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Advances in Atmospheric Sciences

, Volume 36, Issue 7, pp 697–710 | Cite as

LASG Global AGCM with a Two-moment Cloud Microphysics Scheme: Energy Balance and Cloud Radiative Forcing Characteristics

  • Lei Wang
  • Qing BaoEmail author
  • Wei-Chyung Wang
  • Yimin Liu
  • Guo-Xiong Wu
  • Linjiong Zhou
  • Jiandong Li
  • Hua Gong
  • Guokui Nian
  • Jinxiao Li
  • Xiaocong Wang
  • Bian He
Original Paper
  • 28 Downloads

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.

Key words

two-moment cloud microphysics scheme aerosol-cloud interactions energy balance cloud radiative forcing Asian monsoon region 

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.

关键词

双参数云微物理参数化方案 气溶胶-云相互作用 能量收支 云辐射强迫 东亚季风区 

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Notes

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).

References

  1. 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. CrossRefGoogle Scholar
  2. 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. CrossRefGoogle Scholar
  3. 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. CrossRefGoogle Scholar
  4. 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. CrossRefGoogle Scholar
  5. 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. CrossRefGoogle Scholar
  6. 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. Google Scholar
  7. 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. CrossRefGoogle Scholar
  8. 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. CrossRefGoogle Scholar
  9. 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. CrossRefGoogle Scholar
  10. 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. CrossRefGoogle Scholar
  11. 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. CrossRefGoogle Scholar
  12. 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)Google Scholar
  13. 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. Google Scholar
  14. 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. CrossRefGoogle Scholar
  15. 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. CrossRefGoogle Scholar
  16. 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. CrossRefGoogle Scholar
  17. 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. CrossRefGoogle Scholar
  18. 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. Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.
  22. 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. CrossRefGoogle Scholar
  23. 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. Google Scholar
  24. 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. CrossRefGoogle Scholar
  25. 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. CrossRefGoogle Scholar
  26. 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. CrossRefGoogle Scholar
  27. 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. CrossRefGoogle Scholar
  28. 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. CrossRefGoogle Scholar
  29. 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. CrossRefGoogle Scholar
  30. 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. CrossRefGoogle Scholar
  31. 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. CrossRefGoogle Scholar
  32. 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. Google Scholar
  33. 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. CrossRefGoogle Scholar
  34. 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. CrossRefGoogle Scholar
  35. 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. CrossRefGoogle Scholar
  36. 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. CrossRefGoogle Scholar
  37. 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. CrossRefGoogle Scholar
  38. 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.
  39. 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. CrossRefGoogle Scholar
  40. 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. CrossRefGoogle Scholar
  41. 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. CrossRefGoogle Scholar
  42. 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. CrossRefGoogle Scholar
  43. 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. CrossRefGoogle Scholar
  44. 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. CrossRefGoogle Scholar
  45. 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. CrossRefGoogle Scholar
  46. 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. CrossRefGoogle Scholar
  47. 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.
  48. 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. CrossRefGoogle Scholar
  49. 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. CrossRefGoogle Scholar
  50. 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. CrossRefGoogle Scholar
  51. 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. CrossRefGoogle Scholar

Copyright information

© Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lei Wang
    • 1
    • 3
  • Qing Bao
    • 1
    • 2
    Email author
  • Wei-Chyung Wang
    • 4
  • Yimin Liu
    • 1
    • 2
    • 3
  • Guo-Xiong Wu
    • 1
    • 3
  • Linjiong Zhou
    • 5
  • Jiandong Li
    • 1
  • Hua Gong
    • 1
    • 3
  • Guokui Nian
    • 1
    • 3
  • Jinxiao Li
    • 3
  • Xiaocong Wang
    • 1
    • 2
  • Bian He
    • 1
    • 2
  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid DynamicsInstitute of Atmospheric Physics, Chinese Academy of SciencesBeijingChina
  2. 2.CAS Center for Excellence in Tibetan Plateau Earth SciencesChinese Academy of SciencesBeijingChina
  3. 3.College of Earth and Planetary SciencesUniversity of the Chinese Academy of SciencesBeijingChina
  4. 4.Atmospheric Sciences Research CenterState University of New YorkAlbanyUSA
  5. 5.National Oceanic and Atmospheric AdministrationGeophysical Fluid Dynamics LaboratoryPrincetonUSA

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