Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long-range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~60km) and lead (2–5 months) considered here, the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June–August) scale. Eight-member ensemble-average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented.
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We acknowledge NCEP and IMD for the data support. The authors acknowledge the APCC MME Producing Centers for making their hindcast/forecast data available for analysis, the APEC Climate Center for collecting and archiving the data, as well as for producing APCC MME predictions. We acknowledge Department of Science and Technology for the project support.
Availability of data and material
The APCC model simulation, NCEP reanalysis datasets are freely available on the web sites. Gridded rainfall observation data are collected from India Meteorological Department.
Model results are available from the corresponding author upon request.
This work is supported by the projects funded by the Department of Science and Technology (DST) and National Mission on Himalayan Studies (NMHS) of Ministry of Environment, forest and climate change, Govt. of India
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The original online version of this article was revised: The presentation of Table 2 was incorrect.
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Gouda, K.C., Joshi, S. & Bhat, N. An optimum initial manifold for improved skill and lead in long-range forecasting of monsoon variability. Theor Appl Climatol (2021). https://doi.org/10.1007/s00704-021-03589-x