Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-621-2023
© 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-16-621-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
27 Jan 2023
Model experiment description paper |
| 27 Jan 2023
| 27 Jan 2023
A modern-day Mars climate in the Met Office Unified Model: dry simulations
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Denis E. Sergeev
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Nathan Mayne
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Matthew Bate
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
James Manners
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Ian Boutle
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Benjamin Drummond
Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Kristzian Kohary
Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
Related authors
No articles found.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavčič, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 16, 5601–5626, https://doi.org/10.5194/gmd-16-5601-2023, https://doi.org/10.5194/gmd-16-5601-2023, 2023
Short summary
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.
Angela Mynard, Joss Kent, Eleanor R. Smith, Andy Wilson, Kirsty Wivell, Noel Nelson, Matthew Hort, James Bowles, David Tiddeman, Justin M. Langridge, Benjamin Drummond, and Steven J. Abel
Atmos. Meas. Tech., 16, 4229–4261, https://doi.org/10.5194/amt-16-4229-2023, https://doi.org/10.5194/amt-16-4229-2023, 2023
Short summary
Short summary
Air quality models are key in understanding complex air pollution processes and assist in developing strategies to mitigate the impacts of air pollution. The ability of regional air quality models to skilfully represent pollutant distributions aloft is important to enabling their skilful prediction at the surface. To assist in model development and evaluation, a long-term, quality-assured dataset of the 3-D distribution of key pollutants was collected over the United Kingdom (2019–2022).
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.
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.
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.
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.
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.
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.
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
Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1
Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution
Sensitivity of air quality model responses to emission changes: comparison of results based on four EU inventories through FAIRMODE benchmarking methodology
A simple and realistic aerosol emission approach for use in the Thompson–Eidhammer microphysics scheme in the NOAA UFS Weather Model (version GSL global-24Feb2022)
On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest
The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2
GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations
The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1
Spherical air mass factors in one and two dimensions with SASKTRAN 1.6.0
An improved version of the piecewise parabolic method advection scheme: description and performance assessment in a bidimensional test case with stiff chemistry in toyCTM v1.0.1
INCHEM-Py v1.2: a community box model for indoor air chemistry
Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemistry-transport model using Cloud-J v7.3e
Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations
Simulations of 7Be and 10Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 2: Influence of uncertainty factors
A mountain-induced moist baroclinic wave test case for the dynamical cores of atmospheric general circulation models
The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 1: Understanding expressiveness of schemes for different regions from the mechanism perspective
Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model
Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data
The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data
Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020
Description and evaluation of the new UM–UKCA (vn11.0) Double Extended Stratospheric–Tropospheric (DEST vn1.0) scheme for comprehensive modelling of halogen chemistry in the stratosphere
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)
Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2)
A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx
The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics
Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0
A standardized methodology for the validation of air quality forecast applications (F-MQO): lessons learnt from its application across Europe
Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa
A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application
Evaluation of vertically resolved longwave radiation in SPARTACUS-Urban 0.7.3 and the sensitivity to urban surface temperatures
Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning
QES-Plume v1.0: a Lagrangian dispersion model
A two-way coupled regional urban–street network air quality model system for Beijing, China
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
Emulating lateral gravity wave propagation in a global chemistry–climate model (EMAC v2.55.2) through horizontal flux redistribution
Optimising Urban Measurement Networks for CO2 Flux Estimation: A High-Resolution Observing System Simulation Experiment using GRAMM/GRAL
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America
Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave Radiative Transfer computations
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
Short summary
Short summary
The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 2024
Short summary
Short summary
Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
Short summary
Short summary
In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
Short summary
Short summary
We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
Short summary
Short summary
The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
Short summary
Short summary
Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
Short summary
Short summary
Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary
Short summary
A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
Short summary
Short summary
Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
Short summary
Short summary
Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
Short summary
Short summary
In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
Short summary
Short summary
This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
Short summary
Short summary
We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
Short summary
Short summary
Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
Short summary
Short summary
Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
Short summary
Short summary
Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev., 16, 7223–7235, https://doi.org/10.5194/gmd-16-7223-2023, https://doi.org/10.5194/gmd-16-7223-2023, 2023
Short summary
Short summary
The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
Short summary
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Minjie Zheng, Hongyu Liu, Florian Adolphi, Raimund Muscheler, Zhengyao Lu, Mousong Wu, and Nønne L. Prisle
Geosci. Model Dev., 16, 7037–7057, https://doi.org/10.5194/gmd-16-7037-2023, https://doi.org/10.5194/gmd-16-7037-2023, 2023
Short summary
Short summary
The radionuclides 7Be and 10Be are useful tracers for atmospheric transport studies. Here we use the GEOS-Chem to simulate 7Be and 10Be with different production rates: the default production rate in GEOS-Chem and two from the state-of-the-art beryllium production model. We demonstrate that reduced uncertainties in the production rates can enhance the utility of 7Be and 10Be as tracers for evaluating transport and scavenging processes in global models.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6833–6856, https://doi.org/10.5194/gmd-16-6833-2023, https://doi.org/10.5194/gmd-16-6833-2023, 2023
Short summary
Short summary
In addition to the dominant role of the PBL scheme on the results of the meteorological field, many factors in the model are influenced by large uncertainties. This study focuses on the uncertainties that influence numerical simulation results (including horizontal resolution, vertical resolution, near-surface scheme, initial and boundary conditions, underlying surface update, and update of model version), hoping to provide a reference for scholars conducting research on the model.
Owen K. Hughes and Christiane Jablonowski
Geosci. Model Dev., 16, 6805–6831, https://doi.org/10.5194/gmd-16-6805-2023, https://doi.org/10.5194/gmd-16-6805-2023, 2023
Short summary
Short summary
Atmospheric models benefit from idealized tests that assess their accuracy in a simpler simulation. A new test with artificial mountains is developed for models on a spherical earth. The mountains trigger the development of both planetary-scale and small-scale waves. These can be analyzed in dry or moist environments, with a simple rainfall mechanism. Four atmospheric models are intercompared. This sheds light on the pros and cons of the model design and the impact of mountains on the flow.
Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng
Geosci. Model Dev., 16, 6757–6771, https://doi.org/10.5194/gmd-16-6757-2023, https://doi.org/10.5194/gmd-16-6757-2023, 2023
Short summary
Short summary
This study explores how the variation in the source profiles adopted in chemical transport models (CTMs) impacts the simulated results of chemical components in PM2.5 based on sensitivity analysis. The impact on PM2.5 components cannot be ignored, and its influence can be transmitted and linked between components. The representativeness and timeliness of the source profile should be paid adequate attention in air quality simulation.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6635–6670, https://doi.org/10.5194/gmd-16-6635-2023, https://doi.org/10.5194/gmd-16-6635-2023, 2023
Short summary
Short summary
Most current studies on planetary boundary layer (PBL) parameterization schemes are relatively fragmented and lack systematic in-depth analysis and discussion. In this study, we comprehensively evaluate the performance capability of the PBL scheme in five typical regions of China in different seasons from the mechanism of the scheme and the effects of PBL schemes on the near-surface meteorological parameters, vertical structures of the PBL, PBL height, and turbulent diffusion.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev., 16, 6531–6552, https://doi.org/10.5194/gmd-16-6531-2023, https://doi.org/10.5194/gmd-16-6531-2023, 2023
Short summary
Short summary
It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Angel Liduvino Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, and Janaina P. Nascimento
Geosci. Model Dev., 16, 6413–6431, https://doi.org/10.5194/gmd-16-6413-2023, https://doi.org/10.5194/gmd-16-6413-2023, 2023
Short summary
Short summary
A 1-year simulation of atmospheric CH4 over Europe is performed and evaluated against observations based on the TROPOspheric Monitoring Instrument (TROPOMI). A good general model–observation agreement is found, with discrepancies reaching their minimum and maximum values during the summer peak season and winter months, respectively. A huge and under-explored potential for CH4 inverse modeling using improved TROPOMI XCH4 data sets in large-scale applications is identified.
Zhaojun Tang, Zhe Jiang, Jiaqi Chen, Panpan Yang, and Yanan Shen
Geosci. Model Dev., 16, 6377–6392, https://doi.org/10.5194/gmd-16-6377-2023, https://doi.org/10.5194/gmd-16-6377-2023, 2023
Short summary
Short summary
We designed a new framework to facilitate emission inventory updates in the adjoint of GEOS-Chem model. It allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 meteorological data; this allows us to perform long-term analysis with consistent meteorological data.
Rui Zhu, Zhaojun Tang, Xiaokang Chen, Xiong Liu, and Zhe Jiang
Geosci. Model Dev., 16, 6337–6354, https://doi.org/10.5194/gmd-16-6337-2023, https://doi.org/10.5194/gmd-16-6337-2023, 2023
Short summary
Short summary
A single ozone (O3) tracer mode was developed in this work to build the capability of the GEOS-Chem model for rapid O3 simulation. It is combined with OMI and surface O3 observations to investigate the changes in tropospheric O3 in China in 2015–2020. The assimilations indicate rapid surface O3 increases that are underestimated by the a priori simulations. We find stronger increases in tropospheric O3 columns over polluted areas and a large discrepancy by assimilating different observations.
Ewa M. Bednarz, Ryan Hossaini, N. Luke Abraham, and Martyn P. Chipperfield
Geosci. Model Dev., 16, 6187–6209, https://doi.org/10.5194/gmd-16-6187-2023, https://doi.org/10.5194/gmd-16-6187-2023, 2023
Short summary
Short summary
Development and performance of the new DEST chemistry scheme of UM–UKCA is described. The scheme extends the standard StratTrop scheme by including important updates to the halogen chemistry, thus allowing process-oriented studies of stratospheric ozone depletion and recovery, including impacts from both controlled long-lived ozone-depleting substances and emerging issues around uncontrolled, very short-lived substances. It will thus aid studies in support of future ozone assessment reports.
Shaohui Zhou, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
Geosci. Model Dev., 16, 6247–6266, https://doi.org/10.5194/gmd-16-6247-2023, https://doi.org/10.5194/gmd-16-6247-2023, 2023
Short summary
Short summary
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indices for 13 months remain relatively stable: the forecasting accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev., 16, 6087–6125, https://doi.org/10.5194/gmd-16-6087-2023, https://doi.org/10.5194/gmd-16-6087-2023, 2023
Short summary
Short summary
We implemented an alternative aerosol scheme in the high- and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. This development enables the comparison of different aerosol schemes with different complexity in the same model framework. It identifies improvements compared to a range of observations in both the troposphere and stratosphere.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
Geosci. Model Dev., 16, 6161–6185, https://doi.org/10.5194/gmd-16-6161-2023, https://doi.org/10.5194/gmd-16-6161-2023, 2023
Short summary
Short summary
To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and assess its performance against TROPOMI v2 over power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind directions and prior emissions.
Jiangshan Zhu and Ross Noel Bannister
Geosci. Model Dev., 16, 6067–6085, https://doi.org/10.5194/gmd-16-6067-2023, https://doi.org/10.5194/gmd-16-6067-2023, 2023
Short summary
Short summary
We describe how condensation and evaporation are included in the existing (otherwise dry) simplified ABC model. The new model (Hydro-ABC) includes transport of vapour and condensate within a dynamical core, and it transitions between these two phases via a micro-physics scheme. The model shows the development of an anvil cloud and excitation of atmospheric waves over many frequencies. The covariances that develop between variables are also studied together with indicators of convective motion.
Jiangyong Li, Chunlin Zhang, Wenlong Zhao, Shijie Han, Yu Wang, Hao Wang, and Boguang Wang
Geosci. Model Dev., 16, 6049–6066, https://doi.org/10.5194/gmd-16-6049-2023, https://doi.org/10.5194/gmd-16-6049-2023, 2023
Short summary
Short summary
Photochemical box models, crucial for understanding tropospheric chemistry, face challenges due to slow computational efficiency with large chemical equations. The model introduced in this study, ROMAC, boosts efficiency by up to 96 % using an advanced atmospheric solver and an adaptive optimization algorithm. Moreover, ROMAC exceeds traditional box models in evaluating the impact of physical processes on pollutant concentrations.
Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, and Philippe Thunis
Geosci. Model Dev., 16, 6029–6047, https://doi.org/10.5194/gmd-16-6029-2023, https://doi.org/10.5194/gmd-16-6029-2023, 2023
Short summary
Short summary
Air quality forecasting models play a key role in fostering short-term measures aimed at reducing human exposure to air pollution. Together with this role comes the need for a thorough assessment of the model performances to build confidence in models’ capabilities, in particular when model applications support policymaking. In this paper, we propose an evaluation methodology and test it on several domains across Europe, highlighting its strengths and room for improvement.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
Short summary
Short summary
The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
Short summary
Short summary
We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Megan A. Stretton, William Morrison, Robin J. Hogan, and Sue Grimmond
Geosci. Model Dev., 16, 5931–5947, https://doi.org/10.5194/gmd-16-5931-2023, https://doi.org/10.5194/gmd-16-5931-2023, 2023
Short summary
Short summary
Cities' materials and forms impact radiative fluxes. We evaluate the SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically using the explicit DART (Discrete Anisotropic Radiative Transfer) model. The temperature configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with a low computational time (cf. DART), but has larger errors with sunlit/shaded surface temperatures.
Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee
Geosci. Model Dev., 16, 5895–5914, https://doi.org/10.5194/gmd-16-5895-2023, https://doi.org/10.5194/gmd-16-5895-2023, 2023
Short summary
Short summary
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.
Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll
Geosci. Model Dev., 16, 5729–5754, https://doi.org/10.5194/gmd-16-5729-2023, https://doi.org/10.5194/gmd-16-5729-2023, 2023
Short summary
Short summary
The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, and Zifa Wang
Geosci. Model Dev., 16, 5585–5599, https://doi.org/10.5194/gmd-16-5585-2023, https://doi.org/10.5194/gmd-16-5585-2023, 2023
Short summary
Short summary
This paper developed a two-way coupled module in a new version of a regional urban–street network model, IAQMS-street v2.0, in which the mass flux from streets to background is considered. Test cases are defined to evaluate the performance of IAQMS-street v2.0 in Beijing by comparing it with that simulated by IAQMS-street v1.0 and a regional model. The contribution of local emissions and the influence of on-road vehicle control measures on air quality are evaluated by using IAQMS-street v2.0.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavčič, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 16, 5601–5626, https://doi.org/10.5194/gmd-16-5601-2023, https://doi.org/10.5194/gmd-16-5601-2023, 2023
Short summary
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.
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchař, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
Geosci. Model Dev., 16, 5561–5583, https://doi.org/10.5194/gmd-16-5561-2023, https://doi.org/10.5194/gmd-16-5561-2023, 2023
Short summary
Short summary
The columnar approach of gravity wave (GW) schemes results in dynamical model biases, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray tracer. More spread-out GW drag helps reconcile the model with observations and close the 60°S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
Sanam N. Vardag and Robert Maiwald
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-192, https://doi.org/10.5194/gmd-2023-192, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We use the atmospheric transport model GRAMM/GRAL in an inversion to estimate urban CO2 emissions on neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation into the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
EGUsphere, https://doi.org/10.5194/egusphere-2023-1512, https://doi.org/10.5194/egusphere-2023-1512, 2023
Short summary
Short summary
Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variables. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimizing simulations for better climate projections.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
Geosci. Model Dev., 16, 5493–5514, https://doi.org/10.5194/gmd-16-5493-2023, https://doi.org/10.5194/gmd-16-5493-2023, 2023
Short summary
Short summary
With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-169, https://doi.org/10.5194/gmd-2023-169, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, a milestone that has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs, thus perfectly suited for future CO2 geo-stationery and imager satellites.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-171, https://doi.org/10.5194/gmd-2023-171, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
PyRTlib is an attractive educational software because it provides a flexible and user friendly tool to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols and hydrometeors). PyRTlib is a so called Radiative Transfer model which are commonly used to simulate and understand remote sensing observations from ground-based, airborne or satellite instruments.
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Short summary
Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidentally) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011) and show that it improves the simulation of wet deposition.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Short summary
A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023, https://doi.org/10.5194/gmd-16-5251-2023, 2023
Short summary
Short summary
In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Cited articles
Aharonson, O., Zuber, M. T., Smith, D. E., Neumann, G. A., Feldman, W. C., and
Prettyman, T. H.: Depth, distribution, and density of CO2 deposition on
Mars, J. Geophys. Res.-Planet., 109, E05004,
https://doi.org/10.1029/2003JE002223, 2004. a
Atri, D., Abdelmoneim, N., Dhuri, D. B., and Simoni, M.: Diurnal variation of
the surface temperature of Mars with the Emirates Mars Mission: A comparison
with Curiosity and Perseverance rover measurements, Monthly Notices of the
Royal Astronomical Society: Letters, 518, L1–L6,
https://doi.org/10.1093/mnrasl/slac094, 2023. a
Balkanski, Y., Schulz, M., Claquin, T., and Guibert, S.: Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data, Atmos. Chem. Phys., 7, 81–95, https://doi.org/10.5194/acp-7-81-2007, 2007. a, b
Ball, E. R., Mitchell, D. M., Seviour, W. J. M., Thomson, S. I., and Vallis,
G. K.: The Roles of Latent Heating and Dust in the Structure and Variability
of the Northern Martian Polar Vortex, The Planetary Science Journal, 2, 203,
https://doi.org/10.3847/psj/ac1ba2, 2021. a
Banfield, D., Spiga, A., Newman, C., Forget, F., Lemmon, M., Lorenz, R.,
Murdoch, N., Viudez-Moreiras, D., Pla-Garcia, J., Garcia, R. F.,
Lognonné, P., Karatekin, Ã., Perrin, C., Martire, L., Teanby, N., Hove,
B. V., Maki, J. N., Kenda, B., Mueller, N. T., Rodriguez, S., Kawamura, T.,
McClean, J. B., Stott, A. E., Charalambous, C., Millour, E., Johnson, C. L.,
Mittelholz, A., Määttänen, A., Lewis, S. R., Clinton, J.,
Stähler, S. C., Ceylan, S., Giardini, D., Warren, T., Pike, W. T.,
Daubar, I., Golombek, M., Rolland, L., Widmer-Schnidrig, R., Mimoun, D.,
Beucler, E., Jacob, A., Lucas, A., Baker, M., Ansan, V., Hurst, K.,
Mora-Sotomayor, L., Navarro, S., Torres, J., Lepinette, A., Molina, A.,
Marin-Jimenez, M., Gomez-Elvira, J., Peinado, V., Rodriguez-Manfredi, J. A.,
Carcich, B. T., Sackett, S., Russell, C. T., Spohn, T., Smrekar, S. E., and
Banerdt, W. B.: The atmosphere of Mars as observed by InSight, Nat.
