Articles | Volume 13, issue 1
https://doi.org/10.5194/gmd-13-269-2020
© Author(s) 2020. 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-13-269-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
29 Jan 2020
Development and technical paper |
| 29 Jan 2020
| 29 Jan 2020
The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg
Climate Research Division, Environment and Climate Change Canada, Toronto,
Ontario, M3H 5T4, Canada
Saroja M. Polavarapu
Climate Research Division, Environment and Climate Change Canada, Toronto,
Ontario, M3H 5T4, Canada
Douglas Chan
Climate Research Division, Environment and Climate Change Canada, Toronto,
Ontario, M3H 5T4, Canada
Michael Neish
Climate Research Division, Environment and Climate Change Canada, Toronto,
Ontario, M3H 5T4, Canada
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Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
Short summary
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Vikram Khade, Saroja M. Polavarapu, Michael Neish, Pieter L. Houtekamer, Dylan B. A. Jones, Seung-Jong Baek, Tai-Long He, and Sylvie Gravel
Geosci. Model Dev., 14, 2525–2544, https://doi.org/10.5194/gmd-14-2525-2021, https://doi.org/10.5194/gmd-14-2525-2021, 2021
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A new modeling system has been developed at Environment and Climate Change Canada to ingest observations of carbon monoxide (CO) into a coupled weather and constituent transport model. We show that accounting for the uncertainty in surface flux leads to a better estimate of CO distributions. The benefit of assimilating observations from different simulated networks varies with region. This is the first step towards developing a state and flux estimation system for greenhouse gases.
Brendan Byrne, Dylan B. A. Jones, Kimberly Strong, Saroja M. Polavarapu, Anna B. Harper, David F. Baker, and Shamil Maksyutov
Atmos. Chem. Phys., 19, 13017–13035, https://doi.org/10.5194/acp-19-13017-2019, https://doi.org/10.5194/acp-19-13017-2019, 2019
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Interannual variations in net ecosystem exchange (NEE) estimated from the Greenhouse Gases Observing Satellite (GOSAT) XCO2 measurements are shown to be correlated (P < 0.05) with temperature and FLUXCOM NEE anomalies. Furthermore, the GOSAT-informed NEE anomalies are found to be better correlated with temperature and FLUXCOM anomalies than NEE estimates from most terrestrial biosphere models, suggesting that GOSAT CO2 measurements provide a useful constraint on NEE interannual variability.
Misa Ishizawa, Douglas Chan, Doug Worthy, Elton Chan, Felix Vogel, and Shamil Maksyutov
Atmos. Chem. Phys., 19, 4637–4658, https://doi.org/10.5194/acp-19-4637-2019, https://doi.org/10.5194/acp-19-4637-2019, 2019
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The Canadian Arctic has the potential for enhanced methane (CH4) emissions under global warming. However, the regional CH4 emission (fluxes) estimates range widely. This study analyzes recent Canadian Arctic CH4 observations and estimates the regional emissions. The additional observations yield robust CH4 flux estimates and enable the partitioning of the CH4 sources into wetland and forest fires. The results indicate that years with warmer summer conditions result in more wetland CH4 emissions.
Saroja M. Polavarapu, Feng Deng, Brendan Byrne, Dylan B. A. Jones, and Michael Neish
Atmos. Chem. Phys., 18, 12011–12044, https://doi.org/10.5194/acp-18-12011-2018, https://doi.org/10.5194/acp-18-12011-2018, 2018
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A new diagnostic reveals how fluxes constrained by two different CO2 observing systems inform atmospheric CO2 simulations. The potential for GOSAT data to better resolve zonally asymmetric structures in the tropics year-round and in the northern extratropics in most seasons is shown. Using in situ data yields a better match to independent observations on the global, annual scale. Such complementarity of the observing systems can be exploited in greenhouse gas data assimilation systems.
Bakr Badawy, Saroja Polavarapu, Dylan B. A. Jones, Feng Deng, Michael Neish, Joe R. Melton, Ray Nassar, and Vivek K. Arora
Geosci. Model Dev., 11, 631–663, https://doi.org/10.5194/gmd-11-631-2018, https://doi.org/10.5194/gmd-11-631-2018, 2018
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We assess the impact of using the meteorological fields from GEM-MACH-GHG to drive CLASS-CTEM. This coupling is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.
Elton Chan, Douglas Chan, Misa Ishizawa, Felix Vogel, Jerome Brioude, Andy Delcloo, Yuehua Wu, and Baisuo Jin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-213, https://doi.org/10.5194/gmd-2016-213, 2016
Revised manuscript not accepted
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The main objective of this study is to examine the impacts of errors introduced by different components in our newly developed inversion system on flux estimates with a series of controlled experiments. It is very critical for any inversion system to be fully evaluated prior to applying to real observations. As demonstrated, the results can be very sensitive to the model setup and region. It is not reasonable to expect realistic results can always be obtained using the same approach.
Saroja M. Polavarapu, Michael Neish, Monique Tanguay, Claude Girard, Jean de Grandpré, Kirill Semeniuk, Sylvie Gravel, Shuzhan Ren, Sébastien Roche, Douglas Chan, and Kimberly Strong
Atmos. Chem. Phys., 16, 12005–12038, https://doi.org/10.5194/acp-16-12005-2016, https://doi.org/10.5194/acp-16-12005-2016, 2016
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CO2 predictions are used to compute model–data mismatches when estimating surfaces fluxes using atmospheric observations together with an atmospheric transport model. By isolating the component of transport error which is due to uncertain meteorological analyses, it is demonstrated that CO2 can only be defined on large spatial scales. Thus, there is a spatial scale below which we cannot infer fluxes simply due to the fact that meteorological analyes are imperfect.
E. Chan, D. Chan, M. Ishizawa, F. Vogel, J. Brioude, A. Delcloo, Y. Wu, and B. Jin
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-22715-2015, https://doi.org/10.5194/acpd-15-22715-2015, 2015
Revised manuscript not accepted
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This paper uses synthetic data experiments to investigate the impacts of different error sources associated with prior flux, transport model and optimisation method on the atmospheric greenhouse gas inverse estimates. Results indicate that estimation errors are dominated by the transport model error and can propagate to the flux estimates non-linearly. It is necessary to obtain stable and realistic results in synthetic data experiments before a real observation-based inversion is performed.
