Climate Dynamics

, Volume 49, Issue 9–10, pp 3031–3050 | Cite as

Extraction and prediction of indices for monsoon intraseasonal oscillations: an approach based on nonlinear Laplacian spectral analysis

  • C. T. Sabeerali
  • R. S. Ajayamohan
  • Dimitrios Giannakis
  • Andrew J. Majda


An improved index for real-time monitoring and forecast verification of monsoon intraseasonal oscillations (MISOs) is introduced using the recently developed nonlinear Laplacian spectral analysis (NLSA) technique. Using NLSA, a hierarchy of Laplace–Beltrami (LB) eigenfunctions are extracted from unfiltered daily rainfall data from the Global Precipitation Climatology Project over the south Asian monsoon region. Two modes representing the full life cycle of the northeastward-propagating boreal summer MISO are identified from the hierarchy of LB eigenfunctions. These modes have a number of advantages over MISO modes extracted via extended empirical orthogonal function analysis including higher memory and predictability, stronger amplitude and higher fractional explained variance over the western Pacific, Western Ghats, and adjoining Arabian Sea regions, and more realistic representation of the regional heat sources over the Indian and Pacific Oceans. Real-time prediction of NLSA-derived MISO indices is demonstrated via extended-range hindcasts based on NCEP Coupled Forecast System version 2 operational output. It is shown that in these hindcasts the NLSA MISO indices remain predictable out to \(\sim\)3 weeks.


Monsoon intraseasonal oscillations Extended range prediction Nonlinear Laplacian spectral analysis NCEP CFSv2 



The authors gratefully acknowledge the financial support given by the Earth System Science Organization, Ministry of Earth Sciences (MoES), Government of India (Grant no.MM/SERP/NYU/2014/SSC-01/002) to conduct this research under Monsoon Mission. The Center for Prototype Climate Modelling (CPCM) is fully funded by the Abu Dhabi Government through New York University Abu Dhabi (NYUAD) Research Institute Grant. A. J. M. and D. G. also acknowledge support from ONR MURI Grant 25-74200-F7112. The computations were carried out on the High Performance Computing resources at NYUAD. We thank IITM, Pune, India for providing NCEP CFSv2 model simulations for verification. We thank Dr. M. Rajeevan, secretary, MoES for constant encouragement. We acknowledge the support from Dr. S. A. Rao, Dr. A. K. Sahai and Dr. S. Abhilash for providing model data. We thank the Editor and three anonymous Reviewers for their valuable time and comments.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Center for Prototype Climate ModelingNew York University Abu DhabiAbu DhabiUAE
  2. 2.Department of Mathematics, Center for Atmosphere Ocean Science, Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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