Abstract
Cloud dominates influence factors of atmospheric radiation, while aerosol-cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment (mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/LASG global Finite-volume Atmospheric Model (FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing (CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere (TOA) and at the Earth’s surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2 (1%) and 3 W m-2 (3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region (AMR). The results indicate that FAMIL1.1 well reproduces the summer-winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.
摘要
云是影响大气辐射的主要因子之一, 气溶胶-云相互作用则对云的时空分布具有十分重要的影响. 为了提高大气物理研究所LASG实验室大气环流模式(FAMIL)对气溶胶-云相互作用的模拟能力, 一个基于物理过程的双参数云微物理参数化方案(CLR2)被引入到该模式中, 该参数化方案能够更合理地刻画气溶胶-云相互作用过程, 新的模式被命名为FAMIL1.1. 为了评估新模式的模拟性能, 我们首先将模式模拟的能量收支和云辐射强迫特征与再分析资料和观测资料进行了对比分析. 结果表明, 即使使用预设的气溶胶质量浓度, 新模式也能够合理模拟出大气层顶和地表全球平均的能量收支及其季节循环特征. 最大偏差项为到达地表的长波辐射和地表的潜热通量, 偏差分别为3.5 W m−2(相对偏差为1%)和3 W m−2(相对偏差为3.5%). 模式也能够合理地再现全球大气层顶的净辐射通量和净云辐射强迫的空间分布特征, 与观测结果的空间相关系数可分别达到0.97和0.9. 模式的主要偏差在于对液态云水含量的高估和冰水含量的低估. 此外, 我们也关注模式的区域模拟偏差, 并聚焦于东亚季风区. 结果表明, 新模式能够合理的再现东亚季风区云辐射强迫的空间分布特征以及其显著的冬-夏差异. 文末对模式的偏差和可能的改进方法、以及下一步的研发计划进行了相关讨论.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Abdul-Razzak, H., and S. J. Ghan, 2000: A parameterization of aerosol activation: 2. Multiple aerosol types. J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161.
Bao, Q., G. X. Wu, Y. M. Liu, J. Yang, Z. Z. Wang, and T. J. Zhou, 2010: An introduction to the coupled model FGOALS1.1-s and its performance in East Asia. Adv. Atmos. Sci., 27, 1131–1142, https://doi.org/10.1007/s00376-010-9177-1.
Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1.
Bony, S., and Coauthors, 2006: How well do we understand and evaluate climate change feedback processes? J. Climate, 19, 3445–3482, https://doi.org/10.1175/JCLI3819.1.
Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the community atmosphere model. J. Climate, 22, 3422–3448, https://doi.org/10.1175/2008JCLI2556.1.
Chen, B. D., and X. D. Liu, 2005: Seasonal migration of cirrus clouds over the Asian Monsoon regions and the Tibetan Plateau measured from MODIS/Terra. Geophys. Res. Lett., 32, L01804, https://doi.org/10.1029/2004GL020868.
Chen, G. X., W. C. Wang, and J. P. Chen, 2015: Aerosol-stratocumulus-radiation interactions over the southeast pacific. J. Atmos. Sci., 72, 2612–2621, https://doi.org/10.1175/JAS-D-14-0319.1.
Chen, G. X., J. Yang, Q. Bao, and W. C. Wang. 2018: Intrasea-sonal responses of the East Asia summer rainfall to anthropogenic aerosol climate forcing. Climate Dyn., 51, 3985–3998, https://doi.org/10.1007/s00382-017-3691-0.
Chen, J.-P., and S.-T. Liu, 2004: Physically based two-moment bulkwater parametrization for warm-cloud microphysics. Quart. J. Roy. Meteor. Soc., 130, 51–78, https://doi.org/10.1256/qj.03.41.
Cheng, C.-T., W.-C. Wang, and J.-P. Chen, 2007: A modelling study of aerosol impacts on cloud microphysics and radiative properties. Quart. J. Roy. Meteor. Soc., 133, 283–297, https://doi.org/10.1002/qj.25.
Cheng, C.-T., W.-C. Wang, and J.-P. Chen, 2010: Simulation of the effects of increasing cloud condensation nuclei on mixed-phase clouds and precipitation of a front system. Atmospheric Research, 96, 461–476, https://doi.org/10.1016/j.atmosres.2010.02.005.
Duan, J., and J. T. Mao, 2008: Progress in researches on interaction between aerosol and cloud. Advances in Earth Science, 23, 252–261, https://doi.org/10.11867/j.issn.10018166.2008.03.0252. (in Chinese with English abstract)
Ellis, T. D., T. L’Ecuyer, J. M. Haynes, and G. L. Stephens, 2009: How often does it rain over the global oceans? The perspective from CloudSat. Geophys. Res. Lett., 36, L03815, https://doi.org/10.1029/2008GL036728.
Fan, J. W., Y. Wang, D. Rosenfeld, and X. H. Liu, 2016: Review of aerosol-cloud interactions: Mechanisms, significance, and challenges. J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1.
