A stochastic parameterization for deep convection based on equilibrium statistics

Journal of the Atmospheric Sciences

Volumn 65, Issue 1, 2008, Pages 87-105

  Plant, R S ^a   Craig, G C ^b  

^a UNIVERSITY OF READING   (United Kingdom)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLOUDS; HEAT CONVECTION; MASS TRANSFER; PROBABILITY DENSITY FUNCTION;

CLOUD-RESOLVING MODELS; DEEP CONVECTION; KAIN-FRITSCH PARAMETERIZATION;

ATMOSPHERICS;

ATMOSPHERICS; CLOUDS; HEAT CONVECTION; MASS TRANSFER; PROBABILITY DENSITY FUNCTION;

ATMOSPHERIC CONVECTION; ATMOSPHERIC PLUME; CONVECTIVE CLOUD; MODELING; PARAMETERIZATION; PROBABILITY DENSITY FUNCTION; STOCHASTICITY;

EID: 38849155083     PISSN: 00224928     EISSN: None     Source Type: Journal    
DOI: 10.1175/2007JAS2263.1     Document Type: Article

References (65)

1. Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 2493-2525.
2. 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.
3. Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single-column tests using GATE wave, BOMEX, ATEX and arctic air-mass datasets. Quart. J. Roy. Meteor. Soc., 112, 693-709.
4. Bretherton, C. S., and P. K. Smolarkiewicz, 1989: Gravity waves, compensating subsidence and detrainment around cumulus clouds. J. Atmos. Sci., 46, 740-759.
5. Bright, D. R., and S. L. Mullen, 2002: Short-range ensemble forecasts of precipitation during the southwest monsoon. Wea. Forecasting, 17, 1080-1100.
6. Buizza, R., 1997: Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system. Mon. Wea. Rev., 125, 99-119.
7. Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 2887-2908.
8. Buizza, R., P. L. Houtekamer, Z. Toth, G. Pellerin, M. Wei, and Y. Zhu, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 1076-1096.
9. Cohen, B. G., 2001: Fluctuations in an ensemble of cumulus clouds. Ph.D. thesis, University of Reading, 165 pp.
10. Cohen, B. G., and G. C. Craig, 2004: The response time of a convective cloud ensemble to a change in forcing. Quart. J. Roy. Meteor. Soc., 130, 933-944.
11. Cohen, B. G., and G. C. Craig, 2006: Fluctuations in an equilibrium convective ensemble. Part II: Numerical experiments. J. Atmos. Sci., 63, 2005-2015.
12. Craig, G. C., and B. G. Cohen, 2006: Fluctuations in an equilibrium convective ensemble. Part I: Theoretical formulation. J. Atmos. Sci., 63, 1996-2004.
13. Cullen, M. J. P., 1993: The unified forecast/climate model. Meteor. Mag., 122, 89-94.
14. 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. Quart. J. Roy. Meteor. Soc., 132, 737-756.
15. Donner, L. J., 1993: A cumulus parameterization including mass fluxes, vertical momentum dynamics, and mesoscale effects. J. Atmos. Sci., 50, 889-906.
16. Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.
17. Emanuel, K. A., and M. Bister, 1996: Moist convective velocity and buoyancy scales. J. Atmos. Sci., 53, 3276-3285.
18. Evans, R. E., M. S. J. Harrison, R. J. Graham, and K. R. Mylne, 2000: Joint medium-range ensembles from the Met Office and ECMWF systems. Mon. Wea. Rev., 128, 3104-3127.
19. Ewald, B., C. Penland, and R. Temam, 2004: Accurate integration of stochastic climate models with application to El Niño. Mon. Wea. Rev., 132, 154-164.
20. Frank, W. M., and C. Cohen, 1985: Properties of tropical cloud ensembles estimated using a cloud model and an observed updraft population. J. Atmos. Sci., 42, 1911-1928.
21. Fritsch, J. M., and C. F. Chappell, 1980: Numerical prediction of convectively driven mesoscale pressure systems. Part I: Convective parameterization. J. Atmos. Sci., 37, 1722-1733.
22. Grant, A. L. M., and A. R. Brown, 1999: A similarity hypothesis for shallow-cumulus transports. Quart. J. Roy. Meteor. Soc., 125, 1913-1936.
23. Gregory, D., and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 1483-1506.
24. Guichard, F., and Coauthors, 2004: Modelling the diurnal cycle of deep precipitating convection over land with cloud-resolving models and single-column models. Quart. J. Roy. Meteor. Soc., 130, 3139-3172.
25. Hermanson, L., 2006: Stochastic physics: A comparative study of parameterized temperature tendencies in a global atmospheric model. Ph.D. thesis, University of Reading, 145 pp.
26. Horinouchi, T., and Coauthors, 2003: Tropical cumulus convection and upward-propagating waves in middle-atmospheric GCMs. J. Atmos. Sci., 60, 2765-2782.
27. Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the SAMEX '98 ensemble forecasts. Mon. Wea. Rev., 129, 73-91.
28. Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225-1242.
29. Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170-181.
30. Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/ detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.
31. Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization in mesoscale models: The Kain-Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165-170.
32. Kain, J. S., M. E. Baldwin, and S. J. Weiss, 2003: Parameterized updraft mass flux as a predictor of convective intensity. Wea. Forecasting, 18, 106-116.
