Skill of Indian summer monsoon rainfall prediction in multiple seasonal prediction systems

Climate Dynamics volume 52, pages 5291–5301 (2019)Cite this article

Abstract

We use seasonal forecasts from the Climate Historical Forecast Project (CHFP) to study the skill of multiple climate models in predicting Indian summer monsoon precipitation. The multi-model average of seasonal forecasts from eight prediction systems shows statistically significant skill for predicting Indian monsoon precipitation at seasonal lead times. Rapid convergence of tropical rainfall skill with ensemble size suggests that the skill of seasonal monsoon rainfall forecasts improves only marginally when using multi-model ensemble (MME) means as compared to the single most skillful system. There is also a large range in the skill of individual models. Some individual models show correlation skill as high as 0.6, which is similar to the MME mean, while others show low skill. We also investigate the effect of spatial averaging on the skill of predicting monsoon rainfall and show that the predictions averaged over a larger area than the verifying observations can yield higher skill due to the extended spatial coherence of monsoon rainfall variability. We also document current errors in seasonal prediction systems and show that these are more strongly related to the errors in El-Nino Southern Oscillation (ENSO) teleconnections than they are to mean rainfall biases. Finally, we examine the ENSO-monsoon relationship and confirm that this relationship is likely to be stationary, despite fluctuations in the observed relationship, which can simply be explained as sampling variability on an underlying stationary teleconnection between ENSO and the Indian monsoon.

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Acknowledgements

We acknowledge the WCRP/CLIVAR Working Group on Seasonal to Interannual Prediction (WGSIP) for establishing the Climate-system Historical Forecast Project and the Centro de Investigaciones del Mar y la Atmosfera (CIMA) for providing the models outputs (http://chfps.cima.fcen.uba.ar/DS). We also thank the data providers for making the model output available through CHFP. The GloSea5 data has been taken from the EU SPECS database. We are thankful to Dr. Nicola Martin for providing the GloSea5 data and Dr. Yuhei Takaya for discussion on the JMA data. The GPCP precipitation data has been obtained from the Earth System Research Laboratory (ESRL), National Oceanic Atmospheric Administration (NOAA). AS was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). SJ and AKM are thankful to Head, NCMRWF and SJ is thankful to DST Centre of Excellence in Climate Modeling for their support in this work. We are also thankful to the two anonymous reviewers for their comments and suggestions on this paper.

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Affiliations

  1. National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, Uttar Pradesh, 201309, India

    Shipra Jain & Ashis K. Mitra

  2. Centre for Atmospheric Sciences (CAS), Indian Institute of Technology Delhi (IITD), Hauz Khas, New Delhi, 110016, India

    Shipra Jain

  3. Met Office Hadley Centre, Fitz Roy Road, Exeter, Devon, EX1 3PB, UK

    Adam A. Scaife

  4. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon, UK

    Adam A. Scaife

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  1. Shipra Jain
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Jain, S., Scaife, A.A. & Mitra, A.K. Skill of Indian summer monsoon rainfall prediction in multiple seasonal prediction systems. Clim Dyn 52, 5291–5301 (2019). https://doi.org/10.1007/s00382-018-4449-z

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Keywords

  • Climate Historical Forecasts Project (CHFP)
  • Seasonal prediction
  • Indian monsoon
  • ENSO-monsoon teleconnection
  • Seasonal hindcasts