The improvement of the accuracy of simulated cloud-related variables, such as the cloud fraction, in global climate models (GCMs) is still a challenging problem in climate modeling. In this study, the influence of cloud microphysics schemes (one-moment versus two-moment schemes) and cloud overlap methods (observation-based versus a fixed vertical decorrelation length) on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero. Compared with the fixed decorrelation length method, the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions. The utilization of a two-moment cloud microphysics scheme, on the other hand, notably improved the simulated cloud fraction compared with the one-moment scheme; specifically, the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%. Furthermore, the total cloud fraction bias decreased by 6.6% in the boreal winter (DJF) and 1.64% in the boreal summer (JJA). Cloud radiative forcing globally and in the four regions improved by 0.3%–1.2% and 0.2%–2.0%, respectively. Thus, our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Barker, H. W., 2008: Overlap of fractional cloud for radiation calculations in GCMs: A global analysis using CloudSat and CALIPSO data. J. Geophys. Res. Atmos., 113, D00A01, https://doi.org/10.1029/2007JD009677.
Barker, H. W., G. L. Stephens, and Q. Fu, 1999: The sensitivity of domain-averaged solar fluxes to assumptions about cloud geometry. Quart. J. Roy. Meteor. Soc., 125, 2 127–2 152, https://doi.org/10.1002/qj.49712555810.
Bergman, J. W., and P. J. Rasch, 2002: Parameterizing vertically coherent cloud distributions. J. Atmos. Sci., 59, 2 165–2 182, https://doi.org/10.1175/1520-0469(2002)059<2165:PVCCD>2.0.CO;2.
Collins, W. D., and Coauthors, 2004: Description of the NCAR community atmosphere model (CAM 3.0). NCAR Tech. Note NCAR/TN-464+STR, 226 pp, https://doi.org/10.5065/D63N21CH.
Di Giuseppe, F., 2005: Sensitivity of one-dimensional radiative biases to vertical cloud-structure assumptions: Validation with aircraft data. Quart. J. Roy. Meteor. Soc., 131, 1 655–1 676, https://doi.org/10.1256/qj.03.129.
Di Giuseppe, F., and A. M. Tompkins, 2015: Generalizing cloud overlap treatment to include the effect of wind shear. J. Atmos. Sci., 72, 2 865–2 876, https://doi.org/10.1175/JASD-14-0277.1.
Ding, S. G., C. S. Zhao, G. Y. Shi, and C. A. Wu, 2005: Analysis of global total cloud amount variation over the past 20 years. Journal of Applied Meteorological Science, 16, 670–677, https://doi.org/10.3969/j.issn.1001-7313.2005.05.014. (in Chinese with English abstract)
Fan, T. Y., and Coauthors, 2018: Quantify contribution of aerosol errors to cloud fraction biases in CMIP5 Atmospheric Model Intercomparison Project simulations. International Journal of Climatology, 38, 3 140–3 156, https://doi.org/10.1002/joc.5490.
Flynn, C. M., and T. Mauritsen, 2020: On the climate sensitivity and historical warming evolution in recent coupled model ensembles. Atmospheric Chemistry and Physics, 20, 7 829–7 842, https://doi.org/10.5194/acp-20-7829-2020.
Garrett, T. J., and C. F. Zhao, 2006: Increased Arctic cloud longwave emissivity associated with pollution from mid-latitudes. Nature, 440, 787–789, https://doi.org/10.1038/nature04636.
Ghan, S. J., L. R. Leung, and Q. Hu, 1997: Application of cloud microphysics to NCAR community climate model. J. Geophys. Res. Atmos., 102, 16 507–16 527, https://doi.org/10.1029/97JD00703.
Ghan, S. J., X. Liu, R. C. Easter, R. Zaveri, P. J. Rasch, J.-H. Yoon, and B. Eaton, 2012: Toward a minimal representation of aerosols in climate models: Comparative decomposition of aerosol direct, semidirect, and indirect radiative forcing. J. Climate, 25, 6 461–6 476, https://doi.org/10.1175/JCLI-D-11-00650.1.
Harrison, E. F., P. Minnis, B. R. Barkstrom, V. Ramanathan, R. D. Cess, and G. G. Gibson, 1990: Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. J. Geophys. Res. Atmos., 95, 18 687–18 703, https://doi.org/10.1029/JD095iD11p18687.
Hogan, R. J., and A. J. Illingworth, 2000: Deriving cloud overlap statistics from radar. Quart. J. Roy. Meteor. Soc., 126, 2 903–2 909, https://doi.org/10.1002/qj.49712656914.
Intergovernmental Panel on Climate Change, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp.
