Abstract
Abstract. A long-term time series of ice sheet surface elevation change (SEC) is an essential parameter to assess the impact of climate change. In this study, we used an updated plane-fitting least-squares regression strategy to generate a 30-year surface elevation time series for the Greenland Ice Sheet (GrIS) at monthly temporal resolution and 5×5 km grid spatial resolution using ERS-1 (European Remote Sensing), ERS-2, Envisat, and CryoSat-2 satellite radar altimeter observations obtained between August 1991 and December 2020. The ingenious corrections for intermission bias were applied using an updated plane-fitting least-squares regression strategy. Empirical orthogonal function (EOF) reconstruction was used to supplement the sparse monthly gridded data attributable to poor observations in the early years. Validation using both airborne laser altimeter observations and the European Space Agency GrIS Climate Change Initiative (CCI) product indicated that our merged surface elevation time series is reliable. The accuracy and dispersion of errors of SECs of our results were 19.3 % and 8.9 % higher, respectively, than those of CCI SECs and even 30.9 % and 19.0 % higher, respectively, in periods from 2006–2010 to 2010–2014. Further analysis showed that our merged time series could provide detailed insight into GrIS SEC on multiple temporal (up to 30 years) and spatial scales, thereby providing an opportunity to explore potential associations between ice sheet change and climatic forcing. The merged surface elevation time series data are available at https://doi.org/10.11888/Glacio.tpdc.271658 (Zhang et al., 2021).
Highlights
Over recent decades, the Greenland Ice Sheet (GrIS) has experienced increasing substantial imbalance
Since 1991, various satellite altimetry missions have made continuous observations of ice sheet elevation change (EC) a reality (Shepherd et al, 2019; Simonsen et al, 2021). This approach, which uses measurements of surface EC (SEC) derived from satellite altimetry to monitor ice sheet mass balance, provides an unprecedented advantage in terms of spatiotemporal resolution in comparison with two other satellite-based techniques: gravimetric mass balance derived from satellite gravimetry and input–output balance derived from remotely sensed ice flow (Shepherd et al, 2019; Simonsen et al, 2021)
We developed a 30-year surface elevation (SE) time series over the GrIS by combining ERS-1, ERS-2, Envisat, and CryoSat-2 satellite radar altimeter observations
Summary
The Greenland Ice Sheet (GrIS) has experienced increasing substantial imbalance. It has been proven that using a large amount of surface elevation observations to fine-tune the correction of intermission bias and ascending–descending bias can ensure better self-consistency and reliability of the combined time series of elevation (Zhang et al, 2020) This method is unsuitable for combining data from multiple satellite missions simultaneously because introduction into the fitting model of additional parameters and the increasingly complicated topological relationships between them will lead to regression failure. Certain outliers exist in altimeter observations, especially in relation to the early altimetry missions, e.g., ERS-1 (European Remote Sensing; Schröder et al, 2019) These problems will result in a lack of available data values in certain cells of a joint elevation time series.
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