Related subject area
Atmospheric sciences
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Assessing acetone for the GISS ModelE2.1 Earth system model
Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations
A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0
Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
The implementation of dust mineralogy in COSMO5.05-MUSCAT
Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system
Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1
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)
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
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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
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Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
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This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
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Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
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An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
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Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
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In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
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The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
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In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
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The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
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In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
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PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way 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; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
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We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a 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 in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
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The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
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We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which 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 and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
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Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
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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 variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
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Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
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Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024, https://doi.org/10.5194/gmd-17-1111-2024, 2024
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This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
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With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
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GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 17, 759–780, https://doi.org/10.5194/gmd-17-759-2024, https://doi.org/10.5194/gmd-17-759-2024, 2024
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In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for numbers of SDs much larger than typically used in simulations.
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
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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
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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
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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
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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.
Nathan Patrick Arnold
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-245, https://doi.org/10.5194/gmd-2023-245, 2024
Revised manuscript accepted for GMD
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Earth System Models often represent the land surface at smaller scales than the atmosphere, but surface-atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
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
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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
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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.
Gerrit Kuhlmann, Erik F. M. Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2936, https://doi.org/10.5194/egusphere-2023-2936, 2024
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We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter Notebooks included in the library.
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
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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
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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
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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
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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.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-234, https://doi.org/10.5194/gmd-2023-234, 2024
Revised manuscript accepted for GMD
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air quality-challenged arid/semi-arid region in the US. The unique characteristics of semi-arid/arid regions, e.g., intense heat, minimal moisture, persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
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
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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.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
EGUsphere, https://doi.org/10.5194/egusphere-2023-2740, https://doi.org/10.5194/egusphere-2023-2740, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure facilitating further processing to allow emission processing from continental to street scale.
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-226, https://doi.org/10.5194/gmd-2023-226, 2024
Revised manuscript accepted for GMD
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Reanalysis data have been widely used as an initial condition for the daily forecast of the atmosphere or boundary conditions in regional models, for the study of climate change, and as proxies to complement insufficient in situ measurements. This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2023-2962, https://doi.org/10.5194/egusphere-2023-2962, 2024
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There are relatively limited researches on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPU, have distinct advantages in energy efficiency and scalability. In this study, the air quality modeling system can run stably on MIPS CPU platform, and the experiment results verify the stability of scientific computing on the platform. The work provides a technical foundation for the scientific application based on MIPS CPU platforms.
Cited articles
Agustí-Panareda, A., Massart, S., Chevallier, F., Boussetta, S., Balsamo, G., Beljaars, A., Ciais, P., Deutscher, N. M., Engelen, R., Jones, L., Kivi, R., Paris, J.-D., Peuch, V.-H., Sherlock, V., Vermeulen, A. T., Wennberg, P. O., and Wunch, D.: Forecasting global atmospheric CO2, Atmos. Chem. Phys., 14, 11959–11983, https://doi.org/10.5194/acp-14-11959-2014, 2014.
Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D.: Modelling CO2 weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019, 2019.
Ahmadov R., Gerbig, C., Kretschmer, R., Koerner, S., Neininger, B., Dolman,
A. J., and Sarrat, C.: Mesoscale covariance of transport and CO2
fluxes: Evidence from observations and simulations using the WRF-VPRM
coupled atmosphere-biosphere model, J. Geophys. Res., 112, D22107,
https://doi.org/10.1029/2007JD008552, 2007.
Ahmadov, R., Gerbig, C., Kretschmer, R., Körner, S., Rödenbeck, C., Bousquet, P., and Ramonet, M.: Comparing high resolution WRF-VPRM simulations and two global CO2 transport models with coastal tower measurements of CO2, Biogeosciences, 6, 807–817, https://doi.org/10.5194/bg-6-807-2009, 2009.
Andrews, A. E., Kofler, J. D., Trudeau, M. E., Williams, J. C., Neff, D. H., Masarie, K. A., Chao, D. Y., Kitzis, D. R., Novelli, P. C., Zhao, C. L., Dlugokencky, E. J., Lang, P. M., Crotwell, M. J., Fischer, M. L., Parker, M. J., Lee, J. T., Baumann, D. D., Desai, A. R., Stanier, C. O., De Wekker, S. F. J., Wolfe, D. E., Munger, J. W., and Tans, P. P.: CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmos. Meas. Tech., 7, 647–687, https://doi.org/10.5194/amt-7-647-2014, 2014.
Aranami, K., Davies, T., and Wood, N.: A mass restoration scheme for
limited-area models with semi-Lagrangian advection, Q. J. Roy. Meteor.
Soc., 141, 1795–1803, https://doi.org/10.1002/qj.2482, 2015.
Badawy, B., Polavarapu, S., Jones, D. B. A., Deng, F., Neish, M., Melton, J. R., Nassar, R., and Arora, V. K.: Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb), Geosci. Model Dev., 11, 631–663, https://doi.org/10.5194/gmd-11-631-2018, 2018.
Ballav, S., Patra, P. K., Takigawa, M., Ghosh, S., De, U. K., Maksyutov, S.,
Murayama, S., Mukai, H., and Hashimoto, S.: Simulation of CO2
Concentration over East Asia Using the Regional Transport Model
WRF-CO2, J. Meteorol. Soc. Jpn., 90, 959–976,
https://doi.org/10.2151/jmsj.2012-607, 2012.
Barnes, E. A., Parazoo, N., Orbe, C., and Denning, A. S.: Isentropic
transport and the seasonal cycle amplitude of CO2, J. Geophys.
Res.-Atmos., 121, 8106–8124, https://doi.org/10.1002/2016JD025109, 2016.
Bélair, S., Mailhot, J., Strapp, J. W., and MacPherson, J. I.: An
Examination of Local versus Nonlocal Aspects of a TKE-Based Boundary Layer
Scheme in Clear Convective Conditions, J. Appl. Meteorol., 38, 1499–1518,
https://doi.org/10.1175/1520-0450(1999)038<1499:AEOLVN>2.0.CO;2,
1999.
Bélair, S., Mailhot, J., Girard, C., and Vaillancourt, P.: Boundary
layer and shallow cumulus clouds in a medium-range forecast of a large-scale
weather system, Mon. Weather Rev., 133, 1938–1960,
https://doi.org/10.1175/MWR2958.1, 2005.
