Advertisement

Role of SST feedback in the prediction of the boreal summer monsoon intraseasonal oscillation

  • Ying Zhang
  • Meng-Pai Hung
  • Wanqiu WangEmail author
  • Arun Kumar
Article

Abstract

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation (MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air–sea interaction. These results suggest the importance of intraseasonal variability of SST and air–sea interaction in improving the intraseasonal rainfall prediction associated with the MISO.

Notes

Acknowledgements

The authors greatly appreciate the helpful reviews by Zeng-Zhen Hu and Jieshun Zhu, and the anonymous reviewers. We thank Jieshun Zhu for his help with the revision of the manuscript. Ying Zhang gratefully acknowledges the financial support given by the Earth System Science Organization, Ministry of Earth Sciences, Government of India, to conduct this research under the Monsoon Mission. Ying Zhang is also supported by the NOAA Climate Program Office CVP program. The scientific results and conclusions, as well as any view or opinions expressed herein, are those of the author(s) and do not necessarily reflect the views of NWS, NOAA, or the Department of Commerce.

References

  1. Bellenger H, Duvel J-P (2009) An analysis of tropical ocean diurnal warm layers. J Clim 22:3629–3646CrossRefGoogle Scholar
  2. Bernie DJ, Gullyardi E, Madec G, Slingo JM, Woolnough SJ (2007) Impact of resolving the diurnal cycle in an ocean-atmosphere GCM. Part 1: a diurnally forced OGCM. Clim Dyn 29:575–590CrossRefGoogle Scholar
  3. Bernie DJ, Gullyardi E, Madec G, Slingo JM, Woolnough SJ, Cole J (2008) Impact of resolving the diurnal cycle in an ocean-atmosphere GCM. Part 2: a diurnally forced CGCM. Clim Dyn 31:909–925CrossRefGoogle Scholar
  4. Davey M et al (2002) STOIC: a study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420.  https://doi.org/10.1007/s00382-001-0188-6 CrossRefGoogle Scholar
  5. de Szoeke SP, Edson JB, Marion JR, Fairall CW, Bariteau L (2015) The MJO and air–sea interaction in TOGA COARE and DYNAMO. J Clim 28:597–622CrossRefGoogle Scholar
  6. Del Genio AD (2015) Constraints on cumulus parameterization from simulations of observed MJO events. J Clim 28:6419–6442CrossRefGoogle Scholar
  7. Del Genio AD, Chen Y (2015) Cloud-radiative driving of the Madden–Julian oscillation as seen by the A-Train. J Geophys Res Atmos 120:5344–5356.  https://doi.org/10.1002/2015JD023278 CrossRefGoogle Scholar
  8. DeMott CA, Stan C, Randall DA (2013) Northward propagation mechanisms of the boreal summer intraseasonal oscillation in the ERA-Interim and SP-CCSM. J Clim 26:1973–1992CrossRefGoogle Scholar
  9. DeMott CA, Benedict JJ, Klingaman NP, Woolnough SJ, Randall DA (2016) Diagnosing ocean feedbacks to the MJO: SST-modulated surface fluxes and the moist static energy budget. J Geophys Res Atmos 121:8350–8373CrossRefGoogle Scholar
  10. Donlon CJ, Martin M, Stark J, Roberts-Jones J, Fiedler E, Wimmer W (2012) The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sens Environ 116:140–158CrossRefGoogle Scholar
  11. Fu X, Wang B (2004) Differences of boreal summer intraseasonal oscillations simulated in an atmosphere–ocean coupled model and an atmosphere-only model. J Clim 17:1263–1271CrossRefGoogle Scholar
  12. Fu X, Wang B, Li T, McCreary JP (2003) Coupling between northward-propagating, intraseasonal oscillations and sea surface temperature in the Indian Ocean. J Atmos Sci 60:1733–1753CrossRefGoogle Scholar
  13. Fu X, Wang B, Waliser D, Tao L (2007) Impact of atmosphere–ocean coupling on the predictability of monsoon instraseasonal oscillations. J Atmos Sci 64:157–174CrossRefGoogle Scholar
  14. Fu X, Yang B, Bao Q, Wang B (2008) Sea surface temperature feedback extends the predictability of tropical intraseasonal oscillation. Mon Weather Rev 136:577–597CrossRefGoogle Scholar
  15. Fu X, Wang B, Bao Q, Liu P, Lee J-Y (2009) Impacts of initial conditions on monsoon intraseasonal forecasting. Geophys Res Lett 36:L08801.  https://doi.org/10.1029/2009GL037166 Google Scholar
  16. Gadgil S, Rao PRS (2000) Farming strategies for a variable climate—a challenge. Curr Sci 78:1203–1215Google Scholar
