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Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon

  • R. Phani Murali Krishna
  • Suryachandra A. RaoEmail author
  • Ankur Srivastava
  • Hari Prasad Kottu
  • Maheswar Pradhan
  • Prasanth Pillai
  • Ramu A. Dandi
  • C. T. Sabeerali
Article

Abstract

The sensitivity of seasonal predictions of the Indian summer monsoon (ISM) to convection parameterization schemes (CPS) is studied using 37 years of hindcast experiments. The predictions are quite sensitive to changes in these schemes and improve the skill by 18–28%. Though the mean state circulation and rainfall over India improves, the sea surface temperature (SST) biases increase in the sensitivity experiments compared to the control run. The ability of the model to realistically capture the teleconnections associated with monsoon such as the El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) also appears to change with different CPS. It is found that the suitability of a CPS for ISM in the Climate Forecast System version 2 (CFSv2) stems from its ability to capture cloud fractions realistically and keep the SST biases to a minimum. The revised Simplified-Arakawa–Schubert (SAS2, Han and Pan in Weather Forecast 26:520–533.  https://doi.org/10.1175/waf-d-10-05038.1, 2011) scheme gives better prediction skill for ISM compared to the skill score obtained from SAS2 with shallow convection (SAS2sc) primarily because it simulates realistic clouds, without aggravating the SST biases, particularly in the tropical Pacific Ocean, and captures the Indian Ocean teleconnections realistically. SAS2sc significantly under-estimates the low-level clouds over global equatorial region, despite simulating better mid and high-level clouds, higher Nino 3.4 skill, and better inter-annual variability of ISM. The cold SST bias in the tropical basins is large in SAS2sc. Therefore, to exploit the merits of SAS2sc, unrealistic suppression of low clouds needs to be addressed, and the cold SST biases need to be minimized.

Keywords

Indian Summer Monsoon Convective parameterization schemes Teleconnections Clouds Simplified-Arakawa–Schubert scheme 

Notes

Supplementary material

382_2019_4921_MOESM1_ESM.pdf (647 kb)
Supplementary material 1 (PDF 646 kb)

