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.

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 60-Year Time Series Analyses of the Upwelling along the Portuguese Coast

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1285
Author(s):  
Francisco Leitão ◽  
Vânia Baptista ◽  
Vasco Vieira ◽  
Patrícia Laginha Silva ◽  
Paulo Relvas ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Environmental Changes ◽  
Biological Data ◽  
Sea Surface ◽  
Data Set ◽  
Portuguese Coast ◽  
Increase Rate ◽  
Time Series Analyses

Coastal upwelling has a significant local impact on marine coastal environment and on marine biology, namely fisheries. This study aims to evaluate climate and environmental changes in upwelling trends between 1950 and 2010. Annual, seasonal and monthly upwelling trends were studied in three different oceanographic areas of the Portuguese coast (northwestern—NW, southwestern—SW, and south—S). Two sea surface temperature datasets, remote sensing (RS: 1985–2009) and International Comprehensive Ocean—Atmosphere Data Set (ICOADS: 1950–2010), were used to estimate an upwelling index (UPWI) based on the difference between offshore and coastal sea surface temperature. Time series analyses reveal similar yearly and monthly trends between datasets A decrease of the UPWI was observed, extending longer than 20 years in the NW (1956–1979) and SW (1956–1994), and 30 years in the S (1956–1994). Analyses of sudden shifts reveal long term weakening and intensification periods of up to 30 years. This means that in the past 60 years a normal climate UPWI occurred along the Portuguese coast. An intensification of UPWI was recorded in recent decades regardless of the areas (RS: 1985–2009). Such an intensification rate (linear increase in UPWI) is only significant in S in recent decades (increase rate: ICOADS = 0.02 °C decade-1; RS = 0.11 °C decade-1) while in NW and SW the increase rate is meaningless. In NW more stable UPWI conditions were recorded, however average UPWI values increased in autumn and winter in NW in recently decades (RS: 1985–2009). An intensification rate of UPWI was recorded during summer (July, August and September) in SW and S in latter decades (RS: 1985–2009). The average UPWI values increased in recent decades in autumn in S. Marked phenological changes were observed in S in summer (before downwelling conditions prevail whilst recently when UPWI regimes prevail) with UPWI seasonal regime in S in recent decades becoming similar to those found in SW and NW. Results of this work can contribute to a better understanding of how upwelling dynamics affect/are correlated with biological data.

scholarly journals A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature

2020 ◽  
Vol 12 (17) ◽  
pp. 2687 ◽  
Author(s):  
Francisco Pastor ◽  
Jose Antonio Valiente ◽  
Samiro Khodayar
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mediterranean Basin ◽  
Extreme Values ◽  
Hot Spot ◽  
Warming Trend ◽  
Sea Surface ◽  
The Mediterranean ◽  
The Mediterranean Basin

The Mediterranean basin has been classified as a hot-spot for climate change. The Mediterranean Sea plays a fundamental regulatory role in the regional climate. We have analyzed the largest available and complete time series (1982–2019) of blended sea surface temperature (SST) data to study its seasonal cycle and look for a possible warming trend in the basin. From the analysis of the Mediterranean mean SST time series, a new temporal seasonal division is derived that differs from the one used in atmospheric climatology. Then, the SST time series were decomposed into their seasonal and trend components, and a consistent warming trend of 0.035 °C/year was obtained. The nature of this trend has been investigated, indicating a higher warming trend for both maximum and high/summer SST values than for the winter/colder ones. This reinforces the consistency of the SST increase since it is not only based on the presence of extreme values, but on a homogeneous basin global increase of high SST records as well. Although warming is found throughout the Mediterranean basin, the spatial variability found leads to the division of the basin into three distinct subareas regarding warming.

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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