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
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
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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
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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.
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. Discuss., https://doi.org/10.5194/amt-2023-15, https://doi.org/10.5194/amt-2023-15, 2023
Revised manuscript under review for AMT
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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-dimensional distribution of key pollutants has been collected over the UK (2019–2022).
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
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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
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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
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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
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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
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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
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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
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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
Updated isoprene and terpene emission factors for the Interactive BVOC (iBVOC) emission scheme in the United Kingdom Earth System Model (UKESM1.0)
Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model
Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling
Halogen chemistry in volcanic plumes: a 1D framework based on MOCAGE 1D (version R1.18.1) preparing 3D global chemistry modelling
PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
Long-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003–2020)
Emulating aerosol optics with randomly generated neural networks
Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality
Comparison of ozone formation attribution techniques in the northeastern United States
Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution
Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0
Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME
Improving the representation of shallow cumulus convection with the simplified-higher-order-closure–mass-flux (SHOC+MF v1.0) approach
ISAT v2.0: an integrated tool for nested-domain configurations and model-ready emission inventories for WRF-AQM
Estimation of CH4 emission based on an advanced 4D-LETKF assimilation system
Accelerated estimation of sea-spray-mediated heat flux using Gaussian quadrature: case studies with a coupled CFSv2.0-WW3 system
AMORE-Isoprene v1.0: a new reduced mechanism for gas-phase isoprene oxidation
A method for generating a quasi-linear convective system suitable for observing system simulation experiments
The second Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL2
A dynamic ammonia emission model and the online coupling with WRF–Chem (WRF–SoilN–Chem v1.0): development and regional evaluation in China
SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models
Deep learning models for generation of precipitation maps based on numerical weather prediction
An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing
On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions
Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation
The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications
An approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in Beijing-Tianjin-Hebei region
The impact of altering emission data precision on compression efficiency and accuracy of simulations of the community multiscale air quality model
AerSett v1.0: a simple and straightforward model for the settling speed of big spherical atmospheric aerosols
How Does Cloud-Radiative Heating over the North Atlantic Change with Grid Spacing, Convective Parameterization, and Microphysics Scheme?
Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0
Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release
Implementation of HONO into the chemistry–climate model CHASER (V4.0): roles in tropospheric chemistry
Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)
The AirGAM 2022r1 air quality trend and prediction model
Evaluation of a cloudy cold-air pool in the Columbia River basin in different versions of the High-Resolution Rapid Refresh (HRRR) model
Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS
Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble
Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium
Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range
Convective Gusts Nowcasting Based on Radar Reflectivity and a Deep Learning Algorithm
ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application
Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM
The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation
A method for transporting cloud-resolving model variance in a multiscale modeling framework
The Mission Support System (MSS v7.0.4) and its use in planning for the SouthTRAC aircraft campaign
GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): an automatic generation tool of semi-explicit mechanisms
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101, https://doi.org/10.5194/gmd-16-3083-2023, https://doi.org/10.5194/gmd-16-3083-2023, 2023
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The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023, https://doi.org/10.5194/gmd-16-2975-2023, 2023
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The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898, https://doi.org/10.5194/gmd-16-2873-2023, https://doi.org/10.5194/gmd-16-2873-2023, 2023
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We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, https://doi.org/10.5194/gmd-16-2737-2023, 2023
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Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718, https://doi.org/10.5194/gmd-16-2689-2023, https://doi.org/10.5194/gmd-16-2689-2023, 2023
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Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342, https://doi.org/10.5194/gmd-16-2323-2023, https://doi.org/10.5194/gmd-16-2323-2023, 2023
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We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322, https://doi.org/10.5194/gmd-16-2303-2023, https://doi.org/10.5194/gmd-16-2303-2023, 2023
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Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192, https://doi.org/10.5194/gmd-16-2181-2023, https://doi.org/10.5194/gmd-16-2181-2023, 2023
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We use neural-network-based single-image super-resolution to improve the upscaling of meteorological wind fields to be used for particle dispersion models. This deep-learning-based methodology improves the standard linear interpolation typically used in particle dispersion models. The improvement of wind fields leads to substantial improvement in the computed trajectories of the particles.
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213, https://doi.org/10.5194/gmd-16-2193-2023, https://doi.org/10.5194/gmd-16-2193-2023, 2023
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This work aims to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study enables us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit of 40 µg/m3.
Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao
Geosci. Model Dev., 16, 2167–2179, https://doi.org/10.5194/gmd-16-2167-2023, https://doi.org/10.5194/gmd-16-2167-2023, 2023
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The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger
Geosci. Model Dev., 16, 2037–2054, https://doi.org/10.5194/gmd-16-2037-2023, https://doi.org/10.5194/gmd-16-2037-2023, 2023
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Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieved high correlations. The surrogate models run orders of magnitude faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby
Geosci. Model Dev., 16, 1997–2009, https://doi.org/10.5194/gmd-16-1997-2023, https://doi.org/10.5194/gmd-16-1997-2023, 2023
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Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50 000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
Geosci. Model Dev., 16, 1909–1924, https://doi.org/10.5194/gmd-16-1909-2023, https://doi.org/10.5194/gmd-16-1909-2023, 2023
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Low clouds are one of the largest sources of uncertainty in climate prediction. In this paper, we introduce the first version of the unified turbulence and shallow convection parameterization named SHOC+MF developed to improve the representation of shallow cumulus clouds in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). Here, we also show promising preliminary results in a single-column model framework for two benchmark cases of shallow cumulus convection.
Kun Wang, Chao Gao, Kai Wu, Kaiyun Liu, Haofan Wang, Mo Dan, Xiaohui Ji, and Qingqing Tong
Geosci. Model Dev., 16, 1961–1973, https://doi.org/10.5194/gmd-16-1961-2023, https://doi.org/10.5194/gmd-16-1961-2023, 2023
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This study establishes an easy-to-use and integrated framework for a model-ready emission inventory for the Weather Research and Forecasting (WRF)–Air Quality Numerical Model (AQM). A free tool called the ISAT (Inventory Spatial Allocation Tool) was developed based on this framework. ISAT helps users complete the workflow from the WRF nested-domain configuration to a model-ready emission inventory for AQM with a regional emission inventory and a shapefile for the target region.
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, and Kazuyuki Miyazaki
Geosci. Model Dev., 16, 1823–1838, https://doi.org/10.5194/gmd-16-1823-2023, https://doi.org/10.5194/gmd-16-1823-2023, 2023
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In this study, we estimated CH4 fluxes using an advanced 4D-LETKF method. The system was tested and optimized using observation system simulation experiments (OSSEs), where a known surface emission distribution is retrieved from synthetic observations. The availability of satellite measurements has increased, and there are still many missions focused on greenhouse gas observations that have not yet launched. The technique being referred to has the potential to improve estimates of CH4 fluxes.
Ruizi Shi and Fanghua Xu
Geosci. Model Dev., 16, 1839–1856, https://doi.org/10.5194/gmd-16-1839-2023, https://doi.org/10.5194/gmd-16-1839-2023, 2023
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Based on the Gaussian quadrature method, a fast algorithm of sea-spray-mediated heat flux is developed. Compared with the widely used single-radius algorithm, the new fast algorithm shows a better agreement with the full spectrum integral of spray flux. The new fast algorithm is evaluated in a coupled modeling system, and the simulations of sea surface temperature, wind speed and wave height are improved. Thereby, the new fast algorithm has great potential to be used in coupled modeling systems.
Forwood Wiser, Bryan K. Place, Siddhartha Sen, Havala O. T. Pye, Benjamin Yang, Daniel M. Westervelt, Daven K. Henze, Arlene M. Fiore, and V. Faye McNeill
Geosci. Model Dev., 16, 1801–1821, https://doi.org/10.5194/gmd-16-1801-2023, https://doi.org/10.5194/gmd-16-1801-2023, 2023
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We developed a reduced model of atmospheric isoprene oxidation, AMORE-Isoprene 1.0. It was created using a new Automated Model Reduction (AMORE) method designed to simplify complex chemical mechanisms with minimal manual adjustments to the output. AMORE-Isoprene 1.0 has improved accuracy and similar size to other reduced isoprene mechanisms. When included in the CRACMM mechanism, it improved the accuracy of EPA’s CMAQ model predictions for the northeastern USA compared to observations.
Jonathan D. Labriola, Jeremy A. Gibbs, and Louis J. Wicker
Geosci. Model Dev., 16, 1779–1799, https://doi.org/10.5194/gmd-16-1779-2023, https://doi.org/10.5194/gmd-16-1779-2023, 2023
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Observing system simulation experiments (OSSEs) are simulated case studies used to understand how different assimilated weather observations impact forecast skill. This study introduces the methods used to create an OSSE for a tornadic quasi-linear convective system event. These steps provide an opportunity to simulate a realistic high-impact weather event and can be used to encourage a more diverse set of OSSEs.
