Abstract
Reasonable seasonal prediction skill for the Indian summer monsoon rainfall has been achieved using the Monsoon Mission (MM) Seasonal Forecast model, at a lead time of 3 months. The ensembles in the MM model are generated by utilizing lagged initial conditions. The possibility of enhancing the lead time is explored by using the burst ensemble approach. Comprehensive seasonal hindcast experiments carried out in this study reveal that the two methods exhibit similar skill scores for the major tropical phenomenon which govern ISMR variability. In general, the model forecasts are slightly under-dispersive but satisfactorily represent the spread-error relationship for major tropical oceanic climate modes. The ratio between the spread and RMSE is small for ISMR forecasts. Though the skill scores for the majority of indices are similar, the monsoon teleconnections seem to be quite sensitive to the initialization strategy. It is found that the burst initialization method provides a gain of 1-month lead time compared to lagged initialization strategy employed in previous studies without compromising the prediction skill. The gain of a months’ lead time with the burst ensemble approach is a tempting and useful proposition, which can be crucial for the policy- and decision-makers.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adler RF, Huffman GJ, Chang A, et al (2003) The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2
Arribas A, Glover M, Maidens A et al (2011) The GloSea4 ensemble prediction system for seasonal forecasting. Mon Weather Rev 139:1891–1910. https://doi.org/10.1175/2010MWR3615.1
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 28:4499–4502. https://doi.org/10.1029/2001GL013294
Ashok K, Guan Z, Saji NH, Yamagata T (2004) Individual and combined influences of ENSO and the Indian Ocean Dipole on the Indian summer monsoon. J Clim 17:3141–3155. https://doi.org/10.1175/1520-0442(2004)017<3141:IACIOE>2.0.CO;2
Ashok K, Behera SK, Rao SA et al (2007) El Niño Modoki and its possible teleconnection. J Geophys Res Ocean 112:1–27. https://doi.org/10.1029/2006JC003798
Ashok K, Feba F, Teja Tejavath C (2019) The Indian summer monsoon rainfall and ENSO. Mausam 70:443–452
Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman filter part I: theoretical aspects. Mon Weather Rev 129:420–436. https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2
Buizza R, Palmer TN (1995) The singular-vector structure of the atmospheric global circulation. J Atmos Sci 52:1434–1456. https://doi.org/10.1175/1520-0469(1995)052<1434:TSVSOT>2.0.CO;2
Buizza R, Houtekamer PL, Toth Z et al (2005) A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon Weather Rev 133:1076–1097. https://doi.org/10.1175/MWR2905.1
Charney JG, Shukla J (1981) Predictability of monsoons. In: Lighthill J, Pearce RP (eds) Monsoon Dynamics. Cambridge University Press
Chattopadhyay R, Phani R, Sabeerali CT, Dhakate AR, Salunke KD, Mahapatra S, Rao AS, Goswami BN (2015a) Influence of extratropical sea-surface temperature on the Indian summer monsoon: an unexplored source of seasonal predictability. Q J R Meteorol Soc 141:2760–2775. https://doi.org/10.1002/qj.2562
Chattopadhyay R, Rao SA, Sabeerali CT, George G, Rao DN, Dhakate A, Salunke K (2015b) Large scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs. Int J Climatol 36:3297–3313. https://doi.org/10.1002/joc.4556
Chen M, Wang W, Kumar A (2010) Prediction of monthly-mean temperature: the roles of atmospheric and land initial conditions and sea surface temperature. J Clim 23:717–725. https://doi.org/10.1175/2009JCLI3090.1
Chen M, Wang W, Kumar A (2013) Lagged ensembles, forecast configuration, and seasonal predictions. Mon weather Rev 141:3477–3497. https://doi.org/10.1175/MWR-D-12-00184.1
Dai G, Mu M, Jiang Z (2016) Relationships between optimal precursors triggering NAO onset and optimally growing initial errors during NAO prediction. J Atmos Sci 73:293–317. https://doi.org/10.1175/JAS-D-15-0109.1
Du J, Mullen SL, Sanders F (1997) Short-range ensemble forecasting of quantitative precipitation. Mon Weather Rev 125:2427–2459. https://doi.org/10.1175/1520-0493(1997)125<2427:SREFOQ>2.0.CO;2
Eade R, Smith D, Scaife A et al (2014) Do seasonal-to-decadal climate predictions underestimate the predictability of the real world? Geophys Res Lett 41:5620–5628. https://doi.org/10.1002/2014GL061146
Ebisuzaki W, Kalnay E (1991) Ensemble experiments with a new lagged average forecasting scheme. WMO, Research activities in atmospheric and oceanic modeling. Report #15, pp 6.31–6.32. [Available from WMO, C.P. No 2300, CH1211, Geneva, Switzerland]