References

  1. Chattopadhyay R, Phani R, Sabeerali CT et al (2015a) Influence of extratropical sea-surface temperature on the Indian summer monsoon: an unexplored source of seasonal predictability. Q J R Meteorol Soc.  https://doi.org/10.1002/qj.2562 Google Scholar
  2. Chattopadhyay R, Rao SA, Sabeerali CT et al (2015b) Large scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs. Int J Climatol.  https://doi.org/10.1002/joc.4556 Google Scholar
  3. Chen T-C, van Loon H, Chen T-C, van Loon H (1987) Interannual variation of the tropical easterly jet. Mon Weather Rev 115:1739–1759.  https://doi.org/10.1175/1520-0493(1987)115%3c1739:IVOTTE%3e2.0.CO;2 CrossRefGoogle Scholar
  4. Chen T-C, Yen M-C, Chen T-C, Yen M-C (1991) Interaction between Intraseasonal oscillations of the midlatitude flow and tropical convection during 1979 northern summer: the Pacific Ocean. J Climate 4:653–671.  https://doi.org/10.1175/1520-0442(1991)004%3c0653:IBIOOT%3e2.0.CO;2 CrossRefGoogle Scholar
  5. Collins M, AchutaRao K, Ashok K et al (2013) Observational challenges in evaluating climate models. Nat Climate Change 3:940–941.  https://doi.org/10.1038/nclimate2012 CrossRefGoogle Scholar
  6. Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597.  https://doi.org/10.1002/qj.828 CrossRefGoogle Scholar
  7. Ek MB, Mitchell KE, Lin Y et al (2003) Implementation of Noah land surface model advances in the national centers for environmental prediction operational mesoscale Eta model. J Geophys Res Atmos 108:12-1–12-15.  https://doi.org/10.1029/2002jd003296 CrossRefGoogle Scholar
  8. Findlater J (1969) A major low-level air current near the Indian Ocean during the northern summer. Q J R Meteorol Soc 95:362–380.  https://doi.org/10.1002/qj.49709540409 CrossRefGoogle Scholar
  9. 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 60:1733–1753.  https://doi.org/10.1175/1520-0469(2003)060%3c1733:CBNIOA%3e2.0.CO;2 CrossRefGoogle Scholar
  10. Ganai M, Mukhopadhyay P, Krishna RPM, Mahakur M (2015) The impact of revised simplified Arakawa–Schubert convection parameterization scheme in CFSv2 on the simulation of the Indian summer monsoon. Climate Dyn 45:881–902.  https://doi.org/10.1007/s00382-014-2320-4 CrossRefGoogle Scholar
  11. Ganai M, Krishna RPM, Mukhopadhyay P, Mahakur M (2016) The impact of revised simplified Arakawa–Schubert scheme on the simulation of mean and diurnal variability associated with active and break phases of Indian summer monsoon using CFSv2. J Geophys Res Atmos 121:11038–11054.  https://doi.org/10.1002/2016JD024957.Received CrossRefGoogle Scholar
  12. George G, Rao DN, Sabeerali CT et al (2015) Indian summer monsoon prediction and simulation in CFSv2 coupled model. Atmos Sci Lett 64:57–64.  https://doi.org/10.1002/asl.599 Google Scholar
  13. Goswami BN (2005) South Asian Monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability in the atmosphere-ocean climate system. Springer, Berlin, pp 19–61CrossRefGoogle Scholar
  14. Goswami BN, Xavier PK (2005) ENSO control on the south Asian monsoon through the length of the rainy season. Geophys Res Lett.  https://doi.org/10.1029/2005gl023216 Google Scholar
  15. Griffies SM, Harrison MJ, Pacanowski RC, Rosati A (2004) A technical guide to MOM4. GFDL Ocean Gr Tech Rep 5:371Google Scholar
  16. Han J, Pan H-L (2011) Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Weather Forecast 26:520–533.  https://doi.org/10.1175/WAF-D-10-05038.1 CrossRefGoogle Scholar
  17. Hayashi Y (1971) A generalized method of resolving disturbances into progressive and retrogressive waves by space Fourier and time cross-spectral analyses. J Meteorol Soc Japan Ser II 49:125–128.  https://doi.org/10.2151/jmsj1965.49.2_125 CrossRefGoogle Scholar
  18. Hazra A, Chaudhari HS, Rao SA et al (2015) Impact of revised cloud microphysical scheme in CFSv2 on the simulation of the Indian summer monsoon. Int J Climatol 35:4738–4755.  https://doi.org/10.1002/joc.4320 CrossRefGoogle Scholar
  19. He H, McGinnis JW, Song Z et al (1987) Onset of the Asian summer monsoon in 1979 and the effect of the Tibetan Plateau. Mon Weather Rev 115:1966–1995.  https://doi.org/10.1175/1520-0493(1987)115%3c1966:OOTASM%3e2.0.CO;2 CrossRefGoogle Scholar
