Sea Surface Temperature Variability and Marine Heat Waves over the Aegean, Ionian, and Cretan Seas from 2008–2021

The sea surface temperature (SST) is an important factor and indicator of the sea water quality, with various ecological and anthropogenic implications. We used high-resolution satellite-derived SST data, in tandem with field observations and long-term meteorological data, to investigate the spatial and interannual SST variability over the Aegean, Ionian, and Cretan (AIC) Seas during the recent 14-year period (2008–2021). Increasing trends were identified for most of the sub-basins of the AIC Seas. The numbers and durations (days) of the marine heat waves (MHWs) have significantly increased, especially during the last quadrennial period (2018–2021). Changes have been detected in both the maximum and minimum values; however, the trend of the mean annual values is mainly associated with the interannual increases in the lowest values (weaker minima during the cold seasons). The interannual variability and the increasing positive trends of the air temperature are very similar to the SST variations, showing a 5-to-10-day lag between the seasonal time series of the two parameters for all regions; however, extreme atmospheric events (e.g., cold fronts or heat waves) have a more direct impact on the SST variability (zero lag). MHWs were more frequent over the northern Aegean Sea, especially in Thermaikos Gulf, which is characterized as a “hot spot” for MHWs. MHWs were rarer over the southern regions, especially over the southeastern Aegean and Cretan Seas. A stratified upper ocean, controlled by buoyant brackish plumes, such as the Black Sea Waters (BSW) in the northern Aegean, may increase the heat storage capacity of the surface water masses, contributing to the further warming of the ocean. This was the case in the summer of 2021, which was a unique year for the AIC Seas, and especially for the northern Aegean, which revealed the highest SST values among all the study years. The satellite-derived observations of the 2008–2021 period showed increasing trends for all coastal waters, strong trend slopes for most of the coasts of the northern Aegean and central Ionian Seas, and milder trend slopes in the eastern Aegean.

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Long term trends in the sea surface temperature of the Black Sea

Ocean Science ◽

10.5194/os-6-491-2010 ◽

2010 ◽

Vol 6 (2) ◽

pp. 491-501 ◽

Cited By ~ 26

Author(s):

G. I. Shapiro ◽

D. L. Aleynik ◽

L. D. Mee

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Black Sea ◽

Sea Surface ◽

The Black Sea ◽

The North ◽

Sst Variability ◽

Long Term Trends ◽

Cooling Trend

Abstract. There is growing understanding that recent deterioration of the Black Sea ecosystem was partly due to changes in the marine physical environment. This study uses high resolution 0.25° climatology to analyze sea surface temperature variability over the 20th century in two contrasting regions of the sea. Results show that the deep Black Sea was cooling during the first three quarters of the century and was warming in the last 15–20 years; on aggregate there was a statistically significant cooling trend. The SST variability over the Western shelf was more volatile and it does not show statistically significant trends. The cooling of the deep Black Sea is at variance with the general trend in the North Atlantic and may be related to the decrease of westerly winds over the Black Sea, and a greater influence of the Siberian anticyclone. The timing of the changeover from cooling to warming coincides with the regime shift in the Black Sea ecosystem.

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Long term trends in the sea surface temperature of the Black Sea

Ocean Science Discussions ◽

10.5194/osd-7-91-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 91-119

Author(s):

G. I. Shapiro ◽

D. L. Aleynik ◽

L. D. Mee

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Black Sea ◽

Sea Surface ◽

The Black Sea ◽

The North ◽

Sst Variability ◽

Long Term Trends ◽

Cooling Trend

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THE CHARACTERISTIC AND VARIABILITY OF SEA SURFACE TEMPERATURE IN JAVA SEA

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2007.v4.a1219 ◽

2010 ◽

Vol 4 (1) ◽

Author(s):

