scholarly journals Insights from a joint analysis of Indian and Chinese monsoon rainfall data

2011 ◽  
Vol 8 (2) ◽  
pp. 3167-3187 ◽  
Author(s):  
M. Zhou ◽  
F. Tian ◽  
U. Lall ◽  
H. Hu
Keyword(s):  
Agricultural Production ◽  
Monsoon Rainfall ◽  
Temperature Anomaly ◽  
Northern China ◽  
Joint Analysis ◽  
Climate Indices ◽  
Sea Surface Temperature Anomaly ◽  
Indian Rainfall ◽  
Long Term Prediction

Abstract. Monsoon rainfall is of great importance for the agricultural production in both China and India. Understanding its rule and possibility of long term prediction is a challenge for research. This paper gives a joint analysis of Indian monsoon and Chinese monsoon, finds their teleconnection to Sea Surface Temperature anomaly (SSTa) and other climate indices individually and relationship in common. The results show that northern China garners less rainfall when whole Indian rainfall is below normal. Also, with cold SSTa over the Indonesia region, more rainfall would be distributed over India and South China.

scholarly journals Insights from a joint analysis of Indian and Chinese monsoon rainfall data

2011 ◽  
Vol 15 (8) ◽  
pp. 2709-2715 ◽  
Author(s):  
M. Zhou ◽  
F. Tian ◽  
U. Lall ◽  
H. Hu
Keyword(s):  
Large Scale ◽  
Monsoon Rainfall ◽  
Indian Monsoon ◽  
Southern China ◽  
Principal Component ◽  
Northern China ◽  
Rainfall Data ◽  
Joint Analysis ◽  
Climate Indices ◽  
The Common

Abstract. Monsoon rainfall is of great importance for agricultural production in both China and India. Understanding the features of the Indian and Chinese monsoon rainfall and its long term predictability is a challenge for research. In this paper Principal Component Analysis (PCA) method was adopted to analyze Indian monsoon and Chinese monsoon separately as well as jointly during the period 1951 to 2003. The common structure of Indian monsoon and Chinese monsoon rainfall data was explored, and its correlation with large scale climate indices and thus the possibility of prediction were analyzed. The joint PCA results gives a clearer correlation map between Chinese monsoon rainfall and Indian monsoon rainfall. The common rainfall structure presents a significant teleconnection to Sea Surface Temperature anomaly (SSTa), moisture transport and other climate indices. Specifically, our result shows that Northern China would garner less rainfall when whole Indian rainfall is below normal, and with cold SSTa over the Indonesia region more rainfall would be distributed over India and Southern China. The result also shows that SSTa in the previous winter months could be a good indicator for the summer monsoon rainfall in China.

scholarly journals Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Ocean Science ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 301-320 ◽  
Author(s):  
Mei Hong ◽  
Xi Chen ◽  
Ren Zhang ◽  
Dong Wang ◽  
Shuanghe Shen ◽  
...  
Keyword(s):  
Statistical Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
El Niño ◽  
Temperature Anomaly ◽  
El Nino ◽  
Sea Surface ◽  
Sea Surface Temperature Anomaly

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

scholarly journals Forecasting experiments of a dynamical-statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

2017 ◽  
Author(s):  
Mei Hong ◽  
Xi Chen ◽  
Ren Zhang ◽  
Dong Wang ◽  
Shuanghe Shen ◽  
...  
Keyword(s):  
Statistical Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
El Niño ◽  
Temperature Anomaly ◽  
El Nino ◽  
Sea Surface ◽  
Sea Surface Temperature Anomaly

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical-statistical forecast model of sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamic reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical-statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a correlation coefficient of approximately 0.80 and a mean absolute percentage error of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field, but the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in summer and those in winter is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

Spatial variation of East Asian winter monsoon evolution between northern and southern China since the last glacial maximum

2021 ◽  
pp. 1-14
Author(s):  
Qin Li ◽  
Haibin Wu ◽  
Jun Cheng ◽  
Shuya Zhu ◽  
Chunxia Zhang ◽  
...  
Keyword(s):  
East Asian Winter Monsoon ◽  
Southern China ◽  
Ice Sheets ◽  
Northern China ◽  
Winter Monsoon ◽  
Asian Winter Monsoon ◽  
Spatial Differences ◽  
The Last Glacial Maximum ◽  
The Last Glacial

Abstract The East Asian winter monsoon (EAWM) is one of the most dynamic components of the global climate system. Although poorly understood, knowledge of long-term spatial differences in EAWM variability during the glacial–interglacial cycles is important for understanding the dynamic processes of the EAWM. We reconstructed the spatiotemporal characteristics of the EAWM since the last glacial maximum (LGM) using a comparison of proxy records and long-term transient simulations. A loess grain-size record from northern China (a sensitive EAWM proxy) and the sea surface temperature gradient of an EAWM index in sediments of the southern South China Sea were compared. The data–model comparison indicates pronounced spatial differences in EAWM evolution, with a weakened EAWM since the LGM in northern China but a strengthened EAWM from the LGM to the early Holocene, followed by a weakening trend, in southern China. The model results suggest that variations in the EAWM in northern China were driven mainly by changes in atmospheric carbon dioxide (CO2) concentration and Northern Hemisphere ice sheets, whereas orbital insolation and ice sheets were important drivers in southern China. We propose that the relative importance of insolation, ice sheets, and atmospheric CO2 for EAWM evolution varied spatially within East Asia.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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