scholarly journals Sea surface temperature estimates for the mid-Piacenzian Indian Ocean—Ocean Drilling Program sites 709, 716, 722, 754, 757, 758, and 763

2018 ◽  
Author(s):  
Marci M. Robinson ◽  
Harry J. Dowsett ◽  
Danielle K. Stoll
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Ocean Drilling Program ◽  
Ocean Drilling

scholarly journals High-resolution alkenone palaeobarometry indicates relatively stable p CO 2 during the Pliocene (3.3–2.8 Ma)

2013 ◽  
Vol 371 (2001) ◽  
pp. 20130094 ◽  
Author(s):  
Marcus P. S. Badger ◽  
Daniela N. Schmidt ◽  
Andreas Mackensen ◽  
Richard D. Pancost
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temporal Resolution ◽  
Sea Surface ◽  
High Temporal Resolution ◽  
Ocean Drilling Program ◽  
Ocean Drilling ◽  
Temperature Reconstructions ◽  
Sensitivity Studies

Temperature reconstructions indicate that the Pliocene was approximately 3 ° C warmer globally than today, and several recent reconstructions of Pliocene atmospheric CO 2 indicate that it was above pre-industrial levels and similar to those likely to be seen this century. However, many of these reconstructions have been of relatively low temporal resolution, meaning that these records may have failed to capture variations associated with the 41 kyr glacial–interglacial cycles thought to have operated in the Pliocene. Here we present a new, high temporal resolution alkenone carbon isotope-based record of p CO 2 spanning 3.3–2.8 Ma from Ocean Drilling Program Site 999. Our record is of high enough resolution (approx. 19 kyr) to resolve glacial–interglacial changes beyond the intrinsic uncertainty of the proxy method. The record suggests that Pliocene CO 2 levels were relatively stable, exhibiting variation less than 55 ppm. We perform sensitivity studies to investigate the possible effect of changing sea surface temperature (SST), which highlights the importance of accurate and precise SST reconstructions for alkenone palaeobarometry, but demonstrate that these uncertainties do not affect our conclusions of relatively stable p CO 2 levels during this interval.

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.

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