Greenland Ice Sheet Mass Balance (1992–2020) From Calibrated Radar Altimetry

Sebastian B Simonsen,Louise Sandberg Sørensen,William T Colgan,Valentina R Barletta

doi:10.1029/2020gl091216

Abstract

AbstractWe present the first 1992–2020 record of Greenland Ice Sheet (GrIS) mass balance derived from multisatellite Ku‐band altimetry. We employ an empirical approach as an alternative detailed to radar‐propagation modeling, and instead convert elevation changes observed by radar altimetry into mass changes using spatiotemporal calibration fields. This calibration field is derived from a machine learning approach that optimizes the prediction of a previously published mass balance field as a function of ice sheet variables. Our mass balance record shows a GrIS contribution of 12.1 ± 2.3 mm sea‐level equivalent since 1992, with more than 80% of this contribution occurring after 2003. Our record also suggests that the 2017 hydrological year is the first year in the 21st century which, within uncertainties, the GrIS was in balance. Overall, the 28‐year radar‐derived mass balance record we present highlights the potential of the method to provide operational mass balance estimates derived from multisatellite Ku‐band altimetry.

Highlights

The Greenland Ice Sheet (GrIS) is a leading contributor to global sea level rise (Cazenave et al, 2018)
We present the first 1992–2020 record of Greenland Ice Sheet (GrIS) mass balance derived from multisatellite Ku-band altimetry
We employ an empirical approach as an alternative detailed to radar-propagation modeling, and instead convert elevation changes observed by radar altimetry into mass changes using spatiotemporal calibration fields

Summary

Introduction

The Greenland Ice Sheet (GrIS) is a leading contributor to global sea level rise (Cazenave et al, 2018). The data required by these methods are available at different temporal and spatial resolutions, and each method requires different a priori knowledge to derive an ice-sheet mass balance. These methods often have complementary strengths and weaknesses (Shepherd et al, 2012). The IOMB method relies on knowledge of both ice thickness and velocity across flux gates where ice discharge is assessed, as well as regional climate models for estimates of surface mass balance (Colgan et al, 2019; Van Den Broeke et al, 2009)

Results

Discussion

Conclusion