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.
Development and technical paper
| 
27 Feb 2019
Development and technical paper |
| 27 Feb 2019
| 27 Feb 2019
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
Atmos. Chem. Phys., 24, 12903–12924, https://doi.org/10.5194/acp-24-12903-2024, https://doi.org/10.5194/acp-24-12903-2024, 2024
Short summary
Short summary
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, we estimate the cancer risk from inhalation exposure to ambient formaldehyde across the contiguous USA and predict that 40 % of this risk is controllable through reductions in anthropogenic emissions of nitrogen oxides and reactive organic carbon.
Anam M. Khan, Olivia E. Clifton, Jesse O. Bash, Sam Bland, Nathan Booth, Philip Cheung, Lisa Emberson, Johannes Flemming, Erick Fredj, Stefano Galmarini, Laurens Ganzeveld, Orestis Gazetas, Ignacio Goded, Christian Hogrefe, Christopher D. Holmes, Laszlo Horvath, Vincent Huijnen, Qian Li, Paul A. Makar, Ivan Mammarella, Giovanni Manca, J. William Munger, Juan L. Perez-Camanyo, Jonathan Pleim, Limei Ran, Roberto San Jose, Donna Schwede, Sam J. Silva, Ralf Staebler, Shihan Sun, Amos P. K. Tai, Eran Tas, Timo Vesala, Tamas Weidinger, Zhiyong Wu, Leiming Zhang, and Paul C. Stoy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3038, https://doi.org/10.5194/egusphere-2024-3038, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Vegetation removes tropospheric ozone through stomatal uptake, and accurately modeling the stomatal uptake of ozone is important for modeling dry deposition and air quality. We evaluated the stomatal component of ozone dry deposition modeled by atmospheric chemistry models at six sites. We find that models and observation-based estimates agree at times during the growing season at all sites, but some models overestimated the stomatal component during the dry summers at a seasonally dry site.
Paul A. Makar, Philip Cheung, Christian Hogrefe, Ayodeji Akingunola, Ummugulsum Alyuz-Ozdemir, Jesse O. Bash, Michael D. Bell, Roberto Bellasio, Roberto Bianconi, Tim Butler, Hazel Cathcart, Olivia E. Clifton, Alma Hodzic, Iannis Koutsioukis, Richard Kranenburg, Aurelia Lupascu, Jason A. Lynch, Kester Momoh, Juan L. Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Thomas Scheuschner, Mark Shephard, Ranjeet Sokhi, and Stefano Galmarini
EGUsphere, https://doi.org/10.5194/egusphere-2024-2226, https://doi.org/10.5194/egusphere-2024-2226, 2024
Short summary
Short summary
The large range of sulphur and nitrogen deposition estimates from air-quality models results in a large range of predicted impacts. We used models and deposition diagnostics to identify the processes controlling atmospheric sulphur and nitrogen deposition variability. Controlling factors included the uptake of gases and aerosols by droplets, rain, snow, etc., aerosol inorganic chemistry, particle dry deposition, ammonia bidirectional fluxes, and gas deposition via plant cuticles and soil.
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
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Orbital-Radar v1.0.0: A tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
The MESSy DWARF (based on MESSy v2.55.2)
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
Development of the adjoint of the unified tropospheric–stratospheric chemistry extension (UCX) in GEOS-Chem adjoint v36
New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2
ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors
The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations
Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3
The CHIMERE chemistry-transport model v2023r1
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary
Short summary
We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
Short summary
Short summary
We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
Short summary
A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Short summary
Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
Short summary
Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
Short summary
Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Short summary
This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary
Short summary
Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
Short summary
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
Short summary
Short summary
This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
Short summary
Short summary
In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
Short summary
Short summary
The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
Short summary
Short summary
The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
Short summary
Short summary
Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
Short summary
Short summary
Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
Short summary
Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
Short summary
Short summary
Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
Short summary
Short summary
Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
Short summary
Short summary
RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
Short summary
We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
Short summary
Short summary
We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
Short summary
Short summary
Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
Short summary
Short summary
Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
Short summary
Short summary
AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
Short summary
Short summary
Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
Short summary
Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-129, https://doi.org/10.5194/gmd-2024-129, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Orbital-radar is a Python tool transferring sub-orbital radar data (ground-based, airborne, and forward-simulated NWP) into synthetical space-borne cloud profiling radar data mimicking the platform characteristics, e.g. EarthCARE or CloudSat CPR. The novelty of orbital-radar is the simulation platform characteristic noise floors and errors. By this long time data sets can be transformed into synthetic observations for Cal/Valor sensitivity studies for new or future satellite missions.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
Short summary
Short summary
The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
Short summary
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
Short summary
A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary
Short summary
The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Short summary
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
Short summary
Short summary
This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
Short summary
Short summary
Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
Short summary
Short summary
We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
Short summary
Short summary
This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
Short summary
Short summary
Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
Short summary
Short summary
TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems 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.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
Short summary
Short summary
Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
Short summary
Short summary
We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
Short summary
Short summary
A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
Short summary
Short summary
The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 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 in 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's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
Short summary
Short summary
In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
Short summary
Short summary
A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
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
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...