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
Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10143–10162. https://doi.org/10.1029/94jc00572
Fortin V, Abaza M, Anctil F, Turcotte R (2014) Why should ensemble spread match the RMSE of the ensemble mean? J Hydrometeorol 15:1708–1713. https://doi.org/10.1175/JHM-D-14-0008.1
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. Clim Dyn 45:881–902. https://doi.org/10.1007/s00382-014-2320-4
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
Goswami BN, Shukla J (1991) Predictability of a coupled ocean-atmosphere model. J Clim 4:3–22
Griffies SM, Harrison MJ, Pacanowski RC, Rosati A (2004) A technical guide to MOM4. GFDL Ocean Gr Tech Rep 5:371
Hoffman RN, Kalnay E (1983) Lagged average forecasting, an alternative to Monte Carlo forecasting. Tellus A 35(A):100–118. https://doi.org/10.1111/j.1600-0870.1983.tb00189.x
Houtekamer PL, Mitchell HL (1998) Data assimilation using an ensemble Kalman filter technique. Mon Weather Rev 126:796–811. https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
Huang B, Thorne PW, Banzon VF et al (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J Clim 30:8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1
Kalnay E (2003) Atmospheric modeling, data assimilation, and predictability. Cambridge University Press, p 341
Krishna RPM, Rao SA, Srivastava A et al (2019) Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon. Clim Dyn:1–17. https://doi.org/10.1007/s00382-019-04921-y
Krishnamurthy L, Krishnamurthy V (2014a) Decadal scale oscillations and trend in the Indian monsoon rainfall. Clim Dyn 43:319–331. https://doi.org/10.1007/s00382-013-1870-1
Krishnamurthy L, Krishnamurthy V (2014b) Influence of PDO on south Asian summer monsoon and monsoon-ENSO relation. Clim Dyn 42:2397–2410. https://doi.org/10.1007/s00382-013-1856-z
Kumar A, Hoerling MP (1995) Prospects and limitations of seasonal atmospheric GCM predictions. Bull Am Meteorol Soc 76:335–345. https://doi.org/10.1175/1520-0477(1995)076<0335:PALOSA>2.0.CO;2
Kumar A, Hoerling MP (2000) Analysis of a conceptual model of seasonal climate variability and implications for seasonal prediction. Bull Am Meteorol Soc 81:255–264. https://doi.org/10.1175/1520-0477(2000)081<0255:AOACMO>2.3.CO;2
Kumar A, Chen M, Wang W (2011) An analysis of prediction skill of monthly mean climate variability. Clim Dyn 37:1119–1131. https://doi.org/10.1007/s00382-010-0901-4
Lau K-M, Kim K-M, Shen SSP (2002) Potential predictability of seasonal precipitation over the United States from canonical ensemble correlation predictions. Geophys Res Lett 29:1097. https://doi.org/10.1029/2001GL014263
Lau KM, Lee JY, Kim KM, Kang IS (2004) The North Pacific as a regulator of summertime climate over Eurasia and North America. J Clim 17:819–833. https://doi.org/10.1175/1520-0442(2004)017<0819:tnpaar>2.0.co;2
Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
Molteni F, Buizza R, Palmer TN, Petroliagis T (1996) The ECMWF ensemble prediction system: methodology and validation. Q J R Meteorol Soc 122:73–119. https://doi.org/10.1002/qj.49712252905
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 Meteorology
Mullen SL, Baumhefner DP (1994) Monte Carlo simulations of explosive cyclogenesis. Mon Weather Rev 122:1548–1567. https://doi.org/10.1175/1520-0493(1994)122<1548:MCSOEC>2.0.CO;2
Nigam S, Guan B, Ruiz-Barradas A (2011) Key role of the Atlantic Multidecadal Oscillation in 20th century drought and wet periods over the Great Plains. Geophys Res Lett 38:1–6. https://doi.org/10.1029/2011GL048650
Palmer T (2019) The ECMWF ensemble prediction system: looking back (more than) 25 years and projecting forward 25 years. Q J R Meteorol Soc 145:12–24. https://doi.org/10.1002/qj.3383
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. Clim Dyn:1–18. https://doi.org/10.1007/s00382-017-3936-y
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)? Clim Dyn 48:3829–3854. https://doi.org/10.1007/s00382-016-3305-2
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
Pokhrel S, Hazra A, Chaudhari HS, Saha SK, Paulose F, Krishna S, Krishna PM, Rao SA (2018) Hindcast skill improvement in Climate Forecast System (CFSv2) using modified cloud scheme. Int J Climatol 38:2994–3012. https://doi.org/10.1002/joc.5478
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 91:296–306
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:1752–1775. https://doi.org/10.1002/2015JD023538.Effect
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