Geosci., 13, 190–198, https://doi.org/10.1038/s41561-020-0534-0, 2020. a
Benacchio, T. and Wood, N.: Semi-implicit semi-Lagrangian modelling of the
atmosphere: a Met Office perspective, Communications in Applied and
Industrial Mathematics, 7, 4–25, https://doi.org/10.1515/caim-2016-0020, 2016. a
Bonev, B. P., Hansen, G. B., Glenar, D. A., James, P. B., and Bjorkman, J. E.:
Albedo models for the residual south polar cap on Mars: Implications for the
stability of the cap under near-perihelion global dust storm conditions,
Planet. Space Sci., 56, 181–193, https://doi.org/10.1016/j.pss.2007.08.003,
2008. 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, c
Brown, A. J., Piqueux, S., and Titus, T. N.: Interannual observations and
quantification of summertime H2O ice deposition on the Martian CO2 ice south
polar cap, Earth Planet. Sc. Lett., 406, 102–109,
https://doi.org/10.1016/j.epsl.2014.08.039, 2014. a
Chaffin, M. S., Kass, D. M., Aoki, S., Fedorova, A. A., Deighan, J., Connour,
K., Heavens, N. G., Kleinböhl, A., Jain, S. K., Chaufray, J.-Y.,
Mayyasi, M., Clarke, J. T., Stewart, A. I. F., Evans, J. S., Stevens, M. H.,
McClintock, W. E., Crismani, M. M. J., Holsclaw, G. M., Lefevre, F., Lo,
D. Y., Montmessin, F., Schneider, N. M., Jakosky, B., Villanueva, G., Liuzzi,
G., Daerden, F., Thomas, I. R., Lopez-Moreno, J.-J., Patel, M. R., Bellucci,
G., Ristic, B., Erwin, J. T., Vandaele, A. C., Trokhimovskiy, A., and
Korablev, O. I.: Martian water loss to space enhanced by regional dust
storms, Nature Astronomy, 5, 1036–1042, https://doi.org/10.1038/s41550-021-01425-w,
2021. a
Chapman, R. M., Lewis, S. R., Balme, M., and Steele, L. J.: Diurnal variation
in martian dust devil activity, Icarus, 292, 154–167,
https://doi.org/10.1016/j.icarus.2017.01.003, 2017. a
Colaïtis, A., Spiga, A., Hourdin, F., Rio, C., Forget, F., and Millour,
E.: A thermal plume model for the Martian convective boundary layer,
J. Geophys. Res.-Planet., 118, 1468–1487,
https://doi.org/10.1002/jgre.20104, 2013. a, b
Cooper, B., Torre Juárez, M., Mischna, M., Lemmon, M., Martínez,
G., Kass, D., Vasavada, A. R., Campbell, C., and Moores, J.: Thermal Forcing
of the Nocturnal Near Surface Environment by Martian Water Ice Clouds,
J. Geophys. Res.-Planet., 126, e2020JE006737,
https://doi.org/10.1029/2020je006737, 2021. a
Drummond, B., Mayne, N. J., Baraffe, I., Tremblin, P., Manners, J., Amundsen,
D. S., Goyal, J., and Acreman, D.: The effect of metallicity on the
atmospheres of exoplanets with fully coupled 3D hydrodynamics, equilibrium
chemistry, and radiative transfer, Astron. Astrophys., 612, A105,
https://doi.org/10.1051/0004-6361/201732010, 2018. a, b
Eager-Nash, J. K., Reichelt, D. J., Mayne, N. J., Hugo Lambert, F., 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
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/10.1256/smsqj.53106, 1996. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
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, Planetary Science Journal, 3, 213,
https://doi.org/10.3847/PSJ/ac6cf1, 2022. a, b
Fischer, E., Martínez, G. M., Rennó, N. O., Tamppari, L. K., and
Zent, A. P.: Relative Humidity on Mars: New Results From the Phoenix TECP
Sensor, J. Geophys. Res.-Planet., 124, 2780–2792,
https://doi.org/10.1029/2019JE006080, 2019. a
Forget, F., Hourdin, F., Fournier, R., Hourdin, C., Talagrand, O., Collins, M.,
Lewis, S. R., Read, P. L., and Huot, J. P.: Improved general circulation
models of the Martian atmosphere from the surface to above 80 km, J.
Geophys. Res.-Planet., 104, 24155–24175,
https://doi.org/10.1029/1999JE001025, 1999. a, b, c, d, e, f, g, h, i, j, k, l
Gary-Bicas, C. E., Hayne, P. O., Horvath, T., Heavens, N. G., Kass, D. M.,
Kleinböhl, A., Piqueux, S., Shirley, J. H., Schofield, J. T., and
McCleese, D. J.: Asymmetries in Snowfall, Emissivity, and Albedo of Mars'
Seasonal Polar Caps: Mars Climate Sounder Observations, J.
Geophys. Res.-Planet., 125, e2019JE006150, https://doi.org/10.1029/2019JE006150, 2020. a
Gebhardt, C., Abuelgasim, A., Fonseca, R. M., Martín-Torres, J., and
Zorzano, M. P.: Fully Interactive and Refined Resolution Simulations of the
Martian Dust Cycle by the MarsWRF Model, J. Geophys. Res.-Planet., 125, e2019JE006253, https://doi.org/10.1029/2019JE006253, 2020. a
Gierasch, P. J. and Toon, O. B.: Atmospheric Pressure Variation and the
Climate of Mars, J. Atmos. Sci., 30, 1502–1508,
https://doi.org/10.1175/1520-0469(1973)030<1502:APVATC>2.0.CO;2, 1973. a
González-Galindo, F., Bougher, S. W., López-Valverde, M. A.,
Forget, F., and Murphy, J.: Thermal and wind structure of the Martian
thermosphere as given by two General Circulation Models, Planet. Space
Sci., 58, 1832–1849, https://doi.org/10.1016/j.pss.2010.08.013, 2010. a
González-Galindo, F., López-Valverde, M. A., Forget, F.,
García-Comas, M., Millour, E., and Montabone, L.: Variability of the
Martian thermosphere during eight Martian years as simulated by a
ground-to-exosphere global circulation model, J. Geophys.