Bergamaschi, P., Danila, A., Weiss, R. F., Ciais, P., Thompson, R. L.,
Brunner, D., Levin, I., Meijer, Y., Chevallier, F., Janssens-Maenhout, G.,
Bovensmann, H., Crisp, D., Basu, S., Dlugokencky, E., Engelen, R., Gerbig,
C., Günther, D., Hammer, S., Henne, S., Houweling, S., Karstens, U.,
Kort, E., Maione, M., Manning, A. J., Miller, J., Montzka, S., Pandey, S.,
Peters, W., Peylin, P., Pinty, B., Ramonet, M., Reimann, S., Röckmann,
T., Schmidt, M., Strogies, M., Sussams, J., Tarasova, O., van Aardenne, J.,
Vermeulen, A. T., and Vogel, F.: Atmospheric monitoring and inverse
modelling for verification of greenhouse gas inventories, Publications
Office of the European Union, Luxembourg, https://doi.org/10.2760/759928, 2018.
Bermejo, R. and Conde, J.: A conservative quasi-monotone semi-Lagrangian
scheme, Mon. Weather Rev. 130, 423–430, 2002.
Bruhwiler, L. M. P., Michalak, A. M., and Tans, P. P.: Spatial and temporal resolution of carbon flux estimates for 1983–2002, Biogeosciences, 8, 1309–1331, https://doi.org/10.5194/bg-8-1309-2011, 2011.
Buchwitz, M., Reuter, M., Schneising, O., Noël, S., Gier, B., Bovensmann, H., Burrows, J. P., Boesch, H., Anand, J., Parker, R. J., Somkuti, P., Detmers, R. G., Hasekamp, O. P., Aben, I., Butz, A., Kuze, A., Suto, H., Yoshida, Y., Crisp, D., and O'Dell, C.: Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003–2016, Atmos. Chem. Phys., 18, 17355–17370, https://doi.org/10.5194/acp-18-17355-2018, 2018.
Buehner, M., McTaggart-Cowan, R., Beaulne, A., Charette, C., Garand, L.,
Heilliette, S., Lapalme, E., Laroche, S., Macpherson, S. R., Morneau, J.,
and Zadra, A.: Implementation of Deterministic Weather Forecasting Systems
based on Ensemble-Variational Data Assimilation at Environment Canada. Part
I: The Global System, Mon. Weather Rev., 143, 2532–2559,
https://doi.org/10.1175/MWR-D-14-00354.1, 2015.
Bush, E. and Lemmen, D. S. (Eds.): Canada's Changing Climate Report,
Government of Canada, Ottawa, Canada, 444 pp., 2019.
Bush, E., Gillett, N., Watson, E., Fyfe, J., Vogel, F., and Swart, N.:
Understanding Observed Global Climate Change, Chapter 2 in: Canada's Changing
Climate Report, edited by: Bush, E. and Lemmen, D. S., Government of Canada,
Ottawa, Canada, 24–72, 2019.
Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B.,
Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillett, N. P., Houghton, R.
A., and Marland, G.: Contributions to accelerating atmospheric CO2
growth from economic activity, carbon intensity, and efficiency of natural
sinks, P. Natl. Acad. Sci. USA, 104, 18866–18870,
https://doi.org/10.1073/pnas.0702737104, 2007.
Caron, J.-F., Milewski, T., Buehner, M., Fillion, L., Reszka, M.,
Macpherson, S., and St-James, J.: Implementation of deterministic weather
forecasting systems based on ensemble–variational data assimilation at
Environment Canada. Part II: The regional system, Mon. Weather Rev., 143,
2560–2580, https://doi.org/10.1175/MWR-D-14-00353.1, 2015.
Chan, D., Yuen, C. W., Higuchi, K., Shashkov, A., Liu, J., Chen, J., and
Worthy, D.: On the CO2 exchange between the atmosphere and the
biosphere: the role of synoptic and mesoscale processes, Tellus B, 56, 194–212,
https://doi.org/10.3402/tellusb.v56i3.16424, 2004.
Chevallier, F., Feng, L., Bösch, H., Palmer, P. I., and Rayner, P. J.: On the
impact of transport model errors for the estimation of CO2 surface
fluxes from GOSAT observations, Geophys. Res. Lett., 37, L21803,
https://doi.org/10.1029/2010GL044652, 2010.
Chevallier, F., Palmer, P. I., Feng, L., Boesch, H., O'Dell, C. W., and
Bousquet, P.: Toward robust and consistent regional CO2 flux estimates
from in situ and spaceborne measurements of atmospheric CO2, Geophys.
Res. Lett., 41, 1065–1070, https://doi.org/10.1002/2013GL058772, 2014.
Ciais, P., Rayner, P., Chevallier, F., Bousquet, P., Logan, M., Peylin, P.,
and Ramonet, M.: Atmospheric inversions for estimating CO2 fluxes:
methods and perspectives, Climatic Change, 103, 69–92,
https://doi.org/10.1007/s10584-010-9909-3, 2010.
Côté, J., Gravel, S., Méthot, A., Patoine, A., Roch, M., and
Staniforth, A.: The operational CMC–MRB Global Environmental Multiscale
(GEM) model. Part I: Design considerations and formulation, Mon. Weather
Rev., 126, 1373–1395, https://doi.org/10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2, 1998a.
Côté, J., Desmarais, J.-G., Gravel, S., Méthot, A., Patoine, A.,
Roch, M., and Staniforth, A.: The Operational CMC/MRB Global Environment
Multiscale (GEM) Model. Part II: Results, Mon. Weather Rev., 126,
1397–1418, 1998b.
Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier,
Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F., Liu, J., Deng, F., Feng, L., McKain, K., Chatterjee, A., Miller, J. B., Stephens, B. B., Eldering, A., Crisp, D., Schimel, D., Nassar, R., O'Dell, C. W., Oda, T., Sweeney, C., Palmer, P. I., and Jones, D. B. A.: The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network, Atmos. Chem. Phys., 19, 9797–9831, https://doi.org/10.5194/acp-19-9797-2019, 2019.
