Advances in Atmospheric Sciences

, Volume 32, Issue 6, pp 743–758 | Cite as

Analysis and evaluation of the global aerosol optical properties simulated by an online aerosol-coupled non-hydrostatic icosahedral atmospheric model

  • Tie Dai
  • Guangyu Shi
  • Teruyuki Nakajima
Open Access


Aerosol optical properties are simulated using the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-hydrostatic ICosahedral Atmospheric Model (NICAM). The 3-year global mean all-sky aerosol optical thickness (AOT) at 550 nm, the Ångström Exponent (AE) based on AOTs at 440 and 870 nm, and the single scattering albedo (SSA) at 550 nm are estimated at 0.123, 0.657 and 0.944, respectively. For each aerosol species, the mean AOT is within the range of the AeroCom models. Both the modeled all-sky and clear-sky results are compared with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Aerosol Robotic Network (AERONET). The simulated spatiotemporal distributions of all-sky AOTs can generally reproduce the MODIS retrievals, and the correlation and model skill can be slightly improved using the clear-sky results over most land regions. The differences between clear-sky and all-sky AOTs are larger over polluted regions. Compared with observations from AERONET, the modeled and observed all-sky AOTs and AEs are generally in reasonable agreement, whereas the SSA variation is not well captured. Although the spatiotemporal distributions of all-sky and clear-sky results are similar, the clear-sky results are generally better correlated with the observations. The clear-sky AOT and SSA are generally lower than the all-sky results, especially in those regions where the aerosol chemical composition is contributed to mostly by sulfate aerosol. The modeled clear-sky AE is larger than the all-sky AE over those regions dominated by hydrophilic aerosol, while the opposite is found over regions dominated by hydrophobic aerosol.

Key words

aerosol optical properties non-hydrostatic icosahedral atmospheric model Moderate Resolution Imaging Spectroradiometer Aerosol Robotic Network 