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
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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.
Chuanhua Ren, Xin Huang, Tengyu Liu, Yu Song, Zhang Wen, Xuejun Liu, Aijun Ding, and Tong Zhu
Geosci. Model Dev., 16, 1641–1659, https://doi.org/10.5194/gmd-16-1641-2023, https://doi.org/10.5194/gmd-16-1641-2023, 2023
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Ammonia in the atmosphere has wide impacts on the ecological environment and air quality, and its emission from soil volatilization is highly sensitive to meteorology, making it challenging to be well captured in models. We developed a dynamic emission model capable of calculating ammonia emission interactively with meteorological and soil conditions. Such a coupling of soil emission with meteorology provides a better understanding of ammonia emission and its contribution to atmospheric aerosol.
Linlu Mei, Vladimir Rozanov, Alexei Rozanov, and John P. Burrows
Geosci. Model Dev., 16, 1511–1536, https://doi.org/10.5194/gmd-16-1511-2023, https://doi.org/10.5194/gmd-16-1511-2023, 2023
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This paper summarizes recent developments of aerosol, cloud and surface reflectance databases and models in the framework of the software package SCIATRAN. These updates and developments extend the capabilities of the radiative transfer modeling, especially by accounting for different kinds of vertical inhomogeneties. Vertically inhomogeneous clouds and different aerosol types can be easily accounted for within SCIATRAN (V4.6). The widely used surface models and databases are now available.
Adrian Rojas-Campos, Michael Langguth, Martin Wittenbrink, and Gordon Pipa
Geosci. Model Dev., 16, 1467–1480, https://doi.org/10.5194/gmd-16-1467-2023, https://doi.org/10.5194/gmd-16-1467-2023, 2023
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Our paper presents an alternative approach for generating high-resolution precipitation maps based on the nonlinear combination of the complete set of variables of the numerical weather predictions. This process combines the super-resolution task with the bias correction in a single step, generating high-resolution corrected precipitation maps with a lead time of 3 h. We used using deep learning algorithms to combine the input information and increase the accuracy of the precipitation maps.
Robin N. Thor, Mariano Mertens, Sigrun Matthes, Mattia Righi, Johannes Hendricks, Sabine Brinkop, Phoebe Graf, Volker Grewe, Patrick Jöckel, and Steven Smith
Geosci. Model Dev., 16, 1459–1466, https://doi.org/10.5194/gmd-16-1459-2023, https://doi.org/10.5194/gmd-16-1459-2023, 2023
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We report on an inconsistency in the latitudinal distribution of aviation emissions between two versions of a data product which is widely used by researchers. From the available documentation, we do not expect such an inconsistency. We run a chemistry–climate model to compute the effect of the inconsistency in emissions on atmospheric chemistry and radiation and find that the radiative forcing associated with aviation ozone is 7.6 % higher when using the less recent version of the data.
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
Geosci. Model Dev., 16, 1379–1394, https://doi.org/10.5194/gmd-16-1379-2023, https://doi.org/10.5194/gmd-16-1379-2023, 2023
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The long-term comparison between observed and simulated outgoing longwave radiances represents a strict test to evaluate climate model performance. In this work, 9 years of synthetic spectrally resolved radiances, simulated online on the basis of the atmospheric fields predicted by the EC-Earth global climate model (v3.3.3) in clear-sky conditions, are compared to IASI spectral radiance climatology in order to detect model biases in temperature and humidity at different atmospheric levels.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
EGUsphere, https://doi.org/10.5194/egusphere-2023-124, https://doi.org/10.5194/egusphere-2023-124, 2023
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The Fire INventory from NCAR (FINN) provides daily, global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detections, and better represents both large and small fires.. FINNv2.5 generates more emissions than FINNv1, in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-10, https://doi.org/10.5194/gmd-2023-10, 2023
Revised manuscript accepted for GMD
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We refine the ground meteorological stations by a nonlinear approach for improving the regional PM2.5 forecasts. The refined observation network (about 60 % of the current stations) can achieve almost the same improvements in PM2.5 forecasts as all the current station observations. The study will provide a scientific guidance to optimize the ground meteorological stations relative to PM2.5 forecasts and suggests an idea of cost-effective data assimilation for enhancing the PM2.5 forecast skills.
