Advanced

The effects of ocean SST dipole on Mongolian summer rainfall

Yasuda, Hiroshi; Nandintsetseg, Banzragch; Berndtsson, Ronny LU ; Amgalan, Ganbat; Shinoda, Masato and Kawai, Takayuki (2017) In Geofizika 34(1). p.199-218
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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial neural networks, Dryland, Mongolian rainfall, Rainfall prediction, SST teleconnection
in
Geofizika
volume
34
issue
1
pages
20 pages
publisher
Geofizicki Zavod
external identifiers
  • scopus:85029410373
  • wos:000408100600010
ISSN
0352-3659
DOI
10.15233/gfz.2017.34.10
language
English
LU publication?
yes
id
223ab651-5011-4059-bf83-fd632d91dda9
date added to LUP
2017-10-05 08:03:11
date last changed
2018-01-16 13:20:53
@article{223ab651-5011-4059-bf83-fd632d91dda9,
  abstract     = {<p>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.</p>},
  author       = {Yasuda, Hiroshi and Nandintsetseg, Banzragch and Berndtsson, Ronny and Amgalan, Ganbat and Shinoda, Masato and Kawai, Takayuki},
  issn         = {0352-3659},
  keyword      = {Artificial neural networks,Dryland,Mongolian rainfall,Rainfall prediction,SST teleconnection},
  language     = {eng},
  number       = {1},
  pages        = {199--218},
  publisher    = {Geofizicki Zavod},
  series       = {Geofizika},
  title        = {The effects of ocean SST dipole on Mongolian summer rainfall},
  url          = {http://dx.doi.org/10.15233/gfz.2017.34.10},
  volume       = {34},
  year         = {2017},
}