  1. Acker, J. G., and G. Leptoukh, 2007: Online analysis enhances use of NASA Earth science data. Eos, Trans. Amer. Geophys. Union, 88(2), 14–17.CrossRefGoogle Scholar
  2. Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear sky from clouds with MODIS. J. Geophys. Res., 103(D24), 32141–32157.CrossRefGoogle Scholar
  3. Adams, P. J., and J. H. Seinfeld, 2002: Predicting global aerosol size distributions in general circulation models. J. Geophys. Res.: Atmos., 107(D19), AAC 4-1–AAC 4-23.Google Scholar
  4. Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the Large-Scale environment, Part I. J. Atmos. Sci., 31(3), 674–701.CrossRefGoogle Scholar
  5. Barnes, W. L., T. S. Pagano, and V. V. Salomonson, 1998: Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1. IEEE Trans. Geosci. Remote Sens., 36(4), 1088–1100.CrossRefGoogle Scholar
  6. Bi, J. R., J. P. Huang, Q. Fu, X. Wang, J. S. Shi, W. Zhang, H. W. Huang, and B. D. Zhang, 2011: Toward characterization of the aerosol optical properties over Loess Plateau of Northwestern China. Journal of Quantitative Spectroscopy and Radiative Transfer, 112(2), 346–360.CrossRefGoogle Scholar
  7. Chand, D., and Coauthors, 2012: Aerosol optical depth increase in partly cloudy conditions. J. Geophys. Res.: Atmos., 117(D17), D17207, doi: 10.1029/2012JD017894.Google Scholar
  8. Chin, M., and Coauthors, 2002: Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements. J. Atmos. Sci., 59(3), 461–483.CrossRefGoogle Scholar
  9. Chin, M., T. Diehl, O. Dubovik, T. F. Eck, B. N. Holben, A. Sinyuk, and D. G. Streets. 2009: Light absorption by pollution, dust, and biomass burning aerosols: A global model study and evaluation with AERONET measurements. Ann. Geophys., 27, 3439–3464.CrossRefGoogle Scholar
  10. Chin, M., and Coauthors, 2014: Multi-decadal aerosol variations from 1980 to 2009: A perspective from observations and a global model. Atmospheric Chemistry and Physics, 14(7), 3657–3690.CrossRefGoogle Scholar
  11. Chung, C. E., V. Ramanathan, and D. Decremer, 2012: Observationally constrained estimates of carbonaceous aerosol radiative forcing. Proceedings of the National Academy of Sciences of the United States of America, 109(29), 11624–11629.CrossRefGoogle Scholar
  12. Colarco, P., A. da Silva, M. Chin, and T. Diehl, 2010: Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol optical depth. J. Geophy. Res.: Atmos., 115(D14), D14207, doi: 10.1029/2009JD012820.CrossRefGoogle Scholar
  13. Cooke, W. F., and J. J. N. Wilson, 1996: A global black carbon aerosol model. J. Geophys. Res., 101(D14), 19 395–19 409.CrossRefGoogle Scholar
  14. Dai, T., N. A. J. Schutgens, and T. Nakajima, 2013: Applying a local Ensemble transform Kalman filter assimilation system to the NICAM-SPRINTARS model. AIP Conference Proceedings, 1531(1), 744–747.CrossRefGoogle Scholar
  15. Dai, T., D. Goto, N. A. J. Schutgens, X. Dong, G. Shi, and T. Nakajima, 2014a: Simulated aerosol key optical properties over global scale using an aerosol transport model coupled with a new type of dynamic core. Atmos. Enviro., 82, 71–82.CrossRefGoogle Scholar
  16. Dai, T., N. A. J. Schutgens, D. Goto, G. Shi, and T. Nakajima, 2014b: Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model. Environmental Pollution, 195, 319–329.CrossRefGoogle Scholar
  17. Diehl, T., A. Heil, M. Chin, X. Pan, D. Streets, M. Schultz, and S. Kinne, 2012: Anthropogenic, biomass burning, and volcanic emissions of black carbon, organic carbon, and SO2 from 1980 to 2010 for hindcast model experiments. Atmospheric Chemistry and Physics Discussions, 12(9), 24 895–24 954.CrossRefGoogle Scholar
  18. Dubovik, O., and M. D. King, 2000: A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements. J. Geophys. Res., 105(D16), 20 673–20 696.CrossRefGoogle Scholar
  19. Dubovik, O., A. Smirnov, B. N. Holben, M. D. King, Y. J. Kaufman, T. F. Eck, and I. Slutsker, 2000: Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements. J. Geophys. Res., 105(D8), 9791–9806.CrossRefGoogle Scholar