Rao SA, Goswami BN, Sahai AK et al (2019) Monsoon mission a targeted activity to improve monsoon prediction across scales. Bull Am Meteorol Soc 100:2509–2532. https://doi.org/10.1175/BAMS-D-17-0330.1
Ren HL, Jin FF, Song L et al (2017) Prediction of primary climate variability modes at the Beijing Climate Center. J Meteorol Res 31:204–223. https://doi.org/10.1007/s13351-017-6097-3
Saha S, Moorthi S, Pan H-L et al (2010) The NCEP Climate Forecast System Reanalysis. Bull Amer Meteor Soc 91:1015–1058. https://doi.org/10.1175/2010BAMS3001.1
Saha S, Nadiga S, Thiaw C et al (2006) The NCEP Climate Forecast System. J Clim 19:3483–3517. https://doi.org/10.1175/JCLI3812.1
Saha S, Moorthi S, Wu X 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
Saha SK, Hazra A, Pokhrel S et al (2019) Unraveling the mystery of Indian summer monsoon prediction: improved estimate of predictability limit. J Geophys Res Atmos 124:1962–1974. https://doi.org/10.1029/2018JD030082
Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363. https://doi.org/10.1038/43854
Shukla J (1998) Predictability in the midst of Chaos: a scientific basis for climate forecasting. Science 282(80):728–731. https://doi.org/10.1126/science.282.5389.728
Slingo JM, Sperber KR, Boyle JS et al (1996) Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject. Clim Dyn 12(5):325–357. https://doi.org/10.1007/BF00231106
Srivastava A, Rao SA, Rao DN, George G, Pradhan M (2017) Structure, characteristics, and simulation of monsoon low-pressure systems in CFSv2 coupled model. J Geophys Res Ocean 122:6394–6415. https://doi.org/10.1002/2016JC012322
Stockdale TN, Anderson DLT, Balmaseda MA et al (2011) ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Clim Dyn 37:455–471. https://doi.org/10.1007/s00382-010-0947-3
The NCAR Command Language (Version 6.6.2) [Software]. (2016) Boulder, Colorado: UCAR/NCAR/CISL/TDD. https://doi.org/10.5065/D6WD3XH5
Tompkins AM, Ortiz De Zárate MI, Saurral RI et al (2017) The climate-system historical forecast project: providing open access to seasonal forecast ensembles from centers around the globe. Bull Am Meteorol Soc 98:2293–2301. https://doi.org/10.1175/BAMS-D-16-0209.1
Toth Z, Kalnay E (1993) Ensemble forecasting at NMC: the generation of perturbations. Bull Am Meteorol Soc 74:2317–2330. https://doi.org/10.1175/1520-0477(1993)074<2317:efantg>2.0.co;2
Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Weather Rev 125:3297–3319. https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2
Wang X, Bishop CH (2003) A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J Atmos Sci 60:1140–1158. https://doi.org/10.1175/1520-0469(2003)060<1140:ACOBAE>2.0.CO;2
Weigel AP, Baggenstos D, Liniger MA et al (2008) Probabilistic verification of monthly temperature forecasts. Mon Weather Rev 136:5162–5182. https://doi.org/10.1175/2008MWR2551.1
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<0525:ARTLSI>2.0.CO;2
Wu Y, Tang Y (2019) Seasonal predictability of the tropical Indian Ocean SST in the north American multimodel ensemble. Clim Dyn 53:3361–3372. https://doi.org/10.1007/s00382-019-04709-0
Zheng Y, Shinoda T, Lin JL, Kiladis GN (2011) Sea surface temperature biases under the stratus cloud deck in the Southeast Pacific Ocean in 19 IPCC AR4 coupled general circulation models. J Clim 24:4139–4164. https://doi.org/10.1175/2011JCLI4172.1
Acknowledgments
The Indian Institute of Tropical Meteorology, Pune, India, is fully funded by the Ministry of Earth Sciences, Government of India, New Delhi. We thank NCAR for making the NCAR Command Language (NCL 2016) available. The authors thank the Editor, the anonymous reviewers, for their valuable comments and constructive feedback which have helped immensely in improving the manuscript.
Author information
Authors and Affiliations
Contributions
Ankur Srivastava did the model runs and data analysis and prepared the manuscript. Suryachandra A. Rao designed the hypothesis and experiments and contributed to manuscript preparation. Maheswar Pradhan assisted in data analysis. Prasanth A. Pillai assisted in manuscript preparation. V. S. Prasad created the initial conditions for the PE runs using data assimilation system.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this article.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Srivastava, A., Rao, S.A., Pradhan, M. et al. Gain of one-month lead time in seasonal prediction of Indian summer monsoon prediction: comparison of initialization strategies. Theor Appl Climatol 143, 1083–1096 (2021). https://doi.org/10.1007/s00704-020-03470-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-020-03470-3