scholarly journals Climatology of the Eastern Arabian Sea during the last glacial Cycle reconstructed from paired measurement of foraminiferal δ18O and Mg/Ca

2010 ◽  
Vol 73 (3) ◽  
pp. 535-540 ◽  
Author(s):  
V.K. Banakar ◽  
B.S. Mahesh ◽  
G. Burr ◽  
A.R. Chodankar

Paired measurements of Mg/Ca and δ18O of Globigerenoides sacculifer from an Eastern Arabian Sea (EAS) sediment core indicate that sea-surface temperature (SST) varied within 2°C and sea-surface salinity within 2 psu during the last 100 ka. SST was coldest (∽ 27°C) during Marine Isotope Stage (MIS) 4 and 2. Sea-surface salinity was highest (∽ 37.5 psu) during most of the last glacial period (∽ 60–18 ka), concurrent with increased δ18O G.sacculifer and C/N ratios of organic matter and indicative of sustained intense winter monsoons. SST time series are influenced by both Greenland and Antarctic climates. However, the sea-surface salinity time series and the deglacial warming in the SST record (beginning at ∽18 ka) compare well with the LR04 benthic δ18O-stack and Antarctic temperatures. This suggests a teleconnection between the climate in the Southern Hemisphere and the EAS. Therefore, the last 100-ka variability in EAS climatology appears to have evolved in response to a combination of global climatic forcings and regional monsoons. The most intense summer monsoons within the Holocene occurred at ∽8 ka and are marked by SST cooling of ∽ 1°C, sea-surface salinity decrease of 0.5 psu, and δ18O G.sacculifer decrease of 0.2‰.

2005 ◽  
Vol 219 (2-3) ◽  
pp. 99-108 ◽  
Author(s):  
V.K. Banakar ◽  
T. Oba ◽  
A.R. Chodankar ◽  
T. Kuramoto ◽  
M. Yamamoto ◽  
...  

2014 ◽  
Vol 10 (5) ◽  
pp. 3661-3688 ◽  
Author(s):  
P. D. Naidu ◽  
N. Niitsuma ◽  
S. Naik

Abstract. The variation of stable isotopes between individual shells of planktic foraminifera of a given species and size may provide short-term seasonal insight on Paleoceanography. In this context, oxygen isotope analyses of individual Globigerinoides sacculifer and Neogloboquadrina dutertrei were carried out from the Ocean Drilling Program Site 723A in the western Arabian Sea to unravel the seasonal changes for the last 22 kyr. δ18O values of single shells of G. sacculifer range from of 0.54 to 2.09‰ at various depths in the core which cover a time span of the last 22 kyr. Maximum inter-shell δ18O variability and high standard deviation is noticed from 20 to 10 kyr, whereas from 10 kyr onwards the inter shell δ18O variability decreased. The individual contribution of sea surface temperature (SST) and sea surface salinity (SSS) on the inter shell δ18O values of G. sacculifer were quantified. Maximum seasonal SST between 20 and 14 ka was caused due to weak summer monsoon upwelling and strong cold winter arid continental winds. Maximum SSS differences between 18 and 10 ka is attributed to the increase of net evaporation minus precipitation due to the shift of ITCZ further south. Overall, winter dominated SST signal in Greenland would be responsible to make a teleconnection between Indian monsoon and Greenland temperature. Thus the present study has wider implications in understanding wether the forcing mechanisms of tropical monsoon climate lies in high latitudes or in the tropics.


The Sea ◽  
2013 ◽  
Vol 18 (4) ◽  
pp. 163-177 ◽  
Author(s):  
Hee-Dong Jeong ◽  
Sang-Woo Kim ◽  
Jin-Wook Lim ◽  
Yong-Kyu Choi ◽  
Jong-Hwa Park

2021 ◽  
Author(s):  
Xavier Perrot ◽  
Jacqueline Boutin ◽  
Jean Luc Vergely ◽  
Frédéric Rouffi ◽  
Adrien Martin ◽  
...  