  20. Geleyn, J. F., and A. Hollingsworth, 1979: An economical analytical method for the computation of the interaction between scattering and line absorption of radiation. Beitr. Phys. Atmos., 52, 1–16.Google Scholar
  21. Goto, D., T. Nakajima, T. Takemura, and K. Sudo, 2011a: A study of uncertainties in the sulfate distribution and its radiative forcing associated with sulfur chemistry in a global aerosol model. Atmos. Chem. Phys., 11(21), 10 889–10 910.CrossRefGoogle Scholar
  22. Goto, D., N. A. J. Schutgens, T. Nakajima, and T. Takemura, 2011b: Sensitivity of aerosol to assumed optical properties over Asia using a global aerosol model and AERONET. Geophys. Res. Lett., 38(17), L17810, doi: 10.1029/2011GL048675.CrossRefGoogle Scholar
  23. Goto, D., S. Kanazawa, T. Nakajima, and T. Takemura, 2012: Evaluation of a relationship between aerosols and surface downward shortwave flux through an integrative analysis of modeling and observation. Atmos. Environ., 49, 294–301.CrossRefGoogle Scholar
  24. Holben, B., and Coauthors, 1998: AERONET—A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ., 66(1), 1–16.CrossRefGoogle Scholar
  25. Kampa, M., and E. Castanas, 2008: Human health effects of air pollution. Environmental Pollution, 151(2), 362–367.CrossRefGoogle Scholar
  26. Kaufman, Y. J., D. Tanré L. A. Remer, E. F. Vermote, A. Chu, and B. N. Holben, 1997: Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res.: Atmos., 102(D14), 17 051–17 067.CrossRefGoogle Scholar
  27. King, M. D., Y. J. Kaufman, W. P. Menzel, and D. Tanre, 1992: Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS). IEEE Trans. Geosci. Remote Sens., 30(1), 2–27.CrossRefGoogle Scholar
  28. King, M. D., and Coauthors, 2003: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Trans. Geosci. Remote Sens., 41(2), 442–458.CrossRefGoogle Scholar
  29. Kinne, S., and Coauthors, 2003: Monthly averages of aerosol properties: A global comparison among models, satellite data, and AERONET ground data. J. Geophys. Res.: Atmos., 108(D20), 4634, doi: 10.1029/2001JD001253.CrossRefGoogle Scholar
  30. Kinne, S., and Coauthors, 2006: An AeroCom initial assessment-optical properties in aerosol component modules of global models. Atmos. Chem. Phys., 6(7), 1815–1834.CrossRefGoogle Scholar
  31. Le Trent, H., and Z.-X. Li, 1991: Sensitivity of an atmospheric general circulation model to prescribed SST changes: Feedback effects associated with the simulation of cloud optical properties. Climate Dyn., 5(3), 175–187.CrossRefGoogle Scholar
  32. Lee, L. A., K. J. Pringle, C. L. Reddington, G. W. Mann, P. Stier, D. V. Spracklen, J. R. Pierce, and K. S. Carslaw, 2013: The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei. Atmospheric Chemistry and Physics, 13(17), 8879–8914.CrossRefGoogle Scholar
  33. Lee, Y. H., and P. J. Adams, 2010: Evaluation of aerosol distributions in the GISS-TOMAS global aerosol microphysics model with remote sensing observations. Atmospheric Chemistry and Physics, 10(5), 2129–2214.CrossRefGoogle Scholar
  34. Levy, R. C., S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, F. Patadia, and N. C. Hsu, 2013: The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6(11), 2989–3034.CrossRefGoogle Scholar
  35. Logan, T., B. Xi, X. Dong, R. Obrecht, Z. Li, and M. Cribb, 2010: A study of Asian dust plumes using satellite, surface, and aircraft measurements during the INTEX-B field experiment. J. Geophys. Res., 115, D00K25, doi: 10.1029/2010JD014134.Google Scholar
  36. Logan, T., B. Xi, X. Dong, Z. Li, and M. Cribb, 2013: Classification and investigation of Asian aerosol absorptive properties. Atmospheric Chemistry and Physics, 13(4), 2253–2265.CrossRefGoogle Scholar