Davis, K. J., Bakwin, P. S., Yi, C., Berger, B. W., Zhao, C., Teclaw, R. M.,
and Isebrands, J. G.: The annual cycles of CO2 and H2O exchange
over a northern mixed forest as observed from a very tall tower, Glob.
Change Biol., 9, 1278–1293,
https://doi.org/10.1046/j.1365-2486.2003.00672.x, 2003.
Díaz-Isaac, L. I.,
Lauvaux, T., Davis, K. J., Miles, N. L., Richardson, S. J., Jacobson, A. R.,
and Andrews, A. E.: Model-data comparison of MCI field campaign atmospheric
CO2 mole fractions, J. Geophys. Res.-Atmos. 119, 10536–10551,
https://doi.org/10.1002/2014JD021593, 2014.
Díaz-Isaac, L. I., Lauvaux, T., and Davis, K. J.: Impact of physical parameterizations and initial conditions on simulated atmospheric transport and CO2 mole fractions in the US Midwest, Atmos. Chem. Phys., 18, 14813–14835, https://doi.org/10.5194/acp-18-14813-2018, 2018.
Engelen, R. J., Denning, A. S., Gurney, K. R., and TransCom3 modelers: On
error estimation in atmospheric CO2 inversions, J. Geophys. Res., 107,
4635, https://doi.org/10.1029/2002JD002195, 2002.
Enting, I. G.: Inverse Problems in Atmospheric Constituent Transport,
Cambridge University Press, Edward Tipping, Centre for Ecology &
Hydrology, Windermere Series, Cambridge Atmospheric and Space Science Series,
ISBN 9780521018081, 2002.
Feng, S., Lauvaux, T., Newman, S., Rao, P., Ahmadov, R., Deng, A., Díaz-Isaac, L. I., Duren, R. M., Fischer, M. L., Gerbig, C., Gurney, K. R., Huang, J., Jeong, S., Li, Z., Miller, C. E., O'Keeffe, D., Patarasuk, R., Sander, S. P., Song, Y., Wong, K. W., and Yung, Y. L.: Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO2 emissions, Atmos. Chem. Phys., 16, 9019–9045, https://doi.org/10.5194/acp-16-9019-2016, 2016.
Fernández-Martínez, M., Sardans, J., Chevallier, F., Ciais, P.,
Obersteiner, M., Vicca, S., Canadell, J. G., Bastos, A., Friedlingstein, P.,
Sitch, S., Piao, S. L., Janssens, I. A., and Peñuelas, J.: Global trends
in carbon sinks and their relationships with CO2 and temperature, Nat.
Clim. Change, 9, 73–79, https://doi.org/10.1038/s41558-018-0367-7, 2018.
Fillion, L., Tanguay, M., Lapalme, E., Denis, B., Desgagné, M., Lee, V.,
Ek, N., Liu, Z., Lajoie, M., Caron, J.-F., and Pagé, C.: The Canadian
regional data assimilation and forecasting system, Weather Forecast., 25,
1645–1669, 2010.
Gaubert, B., Stephens, B. B., Basu, S., Chevallier, F., Deng, F., Kort, E. A., Patra, P. K., Peters, W., Rödenbeck, C., Saeki, T., Schimel, D., Van der Laan-Luijkx, I., Wofsy, S., and Yin, Y.: Global atmospheric CO2 inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate, Biogeosciences, 16, 117–134, https://doi.org/10.5194/bg-16-117-2019, 2019.
Geels, C., Doney, S., Dargaville, R., Brandt, J., and Christensen, J.:
Investigating the sources of synoptic variability in atmospheric CO2
measurements over the Northern Hemisphere continents: a regional model
study, Tellus B, 56, 35–50, 2004.
Geels, C., Gloor, M., Ciais, P., Bousquet, P., Peylin, P., Vermeulen, A. T., Dargaville, R., Aalto, T., Brandt, J., Christensen, J. H., Frohn, L. M., Haszpra, L., Karstens, U., Rödenbeck, C., Ramonet, M., Carboni, G., and Santaguida, R.: Comparing atmospheric transport models for future regional inversions over Europe – Part 1: mapping the atmospheric CO2 signals, Atmos. Chem. Phys., 7, 3461–3479, https://doi.org/10.5194/acp-7-3461-2007, 2007.
Gerbig, C., Lin, J. C., Wofsy, S. C., Daube, B. C., Andrews, A. E.,
Stephens, B. B., Bakwin, P. S., and Grainger, C. A.: Toward constraining
regional-scale fluxes of CO2 with atmospheric observations over a
continent: 1. Observed spatial variability from airborne platforms, J.
Geophys. Res., 108, 4756, https://doi.org/10.1029/2002JD003018, 2003.
Girard, C., Plante, A., Desgagné, M., McTaggart-Cowan, R., Côté,
J., Charron, M., Gravel, S., Lee, V., Patoine, A., Qaddouri, A., Roch, M.,
Spacek, L., Tanguay, M., Vaillancourt, P., and Zadra, A.: Staggered Vertical
Discretization of the Canadian Environmental Multiscale (GEM) model using a
coordinate of the loghydrostatic-pressure type, Mon. Weather Rev., 142,
1183–1196, 2014.
Gloor, M., Fan, S.-M., Pacala, S., Sarmiento, J., and Ramonet, M.: A
model-based evaluation of inversions of atmospheric transport, using annual
mean mixing ratios, as a tool to monitor fluxes of nonreactive trace
substances like CO2 on a continental scale, J. Geophys. Res., 104,
14245, https://doi.org/10.1029/1999JD900132, 1999.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D.,
Bousquet, P., Bruhwiler, L., Chen, Y.-H., Ciais, P., Fan, S., Fung, I. Y.,
Gloor, M., Heimann, M., Higuchi, K., John, J., Maki, T., Maksyutov, S.,
Masari, K., Peylin, P., Prather, M., Pak, B. C., Randerson, J., Sarmiento,
J., Taguchi, S., Takahashi, T., and Yuen, C.-W.: Towards robust regional
estimates of CO2 sources and sinks using atmospheric transport models,
Nature, 415, 626–630, 2002.
Hines, C. O.: Doppler-spread parameterization of gravity wave momentum
deposition in the middle atmosphere. Part 1: Basic formulation, J. Atmos.