Jing, X. W., H. Zhang, J. Peng, J. N. Li, and H. W. Barker, 2016: Cloud overlapping parameter obtained from CloudSat/CALIPSO dataset and its application in AGCM with McICA scheme. Atmospheric Research, 170, 52–65, https://doi.org/10.1016/j.atmosres.2015.11.007.
Jing, X. W., H. Zhang, M. Satoh, and S. Y. Zhao, 2018: Improving representation of tropical cloud overlap in GCMs based on cloud-resolving model data. J. Meteor. Res., 32, 233–245, https://doi.org/10.1007/s13351-018-7095-9.
Kato, S., S. Sun — Mack, W. F. Miller, F. G. Rose, Y. Chen, P. Minnis, and B. A. Wielicki, 2010: Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles. J. Geophys. Res. Atmos., 115, D00H28, https://doi.org/10.1029/2009JD012277.
Klinger, C., G. Feingold, and T. Yamaguchi, 2019: Cloud droplet growth in shallow cumulus clouds considering 1-D and 3-D thermal radiative effects. Atmospheric Chemistry and Physics, 19, 6 295–6 313, https://doi.org/10.5194/acp-19-6295-2019.
Kumar, S., Y.-S. Vidal, A. S. Moya-Álvarez, and D. Martínez-Castro, 2019: Effect of the surface wind flow and topography on precipitating cloud systems over the Andes and associated Amazon basin: GPM observations. Atmospheric Research, 225, 193–208, https://doi.org/10.1016/j.atmosres.2019.03.027.
Li, J. M., Q. Y. Lv, B. D. Jian, M. Zhang, C. F. Zhao, Q. Fu, K. Kawamoto, and H. Zhang, 2018: The impact of atmospheric stability and wind shear on vertical cloud overlap over the Tibetan Plateau. Atmospheric Chemistry and Physics, 18, 7 329–7 343, https://doi.org/10.5194/acp-18-7329-2018.
Li, J. M., B. D. Jian, C. F. Zhao, Y. X. Zhao, J. Wang, and J. P. Huang, 2019: Atmospheric instability dominates the long — term variation of cloud vertical overlap over the southern great plains site. J. Geophys. Res. Atmos., 124, 9 691–9 701, https://doi.org/10.1029/2019JD030954.
Loeb, N. G., and Coauthors, 2018: Clouds and the Earth’s radiant energy system (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1.
Lohmann, U., P. Stier, C. Hoose, S. Ferrachat, S. Kloster, E. Roeckner, and J. Zhang, 2007: Cloud microphysics and aerosol indirect effects in the global climate model ECHAM5-HAM. Atmospheric Chemistry and Physics, 7, 3 425–3 446, https://doi.org/10.5194/acp-7-3425-2007.
Lu, P., H. Zhang, and J. N. Li, 2011: Correlated k-distribution treatment of cloud optical properties and related radiative impact. J. Atmos. Sci., 68, 2 671–2 688, https://doi.org/10.1175/JAS-D-10-05001.1.
Lu, R. Y., B. W. Dong, R. D. Cess, and G. L. Potter, 2004: The 1997/98 El Niño: A test for climate models. Geophys. Res. Lett., 31, L12216, https://doi.org/10.1029/2004GL019956.
Ma, Z. S., Q. J. Liu, C. F. Zhao, X. S. Shen, Y. Wang, J. H. Jiang, Z. Li, and Y. Yung, 2018: Application and evaluation of an explicit prognostic cloud — cover scheme in GRAPES global forecast system. Journal of Advances in Modeling Earth Systems, 10, 652–667, https://doi.org/10.1002/2017MS001234.
Mace, G. G., and S. Benson-Troth, 2002: Cloud-layer overlap characteristics derived from long-term cloud radar data. J. Climate, 15, 2 505–2 515, https://doi.org/10.1175/1520-0442(2002)015<2505:CLOCDF>2.0.CO;2.
Mather, J. H., S. A. McFarlane, M. A. Miller, and K. L. Johnson, 2007: Cloud properties and associated radiative heating rates in the tropical western Pacific. J. Geophys. Res. Atmos., 112, D05201, https://doi.org/10.1029/2006JD007555.
Minnis, P., D. Doelling, L. Nguyen, R. Palikonda, D. A. Spangenberg, G. Hong, and H. Yi, 2011: Improved cloud and surface properties by combining conventional and L-1 satellite imager data. Preprints, AGU Fall Meeting 2011, San Francisco, CA, USA.
Morrison, H., and A. Gettelman, 2008: 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, 3 642–3 659, https://doi.org/10.1175/2008JCLI2105.1.
Naud, C. M., A. Del Genio, G. G. Mace, S. Benson, E. E. Clothiaux, and P. Kollias, 2008: Impact of dynamics and atmospheric state on cloud vertical overlap. J. Climate, 21, 1 758–1 770, https://doi.org/10.1175/2007JCLI1828.1.
