Climate Dynamics

, Volume 48, Issue 5–6, pp 1447–1465 | Cite as

An assessment of Indian monsoon seasonal forecasts and mechanisms underlying monsoon interannual variability in the Met Office GloSea5-GC2 system

  • Stephanie J. Johnson
  • Andrew Turner
  • Steven Woolnough
  • Gill Martin
  • Craig MacLachlan
Article

Abstract

We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art seasonal forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992–2011), the El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 Indian summer monsoon seasonal prediction skill, providing targets for model improvement.

Keywords

Indian monsoon Seasonal forecasting Indian Ocean dipole 

References

  1. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979—present). J Clim 4:1147–1167Google Scholar
  2. Annamalai H, Murtugudde R, Potemra J, Xie SP, Liu P, Wang B (2003) Coupled dynamics over the Indian Ocean: spring initiation of the Zonal Mode. Deep Sea Res II 50:2305–2330. doi:10.1016/S0967-0645(03)00058-4 CrossRefGoogle Scholar
  3. Ashok K, Guan Z, Yamagata T (2001) Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO. Geophys Res Lett 28(23):4499–4502CrossRefGoogle Scholar
  4. Balsamo G, Albergel C, Beljaars A, Boussetta S, Brun E, Cloke H, Dee D, Dutra E, Munoz-Sabater J, Pappenberger F, Rosnay P, Stockdale T, Vitart F (2015) ERA-Interim/Land: a global land surface reanalysis data set. Hydrol Earth Syst Sci 19:389–407. doi:10.5194/hess-19-389-2015 CrossRefGoogle Scholar
  5. Best MJ, Pryor M, Clark DB, Rooney GG, Essery RLH, Menard CB, Edwards JM, Hendry MA, Porson A, Gedney N, Mercado LM, Sitch S, Blyth E, Boucher O, Cox PM, Grimmond CSB, Harding RJ (2011) The Joint UK Land Environment Simulator (JULES), model description part 1: energy and water fluxes. Geosci Model Dev 4:677–699. doi:10.5194/gmd-4-677-2011 CrossRefGoogle Scholar
  6. Bevington PR (1969) Data reduction and error analysis for the physical sciences, 1st edn. McGraw-Hill, New YorkGoogle Scholar
  7. Blanford HF (1884) On the connexion of the Himalaya snowfall with dry winds and seasons of drought in India. Proc R Soc Lond 37:3–22CrossRefGoogle Scholar
  8. Blockley EW, Martin MJ, Mclaren AJ, Ryan AG, Waters J, Lea DJ, Mirouze I, Peterson KA, Sellar A, Storkey D (2014) Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts. Geosci Model Dev 7:2613–2638. doi:10.5194/gmd-7-2613-2014 CrossRefGoogle Scholar
  9. Bowler NE, Arribas A, Beare SE, Mylne KR, Shutts GJ (2009) The local ETKF and SKEB: upgrades to the MOGREPS short-range ensemble prediction system. Q J R Meteorol Soc 135:767–776. doi:10.1002/qj CrossRefGoogle Scholar
  10. Bush SJ, Turner AG, Woolnough SJ, Martin M, Klingaman NP (2015) The effect of increased convective entrainment on Asian monsoon biases in the MetUM general circulation model. Q J R Meteorol Soc 141:311–326. doi:10.1002/qj.2371 CrossRefGoogle Scholar
  11. Chang P, Fang Y, Saravanan R, Ji L, Seidel H (2006) The cause of the fragile relationship between the Pacific El Nino and the Atlantic Nino. Nature 443:324–328. doi:10.1038/nature05053 CrossRefGoogle Scholar
  12. Charney JG, Shukla J (1981) Predictability of monsoons, chap 6. Cambridge University Press, CambridgeGoogle Scholar
  13. Cionni I, Eyring V, Lamarque JF, Randel WJ, Stevenson DS, Wu F, Bodeker GE, Shepherd TG, Shindell DT, Waugh DW (2011) Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing. Atmos Chem Phys 11:11267–11292. doi:10.5194/acp-11-11267-2011 CrossRefGoogle Scholar
