Articles | Volume 12, issue 2
https://doi.org/10.5194/gmd-12-849-2019
© Author(s) 2019. 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-12-849-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mechanistic representation of soil nitrogen emissions in the Community Multiscale Air Quality (CMAQ) model v 5.1
Department of Civil and Environmental Engineering, Rice
University, Houston, Texas, USA
currently at: Department of Environmental Science and Engineering, UNC-Chapel Hill, NC, USA
Jesse O. Bash
Computational Exposure Division, National Exposure
Research Laboratory, Office of Research and Development,
US Environmental
Protection Agency, RTP, NC, USA
Daniel S. Cohan
Department of Civil and Environmental Engineering, Rice
University, Houston, Texas, USA
Related authors
Ryan Schmedding, Quazi Z. Rasool, Yue Zhang, Havala O. T. Pye, Haofei Zhang, Yuzhi Chen, Jason D. Surratt, Felipe D. Lopez-Hilfiker, Joel A. Thornton, Allen H. Goldstein, and William Vizuete
Atmos. Chem. Phys., 20, 8201–8225, https://doi.org/10.5194/acp-20-8201-2020, https://doi.org/10.5194/acp-20-8201-2020, 2020
Short summary
Short summary
Accurate model prediction of aerosol concentrations is a known challenge. It is assumed in many modeling systems that aerosols are in a homogeneously mixed phase state. It has been observed that aerosols do phase separate and can form a highly viscous organic shell with an aqueous core impacting the formation processes of aerosols. This work is a model implementation to determine an aerosol's phase state using glass transition temperature and aerosol composition.
Chi-Tsan Wang, Christine Wiedinmyer, Kirsti Ashworth, Peter C. Harley, John Ortega, Quazi Z. Rasool, and William Vizuete
Atmos. Chem. Phys., 19, 13973–13987, https://doi.org/10.5194/acp-19-13973-2019, https://doi.org/10.5194/acp-19-13973-2019, 2019
Short summary
Short summary
The legal commercialization of cannabis has created a new and almost unregulated industry. Here we present the first inventory of volatile organic compound emissions from cannabis cultivation facilities (CCFs) for Colorado. When applied within a regulatory air quality model to predict regional ozone impacts, our inventory results in net ozone formation near CCFs with the largest increases in Denver County. However, our inventory is highly uncertain and we identify future critical data needs.
Quazi Z. Rasool, Rui Zhang, Benjamin Lash, Daniel S. Cohan, Ellen J. Cooter, Jesse O. Bash, and Lok N. Lamsal
Geosci. Model Dev., 9, 3177–3197, https://doi.org/10.5194/gmd-9-3177-2016, https://doi.org/10.5194/gmd-9-3177-2016, 2016
Short summary
Short summary
This study updates the representation of soil NO emissions in a regional air quality model. The implementation enhances the representation of biome types and dynamic fertilizer use. Previous modeling of soil NO in CMAQ had tended to under-estimate emissions and misrepresent their response to soil conditions and meteorology. We evaluate results against satellite observations of NO2, and quantify the impacts of the new parameterization on simulations of ozone and particulate matter.
T. Nash Skipper, Emma L. D'Ambro, Forwood C. Wiser, V. Faye McNeill, Rebecca H. Schwantes, Barron H. Henderson, Ivan R. Piletic, Colleen B. Baublitz, Jesse O. Bash, Andrew R. Whitehill, Lukas C. Valin, Asher P. Mouat, Jennifer Kaiser, Glenn M. Wolfe, Jason M. St. Clair, Thomas F. Hanisco, Alan Fried, Bryan K. Place, and Havala O. T. Pye
EGUsphere, https://doi.org/10.5194/egusphere-2024-1680, https://doi.org/10.5194/egusphere-2024-1680, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Here, we develop the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 2 to improve predictions of formaldehyde in ambient air compared to satellite-, aircraft-, and ground-based observations. With the updated chemistry representation, we then estimate the cancer risk in the contiguous US from exposure to ambient formaldehyde and estimate 40 % of this risk is controllable through reductions in anthropogenic emissions of nitrogen oxides and reactive organic carbon.
Olivia E. Clifton, Donna Schwede, Christian Hogrefe, Jesse O. Bash, Sam Bland, Philip Cheung, Mhairi Coyle, Lisa Emberson, Johannes Flemming, Erick Fredj, Stefano Galmarini, Laurens Ganzeveld, Orestis Gazetas, Ignacio Goded, Christopher D. Holmes, László Horváth, Vincent Huijnen, Qian Li, Paul A. Makar, Ivan Mammarella, Giovanni Manca, J. William Munger, Juan L. Pérez-Camanyo, Jonathan Pleim, Limei Ran, Roberto San Jose, Sam J. Silva, Ralf Staebler, Shihan Sun, Amos P. K. Tai, Eran Tas, Timo Vesala, Tamás Weidinger, Zhiyong Wu, and Leiming Zhang
Atmos. Chem. Phys., 23, 9911–9961, https://doi.org/10.5194/acp-23-9911-2023, https://doi.org/10.5194/acp-23-9911-2023, 2023
Short summary
Short summary
A primary sink of air pollutants is dry deposition. Dry deposition estimates differ across the models used to simulate atmospheric chemistry. Here, we introduce an effort to examine dry deposition schemes from atmospheric chemistry models. We provide our approach’s rationale, document the schemes, and describe datasets used to drive and evaluate the schemes. We also launch the analysis of results by evaluating against observations and identifying the processes leading to model–model differences.
Christian Hogrefe, Jesse O. Bash, Jonathan E. Pleim, Donna B. Schwede, Robert C. Gilliam, Kristen M. Foley, K. Wyat Appel, and Rohit Mathur
Atmos. Chem. Phys., 23, 8119–8147, https://doi.org/10.5194/acp-23-8119-2023, https://doi.org/10.5194/acp-23-8119-2023, 2023
Short summary
Short summary
Under the umbrella of the fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4), this study applies AQMEII4 diagnostic tools to better characterize how dry deposition removes pollutants from the atmosphere in the widely used CMAQ model. The results illustrate how these tools can provide insights into similarities and differences between the two CMAQ dry deposition options that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.
Havala O. T. Pye, Bryan K. Place, Benjamin N. Murphy, Karl M. Seltzer, Emma L. D'Ambro, Christine Allen, Ivan R. Piletic, Sara Farrell, Rebecca H. Schwantes, Matthew M. Coggon, Emily Saunders, Lu Xu, Golam Sarwar, William T. Hutzell, Kristen M. Foley, George Pouliot, Jesse Bash, and William R. Stockwell
Atmos. Chem. Phys., 23, 5043–5099, https://doi.org/10.5194/acp-23-5043-2023, https://doi.org/10.5194/acp-23-5043-2023, 2023
Short summary
Short summary
Chemical mechanisms describe how emissions from vehicles, vegetation, and other sources are chemically transformed in the atmosphere to secondary products including criteria and hazardous air pollutants. The Community Regional Atmospheric Chemistry Multiphase Mechanism integrates gas-phase radical chemistry with pathways to fine-particle mass. New species were implemented, resulting in a bottom-up representation of organic aerosol, which is required for accurate source attribution of pollutants.