Nenes, A., and J. H. Seinfeld, 2003: Parameterization of cloud droplet formation in global climate models. J. Geophys. Res. Atmos., 108, 4415, https://doi.org/10.1029/2002JD002911.
Oreopoulos, L., D. Lee, Y. C. Sud, and M. J. Suarez, 2012: Radiative impacts of cloud heterogeneity and overlap in an atmospheric General Circulation Model. Atmospheric Chemistry and Physics, 12, 9 097–9 111, https://doi.org/10.5194/acp-12-9097-2012.
Pincus, R., H. W. Barker, and J.-J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res. Atmos., 108, 4376, https://doi.org/10.1029/2002JD003322.
Potter, G. L., and R. D. Cess, 2004: Testing the impact of clouds on the radiation budgets of 19 atmospheric general circulation models. J. Geophys. Res. Atmos., 109, D02106, https://doi.org/10.1029/2003JD004018.
Räisänen, P., and H. W. Barker, 2004: Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation. Quart. J. Roy. Meteor. Soc., 130, 2 069–2 085, https://doi.org/10.1256/qj.03.215.
Räisänen, P., H. W. Barker, M. F. Khairoutdinov, J. N. Li, and D. A. Randall, 2004: Stochastic generation of subgrid-scale cloudy columns for large-scale models. Quart. J. Roy. Meteor. Soc., 130, 2 047–2 067, https://doi.org/10.1256/qj.03.99.
Randles, C. A., and Coauthors, 2013: Intercomparison of shortwave radiative transfer schemes in global aerosol modeling: Results from the AeroCom Radiative Transfer Experiment. Atmospheric Chemistry and Physics, 13, 2 347–2 379, https://doi.org/10.5194/acp-13-2347-2013.
Rasch, P. J., and J. E. Kristjánsson, 1998: A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Climate, 11, 1 587–1 614, https://doi.org/10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2.
Sato, T., F. Kimura, and A. S. Hasegawa, 2007: Vegetation and topographic control of cloud activity over arid/semiarid Asia. J. Geophys. Res. Atmos., 112, D24109, https://doi.org/10.1029/2006JD008129.
Shonk, J. K. P., R. J. Hogan, J. M. Edwards, and G. G. Mace, 2010: Effect of improving representation of horizontal and vertical cloud structure on the Earth’s global radiation budget. Part I: Review and parametrization. Quart. J. Roy. Meteor. Soc., 136, 1 191–1 204, https://doi.org/10.1002/qj.647.
Stephens, G. L., and Coauthors, 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res. Atmos., 113, D00A18, https://doi.org/10.1029/2008JD009982.
Tan, I., T. Storelvmo, and M. D. Zelinka, 2016: Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science, 352, 224–227, https://doi.org/10.1126/science.aad5300.
Tompkins, A. M., and F. Di Giuseppe, 2015: An interpretation of cloud overlap statistics. J. Atmos. Sci., 72, 2 877–2 889, https://doi.org/10.1175/JAS-D-14-0278.1.
Wang, H. B., H. Zhang, X. W. Jing, and B. Xie, 2018: Effects of different cloud overlapping parameters on simulated total cloud fraction over the globe and East Asian region. Acta Meteorologica Sinica, 76, 767–778, https://doi.org/10.11676/qxxb2018.027. (in Chinese with English abstract)
Wang, P.-H., P. Minnis, M. P. McCormick, G. S. Kent, G. K. Yue, D. F. Young, and K. M. Skeens, 1998: A study of the vertical structure of tropical (20°S–20°N) optically thin clouds from SAGE II observations. Atmospheric Research, 47–48, 599–614, https://doi.org/10.1016/S0169-8095(97)00085-9.
Wang, Z. L., H. Zhang, and P. Lu, 2014: Improvement of cloud microphysics in the aerosol-climate model BCC_AGCM 2.0.1 _CUACE/Aero, evaluation against observations, and updated aerosol indirect effect. J. Geophys. Res. Atmos., 119, 8 400–8 417, https://doi.org/10.1002/2014JD021886.
Webb, M., C. Senior, S. Bony, and J.-J. Morcrette, 2001: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Climate Dyn., 17, 905–922, https://doi.org/10.1077/s003820100157.
Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 2 373–2 423, https://doi.org/10.1175/MWR-D-11-00121.1.
Xie, S. C., X. H. Liu, C. F. Zhao, and Y. Y. Zhang, 2013: Sensitivity of CAM5-simulated arctic clouds and radiation to ice nucleation parameterization. J. Climate, 26, 5 981–5 999, https://doi.org/10.1175/JCLI-D-12-00517.1.
