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Published Online: 22 February 2017
AIP Conference Proceedings 1810, 100002 (2017); https://doi.org/10.1063/1.4975557
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A high-performance computing resource allows us to conduct numerical simulations with a horizontal grid spacing that is sufficiently high to resolve cloud systems. The cutting-edge computational capability, which was provided by the K computer at RIKEN in Japan, enabled the authors to perform long-term, global simulations of air pollutions and clouds with unprecedentedly high horizontal resolutions. In this study, a next generation model capable of simulating global air pollutions with O(10 km) grid spacing by coupling an atmospheric chemistry model to the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) was performed. Using the newly developed model, month-long simulations for July were conducted with 14 km grid spacing on the K computer. Regarding the global distributions of aerosol optical thickness (AOT), it was found that the correlation coefficient (CC) between the simulation and AERONET measurements was approximately 0.7, and the normalized mean bias was -10%. The simulated AOT was also compared with satellite-retrieved values; the CC was approximately 0.6. The radiative effects due to each chemical species (dust, sea salt, organics, and sulfate) were also calculated and compared with multiple measurements. As a result, the simulated fluxes of upward shortwave radiation at the top of atmosphere and the surface compared well with the observed values, whereas those of downward shortwave radiation at the surface were underestimated, even if all aerosol components were considered. However, the aerosol radiative effects on the downward shortwave flux at the surface were found to be as high as 10 W/m2 in a global scale; thus, simulated aerosol distributions can strongly affect the simulated air temperature and dynamic circulation.
  1. 1. O. Boucher et al., “Clouds and aerosols”, in Climate Change 2013: The Physical Science Basis, edited by T. F. Stocker et al. (Cambridge University Press, Cambridge, UK and New York, USA, 2013), pp. 571–658. Google Scholar
  2. 2. G. Myhre et al., Atmos. Chem. Phys. 13, 1853–1877 (2013) https://doi.org/10.5194/acp-13-1853-2013, Google ScholarCrossref
  3. 3. J.-L. F. Li et al., Geophys. Res. Lett. 35, L19710 (2008) https://doi.org/10.1029/2008GL035427, Google ScholarCrossref
  4. 4. D. Goto et al., J. Geophys. Res. Atmos. 120(12), 6247–6270 (2015) https://doi.org/10.1002/2014JD021693, Google ScholarCrossref
  5. 5. H. Tomita and M. Satoh, Fluid Dyn. Res. 34, 357–400 (2004) https://doi.org/10.1016/j.fluiddyn.2004.03.003, Google ScholarCrossref
  6. 6. M. Satoh et al., J. Comput. Phys. 227, 3486–3514 (2008) https://doi.org/10.1016/j.jcp.2007.02.006, Google ScholarCrossref
  7. 7. M. Satoh et al., PEPS 1, 18 (2014) Google Scholar
  8. 8. B. N. Holben et al., Remote Sens. Environ. 66, 1–16 (1998) https://doi.org/10.1016/S0034-4257(98)00031-5, Google ScholarCrossref
  9. 9. D. M. Winker et al., Bull. Amer. Meteor. Soc., 91, 1211–1229 (2010) https://doi.org/10.1175/2010BAMS3009.1, Google ScholarCrossref
  10. 10. R. C. Levy et al., Atmos. Meas. Tech. 6, 2989–3034 (2013) https://doi.org/10.5194/amt-6-2989-2013, Google ScholarCrossref
  11. 11. K. Suzuki et al., Geophys. Res. Lett. 35, L19817 (2008) https://doi.org/10.1029/2008GL035449, Google ScholarCrossref
  12. 12. D. Goto et al. Geosci. Model Dev. 8, 235–259 (2015) https://doi.org/10.5194/gmd-8-235-2015, Google ScholarCrossref
  13. 13. D. Goto et al. Atmos. Environ. 140, 320–332 (2016) https://doi.org/10.1016/j.atmosenv.2016.06.015, Google ScholarCrossref
  14. 14. T. Nakajima et al. submitted to PEPS (2016) Google Scholar
  15. 15. H. Tomita J. Meteo. Soc. Jpn. 86A, 107–119 (2008) https://doi.org/10.2151/jmsj.86A.107, Google ScholarCrossref
  16. 16. T. Takemura et al. J. Geophys. Res. 110, D02202 (2005). Google ScholarCrossref
  17. 17. M. Watanabe et al. J. Clim. 23, 6312–6335 (2010) https://doi.org/10.1175/2010JCLI3679.1, Google ScholarCrossref
  18. 18. G. Janssens-Maenhout et al., Atmos. Chem. Phys. 15, 11411–11432 (2015) https://doi.org/10.5194/acp-15-11411-2015, Google ScholarCrossref
  19. 19. R. F. Adler et al., J. Hydrometeorol 4, 1147–1167 (2003) https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, Google ScholarCrossref
  20. 20. E. Oikawa et al., J. Geophys. Res. Atmos. 118, 3687–3708 (2013) https://doi.org/10.1002/jgrd.50227, Google ScholarCrossref
  21. 21. K. E. Trenbirth et al. BAMS, 311–323 (2009) https://doi.org/10.1175/2008BAMS2634.1, Google ScholarCrossref
  22. 22. M. Wild et al. Clim. Dyn. 44, 3393–3429 (2014) https://doi.org/10.1007/s00382-014-2430-z, Google ScholarCrossref
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