scholarly journals Remote sea-surface temperature variations (2001–2019) in Kuwait Bay: Time series analysis in frequency and time domains

2021 ◽  
Author(s):  
Ali K. Saleh ◽  
◽  
Bader S. Al-Anzi ◽  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Sea Surface ◽  
Moderate Intensity ◽  
Trend Test ◽  
Kuwait Bay ◽  
Series Analysis

There is a recognized need to analyze the temporal changes of sea surface temperature in various water bodies, especially the semi-enclosed ones, because of the direct link between sea temperature and aquatic biodiversity. There has been substantial research undertaken on the role of time series analysis as a powerful technique for studying the characteristics of long-term SST changes at regular time intervals. The present paper aimed to study the monthly-averaged MODIS SST data (2001–2019) over Kuwait Bay, i.e., the northwestern corner of the Arabian Gulf. Because different approaches can yield different results, the analysis of the SST time series was conducted using time and frequency domains. The preliminary analysis of the time series reported a significant SST peak in August 2010 that reached nearly 34.2 °C (SD = 0.17 °C) due to the moderate intensity El Niño event in 2010. However, in the preceding year, we observed a cool SST anomaly in the range of –0.5 °C to –2.4 °C. From the SMK trend test, we found that monthly climatological SST in September exhibited a significant upward trend (𝑆9 = 103, 𝜏 = 0.6, 𝑃 = 0.0004). Pettitt’s changepoint test indicated a significant change in the central tendency of SST data after April 2012. The annual periodicity of the SST in Kuwait Bay was constant over the 19 years. Furthermore, a very weak periodicity of 6-month has been barely noticed. Our present results provide large-scale guidance that affirms the importance of highlighting the severe SST fluctuations in Kuwait’s water in order to understand and improve its marine environmental status.

Time Series Analysis of CMIP5 Model and Observed Sea Surface Temperature Anomaly Along Indian Coastal Zones

2019 ◽  
Vol 86 (sp1) ◽  
pp. 239
Author(s):  
Dhanya Joseph ◽  
Vazhamattom Benjamin Liya ◽  
Girindran Rojith ◽  
Pariyappanal Ulahannan Zacharia ◽  
George Grinson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
Sea Surface ◽  
Coastal Zones ◽  
Cmip5 Model ◽  
Series Analysis ◽  
Sea Surface Temperature Anomaly

scholarly journals Time series analysis of data for sea surface temperature and upwelling components from the southwest coast of Portugal

2016 ◽  
Vol 163 ◽  
pp. 12-22 ◽  
Author(s):  
Priscila Costa Goela ◽  
Clara Cordeiro ◽  
Sergei Danchenko ◽  
John Icely ◽  
Sónia Cristina ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Southwest Coast ◽  
Series Analysis

Relationship of cholera incidence to El Niño and solar activity elucidated by time-series analysis

2009 ◽  
Vol 138 (1) ◽  
pp. 99-107 ◽  
Author(s):  
K. OHTOMO ◽  
N. KOBAYASHI ◽  
A. SUMI ◽  
N. OHTOMO
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sunspot Number ◽  
El Niño ◽  
El Nino ◽  
Sea Surface ◽  
Series Analysis ◽  
Relationship Of

SUMMARYUsing time-series analysis, we investigated the monthly cholera incidence in Dhaka, Bangladesh during an 18-year period for its relationship to the sea surface temperature (SST) linked to El Niño, and to the sunspot number. Dominant periodic modes identified for cholera incidence were 11·0, 4·8, 3·5, 2·9, 1·6, 1·0 and 0·5 years. The majority of these modes, e.g. the 11·0-, 4·8-, 3·5-, 1·6- and 1·0-year modes, were essentially consistent with those obtained for the SST data (dominant modes: 5·1, 3·7, 2·5, 2·1, 1·5, 1·0 years) and the sunspot number data (dominant modes: 22·1, 11·1, 7·3, 4·8, 3·1 years). We confirmed that the variations of cholera incidence were synchronous with SSTs, and were inversely correlated to the sunspot numbers. These results suggest that the cholera incidence in Bangladesh may have been influenced by the occurrence of El Niño and also by the periodic change of solar activity.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

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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