A 30-year monthly 5 km gridded surface elevation time series for the Greenland Ice Sheet from multiple satellite radar altimeters

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).

Reconstructing Continuous Ice Sheet Elevation Changes in the Amundsen Sea Sector During 2003–2021 by Merging Envisat, ICESat, CryoSat‐2, and ICESat‐2 Multi‐Altimeter Observations

“…Zhang et al (2022) estimated the terms of biases from different altimeters by two-step former fitting and subtracted them from the corresponding altimetric observations to construct consistent heights. Different from previous plane fitting studies (Simonsen & Sørensen, 2017;Zhang et al, 2022), the relative surface elevation changes containing the time variable signal with the common reference epoch are concerned more particularly in our method. Nilsson et al (2022) also removed the time-invariant topography to get the elevation anomalies within a specified search radius, which is 500 or 1,000 m for different missions in the space domain.…”

“…Instead of the plane fitted to repeat-track boxes (Flament & Rémy, 2012;Legrésy et al, 2006;Moholdt et al, 2010), all multi-mission measurements were regrouped into regular grid cells with 5 km spacing in our process, following the plane grid partition of Schröder et al (2019) and Zhang et al (2022). Compared to Zhang et al (2022), however, the least-squares model of our methodology does not contain the local topography correction and parameterize the intramission and intermission biases explicitly. The principle of our parameter choice is to retain the necessary correction items and keep the number of parameters as small as possible, which can speed up the calculation of regression for the 5 km gridded cells.…”

“…The principle of our parameter choice is to retain the necessary correction items and keep the number of parameters as small as possible, which can speed up the calculation of regression for the 5 km gridded cells. Zhang et al (2022) estimated the terms of biases from different altimeters by two-step former fitting and subtracted them from the corresponding altimetric observations to construct consistent heights. Different from previous plane fitting studies (Simonsen & Sørensen, 2017;Zhang et al, 2022), the relative surface elevation changes containing the time variable signal with the common reference epoch are concerned more particularly in our method.…”

“…The RA‐2 Geophysical Data Record (GDR) Full Mission Reprocessing v3 product (ESA, 2018) provides the retracked elevation using the ICE‐1 waveform retracker with a threshold of 0.25 for the Offset Center Of Gravity (OCOG) (Wingham et al., 1986) amplitude. The ICE‐1 retracker (Bamber, 1994) can reduce the ascending‐descending crossover differences better than ICE‐2 (Schröder et al., 2017) and it is less sensitive to fluctuations in penetration, indicating that it is more precise for ice sheet elevation measurements (Zhang et al., 2022). The tropospheric and ionospheric corrections were applied to the 20 Hz Ku‐band height measurements, while the backscatter coefficient waveform parameter was used to account for the effect of surface snow penetration from radar signals (Simonsen & Sørensen, 2017).…”

“…First, we assess the precision of the surface elevation retrieved by these different altimeters. And we develop an improved least-squares plane fitting method (ILSPFM) to merge the multi-source data of radar (Envisat and CryoSat-2) and laser altimetry (ICESat/ICESat-2), in which the multi-mission biases are corrected by the combination of relative surface elevation change for each altimeter, instead of explicitly parameterizing the intramission and intermission biases in previous plane fitting studies (Simonsen & Sørensen, 2017;Zhang et al, 2022). Based on the ILSPFM and its construction of SEC, we produce a high-precision, monthly and long-term (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021) ice sheet continuous surface elevation change (CSEC) data set in the Amundsen Sea (AS) sector, West Antarctica.…”