Articles | Volume 17, issue 5
https://doi.org/10.5194/acp-17-3769-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-3769-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Global sensitivity analysis of the GEOS-Chem chemical transport model: ozone and hydrogen oxides during ARCTAS (2008)
Kenneth E. Christian
CORRESPONDING AUTHOR
Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
William H. Brune
Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
Jingqiu Mao
Geophysical Institute and Department of Chemistry and Biochemistry, University of Alaska at Fairbanks, Fairbanks, AK, USA
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22 citations as recorded by crossref.
- Correcting model biases of CO in East Asia: impact on oxidant distributions during KORUS-AQ B. Gaubert et al. 10.5194/acp-20-14617-2020
- Investigating the Understanding of Oxidation Chemistry Using 20 Years of Airborne OH and HO2 Observations D. Miller & W. Brune 10.1029/2021JD035368
- Surface ozone and its precursors at Summit, Greenland: comparison between observations and model simulations Y. Huang et al. 10.5194/acp-17-14661-2017
- Ozone Profile Retrieval Algorithm Based on GEOS-Chem Model in the Middle and Upper Atmosphere Y. An et al. 10.3390/rs16081335
- Global sensitivity analysis of GEOS-Chem modeled ozone and hydrogen oxides during the INTEX campaigns K. Christian et al. 10.5194/acp-18-2443-2018
- Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study K. Aleksankina et al. 10.5194/gmd-11-1653-2018
- Characterization of aerosol growth events over Ellesmere Island during the summers of 2015 and 2016 S. Tremblay et al. 10.5194/acp-19-5589-2019
- Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model K. Aleksankina et al. 10.5194/acp-19-2881-2019
- Climate and Tropospheric Oxidizing Capacity A. Fiore et al. 10.1146/annurev-earth-032320-090307
- Particle-Phase Photoreactions of HULIS and TMIs Establish a Strong Source of H2O2 and Particulate Sulfate in the Winter North China Plain C. Ye et al. 10.1021/acs.est.1c00561
- A better representation of volatile organic compound chemistry in WRF-Chem and its impact on ozone over Los Angeles Q. Zhu et al. 10.5194/acp-24-5265-2024
- Tropospheric ozone over the Indian subcontinent from 2000 to 2015: Data set and simulation using GEOS-Chem chemical transport model L. David et al. 10.1016/j.atmosenv.2019.117039
- Exploring Oxidation in the Remote Free Troposphere: Insights From Atmospheric Tomography (ATom) W. Brune et al. 10.1029/2019JD031685
- Observations of atmospheric oxidation and ozone production in South Korea W. Brune et al. 10.1016/j.atmosenv.2021.118854
- The number fraction of iron-containing particles affects OH, HO<sub>2</sub> and H<sub>2</sub>O<sub>2</sub> budgets in the atmospheric aqueous phase A. Khaled et al. 10.5194/acp-22-1989-2022
- Coordinated effects of energy transition on air pollution mitigation and CO2 emission control in China R. Yuan et al. 10.1016/j.scitotenv.2022.156482
- Atmospheric Aerosol Distribution in 2016–2017 over the Eastern European Region Based on the GEOS-Chem Model G. Milinevsky et al. 10.3390/atmos11070722
- 临近空间紫外辐射环境及影响因素研究 安. AN Yuan et al. 10.3788/gzxb20245303.0301001
- Atmospheric oxidation in the presence of clouds during the Deep Convective Clouds and Chemistry (DC3) study W. Brune et al. 10.5194/acp-18-14493-2018
- Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity O. Wild et al. 10.5194/acp-20-4047-2020
- Indigenized Characterization Factors for Health Damage Due to Ambient PM2.5 in Life Cycle Impact Assessment in China Q. Ma et al. 10.1021/acs.est.3c08122
- Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning C. Fan et al. 10.3390/atmos15111337
1 citations as recorded by crossref.
Latest update: 23 Nov 2024
Short summary
To better understand the sources of uncertainty in modeled Arctic tropospheric oxidants, we created and analyzed an ensemble of chemical transport model runs with inputs perturbed according to their respective uncertainties. Ozone and OH were most sensitive to various emissions and chemical factors. HO2 was overwhelmingly sensitive to aerosol particle uptake. When compared to airborne measurements, better agreement was found when the model used lower aerosol particle uptake rates.
To better understand the sources of uncertainty in modeled Arctic tropospheric oxidants, we...
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