Uncertainties in mass balance estimation of the Antarctic Ice Sheet using the input and output method

The input-output method (IOM) is one of the most popular methods of estimating the ice sheet mass balance (MB), with a significant advantage in presenting the dynamics response of ice to climate change. Assessing the uncertainties of the MB estimation using the IOM is crucial to gaining a clear understanding of the Antarctic ice-sheet mass budget. Here, we introduce a framework for assessing the uncertainties in the MB estimation due to the methodological differences in the IOM, the impact of the parameterization and scale effect on the modeled surface mass balance (SMB, input), and the impact of the uncertainties of ice thickness, ice velocity, and grounding line data on ice discharge (D, output). For the assessment of the D’s uncertainty, we present D at a fine scale. Compared with the goal of determining the Antarctic MB within an uncertainty of 15 Gt yr−1, we found that the different strategies employed in the methods cause considerable uncertainties in the annual MB estimation. The uncertainty of the RACMO2.3 SMB caused by its parameterization can reach 20.4 Gt yr−1, while that due to the scale effect is up to 216.7 Gt yr−1. The observation precisions of the MEaSUREs InSAR-based velocity (1–17 m yr−1), the airborne radio-echo sounder thickness (±100 m), and the MEaSUREs InSAR-based grounding line (±100 m) contribute uncertainties of 17.1 Gt yr−1, 10.5 ± 2.7 Gt yr−1 and 8.0~27.8 Gt yr−1 to the D, respectively. However, the D’s uncertainty due to the remarkable ice thickness data gap, which is represented by the thickness difference between the BEDMAP2 and the BedMachine reaches 101.7 Gt yr−1, which indicates its dominant cause of the future D’s uncertainty. In addition, the interannual variability of D caused by the annual changes in the ice velocity and ice thickness are considerable compared with the target uncertainty of 15 Gt yr−1, which cannot be ignored in annual MB estimations.

Processing methodology for the ITS_LIVE Sentinel-1 ice velocity product

The NASA MEaSUREs Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project seeks to accelerate understanding of critical glaciers and ice sheet processes by providing researchers with global, low-latency, comprehensive and state-of-the-art records of surface velocities and elevations as observed from space. Here we describe the image-pair ice velocity product and processing methodology for ESA Sentinel-1 radar data. We demonstrate improvements to the core processing algorithm for dense offset tracking, “autoRIFT”, that provides finer resolution and higher accuracy data products with improved computational efficiency when compared to earlier versions. A novel calibration is applied to the data to correct for Sentinel-1A/B subswath- and full swath-dependent geolocation errors caused by systematic issues with the instruments. Sentinel-1’s C-band images are affected by variations in the total electron content of the ionosphere that results in large velocity errors in the azimuth (along-track) direction. To reduce these effects slant-range (line-of-sight or LOS) velocities are used and accompanied by LOS parameters that support map coordinate (x/y) velocity inversion from ascending and descending slant-range offset measurements, as derived from 2 image-pairs. The described product and methods comprise the MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 (https://its-live.jpl.nasa.gov).

Validation of a daily satellite-derived Antarctic sea ice velocity product: impacts on ice kinematics

