Role of longwave cloud-radiation feedback in the simulation of the Madden-Julian oscillation

Journal of Climate

Volumn 28, Issue 17, 2015, Pages 6979-6994

  Kim, Daehyun ^a   Ahn, Min Seop ^b   Kang, In Sik ^b   Del Genio, Anthony D ^c  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Seoul National University   (South Korea)

^c NASA GODDARD INSTITUTE FOR SPACE STUDIES   (United States)

Author keywords

Climate models; Cloud parameterizations; Intraseasonal variability; Longwave radiation; Madden Julian oscillation

Indexed keywords

CLIMATE MODELS; GREENHOUSES;

CLIMATE MODEL SIMULATIONS; CLOUD PARAMETERIZATIONS; CLOUD-RADIATION INTERACTIONS; INTRASEASONAL VARIABILITY; LONG-WAVE RADIATION; MADDEN-JULIAN OSCILLATION; OUTGOING LONGWAVE RADIATION; STATISTICAL RELATIONSHIP;

CLIMATOLOGY;

CLIMATE MODELING; CLOUD RADIATIVE FORCING; GREENHOUSE EFFECT; LONGWAVE RADIATION; MADDEN-JULIAN OSCILLATION; PARAMETERIZATION; PRECIPITATION (CLIMATOLOGY); SEASONAL VARIATION;

EID: 84942864304     PISSN: 08948755     EISSN: None     Source Type: Journal    
DOI: 10.1175/JCLI-D-14-00767.1     Document Type: Article

References (92)

