MJO prediction skill of the subseasonal-to-seasonal prediction models

Journal of Climate

Volumn 31, Issue 10, 2018, Pages 4075-4094

  Lim, Yuna ^a   Son, Seok Woo ^a   Kim, Daehyun ^b  

^a Seoul National University   (South Korea)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Atmosphere; Forecast verification skill; Intraseasonal variability; Madden Julian oscillation

Indexed keywords

EARTH ATMOSPHERE; FORECASTING; MOISTURE; TROPICS; WIND EFFECTS;

AMPLITUDE AND PHASE ERROR; CONVECTIVE ENVELOPE; FORECAST VERIFICATION/SKILL; INTRASEASONAL VARIABILITY; MADDEN-JULIAN OSCILLATION; QUANTITATIVE EVALUATION; SEASONAL PREDICTION; TROPICAL INTRASEASONAL VARIABILITY;

CLIMATOLOGY;

CLIMATE FEEDBACK; CLIMATE MODELING; CLOUD RADIATIVE FORCING; ERROR ANALYSIS; LONGWAVE RADIATION; MADDEN-JULIAN OSCILLATION; PREDICTION; QUANTITATIVE ANALYSIS; SEASONAL VARIATION; WEATHER FORECASTING;

EID: 85055270526     PISSN: 08948755     EISSN: None     Source Type: Journal    
DOI: 10.1175/JCLI-D-17-0545.1     Document Type: Article

References (53)