Antarctic sea ice kinematic plays a crucial role in shaping the polar climate and ecosystems. Satellite passive microwave-derived sea ice motion data have been used widely for studying sea ice motion and deformation processes, and provide daily, global coverage at a relatively low spatial-resolution (in the order of 60 × 60 km). In the Arctic, several validated data sets of satellite observations are available and used to study sea ice kinematics, but far fewer validation studies exist for the Antarctic. Here, we compare the widely-used passive microwave-derived Antarctic sea ice motion product by Kimura et al. (2013) with buoy-derived velocities, and interpret the effects of satellite observational configuration on the representation of Antarctic sea ice kinematics. We identify two issues in the Kimura et al. (2013) product: (i) errors in two large triangular areas within the eastern Weddell Sea and western Amundsen Sea relating to an error in the input satellite data composite, and (ii) a more subtle error relating to invalid assumptions for the average sensing time of each pixel. Upon rectification of these, performance of the daily composite sea ice motion product is found to be a function of latitude, relating to the number of satellite swaths incorporated (more swaths further south as tracks converge), and the heterogeneity of the underlying satellite signal (brightness temperature here). Daily sea ice motion vectors calculated using ascending- and descending-only satellite tracks (with a true ~24 h time-scale) are compared with the widely-used combined product (ascending and descending tracks combined together, with an inherent ~39 h time-scale). This comparison reveals that kinematic parameters derived from the shorter time-scale velocity datasets are higher in magnitude than the combined dataset, indicating a high degree of sensitivity to observation time-scale. We conclude that the new generation of “swath-to-swath” (S2S) sea ice velocity datasets, encompassing a range of observational time scales, is necessary to advance future research into sea ice kinematics.

A unified model for transient subglacial water pressure and basal sliding

Changes in water pressure at the beds of glaciers greatly modify their sliding rate, affecting rates of ice mass loss and sea level change. However, there is still no agreement about the physics of subglacial sliding or how water affects it. Here, we present a new simplified physical model for the effect of transient subglacial hydrology on basal ice velocity. This model assumes that a fraction of the glacier bed is connected by an active hydrologic system that, when averaged over an appropriate scale, is governed by two parameters with limited spatial variability. The sliding model is reminiscent of Budd's empirical sliding law but with fundamental differences including a dependence on the fractional area of the active hydrologic system. With periodic surface meltwater forcing, the model displays classic diffusion-wave behavior, with a downstream time lag and decay of subglacial water pressure perturbations. Testing the model against Greenland observations suggests that, despite its simplicity, it captures key features of observed proglacial discharges and ice velocities with reasonable physical parameter values. Given these encouraging findings, including this sliding model in predictive ice-sheet models may improve their ability to predict time-evolving velocities and associated sea level change and reduce the related uncertainties.

The distribution and evolution of supraglacial lakes on 79° N Glacier (north-eastern Greenland) and interannual climatic controls

The Nioghalvfjerdsfjorden glacier (also known as the 79∘ North Glacier) drains approximately 8 % of the Greenland Ice Sheet. Supraglacial lakes (SGLs), or surface melt ponds, are a persistent summertime feature and are thought to drain rapidly to the base of the glacier and influence seasonal ice velocity. However, seasonal development and spatial distribution of SGLs in the north-east of Greenland are poorly understood, leaving a substantial error in the estimate of meltwater and its impacts on ice velocity. Using results from an automated detection of melt ponds, atmospheric and surface mass balance modelling, and reanalysis products, we investigate the role of specific climatic conditions in melt onset, extent, and duration from 2016 to 2019. The summers of 2016 and 2019 were characterised by above-average air temperatures, particularly in June, as well as a number of rainfall events, which led to extensive melt ponds to elevations up to 1600 m. Conversely, 2018 was particularly cold, with a large accumulated snowpack, which limited the development of lakes to altitudes less than 800 m. There is evidence of inland expansion and increases in the total area of lakes compared to the early 2000s, as projected by future global warming scenarios.

Greenland ice-sheet wide glacier classification based on two distinct seasonal ice velocity behaviors

Greenland glaciers exhibit variable seasonal velocity signals that may reflect differences in subglacial hydrology. Here, we conduct a first GrIS-wide glacier classification based on seasonal velocity patterns derived from 2017 Sentinel-1 radar data. Our classification focuses on two distinct seasonal ice velocity patterns, with the first (type-2 from Moon and others, 2014) showing periods of both speedup and slowdown during the melt season, and the second (type-3) instead showing a longer period of slowdown from elevated velocities in the winter and spring. We analyze 221 glaciers in 2017 and show that 48 exhibit type-2 behavior, and 72 exhibit type-3 behavior. We extend the classification to 2018 and 2019 and find that while the glaciers meeting each criterion vary year to year, type-2 is consistently more common in the northern regions and type-3 is more common in the south. Our results highlight the varied impact of meltwater on subglacial drainage systems and glacier flow in Greenland.

