The central Pacific as the export region of the El Niño-Southern Oscillation sea surface temperature anomaly to Antarctic sea ice

2011 ◽  
Vol 116 (D21) ◽  
Author(s):  
Hyo-Jong Song ◽  
Eunho Choi ◽  
Gyu-Ho Lim ◽  
Young Ho Kim ◽  
Jong-Seong Kug ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Central Pacific ◽  
Antarctic Sea Ice ◽  
Sea Surface Temperature Anomaly

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).

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 Synchronized spatial shifts of Hadley and Walker circulations

2021 ◽  
Vol 12 (1) ◽  
pp. 121-132
Author(s):  
Kyung-Sook Yun ◽  
Axel Timmermann ◽  
Malte F. Stuecker
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
Climate Model ◽  
El Nino ◽  
Southern Oscillation ◽  
Walker Circulation ◽  
Warm Pool ◽  
Sea Surface ◽  
Central Pacific

Abstract. The El Niño–Southern Oscillation (ENSO) influences the most extensive tropospheric circulation cells on our planet, known as Hadley and Walker circulations. Previous studies have largely focused on the effect of ENSO on the strength of these cells. However, what has remained uncertain is whether interannual sea surface temperature anomalies can also cause synchronized spatial shifts of these circulations. Here, by examining the spatiotemporal relationship between Hadley and Walker cells in observations and climate model experiments, we demonstrate that the seasonally evolving warm-pool sea surface temperature (SST) anomalies in the decay phase of an El Niño event generate a meridionally asymmetric Walker circulation response, which couples the zonal and meridional atmospheric overturning circulations. This process, which can be characterized as a phase-synchronized spatial shift in Walker and Hadley cells, is accompanied by cross-equatorial northwesterly low-level flow that diverges from an area of anomalous drying in the western North Pacific and converges towards a region with anomalous moistening in the southern central Pacific. Our results show that the SST-induced concurrent spatial shifts of the two circulations are climatically relevant as they can further amplify extratropical precipitation variability on interannual timescales.

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