scholarly journals Interannual coherent variability of SSTA and SSHA in the Tropical Indian Ocean

2012 ◽  
Vol 9 (1) ◽  
pp. 1-24
Author(s):  
J. Q. Feng
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Rossby Wave ◽  
Tropical Indian Ocean ◽  
Sea Surface Height ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Height Anomaly ◽  
Basin Scale

Abstract. Sea surface height derived from the multiple ocean satellite altimeter missions (TOPEX/Poseidon, Jason-1, ERS, Envisat et al.) and sea surface temperature from National Centers for Environmental Prediction (NCEP) over 1993–2008 are analyzed to investigate the coherent patterns between the interannual variability of the sea surface and subsurface in the Tropical Indian Ocean, by jointly adopting Singular Value Decomposition (SVD) and Extended Associate Pattern Analysis (EAPA) methods. Results show that there are two dominant coherent modes with the nearly same main period of about 3–5 yr, accounting for 86 % of the total covariance in all, but 90° phase difference between them. The primary pattern is characterized by a east-west dipole mode associated with the mature phase of ENSO, and the second presents a sandwich mode having one sign anomalies along Sumatra-Java coast and northeast of Madagascar, whilst an opposite sign between the two regions. The robust correlations of the sea surface height anomaly (SSHA) with sea surface temperature anomaly (SSTA) in the leading modes indicate a strong interaction between them, though the highest correlation coefficient appears with a time lag. And there may be some physical significance with respect to ocean dynamics implied in SSHA variability. Analyzing results show that the features of oceanic waves with basin scale, of which the Rossby wave is prominent, are apparent in the dominant modes. It is further demonstrated from the EAPA that the equatorial eastward Kelvin wave and off-equatorial westward Rossby wave as well as their reflection in the east and west boundary, respectively, are important dynamic mechanisms in the evolution of the two leading coherent patterns. Results of the present study suggest that the upper ocean thermal variations on the timescale of interannual coherent with the ocean dynamics in spatial structure and temporal evolution are mainly attributed to the ocean waves.

scholarly journals Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

2018 ◽  
Vol 35 (7) ◽  
pp. 1441-1455 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Prediction Skill ◽  
Basin Scale ◽  
Ann Models ◽  
Artificial Neural

AbstractThe prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

Relationship Between Cyclone Intensities and Sea Surface Temperature in the Tropical Indian Ocean

2013 ◽  
Vol 10 (4) ◽  
pp. 841-844 ◽  
Author(s):  
M. M. Ali ◽  
D. Swain ◽  
T. Kashyap ◽  
J. P. McCreary ◽  
P. V. Nagamani
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Indian Ocean ◽  
Sea Surface

scholarly journals Nutrient enrichment induced by tropical cyclone Seroja in the southeastern tropical Indian Ocean

2021 ◽  
Vol 925 (1) ◽  
pp. 012021
Author(s):  
D W Purnaningtyas ◽  
F Khadami ◽  
Avrionesti
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Nutrient Enrichment ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Nutrient Concentrations ◽  
Biochemical Alterations ◽  
Southeastern Tropical Indian Ocean

Abstract Tropical cyclone (TC) passage triggers a complex response from the adjacent ocean, including vertical mixing, leading to biochemical alterations and affecting the surrounding ecosystem’s dynamics. In previous studies, increased nutrient concentrations and primary production were observed along the cyclone track after the storm. TC Seroja was awakened near the equator in the southeastern tropical Indian Ocean, making it interesting to investigate how the ambient ecosystem responds. Hence, we analyzed the sea surface temperature and nutrient changes during the Seroja event using multi-satellite remote sensing and numerical model data in the south of Indonesia and East Timor along the Seroja track between April 2 and 10, 2021. Immediately after the TC Seroja passed, the sea surface temperature cooled to 3 °C around the TC lane. At the same time, the spatial distribution patterns showed the upsurge of some nutrients in response to the passage of TC Seroja; the surface nitrate swells up to 1.5 mmol/m3, while phosphate increased up to 0.2 mmol/m3, and the dissolved silicate concentration enhanced up to 1.0 mmol/m3. The responses recover within 2-7 days. These results indicate that tropical cyclones contribute to nutrient enrichment in oligotrophic areas outside of their usual annual upwelling time, thereby further supporting ecosystem sustainability.

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