Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-651-2024
https://doi.org/10.5194/gmd-17-651-2024
Model evaluation paper
 | 
26 Jan 2024
Model evaluation paper |  | 26 Jan 2024

Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution

Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura

Related authors

Improving estimation of a record-breaking east Asian dust storm emission with lagged aerosol Ångström exponent observations
Yueming Cheng, Tie Dai, Junji Cao, Daisuke Goto, Jianbing Jin, Teruyuki Nakajima, and Guangyu Shi
Atmos. Chem. Phys., 24, 12643–12659, https://doi.org/10.5194/acp-24-12643-2024,https://doi.org/10.5194/acp-24-12643-2024, 2024
Short summary
Assessing the assimilation of Himawari-8 observations on aerosol forecasts and radiative effects during pollution transport from South Asia to the Tibetan Plateau
Min Zhao, Tie Dai, Daisuke Goto, Hao Wang, and Guangyu Shi
Atmos. Chem. Phys., 24, 235–258, https://doi.org/10.5194/acp-24-235-2024,https://doi.org/10.5194/acp-24-235-2024, 2024
Short summary
Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem
Tie Dai, Yueming Cheng, Daisuke Goto, Yingruo Li, Xiao Tang, Guangyu Shi, and Teruyuki Nakajima
Atmos. Chem. Phys., 21, 4357–4379, https://doi.org/10.5194/acp-21-4357-2021,https://doi.org/10.5194/acp-21-4357-2021, 2021
Short summary
The Nonhydrostatic ICosahedral Atmospheric Model for CMIP6 HighResMIP simulations (NICAM16-S): experimental design, model description, and impacts of model updates
Chihiro Kodama, Tomoki Ohno, Tatsuya Seiki, Hisashi Yashiro, Akira T. Noda, Masuo Nakano, Yohei Yamada, Woosub Roh, Masaki Satoh, Tomoko Nitta, Daisuke Goto, Hiroaki Miura, Tomoe Nasuno, Tomoki Miyakawa, Ying-Wen Chen, and Masato Sugi
Geosci. Model Dev., 14, 795–820, https://doi.org/10.5194/gmd-14-795-2021,https://doi.org/10.5194/gmd-14-795-2021, 2021
Short summary
Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model
Daisuke Goto, Yousuke Sato, Hisashi Yashiro, Kentaroh Suzuki, Eiji Oikawa, Rei Kudo, Takashi M. Nagao, and Teruyuki Nakajima
Geosci. Model Dev., 13, 3731–3768, https://doi.org/10.5194/gmd-13-3731-2020,https://doi.org/10.5194/gmd-13-3731-2020, 2020
Short summary

Related subject area

Atmospheric sciences
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025,https://doi.org/10.5194/gmd-18-621-2025, 2025
Short summary
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025,https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025,https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025,https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025,https://doi.org/10.5194/gmd-18-405-2025, 2025
Short summary

Cited articles

Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2 Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000. 
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., and Arkin, P.: The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis 1979-present, J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. 
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
Alfaro-Contreras, R., Zhang, J., Reid, J. S., and Christopher, S.: A study of 15-year aerosol optical thickness and direct shortwave aerosol radiative effect trends using MODIS, MISR, CALIOP and CERES, Atmos. Chem. Phys., 17, 13849–13868, https://doi.org/10.5194/acp-17-13849-2017, 2017. 
Bates, T. S., Charlson, R. J., and Gammon, R. H.: Evidence for the climate role of marine biogenic sulphur, Nature, 329, 319–321, 1987. 
Download
Short summary
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.
Share