Journal of Meteorological Research

, Volume 32, Issue 4, pp 517–533 | Cite as

Long-Term Integration of a Global Non-Hydrostatic Atmospheric Model on an Aqua Planet

  • Xiaohan Li
  • Xindong Peng


A global non-hydrostatic atmospheric model, i.e., GRAPES_YY (Global/Regional Assimilation and Prediction System on the Yin–Yang grid), with a semi-implicit semi-Lagrangian (SISL) dynamical core developed on the Yin–Yang grid was coupled with the physical parameterization package of the operational version of GRAPES. A 3.5-yr integration was carried out on an aqua planet to assess the numerical performance of this non-hydrostatic model relative to other models. Specific aspects of precipitation and general circulation under two different sea surface temperature (SST) conditions (CONTROL and FLAT) were analyzed. The CONTROL SST peaked at the equator. The FLAT SST had its maximum gradient at about 20° latitude, giving a broad equatorial SST maximum in the tropics and flat profile approaching the equator. The tropical precipitation showed different propagation features in the CONTROL and FLAT simulations. The CONTROL showed tropical precipitation bands moving eastward with some envelopes of westward convective-scale disturbance. Less organized westward-propagating rainfall cells and bands were seen in the FLAT and the propagation of the tropical wave varied with the SST gradient. The Inter Tropical Convergence Zone (ITCZ), Hadley cell, and westerly jet core were weaker and more poleward as the SST profile flattened from the CONTROL to FLAT. The climatological structures simulated by GRAPES_YY, such as the distribution of precipitation and the large-scale circulation, fell within the bounds from other models. The stronger ITCZ precipitation, accompanied with stronger Hadley cells and convective heating in the CONTROL simulation, may be summed up as a result of stronger parameterized convection and the non-hydrostatic effects in GRAPES_YY. In addition, mechanism of the zonal mean circulation maintaining is analyzed for the different SST patterns referring the transient eddy flux.

Key words

non-hydrostatic model Yin–Yang grid physical parameterizations aqua planet experiment 


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We appreciate the editors and the anonymous reviewers’ constructive comments and valuable suggestions. We would like to thank Qijun Liu, Xingliang Li, and Zhanshan Ma for their help in developing the model code and providing insightful comments on the manuscript.


  1. Abiodun, B. J., J. M. Prusa, and W. J. Gutowski Jr, 2008: Implementation of a non-hydrostatic, adaptive-grid dynamics core in CAM3. Part I: Comparison of dynamics cores in aquaplanet simulations. Climate Dyn., 31, 795–810, doi: 10.1007/s00382-008-0381-y.Google Scholar
  2. Ait-Chaalal, F., and T. Schneider, 2015: Why eddy momentum fluxes are concentrated in the upper troposphere. J. Atmos. Sci., 72, 1585–1604, doi: 10.1175/JAS-D-14-0243.1.CrossRefGoogle Scholar
  3. Allen, T., and M. Zerroukat, 2016: A deep non-hydrostatic compressible atmospheric model on a Yin–Yang grid. J. Comput. Phys., 319, 44–60, doi: 10.1016/ 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, 674–701, doi: 10.1175/1520-0469(1974)031 <0674:IOACCE>2.0.CO;2.CrossRefGoogle Scholar
  5. Arakawa, A., and V. R. Lamb, 1997: Computational design of the basic dynamical processes of the UCLA General Circulation Model. Methods Comput. Phys. Adv. Res. Appl., 17, 173–265, doi: 10.1016/B978-0-12-460817-7.50009-4.CrossRefGoogle Scholar
