A simple model of convection with memory
Davies, L., Plant, R. S. and Derbyshire, S. H. (2009) A simple model of convection with memory. Journal of Geophysical Research, 114. D17202. ISSN 0148-0227
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To link to this article DOI: 10.1029/2008JD011653
There are at least three distinct time scales that are relevant for the evolution of atmospheric convection. These are the time scale of the forcing mechanism, the time scale governing the response to a steady forcing, and the time scale of the response to variations in the forcing. The last of these, tmem, is associated with convective life cycles, which provide an element of memory in the system. A highly simplified model of convection is introduced, which allows for investigation of the character of convection as a function of the three time scales. For short tmem, the convective response is strongly tied to the forcing as in conventional equilibrium parameterization. For long tmem, the convection responds only to the slowly evolving component of forcing, and any fluctuations in the forcing are essentially suppressed. At intermediate tmem, convection becomes less predictable: conventional equilibrium closure breaks down and current levels of convection modify the subsequent response.
Arakawa, A. (2004), The cumulus parameterisation problem: Past, present and future, J. Atmos. Sci., 17, 2493 2525. Arakawa, A., and W. Schubert (1974), Interaction of a cumulus cloud ensemble with the large-scale environment: Part 1, J. Atmos. Sci., 31, 674701. Bechtold, P., E. Bazile, F. Guichard, and E. Richard (2001), A mass-flux convection scheme for regional and global models, Q. J. R. Meteorol. Soc., 127, 869886. Bechtold, P., M. Kohler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. J. Rodwell, F. Vitart, and G. Balsamo (2008), Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. R. Meteorol. Soc., 134, 1337 1351. Cho, H.-R. (1977), Contributions of cumulus cloud life-cycle effects to the large-scale heat and moisture budget equations, J. Atmos. Sci., 34, 87 97. Cohen, B., and G. Craig (2004), The time response of a convective cloud ensemble to a change in forcing, Q. J. R. Meteorol. Soc., 130, 933 944. Davies, L. (2008), Self organisation of convection as a mechanism for memory, Ph.D. thesis, 165 pp., Univ. of Reading, Reading, U. K. Derbyshire, S. H., I. Beau, P. Bechtold, J. Y. Grandpeix, J. M. Piriou, J. L. Redelsperger, and P. M. Soares (2004), Sensitivity of moist convection to environmental humidity, Q. J. R. Meteorol. Soc., 130, 30553079. Done, J. M., G. C. Craig, S. L. Gray, P. A. Clark, and M. E. B. Gray (2006), Mesoscale simulations of organized convection: Importance of convective equilibrium, Q. J. R. Meteorol. Soc., 132, 737756. Emanuel, K. A. (1994), Atmospheric Convection, 1st ed., Oxford Univ. Press, Oxford, U. K. Gregory, D. (1997), The mass flux approach to the parameterization of deep convection, in The Physics and Parameterization of Moist Atmospheric Convection, edited by R. K. Smith, pp. 297 319, Kluwer Acad., Norwell, Mass. Kain, J. S. (2004), The Kain-Fritsch convective parameterization: An update, J. Appl. Meteorol., 43, 170 181. Lin, X., D. A. Randall, and L. D. Fowler (2000), Diurnal variability of the hydrologic cycle and radiative fluxes: Comparisons between observations and a GCM, J. Clim., 13, 4159 4179. Marsham, J. H., and D. J. Parker (2006), Secondary initiation of multiple bands of cumulonimbus over southern Britain. II: Dynamics of secondary initiation, Q. J. R. Meteorol. Soc., 132, 1053 1072. Nieuwstadt, F. T. M., and R. A. Brost (1986), The decay of convective turbulence, J. Atmos. Sci., 43, 532 546. Pan, D. M., and D. A. Randall (1998), A cumulus parameterisation with a prognostic closure, Q. J. R. Meteorol. Soc., 124, 949981. Piriou, J. M., J. L. Redelsperger, J. F. Geleyn, J. P. Lafore, and F. Guichard (2007), An approach for convective parameterization with memory: Separating microphysics and transport in grid-scale equations, J. Atmos. Sci., 64, 4127 4139. Randall, D. A., and D.-M. Pan (1993), Implementation of the Arakawa- Schubert cumulus parameterization with a prognostic closure, Meteorol. Monogr., 24(11), 137 144. Randall, D. A., et al. (2007), Climate models and their evaluation, in Climate Change 2007: The Physical Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., pp. 590 662, Cambridge Univ. Press, Cambridge, U. K. Stirling, A., and J. Petch (2004), The impacts of spatial variability on the development of convection, Q. J. R. Meteorol. Soc., 130, 31893206. Thompson, J. M. T., and H. B. Stewart (2001), Nonlinear Dynamics and Chaos, 2nd ed., John Wiley, Hoboken, N. J. Tompkins, A. M., and G. Craig (1998), Radiative-convective equilibrium in a three-dimensional cloud-ensemble model, Q. J. R. Meteorol. Soc., 124, 2073 2097. Wu, X., X. Z. Liang, and S. Park (2007), Cloud-resolving model simulations over the ARM SGP, Mon. Weather Rev., 135, 2841 2853. Xu, K. M., and D. A. Randall (1998), Influence of large-scale advective cooling and moistening effects on the quasi-equilibrium behavior of explicitly simulated cumulus ensembles, J. Atmos. Sci., 55, 896 909. Xu, K. M., et al. (2002), An intercomparison of cloud-resolving models with the atmospheric radiation measurement summer 1997 intensive observation period data, Q. J. R. Meteorol. Soc., 580, 593624. Yang, G. Y., and J. Slingo (2001), The diurnal cycle in the tropics, Mon. Weather Rev., 129, 784801. Yuter, S. E., R. A. Houze Jr., E. A. Smith, T. T. Wilheit, and E. Zipser (2005), Physical characterization of tropical oceanic convection observed in KWAJEX, J. Appl. Meteorol., 44, 385 415.
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