Sol.-Terr. Phys., 59, 371–386, 1997a.
Hines, C. O.: Doppler-spread parameterization of gravity-wave momentum
deposition in the middle atmosphere. Part 2: Broad and quasi monochromatic
spectra, and implementation, J. Atmos. Sol.-Terr. Phys., 59, 387–400, 1997b.
Houtekamer, P. L., Deng, X., Mitchell, H. L., Baek, S.-J., and Gagnon, N.:
Higher Resolution in an Operational Ensemble Kalman Filter, Mon. Weather
Rev., 142, 1143–1162, https://doi.org/10.1175/MWR-D-13-00138.1, 2014.
Houweling, S., Aben, I., Breon, F.-M., Chevallier, F., Deutscher, N., Engelen, R., Gerbig, C., Griffith, D., Hungershoefer, K., Macatangay, R., Marshall, J., Notholt, J., Peters, W., and Serrar, S.: The importance of transport model uncertainties for the estimation of CO2 sources and sinks using satellite measurements, Atmos. Chem. Phys., 10, 9981–9992, https://doi.org/10.5194/acp-10-9981-2010, 2010.
Ishizawa, M., Chan, D., Worthy, D., Chan, E., Vogel, F., and Maksyutov, S.: Analysis of atmospheric CH4 in Canadian Arctic and estimation of the regional CH4 fluxes, Atmos. Chem. Phys., 19, 4637–4658, https://doi.org/10.5194/acp-19-4637-2019, 2019.
Kain, J. S.: The Kain-Fritsch convective parameterization: an update, J.
Appl. Meteorol., 43, 170–181, 2004.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/detraining
plume model and its application in convective parameterizations, J. Atmos.
Sci., 47, 2784–2802, 1990.
Kim, J. and Neish, M.:
Model output data of The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg, available at: http://crd-data-donnees-rdc.ec.gc.ca/CCMR/pub/2019_Kim_GMD_Canadian_atmospheric_transport_model_for_simulating_greenhouse_gas_evolution_on_regional_scales/,
last access: 14 January 2020.
Kjellström, E., Holmén, K., Eneroth, K., and Engardt, M.: Summertime
Siberian CO2 simulations with the regional transport model MATCH: a
feasibility study of carbon uptake calculations from EUROSIB data, Tellus,
54B, 834–849, https://doi.org/10.3402/tellusb.v54i5.16733, 2002.
Kretschmer, R., Gerbig, C., Karstens, U., Biavati, G., Vermeulen, A., Vogel, F., Hammer, S., and Totsche, K. U.: Impact of optimized mixing heights on simulated regional atmospheric transport of CO2, Atmos. Chem. Phys., 14, 7149–7172, https://doi.org/10.5194/acp-14-7149-2014, 2014.
Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, https://doi.org/10.5194/acp-5-417-2005, 2005.
Kurz, W. A., Shaw, C. H., Boisvenue, C., Stinson, G., Metsaranta, J.,
Leckie, D., Dyk, A., and Smyth, C.: Carbon in Canada's boreal forecast – A
synthesis, Environ. Rev., 21, 260–292, https://doi.org/10.1139/er-2013-0041,
2013.
Lauvaux, T. and Davis, K. J.: Planetary boundary layer errors in mesoscale
inversions of column-integrated CO2 measurements, J. Geophys.
Res.-Atmos., 119, 490–508, https://doi.org/10.1002/2013jd020175, 2014.
Lauvaux, T., Uliasz, M., Sarrat, C., Chevallier, F., Bousquet, P., Lac, C., Davis, K. J., Ciais, P., Denning, A. S., and Rayner, P. J.: Mesoscale inversion: first results from the CERES campaign with synthetic data, Atmos. Chem. Phys., 8, 3459–3471, https://doi.org/10.5194/acp-8-3459-2008, 2008.
Lauvaux, T., Schuh, A. E., Bocquet, M., Wu, L., Richardson, S., Miles, N.,
and Davis, K. J.: Network design for mesoscale inversions of CO2
sources and sinks, Tellus B, 64, 17980, https://doi.org/10.3402/tellusb.v64i0.17980,
2012a.
Lauvaux, T., Schuh, A. E., Uliasz, M., Richardson, S., Miles, N., Andrews, A. E., Sweeney, C., Diaz, L. I., Martins, D., Shepson, P. B., and Davis, K. J.: Constraining the CO2 budget of the corn belt: exploring uncertainties from the assumptions in a mesoscale inverse system, Atmos. Chem. Phys., 12, 337–354, https://doi.org/10.5194/acp-12-337-2012, 2012b.
Law, R. M., Rayner, P. J., Denning, A. S., Erickson, D., Fung, I. Y.,
Heimann, M., Piper, S. C., Romonet, M., Taguchi, S., Taylor, J. A.,
Trudinger, C. M., and Watterson, I. G.: Variations in modeled atmospheric
transport of carbon dioxide and the consequences for CO2 inversions,
Global Biogeochem. Cy., 10, 783–796, 1996.
Law, R. M., Peters, W., Rödenbeck, C., Aulagnier, C., Baker, I.,
Bergmann, D. J., Bousquet, P., Brandt, J., Bruhwiler, L., Cameron-Smith, P.
J., Christensen, J. H., Delage, F., Denning, A. S., Fan, S., Geels, C.,
Houweling, S., Imasu, R., Karstens, U., Kawa, S. R., Kleist, J., Krol, M.
C., Lin, S.-J., Lokupitiya, R., Maki, T., Maksyutov, S., Niwa, Y., Onishi,
R., Parazoo, N., Patra, P. K., Pieterse, G., Rivier, L., Satoh, M., Serrar,
S., Taguchi, S., Takigawa, M., Vautard, R., Vermeulen, A. T., and Zhu, Z.:
TransCom model simulations of hourly atmospheric CO2: Experimental
overview and diurnal cycle results for 2002, Global Biogeochem. Cy., 22,
GB3009, https://doi.org/10.1029/2007GB003050, 2008.