1. Andersen, J. A., and Z. Kuang, 2012: Moist static energy budget of MJO-like disturbances in the atmosphere of a zonally symmetric aquaplanet. J. Climate, 25, 2782-2804, doi:10.1175/JCLI-D-11-00168.1.
2. Anderson, J. L., and Coauthors, 2004: The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations. J. Climate, 17, 4641-4673, doi:10.1175/JCLI-3223.1.
3. 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,. doi:10.1175/1520-0469(1974)031,<0674:IOACCE>2.0.CO;2.
4. Arnold, N. P., Z. Kuang, and E. Tziperman, 2013: Enhanced MJO-like variability at high SST. J. Climate, 26, 988-1001, doi:10.1175/JCLI-D-12-00272.1.
5. Benedict, J. J., E. D. Maloney, A. H. Sobel, and D. M. W. Frierson, 2014: Gross moist stability and MJO simulation skill in three full-physics GCMs. J. Atmos. Sci., 71, 3327-3349, doi:10.1175/JAS-D-13-0240.1.
6. Berg, W., T. L'Ecuyer, and J. M. Haynes, 2010: The distribution of rainfall over oceans from spaceborne radars. J. Appl. Meteor. Climatol., 49, 535-543, doi:10.1175/2009JAMC2330.1.
7. Betts, A. K., 1986: A new convective adjustment scheme. Part I. Observational and theoretical basis. Quart. J. Roy. Meteor. Soc., 112, 677-691, doi:10.1002/qj.49711247307.
8. Bony, S., and K. A. Emanuel, 2001: A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data. J. Atmos. Sci., 58, 3158-3183,. doi:10.1175/1520-0469(2001)058,<3158:APOTCA>2.0.CO;2.
9. Bony, S., and K. A. Emanuel, 2005: On the role of moist processes in tropical intraseasonal variability: Cloud-radiation and moisture-convection feedbacks. J. Atmos. Sci., 62, 2770-2789, doi:10.1175/JAS3506.1.
10. Bougeault, P., 1985: A simple parameterization of the large-scale effects of cumulus convection. Mon. Wea. Rev., 113, 2108-2121,. doi:10.1175/1520-0493(1985)113,<2108:ASPOTL>2.0.CO;2.
11. Bretherton, C. S., and A. H. Sobel, 2002: A simple model of a convectively coupled Walker circulation using the weak temperature gradient approximation. J. Climate, 15, 2907-2920,. doi:10.1175/1520-0442(2002)015,<2907:ASMOAC>2.0.CO;2.
12. Chikira, M., 2014: Eastward-propagating intraseasonal oscillation represented by Chikira-Sugiyama cumulus parameterization. Part II: Understanding moisture variation under weak temperature gradient balance. J. Atmos. Sci., 71, 615-639, doi:10.1175/JAS-D-13-038.1.
13. Chikira, M., and M. Sugiyama, 2010: A cumulus parameterization with state-dependent entrainment rate. Part I: Description and sensitivity to temperature and humidity profiles. J. Atmos. Sci., 67, 2171-2193, doi:10.1175/2010JAS3316.1.
14. Del Genio, A. D., J. Wu, A. B. Wolf, Y. Chen, M.-S. Yao, and D. Kim, 2015: Constraints on cumulus parameterization from simulation of observed MJO events. J. Climate, 28, 6419-6442, doi:10.1175/JCLI-D-14-00832.1.
15. Derbyshire, S. H., A. V. Maidens, S. F. Milton, R. A. Stratton, and M. R. Willett, 2011: Adaptive detrainment in a convective parametrization. Quart. J. Roy. Meteor. Soc., 137, 1856-1871, doi:10.1002/qj.875.
16. Donner, L. J., 1993: A cumulus parameterization including mass fluxes, vertical momentum dynamics, and mesoscale effects. J. Atmos. Sci., 50, 889-906,. doi:10.1175/1520-0469(1993)050,<0889:ACPIMF>2.0.CO;2.
17. Donner, L. J., C. J. Seman, R. S. Hemler, and S. Fan, 2001: A cumulus parameterization including mass fluxes, convective vertical velocities, and mesoscale effects: Thermodynamic and hydrological aspects in a general circulation model. J. Climate, 14, 3444-3463,. doi:10.1175/1520-0442(2001)014,<3444:ACPIMF>2.0.CO;2.
18. ECMWF, 2004: IFS Documentation CY28r1, Part IV, chapter 6 (Clouds and large-scale precipitation). European Centre for Medium-Range Weather Forecasts. [Available online at https://software.ecmwf.int/wiki/display/IFS/CY28R11Official1 IFS1Documentation.]
19. Emanuel, K. A., 1991: A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, 2313-2329,. doi:10.1175/1520-0469(1991)048,<2313:ASFRCC>2.0.CO;2.
20. Emori, S., T. Nozawa, A. Numaguti, and I. Uno, 2001: Importance of cumulus parameterization for precipitation simulation over East Asia in June. J. Meteor. Soc. Japan, 79, 939-947, doi:10.2151/jmsj.79.939.