Res.-Planet., 120, 2020–2035, https://doi.org/10.1002/2015JE004925, 2015. a, b
Gronoff, G., Arras, P., Baraka, S., Bell, J. M., Cessateur, G., Cohen, O.,
Curry, S. M., Drake, J. J., Elrod, M., Erwin, J., Garcia-Sage, K., Garraffo,
C., Glocer, A., Heavens, N. G., Lovato, K., Maggiolo, R., Parkinson, C. D.,
Simon Wedlund, C., Weimer, D. R., and Moore, W. B.: Atmospheric Escape
Processes and Planetary Atmospheric Evolution, J. Geophys.
Res.-Space, 125, e2019JA027639, https://doi.org/10.1029/2019JA027639, 2020. a
Haberle, R. M., McKay, C. P., Schaeffer, J., Cabrol, N. A., Grin, E. A., Zent,
A. P., and Quinn, R.: On the possibility of liquid water on present-day
Mars, J. Geophys. Res.-Planet., 106, 23317–23326,
https://doi.org/10.1029/2000JE001360, 2001. a
Haberle, R. M., Forget, F., Colaprete, A., Schaeffer, J., Boynton, W. V.,
Kelly, N. J., and Chamberlain, M. A.: The effect of ground ice on the
Martian seasonal CO2 cycle, Planet. Space Sci., 56, 251–255,
https://doi.org/10.1016/j.pss.2007.08.006, 2008. a, b
Haberle, R. M., Kahre, M. A., Hollingsworth, J. L., Montmessin, F., Wilson,
R. J., Urata, R. A., Brecht, A. S., Wolff, M. J., Kling, A. M., and
Schaeffer, J. R.: Documentation of the NASA/Ames Legacy Mars Global Climate
Model: Simulations of the present seasonal water cycle, Icarus, 333,
130–164, https://doi.org/10.1016/j.icarus.2019.03.026, 2019. a
Hayne, P. O., Paige, D. A., Schofield, J. T., Kass, D. M., Kleinbhl, A.,
Heavens, N. G., and McCleese, D. J.: Carbon dioxide snow clouds on Mars:
South polar winter observations by the Mars Climate Sounder, J.
Geophys. Res.-Planet., 117, E08014, https://doi.org/10.1029/2011JE004040,
2012. a
Heavens, N. G., Richardson, M. I., Kleinböhl, A., Kass, D. M., McCleese,
D. J., Abdou, W., Benson, J. L., Schofield, J. T., Shirley, J. H., and
Wolkenberg, P. M.: Vertical distribution of dust in the Martian atmosphere
during northern spring and summer: High-altitude tropical dust maximum at
northern summer solstice, J. Geophys. Res.-Planet., 116,
E01007, https://doi.org/10.1029/2010JE003692, 2011. a
Hébrard, E., Listowski, C., Coll, P., Marticorena, B., Bergametti, G.,
Määttänen, A., Montmessin, F., and Forget, F.: An
aerodynamic roughness length map derived from extended Martian rock abundance
data, J. Geophys. Res.-Planet., 117, E04008,
https://doi.org/10.1029/2011JE003942, 2012. a, b, c
Hinson, D. P. and Wilson, R. J.: Temperature inversions, thermal tides, and
water ice clouds in the Martian tropics, J. Geophys. Res.-Planet., 109, E01002, https://doi.org/10.1029/2003je002129, 2004. a
Hinson, D. P., Asmar, S. W., Kahan, D. S., Akopian, V., Haberle, R. M., Spiga,
A., Schofield, J. T., Kleinböhl, A., Abdou, W. A., Lewis, S. R., Paik,
M., and Maalouf, S. G.: Initial results from radio occultation measurements
with the Mars Reconnaissance Orbiter: A nocturnal mixed layer in the tropics
and comparisons with polar profiles from the Mars Climate Sounder, Icarus,
243, 91–103, https://doi.org/10.1016/j.icarus.2014.09.019, 2014. a
Holmes, J. A., Lewis, S. R., Patel, M. R., and Lefèvre, F.: A reanalysis
of ozone on Mars from assimilation of SPICAM observations, Icarus, 302,
308–318, https://doi.org/10.1016/j.icarus.2017.11.026, 2018. a
Houben, H., Haberle, R. M., Young, R. E., and Zent, A. P.: Evolution of the
Martian water cycle, Adv. Space Res., 19, 1233–1236,
https://doi.org/10.1016/S0273-1177(97)00274-3, 1997. a
Hourdin, F., Le Van, P., Forget, F., and Talagrand, O.: Meteorological
variability and the annual surface pressure cycle on Mars, J.
Atmos. Sci., 50, 3625–3640,
https://doi.org/10.1175/1520-0469(1993)050<3625:MVATAS>2.0.CO;2, 1993. a
Hourdin, F., Forget, F., and Talagrand, O.: The sensitivity of the Martian
surface pressure and atmospheric mass budget to various parameters: A
comparison between numerical simulations and Viking observations, J.
