Tropical rainfall predictions from multiple seasonal forecast systems

Adam A. Scaife, Laura Ferranti, Oscar Alves, Panos Athanasiadis, Johanna Baehr, Michel Dequé, Tina Dippe, Nick Dunstone, David Fereday, Richard G. Gudgel, Richard J. Greatbatch, Leon Hermanson, Yukiko Imada, Shipra Jain, Arun Kumar, Craig MacLachlan, William Merryfield, Wolfgang A. Müller, Hong Li Ren, Doug SmithYuhei Takaya, Gabriel Andres Vecchi, Xiaosong Yang

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year-to-year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter-basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi-model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter-annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20% of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter-basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real-time forecasts.

Original languageEnglish (US)
Pages (from-to)974-988
Number of pages15
JournalInternational Journal of Climatology
Volume39
Issue number2
DOIs
StatePublished - Feb 1 2019

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Keywords

  • ENSO
  • NAO
  • PNA
  • ensemble
  • seasona prediction
  • tropical rainfall

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    Scaife, A. A., Ferranti, L., Alves, O., Athanasiadis, P., Baehr, J., Dequé, M., Dippe, T., Dunstone, N., Fereday, D., Gudgel, R. G., Greatbatch, R. J., Hermanson, L., Imada, Y., Jain, S., Kumar, A., MacLachlan, C., Merryfield, W., Müller, W. A., Ren, H. L., ... Yang, X. (2019). Tropical rainfall predictions from multiple seasonal forecast systems. International Journal of Climatology, 39(2), 974-988. https://doi.org/10.1002/joc.5855