  6. Baba, Y., K. Takahashi, and T. Sugimura, 2010: Dynamical core of an atmospheric general circulation model on a Yin–Yang grid. Mon. Wea. Rev., 138, 3988–4005, doi: 10.1175/2010MWR3375.1.CrossRefGoogle Scholar
  7. Beljaars, A. C. M., 1995: The parametrization of surface fluxes in large-scale models under free convection. Quart. J. Roy. Meteor. Soc., 121, 255–270, doi: 10.1002/qj.49712152203.CrossRefGoogle Scholar
  8. Blackburn, M., and B. J. Hoskins, 2013: Context and aims of the Aqua-Planet Experiment. J. Meteor. Soc. Japan, 91, 1–15, doi: 10.2151/jmsj.2013-A01.CrossRefGoogle Scholar
  9. Blackburn, M., D. L. Williamson, K. Nakajima, et al., 2013: The Aqua-Planet Experiment (APE): CONTROL SST simulation. J. Meteor. Soc. Japan, 91, 17–56, doi: 10.2151/jmsj.2013-A02.CrossRefGoogle Scholar
  10. Charney, J. G., and N. A. Phillips, 1953: Numerical integration of the quasi-geostrophic equations for barotropic and simple baroclinic flows. J. Meteor., 10, 71–99, doi: 10.1175/1520-0469(1953)010<0071:NIOTQG>2.0.CO;2.CrossRefGoogle Scholar
  11. Chen, D. H., J. S. Xue, X. S. Yang, et al., 2008: New generation of multi-scale NWP system (GRAPES): General scientific design. Chinese Sci. Bull., 53, 3433–3445, doi: 10.1007/s11434-008-0494-z.Google Scholar
  12. Dee, D. P., S. M. Uppala, A. J. Simmons, et al., 2011: The ERAInterim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi: 10.1002/qj.828.CrossRefGoogle Scholar
  13. Dennis, J., A. Fournier, W. F. Spotz, et al., 2005: High-resolution mesh convergence properties and parallel efficiency of a spectral element atmospheric dynamical core. Int. J. High Perform. Comput. Appl., 19, 225–235, doi: 10.1177/1094342005056108.CrossRefGoogle Scholar
  14. Han, J., and H. L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520–533, doi: 10.1175/WAF-D-10-05038.1.CrossRefGoogle Scholar
  15. Held, I. M., and A. Y. Hou, 1980: Nonlinear axially symmetric circulations in a nearly inviscid atmosphere. J. Atmos. Sci., 37, 515–533, doi: 10.1175/1520-0469(1980)037<0515:NASCIA> 2.0.CO;2.CrossRefGoogle Scholar
  16. Hess, P. G., D. S. Battisti, and P. J. Rasch, 1993: Maintenance of the intertropical convergence zones and the large-scale tropical circulation on a water-covered earth. J. Atmos. Sci., 50, 691–713, doi: 10.1175/1520-0469(1993)050<0691:MOTICZ> 2.0.CO;2.CrossRefGoogle Scholar
  17. Hong, S. Y., and H. L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339, doi: 10.1175/1520-0493(1996)124<2322: NBLVDI>2.0.CO;2.CrossRefGoogle Scholar
  18. Hu, Z. J., X. F. Lou, S. W. Bao, et al., 1998: A simplified explicit scheme of phase-mixed cloud and precipitation. Quart. J. Appl. Meteor., 9, 257–264. (in Chinese)Google Scholar
  19. Iacono, M. J., J. S. Delamere, E. J. Mlawer, et al., 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. Atmos., 113, D13103, doi: 10.1029/2008JD009944.CrossRefGoogle Scholar
  20. Jablonowski, C., P. H. Lauritzen., R. D. Nair, et al., 2008: Idealized test cases for the dynamical cores of atmospheric general circulation models: A proposal for the NCAR ASP 2008 summer colloquium. [Available online at]Google Scholar
  21. Kageyama, A., and T. Sato, 2004: “Yin–Yang grid”: An overset grid in spherical geometry. Geochem. Geophys. Geosyst., 5, Q09005, doi: 10.1029/2004GC000734.CrossRefGoogle Scholar
  22. Li, X. H., X. D. Peng, and X. L. Li, 2015: An improved dynamic core for a non-hydrostatic model system on the Yin–Yang grid. Adv. Atmos. Sci., 32, 648–658, doi: 10.1007/s00376-014-4120-5.CrossRefGoogle Scholar