Michael S. Walters and David C. Wong
Geosci. Model Dev., 16, 1179–1190, https://doi.org/10.5194/gmd-16-1179-2023, https://doi.org/10.5194/gmd-16-1179-2023, 2023
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A typical numerical simulation that associates with a large amount of input and output data, applying popular compression software, gzip or bzip2, on data is one good way to mitigate data storage burden. This article proposes a simple technique to alter input, output, or input and output by keeping a specific number of significant digits in data and demonstrates an enhancement in compression efficiency on the altered data but maintains similar statistical performance of the numerical simulation.
Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel
Geosci. Model Dev., 16, 1119–1127, https://doi.org/10.5194/gmd-16-1119-2023, https://doi.org/10.5194/gmd-16-1119-2023, 2023
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Large or even
giantparticles of mineral dust exist in the atmosphere but, so far, solving an non-linear equation was needed to calculate the speed at which they fall in the atmosphere. The model we present, AerSett v1.0 (AERosol SETTling version 1.0), provides a new and simple way of calculating their free-fall velocity in the atmosphere, which will be useful to anyone trying to understand and represent adequately the transport of giant dust particles by the wind.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
EGUsphere, https://doi.org/10.5194/egusphere-2023-109, https://doi.org/10.5194/egusphere-2023-109, 2023
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Clouds absorb and reemit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing, whether we describe convection approximately or exactly, and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Yen-Sen Lu, Garrett H. Good, and Hendrik Elbern
Geosci. Model Dev., 16, 1083–1104, https://doi.org/10.5194/gmd-16-1083-2023, https://doi.org/10.5194/gmd-16-1083-2023, 2023
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The Weather Forecasting and Research (WRF) model consists of many parameters and options that can be adapted to different conditions. This expansive sensitivity study uses a large-scale simulation system to determine the most suitable options for predicting cloud cover in Europe for deterministic and probabilistic weather predictions for day-ahead forecasting simulations.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, https://doi.org/10.5194/gmd-16-1039-2023, 2023
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When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Phuc Thi Minh Ha, Yugo Kanaya, Fumikazu Taketani, Maria Dolores Andrés Hernández, Benjamin Schreiner, Klaus Pfeilsticker, and Kengo Sudo
Geosci. Model Dev., 16, 927–960, https://doi.org/10.5194/gmd-16-927-2023, https://doi.org/10.5194/gmd-16-927-2023, 2023
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HONO affects tropospheric oxidizing capacity; thus, it is implemented into the chemistry–climate model CHASER. The model substantially underpredicts daytime HONO, while nitrate photolysis on surfaces can supplement the daytime HONO budget. Current HONO chemistry predicts reductions of 20.4 % for global tropospheric NOx, 40–67 % for OH, and 30–45 % for O3 in the summer North Pacific. In contrast, OH and O3 winter levels in China are greatly enhanced.
Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
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Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
Sam-Erik Walker, Sverre Solberg, Philipp Schneider, and Cristina Guerreiro
Geosci. Model Dev., 16, 573–595, https://doi.org/10.5194/gmd-16-573-2023, https://doi.org/10.5194/gmd-16-573-2023, 2023
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We have developed a statistical model for estimating trends in the daily air quality observations of NO2, O3, PM10 and PM2.5, adjusting for trends and short-term variations in meteorology. The model is general and may also be used for prediction purposes, including forecasting. It has been applied in a recent comprehensive study in Europe. Significant declines are shown for the pollutants from 2005 to 2019, mainly due to reductions in emissions not attributable to changes in meteorology.
Bianca Adler, James M. Wilczak, Jaymes Kenyon, Laura Bianco, Irina V. Djalalova, Joseph B. Olson, and David D. Turner
Geosci. Model Dev., 16, 597–619, https://doi.org/10.5194/gmd-16-597-2023, https://doi.org/10.5194/gmd-16-597-2023, 2023
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Rapid changes in wind speed make the integration of wind energy produced during persistent orographic cold-air pools difficult to integrate into the electrical grid. By evaluating three versions of NOAA’s High-Resolution Rapid Refresh model, we demonstrate how model developments targeted during the second Wind Forecast Improvement Project improve the forecast of a persistent cold-air pool event.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
EGUsphere, https://doi.org/10.5194/egusphere-2022-1382, https://doi.org/10.5194/egusphere-2022-1382, 2023
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Stakeholders need high-resolution regional climate data for applications such as assessing water availability, mountain snowpack, etc. This study examines 3- and 24-hr historical precipitation over the contiguous United States in the 12-km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
John Douros, Henk Eskes, Jos van Geffen, K. Folkert Boersma, Steven Compernolle, Gaia Pinardi, Anne-Marlene Blechschmidt, Vincent-Henri Peuch, Augustin Colette, and Pepijn Veefkind
Geosci. Model Dev., 16, 509–534, https://doi.org/10.5194/gmd-16-509-2023, https://doi.org/10.5194/gmd-16-509-2023, 2023
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We focus on the challenges associated with comparing atmospheric composition models with satellite products such as tropospheric NO2 columns. The aim is to highlight the methodological difficulties and propose sound ways of doing such comparisons. Building on the comparisons, a new satellite product is proposed and made available, which takes advantage of higher-resolution, regional atmospheric modelling to improve estimates of troposheric NO2 columns over Europe.
