scholarly journals Biogeographic classification of the Caspian Sea

2014 ◽  
Vol 11 (22) ◽  
pp. 6451-6470 ◽  
Author(s):  
F. Fendereski ◽  
M. Vogt ◽  
M. R. Payne ◽  
Z. Lachkar ◽  
N. Gruber ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Caspian Sea ◽  
Benthic Invertebrate ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Agglomerative Clustering ◽  
The Caspian Sea ◽  
Surface Salinity ◽  
Chl A

Abstract. Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the hierarchical agglomerative clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow nearshore waters from those offshore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (one-way ANOVA, P < 0.05). In particular, we found differences in phytoplankton phenology, with differences in the date of bloom initiation, its duration and amplitude between ecoregions. A first qualitative evaluation of differences in community composition based on recorded presence–absence patterns of 25 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

scholarly journals Bio-geographic classification of the Caspian Sea

2014 ◽  
Vol 11 (3) ◽  
pp. 4409-4450
Author(s):  
F. Fendereski ◽  
M. Vogt ◽  
M. R. Payne ◽  
Z. Lachkar ◽  
N. Gruber ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Caspian Sea ◽  
Benthic Invertebrate ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Rank Test ◽  
Agglomerative Clustering ◽  
The Caspian Sea ◽  
Surface Salinity

Abstract. Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in-situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the Hierarchical Agglomerative Clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow near-shore waters from those off-shore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (Kruskal–Wallis rank test, P < 0.05). In particular, we found differences in phytoplankton phenology, with differences in the date of bloom initiation, its duration and amplitude between ecoregions. A first qualitative evaluation of differences in community composition based on recorded presence-absence patterns of 27 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

scholarly journals Evidence of intense climate variation and reduced ENSO activity from δ<sup>18</sup>O of <i>Tridacna</i> 3700 years ago

2019 ◽  
Author(s):  
Yue Hu ◽  
Xiaoming Sun ◽  
Hai Cheng ◽  
Hong Yan
Keyword(s):  
Seasonal Variation ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Tropical Ocean ◽  
Xisha Islands ◽  
The North

Abstract. Tridacna is the largest marine bivalves in the tropical ocean, and its carbonate shell can shed light on high-resolution paleoclimate reconstruction. In this contribution, δ18Oshell was used to estimate the climatic variation in the Xisha Islands of the South China Sea. We first evaluate the sea surface temperature (SST) and sea surface salinity (SSS) influence on modern rehandled monthly (r-monthly) resolution Tridacna gigas δ18Oshell. The obtained results reveal that δ18Oshell seasonal variation is mainly controlled by SST and appear insensitive to local SSS change. Thus, the δ18O of Tridacna shells can be roughly used as a proxy of the local SST: a 1 ‰ δ18Oshell change is roughly equal to 4.41 °C of SST. R-monthly δ18O of a 40-year Tridacna squamosa (3673 ± 28 BP) from the North Reef of Xisha Islands was analyzed and compared with the modern specimen. The difference between the average δ18O of fossil Tridacna shell (δ18O = −1.34 ‰) and modern Tridacna specimen (δ18O = −1.15 ‰) probably implies a warm climate with roughly 0.84°C higher in 3700 years ago. The seasonal variation in 3700 years ago was slightly decreased compared with that suggested by the instrument data, and the switching between warm and cold-seasons was rapid. Higher amplitude in r-monthly and r-annual reconstructed SST anomalies implies an enhanced climate variability in this past warm period. Investigation of the El Ninõ-Southern Oscillation (ENSO) variation (based on the reconstructed SST series) indicates a reduced ENSO frequency but more extreme El Ninõ events in 3700 years ago.

scholarly journals An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2769
Author(s):  
Yingying Gai ◽  
Dingfeng Yu ◽  
Yan Zhou ◽  
Lei Yang ◽  
Chao Chen ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Wind Waves ◽  
Biological Indicator ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
New Model ◽  
Chl A ◽  
Improved Model

Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (R2) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

scholarly journals MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2069 ◽  
Author(s):  
Saleh Daqamseh ◽  
A’kif Al-Fugara ◽  
Biswajeet Pradhan ◽  
Anas Al-Oraiqat ◽  
Maan Habib
Keyword(s):  
Linear Regression ◽  
Chlorophyll A ◽  
Red Sea ◽  
High Potential ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Chl A ◽  
Modis Data ◽  
The Impact

In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

scholarly journals Arctic Ocean CO<sub>2</sub> uptake: an improved multi-year estimate of the air–sea CO<sub>2</sub> flux incorporating chlorophyll-a concentrations

2017 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Eko Siswanto ◽  
Are Olsen ◽  
Mario Hoppema ◽  
Eiji Watanabe ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Chlorophyll A ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Co2 Uptake ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Chl A

Abstract. We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60° N from 1997 to 2014, after mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll-a concentration (Chl-a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. The overall relationship between pCO2w and Chl-a is negative in most regions when Chl-a ≤ 1 mg m−3, whereas there is no significant relationship when Chl-a > 1 mg m−3. In the Kara Sea and the East Siberian Sea and the Bering Strait, however, the relationship is typically positive in summer. The addition of Chl-a as a parameter in the SOM process enabled us to improve the estimate of pCO2w via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. Mainly as a result of the inclusion of Chl-a, the uncertainty in the CO2 flux estimate was reduced, and a net annual Arctic Ocean CO2 uptake of 180 ± 130 TgC y−1 was determined to be significant.

scholarly journals Study of Seasonal Variation of Sea Surface Salinity in Java Sea and its Surrounding Seas using SMAP Satellite

2019 ◽  
Vol 246 ◽  
pp. 012043
Author(s):  
Amirotul Bahiyah ◽  
Anindya Wirasatriya ◽  
Jarot Marwoto ◽  
Gentur handoyo ◽  
D. S. P. Agus Anugrah
Keyword(s):  
Seasonal Variation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Java Sea

scholarly journals MEASURING SEA SURFACE SALINITY OF THE JAKARTA BAY USING REMOTELY SENSED OF OCEAN COLOR DATA ACQUIRED BY MODIS SENSOR

2015 ◽  
Vol 36 (2) ◽  
pp. 51-70 ◽  
Author(s):  
Sam Wouthuyzen
Keyword(s):  
Chlorophyll A ◽  
Sediment Resuspension ◽  
Ocean Color ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Minor Role ◽  
Water Runoff ◽  
Surface Salinity ◽  
Chl A

Observations on oceanographic parameters using remote sensing techniques intensively have been done for more than 3 decades for estimating and mapping the sea surface temperature (SST) and the abundance of phytoplankton expressed as the concentration of chlorophyll-a and applied them in studying the ocean phenomenon. As a result, the product of these 2 parameters for all over the oceans in the world has been established and available in daily basis. However, on the contrary, there is still limited application for sea surface salinity (SSS) which is also one of the most important oceanographic features. This paper describes a novel method of deriving SSS from remotely sensed ocean color. The method is based on two important observations of optical properties in regions of freshwater influences. The first is the strong effect of Colored Dissolved Organic Matter (CDOM or yellow substance) on ocean color when present in relatively high concentrations. The second is the close relationship between salinity and CDOM originating from fresh water runoff. In this paper, these relationships are demonstrated for the Jakarta Bay, Indonesia. The MODIS sensor in Terra and Aqua satellites imageries and 10 in situ measurements conducted near-simultaneously with the satellites over flight over the bay in 2004 and 2006 were implemented for deriving CDOM and SSS. The empirical relationships demonstrated in this study allow the satisfactory prediction of CDOM and SSS in the Jakarta Bay from remotely sensed ocean color. The root mean square (r.m.s) error difference between the observed and predicted parameters are 0.14 m-1 and 0.93 psu for CDOM g440 g and SSS, respectively, over a range of salinity from 24 to 33 psu. This range is in good agreement with field surveys. Parameters that may influence CDOM, such as Chlorophyll-a (CHL-a) and total suspended material (TSM) concentrations were also analyzed. Results showed that there were no relationship at all between CDOM and CHL-a, and between CDOM and TSM. These indicate that phytoplankton plays a minor role in regulating CDOM abundance, and also suggest that CDOM contribution from sediment and/or from sediment resuspension is negligible. Thus, CDOM sources in the Jakarta Bay are mainly from riverine inputs. SSS maps created from the satellite-retrieved ocean color identify features in the surface salinity distribution such as salinity front of > 32 psu that migrated in and out of the bay according to seasons. Therefore, the ability to obtain synoptic views of SSS such as presented in this paper provides great potential in furthering the understanding of coastal environments.