John T. Walker, Xi Chen, Zhiyong Wu, Donna Schwede, Ryan Daly, Aleksandra Djurkovic, A. Christopher Oishi, Eric Edgerton, Jesse Bash, Jennifer Knoepp, Melissa Puchalski, John Iiames, and Chelcy F. Miniat
Biogeosciences, 20, 971–995, https://doi.org/10.5194/bg-20-971-2023, https://doi.org/10.5194/bg-20-971-2023, 2023
Short summary
Short summary
Better estimates of atmospheric nitrogen (N) deposition are needed to accurately assess ecosystem risk and impacts from deposition of nutrients and acidity. Using measurements and modeling, we estimate total N deposition of 6.7 kg N ha−1 yr−1 at a forest site in the southern Appalachian Mountains, a region sensitive to atmospheric deposition. Reductions in deposition of reduced forms of N (ammonia and ammonium) will be needed to meet the lowest estimates of N critical loads for the region.
Sarah E. Benish, Jesse O. Bash, Kristen M. Foley, K. Wyat Appel, Christian Hogrefe, Robert Gilliam, and George Pouliot
Atmos. Chem. Phys., 22, 12749–12767, https://doi.org/10.5194/acp-22-12749-2022, https://doi.org/10.5194/acp-22-12749-2022, 2022
Short summary
Short summary
We assess Community Multiscale Air Quality (CMAQ) model simulations of nitrogen and sulfur deposition over US climate regions to evaluate the model ability to reproduce long-term deposition trends and total deposition budgets. A measurement–model fusion technique is found to improve estimates of wet deposition. Emission controls set by the Clean Air Act successfully decreased oxidized nitrogen deposition across the US; we find increasing amounts of reduced nitrogen to the total nitrogen budget.
Stefano Galmarini, Paul Makar, Olivia E. Clifton, Christian Hogrefe, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Tim Butler, Jason Ducker, Johannes Flemming, Alma Hodzic, Christopher D. Holmes, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Juan Luis Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Sam Silva, and Ralf Wolke
Atmos. Chem. Phys., 21, 15663–15697, https://doi.org/10.5194/acp-21-15663-2021, https://doi.org/10.5194/acp-21-15663-2021, 2021
Short summary
Short summary
This technical note presents the research protocols for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This initiative has three goals: (i) to define the state of wet and dry deposition in regional models, (ii) to evaluate how dry deposition influences air concentration and flux predictions, and (iii) to identify the causes for prediction differences. The evaluation compares LULC-specific dry deposition and effective conductances and fluxes.
Benjamin N. Murphy, Christopher G. Nolte, Fahim Sidi, Jesse O. Bash, K. Wyat Appel, Carey Jang, Daiwen Kang, James Kelly, Rohit Mathur, Sergey Napelenok, George Pouliot, and Havala O. T. Pye
Geosci. Model Dev., 14, 3407–3420, https://doi.org/10.5194/gmd-14-3407-2021, https://doi.org/10.5194/gmd-14-3407-2021, 2021
Short summary
Short summary
The algorithms for applying air pollution emission rates in the Community Multiscale Air Quality (CMAQ) model have been improved to better support users and developers. The new features accommodate emissions perturbation studies that are typical in atmospheric research and output a wealth of metadata for each model run so assumptions can be verified and documented. The new approach dramatically enhances the transparency and functionality of this critical aspect of atmospheric modeling.
K. Wyat Appel, Jesse O. Bash, Kathleen M. Fahey, Kristen M. Foley, Robert C. Gilliam, Christian Hogrefe, William T. Hutzell, Daiwen Kang, Rohit Mathur, Benjamin N. Murphy, Sergey L. Napelenok, Christopher G. Nolte, Jonathan E. Pleim, George A. Pouliot, Havala O. T. Pye, Limei Ran, Shawn J. Roselle, Golam Sarwar, Donna B. Schwede, Fahim I. Sidi, Tanya L. Spero, and David C. Wong
Geosci. Model Dev., 14, 2867–2897, https://doi.org/10.5194/gmd-14-2867-2021, https://doi.org/10.5194/gmd-14-2867-2021, 2021
Short summary
Short summary
This paper details the scientific updates in the recently released CMAQ version 5.3 (and v5.3.1) and also includes operational and diagnostic evaluations of CMAQv5.3.1 against observations and the previous version of the CMAQ (v5.2.1). This work was done to improve the underlying science in CMAQ. This article is used to inform the CMAQ modeling community of the updates to the modeling system and the expected change in model performance from these updates (versus the previous model version).
Yilin Chen, Huizhong Shen, Jennifer Kaiser, Yongtao Hu, Shannon L. Capps, Shunliu Zhao, Amir Hakami, Jhih-Shyang Shih, Gertrude K. Pavur, Matthew D. Turner, Daven K. Henze, Jaroslav Resler, Athanasios Nenes, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Tianfeng Chai, Lieven Clarisse, Pierre-François Coheur, Martin Van Damme, and Armistead G. Russell
Atmos. Chem. Phys., 21, 2067–2082, https://doi.org/10.5194/acp-21-2067-2021, https://doi.org/10.5194/acp-21-2067-2021, 2021
Short summary
Short summary
Ammonia (NH3) emissions can exert adverse impacts on air quality and ecosystem well-being. NH3 emission inventories are viewed as highly uncertain. Here we optimize the NH3 emission estimates in the US using an air quality model and NH3 measurements from the IASI satellite instruments. The optimized NH3 emissions are much higher than the National Emissions Inventory estimates in April. The optimized NH3 emissions improved model performance when evaluated against independent observation.
Ryan Schmedding, Quazi Z. Rasool, Yue Zhang, Havala O. T. Pye, Haofei Zhang, Yuzhi Chen, Jason D. Surratt, Felipe D. Lopez-Hilfiker, Joel A. Thornton, Allen H. Goldstein, and William Vizuete
Atmos. Chem. Phys., 20, 8201–8225, https://doi.org/10.5194/acp-20-8201-2020, https://doi.org/10.5194/acp-20-8201-2020, 2020
Short summary
Short summary
Accurate model prediction of aerosol concentrations is a known challenge. It is assumed in many modeling systems that aerosols are in a homogeneously mixed phase state. It has been observed that aerosols do phase separate and can form a highly viscous organic shell with an aqueous core impacting the formation processes of aerosols. This work is a model implementation to determine an aerosol's phase state using glass transition temperature and aerosol composition.
Shunliu Zhao, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Jaroslav Resler, Huizhong Shen, Armistead G. Russell, Athanasios Nenes, Amanda J. Pappin, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Charles O. Stanier, and Tianfeng Chai
Geosci. Model Dev., 13, 2925–2944, https://doi.org/10.5194/gmd-13-2925-2020, https://doi.org/10.5194/gmd-13-2925-2020, 2020
Chi-Tsan Wang, Christine Wiedinmyer, Kirsti Ashworth, Peter C. Harley, John Ortega, Quazi Z. Rasool, and William Vizuete
Atmos. Chem. Phys., 19, 13973–13987, https://doi.org/10.5194/acp-19-13973-2019, https://doi.org/10.5194/acp-19-13973-2019, 2019
Short summary
Short summary
The legal commercialization of cannabis has created a new and almost unregulated industry. Here we present the first inventory of volatile organic compound emissions from cannabis cultivation facilities (CCFs) for Colorado. When applied within a regulatory air quality model to predict regional ozone impacts, our inventory results in net ozone formation near CCFs with the largest increases in Denver County. However, our inventory is highly uncertain and we identify future critical data needs.
