087, https://doi.org/10.5194/gmd-7-3059-2014, 2014b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u
Mayne, N. J., Drummond, B., Debras, F., Jaupart, E., Manners, J., Boutle,
I. A., Baraffe, I., and Kohary, K.: The Limits of the Primitive
Equations of Dynamics for Warm, Slowly Rotating Small Neptunes
and Super Earths, Astrophys. J., 871, 56,
https://doi.org/10.3847/1538-4357/aaf6e9, 2019. a, b
McCulloch, D., Sergeev, D. E., Mayne, N., Bate, M., Manners, J., Boutle, I., Drummond, B., and Kohary, K.: A modern-day Mars climate in the Met Office Unified Model: dry simulations, Geosci. Model Dev., 16, 621–657, https://doi.org/10.5194/gmd-16-621-2023, 2023. a
Melvin, T., Dubal, M., Wood, N., Staniforth, A., and Zerroukat, M.: An
inherently mass-conserving iterative semi-implicit semi-Lagrangian
discretization of the non-hydrostatic vertical-slice equations, Q.
J. Roy. Meteor. Soc., 136, 799–814,
https://doi.org/10.1002/qj.603, 2010. a
Mendonça, J. M.: Mass transport in a moist planetary climate model, Astron.
Astrophys., 659, A43, https://doi.org/10.1051/0004-6361/202141638, 2022. a
Mendonça, J. M., Grimm, S. L., Grosheintz, L., and Heng, K.: THOR: a new
and flexible global circulation model to explore planetary
atmospheres, Astrophys. J., 829, 115,
https://doi.org/10.3847/0004-637X/829/2/115, 2016. a, b
Met Office: Iris: A Python library for analysing and visualising
meteorological and oceanographic data sets,
http://scitools.org.uk/ (last access: 4 September 2023), 2022. a
Met Office: iris-esmf-regrid: A collection of structured and unstructured ESMF regridding schemes for Iris, Github [code], https://github.com/SciTools-incubator/iris-esmf-regrid, last access: 4 September 2023. a
Met Office Science Repository Service: LFRic-Atmosphere, https://code.metoffice.gov.uk, last access: 4 September 2023. a
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015. a
Noda, S., Ishiwatari, M., Nakajima, K., Takahashi, Y., Takehiro, S., Onishi,
M., Hashimoto, G., Kuramoto, K., and Hayashi, Y.-Y.: The circulation pattern
and day-night heat transport in the atmosphere of a synchronously rotating
aquaplanet: Dependence on planetary rotation rate, Icarus, 282, 1–18,
https://doi.org/10.1016/j.icarus.2016.09.004, 2017. a, b, c
Paradise, A., Macdonald, E., Menou, K., Lee, C., and Fan, B. L.: ExoPlaSim:
Extending the Planet Simulator for exoplanets, Mon. Not. R. Astron. Soc., 511, 3272–3303, https://doi.org/10.1093/mnras/stac172,
2022. a, b, c
Polichtchouk, I., Cho, J.-K., Watkins, C., Thrastarson, H., Umurhan, O., and
de la Torre Juárez, M.: Intercomparison of general circulation models for
hot extrasolar planets, Icarus, 229, 355–377,
https://doi.org/10.1016/j.icarus.2013.11.027, 2014. a, b
Rajpurohit, A. S., Reylé, C., Allard, F., Homeier, D., Schultheis, M.,
Bessell, M. S., and Robin, A. C.: The effective temperature scale of M
dwarfs, Astron. Astrophys., 556, A15,
https://doi.org/10.1051/0004-6361/201321346, 2013. a
Ridgway, R. J., Zamyatina, M., Mayne, N. J., Manners, J., Lambert, F. H.,
Braam, M., Drummond, B., Hébrard, E., Palmer, P. I., and Kohary, K.: 3D
modelling of the impact of stellar activity on tidally locked terrestrial
exoplanets: atmospheric composition and habitability, Mon. Not.
R. Astron. Soc., 518, 2472–2496, https://doi.org/10.1093/mnras/stac3105,
2023. a, b
Saffin, L., Lock, A., Tomassini, L., Blyth, A., Böing, S., Denby, L., and
Marsham, J.: Kilometer-Scale Simulations of Trade-Wind Cumulus
Capture Processes of Mesoscale Organization, J. Adv.
