The effects of ocean SST dipole on Mongolian summer rainfall

Research output: Contribution to journalArticle

Abstract

Cross-correlations between inter-annual summer rainfall time series (June to August: JJA) for arid Mongolia and global sea surface temperatures (GSST) were calculated for prediction purposes. Prediction of summer rainfall for four vegetation zones, Desert Steppe (DS), Steppe (ST), Forest Steppe (FS), and High Mountain (HM) using GSSTs for time lags of 5, 6, and 7 months prior to JJA rainfall was evaluated. Mongolian summer rainfall is correlated with global SSTs. In particular, the summer rainfall of FS and HM displayed high and statistically sigtime series of the SST differences between SST dipoles (positive – negative) with the summer rainfall time series was larger than the original correlations. To preused. Time series of the SST difference that represents the strength of the dipole were used as input to the ANN model, and Mongolian summer rainfall was predicted 5, 6, and 7 months ahead in time. The predicted summer rainfall compared reasonably well with the observed rainfall in the four different vegetation zones. This implies that the model can be used to predict summer rainfall for the four main Mongolian vegetation zones with good accuracy.

Details

Authors
  • Hiroshi Yasuda
  • Banzragch Nandintsetseg
  • Ronny Berndtsson
  • Ganbat Amgalan
  • Masato Shinoda
  • Takayuki Kawai
Organisations
External organisations
  • Nagoya University
  • Tottori University
  • Information and Research Institute of Meteorology, Hydrology and Environment
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Oceanography, Hydrology, Water Resources

Keywords

  • Artificial neural networks, Dryland, Mongolian rainfall, Rainfall prediction, SST teleconnection
Original languageEnglish
Pages (from-to)199-218
Number of pages20
JournalGeofizika
Volume34
Issue number1
Publication statusPublished - 2017
Publication categoryResearch
Peer-reviewedYes