  23. Li, X. L., D. H. Chen, X. D. Peng, et al., 2006: Implementation of the semi-lagrangian advection scheme on a quasi-uniform overset grid on a sphere. Adv. Atmos. Sci., 23, 792–801, doi: 10.1007/s00376-006-0792-9.CrossRefGoogle Scholar
  24. Li, X. L., D. H. Chen, X. D. Peng, et al., 2008: A multimoment finite-volume shallow-water model on the Yin–Yang overset spherical grid. Mon. Wea. Rev., 136, 3066–3086, doi: 10.1175/2007MWR2206.1.CrossRefGoogle Scholar
  25. Liang, X. Z., and W. C. Wang, 1996: Atmospheric ozone climatology for use in General Circulation Models. PCMDI Report No. 43: UCRL-MI-125650; 25 pp. [Accessible online at Google Scholar
  26. Lien, G. Y., E. Kalnay, T. Miyoshi, et al., 2016: Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation. Mon. Wea. Rev., 144, 663–679, doi: 10.1175/MWR-D-15-0150.1.CrossRefGoogle Scholar
  27. Liu, K., Q. Y. Chen, and J. Sun, 2015: Modification of cumulus convection and planetary boundary layer schemes in the GRAPES global model. J. Meteor. Res., 29, 806–882, doi: 10.1007/s13351-015-5043-5.CrossRefGoogle Scholar
  28. Liu, Q. J., Z. J. Hu, and X. J. Zhou, 2003: Explicit cloud schemes of HLAFS and simulation of heavy rainfall and clouds. Part I: Explicit cloud schemes. J. Appl. Meteor. Sci., 14, 60–67, doi: 10.3969/j.issn.1001-7313.2003.z1.008. (in Chinese)Google Scholar
  29. Liu, Y. M., and Y. H. Ding, 2002: Modified mass flux cumulus convective parameterization scheme and its simulation experiment. Part I: Mass flux scheme and its simulation of the 1991 flood event. Acta Meteor. Sinica, 16, 37–49.Google Scholar
  30. Lock, A. P., A. R. Brown, M. R. Bush, et al., 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 3187–3199, doi:1 0.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2.Google Scholar
  31. Louis, J. F., 1979: A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor., 17, 187–202, doi: 10.1007/BF00117978.CrossRefGoogle Scholar
  32. Ma, Z. S., Q. J. Liu., C. F. Zhao, et al., 2018: Application and evaluation of an explicit prognostic cloud-cover scheme in GRAPES global forecast system. J. Adv. Model. Earth Syst., 10, 652–667, doi: 10.1002/2017MS001234.CrossRefGoogle Scholar
  33. Medeiros, B., and B. Stevens, 2011: Revealing differences in GCM representations of low clouds. Climate Dyn., 36, 385–399, doi: 10.1007/s00382-009-0694-5.CrossRefGoogle Scholar
  34. Medeiros, B., B. Stevens, and S. Bony, 2015: Using aquaplanets to understand the robust responses of comprehensive climate models to forcing. Climate Dyn., 44, 1957–1977, doi: 10.1007/s00382-014-2138-0.CrossRefGoogle Scholar
  35. Medeiros, B., D. L. Williamson, and J. G. Olson, 2016: Reference aquaplanet climate in the Community Atmosphere Model, Version 5. J. Adv. Model. Earth Syst., 8, 406–424, doi: 10.1002/2015MS000593.CrossRefGoogle Scholar
  36. Mishra, S. K., M. A. Taylor, R. D. Nair, et al., 2011: Evaluation of the HOMME dynamical core in the aquaplanet configuration of NCAR CAM4: Rainfall. J. Climate, 24, 4037–4055, doi: 10.1175/2011JCLI3860.1.CrossRefGoogle Scholar
  37. Mlawer, E. J., S. J. Taubman, P. D. Brown, et al., 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. Atmos., 102, 16663–16682, doi: 10.1029/97JD00237.CrossRefGoogle Scholar
  38. Nasuno, T., and M. Satoh, 2011: Properties of precipitation and incloud vertical motion in a global nonhydrostatic aquaplanet experiment. J. Meteor. Soc. Japan, 89, 413–439, doi: 10.2151/jmsj.2011-502.CrossRefGoogle Scholar