21. Fuchs, Ž., and D. J. Raymond, 2002: Large-scale modes of a non-rotating atmosphere with water vapor and cloud-radiation feedbacks. J. Atmos. Sci., 59, 1669-1679,. doi:10.1175/1520-0469(2002)059,<1669:LSMOAN>2.0.CO;2.
22. Fuchs, Z., and D. J. Raymond, 2005: Large-scale modes in a rotating atmosphere with radiative-convective instability and WISHE. J. Atmos. Sci., 62, 4084-4094, doi:10.1175/JAS3582.1.
23. Fuchs, Z., and D. J. Raymond, 2007: A simple, vertically resolved model of tropical disturbances with a humidity closure. Tellus, 59A, 344-354, doi:10.1111/j.1600-0870.2007.00230.x.
24. Grandpeix, J.-Y., and J.-P. Lafore, 2010: A density current parameterization coupled with Emanuel's convection scheme. Part I: The models. J. Atmos. Sci., 67, 881-897, doi:10.1175/2009JAS3044.1.
25. Grandpeix, J.-Y., and J.-P. Lafore, and F. Cheruy, 2010: A density current parameterization coupled with Emanuel's convection scheme. Part II: 1D simulations. J. Atmos. Sci., 67, 898-922, doi:10.1175/2009JAS3045.1.
26. Gregory, D., 1995: The representation of moist convection in atmospheric models. Hadley Centre Climate Research Tech. Rep. 54, 36 pp.
27. 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,. doi:10.1175/1520-0493(1990)118,<1483:AMFCSW>2.0.CO;2.
28. 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, 420-440, doi:10.1002/2013MS000272.
29. Hourdin, F., and Coauthors, 2013: LMDZ5B: The atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Climate Dyn., 40, 2193-2222, doi:10.1007/s00382-012-1343-y.
30. Huffman, G. J., and Coauthors, 2001: Global precipitation at onedegree daily resolution from multisatellite observations. J. Hydrometeor., 2, 36-50,. doi:10.1175/1525-7541(2001)002,<0036:GPAODD>2.0.CO;2.
31. Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38-55, doi:10.1175/JHM560.1.
32. Hung, M.-P., J.-L. Lin, W. Wang, D. Kim, T. Shinoda, and S. J. Weaver, 2013: MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. J. Climate, 26, 6185-6214, doi:10.1175/JCLI-D-12-00541.1.
33. Jakob, C., 2000: The representation of cloud cover in atmospheric general circulation models. Ph.D. thesis, Ludwig-MaximiliansUniversitaet Muenchen, 193 pp.
34. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437-471,. doi:10.1175/1520-0477(1996)077,<0437:TNYRP>2.0.CO;2.
35. Kim, D., and Coauthors, 2009: Application of MJO simulation diagnostics to climate models. J. Climate, 22, 6413-6436, doi:10.1175/2009JCLI3063.1.
36. Kim, D., A. H. Sobel, E. D. Maloney, D. M. W. Frierson, and I.-S. Kang, 2011: A systematic relationship between intraseasonal variability and mean state bias in AGCM simulations. J. Climate, 24, 5506-5520, doi:10.1175/2011JCLI4177.1.
37. Kim, D., J.-S. Kug, and A. H. Sobel, 2014a: Propagating versus nonpropagating Madden-Julian oscillation events. J. Climate, 27, 111-125, doi:10.1175/JCLI-D-13-00084.1.
38. Kim, D., and Coauthors, 2014b: Process-oriented MJO simulation diagnostic: Moisture sensitivity of simulated convection. J. Climate, 27, 5379-5395, doi:10.1175/JCLI-D-13-00497.1.
39. Landu, K., and E. D. Maloney, 2011a: Effect of SST distribution and radiative feedbacks on the simulation of intraseasonal variability in an aquaplanet GCM. J. Meteor. Soc. Japan, 89, 195-210, doi:10.2151/jmsj.2011-302.
40. Landu, K., and E. D. Maloney, 2011b: Intraseasonal moist static energy budget in reanalysis data. J. Geophys. Res., 116, D21117, doi:10.1029/2011JD016031.
41. Lau, W. K. M., and D. E. Waliser, 2011: Intraseasonal Variability in the Atmosphere-Ocean Climate System. 2nd ed. Springer, 614 pp.
42. Lee, M.-I., I.-S. Kang, J.-K. Kim, and B. E. Mapes, 2001: Influence of cloud-radiation interaction on simulating tropical intraseasonal oscillation with an atmospheric general circulation model. J. Geophys. Res., 106, 14 219-14 233, doi:10.1029/2001JD900143.
43. Le Treut, H., and Z.-X. Li, 1991: Sensitivity of an atmospheric general circulation model to prescribed SST changes: Feedback effects associated with the simulation of cloud optical properties. Climate Dyn., 5, 175-187, doi:10.1007/BF00251808.
44. Li, C., X. Jia, J. Ling, W. Zhou, and C. Zhang, 2009: Sensitivity of MJO simulations to diabatic heating profiles. Climate Dyn., 32, 167-187, doi:10.1007/s00382-008-0455-x.
45. Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275-1277.
46. Lin, J.-L., and B. E. Mapes, 2004a: Radiation budget of the tropical intraseasonal oscillation. J. Atmos. Sci., 61, 2050-2062,. doi:10.1175/1520-0469(2004)061,<2050:RBOTTI>2.0.CO;2.
47. Lin, J.-L., and B. E. Mapes, 2004b: Wind shear effects on cloud-radiation feedback in the western Pacific warm pool. Geophys. Res. Lett., 31, L16118, doi:10.1029/2004GL020199.
48. Lin, J.-L., and Coauthors, 2006: Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals. J. Climate, 19, 2665-2690, doi:10.1175/JCLI3735.1.
49. Liu, H., and G. Wu, 1997: Impacts of land surface on climate of July and onset of summer monsoon: A study with an AGCM plus SSiB. Adv. Atmos. Sci., 14, 289-308, doi:10.1007/s00376-997-0051-8.
50. Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the Earth's top-of-atmosphere radiation budget. J. Climate, 22, 748-766, doi:10.1175/2008JCLI2637.1.
51. Lohmann, U., and E. Roeckner, 1996: Design and performance of a new cloud microphysics scheme developed for the ECHAM4 general circulation model. Climate Dyn., 12, 557-572, doi:10.1007/BF00207939.
52. Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40-50 day period. J. Atmos. Sci., 29, 1109-1123,. doi:10.1175/1520-0469(1972)029,<1109:DOGSCC>2.0.CO;2.
53. McFarlane, N. A., J. F. Scinocca, M. Lazare, R. Harvey, D. Verseghy, and J. Li, 2005: The CCCma third generation atmospheric general circulation model. CCCma Internal Rep., 25 pp.
54. Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa-Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 978-1002,. doi:10.1175/1520-0493(1992)120,<0978:RASAPO>2.0.CO;2.
55. Morrison, H., and A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests. J. Climate, 21, 3642-3659, doi:10.1175/2008JCLI2105.1.
56. Neale, R. B., J. H. Richter, and M. Jochum, 2008: The impact of convection on ENSO: From a delayed oscillator to a series of events. J. Climate, 21, 5904-5924, doi:10.1175/2008JCLI2244.1.
57. Nordeng, T. E., 1994: Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Tech. Memo. 206, 41 pp.
58. Pan, D.-M., and D. A. Randall, 1998: A cumulus parameterization with a prognostic closure. Quart. J. Roy. Meteor. Soc., 124, 949-981, doi:10.1002/qj.49712454714.
59. Rasch, P. J., and J. E. Kristjansson, 1998: A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Climate, 11, 1587-1614,. doi:10.1175/1520-0442(1998)011,<1587:ACOTCM>2.0.CO;2.
60. Raymond, D. J., 2001: A new model of the Madden-Julian oscillation. J. Atmos. Sci., 58, 2807-2819,. doi:10.1175/1520-0469(2001)058,<2807:ANMOTM>2.0.CO;2.
61. Raymond, D. J., and Ž. Fuchs, 2009: Moisture modes and the Madden-Julian oscillation. J. Climate, 22, 3031-3046,. doi:10.1175/2008JCLI2739
62. Richter, J. H., and P. J. Rasch, 2008: Effects of convective momentum transport on the atmospheric circulation in the Community Atmosphere Model, version 3. J. Climate, 21, 1487-1499, doi:10.1175/2007JCLI1789.1.
63. Rotstayn, L. D., 1997: A physically based scheme for the treatment of stratiform clouds and precipitation in large-scale models. I: Description and evaluation of the microphysical processes. Quart. J. Roy. Meteor. Soc., 123, 1227-1282, doi:10.1002/qj.49712354106.
64. Rotstayn, L. D., 1998: A physically based scheme for the treatment of stratiform clouds and precipitation in large-scale models. II: Comparison of modelled and observed climatological fields. Quart. J. Roy. Meteor. Soc., 124, 389-415, 10.1002/qj.49712454603.
65. Rotstayn, L. D., 2000: On the "tuning" of autoconversion parameterizations in climate models. J. Geophys. Res., 105, 15 495-15 507, doi:10.1029/2000JD900129.
66. Schumacher, C., and R. A. Houze Jr., 2006: Stratiform precipitation production over sub-Saharan Africa and the tropical East Atlantic as observed by TRMM. Quart. J. Roy. Meteor. Soc., 132, 2235-2255, doi:10.1256/qj.05.121.
67. 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., 116, 435-460, doi:10.1002/qj.49711649210.
68. Sobel, A. H., and H. Gildor, 2003: A simple time-dependent model of SST hot spots. J. Climate, 16, 3978-3992,. doi:10.1175/1520-0442(2003)016,<3978:ASTMOS>2.0.CO;2.