Le Quéré, C., Raupach, M. R., Canadell, J. G., Marland, G., Bopp,
L., Ciais, P., Conway, T. J., Doney, S. C., Feely, R. A., Foster, P.,
Friedlingstein, P., Gurney, K., Houghton, R. A., House, J. I., Huntingford,
C., Levy, P. E., Lomas, M. R., Majkut, J., Metzl, N., Ometto, J. P., Peters,
G. P., Prentice, I. C., Randerson, J. T., Running, S. W., Sarmiento, J. L.,
Schuster, U., Sitch, S., Takahashi, T., Viovy, N., van der Werf, G. R., and
Woodward, F. I.: Trends in the sources and sinks of carbon dioxide, Nat.
Geosci., 2, 831–836, https://doi.org/10.1038/NGEO689, 2009.
Li, J. and Barker, H. W.: A radiation algorithm with correlated
k-distribution. Part I: local thermal equilibrium, J. Atmos. Sci., 62,
286–309, 2005.
Li, R., Zhang, M., Chen, L., Kou, X., and Skorokhod, A.: CMAQ simulation of
atmospheric CO2 concentration in East Asia: Comparison with GOSAT
observations and ground measurements, Atmos. Environ., 160, 176–185,
2017.
Lin, J. C., Gerbig, C., Wofsy, S. C., Daube, B. C., Matross, D. M., Chow, V.
Y., Gottlieb, E., Andrews, A. E., Pathmathevan, M., and Munger, J. W.: What
have we learned from intensive atmospheric sampling field programmes of
CO2?, Tellus B, 58, 331–343, https://doi.org/10.1111/j.1600-0889.2006.00202.x,
2006.
Lin, X., Ciais, P., Bousquet, P., Ramonet, M., Yin, Y., Balkanski, Y., Cozic, A., Delmotte, M., Evangeliou, N., Indira, N. K., Locatelli, R., Peng, S., Piao, S., Saunois, M., Swathi, P. S., Wang, R., Yver-Kwok, C., Tiwari, Y. K., and Zhou, L.: Simulating CH4 and CO2 over South and East Asia using the zoomed chemistry transport model LMDz-INCA, Atmos. Chem. Phys., 18, 9475–9497, https://doi.org/10.5194/acp-18-9475-2018, 2018.
Liu, J., Fung, I., Kalnay, E., and Kang, J.-S.: CO2 transport
uncertainties from the uncertainties in meteorological fields, Geophys. Res.
Lett., 38, L12808, https://doi.org/10.1029/2011GL047213, 2011.
Liu, J., Bowman, K. W., Schimel, D. S., Parazoo, N. C., Jiang, Z., Lee, M.,
Bloom, A. A., Wunch, D., Frankenberg, C., Sun, Y., O'Dell, C. W., Gurney, K.
R., Menemenlis, D., Gierach, M., Crisp, D., and Eldering, A.: Contrasting
carbon cycle responses of the tropical continents to the 2015–2016 El
Niño, Science, 358, eaam5690, https://doi.org/10.1126/science.aam5690,
2017.
Locatelli, R., Bousquet, P., Chevallier, F., Fortems-Cheney, A., Szopa, S., Saunois, M., Agusti-Panareda, A., Bergmann, D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Gloor, E., Houweling, S., Kawa, S. R., Krol, M., Patra, P. K., Prinn, R. G., Rigby, M., Saito, R., and Wilson, C.: Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling, Atmos. Chem. Phys., 13, 9917–9937, https://doi.org/10.5194/acp-13-9917-2013, 2013.
Locatelli, R., Bousquet, P., Hourdin, F., Saunois, M., Cozic, A., Couvreux, F., Grandpeix, J.-Y., Lefebvre, M.-P., Rio, C., Bergamaschi, P., Chambers, S. D., Karstens, U., Kazan, V., van der Laan, S., Meijer, H. A. J., Moncrieff, J., Ramonet, M., Scheeren, H. A., Schlosser, C., Schmidt, M., Vermeulen, A., and Williams, A. G.: Atmospheric transport and chemistry of trace gases in LMDz5B: evaluation and implications for inverse modelling, Geosci. Model Dev., 8, 129–150, https://doi.org/10.5194/gmd-8-129-2015, 2015.
Maihot, J., Bélair, S., Benoit, R., Bilodeau, B., Delage, Y., Fillion,
L., Garand, L., Girard, C., and Tremblay, A.: Scientific description of RPN
physics library – Version 3.6, Atmospheric Environment Service Tech. Rep.,
188 pp., available at:
http://collaboration.cmc.ec.gc.ca/science/rpn/physics/physic98.pdf (last
access: 13 February 2019), 1998.
Makar, P. A., Gong, W., Milbrandt, J., Hogrefe., C., Zhang, Y., Curci, G.,
Žabkar, R., Im, U., Balzarini, A., Baró, R., Bianconi, R., Cheung,
P., Forkel, R., Gravel, S., Hirtl, M., Honzak, L., Hou, A.,
Jiménez-Guerrero, P., Langer, M., Moran, M. D., Pabla, B., Pérez, J.
L., Pirovano, G., San José, R., Tuccella, P., Werhahn, J., Zhang, J.,
and Galmarini, S.: Feedbacks between air pollution and weather, Part 1:
Effects on weather, Atmos. Environ., 115, 442–469,
https://doi.org/10.1016/j.atmosenv.2014.12.003, 2015.
Masarie, K. A., Peters, W., Jacobson, A. R., and Tans, P. P.: ObsPack: a framework for the preparation, delivery, and attribution of atmospheric greenhouse gas measurements, Earth Syst. Sci. Data, 6, 375–384, https://doi.org/10.5194/essd-6-375-2014, 2014.
McFarlane, N. A.: The effect of orographically excited gravity wave drag on
the circulation of the lower stratosphere and troposphere, J. Atmos. Sci.,
44, 1775–1800, https://doi.org/10.1175/1520-0469(1987)044<1775:TEOOEG>2.0.CO;2, 1987.
Milbrandt, J. A., Bélair, S., Faucher, M., Vallée, M., Carrera, M.
L., and Glazer, A.: The Pan-Canadian High Resolution (2.5 km) Deterministic
Prediction System, Weather Forecast., 31, 1791–1816,
https://doi.org/10.1175/WAF-D-16-0035.1, 2016.
Miller, S. M., Hayek, M. N., Andrews, A. E., Fung, I., and Liu, J.: Biases in atmospheric CO2 estimates from correlated meteorology modeling errors, Atmos. Chem. Phys., 15, 2903–2914, https://doi.org/10.5194/acp-15-2903-2015, 2015.