1. Adames, á. F., and J. M. Wallace, 2015: Three-dimensional structure and evolution of the moisture field in the MJO. J. Atmos. Sci., 72, 3733-3754, https://doi.org/10.1175/JAS-D-15-0003.1
2. Adames, á. F., and J. M. Wallace, and D. Kim, 2016: The MJO as a dispersive, convectively coupled moisture wave: Theory and observations. J. Atmos. Sci., 73, 913-941, https://doi.org/10.1175/JAS-D-15-0170.1
3. 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, https://doi.org/ 10.1175/JCLI-D-11-00168.1
4. Bretherton, C. S., J. R. McCaa, and H. Grenier, 2004: A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: Description and 1D results. Mon. Wea. Rev., 132, 864-882, https://doi.org/10.1175/1520-0493(2004)132, 0864: ANPFSC.2.0.CO;2
5. Cassou, C., 2008: Intraseasonal interaction between the Madden-Julian oscillation and the North Atlantic Oscillation. Nature, 455, 523-527, https://doi.org/10.1038/nature07286
6. Charney, J. G., 1963: A note on large-scale motions in the tropics. J. Atmos. Sci., 20, 607-609, https://doi.org/10.1175/1520-0469 (1963)020, 0607:ANOLSM.2.0.CO;2
7. 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, https:// doi.org/10.1175/JAS-D-13-038.1
8. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553-597, https://doi.org/ 10.1002/qj.828
9. Fuchs, Ž., and D. J. Raymond, 2017: A simple model of intraseasonal oscillations. J. Adv. Model. Earth Syst., 9, 1195-1211, https://doi.org/10.1002/2017MS000963
10. Gonzalez, A. O., and X. Jiang, 2017: Winter mean lower tropospheric moisture over the Maritime Continent as a climate model diagnostic metric for the propagation of the Madden-Julian oscillation. Geophys. Res. Lett., 44, 2588-2596, https:// doi.org/10.1002/2016GL072430
11. Gottschalck, J., and Coauthors, 2010: A framework for assessing operational Madden-Julian oscillation forecasts: A CLIVAR MJO Working Group project. Bull. Amer. Meteor. Soc., 91, 1247-1258, https://doi.org/10.1175/2010BAMS2816.1
12. Hall, J. D., A. J. Matthews, and D. J. Karoly, 2001: The modulation of tropical cyclone activity in the Australian region by the Madden-Julian oscillation. Mon. Wea. Rev., 129, 2970-2982, https://doi.org/10.1175/1520-0493(2001)129, 2970: TMOTCA.2.0.CO;2
13. Hendon, H. H., and B. Liebmann, 1990: A composite study of onset of the Australian summer monsoon. J. Atmos. Sci., 47, 2227-2250, https://doi.org/10.1175/1520-0469(1990)047, 2227: ACSOOO.2.0.CO;2
14. Hou, A. Y., S.Q.Zhang, A.M. da Silva, W. S. Olson, C. D.Kummerow, and J. Simpson, 2001: Improving global analysis and shortrange forecast using rainfall and moisture observations derived from TRMM and SSM/I passive microwave sensors. Bull. Amer. Meteor. Soc., 82, 659-679, https://doi.org/ 10.1175/1520-0477(2001)082, 0659:IGAASF.2.3.CO;2
15. Huffman, G. J., R. F. Adler, M. M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 36-50, https:// doi.org/10.1175/1525-7541(2001)002, 0036:GPAODD.2.0.CO;2
16. Janiga, M. A., and C. Zhang, 2016: MJO moisture budget during DYNAMO in a cloud-resolving model. J. Atmos. Sci., 73, 2257-2278, https://doi.org/10.1175/JAS-D-14-0379.1
17. Jeong, J.-H., C.-H. Ho, B.-M. Kim, and W.-T. Kwon, 2005: Influence of the Madden-Julian oscillation on wintertime surface air temperature and cold surges in East Asia. J. Geophys. Res., 110, D11104, https://doi.org/10.1029/2004JD005408
18. Kim, D., M.-S. Ahn, I.-S. Kang, and A. D. Del Genio, 2015: Role of longwave cloud-radiation feedback in the simulation of the Madden-Julian oscillation. J. Climate, 28, 6979-6994, https:// doi.org/10.1175/JCLI-D-14-00767.1
19. Kim, H.-M., 2017: The impact of the mean moisture bias on the key physics of MJO propagation in the ECMWF reforecast. J. Geophys. Res. Atmos., 122, 7772-7784, https://doi.org/ 10.1002/2017JD027005
20. Kim, H.-M., P. J. Webster, V. E. Toma, and D. Kim, 2014: Predictability and prediction skill of the MJO in two operational forecasting systems. J. Climate, 27, 5364-5378, https://doi.org/10.1175/ JCLI-D-13-00480.1
21. Kim, H.-M., D. Kim, F. Vitart, V.E.Toma, J.-S.Kug, and P. J.Webster, 2016: MJO propagation across the Maritime Continent in the ECMWF ensemble prediction system. J. Climate, 29, 3973-3988, https://doi.org/10.1175/JCLI-D-15-0862.1
22. Kiranmayi, L., and E. D. Maloney, 2011: Intraseasonal moist static energy budget in reanalysis data. J. Geophys. Res., 116, D21117, https://doi.org/10.1029/2011JD016031
23. Lavender, S. L., and A. J. Matthews, 2009: Response of the West African monsoon to the Madden-Julian oscillation. J. Climate, 22, 4097-4116, https://doi.org/10.1175/2009JCLI2773.1
24. Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275-1277
25. Lin, H., G. Brunet, and J.Derome, 2008: Forecast skill of theMadden-Julian oscillation in twoCanadian atmosphericmodels. Mon. Wea. Rev., 136, 4130-4149, https://doi.org/10.1175/2008MWR2459.1
26. Lin, H., G. Brunet, and J.Derome, 2009: An observed connection between the North Atlantic Oscillation and the Madden-Julian oscillation. J. Climate, 22, 364-380, https://doi.org/10.1175/2008JCLI2515.1
27. Lin, J.-L., and B. E. Mapes, 2004: Radiation budget of the tropical intraseasonal oscillation. J. Atmos. Sci., 61, 2050-2062, https:// doi.org/10.1175/1520-0469(2004)061, 2050:RBOTTI.2.0.CO;2
