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.
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Notes
- 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Loriaux, J.M., G. Lenderink, and A.P. Siebesma. 2017. Large-scale controls on extreme precipitation. Journal of Climate 30 (3): 955–968.
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).
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.
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.
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.
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.
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.
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.
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.
Tiedtke, M. 1989. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Monthly Weather Review 117 (8): 1779–1800.
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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
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