Moran, M. D., Ménard, S., Talbot, D., Huang, P., Makar, P. A., Gong, W.,
Landry, H., Gravel, S., Gong, S., Crevier, L.-P., Kallaur, A., and Sassi,
M.: Particulate-matter forecasting with GEM-MACH15, a new Canadian
air-quality forecast model, in: Air Pollution Modelling and Its Application
XX, edited by: Steyn, D. G. and Rao, S. T., Springer, Dordrecht, 289–292,
2010.
Neish, M., Tanguay, M., Semeniuk, K., Polavarapu, S. M., DeGrandpre, J.,
Girard, C., Qaddouri, A., Gravel, S., Chan, D., Ren, S., and GEM-MACH
development team: GEM-MACH-GHG revision 137, Zenodo,
https://doi.org/10.5281/zenodo.3246556, 2019.
Nisbet, E. and Weiss, R.: Top-down versus bottom-up, Science, 328,
1241–1243, https://doi.org/10.1126/science.1189936, 2010.
ObsPack: Cooperative Global Atmospheric Data Integration Project; Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957–2016; obspack_co2_1_GLOBALVIEWplus_v3.1_2017_10_18; NOAA Earth System Research Laboratory, Global Monitoring Division, https://doi.org/10.15138/G3T055, 2017.
Ott, L. E., Pawson, S., Collatz, G. J., Gregg, W. W., Menemenlis, D., Brix,
H., Rousseaux, C. S., Bowman, K. W., Liu, J., Eldering, A., Gunson, M. R.,
and Kawa, S. R.: Assessing the magnitude of CO2 flux uncertainty in
atmospheric CO2 records using products from NASA's Carbon Monitoring
Flux Pilot Project, J. Geophys. Res.-Atmos., 120, 734–765,
https://doi.org/10.1002/2014JD022411, 2015.
Parazoo, N. C., Denning, A. S., Kawa, S. R., Corbin, K. D., Lokupitiya, R. S., and Baker, I. T.: Mechanisms for synoptic variations of atmospheric CO2 in North America, South America and Europe, Atmos. Chem. Phys., 8, 7239–7254, https://doi.org/10.5194/acp-8-7239-2008, 2008.
Parazoo, N. C., Denning, A. S., Berry, J. A., Wolf, A., Randall, D. A.,
Kawa, S. R., Pauluis, O., and Doney, S. C.: Moist synoptic transport of
CO2 along the mid-latitude storm track, Geophys. Res. Lett., 38,
L09804, https://doi.org/10.1029/2011GL047238, 2011.
Parazoo, N. C., Denning, A. S., Kawa, S. R., Pawson, S., and Lokupitiya, R.: CO2 flux estimation errors associated with moist atmospheric processes, Atmos. Chem. Phys., 12, 6405–6416, https://doi.org/10.5194/acp-12-6405-2012, 2012.
Patra, P. K., Law, R. M., Peters, W., Rödenbeck, C., Takigawa, M.,
Aulagnier, C., Baker, I., Bergmann, D. J., Bousquet, P., Brandt, J.,
Bruhwiler, L., Cameron-Smith, P. J., Christensen, J. H., Delage, F.,
Denning, A. S., Fan, S., Geels, C., Houweling, S., Imasu, R., Karstens, U.,
Kawa, S. R., Kleist, J., Krol, M. C., Lin, S.-J., Lokupitiya, R., Maki, T.,
Maksyutov, S., Niwa, Y., Onishi, R., Parazoo, N., Pieterse, G., Rivier, L.,
Satoh, M., Serrar, S., Taguchi, S., Vautard, R., Vermeulen, A. T., and Zhu,
Z.: TransCom model simulations of hourly atmospheric CO2: Analysis of
synoptic-scale variations for the period 2002–2003, Global Biogeochem. Cy.,
22, GB4013, https://doi.org/10.1029/2007GB003081, 2008.
Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J.,
Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Pétron, G., Hirsch, A.
I.,Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P. O.,
Krol, M. C., and Tans, P. P.: An atmospheric perspective on North American
carbon dioxide exchange: CarbonTracker, P. Natl. Acad. Sci. USA, 104,
18925–18930, 2007.
Peylin, P., Houweling, S., Krol, M. C., Karstens, U., Rödenbeck, C., Geels, C., Vermeulen, A., Badawy, B., Aulagnier, C., Pregger, T., Delage, F., Pieterse, G., Ciais, P., and Heimann, M.: Importance of fossil fuel emission uncertainties over Europe for CO2 modeling: model intercomparison, Atmos. Chem. Phys., 11, 6607-6622, https://doi.org/10.5194/acp-11-6607-2011, 2011.
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013.
Pillai, D., Gerbig, C., Marshall, J., Ahmadov, R., Kretschmer, R., Koch, T., and Karstens, U.: High resolution modeling of CO2 over Europe: implications for representation errors of satellite retrievals, Atmos. Chem. Phys., 10, 83–94, https://doi.org/10.5194/acp-10-83-2010, 2010.
Pillai, D., Gerbig, C., Ahmadov, R., Rödenbeck, C., Kretschmer, R., Koch, T., Thompson, R., Neininger, B., and Lavrié, J. V.: High-resolution simulations of atmospheric CO2 over complex terrain – representing the Ochsenkopf mountain tall tower, Atmos. Chem. Phys., 11, 7445–7464, https://doi.org/10.5194/acp-11-7445-2011, 2011.
Polavarapu, S. M., Neish, M., Tanguay, M., Girard, C., de Grandpré, J., Semeniuk, K., Gravel, S., Ren, S., Roche, S., Chan, D., and Strong, K.: Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2, Atmos. Chem. Phys., 16, 12005–12038, https://doi.org/10.5194/acp-16-12005-2016, 2016.
Polavarapu, S. M., Deng, F., Byrne, B., Jones, D. B. A., and Neish, M.: A comparison of posterior atmospheric CO2 adjustments obtained from in situ and GOSAT constrained flux inversions, Atmos. Chem. Phys., 18, 12011–12044, https://doi.org/10.5194/acp-18-12011-2018, 2018.
