The Antarctic mass trends have been collated from a combination of different remote sensing datasets. These are trends of yearly elevation changes over Antarctica for the period 2003-2013 due to the different geophysical processes driving changes in Antarctica: ice dynamics, surface mass balance and glacio-isostatic adjustment (GIA). Net trends can be easily calculated by adding together surface and ice dynamics trends. 20 km gridded datasets have been produced for each process, per year (except the GIA solution which is time-invariant). To convert elevation to mass trends, we also provide the density fields for surface (SMB) and GIA processes used in Martin-Espanol et al (2016). These can be directly multiplied by the dh/dt. To convert dh/dt from ice dynamics, simply multiply by the density of ice. Mass smb = dh/dt smb * d surf Mass ice = dh/dt ice * d ice (not provided) Mass gia = dh/dt gia * d rock NERC grant: NE/I027401/1
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).
Ground-penetrating radar (GPR) was used to test glacier ice thickness/glacier bed detectability on debris-covered Himalayan glaciers at a range of frequencies in glacier long- and cross- profiles and at static points. The survey sites were of the Lirung and Langtang Glaciers in the Langtang National Park, Nepal, where debris cover thickness varied from centimetres to several metres. The radar used was the BAS DELORES dipole pulse radar system, operating at 5MHz, 10MHz, 20MHZ and 40MHz. Data were acquired as a stop-go survey at 2-4m intervals on partially snow-covered and entirely debris-covered glacier surfaces in temperatures close to freezing, with a diurnal freeze-thaw cycle. Funding was provided by the NERC grant NE/L013258/1.
During the 2014/2015 season six temporary GNSS stations were deployed on rocky outcrops west of the former Larsen B ice shelf by the University of Leeds and BAS, in the region of the Flask and Leppard Glaciers. This was carried out as part of the NERC funded UKANET project. This is an area we have targeted with a space-based technique called radar interferometry (InSAR), which can provide dense measurements of uplift rates, and the temporary GNSS network were deployed to better understand the contribution of atmospheric noise to the InSAR results. Four were taken out in the same season, while the other two were pulled in the 2015/2016 season. Funding was provided by NERC grant NE/L006065/1.
Uncertainties in future sea level projections are dominated by our limited understanding of the dynamical processes that control instabilities of marine ice sheets. A valuable case to examine these processes is the last deglaciation of the British-Irish Ice Sheet. The Minch Ice Stream, which drained a large proportion of ice from the northwest sector of the British-Irish Ice Sheet during the last deglaciation, is well constrained, with abundant empirical data which could be used to inform, validate and analyse numerical ice sheet simulations. We use BISICLES, a higher-order ice sheet model, to examine the dynamical processes that controlled the retreat of the Minch Ice Stream. We simulate retreat from the shelf edge under constant "warm" surface mass balance and subshelf melt, to isolate the role of internal ice dynamics from external forcings. The model simulates a slowdown of retreat as the ice stream becomes laterally confined at a "pinning-point" between mainland Scotland and the Isle of Lewis. At this stage, the presence of ice shelves became a major control on deglaciation, providing buttressing to upstream ice. Subsequently, the presence of a reverse slope inside the Minch Strait produces an acceleration in retreat, leading to a "collapsed" state, even when the climate returns to the initial "cold" conditions. Our simulations demonstrate the importance of the Marine Ice Sheet Instability and ice shelf buttressing during the deglaciation of parts of the British-Irish Ice Sheet. Thus, geological data could be used to constrain these processes in ice sheet models used for projecting the future of our contemporary ice sheets. Funding was provided by the Natural Environment Research Council (NERC) SPHERES Doctoral Training Partnership (NE/L002574/1) with CASE support from the British Geological Survey.
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.