12 research outputs found

    Unraveling the Influence of Equatorial Waves on Post-Monsoon Sea Surface Salinity Anomalies in the Bay of Bengal

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    In this study, we investigate the connection between planetary equatorial waves, modulated by the Indian Ocean dipole (IOD) and El Niño Southern Oscillation (ENSO), and the interannual variabilities of the salinity distribution in the Bay of Bengal (BoB) in October–December (OND), along with its associated dynamics, using satellite and reanalysis datasets. In OND 2010 and 2016 (1994, 1997, 2006, and 2019), positive (negative) sea surface salinity anomalies (SSSAs) were distributed in the eastern equatorial Indian Ocean (EIO) and Andaman Sea. Moreover, the southward movement of negative (positive) SSSAs along the eastern Indian coast was observed. This phenomenon was caused by large-scale anomalous currents associated with zonal wind over the EIO. During OND 2010 and 2016 (1994, 1997, 2006, and 2019), due to anomalous westerlies (easterlies) over the EIO and anomalous downwelling (upwelling) Kelvin waves, the strengthened (weakened) Wyrtki jet and the basin-scale anomalous cyclonic (anticyclonic) circulation in the BoB gave rise to positive (negative) SSSAs within the eastern EIO and Andaman Sea. In addition, the intensified (weakened) eastern Indian coastal currents led to the southward movement of negative (positive) SSSAs. It is worth noting that downwelling Kelvin waves reached the western coast of India during OND 2010 and 2016, while upwelling Kelvin waves were only confined to the eastern coast of India during OND 1994, 1997, 2006, and 2019. Furthermore, westward salinity signals associated with reflected westward Rossby waves could modulate the spatial pattern of salinity. The distribution of salinity anomalies could potentially influence the formation of the barrier layer, thereby impacting the sea surface temperature variability and local convection

    Sea Surface Salinity Anomaly in the Bay of Bengal during the 2010 Extremely Negative IOD Event

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    Based on Soil Moisture and Ocean Salinity (SMOS) data and the Ocean Reanalysis System 5 (ORAS5) dataset, positive salinity anomalies exceeding 2 psu in the northern Bay of Bengal (BoB) and negative salinity anomalies with the peak of the freshening anomalies reaching −2 psu around Sri Lanka were observed in autumn 2010. Here, an analysis of the anomalous salt budget revealed that anomalous horizontal advection contributed most to the variability in salinity in the BoB. With the development of La Niña and negative Indian Ocean dipole (nIOD) in summer and autumn, the strong summer monsoon current and Wyrtki jet combined with the anomalous basin-scale cyclonic circulation led to more high-salinity water entering the northern BoB. In addition, more freshwater was transported southward along the eastern coast of India by east Indian coastal current (EICC) in autumn, resulting in extremely negative salinity anomalies around Sri Lanka and positive salinity anomalies in the northern BoB. Moreover, the freshwater around Sri Lanka was carried farther into the southeastern Arabian Sea by the west Indian coastal current (WICC) in November, which affected the salinity stratification in winter and then influenced the variation of the Arabian Sea Mini Warm Pool (ASMWP) in the following spring. The ASMWP could affect the Indian summer monsoon (ISM) through its influence on the monsoon onset vortex (MOV) over the southeast Arabian Sea (SEAS)

    Sea Surface Salinity Anomaly in the Bay of Bengal during the 2010 Extremely Negative IOD Event

    No full text
    Based on Soil Moisture and Ocean Salinity (SMOS) data and the Ocean Reanalysis System 5 (ORAS5) dataset, positive salinity anomalies exceeding 2 psu in the northern Bay of Bengal (BoB) and negative salinity anomalies with the peak of the freshening anomalies reaching −2 psu around Sri Lanka were observed in autumn 2010. Here, an analysis of the anomalous salt budget revealed that anomalous horizontal advection contributed most to the variability in salinity in the BoB. With the development of La Niña and negative Indian Ocean dipole (nIOD) in summer and autumn, the strong summer monsoon current and Wyrtki jet combined with the anomalous basin-scale cyclonic circulation led to more high-salinity water entering the northern BoB. In addition, more freshwater was transported southward along the eastern coast of India by east Indian coastal current (EICC) in autumn, resulting in extremely negative salinity anomalies around Sri Lanka and positive salinity anomalies in the northern BoB. Moreover, the freshwater around Sri Lanka was carried farther into the southeastern Arabian Sea by the west Indian coastal current (WICC) in November, which affected the salinity stratification in winter and then influenced the variation of the Arabian Sea Mini Warm Pool (ASMWP) in the following spring. The ASMWP could affect the Indian summer monsoon (ISM) through its influence on the monsoon onset vortex (MOV) over the southeast Arabian Sea (SEAS)

    Erratum to: Performance evaluation of operational atmospheric correction algorithms over the East China Seas (Chinese Journal of Oceanology and Limnology, (2017), 35, 1, (1-22), 10.1007/s00343-016-5170-6)

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    Unfortunately for all articles of Vol. 35 No. 1 the future journal title “Journal of Oceanology and Limnology” was used instead of the current journal title “Chinese Journal of Oceanology and Limnology”. All articles in the issue are affected. Please make sure to cite the articles with the following Vol. and No. info: Chinese Journal of Oceanology and Limnology, Vol. 35 No. 1, [page range]

    Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation

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    The panda lineage dates back to the late Miocene and ultimately leads to only one extant species, the giant panda (Ailuropoda melanoleuca). Although global climate change and anthropogenic disturbances are recognized to shape animal population demography their contribution to panda population dynamics remains largely unknown. We sequenced the whole genomes of 34 pandas at an average 4.7-fold coverage and used this data set together with the previously deep-sequenced panda genome to reconstruct a continuous demographic history of pandas from their origin to the present. We identify two population expansions, two bottlenecks and two divergences. Evidence indicated that, whereas global changes in climate were the primary drivers of population fluctuation for millions of years, human activities likely underlie recent population divergence and serious decline. We identified three distinct panda populations that show genetic adaptation to their environments. However, in all three populations, anthropogenic activities have negatively affected pandas for 3,000 years
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