The data is the result of the prediction of Arctic sea ice density and sea ice coverage by the climate system model FGOALS independently developed by the project members. The correct selection of assimilation technology is an important factor for Arctic sea ice prediction. In the sea ice data assimilation technology, the singular value evolutionary interpolation Kalman filter (seik) is a relatively early but still commonly used filtering algorithm. However, due to the calculation of error covariance between all grid points, there is a false teleconnection error. Therefore, it is considered to develop a local filtering method to assimilate sea ice density and sea ice thickness. In the climate system model FGOALS, the project will initialize and process the sea ice thickness data retrieved by the European Space Agency (ESA) cryosat-2 and soil moisture and ocean salinity (SMOs) satellite remote sensing.

File naming and required software

F1.3-20210607-11.cice.h1.2021-07-01.nc represents the data in NetCDF format of sea ice density predicted by FGOALS climate model on July 1, 2021, which can be opened by MATLAB, NCL, Python and other general software.

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Cite as:

Song, M. (2022). Prediction data of sea ice density and sea ice coverage in the Arctic (July September 2021). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Cryos.tpdc.272788. CSTR: 18406.11.Cryos.tpdc.272788. (Download the reference： RIS | Bibtex )

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

CASEarth:Big Earth Data for Three Poles（grant No. XDA19070000） (No:XDA19000000)

Copyright & License

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)