Model. Earth Sy., 15, e2022MS003295, https://doi.org/10.1029/2022MS003295, 2023. a
Sergeev, D. E.: dennissergeev/lfric_exo_bench_code: Version 0, Zenodo [data set and code],
https://doi.org/10.5281/zenodo.7818107, 2023. a, b
Sergeev, D. E. and Zamyatina, M.: Aeolus – a Python library for the analysis
and visualisation of climate model output, Zenodo [code], https://doi.org/10.5281/ZENODO.6478085,
2022. a
Sergeev, D. E., Lambert, F. H., Mayne, N. J., Boutle, I. A., Manners, J., and
Kohary, K.: Atmospheric Convection Plays a Key Role in the Climate
of Tidally Locked Terrestrial Exoplanets: Insights from
High-resolution Simulations, Astrophys. J., 894, 84,
https://doi.org/10.3847/1538-4357/ab8882,
2020. a, b, c, d, e
Sergeev, D. E., Fauchez, T. J., Turbet, M., Boutle, I. A., Tsigaridis, K., Way,
M. J., Wolf, E. T., Domagal-Goldman, S. D., Forget, F., Haqq-Misra, J.,
Kopparapu, R. K., Lambert, F. H., Manners, J., and Mayne, N. J.: The
TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). II.
Moist Cases – The Two Waterworlds, Planet. Sci. J.,
3, 212, https://doi.org/10.3847/PSJ/ac6cf2,
2022a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u
Shashkin, V. V. and Goyman, G. S.: Semi-Lagrangian shallow water equations
solver on the cubed-sphere grid as a prototype of new-generation global
atmospheric model, J. Phys. Conf. Ser., 1740, 012073,
https://doi.org/10.1088/1742-6596/1740/1/012073, 2021. a
Showman, A. P., Fortney, J. J., Lian, Y., Marley, M. S., Freedman, R. S.,
Knutson, H. A., and Charbonneau, D.: Atmospheric circulation of hot
jupiters: coupled radiative-dynamical general circulation model
simulations of HD 189733b and HD 209458b, Astrophys. J.,
699, 564–584, https://doi.org/10.1088/0004-637X/699/1/564, 2009. a, b
Smith, R. N. B.: A scheme for predicting layer clouds and their water content
in a general circulation model, Q. J. Roy. Meteor. Soc., 116, 435–460, https://doi.org/10.1002/qj.49711649210, 1990. a
Staniforth, A. and Thuburn, J.: Horizontal grids for global weather and climate
prediction models: a review, Q. J. Roy. Meteor.
Soc., 138, 1–26, https://doi.org/10.1002/qj.958, 2012. a, b, c, d
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X.,
Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh,
L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R.,
Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the
DYnamics of the Atmospheric general circulation Modeled On
Non-hydrostatic Domains, Prog. Earth Planet. Sc., 6, 61,
https://doi.org/10.1186/s40645-019-0304-z, 2019. a
Turbet, M., Leconte, J., Selsis, F., Bolmont, E., Forget, F., Ribas, I.,
Raymond, S. N., and Anglada-Escudé, G.: The habitability of Proxima
Centauri b, Astron. Astrophys., 596, A112,
https://doi.org/10.1051/0004-6361/201629577, 2016. a
Turbet, M., Bolmont, E., Bourrier, V., Demory, B.-O., Leconte, J., Owen, J.,
and Wolf, E. T.: A Review of Possible Planetary Atmospheres in the
TRAPPIST-1 System, Space Sci. Rev., 216, 100,
https://doi.org/10.1007/s11214-020-00719-1, 2020. a
Turbet, M., Bolmont, E., Chaverot, G., Ehrenreich, D., Leconte, J., and Marcq,
E.: Day–night cloud asymmetry prevents early oceans on Venus but not on
Earth, Nature, 598, 276–280, https://doi.org/10.1038/s41586-021-03873-w, 2021. a
Turbet, M., Fauchez, T. J., Sergeev, D. E., Boutle, I. A., Tsigaridis, K., Way,
M. J., Wolf, E. T., Domagal-Goldman, S. D., Forget, F., Haqq-Misra, J.,
Kopparapu, R. K., Lambert, F. H., Manners, J., Mayne, N. J., and Sohl, L.:
The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). I.
Dry Cases – The Fellowship of the GCMs, Planet. Sci.