  37. Lohmann, U., and Coauthors, 2010: Total aerosol effect: radiative forcing or radiative flux perturbation? Atmospheric Chemistry and Physics, 10(7), 3235–3246.CrossRefGoogle Scholar
  38. Mann, G. W., and Coauthors, 2014: Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity. Atmospheric Chemistry and Physics, 14(9), 4679–4713.CrossRefGoogle Scholar
  39. Martins, J. V., D. Tanré L. Remer, Y. Kaufman, S. Mattoo, and R. Levy, 2002: MODIS Cloud screening for remote sensing of aerosols over oceans using spatial variability. Geophys. Res. Lett., 29(12), MOD4-1–MOD4-4.CrossRefGoogle Scholar
  40. Mellor, G. L., and T. Yamada, 1974: A hierarchy of turbulence closure models for planetary boundary layers. J. Atmos. Sci., 31(7), 1791–1806.CrossRefGoogle Scholar
  41. Miura, H., M. Satoh, T. Nasuno, A. T. Noda, and K. Oouchi, 2007: A Madden-Julian oscillation event realistically simulated by a global Cloud-Resolving model. Science, 318(5857), 1763–1765.CrossRefGoogle Scholar
  42. Nakajima, T., M. Tsukamoto, Y. Tsushima, A. Numaguti, and T. Kimura, 2000: Modeling of the radiative process in an atmospheric general circulation model. Appl. Opt., 39(27), 4869–4878.CrossRefGoogle Scholar
  43. Niwa, Y., and Coauthors, 2011a: Three-dimensional variations of atmospheric CO2: aircraft measurements and multi-transport model simulations. Atmospheric Chemistry and Physics, 11(24), 13359–13375.CrossRefGoogle Scholar
  44. Niwa, Y., H. Tomita, M. Satoh, and R. Imasu, 2011b: A threedimensional icosahedral grid advection scheme preserving monotonicity and consistency with continuity for atmospheric tracer transport. J. Meteor. Soc. Japan, 89(3), 255–268.CrossRefGoogle Scholar
  45. Peng, Y., K. von Salzen, and J. Li, 2012: Simulation of mineral dust aerosol with Piecewise Log-normal Approximation (PLA) in CanAM4-PAM. Atmospheric Chemistry and Physics, 12(15), 6891–6914.CrossRefGoogle Scholar
  46. Prados, A. I., S. Kondragunta, P. Ciren, and K. R. Knapp, 2007: GOES Aerosol/Smoke Product (GASP) over North America: Comparisons to AERONET and MODIS observations. J. Geophys. Res.: Atmos., 112(D15), D15201, doi: 10.1029/2006JD007968.CrossRefGoogle Scholar
  47. Remer, L. A., and Y. J. Kaufman, 2006: Aerosol direct radiative effect at the top of the atmosphere over cloud free ocean derived from four years of MODIS data. Atmospheric Chemistry and Physics, 6(1), 237–253.CrossRefGoogle Scholar
  48. Remer, L. A., and Coauthors, 2005: The MODIS aerosol algorithm, products, and validation. J. Atmos. Sci., 62(4), 947–973.CrossRefGoogle Scholar
  49. Ridley, D. A., C. L. Heald, and B. Ford, 2012: North African dust export and deposition: A satellite and model perspective. J. Geophys. Res., 117(D2), D02202, doi: 10.1029/2011JD016794.Google Scholar
  50. Salomonson, V. V., W. L. Barnes, P. W. Maymon, H. E. Montgomery, and H. Ostrow, 1989: MODIS: Advanced facility instrument for studies of the Earth as a system. IEEE Trans. Geosci. Remote Sens., 27(2), 145–153.CrossRefGoogle Scholar
  51. Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2008: Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227(7), 3486–3514.CrossRefGoogle Scholar
  52. Seiki, T., and T. Nakajima, 2014: Aerosol effects of the condensation process on a convective cloud simulation. J. Atmos. Sci., 71(2), 833–853.CrossRefGoogle Scholar
  53. Seiki, T., M. Satoh, H. Tomita, and T. Nakajima, 2014: Simultaneous evaluation of ice cloud microphysics and nonsphericity of the cloud optical properties using hydrometeor video sonde and radiometer sonde in situ observations. J. Geophys. Res.: Atmos., 119(11), 6681–6701.Google Scholar
  54. Sekiguchi, M., and T. Nakajima, 2008: A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model. Journal of Quantitative Spectroscopy and Radiative Transfer, 109(17–18), 2779–2793.CrossRefGoogle Scholar
  55. Su, L., and O. B. Toon, 2011: Saharan and Asian dust: Similarities and differences determined by CALIPSO, AERONET, and a coupled climate-aerosol microphysical model. Atmos. Chem. Phys., 11(7), 3263–3280.CrossRefGoogle Scholar