Geophys. Res., 100, 5501–5523, https://doi.org/10.1029/94je03079, 1995. a
Husain, S. Z., Girard, C., Qaddouri, A., and Plante, A.: A new dynamical core
of the Global Environmental Multiscale (GEM) model with a height-based
terrain-following vertical coordinate, Mon. Weather Rev., 147,
2555–2578, https://doi.org/10.1175/MWR-D-18-0438.1, 2019. a
Jakosky, B. M. and Edwards, C. S.: Inventory of CO2 available for terraforming
Mars, Nature Astronomy, 2, 634–639, https://doi.org/10.1038/s41550-018-0529-6, 2018. a
Kahre, M. A. and Haberle, R. M.: Mars CO2 cycle: Effects of airborne dust and
polar cap ice emissivity, Icarus, 207, 648–653,
https://doi.org/10.1016/j.icarus.2009.12.016, 2010. a, b, c
Kahre, M. A., Murphy, J. R., and Haberle, R. M.: Modelling the Martian dust
cycle and surface dust reservoirs with the NASA Ames general circulation
model, J. Geophys. Res.-Planet., 111, E06008,
https://doi.org/10.1029/2005JE002588, 2006. a
Kahre, M. A., Murphy, J. R., Newman, C. E., Wilson, R. J., Cantor, B. A.,
Lemmon, M. T., and Wolff, M. J.: The Mars Dust Cycle, in: The Atmosphere
and Climate of Mars, chap. 10, Cambridge University Press, 295–337,
https://doi.org/10.1017/9781139060172.010, 2017. a
Kass, D. M., Schofield, J. T., Michaels, T. I., Rafkin, S. C., Richardson,
M. I., and Toigo, A. D.: Analysis of atmospheric mesoscale models for entry,
descent, and landing, J. Geophys. Res.-Planet., 108,
8090, https://doi.org/10.1029/2003je002065, 2003. a
Kass, D. M., Schofield, J. T., Kleinböhl, A., McCleese, D. J., Heavens,
N. G., Shirley, J. H., and Steele, L. J.: Mars Climate Sounder Observation
of Mars' 2018 Global Dust Storm, Geophys. Res. Lett., 47, e2019GL083931,
https://doi.org/10.1029/2019GL083931, 2020. a
Kieffer, H. H., Martin, T. Z., Peterfreund, A. R., Jakosky, B. M., Miner,
E. D., and Palluconi, F. D.: Thermal and albedo mapping of Mars during the
Viking primary mission, J. Geophys. Res., 82, 4249–4291,
https://doi.org/10.1029/js082i028p04249, 1977. a, b, c, d
Lefèvre, F., Bertaux, J.-L., Clancy, R. T., Encrenaz, T., Fast, K.,
Forget, F., Lebonnois, S., Montmessin, F., and Perrier, S.: Heterogeneous
chemistry in the atmosphere of Mars, Nature, 454, 971–975,
https://doi.org/10.1038/nature07116, 2008. a
Lines, S., Manners, J., Mayne, N. J., Goyal, J., Carter, A. L., Boutle, I. A.,
Lee, G. K., Helling, C., Drummond, B., Acreman, D. M., and Sing, D. K.:
Exonephology: Transmission spectra from a 3D simulated cloudy atmosphere of
HD 209458b, Mon. Not. R. Astron. Soc., 481,
194–205, https://doi.org/10.1093/mnras/sty2275, 2018. 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
Lora, J. M., Tokano, T., Vatant d'Ollone, J., Lebonnois, S., and Lorenz, R. D.:
A model intercomparison of Titan's climate and low-latitude environment,
Icarus, 333, 113–126, https://doi.org/10.1016/j.icarus.2019.05.031, 2019. a
Lott, F. and Miller, M. J.: A new subgrid-scale orographic drag
parametrization: Its formulation and testing, Q. J. Roy.
Meteor. Soc., 123, 101–127, https://doi.org/10.1002/qj.49712353704, 1997. a
Madeleine, J.-B., Forget, F., Millour, E., Navarro, T., and Spiga, A.: The
influence of radiatively active water ice clouds on the Martian climate,
Geophys. Res. Lett., 39, L23202, https://doi.org/10.1029/2012GL053564, 2012. a
Malin, M. C., Caplinger, M. A., and Davis, S. D.: Observational evidence for
an active surface reservoir of solid carbon dioxide on Mars, Science, 294,
2146–2148, https://doi.org/10.1126/science.1066416, 2001. a
Manners, J., Vosper, S. B., and Roberts, N.: Radiative transfer over resolved
topographic features for high-resolution weather prediction, Q.
J. Roy. Meteor. Soc., 138, 720–733,
https://doi.org/10.1002/qj.956, 2012. a
Martínez, G. M., Newman, C. N., De Vicente-Retortillo, A., Fischer, E.,
Renno, N. O., Richardson, M. I., Fairén, A. G., Genzer, M., Guzewich,
S. D., Haberle, R. M., Harri, A. M., Kemppinen, O., Lemmon, M. T., Smith,
M. D., de la Torre-Juárez, M., and Vasavada, A. R.: The Modern
Near-Surface Martian Climate: A Review of In-situ Meteorological Data from
Viking to Curiosity, Space Sci. Rev., 212, 295–338,
https://doi.org/10.1007/s11214-017-0360-x, 2017. a, b, c, d, e, f
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, 2014. a, b
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
McCulloch, D., Sergeev, D., Mayne, N., Bate, M., Manners, J., Boutle, I., and
Drummond, B.: UM post-processed Mars dataset, Version 1, Zenodo [code and data set], https://doi.org/10.5281/zenodo.6974260,
2022. a, b
Mellon, M. T., Fergason, R. L., and Putzig, N. E.: The thermal inertia of the
surface of Mars, in: The Martian Surface, Cambridge University
Press, 399–427, https://doi.org/10.1017/CBO9780511536076.019, 2008. a, b
Millour, E., Forget, F., Spiga, A., López-Valverde, M. A., Vals, M.,
Zakharov, A. V., Montabone, L., Lefevre, F., Montmessin, F., Chaufray, J. Y.,
González-Galindo, F., Lewis, S. R., Read, P. L., Desjean, M.-C., and
Cipriani, F.: The Mars Climate Database (Version 5.3), in: Scientific
Workshop: From Mars Express to ExoMars, ESAC Madrid, Spain,
https://www.cosmos.esa.int/documents/1499429/1583871/Millour_E.pdf (last access: 16 January 2023),
2018. a, b
Montabone, L., Lewis, S. R., Read, P. L., and Withers, P.: Reconstructing the
weather on Mars at the time of the MERs and Beagle 2 landings, Geophys.
Res. Lett., 33, L19202, https://doi.org/10.1029/2006GL026565, 2006. a
Montabone, L., Forget, F., Millour, E., Wilson, R. J., Lewis, S. R., Cantor,
B., Kass, D., Kleinböhl, A., Lemmon, M. T., Smith, M. D., and Wolff,
M. J.: Eight-year climatology of dust optical depth on Mars, Icarus, 251,
65–95, https://doi.org/10.1016/j.icarus.2014.12.034, 2015. a, b, c, d
Montabone, L., Spiga, A., Kass, D. M., Kleinböhl, A., Forget, F., and
Millour, E.: Martian Year 34 Column Dust Climatology from Mars Climate
Sounder Observations: Reconstructed Maps and Model Simulations, J.