Yuqiang Zhang, J. Jason West, Rohit Mathur, Jia Xing, Christian Hogrefe, Shawn J. Roselle, Jesse O. Bash, Jonathan E. Pleim, Chuen-Meei Gan, and David C. Wong
Atmos. Chem. Phys., 18, 15003–15016, https://doi.org/10.5194/acp-18-15003-2018, https://doi.org/10.5194/acp-18-15003-2018, 2018
Short summary
Short summary
Here we use a fine-resolution (36 km) self-consistent 21-year air quality simulation from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates to estimate annual mortality burdens from PM2.5 and O3 in the US, and also the contributions to the trends. We found that the PM2.5-related mortality burden has steadily decreased by 53 %, while the O3-related mortality burden has increased by 13 %, with larger inter-annual variabilities.
Yuqiang Zhang, Rohit Mathur, Jesse O. Bash, Christian Hogrefe, Jia Xing, and Shawn J. Roselle
Atmos. Chem. Phys., 18, 9091–9106, https://doi.org/10.5194/acp-18-9091-2018, https://doi.org/10.5194/acp-18-9091-2018, 2018
Short summary
Short summary
For this study, we evaluated the WRF–CMAQ coupled model's ability to simulate the long-term trends of wet deposition of nitrogen and sulfur from 1990 to 2010 by comparing the model results with long-term observation datasets in the US. The model generally underestimates the wet deposition of both nitrogen and sulfur but captured well the decreasing trends for the deposition. Then we estimated the deposition budget in the US, including wet deposition and dry deposition from model simulations.
K. Wyat Appel, Sergey L. Napelenok, Kristen M. Foley, Havala O. T. Pye, Christian Hogrefe, Deborah J. Luecken, Jesse O. Bash, Shawn J. Roselle, Jonathan E. Pleim, Hosein Foroutan, William T. Hutzell, George A. Pouliot, Golam Sarwar, Kathleen M. Fahey, Brett Gantt, Robert C. Gilliam, Nicholas K. Heath, Daiwen Kang, Rohit Mathur, Donna B. Schwede, Tanya L. Spero, David C. Wong, and Jeffrey O. Young
Geosci. Model Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, https://doi.org/10.5194/gmd-10-1703-2017, 2017
Short summary
Short summary
The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system. The CMAQ model is used extensively throughout the world to simulate air pollutants for many purposes, including regulatory and air quality forecasting applications. This work describes the scientific updates made to the latest version of the CMAQ modeling system (CMAQv5.1) and presents an evaluation of the new model against observations and results from the previous model version.
Quazi Z. Rasool, Rui Zhang, Benjamin Lash, Daniel S. Cohan, Ellen J. Cooter, Jesse O. Bash, and Lok N. Lamsal
Geosci. Model Dev., 9, 3177–3197, https://doi.org/10.5194/gmd-9-3177-2016, https://doi.org/10.5194/gmd-9-3177-2016, 2016
Short summary
Short summary
This study updates the representation of soil NO emissions in a regional air quality model. The implementation enhances the representation of biome types and dynamic fertilizer use. Previous modeling of soil NO in CMAQ had tended to under-estimate emissions and misrepresent their response to soil conditions and meteorology. We evaluate results against satellite observations of NO2, and quantify the impacts of the new parameterization on simulations of ozone and particulate matter.
Jesse O. Bash, Kirk R. Baker, and Melinda R. Beaver
Geosci. Model Dev., 9, 2191–2207, https://doi.org/10.5194/gmd-9-2191-2016, https://doi.org/10.5194/gmd-9-2191-2016, 2016
Short summary
Short summary
Biogenic volatile organic compounds (BVOCs) participate in reactions that can lead to secondarily formed ozone and particulate matter impacting air quality and climate and are important inputs for atmospheric models. BVOC emissions are sensitive to the vegetation species and leaf temperature. Here, we have improved the vegetation data and modeled leaf temperature of the Biogenic Emission Inventory System model. Updated algorithms improved model evaluation against observations in California.
M. W. Shephard, C. A. McLinden, K. E. Cady-Pereira, M. Luo, S. G. Moussa, A. Leithead, J. Liggio, R. M. Staebler, A. Akingunola, P. Makar, P. Lehr, J. Zhang, D. K. Henze, D. B. Millet, J. O. Bash, L. Zhu, K. C. Wells, S. L. Capps, S. Chaliyakunnel, M. Gordon, K. Hayden, J. R. Brook, M. Wolde, and S.-M. Li
Atmos. Meas. Tech., 8, 5189–5211, https://doi.org/10.5194/amt-8-5189-2015, https://doi.org/10.5194/amt-8-5189-2015, 2015
Short summary
Short summary
This study provides direct validations of Tropospheric Emission Spectrometer (TES) satellite retrieved profiles against coincident aircraft profiles of carbon monoxide, ammonia, methanol, and formic acid, all of which are of interest for air quality. The comparisons are performed over the Canadian oil sands region during an intensive field campaign in support of the Joint Canada-Alberta Implementation Plan for the Oil Sands Monitoring (JOSM). Initial model evaluations are also provided.
L. Zhu, D. Henze, J. Bash, G.-R. Jeong, K. Cady-Pereira, M. Shephard, M. Luo, F. Paulot, and S. Capps
Atmos. Chem. Phys., 15, 12823–12843, https://doi.org/10.5194/acp-15-12823-2015, https://doi.org/10.5194/acp-15-12823-2015, 2015
Short summary
Short summary
We implement new diurnal variation scheme for ammonia livestock emissions and bidirectional exchange scheme and its adjoint in the GEOS-Chem global chemical transport model. Updated diurnal variability improves modeled-to-hourly in situ measurements comparison. The ammonium soil pool in the bidirectional exchange model largely extends the ammonia lifetime in the atmosphere. Large model biases remain as livestock emissions are still underestimated.
B. Gantt, J. T. Kelly, and J. O. Bash
Geosci. Model Dev., 8, 3733–3746, https://doi.org/10.5194/gmd-8-3733-2015, https://doi.org/10.5194/gmd-8-3733-2015, 2015
X. Fu, S. X. Wang, L. M. Ran, J. E. Pleim, E. Cooter, J. O. Bash, V. Benson, and J. M. Hao
Atmos. Chem. Phys., 15, 6637–6649, https://doi.org/10.5194/acp-15-6637-2015, https://doi.org/10.5194/acp-15-6637-2015, 2015
Short summary
Short summary
In this study, we estimate, for the first time, the NH3 emission from the agricultural fertilizer application in China online using the bi-directional CMAQ model coupled to an agro-ecosystem model. Compared with previous researches, this method considers more influencing factors, such as meteorological fields, soil and the fertilizer application, and provides improved NH3 emission with higher spatial and temporal resolution.