  17. Gao Y, Klingaman NP, Demott CA, Hsu P-C (2018) Diagnosing ocean feedbacks to the BSISO: SST-modulated surface fluxes and the moist static energy budget. J Geophys Res Atmos 124:146–170.  https://doi.org/10.1029/2018JD029303 Google Scholar
  18. Ge X, Wang W, Kumar A, Zhang Y (2017) Importance of the vertical resolution in simulating SST diurnal and intraseasonal variability in an oceanic general circulation model. J Clim 30:3963–3978.  https://doi.org/10.1175/JCLI-D-16-0689.1 CrossRefGoogle Scholar
  19. Goswami BN, Ajayamohan RS (2001) Intraseasonal oscillation and interannual variability of the Indian summer monsoon. J Clim 14:1180–1198CrossRefGoogle Scholar
  20. Griffies SM (2012) Elements of the Modular Ocean Model (MOM) (2012 release): GFDL Ocean Group Tech. Rep. No. 7. NOAA/Geophysical Fluid Dynamics LaboratoryGoogle Scholar
  21. Inness PM, Slingo JM (2003) Simulation of the Madden–Julian Oscillation in a coupled general circulation model. Part I: comparisons with observations and an atmosphere-only GCM. J Clim 16:345–364CrossRefGoogle Scholar
  22. Inness PM, Slingo JM, Guilyardi E, Cole J (2003) Simulation of the Madden–Julian oscillation in a coupled general circulation model. Part II: The role of the basic state. J Clim 16:365–382CrossRefGoogle Scholar
  23. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503CrossRefGoogle Scholar
  24. Kemball-Cook SR, Weare BC (2001) The onset of convection in the Madden–Julian oscillation. J Clim 14:780–793CrossRefGoogle Scholar
  25. Kim D, Ahn M-S, Kang I-S, Del Genio AD (2015) Role of longwave cloud-radiation feedback in the simulation of the Madden–Julian Oscillation. J Clim 28(17):6979–6994.  https://doi.org/10.1175/JCLI-D-14-00767.1 CrossRefGoogle Scholar
  26. Klingaman NP, Inness PM, Weller H, Slingo JM (2008a) The importance of high-frequency sea surface temperature variability to the intraseasonal oscillation of Indian monsoon rainfall. J Clim 21:6119–6140CrossRefGoogle Scholar
  27. Klingaman NP, Weller H, Slingo JM, Innes PJ (2008b) The intraseasonal variability of the Indian summer monsoon using TMI sea-surface temperatures and ECMWF reanalysis. J Clim 21:2519–2539CrossRefGoogle Scholar
  28. Klingaman NP, Woolnough SJ, Weller H, Slingo JM (2011) The impact of finer-resolution air–sea coupling on the intraseasonal oscillation of the Indian monsoon. J Clim 24:2451–2468CrossRefGoogle Scholar
  29. Krishnamurti TN, Ardanuy P (1980) The 10 to 20-day westward propagating mode and “Breaks in the Monsoons”. Tellus 32:15–26Google Scholar
  30. Krishnamurti TN, Subrahmanyam D (1982) The 30-50-day mode at 850 mb during MONEX. J Atmos Sci 39:2088–2095CrossRefGoogle Scholar
  31. Krishnamurti TN, Osterhof DK, Mehta AV (1988) Air–sea interaction on the time scale of 30 to 50 days. J Atmos Sci 45:1304–1322CrossRefGoogle Scholar
  32. Lau K-M, Chan PH (1986) Aspects of the 40–50 oscillation during the northern summer as inferred from the outgoing longwave radiation. Mon Weather Rev 14:1354–1367CrossRefGoogle Scholar
  33. Lawrence DM, Webster PJ (2002) The boreal summer intraseaonal oscillation: relationship between northward and eastward movement of convection. J Atmos Sci 59:1593–1606CrossRefGoogle Scholar
  34. Li G, Xie S-P, Du Y (2015) Monsoon-induced biases of climate models over the tropical Indian Ocean. J Clim 28:3058–3072CrossRefGoogle Scholar
  35. Li Y, Han W, Wang W, Ravichandran M (2016) Intraseasonal variability of SST and precipitation in the Arabian Sea during the Indian Summer Monsoon: impact of ocean mixed layer depth. J Clim 29:7889–7910CrossRefGoogle Scholar
  36. Moorthi, S., and M. J. Suarez, 1999: Documentation of version 2 of relaxed Arakawa–Schubert cumulus parameterization with convective downdrafts. NOAA Tech. Note NWS/NCEP 99-01Google Scholar
  37. Moorthi S, Suarez MJ (1992) Relaxed Arakawa–Schubert: a parameterization of moist convection for general circulation models. Mon Weather Rev 120:978–1002.  https://doi.org/10.1175/1520-0493(1992)120%3c0978:RASAPO%3e2.0.CO;2 CrossRefGoogle Scholar
  38. Murakami M (1976) Analysis of summer monsoon fluctuations over India. J Meteorol Soc Jpn 54:15–31CrossRefGoogle Scholar
  39. Pan H-L, Wu W-S (1995) Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. NMC Office Note 409Google Scholar