  14. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, Berg LVD, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Dee DP (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi:10.1002/qj.828 CrossRefGoogle Scholar
  15. Fasullo J (2004) A stratified diagnosis of the Indian monsoon Eurasian snow cover relationship. J Clim 17:1110–1122CrossRefGoogle Scholar
  16. Gill AE (1980) Some simple solutions for heat-induced tropical circulation. Q J R Meteorol Soc 106:447–462CrossRefGoogle Scholar
  17. Goddard L, Mason SJ, Zebiak SE, Ropelewski CF, Basher R, Cane MA (2001) Current approaches to seasonal-to-interannual climate predictions. Int J Climatol 21:1111–1152. doi:10.1002/joc.636 CrossRefGoogle Scholar
  18. Good SA, Martin MJ, Rayner NA (2013) EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J Geophys Res 118:6704–6716. doi:10.1002/2013JC009067 CrossRefGoogle Scholar
  19. Gouretski V, Reseghetti F (2010) On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database. Deep Sea Res Part I 57:812–833. doi:10.1016/j.dsr.2010.03.011 CrossRefGoogle Scholar
  20. Ihara C, Kushnir Y, Cane A, Pena VHDL (2007) Indian summer monsoon rainfall and its link with ENSO and Indian Ocean climate indices. Int J Climatol 27:179–187. doi:10.1002/joc CrossRefGoogle Scholar
  21. Jayakumar A, Turner A, Johnson SJ, Rajagopal EN, Mohandas S, Mitra AK (2016) Boreal summer sub-seasonal variability of the South Asian monsoon in the Met Office GloSea5-GC2 initialized coupled model. Clim Dyn (review)Google Scholar
  22. Ju J, Slingo J (1995) The Asian summer monsoon and ENSO. Q J R Meteorol Soc 121:1133–1168CrossRefGoogle Scholar
  23. Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo JJ, Fiorino M,Potter G (2002) NCEPDOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc. doi:10.1175/BAMS-83-11-1631
  24. Kang IS, Shukla J (2006) Dynamic seasonal prediction and predictability of the monsoon, chap 15. Springer/Praxis, ChichesterGoogle Scholar
  25. Kim HM, Webster PJ, Curry JA, Toma VE (2012) Asian summer monsoon prediction in ECMWF System 4 and NCEP CFSv2 retrospective seasonal forecasts. Clim Dyn 39:2975–2991. doi:10.1007/s00382-012-1470-5 CrossRefGoogle Scholar
  26. Krishna Kumar K, Rajagopalan B, Hoerling M, Bates G, Cane M (2006) Unraveling the mystery of Indian monsoon failure during El Nino. Science 314:115–119. doi:10.1126/science.1131152 CrossRefGoogle Scholar
  27. Krishnamurthy V, Shukla J (2000) Intraseasonal and interannual variability of rainfall over India. J Clim 13:4366–4377CrossRefGoogle Scholar
  28. Krishnamurthy V, Shukla J (2007) Intraseasonal and seasonally persisting patterns of Indian monsoon rainfall. J Clim 20:3–20. doi:10.1175/JCLI3981.1 CrossRefGoogle Scholar
  29. Kucharski F, Bracco A, Yoo JH, Moltini F (2007) Low-frequency variability of the Indian monsoon ENSO relationship and the tropical atlantic: the weakening of the 1980s and 1990s. J Clim 20:4255–4266. doi:10.1175/JCLI4254.1 CrossRefGoogle Scholar
  30. Kucharski F, Bracco A, Yoo JH, Molteni F (2008) Atlantic forced component of the Indian monsoon interannual variability. Geophys Res Lett 35:1–5. doi:10.1029/2007GL033037 CrossRefGoogle Scholar
