The elevation change of ice sheet is the comprehensive result of ice dynamic process and ice sheet surface process, and is sensitive to climate change. The long-term time series of ice sheet surface elevation is of great scientific value to study the stability of ice sheet and its response to climate change. Satellite altimetry observation missions have provided a large number of surface elevation observations over ice sheet. However, the life of a single satellite altimetry mission is limited. To obtain a long-term ice sheet surface elevation time series, different satellite altimetry missions need to be linked. We use an updated strategy of Plane-fit method to achieve cross-calibration the missions. After correcting the ascending-descending bias more fully, a larger amount of observations is used to correct the intermission bias. Meanwhile, an interpolation method based on the EOF reconstruction is used to suppress the influence of interpolation error. Finally, by combining the observations of ERS-1, ERS-2, Envisat and CryoSat-2, we successfully constructed the monthly surface elevation time series with 5-km grid resolution of the Greenland ice sheet for 30 years from 1991 to 2020. Subsequently, we used the airborne laser altimeter data from Operation IceBridge and the Greenland ice sheet surface elevation change product provided by ESA Climate Change Initiative (CCI) to validate the time series. It is found that our time series are reliable. The accuracy of ice sheet surface elevation changes obtained from our time series is 19.3% higher than that of ESA CCI products. Benefiting from our more accurate correction of intermission bias, the accuracy across the over the overlapping observation period of Envisat and CryoSat-2 missions are improved more, up to 30.9%. Based on this time series, we find that the volume of Greenland ice sheet has accelerated at an initial rate of -53.8 ± 4.5 km3/yr and an acceleration of -2.2 ± 0.3 km3/yr2 in recent 30 years. We also find that the transformation of the North Atlantic Oscillation has significant impacts on the surface elevation changes of the Greenland ice sheet. In addition, the dataset can be used as fundamental data for assessing the mass balance of Greenland ice sheet and its contribution to global sea level rise and studying the response process and mechanism of Greenland ice sheet to climate change.

File naming and required software

File name: the Greenland ice sheet elevation time series data is stored in NetCDF (. NC) format, and the file name is surface_ Elevation_ Anomaly_ Greenland_ Monthly_ 5km_ Grid.nc, including longitude (lon), latitude (lon), time (time), elevation anomaly before interpolation and its uncertainty (elev, elev_uncer), elevation anomaly after interpolation and its uncertainty (elev_ interp，elev_ uncer_interp), the drainage systems number (basin) and the flag of interpolation（flag_interp). The specific information has been indicated in the data file.

Data reading method: the data file can be directly opened with MATLAB, Python and other software.

Data Citations Data citation guideline What's data citation?

Cite as:

Zhang, B., Wang, Z., An, J., Liu, T., Geng, H. (2021). Surface elevation time series over the Greenland Ice Sheet (1991-2020). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Glacio.tpdc.271658. CSTR: 18406.11.Glacio.tpdc.271658. (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

National Key Research and Development Program of China (No:2018YFC1406102)

National Natural Science Foundation of China (42006184) (No:42006184)

Strategic Priority Research Program of the Chinese Academy of Sciences (No:XDA19070100)

National Natural Science Foundation of China (41941010) (No:41941010)

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

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