sea surface salinity;
ocean reanalysis;
moored buoy;
in situ measurements;
validation;
RESEARCH MOORED ARRAY;
SATELLITE;
AQUARIUS;
PERFORMANCE;
SYSTEM;
D O I:
10.3390/jmse11010054
中图分类号:
U6 [水路运输];
P75 [海洋工程];
学科分类号:
0814 ;
081505 ;
0824 ;
082401 ;
摘要:
Sea surface salinity (SSS) is one of the Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS). Acquiring high-quality SSS datasets with high spatial-temporal resolution is crucial for research on the hydrological cycle and the earth climate. This study assessed the quality of SSS data provided by five high-resolution ocean reanalysis products, including the Hybrid Coordinate Ocean Model (HYCOM) 1/12 degrees global reanalysis, the Copernicus Global 1/12 degrees Oceanic and Sea Ice GLORYS12 Reanalysis, the Simple Ocean Data Assimilation (SODA) reanalysis, the ECMWF Oceanic Reanalysis System 5 (ORAS5) product and the Estimating the Circulation and Climate of the Ocean Phase II (ECCO2) reanalysis. Regional comparison in the Mediterranean Sea shows that reanalysis largely depicts the accurate spatial SSS structure away from river mouths and coastal areas but slightly underestimates the mean SSS values. Better SSS reanalysis performance is found in the Levantine Sea while larger SSS uncertainties are found in the Adriatic Sea and the Aegean Sea. The global comparison with CMEMS level-4 (L4) SSS shows generally consistent large-scale structures. The mean Delta SSS between monthly gridded reanalysis data and in situ analyzed data is -0.1 PSU in the open seas between 40 degrees S and 40 degrees N with the mean Root Mean Square Deviation (RMSD) generally smaller than 0.3 PSU and the majority of correlation coefficients higher than 0.5. A comparison with collocated buoy salinity shows that reanalysis products well capture the SSS variations at the locations of tropical moored buoy arrays at weekly scale. Among all of the five products, the data quality of HYCOM reanalysis SSS is highest in marginal sea, GLORYS12 has the best performance in the global ocean especially in tropical regions. Comparatively, ECCO2 has the overall worst performance to reproduce SSS states and variations by showing the largest discrepancies with CMEMS L4 SSS.
机构:
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University
Collaborative Innovation Center of South China Sea Studies
Collaborative Innovation Center of Novel Software Technology and IndustrializationJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University
XIA Shenzhen
KE Changqing
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机构:
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University
Collaborative Innovation Center of South China Sea Studies
Collaborative Innovation Center of Novel Software Technology and IndustrializationJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University
KE Changqing
ZHOU Xiaobing
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机构:
Department of Geophysical Engineering, Montana Tech of the University of MontanaJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University
ZHOU Xiaobing
ZHANG Jie
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机构:
The First Institute of Oceanography, State Oceanic AdministrationJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University
机构:
Univ Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, BrazilUniv Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, Brazil
Dotto, T. S.
Kerr, R.
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机构:
Univ Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, BrazilUniv Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, Brazil
Kerr, R.
Mata, M. M.
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机构:
Univ Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, BrazilUniv Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, Brazil
Mata, M. M.
Azaneu, M.
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机构:
Univ Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, BrazilUniv Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, Brazil
Azaneu, M.
Wainer, I.
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机构:
Univ Sao Paulo, Inst Oceanog, Lab Oceanog Fis Clima & Criosfera, BR-05508120 Sao Paulo, BrazilUniv Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, Brazil
Wainer, I.
Fahrbach, E.
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机构:
Fachbereich Klimawissensch, Helmholtz Gemeinsch, Stiftung Alfred Wegener Inst Polar & Meeresforsch, D-120121 Bremerhaven, GermanyUniv Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, Brazil
Fahrbach, E.
Rohardt, G.
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机构:
Fachbereich Klimawissensch, Helmholtz Gemeinsch, Stiftung Alfred Wegener Inst Polar & Meeresforsch, D-120121 Bremerhaven, GermanyUniv Fed Rio Grande FURG, Inst Oceanog, Lab Estudos Oceanos & Clima, BR-96203900 Rio Grande, RS, Brazil
机构:
Penn State Univ, Ctr Earth Syst Sci, Inst Environm, University Pk, PA 16802 USAPenn State Univ, Ctr Earth Syst Sci, Inst Environm, University Pk, PA 16802 USA
Seidov, D
Haupt, BJ
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机构:
Penn State Univ, Ctr Earth Syst Sci, Inst Environm, University Pk, PA 16802 USAPenn State Univ, Ctr Earth Syst Sci, Inst Environm, University Pk, PA 16802 USA