Articles | Volume 17, issue 19
https://doi.org/10.5194/acp-17-12145-2017
© 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-17-12145-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Oct 2017
Research article |
| 12 Oct 2017
Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects
Bethan White
CORRESPONDING AUTHOR
Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK
Edward Gryspeerdt
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Philip Stier
Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK
Hugh Morrison
National Center for Atmospheric Research, Boulder, Colorado, USA
Gregory Thompson
National Center for Atmospheric Research, Boulder, Colorado, USA
Zak Kipling
European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, UK
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44 citations as recorded by crossref.
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- Representation of the autoconversion from cloud to rain using a weighted ensemble approach: a case study using WRF v4.1.3 J. Yin et al. 10.5194/gmd-15-771-2022
- Effects of Cloud Liquid‐Phase Microphysical Processes in Mixed‐Phase Cumuli Over the Tibetan Plateau X. Xu et al. 10.1029/2020JD033371
- The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI): Development, Evaluation, and Initial Application T. Glotfelty et al. 10.1175/MWR-D-18-0267.1
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- Simulations of orographic precipitation in the Snowy Mountains of Southeastern Australia F. Sarmadi et al. 10.1016/j.atmosres.2019.01.002
- Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems G. Dagan et al. 10.5194/acp-20-4523-2020
- Indications of a Decrease in the Depth of Deep Convective Cores with Increasing Aerosol Concentration during the CACTI Campaign P. Veals et al. 10.1175/JAS-D-21-0119.1
- Aerosol–cloud interaction in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on mesoscale numerical weather prediction under haze pollution conditions in Jing–Jin–Ji in China W. Zhang et al. 10.5194/acp-22-15207-2022
- Bounding Global Aerosol Radiative Forcing of Climate Change N. Bellouin et al. 10.1029/2019RG000660
- Bulk and structural convergence at convection‐resolving scales in real‐case simulations of summertime moist convection over land D. Panosetti et al. 10.1002/qj.3502
- Evaluation of convective cloud microphysics in numerical weather prediction models with dual-wavelength polarimetric radar observations: methods and examples G. Köcher et al. 10.5194/amt-15-1033-2022
- Tropical and Boreal Forest – Atmosphere Interactions: A Review P. Artaxo et al. 10.16993/tellusb.34
- Seamless Modeling of Direct and Indirect Aerosol Effects during April 2020 Wildfire Episode in Ukraine M. Savenets et al. 10.3390/atmos15050550
- Potential of stochastic methods for improving convection-permitting ensemble forecasts of extreme events over the Western Mediterranean A. Hermoso et al. 10.1016/j.atmosres.2021.105571
- Empirical values and assumptions in the microphysics of numerical models F. Tapiador et al. 10.1016/j.atmosres.2018.09.010
- Multifaceted aerosol effects on precipitation P. Stier et al. 10.1038/s41561-024-01482-6
- Erroneous Attribution of Deep Convective Invigoration to Aerosol Concentration A. Varble 10.1175/JAS-D-17-0217.1
- Comparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hail C. Wellmann et al. 10.5194/acp-20-2201-2020
- Tracking the influence of cloud condensation nuclei on summer diurnal precipitating systems over complex topography in Taiwan Y. Chang et al. 10.5194/acp-21-16709-2021
- Updraft dynamics and microphysics: on the added value of the cumulus thermal reference frame in simulations of aerosol–deep convection interactions D. Hernandez-Deckers et al. 10.5194/acp-22-711-2022
- A simple system for moist convection: the Rainy–Bénard model G. Vallis et al. 10.1017/jfm.2018.954
- Satellite observations of smoke–cloud–radiation interactions over the Amazon rainforest R. Herbert & P. Stier 10.5194/acp-23-4595-2023
- The impact of aerosol-cloud interaction on mesoscale numerical weather prediction when low-cloud and haze coexist in winter over major polluted regions of China W. Zhang et al. 10.1016/j.atmosenv.2023.120270
- Comparisons of bin and bulk microphysics schemes in simulations of topographic winter precipitation with radar and radiometer measurements M. Han et al. 10.1002/qj.3393
- On the contribution of fast and slow responses to precipitation changes caused by aerosol perturbations S. Zhang et al. 10.5194/acp-21-10179-2021
- Possible roles of fall speed parameters of different graupel densities on microphysics and electrification in an idealized thunderstorm X. Ouyang et al. 10.1002/qj.3569
- Global response of parameterised convective cloud fields to anthropogenic aerosol forcing Z. Kipling et al. 10.5194/acp-20-4445-2020
- Aerosol indirect effects on the temperature–precipitation scaling N. Da Silva et al. 10.5194/acp-20-6207-2020
- The numerical study on the sensitivity of different auto-conversion parameterization to CCN concentration Y. Li et al. 10.3389/fenvs.2023.1112266
- Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics U. Proske et al. 10.1029/2022MS003571
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Discussed (preprint)
Latest update: 06 Nov 2024
Short summary
Aerosols influence cloud and precipitation by modifying cloud droplet number concentrations (CDNCs). We simulate three different types of convective cloud using two different cloud microphysics parameterisations. The simulated cloud and precipitation depends much more strongly on the choice of microphysics scheme than on CDNC. The uncertainty differs between types of convection. Our results highlight a large uncertainty in cloud and precipitation responses to aerosol in current models.
Aerosols influence cloud and precipitation by modifying cloud droplet number concentrations...
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