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 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 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 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 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.

scholarly journals Sea Surface Salinity Response to Tropical Cyclones Based on Satellite Observations

2021 ◽  
Vol 13 (3) ◽  
pp. 420
Author(s):  
Jingru Sun ◽  
Gabriel Vecchi ◽  
Brian Soden
Keyword(s):  
Tropical Cyclones ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Translation Speed ◽  
Salinity Stratification ◽  
Left Hand ◽  
In Situ Data ◽  
Salinity Response ◽  
The Right

Multi-year records of satellite remote sensing of sea surface salinity (SSS) provide an opportunity to investigate the climatological characteristics of the SSS response to tropical cyclones (TCs). In this study, the influence of TC winds, rainfall and preexisting ocean stratification on SSS evolution is examined with multiple satellite-based and in-situ data. Global storm-centered composites indicate that TCs act to initially freshen the ocean surface (due to precipitation), and subsequently salinify the surface, largely through vertical ocean processes (mixing and upwelling), although regional hydrography can lead to local departure from this behavior. On average, on the day a TC passes, a strong SSS decrease is observed. The fresh anomaly is subsequently replaced by a net surface salinification, which persists for weeks. This salinification is larger on the right (left)-hand side of the storm motion in the Northern (Southern) Hemisphere, consistent with the location of stronger turbulent mixing. The influence of TC intensity and translation speed on the ocean response is also examined. Despite having greater precipitation, stronger TCs tend to produce longer-lasting, stronger and deeper salinification especially on the right-hand side of the storm motion. Faster moving TCs are found to have slightly weaker freshening with larger area coverage during the passage, but comparable salinification after the passage. The ocean haline response in four basins with different climatological salinity stratification reveals a significant impact of vertical stratification on the salinity response during and after the passage of TCs.

scholarly journals Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models

2021 ◽  
Vol 13 (5) ◽  
pp. 831
Author(s):  
Jorge Vazquez-Cuervo ◽  
Chelle Gentemann ◽  
Wenqing Tang ◽  
Dustin Carroll ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Future Research ◽  
Surface Salinity ◽  
Cold Temperatures ◽  
Yukon River ◽  
Arctic Sea ◽  
The Arctic Ocean

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations.

scholarly journals Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018

2021 ◽  
Vol 13 (4) ◽  
pp. 811
Author(s):  
Hao Liu ◽  
Zexun Wei
Keyword(s):  
Time Scales ◽  
Correlation Coefficients ◽  
Dominant Mode ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Multiple Datasets ◽  
Different Time Scales

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

scholarly journals Response of Freshwater Flux and Sea Surface Salinity to Variability of the Atlantic Warm Pool

2013 ◽  
Vol 26 (4) ◽  
pp. 1249-1267 ◽  
Author(s):  
Chunzai Wang ◽  
Liping Zhang ◽  
Sang-Ki Lee
Keyword(s):  
Central America ◽  
North Atlantic ◽  
Warm Pool ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Freshwater Flux ◽  
Surface Salinity ◽  
Atlantic Warm Pool ◽  
Mean Circulation ◽  
Circulation Dynamics

Abstract The response of freshwater flux and sea surface salinity (SSS) to the Atlantic warm pool (AWP) variations from seasonal to multidecadal time scales is investigated by using various reanalysis products and observations. All of the datasets show a consistent response for all time scales: A large (small) AWP is associated with a local freshwater gain (loss) to the ocean, less (more) moisture transport across Central America, and a local low (high) SSS. The moisture budget analysis demonstrates that the freshwater change is dominated by the atmospheric mean circulation dynamics, while the effect of thermodynamics is of secondary importance. Further decomposition points out that the contribution of the mean circulation dynamics primarily arises from its divergent part, which mainly reflects the wind divergent change in the low level as a result of SST change. In association with a large (small) AWP, warmer (colder) than normal SST over the tropical North Atlantic can induce anomalous low-level convergence (divergence), which favors anomalous ascent (decent) and thus generates more (less) precipitation. On the other hand, a large (small) AWP weakens (strengthens) the trade wind and its associated westward moisture transport to the eastern North Pacific across Central America, which also favors more (less) moisture residing in the Atlantic and hence more (less) precipitation. The results imply that variability of freshwater flux and ocean salinity in the North Atlantic associated with the AWP may have the potential to affect the Atlantic meridional overturning circulation.

scholarly journals The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

2007 ◽  
Vol 24 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Sabine Philipps ◽  
Christine Boone ◽  
Estelle Obligis
Keyword(s):  
Soil Moisture ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Auxiliary Data ◽  
Additional Information ◽  
Ocean Salinity

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

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