Mass-conserved ice sheet geometry of Pine Island Glacier, West Antarctica

This gridded dataset provides geometry (ice thickness and bedrock topography) covering the Pine Island Glacier catchment. It has been created using the principle of mass conservation, given observed fields of velocity, surface elevation change and surface mass balance, together with sparse ice thickness data measured along airborne radar flight-lines. Previous ice flow modelling studies show that gridded geometry products that use traditional interpolation techniques (e.g. Bedmap2) can result in a spurious thickening tendency near the grounding line of Pine Island Glacier. Removing the cause of this thickening signal, in order to more accurately model ice flow dynamics, has been the motivation for creating a new geometry that is consistent with the conservation of mass

Attributing decadal climate variability in coastal sea-level trends

The data produced from analysis to be published in Ocean Science Discussions, paper entitled "Attributing decadal climate variability in coastal sea-level trends". NetCDF contains the following sets of fields: 1. Indexing: An index and location (lat, lon) of the coastal grid cells, a locator index attributing each cell to Atlantic, Pacific and Indian Ocean basin, a time (decimal year) index. 2. NEMO model trends (nemo_<component>_trend): Rolling decadal trends at each coastal grid cell from the NEMO model run for steric, manometric (dynamic) and GRD. The sum of these components gives the equivalent to absolute sea level trend.  3. Climate and oceanographic mode indices: The rolling decadal trends in climate indices and the AMOC index calculated from the AMOC model (ci_trend) and their names (ci_index). 4. Empirical Orthogonal Function spatial pattern (eof_<basin>_<component>_D) and Principal Component time series (eof_<basin>_<component>_PC) of the NEMO model trends. 5. Coefficient of linear regression between PC and climate indices (recon_<basin>_<component>_beta) and the rolling trend time series at each grid cell from the reconstruction, sum{ci_trend*beta} (recon_<basin>_<component>_trend). In 4 and 5, the indices are given by basin. The total coastline is a concatenation of the Atlantic, Pacific and Indian basin data in that order. The absolute SSH is given by the sum of components. i.e. the SSH for all coastal cells in order index: recon_sum_trend([index(Atlantic_index); index(Pacific_index); index(Indian_index)] = ...     [recon_Atlantic_manometric_trend+recon_Atlantic_steric_trend+recon_Atlantic_grd_trend; ...      recon_Pacific_manometric_trend+recon_Pacific_steric_trend+recon_Pacific_grd_trend;  ...      recon_Indian_manometric_trend+recon_Indian_steric_trend+recon_Indian_grd_trend

Spatiotemporal mass balance trends for the Antarctic Ice Sheet, 2003-2013

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/

ICESat-2 L4 Grounding Zone for Antarctic Ice Shelves, Version 1

This data set provides an Antarctic ice shelf grounding zone geolocation product, including the landward limit of ice flexure caused by ocean tidal movement (Point F), the seaward limit of ice flexure (Point H), and the break in surface slope (Point Ib) based on the ATLAS/ICESat-2 ATL06 Land Ice Height data set acquired between March 2019 and September 2020. The grounding zone estimates were derived from automated techniques using ICESat-2 repeat tracks

A new synthesis of annual land ice mass trends 1992 to 2016

We have assessed and synthesised land ice mass trend results published, primarily, since the IPCC AR5 (2013) to produce a consistent estimate of land ice mass trends during the satellite era (1992 to 2016). Our resulting synthesis is both consistent and rigorous, drawing on i) the published literature, ii) expert assessment of that literature, and iii) a new analysis of Arctic glacier and ice cap trends combined with statistical modelling. In the associated paper (Bamber et al 2018) we present annual and pentad (five-year mean) time series for the East, West Antarctic and Greenland Ice Sheets and glaciers separately and combined. When averaged over pentads, covering the entire period considered, we obtain a monotonic trend in mass contribution to the oceans, increasing from 0.31±0.35 mm of sea level equivalent for 1992-1996 to 1.85±0.13 for 2012-2016. Our integrated land ice trend is lower than many estimates of GRACE-derived ocean mass change for the same periods. This is due, in part, to a smaller estimate for glacier and ice cap mass trends compared to previous assessments. We discuss this, and other likely reasons, for the difference between GRACE ocean mass and land ice trends

