排序方式： 共有23条查询结果，搜索用时 46 毫秒
1.
2.
3.
B. Praveen Kumar J. Vialard M. Lengaigne V. S. N. Murty M. J. McPhaden M. F. Cronin F. Pinsard K. Gopala Reddy 《Climate Dynamics》2013,40(7-8):2049-2071
In this paper, we present TropFlux wind stresses and evaluate them against observations along with other widely used daily air-sea momentum flux products (NCEP, NCEP2, ERA-I and QuikSCAT). TropFlux wind stresses are computed from the COARE v3.0 algorithm, using bias and amplitude corrected ERA-I input data and an additional climatological gustiness correction. The wind stress products are evaluated against dependent data from the TAO/TRITON, PIRATA and RAMA arrays and independent data from the OceanSITES mooring networks. Wind stress products are more consistent amongst each other than surface heat fluxes, suggesting that 10 m-winds are better constrained than near-surface thermodynamical parameters (2 m-humidity and temperature) and surface downward radiative fluxes. QuikSCAT overestimates wind stresses away from the equator, while NCEP and NCEP2 underestimate wind stresses, especially in the equatorial Pacific. QuikSCAT wind stress quality is strongly affected by rain under the Inter Tropical Convergence Zones. ERA-I and TropFlux display the best agreement with in situ data, with correlations >0.93 and rms-differences <0.012 Nm?2. TropFlux wind stresses exhibit a small, but consistent improvement (at all timescales and most locations) over ERA-I, with an overall 17 % reduction in root mean square error. ERA-I and TropFlux agree best with long-term mean zonal wind stress observations at equatorial latitudes. All products tend to underestimate the zonal wind stress seasonal cycle by ~20 % in the western and central equatorial Pacific. TropFlux and ERA-I equatorial zonal wind stresses have clearly the best phase agreement with mooring data at intraseasonal and interannual timescales (correlation of ~0.9 versus ~0.8 at best for any other product), with TropFlux correcting the ~13 % underestimation of ERA-I variance at both timescales. For example, TropFlux was the best at reproducing westerly wind bursts that played a key role in the 1997–1998 El Niño onset. Hence, we recommend the use of TropFlux for studies of equatorial ocean dynamics. 相似文献
4.
5.
6.
Nisha Kurian Matthieu Lengaigne Gopalakrishna Venkata Vissa Jerome Vialard Stephane Pous Anne-Charlotte Peter Fabien Durand Shweta Naik 《Ocean Dynamics》2013,63(4):329-346
Active and break phases of the Indian summer monsoon are associated with sea surface temperature (SST) fluctuations at 30–90 days timescale in the Arabian Sea and Bay of Bengal. Mechanisms responsible for basin-scale intraseasonal SST variations have previously been discussed, but the maxima of SST variability are actually located in three specific offshore regions: the South-Eastern Arabian Sea (SEAS), the Southern Tip of India (STI) and the North-Western Bay of Bengal (NWBoB). In the present study, we use an eddy-permitting 0.25° regional ocean model to investigate mechanisms of this offshore intraseasonal SST variability. Modelled climatological mixed layer and upper thermocline depth are in very good agreement with estimates from three repeated expendable bathythermograph transects perpendicular to the Indian Coast. The model intraseasonal forcing and SST variability agree well with observed estimates, although modelled intraseasonal offshore SST amplitude is undere-stimated by 20–30 %. Our analysis reveals that surface heat flux variations drive a large part of the intraseasonal SST variations along the Indian coastline while oceanic processes have contrasted contributions depending of the region considered. In the SEAS, this contribution is very small because intraseasonal wind variations are essentially cross-shore, and thus not associated with significant upwelling intraseasonal fluctuations. In the STI, vertical advection associated with Ekman pumping contributes to ～30 % of the SST fluctuations. In the NWBoB, vertical mixing diminishes the SST variations driven by the atmospheric heat flux perturbations by 40 %. Simple slab ocean model integrations show that the amplitude of these intraseasonal SST signals is not very sensitive to the heat flux dataset used, but more sensitive to mixed layer depth. 相似文献
7.
Chaitanya Akurathi Venkata Sai Vialard Jérôme Lengaigne Matthieu d’Ovidio Francesco Riotte Jean Papa Fabrice James Rathinam Arthur 《Ocean Dynamics》2021,71(11-12):1113-1139
Ocean Dynamics - We use satellite-derived currents and a Lagrangian approach to investigate the redistribution of the precipitation minus evaporation (P-E) and river freshwater inputs into Bay of... 相似文献
8.