  31. Levine RC, Turner AG (2012) Dependence of Indian monsoon rainfall on moisture fluxes across the Arabian Sea and the impact of coupled model sea surface temperature biases. Clim Dyn 38:2167–2190. doi:10.1007/s00382-011-1096-z CrossRefGoogle Scholar
  32. Levine RC, Turner AG, Marathayil D, Martin GM (2013) The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future projections of Indian summer monsoon rainfall. Clim Dyn 41:155–172. doi:10.1007/s00382-012-1656-x CrossRefGoogle Scholar
  33. Li G, Sp X (2012) Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys Res Lett 39(L22):703. doi:10.1029/2012GL053777 Google Scholar
  34. Li G, Xie SP (2014) Tropical biases in CMIP5 multimodel ensemble: the excessive equatorial Pacific cold tongue and double ITCZ problems. J Clim 27:1765–1780. doi:10.1175/JCLI-D-13-00337.1 CrossRefGoogle Scholar
  35. MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P, Madec G (2015) Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q J R Meteorol Soc 141(689):1072–1084. doi:10.1002/qj.2396 CrossRefGoogle Scholar
  36. Marathayil D (2013) The Indian Ocean mean state and variability in a high resolution coupled climate model: HiGEM. PhD thesis, University of ReadingGoogle Scholar
  37. Megann A, Storkey D, Aksenov Y, Alderson S, Calvert D, Graham T, Hyder P, Siddorn J, Sinha B (2014) GO5.0: the joint NERC Met Office NEMO global ocean model for use in coupled and forced applications. Geosci Model Dev 7:1069–1092. doi:10.5194/gmd-7-1069-2014 CrossRefGoogle Scholar
  38. Mogensen KS, Balmaseda MA, Weaver A, Martin MJ, Vidard A (2009) NEMOVAR: a variational data assimilation system for the NEMO ocean modelGoogle Scholar
  39. Nanjundiah RS, Francis PA, Ved M, Gadgil S (2013) Predicting the extremes of Indian summer monsoon rainfall with coupled ocean atmosphere models. Curr Sci 104:1380–1393Google Scholar
  40. Palmer T, Anderson D (1994) The prospects for seasonal forecasting—a review paper. Q J R Meteorol Soc 120(518):755–793Google Scholar
  41. Pottapinjara V, Girishkumar MS, Ravichandran M, Murtugudde R (2014) Influence of the Atlantic zonal mode on monsoon depressions in the Bay of Bengal during boreal summer. J Geophys Res Atmos. 119:6456–6469. doi:10.1002/2014JD021494 CrossRefGoogle Scholar
  42. Rae JGL, Hewitt HT, Keen AB, Ridley JK, West AE, Harris CM, Hunke EC, Walters DN (2015) Development of the Global Sea Ice 6.0 CICE configuration for the Met Office global coupled model. Geosci Model Dev 8:2221–2230. doi:10.5194/gmd-8-2221-2015 CrossRefGoogle Scholar
  43. Rajeevan M, Unnikrishnan CK, Preethi B (2012) Evaluation of the ENSEMBLES multi-model seasonal forecasts of Indian summer monsoon variability. Clim Dyn 38:2257–2274. doi:10.1007/s00382-011-1061-x CrossRefGoogle Scholar
  44. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res doi:10.1029/2002JD002670
  45. Rowell DP, Folland CK, Maskell K, Ward MN (1995) Variability of summer rainfall over tropical north Africa (1906–1992): observations and modelling. Q J R Meteorol Soc 121:669–704Google Scholar
  46. Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363Google Scholar
  47. Senan R, Orsolini YJ, Weisheimer A, Vitart F, Balsamo G, Stockdale T, Dutra E, Doblas-Reyes FJ, Basang D (2015) Impact of springtime Himalayan-Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts. Clim Dyn 1–41 (under revision)Google Scholar
