Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7751-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7751-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)
Wentao Zhang
School of Atmospheric Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
School of Atmospheric Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Chunsong Lu
Key Laboratory for Aerosol-Cloud-Precipitation of China
Meteorological Administration, Nanjing University of Information Science and
Technology, Nanjing 210044, China
Related authors
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Junhui Zhang, Yuying Wang, Jialu Xu, Xiaofan Zuo, Chunsong Lu, Bin Zhu, Yuanjian Yang, Xing Yan, and Yele Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-3186, https://doi.org/10.5194/egusphere-2025-3186, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We conducted a year-long study in Nanjing to understand how tiny airborne particles take up water, which affects air quality and climate. We found that particle water uptake varies by season and size, with lower values in summer due to more organic materials. Local pollution mainly influences smaller particles, while larger ones are shaped by air mass transport. These findings help improve climate models and support better air pollution control in fast-growing cities.
Lian Su, Chunsong Lu, Jinlong Yuan, Xiaofei Wang, Qing He, and Haiyun Xia
Atmos. Chem. Phys., 24, 10947–10963, https://doi.org/10.5194/acp-24-10947-2024, https://doi.org/10.5194/acp-24-10947-2024, 2024
Short summary
Short summary
The cold downhill airflow of the Tibetan Plateau leading to the low-level jet weakens the height and intensity of the inversion layer, which reduces the energy demand for the broken inversion layer. The low-level jet causes dust aerosols to accumulate near the ground. The material conditions for the development of the desert atmospheric boundary layer can be quickly transformed into thermal conditions.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Jiaojiao Liu and Xiangjun Shi
Atmos. Chem. Phys., 21, 10609–10624, https://doi.org/10.5194/acp-21-10609-2021, https://doi.org/10.5194/acp-21-10609-2021, 2021
Short summary
Short summary
Cirrus thinning, which reduces the warming effect of cirrus clouds, has been investigated as a new geoengineering approach. In this study, a flexible seeding method is used to exploit the potential cooling effect of cirrus thinning. Simulation results show that the seeding method is essential for estimating the cooling effect. Cirrus thinning with the flexible seeding method could produce a considerable cooling effect, which is much stronger than the fixed seeding method.
Cited articles
Andrews, T., Forster, P. M., Boucher, O., Bellouin, N., and Jones, A.:
Precipitation, radiative forcing and global temperature change, Geophys.
Res. Lett., 37, L14701, https://doi.org/10.1029/2010GL043991, 2010.
Barahona, D., Molod, A., Bacmeister, J., Nenes, A., Gettelman, A., Morrison, H., Phillips, V., and Eichmann, A.: Development of two-moment cloud microphysics for liquid and ice within the NASA Goddard Earth Observing System Model (GEOS-5), Geosci. Model Dev., 7, 1733–1766, https://doi.org/10.5194/gmd-7-1733-2014, 2014.
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C.,
and Schanen, D. P.: Higher-order turbulence closure and its impact on
climate simulations in the Community Atmosphere Model, J. Climate,
26, 9655–9676, https://doi.org/10.1175/jcli-d-13-00075.1, 2013.
Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018.
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus,
R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H.,
Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity,
Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015.
CESM Working Groups of National Center for Atmospheric Research (NCAR): Community Earth System Model, https://escomp.github.io/CESM/versions/cesm2.1/html/downloading_cesm.html, CESM Working Groups of National Center for Atmospheric Research (NCAR) [code], last access: 9 May 2022.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S.,
van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C.,
Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J.,
Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E.,
Polvani, L., Rasch, P. J., and Strand, W. G.: The community earth system
model version 2 (CESM2), J. Adv. Model. Earth Sy., 12,
e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D.,
Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.:
Predicting global atmospheric ice nuclei distributions and their impacts on
climate, P. Natl. Acad. Sci. USA, 107, 11217–11222,
https://doi.org/10.1073/pnas.0910818107, 2010.
Eidhammer, T., Morrison, H., Bansemer, A., Gettelman, A., and Heymsfield, A. J.: Comparison of ice cloud properties simulated by the Community Atmosphere Model (CAM5) with in-situ observations, Atmos. Chem. Phys., 14, 10103–10118, https://doi.org/10.5194/acp-14-10103-2014, 2014.
