Skip to main content
SpringerLink
Log in

A Gray Zone GCM with Full Representation of Cloud Microphysics

  • Chapter
  • First Online:
Current Trends in the Representation of Physical Processes in Weather and Climate Models

Part of the book series: Springer Atmospheric Sciences ((SPRINGERATMO))

Abstract

This chapter describes the development procedure of general circulation model (GCM) with full representation of cloud microphysics at a medium-range horizontal resolution of 50 km and discusses the simulation results of their precipitation climatology and Madden and Julian Oscillation (MJO). One issue of developing such a GCM is to modify the cloud microphysics suitable to the horizontal resolution. In the present study, the modification is made based on sensitivity experiments for the parameters of the important processes sensitive to the model resolution, particularly the condensation process and the terminal velocity. It is demonstrated that shallow convection and scale-dependent deep convection are still needed in the present model of 50 km resolution with cloud microphysics. The present GCM is shown to simulate the precipitation statistic such as the light and heavy precipitation frequencies and the MJO reasonably well, although the MJO intensity is rather strong. Both cloud microphysics and scale-dependent deep convection play important roles in simulating a realistic MJO in the present GCM. Also noted is that the precipitation climatologies of the present atmospheric GCM (AGCM) and the coupled ocean–atmosphere GCM (CGCM) are quite different from each other, indicating that the air–sea interaction plays an important role in determining the climatology, and this result suggests us to tune the model physics and their parameters with CGCM rather than with AGCM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ahn, M.-S., and I.S. Kang. 2018. A practical approach to scale-adaptive deep convection in a GCM by controlling the cumulus base mass flux. npj Climate and Atmospheric Science (in revision).

    Google Scholar 

  • Ahn, M.-S., D. Kim, K.R. Sperber, I.S. Kang, E. Maloney, D. Waliser, H. Hendon on behalf of WGNE MJO Task Force. 2017. MJO simulation in CMIP5 climate models: MJO skill metrics and process oriented diagnosis. Climate Dynamics 49: 4023–4045.

    Article  Google Scholar 

  • Arakawa, A., J.-H. Jung, and C.-M. Wu. 2011. Toward unification of the multiscale modeling of the atmosphere. Atmospheric Chemistry and Physics 11 (8): 3731–3742.

    Article  Google Scholar 

  • Benedict, J.J., and D.A. Randall. 2009. Structure of the Madden-Julian oscillation in the superparameterized CAM. Journal of the Atmospheric Sciences 66 (11): 3277–3296.

    Article  Google Scholar 

  • Bryan, G.H., and H. Morrison. 2012. Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Monthly Weather Review 140 (1): 202–225.

    Article  Google Scholar 

  • Bryan, G.H., J.C. Wyngaard, and J.M. Fritsch. 2003. Resolution requirements for the simulation of deep moist convection. Monthly Weather Review 131 (10): 2394–2416.

    Article  Google Scholar 

  • Chen, C.-T., and T. Knutson. 2008. On the verification and comparison of extreme rainfall indices from climate models. Journal of Climate 21: 1605–1621.

    Article  Google Scholar 

  • DeMott, C.A., D.A. Randall, and M. Khairoutdinov. 2007. Convective precipitation variability as a tool for general circulation model analysis. Journal of Climate 20: 91–112.

    Article  Google Scholar 

  • Grabowski, W.W., X. Wu, M.W. Moncrieff, and W.D. Hall. 1998. Cloud-resolving modeling of cloud systems during Phase III of GATE. Part II: Effects of resolution and the third spatial dimension. Journal of the Atmospheric Sciences 55 (21): 3264–3282.

    Article  Google Scholar 

  • Ham, Yoo-Geun, Jong-Seong Kug, In-Sik Kang, Fei-Fei Jin, and Axel Timmermann. 2010. Impact of diurnal atmosphere-ocean coupling on tropical climate simulations using a coupled GCM. Climate Dynamics 34: 905–917.