Feingold, G., B. Stevens, W. R. Cotton, and R. L. Walko, 1994: An explicit cloud microphysics/LES model designed to simulate the Twomey effect. Atmospheric Research, 33, 207–233, https://doi.org/10.1016/0169-8095(94)90021-3.
Gettelman, A., and S. C. Sherwood, 2016: Processes responsible for cloud feedback. Current Climate Change Reports, 2, 179–189, https://doi.org/10.1007/s40641-016-0052-8.
Harris, L. M., and S. J. Lin, 2014: Global-to-regional nested grid climate simulations in the GFDL high resolution atmospheric model. J. Climate, 27, 4890–4910, https://doi.org/10.1175/JCLI-D-13-00596.1.
Hazra, A., P. Mukhopadhyay, S. Taraphdar, J.-P. Chen, and W. R. Cotton, 2013: Impact of aerosols on tropical cyclones: An investigation using convection-permitting model simulation. J. Geophys. Res., 118, 7157–7168, https://doi.org/10.1002/jgrd.50546.
Holtslag, A. A. M., and B. A. Boville, 1993: Local versus nonlocal boundary-layer diffusion in a global climate model. J. Climate, 6, 1825–1842, https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2.
Hong, S. Y., J. Dudhia, and S. H. Chen, 2002: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.
Jiang, H. L., G. Feingold, W. R. Cotton, and P. G. Duynkerke, 2001: Large-eddy simulations of entrainment of cloud condensation nuclei into the Arctic boundary layer: May 18, 1998, FIRE/SHEBA case study. J. Geophys. Res., 106, 15 113-15 122, https://doi.org/10.1029/2000JD900303.
Lamarque, J. F., and Coauthors, 2012: CAM-chem: Description and evaluation of interactive atmospheric chemistry in the Community Earth System Model. Geoscientific Model Development, 5, 369–411, https://doi.org/10.5194/gmd-5-369-2012.
Lee, S. S., and L. J. Donner, 2011: Effects of cloud parameterization on radiation and precipitation: A comparison between single-moment microphysics and double-moment microphysics. Terrestrial, Atmospheric and Oceanic Sciences, 22, 403–420, https://doi.org/10.3319/TAO.2011.03.03.01.
Li, J. D., W. C. Wang, Z. A. Sun, G. X. Wu, H. Liao, and Y. M. Liu, 2014a: Decadal variation of East Asian radiative forcing due to anthropogenic aerosols during 1850–2100, and the role of atmospheric moisture. Climate Research, 61, 241–257, https://doi.org/10.3354/cr01236.
Li, J. D., J. Y. Mao, and F. Wang, 2017a: Comparative study of five current reanalyses in characterizing total cloud fraction and top-of-the-atmosphere cloud radiative effects over the Asian monsoon region. International Journal of Climatology, 37, 5047–5067, https://doi.org/10.1002/joc.5143.
Li, J.-X., Q. Bao, Y.-M. Liu, and G.-X. Wu, 2017b: Evaluation of the computational performance of the finite-volume atmospheric model of the IAP/LASG (FAMIL) on a high-performance computer. Atmospheric and Oceanic Science Letters, 10, 329–336, https://doi.org/10.1080/16742834.2017.1331111.
Li, L. J., and Coauthors, 2013: The flexible global ocean-atmosphere-land system model, grid-point version 2: FGOALS-g2. Adv. Atmos. Sci., 30, 543–560, https://doi.org/10.1007/s00376-012-2140-6.
Li, L. J., and Coauthors, 2014b: The flexible global ocean-atmosphere-land system model, grid-point version 2: FGOALS-g2. Flexible Global Ocean-Atmosphere-Land System Model: A Modeling Tool for the Climate Change Research Community, T. J. Zhou et al., Eds., Springer, 39–43, https://doi.org/10.1007/978-3-642-41801-3.
Lim, K. S. S., and S. Y. Hong, 2010: Development of an Effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612, https://doi.org/10.1175/2009MWR2968.1.
Lin, Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor., 22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. At-mos. Sci., 62, 1665–1677, https://doi.org/10.1175/JAS3446.1.
Peng, Y. R., U. Lohmann, R. Leaitch, C. Banic, and M. Couture, 2002: The cloud albedo-cloud droplet effective radius relationship for clean and polluted clouds from RACE and FIRE. ACE. J. Geophys. Res., 107, 4106, https://doi.org/10.1029/2000JD000281.
Pinto, J. O., 1998: Autumnal mixed-phase cloudy boundary layers in the arctic. J. Atmos. Sci., 55, 2016–2038, https://doi.org/10.1175/1520-0469(1998)055<2016:AMPCBL>2.0.CO;2.
Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 1071–1107, https://doi.org/10.1002/qj.49712454804.
Roh, W., M. Satoh, and T. Nasuno, 2017: Improvement of a cloud microphysics scheme for a global nonhydrostatic model using TRMM and a satellite simulator. J. Atmos. Sci., 74, 167–184, https://doi.org/10.1175/JAS-D-16-0027.1.
Rosenfeld, D., S. Sherwood, R. Wood, and L. Donner, 2014: Climate effects of aerosol-cloud interactions. Science, 343, 379–380, https://doi.org/10.1126/science.1247490.