W. Tang, D. S. Cohan, A. Pour-Biazar, L. N. Lamsal, A. T. White, X. Xiao, W. Zhou, B. H. Henderson, and B. F. Lash
Atmos. Chem. Phys., 15, 1601–1619, https://doi.org/10.5194/acp-15-1601-2015, https://doi.org/10.5194/acp-15-1601-2015, 2015
Short summary
Short summary
A joint application of multiple satellite-derived model inputs to improve Texas O3 SIP modeling is demonstrated in this study. The GOES-retrieved clouds are applied to correct the modeled photolysis rates, and the DKF inversion approach is incorporated into the CAMx-DDM model to adjust NOx emissions using OMI NO2. Using both GOES-derived photolysis rates and OMI-constrained NOx emissions together improves O3 simulations and makes O3 more sensitive to NOx emissions in the O3 non-attainment areas.
W. Zhou, D. S. Cohan, and B. H. Henderson
Atmos. Chem. Phys., 14, 2777–2788, https://doi.org/10.5194/acp-14-2777-2014, https://doi.org/10.5194/acp-14-2777-2014, 2014
W. Tang, D. S. Cohan, L. N. Lamsal, X. Xiao, and W. Zhou
Atmos. Chem. Phys., 13, 11005–11018, https://doi.org/10.5194/acp-13-11005-2013, https://doi.org/10.5194/acp-13-11005-2013, 2013
J. O. Bash, E. J. Cooter, R. L. Dennis, J. T. Walker, and J. E. Pleim
Biogeosciences, 10, 1635–1645, https://doi.org/10.5194/bg-10-1635-2013, https://doi.org/10.5194/bg-10-1635-2013, 2013
J. T. Walker, M. R. Jones, J. O. Bash, L. Myles, T. Meyers, D. Schwede, J. Herrick, E. Nemitz, and W. Robarge
Biogeosciences, 10, 981–998, https://doi.org/10.5194/bg-10-981-2013, https://doi.org/10.5194/bg-10-981-2013, 2013
Related subject area
Atmospheric sciences
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
RoadSurf 1.1: open-source road weather model library
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
A general comprehensive evaluation method for cross-scale precipitation forecasts
Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Balloon drift estimation and improved position estimates for radiosondes
Emission ensemble approach to improve the development of multi-scale emission inventories
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
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
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote sensing observations
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
TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
Short summary
Short summary
Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
Short summary
Short summary
Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
Short summary
Short summary
Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
Short summary
Short summary
RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
Short summary
Short summary
The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
Short summary
Short summary
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.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
Short summary
Short summary
The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
Short summary
Short summary
By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
Short summary
Short summary
We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
Short summary
Short summary
Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
Short summary
Short summary
The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
Short summary
Short summary
The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
Short summary
Short summary
There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
Short summary
Short summary
This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and 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.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
Short summary
Short summary
This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
Short summary
Short summary
We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
Short summary
Short summary
This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
Short summary
Short summary
Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
Short summary
Short summary
In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
Short summary
Short summary
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 for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
Short summary
Short summary
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
Short summary
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
Short summary
Short summary
HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
Short summary
Short summary
This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
Short summary
An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
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
Short summary
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.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
Short summary
Short summary
Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate of the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kelly M. Núñez Ocasio and Zachary L. Moon
EGUsphere, https://doi.org/10.5194/egusphere-2024-259, https://doi.org/10.5194/egusphere-2024-259, 2024
Short summary
Short summary
TAMS is an open-source mesoscale convective system tracking and classifying Python-based package that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Cited articles
Appel, K. W., Napelenok, S. L., Foley, K. M., Pye, H. O. T., Hogrefe, C.,
Luecken, D. J., Bash, J. O., Roselle, S. J., Pleim, J. E., Foroutan, H.,
Hutzell, W. T., Pouliot, G. A., Sarwar, G., Fahey, K. M., Gantt, B., Gilliam,
R. C., Heath, N. K., Kang, D., Mathur, R., Schwede, D. B., Spero, T. L.,
Wong, D. C., and Young, J. O.: Description and evaluation of the Community
Multiscale Air Quality (CMAQ) modeling system version 5.1, Geosci. Model
Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, 2017.
Barton, L., McLay, C., Schipper, L., and Smith, C.: Annual denitrification
rates in agricultural and forest soils: a review, Soil Res., 37, 1073–1094,
1999.
Bash, J. O., Baker, K. R., and Beaver, M. R.: Evaluation of improved land use
and canopy representation in BEIS v3.61 with biogenic VOC measurements in
California, Geosci. Model Dev., 9, 2191–2207,
https://doi.org/10.5194/gmd-9-2191-2016, 2016.
Bash, J. O., Cooter, E. J., Dennis, R. L., Walker, J. T., and Pleim, J. E.:
Evaluation of a regional air-quality model with bidirectional NH3
exchange coupled to an agroecosystem model, Biogeosciences, 10, 1635–1645,
https://doi.org/10.5194/bg-10-1635-2013, 2013.
Bertram, T. H., Cohen, R. C., Thorn III, W. J., and Chu, P. M.: Consistency
of ozone and nitrogen oxides standards at tropospherically relevant mixing
ratios, J. Air Waste Manage., 55, 1473–1479, 2005.
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B., Fiore, A.
M., Li, Q., Liu, H., Mickley, L. J., and Schultz, M.: Global modeling of
tropospheric chemistry with assimilated meteorology: Model description and
evaluation, J. Geophys. Res., 106, 23073–23096, 2001.
Bucsela, E. J., Krotkov, N. A., Celarier, E. A., Lamsal, L. N., Swartz, W.
H., Bhartia, P. K., Boersma, K. F., Veefkind, J. P., Gleason, J. F., and
Pickering, K. E.: A new stratospheric and tropospheric NO2 retrieval
algorithm for nadir-viewing satellite instruments: applications to OMI,
Atmos. Meas. Tech., 6, 2607–2626, https://doi.org/10.5194/amt-6-2607-2013,
2013.
Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R., and
Zechmeister-Boltenstern, S.: Nitrous oxide emissions from soils: how well do
we understand the processes and their controls?, Philos. T. R. Soc. B, 368,
20130122, https://doi.org/10.1098/rstb.2013.0122, 2013.
Cameron, K., Di, H. J., and Moir, J.: Nitrogen losses from the soil/plant
system: a review, Ann. Appl. Biol., 162, 145–173, 2013.
Cao, P., Lu, C., and Yu, Z.: Agricultural nitrogen fertilizer uses in the continental
US during 1850–2015: a set of gridded time-series data, PANGAEA,
https://doi.org/10.1594/PANGAEA.883585, 2017.
Conrad, R.: Microbiological and biochemical background of production and
consumption of NO and N2O in soil, in: Trace gas exchange in forest
ecosystems, Springer, 2002.
Cooper, O. R., Parrish, D. D., Ziemke, J., Balashov, N. V., Cupeiro, M.,
Galbally, I. E., Gilge, S., Horowitz, L., Jensen, N. R., Lamarque, J. F., and
Naik, V.: Global distribution and trends of tropospheric ozone: An
observation-based review, Elementa, 2, https://doi.org/10.12952/journal.elementa.000029,
2014.
Cooter, E. J., Bash, J. O., Benson, V., and Ran, L.: Linking agricultural
crop management and air quality models for regional to national-scale
nitrogen assessments, Biogeosciences, 9, 4023–4035,
https://doi.org/10.5194/bg-9-4023-2012, 2012.
