scholarly journals Time-dependent response of the tropical atmosphere to a fixed sea surface temperature anomaly

2003 ◽  
Vol 108 (D20) ◽  
Author(s):  
Joseph A. Zehnder
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
Sea Surface ◽  
Time Dependent ◽  
Tropical Atmosphere ◽  
Sea Surface Temperature Anomaly

Time Series Analysis of CMIP5 Model and Observed Sea Surface Temperature Anomaly Along Indian Coastal Zones

2019 ◽  
Vol 86 (sp1) ◽  
pp. 239
Author(s):  
Dhanya Joseph ◽  
Vazhamattom Benjamin Liya ◽  
Girindran Rojith ◽  
Pariyappanal Ulahannan Zacharia ◽  
George Grinson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
Sea Surface ◽  
Coastal Zones ◽  
Cmip5 Model ◽  
Series Analysis ◽  
Sea Surface Temperature Anomaly

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.

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