Salzmann, M., Y. Ming, J. C. Golaz, P. A. Ginoux, H. Morrison, A. Gettelman, M. Krämer, and L. J. Donner, 2010: Twomoment bulk stratiform cloud microphysics in the GFDL AM3 GCM: Description, evaluation, and sensitivity tests. Atmospheric Chemistry and Physics, 10, 8037–8064, https://doi.org/10.5194/acp-10-8037-2010.
Sassen, K., and Z. E. Wang, 2008: Classifying clouds around the globe with the CloudSat radar: 1-year of results. Geo-phys. Res. Lett., 35, L04805, https://doi.org/10.1029/2007GL032591.
Seifert, A., and K. D. Beheng, 2006: A two-moment cloud mi-crophysics parameterization for mixed-phase clouds. Part 1: Model description. Meteor. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4.
Stephens, G. L., and Coauthors, 2012: An update on Earth’s energy balance in light of the latest global observations. Nature Geoscience, 5, 691–696, https://doi.org/10.1038/ngeo1580.
Wang, W. C., J. P. Chen, I. S. A. Isaksen, I. C. Tsai, K. Noone, and K. Mcguffie, 2012: Climate-chemistry interaction: Future tropospheric ozone and aerosols. The Future of the World’s Climate, 2nd ed, A. Henderson-Sellers and K. McGuffie, Eds., Elsevier, 367–399, https://doi.org/10.1016/B978-0-12-386917-3.00013-0.
Wang, W. C., G. X. Chen, and Y. Y. Song, 2017: Modeling aerosol climate effects over monsoon Asia: A collaborative research program. Adv. Atmos. Sci., 34, 1195–1203, https://doi.org/10.1007/s00376-017-6319-8.
Whitby, K. T., 1978: The physical characteristics of sulfur aerosols. Atmos. Environ., 12, 135–159, https://doi.org/10.1016/0004-6981(78)90196-8.
Wild, M., D. Folini, C. Schär, N. Loeb, E. G. Dutton, and G. König-Langlo, 2013: The global energy balance from a surface perspective. Climate Dyn., 40, 3107–3134, https://doi.org/10.1007/s00382-012-1569-8.
Wood, R., P. R. Field, and W. R. Cotton, 2002: Autoconversion rate bias in stratiform boundary layer cloud parameterizations. Atmospheric Research, 65, 109–128, https://doi.org/10.1016/S0169-8095(02)00071-6.
Wu, G. X., H. Liu, Y. C. Zhao, and W. P. Li, 1996: A nine-layer atmospheric general circulation model and its performance. Adv. Atmos. Sci., 13, 1–18, https://doi.org/10.1007/BF02657024.
Yang, J., W. C. Wang, G. X. Chen, Q. Bao, X. Qi, S. Y. Zhou, 2018: Intraseasonal variation of the black carbon aerosol concentration and its impact on atmospheric circulation over the Southeastern Tibetan Plateau. J. Geophys. Res., 123, 10 881-10 894, https://doi.org/10.1029/2018JD029013.
Zelinka, M. D., D. A. Randall, M. J. Webb, and S. A. Klein, 2017: Clearing clouds of uncertainty. Nat. Clim. Change, 7, 674–678, https://doi.org/10.1038/nclimate3402.
Zhang, X. Y., Y. Q. Wang, T. Niu, X. C. Zhang, S. L. Gong, Y. M. Zhang, and J. Y. Sun, 2012: Atmospheric aerosol compositions in China: Spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmospheric Chemistry and Physics, 12, 779–799, https://doi.org/10.5194/acp-12-779-2012.
Zhou, L. J., Y. M. Liu, Q. Bao, H. Y. Yu, and G. X. Wu, 2012: Computational performance of the high-resolution atmospheric model FAMIL. Atmospheric and Oceanic Science Letters, 5, 355–359, https://doi.org/10.1080/16742834.2012.11447024.
Zhou, L. J., and Coauthors, 2015: Global energy and water balance: Characteristics from Finite-volume Atmospheric Model of the IAP/LASG (FAMIL1). Journal of Advances in Modeling Earth Systems, 7, 1–20, https://doi.org/10.1002/2014MS000349.
Acknowledgements
We thank two anonymous reviewers for their careful reading of the manuscript and their many insightful comments and suggestions. This study was jointly funded by the National Natural Science Foundation of China (Grants 41675100, 91737306, and U1811464).
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights
• A physical-based two-moment microphysical scheme is introduced to AGCM FAMIL1.1.
• The model simulates reasonably both the global and regional energy budgets and cloud radiative forcing.
• The model bias as well as the possible solution are also discussed in FAMIL1.1.
Rights and permissions
About this article
Cite this article
Wang, L., Bao, Q., Wang, WC. et al. LASG Global AGCM with a Two-moment Cloud Microphysics Scheme: Energy Balance and Cloud Radiative Forcing Characteristics. Adv. Atmos. Sci. 36, 697–710 (2019). https://doi.org/10.1007/s00376-019-8196-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-019-8196-9
Key words
- two-moment cloud microphysics scheme
- aerosol-cloud interactions
- energy balance
- cloud radiative forcing
- Asian monsoon region