  20. Huang B, Banzon VF, Freeman E et al (2015) Extended reconstructed sea surface temperature version 4 (ERSST.v4). Part I: upgrades and intercomparisons. J Climate 28:911–930.  https://doi.org/10.1175/JCLI-D-14-00006.1 CrossRefGoogle Scholar
  21. Huffman GJ, Adler RF, Morrissey MM et al (2001) Global precipitation at one-degree daily resolution from multisatellite observations. J Hydrometeorol 2:36–50.  https://doi.org/10.1175/1525-7541(2001)002%3c0036:GPAODD%3e2.0.CO;2 CrossRefGoogle Scholar
  22. Huffman GJ, Bolvin DT, Nelkin EJ et al (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55.  https://doi.org/10.1175/JHM560.1 CrossRefGoogle Scholar
  23. Jiang X, Li T, Wang B (2004) Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J Climate 17:1022–1039.  https://doi.org/10.1175/JCLI3861.1 CrossRefGoogle Scholar
  24. Joseph PV, Raman PL (1966) Existence of low-level westerly jet stream over peninsular India during july. Indian J Meteorol Hydrol Geophys 17:407–410Google Scholar
  25. Jourdain NC, Sen Gupta A, Taschetto AS et al (2013) The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations. Climate Dyn 41:3073–3102.  https://doi.org/10.1007/s00382-013-1676-1 CrossRefGoogle Scholar
  26. Krishnamurti TN, Ardanuy P (1980) The 10 to 20-day westward propagating mode and “Breaks in the Monsoon”. Tellus A.  https://doi.org/10.3402/tellusa.v32i1.10476 Google Scholar
  27. Krishnamurti TN, Bhalme HN (1976) Oscillations of a monsoon system. Part I. Observational aspects. J Atmos Sci 33:1937–1954CrossRefGoogle Scholar
  28. Meinen CS, McPhaden MJ (2000) Observations of warm water volume changes in the Equatorial Pacific and their relationship to El Niño and La Niña. J Clim 13:3551–3559.  https://doi.org/10.1175/1520-0442%282000%29013%3C3551%3AOOWWVC>2.0.CO%3B2 CrossRefGoogle Scholar
  29. 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
  30. Moorthi S, Pan HL, Caplan P (2001) Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. National Oceanic and Atmospheric Administration, National Weather Service, Office of MeteorologyGoogle Scholar
  31. Pan HL, Wu WS (1995) Implementing a mass flux convective parameterization package for the NMC medium-range forecast model. NMC Office Note 409Google Scholar
  32. Pattanaik DR, Satyan V (2000) Fluctuations of tropical easterly jet during contrasting monsoons over India: a GCM study. Meteorol Atmos Phys 75:51–60.  https://doi.org/10.1007/s007030070015 CrossRefGoogle Scholar
  33. Pillai PA, Rao SA, Das RS et al (2017a) Potential predictability and actual skill of Boreal Summer Tropical SST and Indian summer monsoon rainfall in CFSv2-T382: role of initial SST and teleconnections. Climate Dyn.  https://doi.org/10.1007/s00382-017-3936-y Google Scholar
  34. Pillai PA, Rao SA, George G et al (2017b) How distinct are the two flavors of El Niño in retrospective forecasts of climate forecast system version 2 (CFSv2)? Climate Dyn 48:3829–3854.  https://doi.org/10.1007/s00382-016-3305-2 CrossRefGoogle Scholar
  35. Pillai PA, Rao SA, Ramu DA et al (2018) Seasonal prediction skill of Indian summer monsoon rainfall in NMME models and monsoon mission CFSv2. Int J Climatol 38:e847–e861.  https://doi.org/10.1002/joc.5413 CrossRefGoogle Scholar
  36. Pokhrel S, Hazra A, Chaudhari HS et al (2018) Hindcast skill improvement in climate forecast system (CFSv2) using modified cloud scheme. Int J Climatol.  https://doi.org/10.1002/joc.5478 Google Scholar
  37. Pradhan M, Suryachandra Rao A, Srivastava A, Dakate A, Salunke K, Shameera KS (2017) Prediction of Indian summer-monsoon onset variability: a season in advance. Sci Rep 7(1):14229CrossRefGoogle Scholar
  38. Prakash S, Mitra AK, Pai DS (2015) Comparing two high-resolution gauge-adjusted multisatellite rainfall products over India for the southwest monsoon period. Meteorol Appl 22:679–688.  https://doi.org/10.1002/met.1502 CrossRefGoogle Scholar
  39. Rajeevan MN, Bhate J, Kale JD, Lal B (2006) High Resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Curr Sci 91:296–306Google Scholar
  40. Ramamurthy K (1969) Monsoon of India: Some aspects of the ‘break’ in the Indian southwest monsoon during July and August. Forecasting Manual 1–57 No. IV 18.3. India Met. Dept., Pune, IndiaGoogle Scholar