Geophys. Res.-Planet., 125, e2019JE006111, https://doi.org/10.1029/2019JE006111, 2020. a, b, c, d, e, f, g, h, i
Mulholland, D. P., Read, P. L., and Lewis, S. R.: Simulating the interannual
variability of major dust storms on Mars using variable lifting thresholds,
Icarus, 223, 344–358, https://doi.org/10.1016/j.icarus.2012.12.003, 2013. a
Navarro, T., Madeleine, J. B., Forget, F., Spiga, A., Millour, E., Montmessin,
F., and Määttänen, A.: Global climate modeling of the
Martian water cycle with improved microphysics and radiatively active water
ice clouds, J. Geophys. Res.-Planet., 119, 1479–1495,
https://doi.org/10.1002/2013JE004550, 2014. a, b, c, d, e, f, g, h
Nazari-Sharabian, M., Aghababaei, M., Karakouzian, M., and Karami, M.: Water
on Mars – A Literature Review, Galaxies, 8, 40,
https://doi.org/10.3390/galaxies8020040, 2020. a
Neakrase, L. D., Balme, M. R., Esposito, F., Kelling, T., Klose, M., Kok,
J. F., Marticorena, B., Merrison, J., Patel, M., and Wurm, G.: Particle
Lifting Processes in Dust Devils, Space Sci. Rev., 203, 347–376,
https://doi.org/10.1007/s11214-016-0296-6, 2016. a, b
Neary, L. and Daerden, F.: The GEM-Mars general circulation model for Mars:
Description and evaluation, Icarus, 300, 458–476,
https://doi.org/10.1016/j.icarus.2017.09.028, 2018. a
Newman, C. E., Lewis, S. R., Read, P. L., and Forget, F.: Modeling the Martian
dust cycle 1. Representations of dust transport processes, J.
Geophys. Res.-Planet., 107, 5123, https://doi.org/10.1029/2002je001910, 2002. a, b
Newman, C. E., de la Torre Juárez, M., Pla-García, J., Wilson,
R. J., Lewis, S. R., Neary, L., Kahre, M. A., Forget, F., Spiga, A.,
Richardson, M. I., Daerden, F., Bertrand, T., Viúdez-Moreiras, D.,
Sullivan, R., Sánchez-Lavega, A., Chide, B., and Rodriguez-Manfredi,
J. A.: Multi-model Meteorological and Aeolian Predictions for Mars 2020 and
the Jezero Crater Region, Space Sci. Rev., 217, 20,
https://doi.org/10.1007/s11214-020-00788-2, 2021. a, b
Newman, C. E., Bertrand, T., Fenton, L. K., Guzewich, S. D., Jackson, B.,
Lewis, S. R., Mischna, M. A., Montabone, L., and Wellington, D. F.: Martian
Dust, 2 edn., January, Elsevier Inc.,
https://doi.org/10.1016/b978-0-12-818234-5.00143-7, 2022. a, b
Oliver, H., Shin, M., Matthews, D., Sanders, O., Bartholomew, S., Clark, A.,
Fitzpatrick, B., Van Haren, R., Drost, N., and Hut, R.: Workflow Automation
for Cycling Systems, Comput. Sci. Eng., 21, 7–21,
https://doi.org/10.1109/MCSE.2019.2906593, 2019 (code available at: https://cylc.github.io/, last access: 16 January 2023). a
Paige, D. A. and Wood, S. E.: Modeling the Martian seasonal CO2 cycle 2.
Interannual variability, Icarus, 99, 15–27,
https://doi.org/10.1016/0019-1035(92)90167-6, 1992. a
Pál, B., Kereszturi, Ã., Forget, F., and Smith, M. D.: Global seasonal
variations of the near-surface relative humidity levels on present-day Mars,
Icarus, 333, 481–495, https://doi.org/10.1016/j.icarus.2019.07.007, 2019. a, b
Palluconi, F. D. and Kieffer, H. H.: Thermal inertia mapping of Mars from
60∘ S to 60∘ N, Icarus, 45, 415–426,
https://doi.org/10.1016/0019-1035(81)90044-0, 1981. a, b
Pollack, J. B., Haberle, R. M., Murphy, J. R., Schaeffer, J., and Lee, H.:
Simulations of the general circulation of the Martian atmosphere. 2.
Seasonal pressure variations, J. Geophys. Res., 98,
3149–3181, https://doi.org/10.1029/92JE02947, 1993. a
Pottier, A., Forget, F., Montmessin, F., Navarro, T., Spiga, A., Millour, E.,
Szantai, A., and Madeleine, J.-B. B.: Unraveling the martian water cycle
with high-resolution global climate simulations, Icarus, 291, 82–106,
https://doi.org/10.1016/j.icarus.2017.02.016, 2017. a, b
Richardson, M. I. and Wilson, R. J.: A topographically forced asymmetry in the
martian circulation and climate, Nature, 416, 298–301,
https://doi.org/10.1038/416298a, 2002. a, b, c
Schmidt, F., Douté, S., Schmitt, B., Vincendon, M., Bibring, J. P., and
Langevin, Y.: Albedo control of seasonal South Polar cap recession on Mars,
Icarus, 200, 374–394, https://doi.org/10.1016/j.icarus.2008.12.014, 2009. a, b
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
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, Planetary Science Journal, 3, 212,
https://doi.org/10.3847/PSJ/ac6cf2, 2022. a, b
Shaposhnikov, D. S., Rodin, A. V., and Medvedev, A. S.: The water cycle in the
general circulation model of the martian atmosphere, Solar System Research,
50, 90–101, https://doi.org/10.1134/S0038094616020039, 2016. a, b
Shaposhnikov, D. S., Rodin, A. V., Medvedev, A. S., Fedorova, A. A., Kuroda,
T., and Hartogh, P.: Modeling the Hydrological Cycle in the Atmosphere of
Mars: Influence of a Bimodal Size Distribution of Aerosol Nucleation
Particles, J. Geophys. Res.-Planet., 123, 508–526,
https://doi.org/10.1002/2017JE005384, 2018. a, b
Singh, D., Flanner, M. G., and Millour, E.: Improvement of Mars Surface Snow
Albedo Modeling in LMD Mars GCM With SNICAR, J. Geophys.
