Original scientific paper

https://doi.org/10.15233/gfz.2017.34.10

The effects of ocean SST dipole on Mongolian summer rainfall

Hiroshi Yasuda ; Arid Land Research Center, Tottori University, Hamasaka, Tottori, Japan
Banzragch Nandintsetseg ; Information and Research Institute of Meteorology, Hydrology and Environment, Ulaanbaatar, Mongolia
Ronny Berndtsson ; Centre for Middle Eastern Studies & Division of Water Resources Engineering , Lund University, Lund , Sweden
Ganbat Amgalan ; Information and Research Institute of Meteorology, Hydrology and Environment, Ulaanbaatar, Mongolia
Masato Shinoda ; Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
Takayuki Kawai ; Arid Land Research Center, Tottori University, Hamasaka, Tottori, Japan

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 significant correlations with SST in specific parts of the oceans. SST dipoles (pairs of positively and negatively correlated areas) were identified, and correlation for time series of the SST differences between SST dipoles (positive - negative) with the summer rainfall time series was larger than the original correlations. To predict the summer rainfall from SST, an artificial neural network (ANN) model was used. 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.

Keywords

artificial neural network; dryland; Mongolian rainfall; rainfall prediction; SST teleconnection

Hrčak ID:

186338

URI

https://hrcak.srce.hr/186338

Publication date:

30.6.2017.

Article data in other languages: croatian

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