Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-927-2023
https://doi.org/10.5194/gmd-16-927-2023
Model evaluation paper
 | 
06 Feb 2023
Model evaluation paper |  | 06 Feb 2023

Implementation of HONO into the chemistry–climate model CHASER (V4.0): roles in tropospheric chemistry

Phuc Thi Minh Ha, Yugo Kanaya, Fumikazu Taketani, Maria Dolores Andrés Hernández, Benjamin Schreiner, Klaus Pfeilsticker, and Kengo Sudo

Related authors

Downward and upward revisions of Chinese emissions of black carbon and CO in bottom-up inventories are still required: an integrated analysis of WRF/CMAQ model and EMeRGe observations in East Asia in spring 2018
Phuc Thi Minh Ha, Yugo Kanaya, Kazuyo Yamaji, Syuichi Itahashi, Satoru Chatani, Takashi Sekiya, Maria Dolores Andrés Hernández, John Philip Burrows, Hans Schlager, Michael Lichtenstern, Mira Poehlker, and Bruna Holanda
EGUsphere, https://doi.org/10.5194/egusphere-2024-2064,https://doi.org/10.5194/egusphere-2024-2064, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Effects of heterogeneous reactions on tropospheric chemistry: a global simulation with the chemistry–climate model CHASER V4.0
Phuc T. M. Ha, Ryoki Matsuda, Yugo Kanaya, Fumikazu Taketani, and Kengo Sudo
Geosci. Model Dev., 14, 3813–3841, https://doi.org/10.5194/gmd-14-3813-2021,https://doi.org/10.5194/gmd-14-3813-2021, 2021
Short summary

Related subject area

Atmospheric sciences
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025,https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025,https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025,https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025,https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025,https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary

Cited articles

Acker, K., Möller, D., Wieprecht, W., Auel, R., Kalass, D., and Tscherwenka, W.: Nitrous and nitric acid measurements inside and outside of clouds at Mt. Brocken, Water Air Soil Poll., 130, 331–336, https://doi.org/10.1023/A:1013808529303, 2001. 
Acker, K., Febo, A., Trick, S., Perrino, C., Bruno, P., Wiesen, P., Moller, D., Wieprecht, W., Auel, R., Giusto, M., Geyer, A., Platt, U., and Allegrini, I.: Nitrous acid in the urban area of Rome, Atmos. Environ., 40, 3123–3133, https://doi.org/10.1016/j.atmosenv.2006.01.028, 2006. 
Akimoto, H., Nagashima, T., Li, J., Fu, J. S., Ji, D., Tan, J., and Wang, Z.: Comparison of surface ozone simulation among selected regional models in MICS-Asia III – effects of chemistry and vertical transport for the causes of difference, Atmos. Chem. Phys., 19, 603–615, https://doi.org/10.5194/acp-19-603-2019, 2019. 
Al-Abadleh, H. A. and Grassian, V. H.: Heterogeneous reaction of NO2 on hexane soot: A Knudsen cell and FT-IR study, J. Phys. Chem. A, 104, 11926–11933, 2000. 
Ammann, M., Kalberer, M., Jost, D. T., Tobler, L., Rossler, E., Piguet, D., Gaggeler, H. W., and Baltensperger, U.: Heterogeneous production of nitrous acid on soot in polluted air masses, Nature, 395, 157–160, 1998. 
Download
Short summary
HONO affects tropospheric oxidizing capacity; thus, it is implemented into the chemistry–climate model CHASER. The model substantially underpredicts daytime HONO, while nitrate photolysis on surfaces can supplement the daytime HONO budget. Current HONO chemistry predicts reductions of 20.4 % for global tropospheric NOx, 40–67 % for OH, and 30–45 % for O3 in the summer North Pacific. In contrast, OH and O3 winter levels in China are greatly enhanced.
Share