Davidson, E. A. and Verchot, L. V.: Testing the Hole-in-the-Pipe Model of
nitric and nitrous oxide emissions from soils using the TRAGNET Database,
Global Biogeochem. Cy., 14, 1035–1043, 2000.
Davidson, E. and Kingerlee, W.: A global inventory of nitric oxide emissions
from soils, Nutr. Cycl. Agroecosys., 48, 37–50,
https://doi.org/10.1023/A:1009738715891, 1997.
Davidson, E. A., David, M. B., Galloway, J. N., Goodale, C. L., Haeuber, R.,
Harrison, J. A., Howarth, R.W., Jaynes, D. B., Lowrance, R. R., Nolan, B. T.,
Peel, J. L., Pinder, R. W., Porter, E., Snyder, C. S., Townsend, A. R.,
and Ward, M. H.: Excess nitrogen in the U.S. environment: trends, risks,
and solutions, Issues in Ecology, Report Number 15, Ecological Society of America, 1–16, 2012.
Davidson, E.: Pulses of nitric oxide and nitrous oxide flux following
wetting of dry soil: an assessment of probable sources and importance
relative to annual fluxes, Ecol. Bull., 42, 149–155, 1992.
Del Grosso, S., Parton, W., Mosier, A., Ojima, D., Kulmala, A., and
Phongpan, S.: General model for N2O and N2 gas emissions from
soils due to dentrification, Global Biogeochem. Cy., 14, 1045–1060, 2000.
Evans, S. E. and Burke, I. C.: Carbon and nitrogen decoupling under an
11-year drought in the shortgrass steppe, Ecosystems, 16, 20–33, 2013.
Firestone, M. K. and Davidson, E. A.: Microbiological basis of NO and
N2O
production and consumption in soil, Life Sci. R., 47, 7–21, 1989.
Frink, C. R., Waggoner, P. E., and Ausubel, J. H.: Nitrogen fertilizer:
retrospect and prospect, P. Natl. Acad. Sci. USA, 96, 1175–1180, 1999.
Gaillard, R. K., Jones, C. D., Ingraham, P., Collier, S., Izaurralde, R. C.,
Jokela, W., Osterholz, W., Salas, W., Vadas, P., and Ruark, M.:
Underestimation of N2O emissions in a comparison of the DayCent,
DNDC, and EPIC models, Ecol. Appl., 28, 694–708, 2018.
Geddes, J. A., Heald, C. L., Silva, S. J., and Martin, R. V.: Land cover
change impacts on atmospheric chemistry: simulating projected large-scale
tree mortality in the United States, Atmos. Chem. Phys., 16, 2323–2340,
https://doi.org/10.5194/acp-16-2323-2016, 2016.
Gödde, M. and Conrad, R.: Influence of soil properties on the turnover
of nitric oxide and nitrous oxide by nitrification and denitrification at
constant temperature and moisture, Biol. Fert. Soils, 32, 120–128, 2000.
Gollehon, N. R., Caswell, M., Ribaudo, M., Kellogg, R. L., Lander, C., and
Letson, D.: Confined Animal Production and Manure Nutrients. Washington, DC,
U.S. Department of Agriculture, Economic Research Service, Agriculture Information Bulletin 771,
available at:
https://ageconsearch.umn.edu/record/33763 (last access: 22 February 2019), 2001.
Griffis, T. J., Chen, Z., Baker, J. M., Wood, J. D., Millet, D. B., Lee, X.,
Venterea, R. T., and Turner, P. A.: Nitrous oxide emissions are enhanced in a
warmer and wetter world, P. Natl. Acad. Sci. USA, 114, 12081–12085, 2017.
Heil, J., Vereecken, H., and Brüggemann, N.: A review of chemical
reactions of nitrification intermediates and their role in nitrogen cycling
and nitrogen trace gas formation in soil, Eur. J. Soil Sci., 67, 23–39,
2016.
Hickman, J. E., Wu, S., Mickley, L. J., and Lerdau, M. T.: Kudzu (Pueraria
montana) invasion doubles emissions of nitric oxide and increases ozone
pollution, P. Natl. Acad. Sci. USA, 107, 10115–10119, 2010.
Holmes, N. S.: A review of particle formation events and growth in the
atmosphere in the various environments and discussion of mechanistic
implications, Atmos. Environ., 41, 2183–2201, 2007.
Homyak, P. M. and Sickman, J. O.: Influence of soil moisture on the
seasonality of nitric oxide emissions from chaparral soils, Sierra Nevada,
California, USA, J. Arid Environ., 103, 46–52, 2014.
Homyak, P. M., Blankinship, J. C., Marchus, K., Lucero, D. M., Sickman, J.
O., and Schimel, J. P.: Aridity and plant uptake interact to make dryland
soils hotspots for nitric oxide (NO) emissions, P. Natl. Acad. Sci. USA, 113,
E2608–E2616, 2016.
Houlton, B., Morford, S., and Dahlgren, R.: Convergent evidence for
widespread rock nitrogen sources in Earth's surface environment, Science,
360, 58–62, 2018.
Hu, H. W., Chen, D., and He, J. Z.: Microbial regulation of terrestrial nitrous oxide formation:
Understanding the biological pathways for prediction of emission rates,
FEMS Microbiol. Rev., 39, 729–749, 2015.
Hudman, R. C., Moore, N. E., Mebust, A. K., Martin, R. V., Russell, A. R.,
Valin, L. C., and Cohen, R. C.: Steps towards a mechanistic model of global
soil nitric oxide emissions: implementation and space based-constraints,
Atmos. Chem. Phys., 12, 7779–7795, https://doi.org/10.5194/acp-12-7779-2012,
2012.
Hudman, R. C., Russell, A. R., Valin, L. C., and Cohen, R. C.: Interannual
variability in soil nitric oxide emissions over the United States as viewed
from space, Atmos. Chem. Phys., 10, 9943–9952,
https://doi.org/10.5194/acp-10-9943-2010, 2010.
Hutchinson, G. and Brams, E.: NO versus N2O emissions from an -amended
Bermuda grass pasture, J. Geophys. Res.-Atmos., 97, 9889–9896, 1992.
IPCC: Climate Change 2013: The Physical Science Basis, Working Group I
Contribution to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M.,
Allen, S. K., Boschung, J., Nauels, A. Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, UK, 2013.
Izaurralde, R. C., McGill, W. B., Williams, J. R., Jones, C. D., Link, R.
P., Manowitz, D. H., Schwab, D. E., Zhang, X., Robertson, G. P., and Millar,
N.: Simulating microbial denitrification with EPIC: Model description and
evaluation, Ecol. Model., 359, 349–362, 2017.
Izaurralde, R. C., McGill, W. B., and Williams, J.: Development and
application of the EPIC model for carbon cycle, greenhouse gas mitigation,
and biofuel studies, in: Managing Agricultural Greenhouse Gases, Elsevier,
2012.
Izaurralde, R., Williams, J. R., Mcgill, W. B., Rosenberg, N. J., and Jakas,
M. Q.: Simulating soil C dynamics with EPIC: Model description and testing
against long-term data, Ecol. Model., 192, 362–384, 2006.