Res.-Planet., 123, 780–791, https://doi.org/10.1002/2017JE005368, 2018. a, b
Smith, D. E., Zuber, M. T., Solomon, S. C., Phillips, R. J., Head, J. W.,
Garvin, J. B., Banerdt, W. B., Muhleman, D. O., Pettengill, G. H., Neumann,
G. A., Lemoine, F. G., Abshire, J. B., Aharonson, O., Brown, C. D., Hauck,
S. A., Ivanov, A. B., McGovern, P. J., Zwally, H. J., and Duxbury, T. C.:
The global topography of Mars and implications for surface evolution,
Science, 284, 1495–1503, https://doi.org/10.1126/science.284.5419.1495, 1999. a, b, c
Spafford, L. and MacDougall, A. H.: Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review, Geosci. Model Dev., 14, 5863–5889, https://doi.org/10.5194/gmd-14-5863-2021, 2021. a
Spiga, A. and Forget, F.: A new model to simulate the Martian mesoscale and
microscale atmospheric circulation: Validation and first results, J.
Geophys. Res.-Planet., 114, E02009, https://doi.org/10.1029/2008JE003242,
2009. a
Spiga, A., Hinson, D. P., Madeleine, J. B., Navarro, T., Millour, E., Forget,
F., and Montmessin, F.: Snow precipitation on Mars driven by cloud-induced
night-time convection, Nat. Geosci., 10, 652–657,
https://doi.org/10.1038/ngeo3008, 2017. a
Staniforth, A. and Wood, N.: The deep-atmosphere Euler equations in a
generalized vertical coordinate, Mon. Weather Rev., 131, 1931–1938,
https://doi.org/10.1175//2564.1, 2003. a
Staniforth, A. and Wood, N.: Aspects of the dynamical core of a
nonhydrostatic, deep-atmosphere, unified weather and climate-prediction
model, J. Comput. Phys., 227, 3445–3464,
https://doi.org/10.1016/j.jcp.2006.11.009, 2008. a
Steele, L. J., Balme, M. R., Lewis, S. R., and Spiga, A.: The water cycle and
regolith–atmosphere interaction at Gale crater, Mars, Icarus, 289, 56–79,
https://doi.org/10.1016/j.icarus.2017.02.010, 2017. a, b
Streeter, P. M., Lewis, S. R., Patel, M. R., Holmes, J. A., and Kass, D. M.:
Surface Warming During the 2018/Mars Year 34 Global Dust Storm, Geophys.
Res. Lett., 47, e2019GL083936, https://doi.org/10.1029/2019GL083936, 2020. a, b, c
Sullivan, C. and Kaszynski, A.: PyVista: 3D plotting and mesh analysis through
a streamlined interface for the Visualization Toolkit (VTK), Journal of Open
Source Software, 4, 1450, https://doi.org/10.21105/joss.01450, 2019. a
Tillman, J. E.: VL1/VL2-M-MET-4-DAILY-AVG-PRESSURE-V1.0, NASA [data set],
https://atmos.nmsu.edu/data_and_services/atmospheres_data/MARS/viking/sol_avg_sur_press_data.html (last access: 16 January 2023),
1989. 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, Planetary Science Journal, 3, 211,
https://doi.org/10.3847/PSJ/ac6cf0, 2022. a, b, c, d, e
Vosper, S. B.: Mountain waves and wakes generated by South Georgia:
Implications for drag parametrization, Q. J. Roy.
Meteor. Soc., 141, 2813–2827, https://doi.org/10.1002/qj.2566, 2015. a
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, d, e, f, g, h
Wang, C., Forget, F., Bertrand, T., Spiga, A., Millour, E., and Navarro, T.:
Parameterization of Rocket Dust Storms on Mars in the LMD Martian GCM:
Modeling Details and Validation, J. Geophys. Res.-Planet.,
123, 982–1000, https://doi.org/10.1002/2017JE005255, 2018. a, b
Wang, H. and Richardson, M. I.: The origin, evolution, and trajectory of large
dust storms on Mars during Mars years 24–30 (1999–2011), Icarus, 251,
112–127, https://doi.org/10.1016/j.icarus.2013.10.033, 2015. a, b
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, b, c, d, e, f
Webster, S., Brown, A. R., Cameron, D. R., and Jones, C. P.: Improvements to
the representation of orography in the Met Office Unified Model, Q.
J. Roy. Meteor. Soc., 129, 1989–2010,
https://doi.org/10.1256/qj.02.133, 2003. 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.
Soc., 134, 2093–2107, https://doi.org/10.1002/qj.333, 2008a. a
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., Morcrette,
C. J., and Bodas-Salcedo, A.: PC2: A prognostic cloud fraction and
condensation scheme. II: Climate model simulations, Q. J.
Roy. Meteor. Soc., 134, 2109–2125, https://doi.org/10.1002/qj.332,
2008b. a
Wolff, M. J., Smith, M. D., Clancy, R. T., Arvidson, R., Kahre, M., Seelos IV,
F., Murchie, S., and Savijärvi, H.: Wavelength dependence of dust
aerosol single scattering albedo as observed by the Compact Reconnaissance
Imaging Spectrometer, J. Geophys. Res.-Planet., 114,
E00D04, https://doi.org/10.1029/2009JE003350, 2009.
a
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
Woodward, S.: Modeling the atmospheric life cycle and radiative impact of
mineral dust in the Hadley Centre climate model, J. Geophys.
Res.-Atmos., 106, 18155–18166,
https://doi.org/10.1029/2000JD900795, 2001. a, b, c, d
Woodward, S., Sellar, A. A., Tang, Y., Stringer, M., Yool, A., Robertson, E., and Wiltshire, A.: The simulation of mineral dust in the United Kingdom Earth System Model UKESM1, Atmos. Chem. Phys., 22, 14503–14528, https://doi.org/10.5194/acp-22-14503-2022, 2022. a, b
Zalucha, A. M., Alan Plumb, R., John Wilson, R., Plumb, R. A., and Wilson,
R. J.: An Analysis of the Effect of Topography on the Martian Hadley Cells,
J. Atmos. Sci., 67, 673–693,
https://doi.org/10.1175/2009JAS3130.1, 2010. a, b, c, d
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.
We present results from the Met Office Unified Model (UM) to study the dry Martian climate. We...