  39. Nasuno, T., H. Tomita, S. Iga, et al., 2008: Convectively coupled equatorial waves simulated on an aquaplanet in a global nonhydrostatic experiment. J. Atmos. Sci., 65, 1246–1265, doi: 10.1175/2007JAS2395.1.CrossRefGoogle Scholar
  40. Neale, R. B., and B. J. Hoskins, 2000a: A standard test for AGCMs including their physical parametrizations. I: The proposal. Atmos. Sci. Lett., 1, 101–107, doi: 10.1006/asle. 2000.0022.Google Scholar
  41. Neale, R. B., and B. J. Hoskins, 2000b: A standard test for AGCMs including their physical parametrizations. II: Results for the Met Office model. Atmos. Sci. Lett., 1, 108–114, doi: 10.1006/asle.2000.0024.Google Scholar
  42. Niu, Y. J., X. D. Peng, and G. Z. Fan, 2017: Impact of the three-dimensional Coriolis force in GRAPES model. Acta. Meteor. Sinica, 76, 473–484, doi: 10.11676/qxxb2018.003. (in Chinese)Google Scholar
  43. Pan, H. L., and W. S. Wu, 1995: Implementing a mass flux convection parameterization package for the NMC Medium-Range Forecast model. NMC Office Note 409, 40 ppGoogle Scholar
  44. Qaddouri, A., and V. Lee, 2011: The Canadian global environmental multiscale model on the Yin–Yang grid system. Quart. J. Roy. Meteor. Soc., 137, 1913–1926, doi: 10.1002/qj.873.CrossRefGoogle Scholar
  45. Qaddouri, A., L. Laayouni, L. Loisel, et al., 2008: Optimized Schwarz methods with an overset grid for the shallow-water equations: Preliminary results. Appl. Numer. Math., 58, 459–471, doi: 10.1016/j.apnum.2007.01.015.CrossRefGoogle Scholar
  46. Qian, J., F. H. M. Semazzi, and J. S. Scroggs, 1998: A global nonhydrostatic semi-lagrangian atmospheric model with orography. Mon. Wea. Rev., 126, 747–771, doi: 10.1175/1520-0493(1998)126<0747:AGNSLA>2.0.CO;2.CrossRefGoogle Scholar
  47. Rayner, N. A., D. E. Parker, E. B. Horton, et al., 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos., 108, 4407, doi: 10.1029/2002JD002670.CrossRefGoogle Scholar
  48. Sadourny, R., 1972: Conservative finite-difference approximations of the primitive equations on quasi-uniform spherical grids. Mon. Wea. Rev., 100, 136–144, doi: 10.1175/1520-0493(1972)100<0136:CFAOTP>2.3.CO;2.CrossRefGoogle Scholar
  49. Sadourny, R., A. Arakawa, and Y. Mintz, 1968: Integration of the nondivergent barotropic vorticity equation with an icosahedral-hexagonal grid for the sphere. Mon. Wea. Rev., 96, 351–356, doi: 10.1175/1520-0493(1968)096<0351:IOTNBV> 2.0.CO;2.CrossRefGoogle Scholar
  50. Semazzi, F. H. M., J. H. Qian, and J. S. Scroggs, 1995: A global nonhydrostatic semi-lagrangian atmospheric model without orography. Mon. Wea. Rev., 123, 2534–2550, doi: 10.1175/1520-0493(1995)123<2534:AGNSLA>2.0.CO;2.CrossRefGoogle Scholar
  51. Shen, X. S., Y. Su, J. L. Hu, et al., 2017: Development and operation transformation of GRAPES global middle-range forecast system. J. Appl. Meteor. Sci., 28, 1–10, doi: 10.11898/1001-7313.20170101. (in Chinese)Google Scholar
  52. Stevens, B., and S. Bony, 2013: What are climate models missing? Science, 340, 1053–1054, doi: 10.1126/science.1237554.CrossRefGoogle Scholar
  53. Tan, C., Q. J. Liu, and Z. S. Ma, 2013: Influences of sub-grid convective processes on cloud forecast in the GRAPES global model. Acta Meteor. Sinica, 71, 867–878, doi: 10.11676/qxxb2013.067. (in Chinese)Google Scholar
  54. Taylor, M. A., J. Edwards, S. Thomas, et al., 2007: A mass and energy conserving spectral element atmospheric dynamical core on the cubed-sphere grid. J. Phys. Conf. Ser., 78, 012074, doi: 10.1088/1742-6596/78/1/012074.CrossRefGoogle Scholar