Widada Sulistya ◽

Agus Hartoko ◽

S.Budi Prayitno

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Water ◽

Temporal Analysis ◽

Sea Surface ◽

Fishing Ground ◽

Sst Variability ◽

Java Sea ◽

Spatio Temporal ◽

Marine Climate

The phenomena of marine climate can be identified by the sea surface temperature, as Illahude (1999) reported that one of the parameters of oceanography which characterized of sea water mass is sea surface temperature (SST). The distribution of sea surface temperature can be used as an indicator of fishing ground. However, as understanding of marine climate variability it does not well enough (Hartoko,2000). The characteristic and variability of ST in Java Sea are not sufficiently enough understood. In order to better understand, we need the Spatial-Temporal Analysis of SST. The Spectral Analysis Method is used to study the characteristic and seasonal variation of SST, while GIS Analysis is used to study SST spatial distribution pattern. Temporally, the highest temperature at Java Sea occurs in April-May and November, whereas the lowest temperature in February and August. The SST fluctuation of Java Sea ranges from 27.48 degree of celcius to 29.66 degree of celcius and its periodic cycle generally occurs for 6 months, 1 year and 8 years. Keyword: SST, Variability, Spatio-Temporal Analysis

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On the factors contributing to heat waves in Europe. A global large ensemble approach with MPAS

10.5194/egusphere-egu21-11831 ◽

2021 ◽

Author(s):

Gerhard Smiatek ◽

Harald Kunstmann

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Heat Waves ◽

Circulation Pattern ◽

Sea Surface ◽

Maximum Temperature ◽

Large Ensemble ◽

Sea Ice Extent ◽

Weather Patterns ◽

Potential Factors

The European summer heat wave of 2003 with record-breaking temperature anomalies was brought into connection with a blocking Omega circulation pattern, soil moisture deficit and high sea surface temperature, especially in the Mediterranean Sea. &#160;We investigate the potential factors influencing extreme heat waves in Europe with a very large ensemble obtained from multiple global integrations of the Model for Prediction Across Scales (MPAS). The global MPAS runs are performed in approximately 60 km resolution with sea surface temperature (SST) and sea ice extent from ERA-Interim data as boundary condition initialized on different days.The contribution investigates the results obtained from a total of 540 simulations. It concentrates on the regional SST and weather patterns and moisture obtained in simulations contributing to the upper 10% of the resulting probability density function (PDF) of the summer daily mean and maximum temperature. The investigation considers in total eight standard evaluation domains in Europe as defined in the PRUDENCE project.

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On the Interpretation of the North Atlantic Averaged Sea Surface Temperature

Journal of Climate ◽

10.1175/jcli-d-19-0158.1 ◽

2020 ◽

Vol 33 (14) ◽

pp. 6025-6045

Author(s):

Jing Sun ◽

Mojib Latif ◽

Wonsun Park ◽

Taewook Park

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

North Atlantic ◽

Climate Models ◽

Ocean Dynamics ◽

Sea Surface ◽

The North ◽

Sst Variability ◽

The North Atlantic ◽

Different Time Scales

AbstractThe North Atlantic (NA) basin-averaged sea surface temperature (NASST) is often used as an index to study climate variability in the NA sector. However, there is still some debate on what drives it. Based on observations and climate models, an analysis of the different influences on the NASST index and its low-pass filtered version, the Atlantic multidecadal oscillation (AMO) index, is provided. In particular, the relationships of the two indices with some of its mechanistic drivers including the Atlantic meridional overturning circulation (AMOC) are investigated. In observations, the NASST index accounts for significant SST variability over the tropical and subpolar NA. The NASST index is shown to lump together SST variability originating from different mechanisms operating on different time scales. The AMO index emphasizes the subpolar SST variability. In the climate models, the SST-anomaly pattern associated with the NASST index is similar. The AMO index, however, only represents pronounced SST variability over the extratropical NA, and this variability is significantly linked to the AMOC. There is a sensitivity of this linkage to the cold NA SST bias observed in many climate models. Models suffering from a large cold bias exhibit a relatively weak linkage between the AMOC and AMO and vice versa. Finally, the basin-averaged SST in its unfiltered form, which has been used to question a strong influence of ocean dynamics on NA SST variability, mixes together multiple types of variability occurring on different time scales and therefore underemphasizes the role of ocean dynamics in the multidecadal variability of NA SSTs.

