Assessment of Dielectric Mixing Models for L-Band Radiometric Measurement of Liquid Water Content in Greenland Ice Sheet

Surface melting and consequent runoff/refreezing play an increasingly crucial role in the Greenland Ice Sheet (GrIS) Surface Mass Balance (SMB) and its contribution to the global sea-level rise. Space-based L-band radiometry offers a promising tool for quantifying the total surface-to-subsurface liquid water amount (LWA) in the firn, in addition to providing the areal extent and duration of seasonal surface snow melt. Here, we evaluate the performance of commonly used microwave dielectric mixing models in determining the total LWA using a snow microwave emission and radiative transfer model in conjunction with L-band (1.4 GHz) brightness temperature (TB) observations from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions. The L-band TB responds to the real and imaginary parts of the firn dielectric constant, which increases markedly with liquid water content (LWC) in the firn. The measured dielectric constant is translated into LWA using a model between snow LWC and the dielectric constant. The formulation of the effective dielectric constant of the ice, air, and water mixture is key to accurately quantifying LWA; as it is independent of the radiometer measurement, it adds an uncertainty component to the LWA retrieval that is solely depending on the accuracy of this dielectric mixing model. We apply different dielectric mixing formulations in the forward model to estimate LWA, which we compare to the corresponding LWA from a locally calibrated ice sheet Energy and Mass Balance (EMB) model and the Glacier Energy and Mass Balance (GEMB) model within NASA’s Icesheet and Sea-Level System Model (ISSM). The EMB model was driven by in situ measurements from automatic weather stations (AWS) of the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and Greenland Climate Network (GC-Net) located in the percolation zone of the GrIS, and the GEMB model was forced with the ERA-5 reanalysis products. Both models were initialized with relevant in situ profiles of density, snow and firn stratigraphy, and the sub-surface temperature measured at the AWS locations. The agreements and discrepancies between the LWA estimates from the mixing models and their comparison with the LWA from firn models will be presented. The analysis assesses the impact of the dielectric mixing model choice on the LWA retrieval algorithm to create an observational dataset of seasonal LWA across GrIS.