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
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Jesse O. Bash, Kirk R. Baker, and Melinda R. Beaver
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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
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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
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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
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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
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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
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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
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The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 2024
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Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
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In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
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We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
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The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
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Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
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Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
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A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
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Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
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Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
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In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
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This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
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We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
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Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
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Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
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Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev., 16, 7223–7235, https://doi.org/10.5194/gmd-16-7223-2023, https://doi.org/10.5194/gmd-16-7223-2023, 2023
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The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
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We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Minjie Zheng, Hongyu Liu, Florian Adolphi, Raimund Muscheler, Zhengyao Lu, Mousong Wu, and Nønne L. Prisle
Geosci. Model Dev., 16, 7037–7057, https://doi.org/10.5194/gmd-16-7037-2023, https://doi.org/10.5194/gmd-16-7037-2023, 2023
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The radionuclides 7Be and 10Be are useful tracers for atmospheric transport studies. Here we use the GEOS-Chem to simulate 7Be and 10Be with different production rates: the default production rate in GEOS-Chem and two from the state-of-the-art beryllium production model. We demonstrate that reduced uncertainties in the production rates can enhance the utility of 7Be and 10Be as tracers for evaluating transport and scavenging processes in global models.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6833–6856, https://doi.org/10.5194/gmd-16-6833-2023, https://doi.org/10.5194/gmd-16-6833-2023, 2023
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In addition to the dominant role of the PBL scheme on the results of the meteorological field, many factors in the model are influenced by large uncertainties. This study focuses on the uncertainties that influence numerical simulation results (including horizontal resolution, vertical resolution, near-surface scheme, initial and boundary conditions, underlying surface update, and update of model version), hoping to provide a reference for scholars conducting research on the model.
Owen K. Hughes and Christiane Jablonowski
Geosci. Model Dev., 16, 6805–6831, https://doi.org/10.5194/gmd-16-6805-2023, https://doi.org/10.5194/gmd-16-6805-2023, 2023
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Atmospheric models benefit from idealized tests that assess their accuracy in a simpler simulation. A new test with artificial mountains is developed for models on a spherical earth. The mountains trigger the development of both planetary-scale and small-scale waves. These can be analyzed in dry or moist environments, with a simple rainfall mechanism. Four atmospheric models are intercompared. This sheds light on the pros and cons of the model design and the impact of mountains on the flow.
Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng
Geosci. Model Dev., 16, 6757–6771, https://doi.org/10.5194/gmd-16-6757-2023, https://doi.org/10.5194/gmd-16-6757-2023, 2023
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This study explores how the variation in the source profiles adopted in chemical transport models (CTMs) impacts the simulated results of chemical components in PM2.5 based on sensitivity analysis. The impact on PM2.5 components cannot be ignored, and its influence can be transmitted and linked between components. The representativeness and timeliness of the source profile should be paid adequate attention in air quality simulation.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6635–6670, https://doi.org/10.5194/gmd-16-6635-2023, https://doi.org/10.5194/gmd-16-6635-2023, 2023
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Most current studies on planetary boundary layer (PBL) parameterization schemes are relatively fragmented and lack systematic in-depth analysis and discussion. In this study, we comprehensively evaluate the performance capability of the PBL scheme in five typical regions of China in different seasons from the mechanism of the scheme and the effects of PBL schemes on the near-surface meteorological parameters, vertical structures of the PBL, PBL height, and turbulent diffusion.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev., 16, 6531–6552, https://doi.org/10.5194/gmd-16-6531-2023, https://doi.org/10.5194/gmd-16-6531-2023, 2023
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It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Angel Liduvino Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, and Janaina P. Nascimento
Geosci. Model Dev., 16, 6413–6431, https://doi.org/10.5194/gmd-16-6413-2023, https://doi.org/10.5194/gmd-16-6413-2023, 2023
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A 1-year simulation of atmospheric CH4 over Europe is performed and evaluated against observations based on the TROPOspheric Monitoring Instrument (TROPOMI). A good general model–observation agreement is found, with discrepancies reaching their minimum and maximum values during the summer peak season and winter months, respectively. A huge and under-explored potential for CH4 inverse modeling using improved TROPOMI XCH4 data sets in large-scale applications is identified.