Jaeglé, L., Martin, R. V., Chance, K., Steinberger, L., Kurosu, T. P.,
Jacob, D. J., Modi, A. I., Yoboué, V., Sigha-Nkamdjou, L., and
Galy-Lacaux, C.: Satellite mapping of rain-induced nitric oxide emissions
from soils, J. Geophys. Res.-Atmos., 109, D21310, https://doi.org/10.1029/2004JD004787,
2004.
Jaeglé, L., Steinberger, L., Martin, R. V., and Chance, K.: Global
partitioning of NOx sources using satellite observations: Relative
roles of fossil fuel combustion, biomass burning and soil emissions, Faraday
Discuss., 130, 407–423, 2005.
Jiang, Z., McDonald, B. C., Worden, H., Worden, J. R., Miyazaki, K., Qu, Z., Henze, D. K.,
Jones, D. B., Arellano, A. F., and Fischer, E. V.: Unexpected slowdown of
US pollutant emission reduction in the past decade,
P. Natl. Acad. Sci. USA, 115, 201801191, https://doi.org/10.1073/pnas.1801191115, 2018.
Kampa, M. and Castanas, E.: Human health effects of air pollution,
Environ. Pollut., 151, 362–367, 2008.
Kellogg, R. L., Lander, C. H., Moffitt, D. C., and Gollehon, N.: Manure
nutrients relative to the capacity of cropland and pastureland to assimilate
nutrients: Spatial and temporal trends for the United States, Proceedings of
the Water Environment Federation, 2000, 18–157, 2000.
Kesik, M., Blagodatsky, S., Papen, H., and Butterbach-Bahl, K.: Effect of
pH, temperature and substrate on N2O, NO and CO2 production
by Alcaligenes faecalis p, J. Appl. Microbiol., 101, 655–667, 2006.
Kim, H. C., Lee, P., Judd, L., Pan, L., and Lefer, B.: OMI NO2 column
densities over North American urban cities: the effect of satellite footprint
resolution, Geosci. Model Dev., 9, 1111–1123,
https://doi.org/10.5194/gmd-9-1111-2016, 2016.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of
the Köppen-Geiger climate classification updated, Meteorol. Z., 15,
259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Kwok, R., Napelenok, S., and Baker, K.: Implementation and evaluation of
PM2.5 source contribution analysis in a photochemical model, Atmos.
Environ., 80, 398–407, 2013.
Lamsal, L. N., Krotkov, N. A., Celarier, E. A., Swartz, W. H., Pickering, K.
E., Bucsela, E. J., Gleason, J. F., Martin, R. V., Philip, S., Irie, H.,
Cede, A., Herman, J., Weinheimer, A., Szykman, J. J., and Knepp, T. N.:
Evaluation of OMI operational standard NO2 column retrievals using in
situ and surface-based NO2 observations, Atmos. Chem. Phys., 14,
11587–11609, https://doi.org/10.5194/acp-14-11587-2014, 2014.
Laville, P., Lehuger, S., Loubet, B., Chaumartin, F., and Cellier, P.:
Effect of management, climate and soil conditions on N2O and NO
emissions from an arable crop rotation using high temporal resolution
measurements, Agr. Forest Meteorol., 151, 228–240, 2011.
Leitner, S., Homyak, P. M., Blankinship, J. C., Eberwein, J., Jenerette, G.
D., Zechmeister-Boltenstern, S., and Schimel, J. P.: Linking NO and
N2O emission pulses with the mobilization of mineral and organic N
upon rewetting dry soils, Soil Biol. Biochem., 115, 461–466, 2017.
Li, Y., Schichtel, B. A., Walker, J. T., Schwede, D. B., Chen, X., Lehmann,
C. M., Puchalski, M. A., Gay, D. A., and Collett, J. L.: Increasing
importance of deposition of reduced nitrogen in the United States, P. Natl.
Acad. Sci. USA, 113, 5874–5879, 2016.
Liu, B., Mørkved, P. T., Frostegård, Å., and Bakken, L. R.:
Denitrification gene pools, transcription and kinetics of NO, N2O and
N2 production as affected by soil pH, FEMS Microbiol. Ecol., 72,
407–417, 2010.
Liu, X., Ju, X., Zhang, Y., He, C., Kopsch, J., and Fusuo, Z.: Nitrogen
deposition in agroecosystems in the Beijing area, Agriculture, Ecosystems
& Environment, 113, 370–377, 2006.
Lu, C. and Tian, H.: Global nitrogen and phosphorus fertilizer use for
agriculture production in the past half century: shifted hot spots and
nutrient imbalance, Earth Syst. Sci. Data, 9, 181–192,
https://doi.org/10.5194/essd-9-181-2017, 2017.
Ludwig, J., Meixner, F., Vogel, B., and Förstner, J.: Soil-air exchange
of nitric oxide: an overview of processes, environmental factors, and
modeling studies, Biogeochemistry, 52, 225–257,
https://doi.org/10.1023/A:1006424330555, 2001.
Machefert, S. E., Dise, N. B., Goulding, K. W. T., and Whitehead, P. G.:
Nitrous oxide emission from a range of land uses across Europe, Hydrol. Earth
Syst. Sci., 6, 325–338, https://doi.org/10.5194/hess-6-325-2002, 2002.
Maljanen, M., Yli-Pirilä, P., Hytönen, J., Joutsensaari, J., and
Martikainen, P. J.: Acidic northern soils as sources of atmospheric nitrous
acid (HONO), Soil Biol. Biochem., 67, 94–97, 2013.
Malm, W. C., Sisler, J. F., Huffman, D., Eldred, R. A., and Cahill, T. A.:
Spatial and seasonal trends in particle concentration and optical extinction
in the United States, J. Geophys. Res.-Atmos., 99, 1347–1370, 1994.
Mamtimin, B., Meixner, F. X., Behrendt, T., Badawy, M., and Wagner, T.: The
contribution of soil biogenic NO and HONO emissions from a managed hyperarid
ecosystem to the regional NOx emissions during growing
season, Atmos. Chem. Phys., 16, 10175–10194,
https://doi.org/10.5194/acp-16-10175-2016, 2016.
Manzoni, S. and Porporato, A.: Soil carbon and nitrogen mineralization:
theory and models across scales, Soil Biol. Biochem., 41, 1355–1379, 2009.
Martin, R. E., Scholes, M., Mosier, A., Ojima, D., Holland, E., and Parton,
W.: Controls on annual emissions of nitric oxide from soils of the Colorado
shortgrass steppe, Global Biogeochem. Cy., 12, 81–91, 1998.
Medinets, S., Skiba, U., Rennenberg, H., and Butterbach-Bahl, K.: A review
of soil NO transformation: Associated processes and possible physiological
significance on organisms, Soil Biol. Biochem., 80, 92–117, 2015.
Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D. E.:
Three-porosity model for predicting the gas diffusion coefficient in
undisturbed soil, Soil Sci. Soc. Am. J., 68, 750–759, 2004.
Montes, F., Meinen, R., Dell, C., Rotz, A., Hristov, A., Oh, J., Waghorn,
G., Gerber, P., Henderson, B., and Makkar, H.: SPECIAL TOPICS – mitigation
of methane and nitrous oxide emissions from animal operations: II. A review
of manure management mitigation options, J. Anim. Sci., 91, 5070–5094, 2013.
