The Interdecadal Reverse of the Relationship and Feedback Mechanism between Sea Surface Temperature and Evaporation over the Indian Ocean during Boreal Autumn

2020 ◽  
Vol 33 (23) ◽  
pp. 10205-10219
Author(s):  
Bicheng Huang ◽  
Tao Su ◽  
Yongping Wu ◽  
Guolin Feng
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Indian Ocean Dipole ◽  
Interaction Mechanism ◽  
Ocean Basin ◽  
Sea Surface ◽  
Indian Ocean Basin Mode ◽  
Low Level ◽  
The Indian Ocean

AbstractThe linkage between sea surface temperature (SST) and evaporation (EVP) plays an important role in air–sea interactions. In this study, the interaction mechanism of SST and EVP during boreal autumn was studied using correlation analysis, composite analysis, the EVP decomposition method, and singular value decomposition. The results showed that the correlation between SST and EVP in the Indian Ocean was reversed from positive to negative in the late 1990s. The significant positive SST–EVP relationship was attributed to the Indian Ocean basin mode forcing upon EVP during 1980–90. The decrease in wind speed–induced EVP and SST warming led to a significant negative SST–EVP relationship during 2005–15. Moreover, the negative SST–EVP correlation occurred when the Indian Ocean dipole (IOD) and subtropical Indian Ocean dipole (SIOD) exhibited inverse phases. The low-level moisture–EVP–SST feedback dominated the negative SST–EVP correlation in the negative IOD and positive SIOD (nIOD–pSIOD) pattern, whereas the wind–EVP–SST feedback played the leading role in the positive IOD and negative SIOD (pIOD–nSIOD) pattern. The EVP anomalies induced by the low-level anomalous anticyclone and cyclone were the main causes of the SST anomalies with inverse phases of the IOD and SIOD. The correlation between the SST and EVP reversal from positive to negative implies that the effect of the atmosphere on the ocean is as important as the external forcing of the ocean on the atmosphere.

scholarly journals The Influence of Indian Ocean Dipole on Production of Tembang Fishing (Sardinella fimbriata) at Sunda Strait Indonesia

2020 ◽  
pp. 51-58
Author(s):  
Delima Mentari Amara ◽  
Yuniar Mulyani ◽  
Alexander M. A. Khan ◽  
Herman Hamdani
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Indian Ocean Dipole ◽  
Pelagic Fish ◽  
Sea Surface ◽  
Dipole Mode ◽  
Dipole Mode Index ◽  
Mode Index ◽  
The Indian Ocean

Tembang is a pelagic fish which is important in Indonesia and the development on the Sunda Strait. The Indian Ocean Dipole could affect oceanography and at the same time will affect the population of fishes. The aim of this study was to determine the effect of IOD and oceanographic factors on the catch of Tembang fish. This research was conducted in the Sunda Strait waters by looking at the Dipole Mode Index (DMI) and oceanographic ocean conditions such as sea surface temperature and chlorophyll as well as the production of fish catches for 11 years from 2008-2018. IOD affects the catch of Tembang fish by 35.8%. Temperature influences the catch of Tembang fish in the Sunda Strait by 9.48%. Klorofil-a influences the catch of Tembang fish in Sunda Strait by 38.6%. DMI, Temperature, and Chlorophyll affect fish catches by 26.9%.

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.

scholarly journals The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean

2007 ◽  
Vol 20 (13) ◽  
pp. 2872-2880 ◽  
Author(s):  
Gary Meyers ◽  
Peter McIntosh ◽  
Lidia Pigot ◽  
Mike Pook
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
El Nino Southern Oscillation ◽  
La Nina ◽  
The Indian Ocean

Abstract The Indian Ocean zonal dipole is a mode of variability in sea surface temperature that seriously affects the climate of many nations around the Indian Ocean rim, as well as the global climate system. It has been the subject of increasing research, and sometimes of scientific debate concerning its existence/nonexistence and dependence/independence on/from the El Niño–Southern Oscillation, since it was first clearly identified in Nature in 1999. Much of the debate occurred because people did not agree on what years are the El Niño or La Niña years, not to mention the newly defined years of the positive or negative dipole. A method that identifies when the positive or negative extrema of the El Niño–Southern Oscillation and Indian Ocean dipole occur is proposed, and this method is used to classify each year from 1876 to 1999. The method is statistical in nature, but has a strong basis on the oceanic physical mechanisms that control the variability of the near-equatorial Indo-Pacific basin. Early in the study it was found that some years could not be clearly classified due to strong decadal variation; these years also must be recognized, along with the reason for their ambiguity. The sensitivity of the classification of years is tested by calculating composite maps of the Indo-Pacific sea surface temperature anomaly and the probability of below median Australian rainfall for different categories of the El Niño–Indian Ocean relationship.

scholarly journals Short-Term Variation of the Surface Flow Pattern South of Lombok Strait Observed from the Himawari-8 Sea Surface Temperature

2019 ◽  
Vol 11 (12) ◽  
pp. 1491 ◽  
Author(s):  
Naokazu Taniguchi ◽  
Shinichiro Kida ◽  
Yuji Sakuno ◽  
Hidemi Mutsuda ◽  
Fadli Syamsudin
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Flow ◽  
Sea Surface ◽  
Surface Velocity ◽  
Thermal Plume ◽  
Short Term ◽  
Flow Variation ◽  
The Indian Ocean

Spatial and temporal information on oceanic flow is fundamental to oceanography and crucial for marine-related social activities. This study attempts to describe the short-term surface flow variation in the area south of the Lombok Strait in the northern summer using the hourly Himawari-8 sea surface temperature (SST). Although the uncertainty of this temperature is relatively high (about 0.6 ∘ C), it could be used to discuss the flow variation with high spatial resolution because sufficient SST differences are found between the areas north and south of the strait. The maximum cross-correlation (MCC) method is used to estimate the surface velocity. The Himawari-8 SST clearly shows Flores Sea water intruding into the Indian Ocean with the high-SST water forming a warm thermal plume on a tidal cycle. This thermal plume flows southward at a speed of about 2 m / s . The Himawari-8 SST indicates a southward flow from the Lombok Strait to the Indian Ocean, which blocks the South Java Current flowing eastward along the southern coast of Nusa Tenggara. Although the satellite data is limited to the surface, we found it useful for understanding the spatial and temporal variations in the surface flow field.

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