Catalina Poraicu, Jean-François Müller, Trissevgeni Stavrakou, Dominique Fonteyn, Frederik Tack, Felix Deutsch, Quentin Laffineur, Roeland Van Malderen, and Nele Veldeman
Geosci. Model Dev., 16, 479–508, https://doi.org/10.5194/gmd-16-479-2023, https://doi.org/10.5194/gmd-16-479-2023, 2023
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High-resolution WRF-Chem simulations are conducted over Antwerp, Belgium, in June 2019 and evaluated using meteorological data and in situ, airborne, and spaceborne NO2 measurements. An intercomparison of model, aircraft, and TROPOMI NO2 columns is conducted to characterize biases in versions 1.3.1 and 2.3.1 of the satellite product. A mass balance method is implemented to provide improved emissions for simulating NO2 distribution over the study area.
Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, and Claudia Plant
Geosci. Model Dev., 16, 251–270, https://doi.org/10.5194/gmd-16-251-2023, https://doi.org/10.5194/gmd-16-251-2023, 2023
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The production of wind energy is increasing rapidly and relies heavily on atmospheric conditions. To ensure power grid stability, accurate predictions of wind speed are needed, especially in the short range and for extreme wind speed ranges. In this work, we demonstrate the forecasting skills of a data-driven deep learning model with model adaptations to suit higher wind speed ranges. The resulting model can be applied to other data and parameters, too, to improve nowcasting predictions.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-272, https://doi.org/10.5194/gmd-2022-272, 2023
Revised manuscript accepted for GMD
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Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CGs nowcasting has remained unattainable. Here, we developed a deep learning model – namely CGsNet – for 0–2 hours of quantitative CGs nowcasting, first achieving minute-kilometer-level forecasts. Based on CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Peter J. M. Bosman and Maarten C. Krol
Geosci. Model Dev., 16, 47–74, https://doi.org/10.5194/gmd-16-47-2023, https://doi.org/10.5194/gmd-16-47-2023, 2023
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We describe an inverse modelling framework constructed around a simple model for the atmospheric boundary layer. This framework can be fed with various observation types to study the boundary layer and land–atmosphere exchange. With this framework, it is possible to estimate model parameters and the associated uncertainties. Some of these parameters are difficult to obtain directly by observations. An example application for a grassland in the Netherlands is included.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
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Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
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Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Walter Hannah and Kyle Pressel
Geosci. Model Dev., 15, 8999–9013, https://doi.org/10.5194/gmd-15-8999-2022, https://doi.org/10.5194/gmd-15-8999-2022, 2022
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A multiscale modeling framework couples two models of the atmosphere that each cover different scale ranges. Traditionally, fluctuations in the small-scale model are not transported by the flow on the large-scale model grid, but this is hypothesized to be responsible for a persistent, unphysical checkerboard pattern. A method is presented to facilitate the transport of these small-scale fluctuations, analogous to how small-scale clouds and turbulence are transported in the real atmosphere.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev., 15, 8983–8997, https://doi.org/10.5194/gmd-15-8983-2022, https://doi.org/10.5194/gmd-15-8983-2022, 2022
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The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurement campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As an example for how the MSS software is used in conjunction with many datasets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Zhizhao Wang, Florian Couvidat, and Karine Sartelet
Geosci. Model Dev., 15, 8957–8982, https://doi.org/10.5194/gmd-15-8957-2022, https://doi.org/10.5194/gmd-15-8957-2022, 2022
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Air quality models need to reliably predict secondary organic aerosols (SOAs) at a reasonable computational cost. Thus, we developed GENOA v1.0, a mechanism reduction algorithm that preserves the accuracy of detailed gas-phase chemical mechanisms for SOA formation, thereby improving the practical use of actual chemistry in SOA models. With GENOA, a near-explicit chemical scheme was reduced to 2 % of its original size and computational time, with an average error of less than 3 %.
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...