69. Sobel, A. H., and E. Maloney, 2012: An idealized semi-empirical framework for modeling the Madden-Julian oscillation. J. Atmos. Sci., 69, 1691-1705, doi:10.1175/JAS-D-11-0118.1.
70. Sobel, A. H., and E. Maloney, 2013: Moisture modes and the eastward propagation of the MJO. J. Atmos. Sci., 70, 187-192, doi:10.1175/JAS-D-12-0189.1.
71. Sobel, A. H., J. Nilsson, and L. M. Polvani, 2001: The weak temperature gradient approximation and balanced tropical moisture waves. J. Atmos. Sci., 58, 3650-3665,. doi:10.1175/1520-0469(2001)058,<3650:TWTGAA>2.0.CO;2.
72. Sobel, A. H., E. D. Maloney, G. Bellon, and D. M. Frierson, 2010: Surface fluxes and tropical intraseasonal variability: A reassessment. J. Adv. Model. Earth Syst., 2 (2), doi:10.3894/JAMES.2010.2.2.
73. Sperber, K. R., and D. Kim, 2012: Simplified metrics for the identification of the Madden-Julian oscillation in models. Atmos. Sci. Lett., 13, 187-193, doi:10.1002/asl.378.
74. Su, H., and Coauthors, 2013: Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using "A-Train" satellite observations and reanalysis data. J. Geophys. Res. Atmos., 118, 2762-2780, doi:10.1029/2012JD018575.
75. Sundqvist, H., E. Berge, and J. E. Kristjansson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev., 117, 1641-1657,. doi:10.1175/1520-0493(1989)117,<1641:CACPSW>2.0.CO;2.
76. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485-498, doi:10.1175/BAMS-D-11-00094.1.
77. Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779-1800,. doi:10.1175/1520-0493(1989)117,<1779:ACMFSF>2.0.CO;2.
78. Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 3040-3061,. doi:10.1175/1520-0493(1993)121,<3040:ROCILS>2.0.CO;2.
79. Tobin, I., S.Bony, C.E.Holloway, J.Y.Grandpeix, G.Sèze, D.Coppin, S. J. Woolnough, and R. Roca, 2013: Does convective aggregation need to be represented in cumulus parameterizations? J. Adv. Model. Earth Syst., 5, 692-703, doi:10.1002/jame.20047.
80. Tompkins, A., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in largescale models and its use to diagnose cloud cover. J. Atmos. Sci., 59, 1917-1942,. doi:10.1175/1520-0469(2002)059,<1917:APPFTS>2.0.CO;2.
81. Watanabe, M., S. Emori, M. Satoh, and H. Miura, 2009: A PDF-based hybrid prognostic cloud scheme for general circulation models. Climate Dyn., 33, 795-816, doi:10.1007/s00382-008-0489-0.
82. Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917-1932,. doi:10.1175/1520-0493(2004)132,<1917:AARMMI>2.0.CO;2.
83. Wilcox, E. M., and L. J. Donner, 2007: The frequency of extreme rain events in satellite rain-rate estimates and an atmospheric general circulation model. J. Climate, 20, 53-69, doi:10.1175/JCLI3987.1.
84. 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, doi:10.1002/qj.49712555707.
85. Yokoi, S., 2015: Multireanalysis comparison of variability in column water vapor and its analysis increment associated with the Madden-Julian oscillation. J. Climate, 28, 793-808, doi:10.1175/JCLI-D-14-00465.1.
86. Yukimoto, S., and Coauthors, 2011: Meteorological Research Institute Earth System Model version 1 (MRI-ESM1)-Model description. MRI Tech. Rep. 64, 83 pp. [Available online at http://www.mri-jma.go.jp/Publish/Technical/index_en.html.]
87. Zhang, C., 2005: Madden-Julian Oscillation. Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.
88. Zhang, C., 2013: Madden-Julian oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 1849-1870, doi:10.1175/BAMS-D-12-00026.1.
89. Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.-Ocean, 33, 407-446, doi:10.1080/07055900.1995.9649539.
90. Zhang, G. J., and M. Mu, 2005: Effects of modifications to the Zhang-McFarlane convection parameterization on the simulation of the tropical precipitation in the National Center for Atmospheric Research Community Climate Model, version 3. J. Geophys. Res., 110, D09109, doi:10.1029/2004JD005617.
91. Zhang, G. J., and X. Song, 2009: Interaction of deep and shallow convection is key to Madden-Julian oscillation simulation. Geophys. Res. Lett., 36, L09708, doi:10.1029/2009GL037340.
92. Zurovac-Jevtić, D., S. Bony, and K. Emanuel, 2006: On the role of clouds and moisture in tropical waves: A two-dimensional model study. J. Atmos. Sci., 63, 2140-2155, doi:10.1175/JAS3738.1.