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

ANSO_ IAP_ Jun_ SIO_ 2020-06-10.nc refers to the data in NetCDF format of sea ice density and sea ice coverage predicted by IAP "the Belt and Road International Cooperation Organization" climate model on June 10, 2020, which can be opened with MATLAB, NCL, Python and other general software.

Data Citations Data citation guideline What's data citation?

Cite as:

Song, M. (2022). Prediction of Sea ice concentration and Sea ice coverage in the Arctic Region (June-September 2020). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Cryos.tpdc.272787. CSTR: 18406.11.Cryos.tpdc.272787. (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)