Eidhammer, T., Morrison, H., Mitchell, D., Gettelman, A., and Erfani, E.:
Improvements in global climate model microphysics using a consistent
representation of ice particle properties, J. Climate, 30, 609–629,
https://doi.org/10.1175/jcli-d-16-0050.1, 2017.
Erfani, E. and Mitchell, D. L.: Developing and bounding ice particle mass- and area-dimension expressions for use in atmospheric models and remote sensing, Atmos. Chem. Phys., 16, 4379–4400, https://doi.org/10.5194/acp-16-4379-2016, 2016.
Gettelman, A. and Morrison, H.: Advanced two-moment bulk microphysics for
global models. Part I: Off-line tests and comparison with other schemes,
J. Climate, 28, 1268–1287,
https://doi.org/10.1175/jcli-d-14-00102.1, 2015.
Heymsfield, A. J.: Properties of tropical and midlatitude ice cloud particle
ensembles. Part II: Applications for mesoscale and climate models, J.
Atmos. Sci., 60, 2592–2611,
https://doi.org/10.1175/1520-0469(2003)060<2592:Potami>2.0.Co;2, 2003.
Heymsfield, A. J., Schmitt, C., and Bansemer, A.: Ice cloud particle size
distributions and pressure-dependent terminal velocities from in situ
observations at temperatures from 0 to −86 ∘C, J. Atmos. Sci.,
70, 4123–4154, https://doi.org/10.1175/JAS-D-12-0124.1, 2013.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O.,
Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., van den
Heever, S. C., and Yano, J. I.: Representation of microphysical processes in
cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322,
https://doi.org/10.1002/2014rg000468, 2015.
King, A. D., Lane, T. P., Henley, B. J., and Brown, J. R.: Global and
regional impacts differ between transient and equilibrium warmer worlds,
Nat. Clim. Change, 10, 42–47, https://doi.org/10.1038/s41558-019-0658-7,
2020.
Korolev, A. V. and Mazin, I. P.: Supersaturation of water vapor in clouds,
J. Atmos. Sci., 60, 2957–2974,
https://doi.org/10.1175/1520-0469(2003)060<2957:Sowvic>2.0.Co;2, 2003.
Krämer, M., Schiller, C., Afchine, A., Bauer, R., Gensch, I., Mangold, A., Schlicht, S., Spelten, N., Sitnikov, N., Borrmann, S., de Reus, M., and Spichtinger, P.: Ice supersaturations and cirrus cloud crystal numbers, Atmos. Chem. Phys., 9, 3505–3522, https://doi.org/10.5194/acp-9-3505-2009, 2009.
Larson, V. E.: CLUBB-SILHS: A parameterization of subgrid variability in the
atmosphere, arXiv [preprint],
https://doi.org/10.48550/arXiv.1711.03675,
2017.
Li, J.-L. F., Waliser, D. E., Chen, W.-T., Guan, B., Kubar, T. L., Stephens, G. L., Ma, H.-Y., Min, D., Donner, L. J., Seman, C. J., and Horowitz, L. W.: An observationally-based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary satellite data, J. Geophys. Res., 117, D16105, https://doi.org/10.1029/2012JD017640, 2012.
Liou, K.: Influence of cirrus clouds on weather and climate processes: A
Global Perspective, Mon. Weather Rev., 114, 1167–1199,
https://doi.org/10.1175/1520-0493(1986)114<1167:IOCCOW>2.0.CO;2, 1986.
Liu, X. H. and Penner, J. E.: Ice nucleation parameterization for global
models, Meteorol. Z., 14, 499–514,
https://doi.org/10.1127/0941-2948/2005/0059, 2005.
Loftus, A. M., Cotton, W. R., and Carrió, G. G.: A triple-moment hail
bulk microphysics scheme. Part I: Description and initial evaluation,
Atmos. Res., 149, 35–57,
https://doi.org/10.1016/j.atmosres.2014.05.013, 2014.
Lohmann, U., Stier, P., Hoose, C., Ferrachat, S., Kloster, S., Roeckner, E., and Zhang, J.: Cloud microphysics and aerosol indirect effects in the global climate model ECHAM5-HAM, Atmos. Chem. Phys., 7, 3425–3446, https://doi.org/10.5194/acp-7-3425-2007, 2007.