Necpálová, M., Anex, R. P., Fienen, M. N., Del Grosso, S. J.,
Castellano, M. J., Sawyer, J. E., Iqbal, J., Pantoja, J. L., and Barker, D.
W.: Understanding the DayCent model, Environ. Modell. Softw., 66, 110–130,
2015.
Neira, M.: The 2014 WHO conference on health and climate, SciELO Public Health,
https://doi.org/10.2471/BLT.14.14389125177064, 2014.
Nemitz, E., Milford, C., and Sutton, M. A.: A two–layer canopy compensation point
model for describing bi-directional biosphere–atmosphere exchange of ammonia,
Q. J. Roy. Meteor. Soc., 127, 815–833, 2001.
Oikawa, P., Ge, C., Wang, J., Eberwein, J., Liang, L., Allsman, L., Grantz,
D., and Jenerette, G.: Unusually high soil nitrogen oxide emissions influence
air quality in a high-temperature agricultural region, Nat. Commun., 6, 8753,
https://doi.org/10.1038/ncomms9753,
2015.
Oswald, R., Behrendt, T., Ermel, M., Wu, D., Su, H., Cheng, Y., Breuninger, C., Moravek, A.,
Mougin, E., Delon, C., Loubet, B., Pommerening-Röser, A., Sörgel, M.,
Pöschl, U., Hoffmann, T., Andreae, M. O., Meixner, F. X., and Trebs, I.:
HONO emissions from soil bacteria as a major source of atmospheric reactive nitrogen,
Science, 341, 1233–1235, https://doi.org/10.1126/science.1242266,2013.
Otte, T. L. and Pleim, J. E.: The Meteorology-Chemistry Interface Processor
(MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1, Geosci.
Model Dev., 3, 243–256, https://doi.org/10.5194/gmd-3-243-2010, 2010.
Parrish, D., Williams, E., Fahey, D., Liu, S., and Fehsenfeld, F.:
Measurement of nitrogen oxide fluxes from soils: Intercomparison of enclosure
and gradient measurement techniques, J. Geophys. Res.-Atmos., 92, 2165–2171,
1987.
Parton, W. J., Holland, E. A., Del Grosso, S. J., Hartman, M. D., Martin, R.
E., Mosier, A. R., Ojima, D. S., and Schimel, D. S.: Generalized model for
NOx and N2O emissions from soils, J. Geophys. Res.-Atmos., 106,
17403–17419, https://doi.org/10.1029/2001JD900101, 2001.
Parton, W. J., Ojima, D. S., Cole, C. V., and Schimel, D. S.: A general
model for soil organic matter dynamics: sensitivity to litter chemistry,
texture and management, SSSA Spec. Publ., 1994, 147–167, 1994.
Pilegaard, K.: Processes regulating nitric oxide emissions from soils,
Philos. T. Roy. Soc. B, 368, 1621, https://doi.org/10.1098/rstb.2013.0126, 2013.
Pleim, J. E. and Xiu, A.: Development of a land surface model. Part II: Data
assimilation, J. Appl. Meteorol., 42, 1811–1822, 2003.
Pleim, J. E., Bash, J. O., Walker, J. T., and Cooter, E. J.: Development and evaluation of an
ammonia bidirectional flux parameterization for air quality models,
J. Geophys. Res., 118, 3794–3806, https://doi.org/10.1002/jgrd.50262, 2013.
Pope, C. A., Burnett, R. T., Krewski, D., Jerrett, M., Shi, Y., Calle, E.
E., and Thun, M. J.: Cardiovascular mortality and exposure to airborne fine
particulate matter and cigarette smoke: shape of the exposure-response
relationship, Circulation, 120, 941–948, 2009.
Potter, P., Navin, R., Elena, M. B., and Simon D. D.: Characterizing the
spatial patterns of global fertilizer application and manure production,
Earth Interact., 14, 1–22, 2010.
Pouliot, G. and Pierce, T.: Integration of the Model of Emissionsof Gases and Aerosols from Nature (MEGAN) into the CMAQModeling System,
18th International Emission Inventory Conference, Baltimore, Maryland, 14–17 April 2009.
Pusede, S. E. and Cohen, R. C.: On the observed response of ozone to NOx and VOC reactivity reductions in San Joaquin Valley California 1995–present, Atmos.
Chem. Phys., 12, 8323–8339, https://doi.org/10.5194/acp-12-8323-2012, 2012.
Rasool, Q. Z., Zhang, R., Lash, B., Cohan, D. S., Cooter, E. J., Bash, J. O.,
and Lamsal, L. N.: Enhanced representation of soil NO emissions in the
Community Multiscale Air Quality (CMAQ) model version 5.0.2, Geosci. Model
Dev., 9, 3177–3197, https://doi.org/10.5194/gmd-9-3177-2016, 2016.
Rasool, Q. Z., Bash, J. O., and Cohan, D. S.: Mechanistic representation of
soil nitrogen emissions in CMAQ version 5.1, ORNL DAAC, Oak Ridge, Tennessee,
USA, https://doi.org/10.3334/ORNLDAAC/1661, 2018.
Redding, M., Shorten, P., Lewis, R., Pratt, C., Paungfoo-Lonhienne, C., and
Hill, J.: Soil N availability, rather than N deposition, controls indirect
N2O emissions, Soil Biol. Biochem., 95, 288–298, 2016.
Ribaudo, M., Key, N., and Sneeringer, S.: The potential role for a nitrogen
compliance policy in mitigating Gulf hypoxia, Appl. Econ. Perspect. P., 39,
458–478, 2016.
Ribaudo, M., Livingston, M., and Williamson, J.: Nitrogen management on us
corn acres, 2001-10, United States Department of Agriculture, Economic
Research Service, 2012.
Ribaudo, M., Gollehon, N., and Agapoff, J.: Land application of manure by
animal feeding operations: Is more land needed?, J. Soil Water Conserv., 58,
30–38, 2003.
Robertson, G. P. and Groffman, P.: Nitrogen transformations, in: Soil
Microbiology, Ecology and Biochemistry, 3rd Edn., Elsevier, 2007.
Romer, P. S., Duffey, K. C., Wooldridge, P. J., Edgerton, E., Baumann, K.,
Feiner, P. A., Miller, D. O., Brune, W. H., Koss, A. R., de Gouw, J. A.,
Misztal, P. K., Goldstein, A. H., and Cohen, R. C.:
Effects of temperature-dependent NOx
emissions on continental ozone production, Atmos. Chem. Phys., 18, 2601–2614, https://doi.org/10.5194/acp-18-2601-2018, 2018.
Schimel, J. P. and Weintraub, M. N.: The implications of exoenzyme activity
on microbial carbon and nitrogen limitation in soil: a theoretical model,
Soil Biol. Biochem., 35, 549–563, 2003.
Schindlbacher, A., Zechmeister-Boltenstern, S., and Butterbach-Bahl, K.:
Effects of soil moisture and temperature on NO, NO2, and N2O
emissions from European forest soils, J. Geophys. Res.-Atmos., 109, 17302–17309,
2004.
