scholarly journals The 2019 Benguela Niño

2021 ◽  
Vol 8 ◽  
Author(s):  
Rodrigue Anicet Imbol Koungue ◽  
Peter Brandt ◽  
Joke Lübbecke ◽  
Arthur Prigent ◽  
Meike Sena Martins ◽  
...  

High interannual sea surface temperature anomalies of more than 2°C were recorded along the coasts of Angola and Namibia between October 2019 and January 2020. This extreme coastal warm event that has been classified as a Benguela Niño, reached its peak amplitude in November 2019 in the Angola Benguela front region. In contrast to classical Benguela Niños, the 2019 Benguela Niño was generated by a combination of local and remote forcing. In September 2019, a local warming was triggered by positive anomalies of near coastal wind-stress curl leading to downwelling anomalies through Ekman dynamics off Southern Angola and by anomalously weak winds reducing the latent heat loss by the ocean south of 15°S. In addition, downwelling coastal trapped waves were observed along the African coast between mid-October 2019 and early January 2020. Those coastal trapped waves might have partly emanated from the equatorial Atlantic as westerly wind anomalies were observed in the central and eastern equatorial Atlantic between end of September to early December 2019. Additional forcing for the downwelling coastal trapped waves likely resulted from an observed weakening of the prevailing coastal southerly winds along the Angolan coast north of 15°S between October 2019 and mid-February 2020. During the peak of the event, latent heat flux damped the sea surface temperature anomalies mostly in the Angola Benguela front region. In the eastern equatorial Atlantic, relaxation of cross-equatorial southerly winds might have contributed to the equatorial warming in November 2019 during the peak of the 2019 Benguela Niño. Moreover, for the first time, moored velocities off Angola (11°S) revealed a coherent poleward flow in the upper 100 m in October and November 2019 suggesting a contribution of meridional heat advection to the near-surface warming during the early stages of the Benguela Niño. During the Benguela Niño, a reduction of net primary production in the Southern Angola and Angola Benguela front regions was observed.

2021 ◽  
pp. 102098
Author(s):  
F. Neptalí Morales-Serna ◽  
Lorenia Olivas-Padilla ◽  
Emigdio Marín-Enriquez ◽  
Juan M. Osuna-Cabanillas ◽  
Hugo Aguirre-Villaseñor ◽  
...  

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.


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