scholarly journals The Interdependency of the Morphological Variations of the Planktonic Foraminiferal Species Globigerina bulloides in Surface Sediments on the Environmental Parameters of the Southwestern Indian Ocean

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Abhijit Mazumder ◽  
Neloy Khare ◽  
Pawan Govil
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Census Data ◽  
Planktonic Foraminifera ◽  
Morphological Characteristics ◽  
Sea Surface ◽  
Globigerina Bulloides ◽  
Foraminiferal Species ◽  
Ecological Parameters

18 surface sediment samples collected from a north-south transect along the Indian Ocean have been analyzed for planktonic Foraminifera content. Among the other planktonic foraminiferal faunas, Globigerina bulloides was present substantially in all samples. Census data of G. bulloides were measured for different parameters (average size, mean proloculus size, coiling direction, and number of chambers) and a Q-mode cluster analysis was applied on these data. Samples were segregated into two homogeneous clusters, each reflecting particular environmental conditions. Two clusters are as follows: (1) Cluster A, comprised of 6 samples and characterized by the highest range of foraminiferal and ecological parameters, except sea surface temperature and salinity which shows the lowest range, and (2) Cluster B, comprised of 12 samples and characterized by the lowest range of foraminiferal parameters and ecological parameters, except sea surface temperature and salinity which shows the highest range. The study suggests that the ecological parameters are the governing factors for the morphological characteristics of planktonic foraminiferal species G. bulloides.

scholarly journals Interannual Variability of Sea Surface Temperature off Java and Sumatra in a Global GCM*

2008 ◽  
Vol 21 (11) ◽  
pp. 2451-2465 ◽  
Author(s):  
Yan Du ◽  
Tangdong Qu ◽  
Gary Meyers
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Heat Budget ◽  
Sea Surface ◽  
Surface Mixed Layer ◽  
Horizontal Advection ◽  
Cold Anomaly ◽  
Sst Anomalies

Abstract Using results from the Simple Ocean Data Assimilation (SODA), this study assesses the mixed layer heat budget to identify the mechanisms that control the interannual variation of sea surface temperature (SST) off Java and Sumatra. The analysis indicates that during the positive Indian Ocean Dipole (IOD) years, cold SST anomalies are phase locked with the season cycle. They may exceed −3°C near the coast of Sumatra and extend as far westward as 80°E along the equator. The depth of the thermocline has a prominent influence on the generation and maintenance of SST anomalies. In the normal years, cooling by upwelling–entrainment is largely counterbalanced by warming due to horizontal advection. In the cooling episode of IOD events, coastal upwelling–entrainment is enhanced, and as a result of mixed layer shoaling, the barrier layer no longer exists, so that the effect of upwelling–entrainment can easily reach the surface mixed layer. Horizontal advection spreads the cold anomaly to the interior tropical Indian Ocean. Near the coast of Java, the northern branch of an anomalous anticyclonic circulation spreads the cold anomaly to the west near the equator. Both the anomalous advection and the enhanced, wind-driven upwelling generate the cold SST anomaly of the positive IOD. At the end of the cooling episode, the enhanced surface thermal forcing overbalances the cooling effect by upwelling/entrainment, and leads to a warming in SST off Java and Sumatra.

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.

scholarly journals Seasonal Covariability of Dryness or Wetness in China and Global Sea Surface Temperature

2020 ◽  
Vol 33 (2) ◽  
pp. 727-747
Author(s):  
Chunxiang Li ◽  
Chunzai Wang ◽  
Tianbao Zhao
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Sea Surface ◽  
Interdecadal Variability ◽  
Coupled Modes ◽  
Indian Ocean Basin Mode ◽  
Global Sea Surface Temperature

AbstractSeasonal covariability of the dryness/wetness in China and global sea surface temperature (SST) is investigated by using the monthly self-calibrated Palmer drought severity index (PDSI) data and other data from 1950 to 2014. The singular value decomposition (SVD) analysis shows two recurring PDSI–SST coupled modes. The first SVD mode of PDSI is associated with the warm phases of the eastern Pacific–type El Niño–Southern Oscillation (ENSO), the interdecadal Pacific oscillation (IPO) or Pacific decadal oscillation (PDO), the Indian Ocean basin mode (IOBM) in the autumn and winter, and the cold phase of the IOBM in the spring. Meanwhile, the Atlantic multidecadal oscillation (AMO) pattern appears in every season except the autumn. The second SVD mode of PDSI is accompanied by a central Pacific–type El Niño developing from the winter to autumn over the tropical Pacific and a positive phase of IPO or PDO from the winter to summer. Moreover, an AMO pattern is observed in all seasons except the summer, whereas the SST over the tropical Indian Ocean shows negligible variations. The further analyses suggest that AMO remote forcing may be a primary factor influencing interdecadal variability of PDSI in China, and interannual to interdecadal variability of PDSI seems to be closely associated with the ENSO-related events. Meanwhile, the IOBM may be a crucial factor in interannual variability of PDSI during its mature phase in the spring. In general, the SST-related dryness/wetness anomalies can be explained by the associated atmospheric circulation changes.

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