Zhaojun Tang, Zhe Jiang, Jiaqi Chen, Panpan Yang, and Yanan Shen
Geosci. Model Dev., 16, 6377–6392, https://doi.org/10.5194/gmd-16-6377-2023, https://doi.org/10.5194/gmd-16-6377-2023, 2023
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We designed a new framework to facilitate emission inventory updates in the adjoint of GEOS-Chem model. It allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 meteorological data; this allows us to perform long-term analysis with consistent meteorological data.
Rui Zhu, Zhaojun Tang, Xiaokang Chen, Xiong Liu, and Zhe Jiang
Geosci. Model Dev., 16, 6337–6354, https://doi.org/10.5194/gmd-16-6337-2023, https://doi.org/10.5194/gmd-16-6337-2023, 2023
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A single ozone (O3) tracer mode was developed in this work to build the capability of the GEOS-Chem model for rapid O3 simulation. It is combined with OMI and surface O3 observations to investigate the changes in tropospheric O3 in China in 2015–2020. The assimilations indicate rapid surface O3 increases that are underestimated by the a priori simulations. We find stronger increases in tropospheric O3 columns over polluted areas and a large discrepancy by assimilating different observations.
Ewa M. Bednarz, Ryan Hossaini, N. Luke Abraham, and Martyn P. Chipperfield
Geosci. Model Dev., 16, 6187–6209, https://doi.org/10.5194/gmd-16-6187-2023, https://doi.org/10.5194/gmd-16-6187-2023, 2023
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Development and performance of the new DEST chemistry scheme of UM–UKCA is described. The scheme extends the standard StratTrop scheme by including important updates to the halogen chemistry, thus allowing process-oriented studies of stratospheric ozone depletion and recovery, including impacts from both controlled long-lived ozone-depleting substances and emerging issues around uncontrolled, very short-lived substances. It will thus aid studies in support of future ozone assessment reports.
Shaohui Zhou, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
Geosci. Model Dev., 16, 6247–6266, https://doi.org/10.5194/gmd-16-6247-2023, https://doi.org/10.5194/gmd-16-6247-2023, 2023
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The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indices for 13 months remain relatively stable: the forecasting accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev., 16, 6087–6125, https://doi.org/10.5194/gmd-16-6087-2023, https://doi.org/10.5194/gmd-16-6087-2023, 2023
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We implemented an alternative aerosol scheme in the high- and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. This development enables the comparison of different aerosol schemes with different complexity in the same model framework. It identifies improvements compared to a range of observations in both the troposphere and stratosphere.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
Geosci. Model Dev., 16, 6161–6185, https://doi.org/10.5194/gmd-16-6161-2023, https://doi.org/10.5194/gmd-16-6161-2023, 2023
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To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and assess its performance against TROPOMI v2 over power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind directions and prior emissions.
Jiangshan Zhu and Ross Noel Bannister
Geosci. Model Dev., 16, 6067–6085, https://doi.org/10.5194/gmd-16-6067-2023, https://doi.org/10.5194/gmd-16-6067-2023, 2023
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We describe how condensation and evaporation are included in the existing (otherwise dry) simplified ABC model. The new model (Hydro-ABC) includes transport of vapour and condensate within a dynamical core, and it transitions between these two phases via a micro-physics scheme. The model shows the development of an anvil cloud and excitation of atmospheric waves over many frequencies. The covariances that develop between variables are also studied together with indicators of convective motion.
Jiangyong Li, Chunlin Zhang, Wenlong Zhao, Shijie Han, Yu Wang, Hao Wang, and Boguang Wang
Geosci. Model Dev., 16, 6049–6066, https://doi.org/10.5194/gmd-16-6049-2023, https://doi.org/10.5194/gmd-16-6049-2023, 2023
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Photochemical box models, crucial for understanding tropospheric chemistry, face challenges due to slow computational efficiency with large chemical equations. The model introduced in this study, ROMAC, boosts efficiency by up to 96 % using an advanced atmospheric solver and an adaptive optimization algorithm. Moreover, ROMAC exceeds traditional box models in evaluating the impact of physical processes on pollutant concentrations.
Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, and Philippe Thunis
Geosci. Model Dev., 16, 6029–6047, https://doi.org/10.5194/gmd-16-6029-2023, https://doi.org/10.5194/gmd-16-6029-2023, 2023
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Air quality forecasting models play a key role in fostering short-term measures aimed at reducing human exposure to air pollution. Together with this role comes the need for a thorough assessment of the model performances to build confidence in models’ capabilities, in particular when model applications support policymaking. In this paper, we propose an evaluation methodology and test it on several domains across Europe, highlighting its strengths and room for improvement.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
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We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Megan A. Stretton, William Morrison, Robin J. Hogan, and Sue Grimmond
Geosci. Model Dev., 16, 5931–5947, https://doi.org/10.5194/gmd-16-5931-2023, https://doi.org/10.5194/gmd-16-5931-2023, 2023
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Cities' materials and forms impact radiative fluxes. We evaluate the SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically using the explicit DART (Discrete Anisotropic Radiative Transfer) model. The temperature configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with a low computational time (cf. DART), but has larger errors with sunlit/shaded surface temperatures.
Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee
Geosci. Model Dev., 16, 5895–5914, https://doi.org/10.5194/gmd-16-5895-2023, https://doi.org/10.5194/gmd-16-5895-2023, 2023
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To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.
Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll
Geosci. Model Dev., 16, 5729–5754, https://doi.org/10.5194/gmd-16-5729-2023, https://doi.org/10.5194/gmd-16-5729-2023, 2023
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The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, and Zifa Wang
Geosci. Model Dev., 16, 5585–5599, https://doi.org/10.5194/gmd-16-5585-2023, https://doi.org/10.5194/gmd-16-5585-2023, 2023
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This paper developed a two-way coupled module in a new version of a regional urban–street network model, IAQMS-street v2.0, in which the mass flux from streets to background is considered. Test cases are defined to evaluate the performance of IAQMS-street v2.0 in Beijing by comparing it with that simulated by IAQMS-street v1.0 and a regional model. The contribution of local emissions and the influence of on-road vehicle control measures on air quality are evaluated by using IAQMS-street v2.0.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavčič, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 16, 5601–5626, https://doi.org/10.5194/gmd-16-5601-2023, https://doi.org/10.5194/gmd-16-5601-2023, 2023
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Three-dimensional climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchař, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
Geosci. Model Dev., 16, 5561–5583, https://doi.org/10.5194/gmd-16-5561-2023, https://doi.org/10.5194/gmd-16-5561-2023, 2023
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The columnar approach of gravity wave (GW) schemes results in dynamical model biases, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray tracer. More spread-out GW drag helps reconcile the model with observations and close the 60°S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
Sanam N. Vardag and Robert Maiwald
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-192, https://doi.org/10.5194/gmd-2023-192, 2023
Revised manuscript accepted for GMD
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We use the atmospheric transport model GRAMM/GRAL in an inversion to estimate urban CO2 emissions on neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation into the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
EGUsphere, https://doi.org/10.5194/egusphere-2023-1512, https://doi.org/10.5194/egusphere-2023-1512, 2023
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Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variables. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimizing simulations for better climate projections.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
Geosci. Model Dev., 16, 5493–5514, https://doi.org/10.5194/gmd-16-5493-2023, https://doi.org/10.5194/gmd-16-5493-2023, 2023
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With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-169, https://doi.org/10.5194/gmd-2023-169, 2023
Revised manuscript accepted for GMD
Short summary
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, a milestone that has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs, thus perfectly suited for future CO2 geo-stationery and imager satellites.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-171, https://doi.org/10.5194/gmd-2023-171, 2023
Revised manuscript accepted for GMD
Short summary
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PyRTlib is an attractive educational software because it provides a flexible and user friendly tool to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols and hydrometeors). PyRTlib is a so called Radiative Transfer model which are commonly used to simulate and understand remote sensing observations from ground-based, airborne or satellite instruments.
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, https://doi.org/10.5194/gmd-16-5323-2023, 2023
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Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidentally) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011) and show that it improves the simulation of wet deposition.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
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A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023, https://doi.org/10.5194/gmd-16-5251-2023, 2023
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In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Cited articles
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...
