Skip to main content

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

Abstract

This chapter starts with an introduction to standard mass-flux parameterisations and closures for moist convection which are used in most large-scale models. The shortcomings of this approach will be discussed, especially for higher resolutions where standard assumptions such as quasi-equilibrium start to break down. New pathways that use a stochastic approach and are scale aware will be discussed. These will also allow to incorporate the effect of mesoscale organisation into parameterisations for moist convection.

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

Access this chapter

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

Notes

  1. 1.

    The precipitation area is not equal to the updraft area but for precipitating cumulus convection it is reasonable to assume that they are proportional to each other.

References

  • Arakawa, A., and H. Schubert. 1974. Interaction of a cumulus cloud ensemble with the large-scale environment. Part I: Theoretical formulation and sensitivity tests. Journal of the Atmospheric Sciences 31: 674–701.

    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 

  • Bengtsson, L., M. Steinheimer, P. Bechtold, and J.-F. Geleyn. 2013. A stochastic parametrization for deep convection using cellular automata. Quarterly Journal of the Royal Meteorological Society 139 (675): 1533–1543.

    Article  Google Scholar 

  • Buizza, R., M. Miller, and T.N. Palmer. 2007. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quarterly Journal of the Royal Meteorological Society 125 (560): 2887–2908.

    Article  Google Scholar 

  • de Roode, S.R., A.P. Siebesma, H.J.J. Jonker, and Y. de Voogd. 2012. Parameterization of the vertical velocity equation for shallow cumulus clouds. Monthly Weather Review 140: 2424–2436.

    Article  Google Scholar 

  • de Rooy, W.C., P. Bechtold, K. Frohlich, C. Hohenegger, H.H.J. Jonker, D. Mironov, A.P. Siebesma, J. Teixeira, and J.-I. Yano. 2013. Entrainment and detrainment in cumulus convection: an overview. QJRMS 139 (670): 1–19.

    Google Scholar 

  • Dee, D.P., S.J. Uppala, A.P. Simmons, P. Berrisford, S. Poli, U. Kobayashi, A. Andrae, M.G. Balmaseda, P. Balsamo, P. Bauer, M. Bechtold, A.C. Beljaars, L.J. van de Berg, N. Bidlot, C. Bormann, R. Delsol, M. Dragani, J. Fuentes, A.L. Geer, B. Haimberger, S.H. Healy, V. Hersbach, E.L. Hlm, P. Isaksen, M. Kllberg, M. Khler, P. Matricardi, A.M. McNally, B. MongeSanz, J.J. Morcrette, B.K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.N. Thpaut, and F. Vitart. 2011. The ERAInterim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society 137 (656): 553–597.

    Article  Google Scholar 

  • Dorrestijn, J., D.T. Crommelin, J.A. Biello, and S.J. Böing. 2013. A data-driven multi-cloud model for stochastic parametrization of deep convection. Philosophical Transactions of the Royal Society A 371 (1991): 20120374.

    Article  Google Scholar 

  • Dorrestijn, J., D.T. Crommelin, A.P. Siebesma, H.J.J. Jonker, and C. Jakob. 2015. Stochastic parameterization of convective area fractions with a multicloud model inferred from observational data. Journal of the Atmospheric Sciences 72 (2): 854–869.

    Article  Google Scholar 

  • Dorrestijn, J., D.T. Crommelin, A.P. Siebesma, H.J.J. Jonker, and F. Selten. 2016. Stochastic convection parameterization with Markov chains in an intermediate-complexity GCM. JAS 73: 1367–1382.

    Article  Google Scholar 

  • Fritsch, J.M., and C.F. Chappell. 1980. Numerical prediction of convectively driven mesoscale pressure systems. Part I: Convective parameterization. Journal of the Atmospheric Sciences 37 (8): 1722–1733.

    Article  Google Scholar 

  • Khouider, B., J. Biello, A.J. Majda, et al. 2010. A stochastic multicloud model for tropical convection. Communications in Mathematical Sciences 8 (1): 187–216.

    Article  Google Scholar 

  • Kumar, V.V., C. Jakob, A. Protat, P.T. May, and L. Davies. 2013. The four cumulus cloud modes and their progression during rainfall events: AC-band polarimetric radar perspective. Journal of Geophysical Research: Atmospheres 118 (15): 8375–8389.

    Google Scholar 

  • Loriaux, J.M., G. Lenderink, and A.P. Siebesma. 2017. Large-scale controls on extreme precipitation. Journal of Climate 30 (3): 955–968.

    Article  Google Scholar 

  • Mapes, B.E., and R.B. Neale. 2011. Parameterizing convective organization to escape the entrainment dilemma. Journal of Advances in Modeling Earth Systems 3 (06).

    Article  Google Scholar 

  • Neggers, R.A.J. 2015. Exploring bin-macrophysics models for moist convective transport and clouds. Journal of Advances in Modeling Earth Systems 7 (4): 2079–2104.

    Article  Google Scholar 

  • Peters, K., T. Crueger, C. Jakob, and B. Möbis. 2017. Improved MJO-simulation in ECHAM6.3 by coupling a stochastic multicloud model to the convection scheme. Journal of Advances in Modeling Earth Systems 9 (1): 193–219.

    Article  Google Scholar 

  • Plant, R.S., and G.C. Craig. 2008. A stochastic parameterization for deep convection based on equilibrium statistics. Journal of the Atmospheric Sciences 65 (1): 87–105.

    Article  Google Scholar 

  • Sakradzija, M., A. Seifert, and T. Heus. 2015. Fluctuations in a quasi-stationary shallow cumulus cloud ensemble. Nonlinear Processes in Geophysics 22 (1): 65–85.

    Article  Google Scholar 

  • Siebesma, A.P. 1998. Shallow cumulus convection. In Buoyant convection in geophysical flows, vol. 513, ed. E.J. Plate, E.E. Fedorovich, D.X. Viegas, and J.C. Wyngaard, 441–486. New York: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Sušelj, K., J. Teixeira, and D. Chung. 2013. A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parameterization. Journal of the Atmospheric Sciences 70 (7): 1929–1953.

    Article  Google Scholar 

  • Teixeira, J., and C.A. Reynolds. 2008. Stochastic nature of physical parameterizations in ensemble prediction: A stochastic convection approach. Monthly Weather Review 136 (2): 483–496.

    Article  Google Scholar 

  • Tiedtke, M. 1989. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Monthly Weather Review 117 (8): 1779–1800.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Pier Siebesma .

Editor information

Editors and Affiliations

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

Siebesma, A.P., Dorrestijn, J. (2019). New Pathways for Moist Convection Parameterisation. 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_16

Download citation

Publish with us

Policies and ethics