Luo, Z. and Rossow, W. B.: Characterizing tropical cirrus life cycle,
evolution, and interaction with upper-tropospheric water vapor using
Lagrangian trajectory analysis of satellite observations, J.
Climate, 17, 4541–4563, https://doi.org/10.1175/3222.1, 2004.
McFarquhar, G. M., Hsieh, T.-L., Freer, M., Mascio, J., and Jewett, B. F.:
The characterization of ice hydrometeor gamma size distributions as volumes
in N0–λ–μ phase space: Implications for microphysical
process modeling, J. Atmos. Sci., 72, 892–909,
https://doi.org/10.1175/jas-d-14-0011.1, 2015.
Milbrandt, J. A. and McTaggart-Cowan, R.: Sedimentation-induced errors in
bulk microphysics schemes, J. Atmos. Sci., 67,
3931–3948, https://doi.org/10.1175/2010jas3541.1, 2010.
Milbrandt, J. A. and Yau, M. K.: A multimoment bulk microphysics
parameterization. Part I: Analysis of the role of the spectral shape
parameter, J. Atmos. Sci., 62, 3051–3064,
https://doi.org/10.1175/jas3534.1, 2005.
Milbrandt, J. A., Morrison, H., Dawson II, D. T., and Paukert, M.: A
triple-moment representation of ice in the Predicted Particle Properties
(P3) microphysics scheme, J. Atmos. Sci., 78, 439–458,
https://doi.org/10.1175/jas-d-20-0084.1, 2021.
Mitchell, D. L.: Evolution of Snow-Size Spectra in Cyclonic Storms. Part II:
Deviations from the Exponential Form, J. Atmos. Sci., 48, 1885–1899,
https://doi.org/10.1175/1520-0469(1991)048<1885:EOSSSI>2.0.CO;2, 1991.
Mitchell, D. L., Rasch, P., Ivanova, D., McFarquhar, G., and
Nousiainen, T.: Impact of small ice crystal assumptions on ice sedimentation
rates in cirrus clouds and GCM simulations, Geophys. Res. Lett., 35, L09806,
https://doi.org/10.1029/2008gl033552, 2008.
Morrison, H. and Gettelman, A.: A new two-moment bulk stratiform cloud
microphysics scheme in the community atmosphere model, version 3 (CAM3),
Part I: Description and numerical tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008jcli2105.1, 2008.
Morrison, H. and Milbrandt, J. A.: Parameterization of cloud microphysics
based on the prediction of bulk ice particle properties. Part I: Scheme
description and idealized tests, J. Atmos. Sci., 72,
287–311, https://doi.org/10.1175/jas-d-14-0065.1, 2015.
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A new double-moment
microphysics parameterization for application in cloud and climate models.
Part I: Description, J. Atmos. Sci., 62, 1665–1677,
https://doi.org/10.1175/jas3446.1, 2005.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van
Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud
and precipitation microphysics, J. Adv. Model. Earth
Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
Paukert, M., Fan, J., Rasch, P. J., Morrison, H., Milbrandt, J. A., Shpund,
J., and Khain, A.: Three-moment representation of rain in a bulk
microphysics model, J. Adv. Model. Earth Sy., 11,
257–277, https://doi.org/10.1029/2018MS001512, 2019.
Proske, U., Ferrachat, S., Neubauer, D., Staab, M., and Lohmann, U.: Assessing the potential for simplification in global climate model cloud microphysics, Atmos. Chem. Phys., 22, 4737–4762, https://doi.org/10.5194/acp-22-4737-2022, 2022.
Salzmann, M., Ming, Y., Golaz, J.-C., Ginoux, P. A., Morrison, H., Gettelman, A., Krämer, M., and Donner, L. J.: Two-moment bulk stratiform cloud microphysics in the GFDL AM3 GCM: description, evaluation, and sensitivity tests, Atmos. Chem. Phys., 10, 8037–8064, https://doi.org/10.5194/acp-10-8037-2010, 2010.
Schmitt, C. G. and Heymsfield, A. J.: The size distribution and
mass-weighted terminal velocity of low-latitude tropopause cirrus crystal
populations, J. Atmos. Sci., 66, 2013–2028,
https://doi.org/10.1175/2009JAS3004.1, 2009.