    Article  Google Scholar 

  • Holloway, C.E., S.J. Woolnough, and G.M.S. Lister. 2013. The effects of explicit versus parameterized convection on the MJO in a large-domain high-resolution tropical case study. Part I: Characterization of large-scale organization and propagation*. Journal of the Atmospheric Sciences 70: 1342–1369.

    Article  Google Scholar 

  • Holloway, C.E., S.J. Woolnough, and G.M.S. Lister. 2015. The effects of explicit versus parameterized convection on the MJO in a large-domain high resolution tropical case study. Part II: Processes leading to differences in MJO development. Journal of the Atmospheric Sciences 72: 2719–2743.

    Article  Google Scholar 

  • Hung, M.-P., J.-L. Lin, W. Wang, D. Kim, T. Shinoda, and S.J. Weaver. 2013. MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. Journal of Climate 26: 6185–6214.

    Article  Google Scholar 

  • Iorio, J., P. Duffy, B. Govindasamy, S. Thompson, M. Khairoutdinov, and D. Randall. 2004. Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental United States. Climate Dynamics 23 (3–4): 243–258.

    Article  Google Scholar 

  • Jung, J.-H., and A. Arakawa. 2004. The resolution dependence of model physics: Illustrations from nonhydrostatic model experiments. Journal of the Atmospheric Sciences 61 (1): 88–102.

    Article  Google Scholar 

  • Kang, I.-S., Y.-M. Yang, and W.-K. Tao. 2015. GCMs with implicit and explicit representation of cloud microphysics for simulation of extreme precipitation frequency. Climate Dynamics 45: 325–335.

    Article  Google Scholar 

  • Kang, I.-S., M.-S. Ahn, and Y.-M. Yang. 2016. A GCM with cloud microphysics and its MJO simulation. Geoscience Letters 3: 16.

    Article  Google Scholar 

  • Kim, D., and I.-S. Kang. 2012. A bulk mass flux convection scheme for climate model: Description and moisture sensitivity. Climate Dynamics 38: 411–429.

    Article  Google Scholar 

  • Klemp, J.B., and R. Wilhelmson. 1978. The simulation of three-dimensional convective storm dynamics. Journal of the Atmospheric Sciences 35: 1070–1096.

    Article  Google Scholar 

  • Kodama, C., Y. Yamada, A.T. Noda, K. Kikuchi, Y. Kajikawa, T. Nasuno, T. Tomita, T. Yamaura, H.G. Takahashi, M. Hara, Y. Kawatani, M. Satoh, and M. Sugi. 2015. A 20-year climatology of a NICAM AMIP-type simulation. Journal of the Meteorological Society of Japan 93: 393–424.

    Article  Google Scholar 

  • 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 Dynamics 5 (3): 175–187.

    Article  Google Scholar 

  • Lee, M.-I., I.-S. Kang, J.-K. Kim, and B.E. Mapes. 2001. Influence of cloud-radiation interaction on simulating tropical intraseasonal oscillation with an atmospheric general circulation model. Journal Geophysical Research 106 (14): 219–233.

    Google Scholar 

  • Li, F., W.D. Collins, M.F. Wehner, D.L. Williamson, J.G. Olson, and C. Algieri. 2011. Impact of horizontal resolution on simulation of precipitation extremes in an aqua-planet version of community atmospheric model (CAM3). Tellus A 63: 884–892.

    Article  Google Scholar 

  • Lin, S.-J. 2004. A “vertically Lagrangian” finite-volume dynamical core for global models. Monthly Weather Review 132 (10): 2293–2307.

    Article  Google Scholar 

  • Lin, Y.-L., R.D. Farley, and H.D. Orville. 1983. Bulk parameterization of the snow field in a cloud model. Journal of Applied Meteorology and Climatology 22 (6): 1065–1092.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Miyakawa, T., M. Satoh, H. Miura, H. Tomita, H. Yashiro, A.T. Noda, Y. Yamada, C. Kodama, M. Kimoto, and K. Yoneyama. 2014. Madden-Julian oscillation prediction skill of a new-generation global model. Nature Communications 5: 3769.

    Article  Google Scholar 

  • Moorthi, S., and M.J. Suarez. 1992. Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models. Monthly Weather Review 120: 978–1002.

