scholarly journals Seasonal predictability of sea surface temperature anomalies over the Kuroshio-Oyashio Extension: Low in summer and high in winter

2016 ◽  
Vol 121 (9) ◽  
pp. 6862-6873 ◽  
Author(s):  
Yujie Wu ◽  
Wansuo Duan ◽  
Xinyao Rong
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Seasonal Predictability ◽  
Temperature Anomalies ◽  
Sea Surface Temperature Anomalies ◽  
The Kuroshio

Copepod and monogenean infection of fish under sea surface temperature anomalies

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

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

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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