Schumann, U., Mayer, B., Gierens, K., Unterstrasser, S., Jessberger, P.,
Petzold, A., Voigt, C., and Gayet, J.-F.: Effective radius of ice particles
in cirrus and contrails, J. Atmos. Sci., 68, 300–321,
https://doi.org/10.1175/2010jas3562.1, 2011.
Sherwood, S. C., Bony, S., Boucher, O., Bretherton, C., Forster, P. M.,
Gregory, J. M., and Stevens, B.: Adjustments in the forcing-feedback
framework for understanding climate change, B. Am.
Meteorol. Soc., 96, 217–228,
https://doi.org/10.1175/bams-d-13-00167.1, 2015.
Shi, X., Liu, X., and Zhang, K.: Effects of pre-existing ice crystals on cirrus clouds and comparison between different ice nucleation parameterizations with the Community Atmosphere Model (CAM5), Atmos. Chem. Phys., 15, 1503–1520, https://doi.org/10.5194/acp-15-1503-2015, 2015.
Spichtinger, P. and Gierens, K. M.: Modelling of cirrus clouds – Part 1a: Model description and validation, Atmos. Chem. Phys., 9, 685–706, https://doi.org/10.5194/acp-9-685-2009, 2009.
Storelvmo, T., Kristjansson, J. E., Muri, H., Pfeffer, M., Barahona, D., and
Nenes, A.: Cirrus cloud seeding has potential to cool climate, Geophys. Res.
Lett., 40, 178–182, https://doi.org/10.1029/2012GL054201, 2013.
Wang, M. and Penner, J. E.: Cirrus clouds in a global climate model with a statistical cirrus cloud scheme, Atmos. Chem. Phys., 10, 5449–5474, https://doi.org/10.5194/acp-10-5449-2010, 2010.
Wang, M., Liu, X., Zhang, K., and Comstock, J. M.: Aerosol effects on cirrus
through ice nucleation in the Community Atmosphere Model CAM5 with a
statistical cirrus scheme, J. Adv. Model. Earth Sy., 6,
756–776, https://doi.org/10.1002/2014MS000339, 2014.
Wyser, K.: The effective radius in ice clouds, J. Climate, 11,
1793–1802, https://doi.org/10.1175/1520-0442(1998)011<1793:Teriic>2.0.Co;2, 1998.
Zhang, G. J. and McFarlane, N. A.: Sensitivity of climate simulations to the
parameterization of cumulus convection in the Canadian climate centre
general circulation model, Atmos.-Ocean, 33, 407–446,
https://doi.org/10.1080/07055900.1995.9649539, 1995.
Zhang, G. J., Kiehl, J. T., and Rasch, P. J.: Response of climate simulation
to a new convective parameterization in the National Center for Atmospheric
Research Community Climate Model (CCM3), J. Climate, 11, 2097–2115,
https://doi.org/10.1175/1520-0442(1998)011<2097:Rocsta>2.0.Co;2, 1998.
Zhang, W., Shi, X., and Lu, C.: Model code, data, and plot scripts for the paper “Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)”, Zenodo [data set and code], https://doi.org/10.5281/zenodo.6409156, 2022.
Zhao, X., Lin, Y., Peng, Y., Wang, B., Morrison, H., and Gettelman, A.: A
single ice approach using varying ice particle properties in global climate
model microphysics, J. Adv. Model. Earth Sy., 9,
2138–2157, https://doi.org/10.1002/2017MS000952, 2017.
Zhou, C., Zelinka, M. D., and Klein, S. A.: Impact of decadal cloud
variations on the Earth's energy budget, Nat. Geosci., 9, 871–874,
https://doi.org/10.1038/ngeo2828, 2016.
Short summary
The two-moment bulk cloud microphysics scheme used in CAM6 was modified to consider the impacts of the ice-crystal size distribution shape parameter (μi). After that, how the μi impacts cloud microphysical processes and then climate simulations is clearly illustrated by offline tests and CAM6 model experiments. Our results and findings are useful for the further development of μi-related parameterizations.
The two-moment bulk cloud microphysics scheme used in CAM6 was modified to consider the impacts...