Observed seasonal variability of heat content in the upper layers of the tropical Indian Ocean from a new global ocean temperature climatology

1998 ◽  
Vol 45 (1) ◽  
pp. 67-89 ◽  
Author(s):  
R.R. Rao ◽  
R. Sivakumar
2014 ◽  
Vol 27 (5) ◽  
pp. 1945-1957 ◽  
Author(s):  
John M. Lyman ◽  
Gregory C. Johnson

Abstract Ocean heat content anomalies are analyzed from 1950 to 2011 in five distinct depth layers (0–100, 100–300, 300–700, 700–900, and 900–1800 m). These layers correspond to historic increases in common maximum sampling depths of ocean temperature measurements with time, as different instruments—mechanical bathythermograph (MBT), shallow expendable bathythermograph (XBT), deep XBT, early sometimes shallower Argo profiling floats, and recent Argo floats capable of worldwide sampling to 2000 m—have come into widespread use. This vertical separation of maps allows computation of annual ocean heat content anomalies and their sampling uncertainties back to 1950 while taking account of in situ sampling advances and changing sampling patterns. The 0–100-m layer is measured over 50% of the globe annually starting in 1956, the 100–300-m layer starting in 1967, the 300–700-m layer starting in 1983, and the deepest two layers considered here starting in 2003 and 2004, during the implementation of Argo. Furthermore, global ocean heat uptake estimates since 1950 depend strongly on assumptions made concerning changes in undersampled or unsampled ocean regions. If unsampled areas are assumed to have zero anomalies and are included in the global integrals, the choice of climatological reference from which anomalies are estimated can strongly influence the global integral values and their trend: the sparser the sampling and the bigger the mean difference between climatological and actual values, the larger the influence.


2005 ◽  
Vol 22 (3) ◽  
pp. 451-462 ◽  
Author(s):  
Hu Ruijin ◽  
Liu Qinyu ◽  
Meng Xiangfeng ◽  
J. Stuart Godfrey

2009 ◽  
Vol 22 (7) ◽  
pp. 1850-1858 ◽  
Author(s):  
Jin-Yi Yu ◽  
Fengpeng Sun ◽  
Hsun-Ying Kao

Abstract The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model’s biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model’s biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3’s warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean–atmosphere coupling and enhance the ENSO intensity.


2007 ◽  
Vol 20 (13) ◽  
pp. 2937-2960 ◽  
Author(s):  
Bohua Huang ◽  
J. Shukla

Abstract To understand the mechanisms of the interannual variability in the tropical Indian Ocean, two long-term simulations are conducted using a coupled ocean–atmosphere GCM—one with active air–sea coupling over the global ocean and the other with regional coupling restricted within the Indian Ocean to the north of 30°S while the climatological monthly sea surface temperatures (SSTs) are prescribed in the uncoupled oceans to drive the atmospheric circulation. The major spatial patterns of the observed upper-ocean heat content and SST anomalies can be reproduced realistically by both simulations, suggesting that they are determined by intrinsic coupled processes within the Indian Ocean. In both simulations, the interannual variability in the Indian Ocean is dominated by a tropical mode and a subtropical mode. The tropical mode is characterized by a coupled feedback among thermocline depth, zonal SST gradient, and wind anomalies over the equatorial and southern tropical Indian Ocean, which is strongest in boreal fall and winter. The tropical mode simulated by the global coupled model reproduces the main observational features, including a seasonal connection to the model El Niño–Southern Oscillation (ENSO). The ENSO influence, however, is weaker than that in a set of ensemble simulations described in Part I of this study, where the observed SST anomalies for 1950–98 are prescribed outside the Indian Ocean. Combining with the results from Part I of this study, it is concluded that ENSO can modulate the temporal variability of the tropical mode through atmospheric teleconnection. Its influence depends on the ENSO strength and duration. The stronger and more persistent El Niño events in the observations extend the life span of the anomalous events in the tropical Indian Ocean significantly. In the regional coupled simulation, the tropical mode is still active, but its dominant period is shifted away from that of ENSO. In the absence of ENSO forcing, the tropical mode is mainly stimulated by an anomalous atmospheric direct thermal cell forced by the fluctuations of the northwestern Pacific monsoon. The subtropical mode is characterized by an east–west dipole pattern of the SST anomalies in the southern subtropical Indian Ocean, which is strongest in austral fall. The SST anomalies are initially forced by surface heat flux anomalies caused by the anomalous southeast trade wind in the subtropical ocean during austral summer. The trade wind anomalies are in turn associated with extratropical variations from the southern annular mode. A thermodynamic air–sea feedback strengthens these subtropical anomalies quickly in austral fall and extends their remnants into the tropical ocean in austral winter. In the simulations, this subtropical variability is independent of ENSO.


