scholarly journals Strong Indian Ocean sea surface temperature signals associated with the Madden-Julian Oscillation in late 2007 and early 2008

2008 ◽  
Vol 35 (19) ◽  
Author(s):  
J. Vialard ◽  
G. R. Foltz ◽  
M. J. McPhaden ◽  
J. P. Duvel ◽  
C. de Boyer Montégut
Author(s):  
Amirul Islam ◽  
Andy Chan ◽  
Matthew Ashfold ◽  
Chel Gee Ooi ◽  
Majid Azari

Maritime Continent (MC) positions in between Asian and Australian summer monsoons zone. Its complex topography and shallow seas around it is a major challenge for the climate researchers to model and understand it. Monsoon in this area is affected by inter-scale ocean-atmospheric interactions like El-Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden-Julian Oscillation. Monsoon rainfall in MC (especially in Indonesia and Malaysia) profoundly exhibits its variability dependency on ocean-atmospheric phenomena in this region. This monsoon shift often introduces to dreadful events like biomass burning (BB) in Southeast Asia (SEA) which sometimes leads to severe trans-boundary haze pollution. In this study, the episode of BB in 2015 of SEA is highlighted and discussed. Observational satellite datasets are tested by performing simulations with numerical weather prediction (NWP) model using WRF-ARW (Advanced research WRF). Observed and model datasets are compared to study the sea surface temperature (SST) and precipitation (rainfall) anomalies influenced by ENSO, IOD and MJO. Correlations have been recognised which explains the delayed rainfall of regular monsoon in MC due to the influence of ENSO, IOD and MJO during 2015 BB episode, eventually leading to intensification of fire and severe haze.


2008 ◽  
Vol 21 (11) ◽  
pp. 2451-2465 ◽  
Author(s):  
Yan Du ◽  
Tangdong Qu ◽  
Gary Meyers

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.


2018 ◽  
Vol 35 (7) ◽  
pp. 1441-1455 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo

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.


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