Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

Journal of Advances in Modeling Earth Systems

Volumn 9, Issue 2, 2017, Pages 1231-1250

  Subramanian, Aneesh C ^a   Palmer, Tim N ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

ensemble forecasts; MJO; moist static energy; precipitation; tropical weather

Indexed keywords

CLIMATE MODELS; CLIMATOLOGY; FORECASTING; PARAMETERIZATION; PRECIPITATION (CHEMICAL); STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TROPICAL ENGINEERING; TROPICS; UNCERTAINTY ANALYSIS;

CLOUD RESOLVING MODEL; ENSEMBLE FORECASTS; ENSEMBLE PREDICTION SYSTEMS; EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS; MOIST STATIC ENERGY; PROBABILISTIC FORECASTS; RANDOM PERTURBATIONS; SUPERPARAMETERIZATION;

WEATHER FORECASTING;

ATMOSPHERIC CONVECTION; CLIMATE MODELING; CLIMATE PREDICTION; ENERGY; ENSEMBLE FORECASTING; GLOBAL CLIMATE; MADDEN-JULIAN OSCILLATION; MODEL TEST; PARAMETERIZATION; PERTURBATION; PRECIPITATION (CLIMATOLOGY); STOCHASTICITY; TROPICAL ENVIRONMENT; TROPICAL METEOROLOGY; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

EID: 85019839431     PISSN: None     EISSN: 19422466     Source Type: Journal    
DOI: 10.1002/2016MS000857     Document Type: Article

References (73)