2008 ◽  
Vol 21 (10) ◽  
pp. 2259-2268 ◽  
Author(s):  
Mark Carson ◽  
D. E. Harrison

Abstract There is great interest in World Ocean temperature trends, yet the historical global ocean database has very uneven coverage in space and time. Previous work on 50-yr upper ocean temperature trends from the NOAA ocean data archive is extended here. Trends at depths from 50 to 1000 m are examined, based on observations gridded over larger regions than in the earlier study. Despite the use of larger grid boxes, most of the ocean does not have significant 50-yr trends at the 90% confidence level (CL). In fact only 30% of the ocean at 50 m has 90% CL trends, and the percentage decreases significantly with increasing depth. As noted in the previous study, there is much spatial structure in 50-yr trends, with areas of strong warming and strong cooling. These trend results are compared with trends calculated from data interpolated to standard levels and from a highly horizontally interpolated version of the dataset that has been used in previous heat content trend studies. The regional trend results can differ substantially, even in the areas with statistically significant trends. Trends based on the more interpolated analyses show more warming. Together with major temporal and spatial sampling limitations, the previously described strong interdecadal and spatial variability of trends makes it very difficult to formally estimate uncertainty in World Ocean averages, but these results suggest that upper ocean heat content integrals and integral trends may be substantially more uncertain than has yet been acknowledged. Further exploration of uncertainties is needed.


2014 ◽  
Vol 28 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Yongjing Zhao ◽  
Sumant Nigam

Abstract The claim for a zonal-dipole structure in interannual variations of the tropical Indian Ocean (IO) SSTs—the Indian Ocean dipole (IOD)—is reexamined after accounting for El Niño–Southern Oscillation’s (ENSO) influence. The authors seek an a priori accounting of ENSO’s seasonally stratified influence on IO SSTs and evaluate the basis of the related dipole mode index, instead of seeking a posteriori adjustments to this index, as common. Scant observational evidence is found for zonal-dipole SST variations after removal of ENSO’s influence from IO SSTs: The IOD poles are essentially uncorrelated in the ENSO-filtered SSTs in both recent (1958–98) and century-long (1900–2007) periods, leading to the breakdown of zonal-dipole structure in surface temperature variability; this finding does not depend on the subtleties in estimation of ENSO’s influence. Deconstruction of the fall 1994 and 1997 SST anomalies led to their reclassification, with a weak IOD in 1994 and none in 1997. Regressions of the eastern IOD pole on upper-ocean heat content, however, do exhibit a zonal-dipole structure but with the western pole in the central-equatorial IO, suggesting that internally generated basin variability can have zonal-dipole structure at the subsurface. The IO SST variability was analyzed using the extended-EOF technique, after removing the influence of Pacific SSTs; the technique targets spatial and temporal recurrence and extracts modes (rather than patterns) of variability. This spatiotemporal analysis also does not support the existence of zonal-dipole variability at the surface. However, the analysis did yield a dipole-like structure in the meridional direction in boreal fall/winter, when it resembles the subtropical IOD pattern (but not the evolution time scale).


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