A. Jayakumar J��r?me Vialard M. Lengaigne C. Gnanaseelan Julian P. McCreary B. Praveen Kumar 《Climate Dynamics》2011,37(11-12):2217-2234
During boreal winter, there is a prominent maximum of intraseasonal sea-surface temperature (SST) variability associated with the Madden?CJulian Oscillation (MJO) along a Thermocline Ridge located in the southwestern Indian Ocean (5°S?C10°S, 60°E?C90°E; TRIO region). There is an ongoing debate about the relative importance of air-sea heat fluxes and oceanic processes in driving this intraseasonal SST variability. Furthermore, various studies have suggested that interannual variability of the oceanic structure in the TRIO region could modulate the amplitude of the MJO-driven SST response. In this study, we use observations and ocean general circulation model (OGCM) experiments to quantify these two effects over the 1997?C2006 period. Observational analysis indicates that Ekman pumping does not contribute significantly (on average) to intraseasonal SST variability. It is, however, difficult to quantify the relative contribution of net heat fluxes and entrainment to SST intraseasonal variability from observations alone. We therefore use a suite of OGCM experiments to isolate the impacts of each process. During 1997?C2006, wind stress contributed on average only about 20% of the intraseasonal SST variability (averaged over the TRIO region), while heat fluxes contributed about 70%, with forcing by shortwave radiation (75%) dominating the other flux components (25%). This estimate is consistent with an independent air-sea flux product, which indicates that shortwave radiation contributes 68% of intraseasonal heat flux variability. The time scale of the heat-flux perturbation, in addition to its amplitude, is also important in controlling the intraseasonal SST signature, with longer periods favouring a larger response. There are also strong year-to-year variations in the respective role of heat fluxes and wind stress. Of the five strong cooling events identified in both observations and the model (two in 1999 and one in 2000, 2001 and 2002), intraseasonal-wind stress dominates the SST signature during 2001 and contributes significantly during 2000. Interannual variations of the subsurface thermal structure associated with the Indian Ocean Dipole or El Ni?o/La Ni?a events modulate the MJO-driven SST signature only moderately (by up to 30%), mainly by changing the temperature of water entrained into the mixed layer. The primary factor that controls year-to-year changes in the amplitude of TRIO, intraseasonal SST anomalies is hence the characteristics of intraseasonal surface flux perturbations, rather than changes in the underlying oceanic state. 相似文献
9.
J. Vialard P. Terray J.-P. Duvel R. S. Nanjundiah S. S. C. Shenoi D. Shankar 《Climate Dynamics》2011,37(3-4):493-507
Most of the annual rainfall over India occurs during the Southwest (June?CSeptember) and Northeast (October?CDecember) monsoon periods. In March 2008, however, Southern peninsular India and Sri Lanka received the largest rainfall anomaly on record since 1979, with amplitude comparable to summer-monsoon interannual anomalies. This anomalous rainfall appeared to be modulated at intraseasonal timescale by the Madden Julian Oscillation, and was synchronous with a decaying La Ni?a event in the Pacific Ocean. Was this a coincidence or indicative of a teleconnection pattern? In this paper, we explore factors controlling rainfall over southern India and Sri Lanka between January and April, i.e. outside of the southwest and northeast monsoons. This period accounts for 20% of annual precipitation over Sri Lanka and 10% over the southern Indian states of Kerala and Tamil Nadu. Interannual variability is strong (about 40% of the January?CApril climatology). Intraseasonal rainfall anomalies over southern India and Sri Lanka are significantly associated with equatorial eastward propagation, characteristic of the Madden Julian Oscillation. At the interannual timescale, we find a clear connection with El Ni?o-Southern Oscillation (ENSO); with El Ni?os being associated with decreased rainfall (correlation of ?0.46 significant at the 98% level). There is also a significant link with local SST anomalies over the Indian Ocean, and in particular with the inter-hemispheric sea surface temperature (SST) gradient over the Indian Ocean (with colder SST south of the equator being conducive to more rainfall, correlation of 0.55 significant at the 99% level). La Ni?as/cold SSTs south of the equator tend to have a larger impact than El Ni?os. We discuss two possible mechanisms that could explain these statistical relationships: (1) subsidence over southern India remotely forced by Pacific SST anomalies; (2) impact of ENSO-forced regional Indian Ocean SST anomalies on convection. However, the length of the observational record does not allow distinguishing between these two mechanisms in a statistically significant manner. 相似文献
10.
A. G. Nidheesh Matthieu Lengaigne Jérôme Vialard A. S. Unnikrishnan H. Dayan 《Climate Dynamics》2013,41(2):381-402
In this study, we analysed decadal and long-term steric sea level variations over 1966–2007 period in the Indo-Pacific sector, using an ocean general circulation model forced by reanalysis winds. The simulated steric sea level compares favourably with sea level from satellite altimetry and tide gauges at interannual and decadal timescales. The amplitude of decadal sea level variability (up to ~5 cm standard deviation) is typically nearly half of the interannual variations (up to ~10 cm) and two to three times larger than long-term sea level variations (up to 2 cm). Zonal wind stress varies at decadal timescales in the western Pacific and in the southern Indian Ocean, with coherent signals in ERA-40 (from which the model forcing is derived), NCEP, twentieth century and WASWind products. Contrary to the variability at interannual timescale, for which there is a tendency of El Niño and Indian Ocean Dipole events to co-occur, decadal wind stress variations are relatively independent in the two basins. In the Pacific, those wind stress variations drive Ekman pumping on either side of the equator, and induce low frequency sea level variations in the western Pacific through planetary wave propagation. The equatorial signal from the western Pacific travels southward to the west Australian coast through equatorial and coastal wave guides. In the Indian Ocean, decadal zonal wind stress variations induce sea level fluctuations in the eastern equatorial Indian Ocean and the Bay of Bengal, through equatorial and coastal wave-guides. Wind stress curl in the southern Indian Ocean drives decadal variability in the south-western Indian Ocean through planetary waves. Decadal sea level variations in the south–western Indian Ocean, in the eastern equatorial Indian Ocean and in the Bay of Bengal are weakly correlated to variability in the Pacific Ocean. Even though the wind variability is coherent among various wind products at decadal timescales, they show a large contrast in long-term wind stress changes, suggesting that long-term sea level changes from forced ocean models need to be interpreted with caution. 相似文献