<p>This study is performed in the frame of the European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (SSS), which aims at generating global SSS fields from all available satellite L-band radiometer measurements over the longest possible period with a great stability. By combining SSS from the Soil Moisture and Ocean Salinity, SMOS, Aquarius and the Soil Moisture Active Passive, SMAP missions, CCI+SSS fields (Boutin et al. 2020) are the only one to provide a 10 year time series of satellite salinity with such quality: global rms difference of weekly 25x25km<span>2 </span>CCI+SSS with respect to in situ Argo SSS of 0.17 pss, correlation coefficient of 0.97 (see https://pimep.ifremer.fr/diffusion/analyses/mdb-database/GO/cci-l4-esa-merged-oi-v2.31-7dr/argo/report/pimep-mdb-report_GO_cci-l4-esa-merged-oi-v2.31-7dr_argo_20201215.pdf). Nevertheless, we found that some systematic biases remained. In this presentation, we will show how they will be reduced in the next CCI+SSS version.</p><p>The key satellite mission ensuring the longest time period, since 2010, at global scale, is SMOS. We implemented a re-processing of the whole SMOS dataset by changing some key points. Firstly we replace the Klein and Swift (1977) dielectric constant parametrization by the new Boutin et al. (2020) one. Secondly we change the reference dataset used to perform a vicarious calibration over the south east Pacific Ocean (the so-called Ocean Target Transformation), by using Argo interpolated fields (ISAS, Gaillard et al. 2016) contemporaneous to the satellite measurements instead of the World Ocean Atlas climatology. And thirdly the auxiliary data (wind, SST, atmospheric parameters) used as priors in the retrieval scheme, which come in the original SMOS processing from the ECMWF forecast model were replaced by ERA5 reanalysis.</p><p>Our results are showing a quantitative improvement in the stability of the SMOS CCI+SSS with respect to in situ measurements for all the period as well as a decrease of the spread of the difference between SMOS and in situ salinity measurements.</p><p>Bibliography:</p><p>J. Boutin et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3030488.</p><p>F. Gaillard et al. (2016), In Situ–Based Reanalysis of the Global Ocean Temperature and Salinity with ISAS: Variability of the Heat Content and Steric Height, Journal of Climate, vol. 29, no. 4, pp. 1305-1323, doi: 10.1175/JCLI-D-15-0028.1.</p><p>L. Klein and C. Swift (1977), An improved model for the dielectric constant of sea water at microwave frequencies, IEEE Transactions on Antennas and Propagation, vol. 25, no. 1, pp. <span>104-111, </span>doi: 10.1109/JOE.1977.1145319.</p><p>Data reference:</p><p>J. Boutin et al. (2020): ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/eacb7580e1b54afeaabb0fd2b0a53828</p>


2020 ◽  
Vol 37 (2) ◽  
pp. 317-325 ◽  
Author(s):  
Tao Song ◽  
Zihe Wang ◽  
Pengfei Xie ◽  
Nisheng Han ◽  
Jingyu Jiang ◽  
...  

AbstractAccurate and real-time sea surface salinity (SSS) prediction is an elemental part of marine environmental monitoring. It is believed that the intrinsic correlation and patterns of historical SSS data can improve prediction accuracy, but they have been not fully considered in statistical methods. In recent years, deep-learning methods have been successfully applied for time series prediction and achieved excellent results by mining intrinsic correlation of time series data. In this work, we propose a dual path gated recurrent unit (GRU) network (DPG) to address the SSS prediction accuracy challenge. Specifically, DPG uses a convolutional neural network (CNN) to extract the overall long-term pattern of time series, and then a recurrent neural network (RNN) is used to track the local short-term pattern of time series. The CNN module is composed of a 1D CNN without pooling, and the RNN part is composed of two parallel but different GRU layers. Experiments conducted on the South China Sea SSS dataset from the Reanalysis Dataset of the South China Sea (REDOS) show the feasibility and effectiveness of DPG in predicting SSS values. It achieved accuracies of 99.29%, 98.44%, and 96.85% in predicting the coming 1, 5, and 14 days, respectively. As well, DPG achieves better performance on prediction accuracy and stability than autoregressive integrated moving averages, support vector regression, and artificial neural networks. To the best of our knowledge, this is the first time that data intrinsic correlation has been applied to predict SSS values.


Sign in / Sign up

Export Citation Format

Share Document