Extreme melt season ice layers reduce firn permeability across Greenland

Culberg, Riley; Schroeder, Dustin M.; Chu, Winnie

doi:10.1038/s41467-021-22656-5

Cited by 47 publications

(76 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the AR-derived spatially coherent melt layer detections are often observed to be adjacent to perennial firn aquifer and ice slab areas, these sub-facies were masked in the original airborne ice-penetrating radar survey analysis by Culberg et al (2021). Spatially coherent melt layers often overlay perennial firn aquifers (e.g., Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Seasonal meltwater was refrozen into spatially coherent melt layers following the 2010 and 2012 melting seasons (Culberg et al, 2021) as well as more recently following the 2015 and 2018 melting seasons identified as part of the temporal L-band signature analysis in this study (Sect. 2.3.1).…”

Section: Can Influencementioning

confidence: 99%

“…2.3.1). As compared to ice slabs, which are dense low-permeability solid-ice layers, spatially coherent melt layers are a network of embedded ice structures primarily consisting of discontinuous horizontally oriented ice layers and ice lenses sparsely connected via vertically oriented ice pipes (Culberg et al, 2021). Spatially coherent melt layers are relatively thin (0.2 cm-2 m) and can rapidly form across the high-elevation (up to 3200 m a.s.l.)…”

Section: Can Influencementioning

confidence: 99%

“…However, electromagnetic energy still propagates downward through the high-reflectivity layer into the deeper firn layers. Culberg et al (2021) recently demonstrated mapping the extent of spatially coherent melt layers formed following the 2012 melting season (Nghiem et al, 2012) via AR (Figs. 1c and 2).…”

Section: Can Influencementioning

confidence: 99%

See 3 more Smart Citations

An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry

et al. 2022

Self Cite

View full text Add to dashboard Cite

Abstract. Perennial firn aquifers are subsurface meltwater reservoirs consisting of a meters-thick water-saturated firn layer that can form on spatial scales as large as tens of kilometers. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting and high snow accumulation. Widespread perennial firn aquifers have been identified within the Greenland Ice Sheet (GrIS) via field expeditions, airborne ice-penetrating radar surveys, and satellite microwave sensors. In contrast, ice slabs are nearly continuous ice layers that can also form on spatial scales as large as tens of kilometers as a result of surface and subsurface water-saturated snow and firn layers sequentially refreezing following multiple melting seasons. They have been observed within the percolation facies of glaciated regions experiencing intense seasonal surface melting but in areas where snow accumulation is at least 25 % lower as compared to perennial firn aquifer areas. Widespread ice slabs have recently been identified within the GrIS via field expeditions and airborne ice-penetrating radar surveys, specifically in areas where perennial firn aquifers typically do not form. However, ice slabs have yet to be identified from space. Together, these two ice sheet features represent distinct, but related, sub-facies within the broader percolation facies of the GrIS that can be defined primarily by differences in snow accumulation, which influences the englacial hydrology and thermal characteristics of firn layers at depth. Here, for the first time, we use enhanced-resolution vertically polarized L-band brightness temperature (TVB) imagery (2015–2019) generated using observations collected over the GrIS by NASA's Soil Moisture Active Passive (SMAP) satellite to map perennial firn aquifer and ice slab areas together as a continuous englacial hydrological system. We use an empirical algorithm previously developed to map the extent of Greenland's perennial firn aquifers via fitting exponentially decreasing temporal L-band signatures to a set of sigmoidal curves. This algorithm is recalibrated to also map the extent of ice slab areas using airborne ice-penetrating radar surveys collected by NASA's Operation IceBridge (OIB) campaigns (2010–2017). Our SMAP-derived maps show that between 2015 and 2019, perennial firn aquifer areas extended over 64 000 km2, and ice slab areas extended over 76 000 km2. Combined together, these sub-facies are the equivalent of 24 % of the percolation facies of the GrIS. As Greenland's climate continues to warm, seasonal surface melting will increase in extent, intensity, and duration. Quantifying the possible rapid expansion of these sub-facies using satellite L-band microwave radiometry has significant implications for understanding ice-sheet-wide variability in englacial hydrology that may drive meltwater-induced hydrofracturing and accelerated ice flow as well as high-elevation meltwater runoff that can impact the mass balance and stability of the GrIS.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Can Influencementioning

confidence: 99%

Section: Can Influencementioning

confidence: 99%

Section: Can Influencementioning

confidence: 99%

See 2 more Smart Citations

An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chemistry, physics, and material science on laser devices, ion energy, and photoelectrochemical power [9]- [11]. Environmental and earth science on climate and ocean dynamics [12]- [14]. Automotive and transportation engineering including self-driving techniques [15]- [17].…”

Section: A Background Of Variable Dependence Detectionmentioning

confidence: 99%

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

et al. 2021

View full text Add to dashboard Cite

For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences among variables, and so measures for that have been proposed. Correlation coefficient and its variants are most common, but they only detect a linear dependence (co-linearity) between two variables. Some recent measures can detect a nonlinear dependence (co-nonlinearity) by means of kernelization or segmentation. They are supposed to handle two variables only and open to discussion with regard to performance in detection and difficulty in setup. There is room for a novel measure based on Neural Networks (NNs), since usual NNs aim at prediction but not at variable dependence detection. For the high-performance detection of co-nonlinearities among multi variables, we propose a measure called NNR-GL based on Neural Network Regression (NNR) regularized by Group Lasso (GL). NNR-GL embodies the detection through multi-input single-output regression by NNR and regularization on the input layer by GL. NNR-GL then calculates how strong the detected co-nonlinearities are by unifying the regression performance and the weights on input variables. We conducted experiments using artificial data to examine the behaviors and fundamental effectiveness of NNR-GL. The performance was estimated by a comprehensive detection performance criterion (CDP-AUC in short), which is the mean of area under curves representing true positive and true negative detections. NNR-GL achieved the values of CDP-AUC from 0.7472 to 0.9681, where 0 means complete failure and 1 means complete success in detection. These values were consistently higher than those from 0.5972 to 0.9259 of the conventional measures for all the different conditions of dependence, data size, and noise rate. Consequently, the effectiveness and robustness of NNR-GL were clearly confirmed.

show abstract

Greenland Ice Sheet Ice Slab Expansion and Thickening

Jullien

Tedstone

Machguth

et al. 2023

Preprint

View full text Add to dashboard Cite

We use airborne accumulation radar data acquired over the Greenland Ice Sheet between 2002 and 2018 to identify changes in ice slab extent and thickness. We show that ice slabs several metres thick were already present at least as early as 2002. Between 2012 and 2018, they expanded 13,400-17,600 inland, or by 37-44%. Our results document that the extremely warm summer of 2012 produced near-surface ice layers at higher elevations, enabling ice slabs to develop in locations with only moderate melting in the following summers. With repeated flights over a transect in southwest Greenland, we show that ice slabs can thicken from above and on their undersides. Moderate melting primarily thickens ice slabs by top-down accretion, while large melting events can also trigger ice accretion below the slabs.

show abstract

Extreme melt season ice layers reduce firn permeability across Greenland

Cited by 47 publications

References 71 publications

An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry

An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

Greenland Ice Sheet Ice Slab Expansion and Thickening

Contact Info

Product

Resources

About