  56. Sudo, K., M. Takahashi, J.-i. Kurokawa, and H. Akimoto, 2002: CHASER: A global chemical model of the troposphere 1. Model description. J. Geophys. Res., 107(D17), ACH 7-1–ACH 7-20.Google Scholar
  57. Suzuki, K., T. Nakajima, M. Satoh, H. Tomita, T. Takemura, T. Y. Nakajima, and G. L. Stephens, 2008: Global cloud-systemresolving simulation of aerosol effect on warm clouds. Geophys. Res. Lett., 35(19), L19817, doi: 10.1029/2008GL035449.CrossRefGoogle Scholar
  58. Takata, K., S. Emori, and T. Watanabe, 2003: Development of the minimal advanced treatments of surface interaction and runoff. Global and Planetary Change, 38(1–2), 209–222.CrossRefGoogle Scholar
  59. Takemura, T., H. Okamoto, Y. Maruyama, A. Numaguti, A. Higurashi, and T. Nakajima, 2000: Global three-dimensional simulation of aerosol optical thickness distribution of various origins. J. Geophys. Res., 105(D14), 17 853–17 873.CrossRefGoogle Scholar
  60. Takemura, T., T. Nakajima, O. Dubovik, B. N. Holben, and S. Kinne, 2002a: Single-scattering albedo and radiative forcing of various aerosol species with a global Three-Dimensional model. J. Climate, 15(4), 333–352.CrossRefGoogle Scholar
  61. Takemura, T., I. Uno, T. Nakajima, A. Higurashi, and I. Sano, 2002b: Modeling study of long-range transport of Asian dust and anthropogenic aerosols from East Asia. Geophys. Res. Lett., 29(24), 11-1–11-4.CrossRefGoogle Scholar
  62. Takemura, T., M. Egashira, K. Matsuzawa, H. Ichijo, R. O’Ishi, and A. Abe-Ouchi, 2009: A simulation of the global distribution and radiative forcing of soil dust aerosols at the Last Glacial Maximum. Atmospheric Chemistry and Physics, 9(9), 3061–3073.CrossRefGoogle Scholar
  63. Tanré, D., Y. J. Kaufman, M. Herman, and S. Mattoo, 1997: Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. J. Geophys. Res., 102(D14), 16 971–16 988.CrossRefGoogle Scholar
  64. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106(D7), 7183–7192.CrossRefGoogle Scholar
  65. Textor, C., and Coauthors, 2006: Analysis and quantification of the diversities of aerosol life cycles within AeroCom. Atmos. Chem. Phys., 6(7), 1777–1813.CrossRefGoogle Scholar
  66. Textor, C., and Coauthors, 2007: The effect of harmonized emissions on aerosol properties in global models-an AeroCom experiment. Atmospheric Chemistry and Physics, 7(17), 4489–4501.CrossRefGoogle Scholar
  67. Tomita, H., 2008: New microphysical schemes with five and six categories by diagnostic generation of cloud ice. J. Meteor. Soc. Japan Ser. II, 86A, 121–142.CrossRefGoogle Scholar
  68. Twomey, S., 1974: Pollution and the planetary albedo. Atmos. Environ., 8(12), 1251–1256.CrossRefGoogle Scholar
  69. Wang, X., J. Huang, M. Ji, and K. Higuchi, 2008: Variability of East Asia dust events and their long-term trend. Atmos. Environ., 42(13), 3156–3165.CrossRefGoogle Scholar
  70. Yang, Y. Q., Q. Hou, C. H. Zhou, H. L. Liu, Y. Q. Wang, and T. Niu, 2008: Sand/dust storm processes in Northeast Asia and associated large-scale circulations. Atmospheric Chemistry and Physics, 8(1), 25–33.CrossRefGoogle Scholar
  71. Zhang, H., and Coauthors, 2012a: Simulation of direct radiative forcing of aerosols and their effects on East Asian climate using an interactive AGCM-aerosol coupled system. Climate Dyn., 38(7–8), 1675–1693.CrossRefGoogle Scholar
  72. Zhang, K., and Coauthors, 2012b: The global aerosol-climate model ECHAM-HAM, version 2: Sensitivity to improvements in process representations. Atmospheric Chemistry and Physics, 12(19), 8911–8949.CrossRefGoogle Scholar

Copyright information

© The Authors 2015

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological AdministrationNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina
  4. 4.Atmosphere and Ocean Research InstituteUniversity of TokyoKashiwaJapan

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