Yang, Y., and Coauthors, 2019: Toward understanding the process-level impacts of aerosols on microphysical properties of shallow cumulus cloud using aircraft observations. Atmospheric Research, 221, 27–33, https://doi.org/10.1016/j.atmosres.2019.01.027.
Zhang, B. C., Z. Guo, X. L. Chen, T. J. Zhou, X. Y. Rong, and J. Li, 2020: Responses of cloud-radiative forcing to strong El Niño events over the western Pacific warm pool as simulated by CAMS-CSM. J. Meteor. Res., 34, 499–514, https://doi.org/10.1007/s13351-020-9161-3.
Zhang, H., 2015: The Study on Atmospheric Absorption Radiation. China Meteorological Press, 179 pp. (in Chinese)
Zhang, H., 2016: BCC_RAD Radiative Transfer Model. China Meteorological Press, 205 pp. (in Chinese)
Zhang, H., and X. W. Jing, 2016: Advances in studies of cloud overlap and its radiative transfer issues in the climate models. Acta Meteorologica Sinica, 74, 103–113, https://doi.org/10.11676/qxxb2016.009.
Zhang, H., T. Nakajima, G. Y. Shi, T. Suzuki, and R. Imasu, 2003: An optimal approach to overlapping bands with correlated k distribution method and its application to radiative calculations. J. Geophys. Res. Atmos., 108, 4641, https://doi.org/10.1029/2002JD003358.
Zhang, H., G. Y. Shi, T. Nakajima, and T. Suzuki, 2006a: The effects of the choice of the k-interval number on radiative calculations. Journal of Quantitative Spectroscopy and Radiative Transfer, 98, 31–43, https://doi.org/10.1016/j.jqsrt.2005.05.090.
Zhang, H., T. Suzuki, T. Nakajima, G. Y. Shi, X. Y. Zhang, and Y. Liu, 2006b: Effects of band division on radiative calculations. Optical Engineering, 45, 016002, https://doi.org/10.1117/1.2160521.
Zhang, H., and Coauthors, 2012: Simulation of direct radiative forcing of aerosols and their effects on East Asian climate using an interactive AGCM-aerosol coupled system. Climate Dyn., 38, 1 675–1 693, https://doi.org/10.1007/s00382-011-1131-0.
Zhang, H., J. Peng, X. W. Jing, and J. N. Li, 2013: The features of cloud overlapping in Eastern Asia and their effect on cloud radiative forcing. Science China Earth Sciences, 56, 737–747, https://doi.org/10.1007/s11430-012-4489-x.
Zhang, H., X. Jing, and J. Li, 2014: Application and evaluation of a new radiation code under McICA scheme in BCC_AGCM2.0.1. Geoscientific Model Development, 7, 737–754, https://doi.org/10.5194/gmd-7-737-2014.
Zhang, H., Q. Chen, and B. Xie, 2015: A new parameterization for ice cloud optical properties used in BCC-RAD and its radiative impact. Journal of Quantitative Spectroscopy and Radiative Transfer, 150, 76–86, https://doi.org/10.1016/j.jqsrt.2014.08.024.
Zhang, H., Z. L. Wang, and S. Y. Zhao, 2017: Atmospheric Aerosols and Their Climate Effects. China Meteorological Press, 204 pp. (in Chinese)
Zhang, H., X. W. Jing, and J. Peng, 2019: Cloud Radiation and Climate. China Meteorological Press, 270 pp. (in Chinese)
Zhao, C. F., and T. J. Garrett, 2015: Effects of Arctic haze on surface cloud radiative forcing, Geophys. Res. Lett., 42, 557–564, https://doi.org/10.1002/2014GL062015.
Zhao, C. F., and Coauthors, 2020: Aerosol characteristics and impacts on weather and climate over the Tibetan Plateau. National Science Review, 7(3), 492–495, https://doi.org/10.1093/nsr/nwz184.
This work was financially supported by the National Key R&D Program of China (2017YFA0603502), (Key) National Natural Science Foundation of China (91644211), S&T Development Fund of CAMS (2021KJ004).
• The utilization of a two-moment cloud microphysics scheme notably improved the simulated cloud-related variables.
• The observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions.
• In the two-moment cloud microphysics scheme, observation-based vertical decorrelation length improved the simulations more obviously than in fixed vertical decorrelation length.
This paper is a contribution to the special issue on Cloud-Aerosol-Radiation-Precipitation Interaction: Progress and Challenges.
Electronic supplementary material
About this article
Cite this article
Wang, H., Zhang, H., Xie, B. et al. Evaluating the Impacts of Cloud Microphysical and Overlap Parameters on Simulated Clouds in Global Climate Models. Adv. Atmos. Sci. (2021). https://doi.org/10.1007/s00376-021-0369-7
- cloud fraction
- cloud microphysics scheme
- cloud radiative forcing
- vertical cloud overlap