  55. Thompson, D. W. J., and J. D. Woodworth, 2014: Barotropic and baroclinic annular variability in the Southern Hemisphere. J. Atmos. Sci., 71, 1480–1493, doi: 10.1175/JAS-D-13-0185.1.CrossRefGoogle Scholar
  56. Tomita, H., and M. Satoh, 2004: A new dynamical framework of nonhydrostatic global model using the icosahedral grid. Fluid Dyn. Res., 34, 357–400, doi: 10.1016/j.fluiddyn.2004.03.003.CrossRefGoogle Scholar
  57. Tomita, H., H. Miura, S. Iga, et al., 2005: A global cloud-resolving simulation: Preliminary results from an aqua planet experiment. Geophys. Res. Lett., 32, L08805, doi: 10.1029/2005GL022459.Google Scholar
  58. Troen, I. B., and L. Mahrt, 1986: A simple model of the atmospheric boundary layer; sensitivity to surface evaporation. Bound.-Layer Meteor., 37, 129–148, doi: 10.1007/BF001 22760.CrossRefGoogle Scholar
  59. Wang, W. C., X. Z. Liang, M. P. Dudek, et al., 1995: Atmospheric ozone as a climate gas. Atmos. Res., 37, 247–256, doi: 10.1016/0169-8095(94)00080-W.CrossRefGoogle Scholar
  60. Wang, Z. Z., J. Y. Mao, and G. X. Wu, 2008: The wavenumberfrequency characteristics of the tropical waves in an aquaplanet GCM. Adv. Atmos. Sci., 25, 541–554, doi: 10.1007/s00376-008-0541-3.CrossRefGoogle Scholar
  61. Wheeler, M., and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber-frequency domain. J. Atmos. Sci., 56, 374–399, doi: 10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO; 2.CrossRefGoogle Scholar
  62. Whitehead, J. P., C. Jablonowski, R. B. Rood, et al., 2011: A stability analysis of divergence damping on a latitude–longitude grid. Mon. Wea. Rev., 139, 2976–2993, doi: 10.1175/2011 MWR3607.1.CrossRefGoogle Scholar
  63. Williamson, D. L., 2007: The evolution of dynamical cores for global atmospheric models. J. Meteor. Soc. Japan, 85, 241–269, doi: 10.2151/jmsj.85B.241.CrossRefGoogle Scholar
  64. Williamson, D. L., 2008: Equivalent finite volume and Eulerian spectral transform horizontal resolutions established from aqua-planet simulations. Tellus A: Dyn. Meteor. Oceanogr., 60, 839–847, doi: 10.1111/j.1600-0870.2008.00340.x.CrossRefGoogle Scholar
  65. Williamson, D. L., M. Blackburn, B. J. Hoskins, et al., 2012: The APE ATLAS. NCAR/TN-484+STR, Boulder, Colorado, National Center for Atmospheric Research, 508 pp, doi: 10.5065/D6FF3QBRGoogle Scholar
  66. Williamson, D. L., M. Blackburn, K. Nakajima, et al., 2013: The Aqua-Planet Experiment (APE): Response to changed meridional SST profile. J. Meteor. Soc. Japan, 91, 57–89, doi: 10.2151/jmsj.2013-A03.CrossRefGoogle Scholar
  67. Xiao, F., and X. D. Peng, 2004: A convexity preserving scheme for conservative advection transport. J. Comput. Phys., 198, 389–402, doi: 10.1016/ Scholar
  68. Yang, Q., L. R. Leung, J. Lu, et al., 2017: Exploring the effects of a nonhydrostatic dynamical core in high-resolution aquaplanet simulations. J. Geophys. Res. Atmos., 122, 3245–3265, doi: 10. 1002/2016JD025287.CrossRefGoogle Scholar
  69. Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.-Ocean, 33, 407–446, doi: 10.1080/07055900.1995. 9649539.CrossRefGoogle Scholar
  70. Zeng, X. B., M. Zhao, and R. E. Dickinson, 1998: Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J. Climate, 11, 2628–2644, doi: 10.1175/1520-0442(1998)011 <2628:IOBAAF>2.0.CO;2.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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