scholarly journals Ocean–Atmosphere Interactions during Hurricanes Marco and Laura (2020)

2021 ◽  
Vol 13 (10) ◽  
pp. 1932
Author(s):  
Emily N. Eley ◽  
Bulusu Subrahmanyam ◽  
Corinne B. Trott
Keyword(s):  
Mississippi River ◽  
Heat Content ◽  
Ocean Model ◽  
Ocean Heat Content ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Chl A ◽  
Upper Level ◽  
Heat And Moisture

During August of the 2020 Atlantic Hurricane Season, the Gulf of Mexico (GoM) was affected by two subsequent storms, Hurricanes Marco and Laura. Hurricane Marco entered the GoM first (22 August) and was briefly promoted to a Category 1 storm. Hurricane Laura followed Marco closely (25 August) and attained Category 4 status after a period of rapid intensification. Typically, hurricanes do not form this close together; this study aims to explain the existence of both hurricanes through the analysis of air-sea fluxes, local thermodynamics, and upper-level circulation. The GoM and its quality of warm, high ocean heat content waters proved to be a resilient and powerful reservoir of heat and moisture fuel for both hurricanes; however, an area of lower ocean heat content due to circulation dynamics was crucial in the evolution of both Marco and Laura. An analysis of wind shear further explained the evolution of both hurricanes. Furthermore, a suite of satellite observations and ocean model outputs were used to evaluate the biophysical modulations in the GoM. The cold core eddy (CCE) and Mississippi River surface plume had the greatest biophysical oceanic responses; the oceanic modulations were initialized by Marco and extended temporally and spatially by Laura. Reduced sea surface temperatures (SST), changes in sea surface salinity (SSS), and changes in Chlorophyll-a (Chl-a) concentrations are related to translation speeds, and respective contributions of hurricane winds and precipitation are evaluated in this work.

scholarly journals Arctic Ocean CO<sub>2</sub> uptake: an improved multiyear estimate of the air–sea CO<sub>2</sub> flux incorporating chlorophyll <i>a</i> concentrations

2018 ◽  
Vol 15 (6) ◽  
pp. 1643-1661 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Eko Siswanto ◽  
Are Olsen ◽  
Mario Hoppema ◽  
Eiji Watanabe ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Co2 Flux ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Co2 Uptake ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Chl A

Abstract. We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60∘ N from 1997 to 2014. This was done by mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll a concentration (Chl a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. We applied new algorithms for extracting Chl a from satellite remote sensing reflectance with close examination of uncertainty of the obtained Chl a values. The overall relationship between pCO2w and Chl a was negative, whereas the relationship varied among seasons and regions. The addition of Chl a as a parameter in the SOM process enabled us to improve the estimate of pCO2w, particularly via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. As a result of the inclusion of Chl a, the uncertainty in the CO2 flux estimate was reduced, with a net annual Arctic Ocean CO2 uptake of 180 ± 130 Tg C yr−1. Seasonal to interannual variation in the CO2 influx was also calculated.

scholarly journals Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

2021 ◽  
Vol 13 (15) ◽  
pp. 2995
Author(s):  
Frederick M. Bingham ◽  
Severine Fournier ◽  
Susannah Brodnitz ◽  
Karly Ulfsax ◽  
Hong Zhang
Keyword(s):  
High Resolution ◽  
Time Window ◽  
Single Point ◽  
Ocean Model ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Argo Floats ◽  
Statistical Measures ◽  
Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

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