J., 3, 211, https://doi.org/10.3847/PSJ/ac6cf0, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Vallis, G. K., Colyer, G., Geen, R., Gerber, E., Jucker, M., Maher, P., Paterson, A., Pietschnig, M., Penn, J., and Thomson, S. I.: Isca, v1.0: a framework for the global modelling of the atmospheres of Earth and other planets at varying levels of complexity, Geosci. Model Dev., 11, 843–859, https://doi.org/10.5194/gmd-11-843-2018, 2018. a, b
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a, b, c
Way, M. J., Aleinov, I., Amundsen, D. S., Chandler, M. A., Clune, T. L., Genio,
A. D. D., Fujii, Y., Kelley, M., Kiang, N. Y., Sohl, L., and Tsigaridis, K.:
Resolving Orbital and Climate Keys of Earth and Extraterrestrial
Environments with Dynamics (ROCKE-3D) 1.0: A General
Circulation Model for Simulating the Climates of Rocky Planets,
Astrophys. J. Suppl. S., 231, 12,
https://doi.org/10.3847/1538-4365/aa7a06, 2017. a
Wedi, N. P. and Smolarkiewicz, P. K.: A framework for testing global
non-hydrostatic models, Q. J. Roy. Meteor.
Soc., 135, 469–484, https://doi.org/10.1002/qj.377,
2009. a
White, A. A., Hoskins, B. J., Roulstone, I., and Staniforth, A.: Consistent
approximate models of the global atmosphere: shallow, deep, hydrostatic,
quasi-hydrostatic and non-hydrostatic, Q. J. Roy.
Meteor. Soc., 131, 2081–2107, https://doi.org/10.1256/qj.04.49, 2005. a
Wilson, D. R. and Ballard, S. P.: A microphysically based precipitation scheme
for the UK meteorological office unified model, Q. J.
Roy. Meteor. Soc., 125, 1607–1636, https://doi.org/10.1002/qj.49712555707,
1999. a
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., and
Morcrette, C. J.: PC2: A prognostic cloud fraction and condensation
scheme. I: Scheme description, Q. J. Roy.
Meteor. So., 134, 2093–2107, https://doi.org/10.1002/qj.333, 2008. a
Wiltshire, A. J., Duran Rojas, M. C., Edwards, J. M., Gedney, N., Harper, A. B., Hartley, A. J., Hendry, M. A., Robertson, E., and Smout-Day, K.: JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2, Geosci. Model Dev., 13, 483–505, https://doi.org/10.5194/gmd-13-483-2020, 2020. a
Wolf, E. T., Kopparapu, R., Haqq-Misra, J., and Fauchez, T. J.: ExoCAM: A
3D Climate Model for Exoplanet Atmospheres, Planet. Sci.
J., 3, 7, https://doi.org/10.3847/PSJ/AC3F3D, 2022. a, b, c
Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M.,
Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An
inherently mass-conserving semi-implicit semi-Lagrangian discretization of
the deep-atmosphere global non-hydrostatic equations, Q. J.
Roy. Meteor. Soc., 140, 1505–1520, https://doi.org/10.1002/qj.2235,
2014. a, b, c, d
Wordsworth, R. and Kreidberg, L.: Atmospheres of Rocky Exoplanets, Annu.
Rev. Astron. Astr., 60, 159–201,
https://doi.org/10.1146/annurev-astro-052920-125632, 2022. a
Wordsworth, R. D., Forget, F., Selsis, F., Millour, E., Charnay, B., and
Madeleine, J.-B.: Gliese 581D is the first discovered
terrestrial-mass exoplanet in the habitable zone,
Astrophys. J., 733, L48, https://doi.org/10.1088/2041-8205/733/2/L48, 2011. a
Yang, J., Cowan, N. B., and Abbot, D. S.: Stabilizing cloud feedback
dramatically expands the habitable zone of tidally locked
planets, Astrophys. J., 771, L45,
https://doi.org/10.1088/2041-8205/771/2/L45, 2013. a
Yang, J., Leconte, J., Wolf, E. T., Merlis, T., Koll, D. D. B., Forget, F., and
Abbot, D. S.: Simulations of Water Vapor and Clouds on Rapidly
Rotating and Tidally Locked Planets: A 3D Model
Intercomparison, Astrophys. J., 875, 46,
https://doi.org/10.3847/1538-4357/ab09f1, 2019. a
Zamyatina, M., Hébrard, E., Drummond, B., Mayne, N. J., Manners, J., Christie,
D. A., Tremblin, P., Sing, D. K., and Kohary, K.: Observability of signatures
of transport-induced chemistry in clear atmospheres of hot gas giant
exoplanets, Mon. Not. R. Astron. Soc., 519,
3129–3153, https://doi.org/10.1093/mnras/stac3432, 2023.
a, b
Zhang, F. (Ed.): The Schur Complement and Its Applications, vol. 4,
Springer-Verlag, New York, https://doi.org/10.1007/b105056, 2005. a
Short summary
Three-dimensional climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
Three-dimensional climate models are one of the best tools we have to study planetary...