1. Ackerman, T. P., T. S. Cress, W. R. Ferrell, and J. H. Mather (2016), The programmatic maturation of the ARM Program, Meteorol. Monogr., 57, 3.1–3.19, doi:10.1175/AMSMONOGRAPHS-D-15-0054.1.
2. Andersen, J. A., and Z. Kuang (2012), Moist static energy budget of MJO-like disturbances in the atmosphere of a zonally symmetric aquaplanet, J. Clim., 25(8), 2782–2804.
3. Arakawa, A., and J. H. Jung (2011), Multiscale modeling of the moist-convective atmosphere—A review, Atmos. Res., 102(3), 263–285.
4. Arakawa, A., and W. H. Schubert (1974), Interaction of a cumulus cloud ensemble with the large-scale environment, Part I, J. Atmos. Sci., 31(3), 674–701.
5. Arakawa, A., and C. Wu (2013), A unified representation of deep moist convection in numerical modeling of the atmosphere, Part I, J. Atmos. Sci., 70(7), 1977–1992.
6. Arnold, N. P., Z. Kuang, and E. Tziperman (2013), Enhanced MJO-like variability at high SST, J. Clim., 26(3), 988–1001.
7. Arnold, N. P., M. Branson, Z. Kuang, and D. A. Randall (2015), MJO intensification with warming in the superparameterized CESM, J. Clim., 28, 2706–2724.
8. Bechtold, P., M. Köhler, 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.
9. Berner, J., K. R. Smith, S. Y. Ha, J. P. Hacker, and C. Snyder (2015), Increasing the skill of probabilistic forecasts: Model-error representations versus calibration and debiasing, Mon. Weather Rev, 143, 1295–1320.
10. Berner, J., et al. (2016), Stochastic parameterization: Towards a new view of weather and climate models, Bull. Am. Meteorol. Soc., 98, 565–588, doi:10.1175/BAMS-D-15-00268.1.
11. Blad, I., and D. L. Hartmann (1993), Tropical intraseasonal oscillations in a simple nonlinear model, J. Atmos. Sci., 50(17), 2922–2939.
12. Buizza, R., M. Milleer, and T. N. Palmer (1999), Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. R. Meteorol. Soc., 125(560), 2887–2908.
13. Charney, J. G. (1963), A note on large-scale motions in the tropics, J. Atmos. Sci., 20(6), 607–609.
14. Craig, G. C., and B. G. Cohen (2006), Fluctuations in an equilibrium convective ensemble: Part I: Theoretical formulation, J. Atmos. Sci., 63, 1996–2004.
15. Deng, Q., B. Khouider, and A. J. Majda (2015), The MJO in a coarse-resolution GCM with a stochastic multicloud parameterization, J. Atmos. Sci., 72(1), 55–74.
16. Emanuel, K. (1994), Atmospheric Convection, Oxford Univ. Press, Oxford, U. K.
17. Grabowski, W. W. (2003), MJO-like coherent structures: Sensitivity simulations using the cloud-resolving convection parameterization (CRCP), J. Atmos. Sci., 60(6), 847–864.
18. Grabowski, W. W., and P. K. Smolarkiewicz (1999), CRCP: A cloud resolving convection parameterization for modeling the tropical convecting atmosphere, Physica D, 133(1–4), 171–178.
19. Grooms, I., and A. J. Majda (2013), Efficient stochastic superparameterization for geophysical turbulence, Proc. Natl. Acad. Sci. U. S. A., 110, 4464–4469.
20. Grooms, I., and A. J. Majda (2014), Stochastic superparameterization in quasigeostrophic turbulence, J. Comput. Phys., 271, 78–98.
21. Grooms, I., A. J. Majda, and K. S. Smith (2015), Stochastic superparameterization in a quasigeostrophic model of the Antarctic circumpolar current, Ocean Modell., 85, 1–15.
22. Hannah, W. M., and E. D. Maloney (2014), The moist static energy budget in NCAR CAM5 hindcasts during dynamo, J. Adv. Model. Earth Syst., 6(2), 420–440.
23. Hendon, H. H., and B. Liebmann (1990), The intraseasonal (30-50 Day) oscillation of the Australian summer monsoon, J. Atmos. Sci., 47(24), 2909–2923.
24. Hohenegger, C., and C. Schär (2007), Predictability and error growth dynamics in cloud-resolving models, J. Atmos. Sci., 64(12), 4467–4478.
25. Hohenegger, C., D. Lüthi, and C. Schär (2006), Predictability mysteries in cloud-resolving models, Mon. Weather Rev., 134(8), 2095–2107.
26. Houze, R. A. (2004), Mesoscale convective systems, Rev. Geophys., 42, G4003, doi:10.1029/2004RG000150.
27. Hu, Q., and D. A. Randall (1994), Low-frequency oscillations in radiative-convective systems, J. Atmos. Sci., 42(4), 1089–1099.
28. Huffman, G. J., D. T. Bolvin, E. J. Nelkin, D. B. Wolff, R. F. Adler, G. Gu, Y. Hong, K. P. Bowman, and E. F. Stocker (2007), The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol., 8(1), 38–55, doi:10.1175/JHM560.1.
29. Khairoutdinov, M., D. Randall, and C. DeMott (2005), Simulations of the atmospheric general circulation using a cloud-resolving model as a superparameterization of physical processes, R. Meteorol. Soc. Interface, 62(7), 2136–2154.
30. Khairoutdinov, M. F., and Y. L. Kogan (1999), A large Eddy simulation model with explicit microphysics: Validation against aircraft observations of a stratocumulus-topped boundary layer, J. Atmos. Sci., 56(13), 2115–2131.
31. Khairoutdinov, M. F., and D. A. Randall (2003), Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities, J. Atmos. Sci., 60(4), 607–625.
32. Khouider, B., and A. J. Majda (2016), Models for multiscale interactions. Part i: A multicloud model parameterization, Meteorol. Monogr., 56, 9–1.
33. Kim, D., et al. (2009), Application of MJO simulation diagnostics to climate models, J. Clim., 22(23), 6413–6436.
34. Klein, R., and A. J. Majda (2006), Systematic multiscale models for deep convection on mesoscales, Clim. Dyn., 20(5–6), 525–551.
35. Kuo, H. L. (1965), On formation and intensification of tropical cyclones through latent heat release by cumulus convection, J. Atmos. Sci., 22(1), 40–63.
36. Leutbecher, M., and T. N. Palmer (2008), Ensemble forecasting, J. Comput. Phys., 227(7), 3515–3539.
37. Lewis, J. M. (2005), Roots of ensemble forecasting, Mon. Weather Rev., 133, 1865–1885.
38. Lin, J., and J. D. Neelin (2003), Toward stochastic deep convective parameterization in general circulation models, Geophys. Res. Lett., 30(4), 1162, doi:10.1029/2002GL016203.
39. Lin, J.-L., et al. (2006), Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals, J. Clim., 19(12), 2665–2690.
