Second peak in the far eastern Pacific sea surface temperature anomaly following strong El Niño events

2013 ◽  
Vol 40 (17) ◽  
pp. 4751-4755 ◽  
Author(s):  
WonMoo Kim ◽  
Wenju Cai
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
El Nino ◽  
Sea Surface ◽  
Eastern Pacific ◽  
Sea Surface Temperature Anomaly ◽  
El Niño Events ◽  
Far Eastern ◽  
Second Peak

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.

scholarly journals New Indices for Better Understanding ENSO by Incorporating Convection Sensitivity to Sea Surface Temperature

2020 ◽  
Vol 33 (16) ◽  
pp. 7045-7061 ◽  
Author(s):  
Ruihuang Xie ◽  
Mu Mu ◽  
Xianghui Fang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Longwave Radiation ◽  
High Impact ◽  
Sea Surface ◽  
Eastern Pacific ◽  
El Niño Events ◽  
El Nino Events

AbstractObserved outgoing longwave radiation (OLR) data indicate that convection is nonlinearly sensitive to sea surface temperature anomalies (SSTA) for background SSTs in the 25.25°–30.25°C high-impact range. In this study, we use that observed convection sensitivity to derive a proxy of the convective responses to SSTA only [referred to as fluctuations of the accumulated convection strength (FACT)]. FACT reproduces the pattern of the observed convection response to ENSO in the central and eastern Pacific, but underestimates the amplitude due to the exclusion of the effect of ENSO-induced atmospheric convergence anomalies on convection. We thus use FACT to define new indices (InFACT) of ENSO diversity that explicitly account for the nonlinear convection–SST sensitivity. The amplitude of InFACT allows us to easily classify El Niño events into weak, moderate, and strong types that markedly differ in terms of SSTA spatial patterns and their convective responses. La Niña events classified by InFACT display much less pattern diversity, and mostly differ through their amplitudes. Finally, our study supports some previous studies that the nonlinear SST–convection relation plays a strong role for the development of extreme El Niño events with the presence of high-impact SSTs and large convection anomalies in the equatorial eastern Pacific.

scholarly journals Java Sea Surface Temperature Variability during ENSO 1997 – 1998 and 2014 – 2015

Omni-Akuatika ◽  
2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Hilda Heryati ◽  
Widodo Setiyo Pranowo ◽  
Noir Primadona Purba ◽  
Achmad Rizal ◽  
Lintang Permata Yuliadi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
Temperature Anomaly ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Strong Linkage ◽  
Sea Surface Temperature Anomaly ◽  
Java Sea

Sea Surface Temperature (SST) is one of the important parameter to describe seawater characteristic. There is a strong linkage between SST and El Nino Southern Oscillation (ENSO). The purpose of this research is to investigate SST of Java Sea during in period 1997—1998 and 2014– 2015. We use datasets from Hycom archieves, INDESO, and SOI. The result shows El Nino is started in March 1997 until April 1998 (peak in March 1998), then La Nina is started in June to December 1998 (peak in July 1998). Maximum Sea Surface Temperature Anomaly (SSTA) is occurred in August – September 1998 (0.8 °C – 0.9 °C). During 2014–2015, a propagation of El Nino is founded. El Nino is started in August until November 2014 (-7.6 < SOI < -11.4, peak in August), and is followed in May to October 2015 (-12 < SOI < -20.2, peak in October). During 2014–2015, a maximum Sea Surface Temperature Anomaly (SSTA) is founded in May 2014 (0.5 °C).

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