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Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty

Journal of Climate ◽

10.1175/jcli-d-12-00837.1 ◽

2014 ◽

Vol 27 (1) ◽

pp. 57-75 ◽

Cited By ~ 229

Author(s):

Shoji Hirahara ◽

Masayoshi Ishii ◽

Yoshikazu Fukuda

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Specific Area ◽

Sea Surface ◽

Sampling Errors ◽

Sea Ice Concentration ◽

Sst Variability ◽

Analysis Errors ◽

Global Mean

Abstract A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice–SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.

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Mapping Sea Surface Temperature and Its Correlation with The Acidity of Sea Water on the Coast of Kwanyar Madura Island

10.11594/nstp.2019.0472 ◽

2020 ◽

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Water ◽

Sea Surface

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Intraseasonal Sea Surface Temperature Variability across the Indonesian Seas*

Journal of Climate ◽

10.1175/jcli-d-14-00758.1 ◽

2015 ◽

Vol 28 (22) ◽

pp. 8710-8727 ◽

Cited By ~ 14

Author(s):

Asmi M. Napitu ◽

Arnold L. Gordon ◽

Kandaga Pujiana

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Intraseasonal Variability ◽

Longwave Radiation ◽

Active Phase ◽

Ocean Model ◽

Sea Surface ◽

Indonesian Seas ◽

Sst Variability ◽

Slab Ocean

Abstract Sea surface temperature (SST) variability at intraseasonal time scales across the Indonesian Seas during January 1998–mid-2012 is examined. The intraseasonal variability is most energetic in the Banda and Timor Seas, with a standard deviation of 0.4°–0.5°C, representing 55%–60% of total nonseasonal SST variance. A slab ocean model demonstrates that intraseasonal air–sea heat flux variability, largely attributed to the Madden–Julian oscillation (MJO), accounts for 69%–78% intraseasonal SST variability in the Banda and Timor Seas. While the slab ocean model accurately reproduces the observed intraseasonal SST variations during the northern winter months, it underestimates the summer variability. The authors posit that this is a consequence of a more vigorous cooling effect induced by ocean processes during the summer. Two strong MJO cycles occurred in late 2007–early 2008, and their imprints were clearly evident in the SST of the Banda and Timor Seas. The passive phase of the MJO [enhanced outgoing longwave radiation (OLR) and weak zonal wind stress) projects on SST as a warming period, while the active phase (suppressed OLR and westerly wind bursts) projects on SST as a cooling phase. SST also displays significant intraseasonal variations in the Sulawesi Sea, but these differ in characteristics from those of the Banda and Timor Seas and are attributed to ocean eddies and atmospheric processes independent from the MJO.

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Results of Monitoring of Temperature Conditions of Migration and Fishing of the Azov Khamsa

UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES ◽

10.18522/1026-2237-2020-1-71-77 ◽

2020 ◽

pp. 71-77

Author(s):

Boris N. Panov ◽

Elena O. Spiridonova ◽

Michail M. Pyatinskiy ◽

Aleksandr S. Arutyunyan

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Water Temperature ◽

Black Sea ◽

Sea Surface ◽

The Black Sea ◽

Sea Of Azov ◽

The Sea Of Azov ◽

Kerch Strait ◽

Flood Zone

The paper presents the results of monitoring the process of migration and fishing of the Azov khamsa in April-May and October-November, 2019. The research used daily maps of sea surface temperature (SST) of the Black and Azov seas, built in the hydrometeorological Center of Russia according to NCDC/NOAA (Operational module Yessim - hmc.meteorf.ru/sea/black/sst/sst_black.htm) and daily fishing information of the Center for Monitoring of Fisheries and Communications. It is shown that in the spring, khamsa clusters begin to disperse and move to feeding places after the water temperature reaches 11 °C, and at a water temperature of 14-15 °C, the fish becomes much more mobile and the clusters finally disperse. In autumn, the Azov khamsa began to concentrate in the pre-flood zone of the Sea of Azov at an average SST of 16-17 °C, with a SST of 14-16 °C, the khamsa went out into the Kerch Strait. The active output of the khamsa into the Black Sea began at the SST of the pre-flood zone of 15 °C and almost stopped at the SST of about 13 °C. The average SST in the Kerch Strait dropped to 11 °C these days.

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