Pugliese, S. C., Murphy, J. G., Vogel, F. R., Moran, M. D., Zhang, J., Zheng, Q., Stroud, C. A., Ren, S., Worthy, D., and Broquet, G.: High-resolution quantification of atmospheric CO2 mixing ratios in the Greater Toronto Area, Canada, Atmos. Chem. Phys., 18, 3387–3401, https://doi.org/10.5194/acp-18-3387-2018, 2018.
Qaddouri, A. and Lee, V.: The Canadian Global Environmental Multiscale model
on the Yin-Yang grid system, Q. J. Roy. Meteor. Soc., 137, 1913–1926,
https://doi.org/10.1002/qj.873, 2011.
Remaud, M., Chevallier, F., Cozic, A., Lin, X., and Bousquet, P.: On the impact of recent developments of the LMDz atmospheric general circulation model on the simulation of CO2 transport, Geosci. Model Dev., 11, 4489–4513, https://doi.org/10.5194/gmd-11-4489-2018, 2018.
Robichaud, A. and Ménard, R.: Multi-year objective analyses of warm season ground-level ozone and PM2.5 over North America using real-time observations and Canadian operational air quality models, Atmos. Chem. Phys., 14, 1769–1800, https://doi.org/10.5194/acp-14-1769-2014, 2014.
Schuh, A. E., Denning, A. S., Corbin, K. D., Baker, I. T., Uliasz, M., Parazoo, N., Andrews, A. E., and Worthy, D. E. J.: A regional high-resolution carbon flux inversion of North America for 2004, Biogeosciences, 7, 1625–1644, https://doi.org/10.5194/bg-7-1625-2010, 2010.
Schuh, A. E., Lauvaux, T., West, T. O., Denning, A. S., Davis, K. J., Miles,
N., Richardson, S., Uliasz, M., Lokupitiya, E., Cooley, D., Andrews, A., and
Ogle, S.: Evaluating atmospheric CO2 inversions at multiple scales over
a highly-inventoried agricultural landscape, Glob. Change Biol., 19,
1424–1439, https://doi.org/10.1111/gcb.12141, 2013.
Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K.,
Chevallier, F., Crowell, S., Davis, K. J., Deng, F., Denning, S., Feng, L.,
Jones, D., Liu, J., and Palmer, P. I.: Quantifying the Impact of Atmospheric
Transport Uncertainty on CO2 Surface Flux Estimates, Global Biogeochem.
Cy., 33, 2018GB006086, https://doi.org/10.1029/2018GB006086, 2019.
Sørensen, B., Kaas, E., and Korsholm, U. S.: A mass-conserving and multi-tracer efficient transport scheme in the online integrated Enviro-HIRLAM model, Geosci. Model Dev., 6, 1029–1042, https://doi.org/10.5194/gmd-6-1029-2013, 2013.
Stephens, B. B., Gurney, K. R., Tans, P. P., Sweeney, C., Peters, W.,
Bruhwiler, L., Ciais, P., Ramonet, M., Bousquet, P., Nakazawa, T., Aoki, S.,
Machida, T., Inoue, G., Vinnichenko, N., Lloyd, J., Jordan, A., Heimann, M.,
Shibistova, O., Langenfelds, R. L., Steele, L. P., Francey, R. J., and
Denning, A. S.: Weak northern and strong tropical land carbon uptake from
vertical profiles of atmospheric CO2, Science, 316, 1732–1735,
https://doi.org/10.1126/science.1137004, 2007.
Sweeney, C., Karion, A., Wolter, S., Newberger, T., Guenther, D., Higgs, J.
A., Andrews, A. E., Lang, P. M., Neff, D., Dlugokencky, E., Miller, J. B.,
Montzka, S. A., Miller, B. R., Masarie, K. A., Biraud, S. C., Novelli, P.
C., Crotwell, M., Crotwell, A. M., Thoning, K., and Tans, P. P.: Seasonal
climatology of CO2 across North America from aircraft measurements in
the NOAA/ESRL Global Greenhouse Gas Reference Network, J. Geophys. Res.-Atmos., 120, 5155–5190, https://doi.org/10.1002/2014JD022591, 2015.
Tans, P. P., Fung, I. Y., and Takahashi, T.: Observational Contrains on the
Global Atmospheric CO2 Budget, Science, 247, 1431–1438,
https://doi.org/10.1126/science.247.4949.1431, 1990.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, 2001.
Wang, J. W., Denning, A. S., Lu, L., Baker, I. T., Corbin, K. D., and Davis,
K. J.: Observations and simulations of synoptic, regional, and local
variations in atmospheric CO2, J. Geophys. Res., 112, D04108,
https://doi.org/10.1029/2006JD007410, 2007.
Wilks, D. S.: Statistical methods in the atmospheric sciences, Academic
Press, 2006.
Worthy, D. E., Platt, J. A., Kessler, R., Ernst, M., Audette, C., and Racki,
S.: An update on the Canadian GHG measurement program, in: Report of the
12th WMO/IAEA Meeting of Experts on Carbon Dioxide Concentration and Related
Tracer Measurement Techniques, Toronto, Canada, September 2003, edited by:
Worthy, D. and Huang, L., World Meteorological Organization Global
Atmosphere Watch, Report 162, 220–231, 2005.
Wu, D., Lin, J. C., Fasoli, B., Oda, T., Ye, X., Lauvaux, T., Yang, E. G., and Kort, E. A.: A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”), Geosci. Model Dev., 11, 4843–4871, https://doi.org/10.5194/gmd-11-4843-2018, 2018.
Zhang, X., Gurney, K. R., Rayner, P., Liu, Y., and Asefi-Najafabady, S.: Sensitivity of simulated CO2 concentration to regridding of global fossil fuel CO2 emissions, Geosci. Model Dev., 7, 2867–2874, https://doi.org/10.5194/gmd-7-2867-2014, 2014.
Short summary
GEM–MACH–GHG is a coupled weather and greenhouse gas transport model at Environment and Climate Change Canada. We have developed the regional version of GEM–MACH–GHG, mainly focusing on Canada and the United States, as a follow-up to the global version of GEM–MACH–GHG. The regional model is superior to our global model in terms of weather forecasts and CO2 simulation.
GEM–MACH–GHG is a coupled weather and greenhouse gas transport model at Environment and Climate...