    Article  Google Scholar 

  • Moncrieff, M.W., and E. Klinker. 1997. Organized convective systems in the tropical western Pacific as a process in general circulation models. Quarterly Journal of the Royal Meteorological Society 123: 805–828.

    Article  Google Scholar 

  • Nakajima, T., M. Tsukamoto, Y. Tsushima, and A. Numaguti. 1995. Modelling of the radiative processes in an AGCM. Climate System Dynamics and Modelling 3: 104–123.

    Google Scholar 

  • Noh, Y., and H.J. Kim. 1999. Simulations of temperature and turbulence structure of the oceanic boundary layer with the improved nearsurface process. Journal of Geophysical Research 104: 15621–15634.

    Article  Google Scholar 

  • Oouchi, K., A.T. Noda, M. Satoh, H. Miura, H. Tomita, T. Nasuno, and S. Iga. 2009. A simulated preconditioning of typhoon genesis controlled by a boreal summer Madden-Julian Oscillation event in a global cloud-system-resolving model. SOLA 5: 65–68.

    Article  Google Scholar 

  • Pauluis, O., and S. Garner. 2006. Sensitivity of radiative-convective equilibrium simulations to horizontal resolution. Journal of the Atmospheric Sciences 63 (7): 1910–1923.

    Article  Google Scholar 

  • Satoh, M., and Coauthors. 2014. The non-hydrostatic icosahedral atmospheric model: Description and development. Progress in Earth and Planetary Science 1: 18.

    Google Scholar 

  • Tao, W.-K., J. Simpson, D. Baker, S. Braun, M.-D. Chou, B. Ferrier, D. Johnson, A. Khain, S. Lang, and B. Lynn. 2003. Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteorology and Atmospheric Physics 82 (1): 97–137.

    Article  Google Scholar 

  • Tiedtke, M. 1984. Sensitivity of the time-mean large-scale flow to cumulus convection in the ECMWF model, 297–316.

    Google Scholar 

  • Tomita, H., H. Miura, S. Iga, T. Nasuno, and M. Satoh. 2005. A global cloud-resolving simulation: Preliminary results from an aqua planet experiment. Geophysical Reseach Letters 32: 1–4. https://doi.org/10.1029/2005GL022459.

    Article  Google Scholar 

  • Wehner, M.F., R.L. Smith, G. Bala, and P. Duffy. 2010. The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model. Climate Dynamics 34: 241–247.

    Article  Google Scholar 

  • Weisman, M.L., W.C. Skamarock, and J.B. Klemp. 1997. The resolution dependence of explicitly modeled convective systems. Monthly Weather Review 125 (4): 527–548.

    Article  Google Scholar 

  • Zhu, H., H. Hendon, and C. Jakob. 2009. Convection in a parameterized and superparameterized model and its role in the representation of the MJO. Journal of the Atmospheric Sciences 66 (9): 2796–2811.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Ocean Operational Oceanographic Research Center, SOED, Second Institute of Oceanography, Hangzhou, China

    In-Sik Kang

  2. Center of Excellence of Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia

    In-Sik Kang

  3. Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA

    Min-Seop Ahn

  4. Department of Oceanography, Chonnam National University, Gwangju, Korea

    Min-Seop Ahn

Authors
  1. In-Sik Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min-Seop Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to In-Sik Kang .

Editor information

Editors and Affiliations

  1. Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA

    David A. Randall

  2. Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru, Karnataka, India

    J. Srinivasan

  3. Indian Institute of Tropical Meteorology, Pune, Maharashtra, India

    Ravi S. Nanjundiah

  4. Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru, Karnataka, India

    Parthasarathi Mukhopadhyay

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kang, IS., Ahn, MS. (2019). A Gray Zone GCM with Full Representation of Cloud Microphysics. In: Randall, D., Srinivasan, J., Nanjundiah, R., Mukhopadhyay, . (eds) Current Trends in the Representation of Physical Processes in Weather and Climate Models. Springer Atmospheric Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-13-3396-5_7

Download citation

Publish with us

Policies and ethics

Search

Navigation