  40. Pegion K, Kirtman B (2008) The impact of air–sea interactions on the simulation of tropical intraseasonal variability. J Clim 21:6616–6635CrossRefGoogle Scholar
  41. Roxy M, Tanimoto Y (2007) Role of SST over the Indian Ocean in influencing the intraseasonal variability of the Indian summer monsoon. J Meteorol Soc Jpn 85:349–358CrossRefGoogle Scholar
  42. Saha S et al (2006) The NCEP Climate Forecast System. J Clim 19:3483–3517CrossRefGoogle Scholar
  43. Saha S et al (2010) The NCEP Climate Forecast System reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
  44. Saha S et al (2014) The NCEP Climate Forecast System version 2. J Clim 27:2185–2208.  https://doi.org/10.1175/JCLI-D-12-00823.1 CrossRefGoogle Scholar
  45. Seo K-H, Schemm J-KE, Wang W, Kumar A (2007) The boreal summer intraseasonal oscillation simulated in the NCEP Climate Forecast System: the effect of sea surface temperature. Mon Weather Rev 135:1807–1827CrossRefGoogle Scholar
  46. Seo H, Subramanian AC, Miller AJ, Cavanaugh NR (2014) Coupled impacts of the diurnal cycle of sea surface temperature on the Madden–Julian oscillation. J Clim 27:8422–8443.  https://doi.org/10.1175/jcli-d-14-00141.1, https://hdl.handle.net/1912/6996
  47. Suhas E, Neena JM, Goswami BN (2013) An Indian monsoon intraseasonal oscillations (MISO) index for real time monitoring and forecast verification. Clim Dyn 40:2605–2616CrossRefGoogle Scholar
  48. Tseng W-L, Tsuang B-J, Keelyside NS, Hsu H-H, Tu C-Y (2014) Resolving the upper-ocean warm layer improves the simulation of the Madden–Jullian oscillation. Clim Dyn 44:1487–1503CrossRefGoogle Scholar
  49. Wang B, Rui H (1990) Synoptic climatology of transient tropical intraseasonal convection anomalies: 1975–1985. Meteorol Atmos Phys 44:43–61CrossRefGoogle Scholar
  50. Wang B, Xie X (1997) A model for the boreal summer intraseasonal oscillation. J Atmos Sci 54:72–86CrossRefGoogle Scholar
  51. Wang B, Xie X (1998) Coupled modes of the warm pool climate system. Part I: the role of air–sea interaction in maintaining Madden–Julian Oscillation. J Clim 11:2116–2135CrossRefGoogle Scholar
  52. Wang W, Chen M, Kumar A (2009) Impacts of ocean surface on the northward propagation of the boreal summer intraseasonal oscillation in the NCEP Climate Forecast System. J Clim 22:6561–6576.  https://doi.org/10.1175/2009JCLI3007.1 CrossRefGoogle Scholar
  53. Wang C, Zhang L, Lee S-K, Wu L, Mechoso CR (2014) A global perspective on CMIP5 climate model biases. Nat Clim Change 4:201–205.  https://doi.org/10.1038/nclimate2118 CrossRefGoogle Scholar
  54. Wang W, Kumar A, Fu J, Hung M-P (2015) What is the role of the sea surface temperature uncertainty in the prediction of tropical convection associated with the MJO? Mon Weather Rev 143:3156–3175.  https://doi.org/10.1175/MWR-D-14-00385.1 CrossRefGoogle Scholar
  55. Webster PJ et al (1998) Monsoons: processes, predictability and the prospects of prediction. J Geophys Res 103:14451–14510CrossRefGoogle Scholar
  56. Woolnough SJ, Slingo JM, Hoskins BJ (2000) The relationship between convection and sea surface temperature on intraseasonal timescales. J Clim 13:2086–2104CrossRefGoogle Scholar
  57. Woolnough SJ, Vitart F, Balmaseda MA (2007) The role of the ocean in the Madden–Julian Oscillation: implications for MJO prediction. Q J R Meteorol Soc 133:117–128CrossRefGoogle Scholar
  58. Yasunari T (1979) Cloudiness fluctuation associated with the Northern Hemisphere summer monsoon. J Meteorol Soc Jpn 57:227–242CrossRefGoogle Scholar
  59. Yasunari T (1980) A quasi-stationary appearance of 30- to 40-day period in the cloudiness fluctuations during the summer monsoon over India. J Meteorol Soc Jpn 58:225–229CrossRefGoogle Scholar
  60. Zhu J, Wang W, Kumar A (2017) Simulations of MJO propagation across the Maritime Continent: impacts of SST feedback. J Clim 30:1689–1704.  https://doi.org/10.1175/JCLIM-D-16-0367.1 CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019

Authors and Affiliations

  1. 1.ESSICUniversity of MarylandCollege ParkUSA
  2. 2.NOAA/NWS/NCEP Climate Prediction CenterCollege ParkUSA
  3. 3.Chinese Culture UniversityTaipeiTaiwan

Personalised recommendations