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This dataset contains rates of mass change and cumulative mass change and their associated uncertainty for the Antarctic Ice Sheet (in its entirety and split into West Antarctica, East Antarctica and the Antarctic Peninsula), the Greenland Ice Sheet, and their sum between 1992 and 2020. The data are reconciled estimates of mass balance from three independent satellite-based techniques: altimetry, gravimetry and input-output method. This dataset is part of the Ice Sheet Mass Balance Intercomparison Exercise (IMBIE). This work is an outcome of the Ice Sheet Mass Balance Inter-Comparison Exercise IMBIE) supported by the ESA Climate Change Initiative and the NASA Cryosphere Program. Andrew Shepherd was additionally supported by a Royal Society Wolfson Research Merit Award and the UK Natural Environment Research Council Centre for Polar Observation and Modelling (cpom30001).
This dataset consists of the time series of mass change of the Greenland Ice Sheet and its contribution to global sea level between 1980 and 2018 derived from satellite measurements. The dataset presented here is a reconciled estimate of mass balance estimates from three independent satellite-based techniques - gravimetry, altimetry and input-output method - and its associated uncertainty. This dataset is part of the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). The total mass change as well as the partition between surface and dynamics mass balance are provided in this dataset. This work is an outcome of the Ice Sheet Mass Balance Inter-Comparison Exercise (IMBIE) supported by the ESA Climate Change Initiative and the NASA Cryosphere Program. Andrew Shepherd was additionally supported by a Royal Society Wolfson Research Merit Award and the UK Natural Environment Research Council Centre for Polar Observation and Modelling (cpom30001). ***** PLEASE BE ADVISED TO USE UPDATED DATA ***** The expanded data set (see ''Related Data Set Metadata'' link below) has an additional 24 months of measurements, and also includes data for Antarctica.
Datasets from the Resolving subglacial properties, hydrological networks and dynamic evolution of ice flow on the Greenland Ice Sheet (RESPONDER) project as published in the paper by Chudley et al. entitled "Controls on water storage and drainage in crevasses on the Greenland Ice Sheet". This dataset consists of remotely sensed observations of water-filled crevasses across a marine-terminating sector of the west Greenland Ice Sheet between 2017 and 2019.The dataset presented here includes all data necessary to replicate the findings presented in the main paper, including UAV photogrammetry-derived raster data (producing a series of orthophotos and digital elevation models) and observations from satellite-derived data (Sentinel-2, ArcticDEM, and MEaSUREs Greenland velocity data) of crevasse presence, water presence, and estimates of surface stress. This research was funded by the European Research Council as part of the RESPONDER project under the European Union''s Horizon 2020 research and innovation program (Grant 683043). Tom Chudley was supported by a Natural Environment Research Council Doctoral Training Partnership Studentship (Grant NE/L002507/1).
This dataset presents the input and output data from a set of sensitivity experiments to simulate the evolution of the Laurentide ice sheet in the Early Holocene (10-7 thousand years ago). These data are presented in the manuscript "Simulating the Early Holocene demise of the Laurentide Ice Sheet with BISICLES (public trunk revision 3298)". Simulating the demise of the Laurentide Ice Sheet covering the Hudson Bay in the early Holocene is important for understanding the role of accelerated changes in ice sheet topography and melt in the ''8.2 ka event'', a century long cooling of the Northern Hemisphere by several degrees. Freshwater released from the ice sheet through a surface mass balance instability (known as the saddle collapse) has been suggested as a major forcing for the 8.2 ka event, but the temporal evolution of this pulse has not been constrained. Dynamical ice loss and marine interactions could have significantly accelerated the ice sheet demise, but simulating such processes requires computationally expensive models that are difficult to configure and are often impractical for simulating past ice sheets. Here, we developed an ice sheet model setup for studying the Laurentide Ice Sheet''s Hudson Bay saddle collapse and the associated meltwater pulse in unprecedented detail using the BISICLES ice sheet model, an efficient marine ice sheet model of the latest generation, capable of refinement to kilometre-scale resolution and higher-order ice flow physics. The setup draws on previous efforts to model the deglaciation of the North American Ice Sheet for initialising the ice sheet temperature, recent ice sheet reconstructions for developing the topography of the region and ice sheet, and output from a general circulation model for a representation of the climatic forcing. The modelled deglaciation is in agreement with the reconstructed extent of the ice sheet and the associated meltwater pulse has realistic timing. Furthermore, the peak magnitude of the modelled meltwater equivalent (0.07-0.13 Sv) is compatible with geological estimates of freshwater discharge through the Hudson Strait. The results demonstrate that while improved representation of the glacial dynamics and marine interactions are key for correctly simulating the pattern of early Holocene ice sheet retreat, surface mass balance introduces by far the most uncertainty. The new model configuration presented here provides future opportunities to quantify the range of plausible amplitudes and durations of a Hudson Bay ice saddle collapse meltwater pulse and its role in forcing the 8.2 ka event. Ilkka Matero was funded by the Leeds-York Natural Environment Research Council (NERC) Spheres Doctoral Training Partnership (NE/L002574/1). The contribution from Ruza Ivanovic was partly supported by NERC grant NE/K008536/1. Lauren Gregoire is funded by a UKRI Future Leaders Fellowship (MR/S016961/1). The work made use of the N8 HPC facilities, which are provided and funded by the N8 consortium and EPSRC (EP/K000225/1) and co-ordinated by the Universities of Leeds and Manchester.