Subglacial bed roughness of Greenland, provided using two independent metrics

These two files (.csv) provide independent methods of quantifying subglacial roughness in Greenland, both calculated from radio-echo sounding (or ice penetrating radar) data collected by the Operation Ice Bridge programme using CReSIS instrumentation. They are an output of the Basal Properties of Greenland (BPOG) project (http://bpog.blogs.ilrt.org/), with funding from NERC grant NE/M000869/1. Roughness here, and in the wider literature, is defined as the variation in bed elevation (in the vertical) at the ice-bed interface, over a given length-scale. These two metrics calculate/quantify this variation in different ways: one shows topographic-scale roughness, calculated from the variation in along-track topography (bed elevation measurements derived from the radar pulse); and the other shows scattering-derived roughness, calculated from quantifying characteristics of each bed-echo (the return from the radar pulse at the ice-bed interface)

Multi-spectral unmanned aerial system imagery, UPE_U, north-west Greenland, July 2018: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification)

This dataset consists of orthomosaics created from flights of an unmanned aerial system imaging platform at UPE_U in north-west Greenland on 24 July 2018. The Level-2 orthomosaics consist of (1) ground reflectance at 5 spectral bands, and (2) a digital elevation model. Level-3 orthomosaics consist of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm which was trained on measurements collected at S6, K-transect, south-west Greenland. Funding was provided by the NERC standard grant NE/M021025/1

Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023, v1

Svalbard has experienced increased climate variability as a result of global warming, leading to significant mass loss in its marine-terminating glaciers over recent decades. Nevertheless, the mechanisms driving this mass loss remain less understood, primarily due to a limited understanding of calving dynamics. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985-2023. This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions. The dataset comprises 149 folders, each representing a distinct glacier. Each glacier folder contains the following five different files: a shapefile recording all the terminus traces of this glacier mapped in our study under the projection EPSG:3995; a shapefile containing the glacier centreline used in measuring the calving front migrations under the projection EPSG:3995; a .CSV file recording the glacier calving front change time series along the centreline in relation to the earliest time stamp; a .PNG file showing the geolocation of this glacier and its calving front traces; a .PNG file showing the calving front change time series along the glacier centreline. Calving Front Trace Shapefile Feature Attribute Table Data Field Description Glacier The Randolph Glacier Inventory (RGI) version 6 (RGI Consortium, 2017) glacier id. Sensor The satellite platform used in mapping glacier calving front, including “Landsat”, “Terra-ASTER”, “Sentinel2” and “Sentinel1”. ImageId The image id of the satellite image used in mapping the glacier calving front. DateString The datetime string of the satellite image in the format of “YYYYMMDD”. CFL_Change The calving front location (CFL) changes in meters along the glacier centreline in relation to the earliest calving front location in the time series

Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023, v2

Svalbard has experienced increased climate variability as a result of global warming, leading to significant mass loss in its marine-terminating glaciers over recent decades. Nevertheless, the mechanisms driving this mass loss remain less understood, primarily due to a limited understanding of calving dynamics. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985-2023. This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions. The information regarding the glacier calving front terminal traces, glacier centrelines, glacier domains, and the along-centreline glacier calving front change time series is consolidated into a single Geopackage file named "Svalbard_Calving_Front_Product.gpkg." The specific file structure for this data file is detailed in Table 1, and the feature attribute table for the different data layers recorded in this data file can be found in Table 2. Furthermore, we have included spatial distribution map plots of the glacier calving front traces and line plots depicting the time series of calving front changes for each individual glacier. These plots are provided in .PNG file format and can be accessed within the Figures folder. Table 1. The layer structure of the Svalbard calving front data product. Layer Name Details traces Line geometries recording the terminal traces of all the glaciers (EPSG:3995). centrelines Line geometries recording the glacier centrelines used in calving front change estimation (EPSG:3995). domains Polygon geometries recording the glacier domains (EPSG:3995). front_change_time_series Point geometries recording the along-centreline glacier calving front change time series (EPSG:4326). Table 2. The feature attribute table of the data layer. Data Field Description Glacier The Randolph Glacier Inventory (RGI) version 6 (RGI Consortium, 2017) glacier id. Sensor The satellite platform used in mapping glacier calving front, including “Landsat”, “Terra-ASTER”, “Sentinel2” and “Sentinel1”. ImageId The image id of the satellite image used in mapping the glacier calving front. DateString The datetime string of the satellite image in the format of “YYYYMMDD”. CFL_Change The calving front location (CFL) changes in meters along the glacier centreline in relation to the earliest calving front location in the time series. glacier_lat The latitude of the glacier location (WGS84 coordinate system). glacier_lon The longitude of the glacier location (WGS84 coordinate system)