  41. Ramu DA, Sabeerali CT, Chattopadhyay R et al (2016) Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution. J Geophys Res Atmos.  https://doi.org/10.1002/2015jd023538.effect Google Scholar
  42. Ramu DA, Rao SA, Pillai PA et al (2017) Prediction of seasonal summer monsoon rainfall over homogenous regions of India using dynamical prediction system. J Hydrol 546:103–112.  https://doi.org/10.1016/j.jhydrol.2017.01.010 CrossRefGoogle Scholar
  43. Rao SA, Luo J-J, Behera SK, Yamagata T (2009) Generation and termination of Indian Ocean dipole events in 2003, 2006 and 2007. Climate Dyn 33:751–767.  https://doi.org/10.1007/s00382-008-0498-z CrossRefGoogle Scholar
  44. Saha S, Moorthi S, Wu X et al (2014) The NCEP climate forecast system version 2. J Climate 27:2185–2208.  https://doi.org/10.1175/JCLI-D-12-00823.1 CrossRefGoogle Scholar
  45. Shukla J (1987) Monsoons. Wiley, New YorkGoogle Scholar
  46. Soraisam B, Karumuri A, Pai DS (2018) Uncertainties in observations and climate projections for the North East India. Glob Planet Change 160:96–108.  https://doi.org/10.1016/J.GLOPLACHA.2017.11.010 CrossRefGoogle Scholar
  47. Srivastava A, Pradhan M, George G, Dhakate A, Salunke K, Rao SA (2015) Long range forecasts of the Indian summer monsoon using the climate forecast system. In: Mujumdar M, Gnanaseelan C, Rajeevan M (eds) A research report on the 2015 southwest monsoon. Indian Institute of Tropical Meteorology, Pune, pp 58–62Google Scholar
  48. Srivastava A, Rao SA, Rao DN et al (2017) Structure, characteristics, and simulation of monsoon low-pressure systems in CFSv2 coupled model. J Geophys Res Ocean.  https://doi.org/10.1002/2016jc012322 Google Scholar
  49. Tanaka M, Tanaka M (1982) Interannual fluctuations of the tropical easterly jet and the summer monsoon in the Asian RegionGoogle Scholar
  50. Tang X, Chen B (2006) Cloud types associated with the Asian summer monsoons as determined from MODIS/TERRA measurements and a comparison with surface observations. Geophys Res Lett 33:L07814.  https://doi.org/10.1029/2006GL026004 CrossRefGoogle Scholar
  51. Tiedtke M, Heckley WA, Slingo J (1988) Tropical forecasting at ECMWF: the influence of physical parametrization on the mean structure of forecasts and analyses. Q J R Meteorol Soc 114:639–664.  https://doi.org/10.1002/QJ.49711448106 CrossRefGoogle Scholar
  52. Walker GT (1918) Correlation in seasonal variation of weather. Q J R Meteorol Soc 44:223–234Google Scholar
  53. Webster PJ (1987) The elementary monsoon. In: Fein JS, Stephens PL (eds) Monsoons. Wiley, New York, pp 3–32Google Scholar
  54. Webster PJ, Magaña VO, Palmer TN et al (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Res 103:14451.  https://doi.org/10.1029/97JC02719 CrossRefGoogle Scholar
  55. Wielicki BA, Barkstrom BR, Harrison EF et al (1996) Clouds and the earth’s radiant energy system (CERES): an earth observing system experiment. Bull Am Meteorol Soc 77:853–868.  https://doi.org/10.1175/1520-0477(1996)077%3c0853:CATERE%3e2.0.CO;2 CrossRefGoogle Scholar
  56. Winton M (2000) A reformulated three-layer sea ice model. J Atmos Ocean Technol 17:525–531.  https://doi.org/10.1175/1520-0426(2000)017%3c0525:ARTLSI%3e2.0.CO;2 CrossRefGoogle Scholar
  57. Yanai M, Li C, Song Z (1992) Seasonal heating of the tibetan plateau and its effects on the evolution of the Asian summer monsoon. J Meteorol Soc Japan Ser II 70:319–351.  https://doi.org/10.2151/jmsj1965.70.1B_319 CrossRefGoogle Scholar
  58. Yasunari T (1981) Structure of an Indian summer monsoon system with around 40-day period. J Meteorol Soc Japan 59:336–354CrossRefGoogle Scholar
  59. Zhao Q, Carr FH (1997) A prognostic cloud scheme for operational NWP models. Mon Weather Rev 125:1931–1953.  https://doi.org/10.1175/1520-0493(1997)125%3c1931:APCSFO%3e2.0.CO;2 CrossRefGoogle Scholar
  60. Zhu J, Kumar A, Wang W et al (2017) Importance of convective parameterization in ENSO predictions. Geophys Res Lett 44:6334–6342.  https://doi.org/10.1002/2017GL073669 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Indian Institute of Tropical MeteorologyPuneIndia
  2. 2.Center for Prototype Climate ModelingNew York University Abu DhabiAbu DhabiUnited Arab Emirates

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