Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models

To study near-surface melt changes over the Greenland ice sheet (GrIS) since 1979, melt extent estimates from two regional climate models were compared with those obtained from spaceborne microwave brightness temperatures using two different remote sensing algorithms. The results from the two models were consistent with those obtained with the remote sensing algorithms at both daily and yearly time scales, encouraging the use of the models for analyzing melting trends before the satellite era (1958–1979), when forcing data is available. Differences between satellite-derived and model-simulated results still occur and are used here to identify (i) biases in the snow models (notably in the albedo parametrization, in the thickness of a snow layer, in the maximum liquid water content within the snowpack and in the snowfall impacting the bare ice appearance in summer) and (ii) limitations in the use of passive microwave data for snowmelt detection at the edge of the ice sheet due to mixed pixel effect (e.g., tundra or rock nearby the ice sheet). The results from models and spaceborne microwave sensors confirm a significant (p-value = 0.01) increase in GrIS surface melting since 1979. The melt extent recorded over the last years (1998, 2003, 2005 and 2007) is unprecedented in the last 50 yr with the cumulated melt area in the 2000's being, on the average, twice that of the 1980's

Using deep learning to model elevation differences between 2 radar and laser altimetry

peer reviewedSatellite and airborne observations of surface elevation are critical in understanding climatic and glaciological processes and quantifying their impact on changes in ice masses and sea level contribution. With the growing number of dedicated airborne campaigns and experimental and operational satellite missions, the science community has access to unprecedented and ever-increasing data. Combining elevation datasets allows potentially greater spatial-temporal coverage and improved accuracy; however, combining data from different sensor types and acquisition modes is difficult by differences in intrinsic sensor properties and processing methods. This study focuses on the combination of elevation measurements derived from ICESat-2 and Operation IceBridge LIDAR instruments and from CryoSat-2’s novel interferometric radar altimeter over Greenland. We develop a deep neural network based on sub-waveform information from CryoSat-2, elevation differences between radar and LIDAR, and additional inputs representing local geophysical information. A time series of maps are created showing observed LIDAR-radar differences and neural network model predictions. Mean LIDAR vs. interferometric radar adjustments and the broad spatial and temporal trends thereof are recreated by the neural network. The neural network also predicts radar-LIDAR differences with respect to waveform parameters better than a simple linear model; however, point level adjustments and the magnitudes of the spatial and temporal trends are underestimated

Modelling the effects of sand extraction, on sediment transport due to tides, on the Kwinte Bank

In recent years, the exploitation of marine aggregates is increasing. As an example, on the Belgian continental shelf, one particular sandbank (the Kwinte Bank) is exploited extensively; this has led to the creation of a 5 m deep depression along its central part. In the present contribution, the influence of these bathymetric changes, on erosion and sedimentation patterns are studied, using numerical modelling, in order to obtain an initial impression of the effect of such intense sand extraction on the stability of the sandbank. Different numerical models are utilised. Twodimensional and three-dimensional hydrodynamic models have been used to derive currents, whilst third generation wave models have been used to simulate the waves. Two different models are presented, which calculate the total load sediment transport as a function of the local currents and waves. These models have been used to investigate the erosional and depositional patterns. The use of two different sediment transport models has some advantages, since the results of sediment transport models are still subject to some important uncertainties. The hydrodynamic model results are validated using ADCP current data, confirming the good performance of the models. Likewise the wave models provide good results, comparing their results with data from a buoy. The sediment transport model results were compared to the residual transport patterns, derived from the asymmetry of dunes. The results obtained seem to be in general agreement with these observations. The numerical models are used to simulate the response of the sediment transport to extensive sand extraction from the sandbank. One ‘worst-case’ scenario and two more realistic scenarios were simulated, whilst the effect of these bathymetric changes on sediment transport was studied. The results show that the intense sand extraction does not seem to influence extensively the stability of the sandbank, but that, as a consequence, there is less erosion and deposition. The model results show, for all of the scenarios, a small amount of deposition on the top of the sandbank; this could be an indication of a regeneration mechanism. A trench, created perpendicular to the crest of the sandbank, could be slowly refilled again. The time-scale of this regeneration and the influence of storms remain uncertain. Although the main emphasis of the paper relates to tidal forcing, a brief discussion is included on the influence of wave action, on sediment transport

Rapid loss of firn pore space accelerates 21st century Greenland mass loss

Mass loss from the two major ice sheets and their contribution to global sea level rise is accelerating. In Antarctica, mass loss is dominated by increased flow velocities of outlet glaciers, following the thinning or disintegration of coastal ice shelves into which they flow. In contrast, ∼55% of post‒1992 Greenland ice sheet (GrIS) mass loss is accounted for by surface processes, notably increased meltwater runoff. A subtle process in the surface mass balance of the GrIS is the retention and refreezing of meltwater, currently preventing ∼40% of the meltwater to reach the ocean. Here we force a high‒resolution atmosphere/snow model with a mid‒range warming scenario (RCP4.5, 1970–2100), to show that rapid loss of firn pore space, by >50% at the end of the 21st century, quickly reduces this refreezing buffer. As a result, GrIS surface mass loss accelerates throughout the 21st century and its contribution to global sea level rise increases to 1.7 ±0.5 mm yr−1, more than four times the current value