Scholes, M., Martin, R., Scholes, R., Parsons, D., and Winstead, E.: NO and
N2O emissions from savanna soils following the first simulated rains of
the season, Nutr. Cycl. Agroecosys., 48, 115– 122,
https://doi.org/10.1023/A:1009781420199, 1997.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics:
from air pollution to climate change, John Wiley & Sons, 2012.
Simon, H., Reff, A., Wells, B., Xing, J., and Frank, N.: Ozone trends across
the United States over a period of decreasing NOx and VOC emissions,
Environ. Sci. Technol., 49, 186–195, 2014.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M.,
Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A description of the
advanced research WRF version 3, NCAR Tech. Note, NCAR/TN-475+STR, 8 pp.,
Natl. Cent. for Atmos. Res., Boulder, Colo., available at:
http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf (last access: 22 February 2019), 2008.
Stehfest, E. and Bouwman, L.: N2O and NO emission from agricultural
fields and soils under natural vegetation: summarizing available measurement
data and modeling of global annual emissions, Nutr. Cycl. Agroecosys., 74,
207–228, https://doi.org/10.1007/s10705-006-9000-7, 2006.
Steinkamp, J. and Lawrence, M. G.: Improvement and evaluation of simulated
global biogenic soil NO emissions in an AC-GCM, Atmos. Chem. Phys., 11,
6063–6082, https://doi.org/10.5194/acp-11-6063-2011, 2011.
Strode, S. A., Rodriguez, J. M., Logan, J. A., Cooper, O. R., Witte, J. C.,
Lamsal, L. N., Damon, M., Van Aartsen, B., Steenrod, S. D., and Strahan, S.
E.: Trends and variability in surface ozone over the United States, J.
Geophys. Res.-Atmos., 120, 9020–9042, https://doi.org/10.1002/2014JD022784, 2015.
Su, H., Cheng, Y., Oswald, R., Behrendt, T., Trebs, I., Meixner, F. X.,
Andreae, M. O., Cheng, P., Zhang, Y., and Pöschl, U.: Soil nitrite as a
source of atmospheric HONO and OH radicals, Science, 333, 1616–1618, 2011.
Tilman, D., Fargione, J., Wolff, B., D'antonio, C., Dobson, A., Howarth, R.,
Schindler, D., Schlesinger, W. H., Simberloff, D., and Swackhamer, D.:
Forecasting agriculturally driven global environmental change, Science, 292,
281–284, 2001.
Townsend, A. R., Howarth, R. W., Bazzaz, F. A., Booth, M. S., Cleveland, C.
C., Collinge, S. K., Dobson, A. P., Epstein, P. R., Holland, E. A., and
Keeney, D. R.: Human health effects of a changing global nitrogen cycle,
Front. Ecol. Environ., 1, 240–246, 2003.
Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu, L., Yu, K., Miller, C. C., Yantosca, R. M., Sulprizio, M. P., Thompson, A. M., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Cohen, R. C., Laughner, J. L., Dibb, J. E., Hall, S. R., Ullmann, K., Wolfe, G. M., Pollack, I. B., Peischl, J., Neuman, J. A., and Zhou, X.: Why do models overestimate surface ozone in the Southeast United States?, Atmos. Chem. Phys., 16, 13561–13577, https://doi.org/10.5194/acp-16-13561-2016, 2016.
Venterea, R. T. and Rolston, D. E.: Mechanisms and kinetics of nitric and
nitrous oxide production during nitrification in agricultural soil, Glob.
Change Biol., 6, 303–316, 2000.
Vinken, G. C. M., Boersma, K. F., Maasakkers, J. D., Adon, M., and Martin, R.
V.: Worldwide biogenic soil NOx emissions inferred from OMI
NO2 observations, Atmos. Chem. Phys., 14, 10363–10381,
https://doi.org/10.5194/acp-14-10363-2014, 2014.
Wade, T., Claassen, R. L., and Wallander, S.: Conservation-practice adoption
rates vary widely by crop and region, United States Department of
Agriculture, Economic Research Service, 2015.
Wang, C., Houlton, B. Z., Dai, W., and Bai, E.: Growth in the global N2 sink
attributed to N fertilizer inputs over 1860 to 2000, Sci. Total Environ.,
574, 1044–1053, 2017.
Wang, L., Xu, J., Yang, J., Zhao, X., Wei, W., Cheng, D., Pan, X., and Su,
J.: Understanding haze pollution over the southern Hebei area of China using
the CMAQ model, Atmos. Environ., 56, 69–79, 2012.
Wang, Y., Logan, J. A., and Jacob, D. J.: Global simulation of tropospheric
O3-NOx-hydrocarbon chemistry: 2. Model evaluation and global
ozone budget, J. Geophys. Res.-Atmos., 103, 10727–10755, 1998.
Wang, Y., Zhang, Q. Q., He, K., Zhang, Q., and Chai, L.:
Sulfate-nitrate-ammonium aerosols over China: response to 2000–2015 emission
changes of sulfur dioxide, nitrogen oxides, and ammonia, Atmos. Chem. Phys.,
13, 2635–2652, https://doi.org/10.5194/acp-13-2635-2013, 2013.
Weier, K., Doran, J., Power, J., and Walters, D.: Denitrification and the
dinitrogen/nitrous oxide ratio as affected by soil water, available carbon,
and nitrate, Soil Sci. Soc. Am. J., 57, 66–72, 1993.
Williams, E. and Fehsenfeld, F.: Measurement of soil nitrogen oxide
emissions at three North American ecosystems, J. Geophys. Res.-Atmos., 96,
1033–1042, 1991.
Williams, E. J., Guenther, A., and Fehsenfeld, F. C.: An inventory of nitric oxide
emissions from soils in the United States, J. Geophys. Res., 97, 7511–7519,
1992.
Williams, J., Izaurralde, R., and Steglich, E.: Agricultural
policy/environmental extender model, Theoretical Documentation, Version, 604,
2008–2017, 2008.
Xu, X., Thornton, P. E., and Post, W. M.: A global analysis of soil
microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems,
Global Ecol. Biogeogr., 22, 737–749, 2013.
Xu, X., Thornton, P., and POTAPOV, P.:
Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1264, 2015.
Yienger, J. and Levy, H.: Empirical model of global soil-biogenic NOx
emissions, J. Geophys. Res.-Atmos., 100, 11447–11464, 1995.
Zhu, L., Henze, D., Bash, J., Jeong, G.-R., Cady-Pereira, K., Shephard, M.,
Luo, M., Paulot, F., and Capps, S.: Global evaluation of ammonia
bidirectional exchange and livestock diurnal variation schemes, Atmos. Chem.
Phys., 15, 12823–12843, https://doi.org/10.5194/acp-15-12823-2015, 2015.
Short summary
Soils have been overlooked as a source of reactive nitrogen (N) emissions that are pronounced in the summer ozone season (growing season) and increasingly important as fertilizer use grows, while fossil fuel combustion sources of N decline. Mechanistic process models of soil N emissions are used in Earth science and soil biogeochemical modeling on a site scale. This work mechanistically models soil N emissions for the first time on a regional scale to better understand their air quality impacts.
Soils have been overlooked as a source of reactive nitrogen (N) emissions that are pronounced in...