40. Lipps, F. B., and R. S. Hemler (1982), A scale analysis of deep moist convection and some related numerical calculations, J. Atmos. Sci., 39(10), 2192–2210.
41. Majda, A. J. (2007), Multiscale models with moisture and systematic strategies for superparameterization, J. Atmos. Sci., 64(7), 2726–2734.
42. Majda, A. J. (2012), Challenges in climate science and contemporary applied mathematics, Commun. Pure Appl. Math., 65(7), 920–948.
43. Majda, A. J., C. Franzke, and B. Khouider (2008), An applied mathematics perspective on stochastic modelling for climate, Philos. Trans. R. Soc. London A, 366(1875), 2427–2453.
44. Maloney, E. D. (2009), The moist static energy budget of a composite tropical intraseasonal oscillation in a climate model, J. Clim., 22(3), 711–729.
45. Manabe, S., J. Smagorinsky, and R. F. Strickler (1965), Simulated climatology of a general circulation model with a hydrologic cycle, Mon. Weather Rev., 93(12), 769–798.
46. Mapes, B., S. Tulich, J. Lin, and P. Zuidema (2006), The mesoscale convection life cycle: Building block or prototype for large-scale tropical waves?, Dyn. Atmos. Oceans, 42(1–4), 3–29.
47. Moncrieff, M. W. (1992), Organized convective systems: Archetypal dynamical models, mass and momentum flux theory, and parametrization, Q. J. R. Meteorol. Soc., 118(507), 819–850.
48. Neelin, J. D., and I. M. Held (1987), Modeling tropical convergence based on the moist static energy budget, Mon. Weather Rev., 2(5), 3–12.
49. Palmer, T. (2014), Build high-resolution global climate models, Nature, 515(7527), 338–339.
50. Palmer, T. N. (2012), Towards the probabilistic Earth-system simulator: A vision for the future of climate and weather prediction, Q. J. R. Meteorol. Soc., 138(665), 841–861.
51. Palmer, T. N., F. J. Doblas-Reyes, and A. Weisheimer (2009a), Toward seamless prediction: Calibration of climate change projections using seasonal forecasts reply, Bull. Am. Meteorol. Soc., 89(4), 459–470.
52. Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. Shutts, M. Steinheimer, and A. Weisheimer (2009b), Stochastic parametrization and model uncertainty, ECMWF Tech. Memo. 598, Eur. Cent. for Medium-Range Weather Forecasts, Reading, U. K.
53. Peters, K., C. Jakob, L. Davies, B. Khouider, and A. J. Majda (2013), Stochastic behavior of tropical convection in observations and a multicloud model, J. Atmos. Sci., 70(11), 3556–3575.
54. Plant, R. S., and G. C. Craig (2008), A stochastic parameterization for deep convection based on equilibrium statistics, J. Atmos. Sci., 65(1), 87–105.
55. Plant, R. S., and J.-I. Yano (2015), Parameterization of Atmospheric Convection, vol. 1, World Sci., London.
56. Randall, D., M. Khairoutdinov, A. Arakawa, and W. Grabowski (2003), Breaking the cloud parameterization deadlock, Bull. Am. Meteorol. Soc., 84(11), 1547–1564.
57. Randall, D. A. (2013), Beyond deadlock, Geophys. Res. Lett., 40, 5970–5976, doi:10.1002/2013GL057998.
58. Raymond, D. J., and Ž. Fuchs (2009), Moisture modes and the Madden–Julian oscillation, J. Clim., 22(11), 3031–3046.
59. Shutts, G., and A. C. Pallarès (2014), Assessing parametrization uncertainty associated with horizontal resolution in numerical weather prediction models, Philos. Trans. R. Soc. A, 372(2018), 20130284, doi:10.1098/rsta.2013.0284.
60. Shutts, G. J., and T. N. Palmer (2007), Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parameterization problem, J. Clim., 20(2), 187–202.
61. Slingo, J. M., et al. (1996), Intraseasonal oscillations in 15 atmospheric general circulation models: Results from an AMIP diagnostic subproject, Clim. Dyn., 12(5), 325–357.
62. Smagorinsky, J. (1960), On the dynamical prediction of large-scale condensation by numerical methods, in Physics of Precipitation: Proceedings of the Cloud Physics Conference, Woods Hole, Massachusetts, June 3-5, 1959, Geophys. Monogr. Ser., edited by H. Weickmann, AGU, Washington, D. C.
63. Smith, R. K. (2013), The Physics and Parameterization of Moist Atmospheric Convection, vol. 505, Springer, Dordrecht, Netherlands.
64. Sobel, A., and E. Maloney (2013), Moisture modes and the eastward propagation of the MJO, J. Atmos. Sci., 70(1), 187–192.
65. Sobel, A. H., J. Nilsson, and L. M. Polvani (2001), The weak temperature gradient approximation and balanced tropical moisture waves, J. Atmos. Sci., 58(23), 3650–3665.
66. Subramanian, A., A. Weisheimer, T. Palmer, F. Vitart, and P. Bechtold (2016), Impact of stochastic physics on tropical precipitation in the coupled ECMWF model, Q. J. R. Meteorol. Soc., 143, 852–865, doi:10.1002/qj.2970.
67. Subramanian, A. C., and G. J. Zhang (2014), Diagnosing MJO hindcast biases in NCAR CAM3 using nudging during the dynamo field campaign, J. Geophys. Res. Atmos., 119, 7231–7253, doi:10.1002/2013JD021370.
68. Wang, Y., G. J. Zhang, and G. C. Craig (2016), Stochastic convective parameterization improves the simulation of tropical precipitation variability in the NCAR CAM5, Geophys. Res. Lett., 43, 6612–6619, doi:10.1002/2016GL069818.
69. Watson, P. A. G., H. M. Christensen, and T. N. Palmer (2015), Does the ECMWF IFS convection parameterization with stochastic physics correctly reproduce relationships between convection and the large-scale state?, J. Atmos. Sci., 72(1), 236–242.
70. Weisheimer, A., S. Corti, and T. Palmer (2014), Addressing model error through atmospheric stochastic physical parametrizations: Impact on the coupled ECMWF seasonal forecasting system, Philos. Trans. R. Soc., 372, 20130290, doi:10.1098/rsta.2013.0290.
71. Wolding, B. O., and E. D. Maloney (2015), Objective diagnostics and the Madden–Julian oscillation. Part II: Application to moist static energy and moisture budgets, J. Clim., 28(19), 7786–7808.
72. Xing, Y., A. J. Majda, and W. W. Grabowski (2009), New efficient sparse space-time algorithms for superparameterization on mesoscales, Mon. Weather Rev., 137(12), 4307–4324.
73. Yano, J.-I. (2016), Subgrid-scale physical parameterization in atmospheric modeling: How can we make it consistent?, J. Phys. A, 49(28), 284001.