  48. Shaffrey LC, Stevens I, Norton WA, Roberts MJ, Vidale PL, Harle JD, Jrrar A, Stevens DP, Woodage MJ, Demory ME, Donners J, Clark DB, Clayton A, Cole JW, Wilson SS, Connelley WM, Davies TM, Iwi AM, Johns TC, King JC, New AL, Slingo JM, Slingo A, Steenman-Clark L, Martin GM (2009) U.K. HiGEM: The New U. K. high-resolution global environment model—model description and basic evaluation. J Clim 22:1861–1896. doi:10.1175/2008JCLI2508.1 CrossRefGoogle Scholar
  49. Shukla J, Paolino DA (1983) The southern oscillation and long-range forecasting of the summer monsoon rainfall over India. Mon Weather Rev 111:1830–1837CrossRefGoogle Scholar
  50. Sperber KR, Annamalai H (2008) Coupled model simulations of boreal summer intraseasonal (30–50 day) variability, part 1: systematic errors and caution on use of metrics. Clim Dyn 31(2–3):345–372. doi:10.1007/s00382-008-0367-9 CrossRefGoogle Scholar
  51. Sperber KR, Slingo J, Annamalai H (2000) Predictability and the relationship between subseasonal and interannual variability. Q J R Meteorol Soc 126:2545–2574CrossRefGoogle Scholar
  52. Sperber KR, Annamalai H, Kang IS, Kitoh A, Moise A, Turner AG, Wang B, Zhou T (2013) The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Clim Dyn 41:2711–2744. doi:10.1007/s00382-012-1607-6 CrossRefGoogle Scholar
  53. Trenberth KE, Stepaniak DP (2001) Indices of El Nino evolution. J Clim 14:1697–1701CrossRefGoogle Scholar
  54. Turner AG, Slingo J (2011) Using idealized snow forcing to test teleconnections with the Indian summer monsoon in the Hadley Centre GCM. Clim Dyn 36(9–10):1717–1735. doi:10.1007/s00382-010-0805-3 CrossRefGoogle Scholar
  55. Valcke S (2013) The OASIS3 coupler: a European climate modelling community software. Geosci Model Dev 6:373–388. doi:10.5194/gmd-6-373-2013 CrossRefGoogle Scholar
  56. Vanniere B, Guilyardi E, Madec G, Doblas-Reyes FJ, Woolnough S (2013) Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO. Clim Dyn. doi:10.1007/s00382-012-1429-6
  57. Walters DN, Williams KD, Boutle IA, Bushell AC, Edwards JM, FieldPR, Lock AP, Morcrette CJ, Stratton RA, Wilkinson JM, Willett MR,Brooks ME, Copsey D, Earnshaw PD, Harris CM, Manners JC, MacLachlanC, Palmer MD, Roberts MJ, Tennant WJ (2015) The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1.0 congurations (in preparation)Google Scholar
  58. Wang B, Fan Z (1999) Choice of South Asian summer monsoon indices. Bull Am Meteorol Soc 80(4):629–638CrossRefGoogle Scholar
  59. Webster PJ, Yang S (1992) Monsoon and ENSO: selectively interactive systems. Q J R Meteorol Soc 118:877–926CrossRefGoogle Scholar
  60. Webster PJ, Moore AM, Loschnigg JP, Leben RR (1999) Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997–1998. Nature 401:356–360CrossRefGoogle Scholar
  61. Wilks D (2006) Statistical methods in the atmospheric sciences, 2nd edn. Elsevier, AmsterdamGoogle Scholar
  62. Williams KD, Harris CM, Camp J, Comer RE (2015) The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci Model Dev Discuss 8:521–565. doi:10.5194/gmdd-8-521-2015 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Stephanie J. Johnson
    • 1
    • 2
  • Andrew Turner
    • 1
  • Steven Woolnough
    • 1
  • Gill Martin
    • 3
  • Craig MacLachlan
    • 3
  1. 1.Department of Meteorology, National Centre for Atmospheric ScienceUniversity of ReadingReadingUK
  2. 2.ECMWFReadingUK
  3. 3.Met Office Hadley CentreExeterUK

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