Articles | Volume 9, issue 5
https://doi.org/10.5194/gmd-9-1959-2016
https://doi.org/10.5194/gmd-9-1959-2016
Development and technical paper
 | 
27 May 2016
Development and technical paper |  | 27 May 2016

Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California

Chun Zhao, Maoyi Huang, Jerome D. Fast, Larry K. Berg, Yun Qian, Alex Guenther, Dasa Gu, Manish Shrivastava, Ying Liu, Stacy Walters, Gabriele Pfister, Jiming Jin, John E. Shilling, and Carsten Warneke

Related authors

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023,https://doi.org/10.5194/gmd-16-3029-2023, 2023
Short summary
The impacts of dust aerosol and convective available potential energy on precipitation vertical structure in southeastern China as seen from multisource observations
Hongxia Zhu, Rui Li, Shuping Yang, Chun Zhao, Zhe Jiang, and Chen Huang
Atmos. Chem. Phys., 23, 2421–2437, https://doi.org/10.5194/acp-23-2421-2023,https://doi.org/10.5194/acp-23-2421-2023, 2023
Short summary
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022,https://doi.org/10.5194/gmd-15-6143-2022, 2022
Short summary
The sensitivity of simulated aerosol climatic impact to domain size using regional model (WRF-Chem v3.6)
Xiaodong Wang, Chun Zhao, Mingyue Xu, Qiuyan Du, Jianqiu Zheng, Yun Bi, Shengfu Lin, and Yali Luo
Geosci. Model Dev., 15, 199–218, https://doi.org/10.5194/gmd-15-199-2022,https://doi.org/10.5194/gmd-15-199-2022, 2022
Short summary
Modeling sensitivities of BVOCs to different versions of MEGAN emission schemes in WRF-Chem (v3.6) and its impacts over eastern China
Mingshuai Zhang, Chun Zhao, Yuhan Yang, Qiuyan Du, Yonglin Shen, Shengfu Lin, Dasa Gu, Wenjing Su, and Cheng Liu
Geosci. Model Dev., 14, 6155–6175, https://doi.org/10.5194/gmd-14-6155-2021,https://doi.org/10.5194/gmd-14-6155-2021, 2021
Short summary

Related subject area

Atmospheric sciences
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024,https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024,https://doi.org/10.5194/gmd-17-2961-2024, 2024
Short summary
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024,https://doi.org/10.5194/gmd-17-2901-2024, 2024
Short summary
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024,https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024,https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary

Cited articles

Andreae, M. and Crutzen, P. J.: Atmospheric Aerosols: Biogeochemical Sources and Role in Atmospheric Chemistry, Science, 276, 1052–1058, https://doi.org/10.1126/science.276.5315.1052, 1997.
Arneth, A., Niinemets, Ü., Pressley, S., Bäck, J., Hari, P., Karl, T., Noe, S., Prentice, I. C., Serça, D., Hickler, T., Wolf, A., and Smith, B.: Process-based estimates of terrestrial ecosystem isoprene emissions: incorporating the effects of a direct CO2-isoprene interaction, Atmos. Chem. Phys., 7, 31–53, https://doi.org/10.5194/acp-7-31-2007, 2007.
Barlage, M., Chen, F., Tewari, M., Ikeda, K., Gochis, D., Dudhia, J., Rasmussen, R., Livneh, B., Ek, M., and Mitchell, K.: Noah land surface model modifications to improve snowpack prediction in the Colorado Rocky Mountains, J. Geophys. Res., 115, D22101, https://doi.org/10.1029/2009JD013470, 2010.
Bonan, G. B.: A land surface model (LSM ver. 1.0) for ecological, hydrological, and atmospheric studies: Technical description and user's guide, NCAR Tech. Note 4171STR, 150 pp., available at: http://opensky.ucar.edu/islandora/object/technotes:185 (last access: 25 May 2016), 1996.
Download
Short summary
In this study, the latest version of MEGAN is coupled within CLM4 in WRF-Chem. In this implementation, MEGAN shares a consistent vegetation map with CLM4. This improved modeling framework is used to investigate the impact of two land surface schemes on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models.