28. Liu, X., and Coauthors, 2017: MJO prediction using the subseasonal to seasonal forecast model of Beijing Climate Center. Climate Dyn., 48, 3283-3307, https://doi.org/10.1007/ s00382-016-3264-7
29. Madden, R. A., and P. R. Julian, 1971: Description of a 40-50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702-708, https://doi.org/10.1175/1520-0469(1971)028, 0702: DOADOI.2.0.CO;2
30. 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, https://doi.org/10.1175/1520-0469(1972)029, 1109: DOGSCC.2.0.CO;2
31. Matthews, A. J., B. J. Hoskins, and M. Masutani, 2004: The global response to tropical heating in the Madden-Julian oscillation during the northern winter. Quart. J. Roy. Meteor. Soc., 130, 1991-2011, https://doi.org/10.1256/qj.02.123
32. Neelin, J. D., and J.-Y. Yu, 1994: Modes of tropical variability under convective adjustment and the Madden-Julian oscillation. Part I: Analytical theory. J. Atmos. Sci., 51, 1876-1894, https:// doi.org/10.1175/1520-0469(1994)051, 1876:MOTVUC.2.0.CO;2
33. Neena, J. M., J. Y. Lee, D. Waliser, B. Wang, and X. Jiang, 2014: Predictability of the Madden-Julian oscillation in the Intraseasonal VariabilityHindcast Experiment (ISVHE). J.Climate, 27, 4531-4543, https://doi.org/10.1175/JCLI-D-13-00624.1
34. Rashid, H. A., H. H. Hendon, M. C. Wheeler, and O. Alves, 2011: Prediction of the Madden-Julian oscillation with thePOAMA dynamical prediction system. Climate Dyn., 36, 649-661, https://doi.org/10.1007/s00382-010-0754-x
35. Raymond, D. J., 2001: A new model of the Madden-Julian oscillation. J. Atmos. Sci., 58, 2807-2819, https://doi.org/10.1175/ 1520-0469(2001)058, 2807:ANMOTM.2.0.CO;2
36. Robertson, A. W., A. Kumar, M. Peña, and F. Vitart, 2015: Improving and promoting subseasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 96, ES49-ES53, https://doi.org/ 10.1175/BAMS-D-14-00139.1
37. Seo, K.-H., and S.-W. Son, 2012: The global atmospheric circulation response to tropical diabatic heating associated with the Madden-Julian oscillation during northern winter. J. Atmos. Sci., 69, 79-96, https://doi.org/10.1175/2011JAS3686.1
38. Sobel, A., and E. Maloney, 2012: An idealized semi-empirical framework for modeling the Madden-Julian oscillation. J. Atmos. Sci., 69, 1691-1705, https://doi.org/10.1175/JAS-D-11-0118.1
39. Sobel, A., and E. Maloney, 2013: Moisture modes and the eastward propagation of the MJO. J. Atmos. Sci., 70, 187-192, https://doi.org/ 10.1175/JAS-D-12-0189.1
40. Sobel, A., and E. Maloney, J. Nilsson, and L. M. Polvani, 2001: The weak temperature gradient approximation and balanced tropical moisture waves. J. Atmos. Sci., 58, 3650-3665, https://doi.org/10.1175/ 1520-0469(2001)058, 3650:TWTGAA.2.0.CO;2
41. Vitart, F., 2014: Evolution of ECMWF sub-seasonal forecast skill scores. Quart. J. Roy. Meteor. Soc., 140, 1889-1899, https:// doi.org/10.1002/qj.2256
42. Vitart, F., 2017: Madden-Julian oscillation prediction and teleconnections in the S2S database. Quart. J. Roy. Meteor. Soc., 143, 2210-2220, https://doi.org/10.1002/qj.3079
43. Vitart, F., and F. Molteni, 2010: Simulation of the Madden-Julian oscillation and its teleconnections in the ECMWF forecast system. Quart. J. Roy. Meteor. Soc., 136, 842-855, https://doi. org/10.1002/qj.623
44. Vitart, F., and F. Molteni, and Coauthors, 2017: The Subseasonal to Seasonal (S2S) prediction project database. Bull. Amer. Meteor. Soc., 98, 163-173, https://doi.org/10.1175/BAMS-D-16-0017.1
45. Wang, L., T. Li, E. Maloney, and B. Wang, 2017: Fundamental causes of propagating and nonpropagating MJOs in MJOTF/ GASS models. J. Climate, 30, 3743-3769, https://doi.org/ 10.1175/JCLI-D-16-0765.1
46. Wang, W., M.-P. Hung, S. J. Weaver, A. Kumar, and X. Fu, 2014: MJO prediction in the NCEP Climate Forecast System version 2. Climate Dyn., 42, 2509-2520, https://doi.org/10.1007/ s00382-013-1806-9
47. Wentz, F. J., K. A. Hilburn, and D. K. Smith, 2012: Remote Sensing Systems DMSP SSM/I daily environmental suite on 0.25 deg grid, version 7. Remote Sensing Systems, accessed 1 February 2017, www.remss.com/missions/ssmi
48. 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, https:// doi.org/10.1175/1520-0493(2004)132, 1917:AARMMI.2.0.CO;2
49. Wolding, B. O., E. D. Maloney, and M. Branson, 2016: Vertically resolved weak temperature gradient analysis of the Madden-Julian oscillation in SP-CESM. J. Adv. Model. Earth Syst., 8, 1586-1619, https://doi.org/10.1002/2016MS000724
50. Xiang, B., M. Zhao, X. Jiang, S.-J. Lin, T. Li, X. Fu, and G. Vecchi, 2015: The 3-4-week MJO prediction skill in a GFDL coupled model. J. Climate, 28, 5351-5364, https://doi.org/10.1175/ JCLI-D-15-0102.1
51. Yasunari, T., 1979: Cloudiness fluctuations associated with the NorthernHemisphere summer monsoon. J.Meteor. Soc. Japan, 57, 227-242, https://doi.org/10.2151/jmsj1965.57.3_227
52. Zhang, C., 2005: Madden-Julian oscillation. Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158
53. Zhang, C., 2013: Madden-Julian oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 1849-1870, https://doi.org/ 10.1175/BAMS-D-12-00026.1