33. Khouider, B., A. J. Majda, and A. Katsoulakis, 2003: Coarse grained stochastic models for tropical convection and climate. Proc. Natl. Acad. Sci. USA, 100, 11 941-11 946.
34. Kuang, Z., and C. S. Bretherton, 2006: A mass-flux scheme view of a high-resolution simulation of a transition from shallow to deep cumulus convection. J. Atmos. Sci., 63, 1895-1909.
35. LeMone, M. A., and E. J. Zipser, 1980: Cumulonimbus vertical velocity events in GATE. Part I: Diameter, intensity and mass flux. J. Atmos. Sci., 37, 2444-2457.
36. Lennard, R., 2004: The distribution of cumulus cloud sizes. M.S. thesis, Department of Meteorology, University of Reading, 67 pp.
37. Lin, J. W.-B., and J. D. Neelin, 2000: Influence of a stochastic moist convective parameterization on tropical climate variability. Geophys. Res. Lett., 27, 3691-3694.
38. Lin, J. W.-B., and J. D. Neelin, 2002: Considerations for stochastic convective parameterization. J. Atmos. Sci., 59, 959-975.
39. Lin, J. W.-B., and J. D. Neelin, 2003: Toward stochastic deep convective parameterization in general circulation models. Geophys. Res. Lett., 30, 1162, doi:10.1029/2002GL016203.
40. Majda, A. J., and B. Khouider, 2002: Stochastic and mesoscopic models for tropical convection. Proc. Natl. Acad. Sci. USA, 99, 1123-1128.
41. Mapes, B. E., 1997: Equilibrium vs. activation control of large-scale variations of tropical deep convection. The Physics and Parameterization of Moist Atmospheric Convection, R. K. Smith, Ed., Kluwer Academic, 321-358.
42. Mylne, K. R., R. E. Evans, and R. T. Clark, 2002: Multi-model multi-analysis ensembles in quasi-operational medium-range forecasting. Quart. J. Roy. Meteor. Soc., 128, 361-384.
43. Nober, F. J., 2003: Development of a convective cloud field model based upon principles of self-organization. Ph.D. thesis, University of Hamburg, 109 pp.
44. Ooyama, K., 1971: A theory on parameterization of cumulus convection. J. Meteor. Soc. Japan, 49, 744-756.
45. Palmer, T. N., 2001: A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models. Quart. J. Roy. Meteor. Soc., 127, 279-304.
46. Penland, C., 2003: A stochastic approach to nonlinear dynamics: A review. Bull. Amer. Meteor. Soc., 84, ES43-ES52.
47. Peters, O., and J. D. Neelin, 2006: Critical phenomena in atmospheric precipitation. Nature Phys., 2, 393-396.
48. Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in Fortran 77: The Art of Scientific Computing. 2nd ed. Cambridge University Press, 934 pp.
49. Ricciardulli, L., and R. R. Garcia, 2000: The excitation of equatorial waves by deep convection in the NCAR Community Climate Model (CCM3). J. Atmos. Sci., 57, 3461-3487.
50. Robe, F. R., and K. A. Emanuel, 1996: Moist convective scaling: Some inferences from three-dimensional cloud ensemble simulations. J. Atmos. Sci., 53, 3266-3275.
51. Shutts, G. J., and M. E. B. Gray, 1999: Numerical simulations of convective equilibrium under prescribed forcing. Quart. J. Roy. Meteor. Soc., 125, 2767-2787.
52. Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 10, 435-460.
53. Smith, R. N. B., and A. Williams, 2000: Surface and boundary layer processes. Met Office Tech. Rep., Unified Model Documentation Paper 24, model version 4.5, 118 pp.
54. Smith, R. N. B., D. Gregory, J. F. B. Mitchell, A. C. Bushell, and D. R. Wilson, 1998: Large-scale precipitation. Met Office Tech. Rep., Unified Model Documentation Paper 26, model version 4.5, 38 pp.
55. Stensrud, D. J., J.-W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 2077-2107.
56. Teixeira, J., and C. A. Reynolds, 2008: Stochastic nature of physical parameterizations in ensemble prediction: A stochastic convection approach. Mon. Wea. Rev., in press.
57. Wilks, D. S., 2005: Effects of stochastic parameterizations in the Lorenz '96 system. Quart. J. Roy. Meteor. Soc., 131, 389-407.
58. Williams, P. D., 2006: Modelling climate change: The role of unresolved processes. Philos. Trans. Roy. Soc. London, 363, 2931-2946.
59. Wilson, D. R., and S. P. Ballard, 1999: A microphysically based precipitation scheme for the UK meteorological office unified model. Quart. J. Roy. Meteor. Soc., 125, 1607-1636.
60. Xu, K.-M., A. Arakawa, and S. K. Krueger, 1992: The macroscopic behavior of cumulus ensembles simulated by a cumulus ensemble model. J. Atmos. Sci., 49, 2402-2420.
61. Yang, Z. W., and R. W. Arritt, 2002: Tests of a perturbed physics ensemble approach for regional climate modeling. J. Climate, 15, 2881-2896.
62. Yano, J.-I., K. Fraedrich, and R. Blender, 2001: Tropical convective variability as 1/f noise. J. Climate, 14, 3608-3616.
63. Yano, J.-I., R. Blender, C. Zhang, and K. Fraedrich, 2004a: 1/f noise and pulse-like events in the tropical atmospheric surface variabilities. Quart. J. Roy. Meteor. Soc., 130, 1697-1721.
64. Yano, J.-I., F. Guichard, J.-P. Lafore, J.-L. Redelsperger, and P. Bechtold, 2004b: Estimations of mass fluxes for cumulus parameterizations from high-resolution spatial data. J. Atmos. Sci., 61, 829-842.
65. Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 1173-1185.