FN Archimer Export Format
PT J
TI Reconstructing ocean subsurface salinity at high resolution using a machinelearning approach
BT 
AF TIAN, Tian
   CHENG, Lijing
   WANG, Gongjie
   ABRAHAM, John
   WEI, Wangxu
   REN, Shihe
   ZHU, Jiang
   SONG, Junqiang
   LENG, Hongze
AS 1:1,2;2:2,3;3:4;4:5;5:2;6:6;7:2;8:1;9:1;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;
C1 College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410073, China
   Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
   Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, 266071, China
   National Climate Center, Chinese Meteorological Administration, Beijing, 100081, China
   School of Engineering, University of St. Thomas, St. Paul, MN 55105, USA
   Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing, 100081, China
C2 NATL UNIV DEF TECHNOL, CHINA
   CHINESE ACAD SCI, CHINA
   CHINESE ACAD SCI, CHINA
   CHINESE METEOROL ADM, CHINA
   UNIV ST THOMAS, USA
   MINIST NAT RESOURCES, CHINA
IN DOAJ
IF 11.4
TC 6
UR https://archimer.ifremer.fr/doc/00824/93589/100352.pdf
   https://archimer.ifremer.fr/doc/00824/93589/100353.pdf
   https://archimer.ifremer.fr/doc/00824/93589/100354.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
AB A gridded ocean subsurface salinity dataset with global coverage is useful for research on climate change and its variability. Here, we explore the feed-forward neural network (FFNN) approach to reconstruct a high-resolution (0.25 degrees x 0.25 degrees) ocean subsurface (1-2000 m) salinity dataset for the period 1993-2018 by merging in situ salinity profile observations with high-resolution (0.25 degrees x 0.25 degrees) satellite remote-sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse-resolution (1 degrees x 1 degrees) gridded salinity product. We show that the FFNN can effectively transfer small-scale spatial variations in ADT, SST, and SSW fields into the 0.25 degrees x 0.25 degrees salinity field. The root-mean-square error (RMSE) can be reduced by -11 % on a global-average basis compared with the 1 degrees x 1 degrees salinity gridded field. The reduction in RMSE is much larger in the upper ocean than the deep ocean because of stronger mesoscale variations in the upper layers. In addition, the new 0.25 degrees x 0.25 degrees reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1 degrees x 1 degrees resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25 degrees x 0.25 degrees data are consistent with the 1 degrees x 1 degrees gridded salinity field, suggesting the persistence of the large-scale signals in the high-resolution reconstruction. The successful application of machine learning in this study provides an alternative approach for ocean and climate data reconstruction that can complement the existing data assimilation and objective analysis methods. 
PY 2022
PD NOV
SO Earth System Science Data
SN 1866-3508
PU Copernicus Gesellschaft Mbh
VL 14
IS 11
UT 000886674600001
BP 5037
EP 5060
DI 10.5194/essd-14-5037-2022
ID 93589
ER

EF