Greenland's bed topography is a primary control on ice flow, grounding line migration, calving dynamics, and subglacial drainage. Moreover, fjord bathymetry regulates the penetration of warm Atlantic water (AW) that rapidly melts and undercuts Greenland's marine‐terminating glaciers. Here we present a new compilation of Greenland bed topography that assimilates seafloor bathymetry and ice thickness data through a mass conservation approach. A new 150 m horizontal resolution bed topography/bathymetric map of Greenland is constructed with seamless transitions at the ice/ocean interface, yielding major improvements over previous data sets, particularly in the marine‐terminating sectors of northwest and southeast Greenland. Our map reveals that the total sea level potential of the Greenland ice sheet is 7.42 ± 0.05 m, which is 7 cm greater than previous estimates. Furthermore, it explains recent calving front response of numerous outlet glaciers and reveals new pathways by which AW can access glaciers with marine‐based basins, thereby highlighting sectors of Greenland that are most vulnerable to future oceanic forcing.

We reconstruct the mass balance of the Greenland Ice Sheet using a comprehensive survey of thickness, surface elevation, velocity, and surface mass balance (SMB) of 260 glaciers from 1972 to 2018. We calculate mass discharge, D, into the ocean directly for 107 glaciers (85% of D) and indirectly for 110 glaciers (15%) using velocity-scaled reference fluxes. The decadal mass balance switched from a mass gain of +47 ± 21 Gt/y in 1972–1980 to a loss of 51 ± 17 Gt/y in 1980–1990. The mass loss increased from 41 ± 17 Gt/y in 1990–2000, to 187 ± 17 Gt/y in 2000–2010, to 286 ± 20 Gt/y in 2010–2018, or sixfold since the 1980s, or 80 ± 6 Gt/y per decade, on average. The acceleration in mass loss switched from positive in 2000–2010 to negative in 2010–2018 due to a series of cold summers, which illustrates the difficulty of extrapolating short records into longer-term trends. Cumulated since 1972, the largest contributions to global sea level rise are from northwest (4.4 ± 0.2 mm), southeast (3.0 ± 0.3 mm), and central west (2.0 ± 0.2 mm) Greenland, with a total 13.7 ± 1.1 mm for the ice sheet. The mass loss is controlled at 66 ± 8% by glacier dynamics (9.1 mm) and 34 ± 8% by SMB (4.6 mm). Even in years of high SMB, enhanced glacier discharge has remained sufficiently high above equilibrium to maintain an annual mass loss every year since 1998.

Bathymetry (seafloor depth), is a critical parameter providing the geospatial context for a multitude of marine scientific studies. Since 1997, the International Bathymetric Chart of the Arctic Ocean (IBCAO) has been the authoritative source of bathymetry for the Arctic Ocean. IBCAO has merged its efforts with the Nippon Foundation-GEBCO-Seabed 2030 Project, with the goal of mapping all of the oceans by 2030. Here we present the latest version (IBCAO Ver. 4.0), with more than twice the resolution (200 × 200 m versus 500 × 500 m) and with individual depth soundings constraining three times more area of the Arctic Ocean (∼19.8% versus 6.7%), than the previous IBCAO Ver. 3.0 released in 2012. Modern multibeam bathymetry comprises ∼14.3% in Ver. 4.0 compared to ∼5.4% in Ver. 3.0. Thus, the new IBCAO Ver. 4.0 has substantially more seafloor morphological information that offers new insights into a range of submarine features and processes; for example, the improved portrayal of Greenland fjords better serves predictive modelling of the fate of the Greenland Ice Sheet.

Many Greenland glaciers have been attacked by the ocean from below and, in turn, contributed to sea-level rise from the ice sheet.

The mass loss of the Greenland Ice Sheet is nearly equally partitioned between a decrease in surface mass balance from enhanced surface melt and an increase in ice dynamics from the acceleration and retreat of its marine-terminating glaciers. Much uncertainty remains in the future mass loss of the Greenland Ice Sheet due to the challenges of capturing the ice dynamic response to climate change in numerical models. Here, we estimate the sea level contribution of the Greenland Ice Sheet over the 21st century using an ice-sheet wide, high-resolution, ice-ocean numerical model that includes surface mass balance forcing, thermal forcing from the ocean, and iceberg calving dynamics. The model is calibrated with ice front observations from the past eleven years to capture the recent evolution of marine-terminating glaciers. Under a business as usual scenario, we find that northwest and central west Greenland glaciers will contribute more mass loss than other regions due to ice front retreat and ice flow acceleration. By the end of century, ice discharge from marine-terminating glaciers will contribute 50 ± 20% of the total mass loss, or twice as much as previously estimated although the contribution from the surface mass balance increases towards the end of the century.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set of Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m (1.89 pixels). We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.