Greenland ice velocity maps from the PROMICE project

We present the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) Ice Velocity product (https://doi.org/10.22008/promice/data/sentinel1icevelocity/greenlandicesheet, Solgaard and Kusk, 2021), which is a time series of Greenland Ice Sheet ice velocity mosaics spanning September 2016 through to the present. The product is based on Sentinel-1 synthetic aperture radar data and has a 500 m grid spacing. A new mosaic is available every 12 d and spans two consecutive Sentinel-1 cycles (24 d). The product is made available within ∼ 10 d of the last acquisition and includes all possible 6 and 12 d pairs within the two Sentinel-1A cycles. We describe our operational processing chain from data selection, mosaicking, and error estimation to final outlier removal. The product is validated against in situ GPS measurements. We find that the standard deviation of the difference between satellite- and GPS-derived velocities (and bias) is 20 m yr−1 (−3 m yr−1) and 27 m yr−1 (−2 m yr−1) for the components in an eastern and northern direction, respectively. Over stable ground the values are 8 m yr−1 (0.1 m yr−1) and 12 m yr−1 (−0.6 m yr−1) in an eastern and northern direction, respectively. This is within the expected values; however, we expect that the GPS measurements carry a considerable part of this uncertainty. We investigate variations in coverage from both a temporal and spatial perspective. The best spatial coverage is achieved in winter due to the comprehensive data coverage by Sentinel-1 and high coherence, while summer mosaics have the lowest coverage due to widespread melt. The southeast Greenland Ice Sheet margin, along with other areas of high accumulation and melt, often has gaps in the ice velocity mosaics. The spatial comprehensiveness and temporal consistency make the product ideal both for monitoring and for studying ice-sheet-wide and glacier-specific ice discharge and dynamics of glaciers on seasonal scales.

Surface Mass-Balance Gradients From Elevation and Ice Flux Data in the Columbia Basin, Canada

The mass-balance—elevation relation for a given glacier is required for many numerical models of ice flow. Direct measurements of this relation using remotely-sensed methods are complicated by ice dynamics, so observations are currently limited to glaciers where surface mass-balance measurements are routinely made. We test the viability of using the continuity equation to estimate annual surface mass balance between flux-gates in the absence of in situ measurements, on five glaciers in the Columbia Mountains of British Columbia, Canada. Repeat airborne laser scanning surveys of glacier surface elevation, ice penetrating radar surveys and publicly available maps of ice thickness are used to estimate changes in surface elevation and ice flux. We evaluate this approach by comparing modeled to observed mass balance. Modeled mass-balance gradients well-approximate those obtained from direct measurement of surface mass balance, with a mean difference of +6.6 ± 4%. Regressing modeled mass balance, equilibrium line altitudes are on average 15 m higher than satellite-observations of the transient snow line. Estimates of mass balance over flux bins compare less favorably than the gradients. Average mean error (+0.03 ± 0.07 m w.e.) between observed and modeled mass balance over flux bins is relatively small, yet mean absolute error (0.55 ± 0.18 m w.e.) and average modeled mass-balance uncertainty (0.57 m w.e.) are large. Mass conservation, assessed with glaciological data, is respected (when estimates are within 1σ uncertainties) for 84% of flux bins representing 86% of total glacier area. Uncertainty on ice velocity, especially for areas where surface velocity is low (<10 m a−1) contributes the greatest error in estimating ice flux. We find that using modeled ice thicknesses yields comparable modeled mass-balance gradients relative to using observations of ice thickness, but we caution against over-interpreting individual flux-bin mass balances due to large mass-balance residuals. Given the performance of modeled ice thickness and the increasing availability of ice velocity and surface topography data, we suggest that similar efforts to produce mass-balance gradients using modern high-resolution datasets are feasible on larger scales.