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General definitions
Level 2 pre-processed (L2P): this is a designation of satellite data processing level. “Level 2” means geophysical variables derived from Level 1 source data on the same grid (typically the satellite swath projection). “Pre-processed” means ancillary data and metadata added following GHRSST Data Specification, adopted in the case of LSWT.
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Threshold requirement: minimum requirement to be met to ensure data are useful.
Scope of the document
This document aims to provide users with the relevant information on requirements and gaps for each of the given products within the Land Hydrology and Cryosphere service. The gaps in this context refer to data availability to enable the ECV products to be produced, or in terms of scientific research required to enable the current ECV products to be evolved to respond to the specified user requirements.
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Initially an overview of each product is provided, including the required input data and auxiliary products, a definition of the retrieval algorithms and processing algorithms versions; including, where relevant, a comment on the current methodology applied for uncertainty estimation. The target requirements for each product is then specified which generally reflect the GCOS ECV requirements. The result of a gap analysis is provided that identifies the envisaged data availability for the next 10-15 years, the requirement for the further development of the processing algorithms, and the opportunities to take full advantage of current, external, research activities. Finally, where possible, areas of required missing fundamental research are highlighted, and a comment on the impact of future instrument missions is provided.
Executive summary
Ice Sheets
The Ice Sheets and Ice Shelves Service represents three ECVs by providing four products. The Ice Velocity (IV) product covers the Greenland Ice Sheet. The Gravimetric Mass Balance (GMB) product covers both the Greenland and Antarctic Ice Sheets in one dataset. Finally, there are two Surface Elevation Change (SEC) products, one for each ice sheet. Although the two products have the same format, they necessarily use different map projections and processing methods, and so have been split to avoid possible confusion.
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Reliance on External Research
Since the C3S programme only supports the implementation, development and operation of the CDR processor, any scientific advances of the C3S products entirely rely on funding provided by external programmes, e.g. CCI+, H-SAF, Horizon2020. Thus, the implementation of new scientific improvements can only be implemented if external funding allows for it. This depends both on the availability of suitable programmes to support the R&D activities and the success of the C3S contractors in winning potential suitable calls.
Ice Sheets and Ice Shelves ECV Service
Introduction
This section aims at providing users with the relevant information on requirements, and gaps, for the Ice Sheets and Ice Shelves Service. It is divided into three sections. Section 1.2 describes the products currently provided by the Service. Section 1.3 provides the target requirements for ice sheet related ECVs. Section 1.4 provides a past, present, and future gap analysis for current and potential future products of the Ice Sheet and Ice Shelf Service covering both gaps in the data availability and scientific gaps that could be addressed by further research activities (outside C3S).
Ice Sheets Product description
The ice sheets and shelves service covers three ECVs with four products :
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This section describes the existing products in more detail.
Greenland Ice Sheet velocity
The velocity grid represents the average annual ice surface velocity (IV) of Greenland in true metres per day. The geographic extent is the Greenland Ice Sheet, including peripheral glaciers. The ice sheet boundaries are based on the latest version of the Randolph Glacier Inventory (RGI 6.0, RGI Consortium, 2017) with updated glacier fronts for marine terminating glaciers. The basic IV product contains the horizontal (Vx, Vy) and vertical (Vz) components of the velocity vector. The horizontal surface velocities are derived from measured displacements in radar geometry (range, azimuth). The vertical velocity is derived from the interpolated height at the end position of the displacement vector minus the elevation at the start position, taken from a DEM (see auxiliary data). The main data variables are defined on a three-dimensional grid (x, y, z), where x and y are defined by the used map projection, i.e. the polar stereographic grid. The velocities are true values and not subject to the distance distortions present in the polar stereographic grid. Along with the ice velocity maps, the products include a valid pixel count map, which provides the number of valid slant range and azimuth displacement estimates at the output pixel position that are used in compiling the averaged map, as well as an uncertainty map (based on the standard deviation).
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Figure 1: Example IV product covering the Greenland Ice Sheet, depicted are from left to right the easting component, the northing component and the magnitude of velocity.Anchor Figure 1 Figure 1
Instruments
The IV product is primarily derived by applying feature tracking on repeat pass Copernicus Sentinel-1 SLC data. Sentinel-1 is a C-band synthetic aperture radar (SAR) mission and the constellation currently comprises two identical satellites (Sentinel-1A and -1B) with a repeat cycle of 6/12-days. The Interferometric Wide (IW) swath mode is the standard operation mode over land surfaces including land ice. It applies the Terrain Observation by Progressive Scans (TOPS) acquisition technology, providing a spatial resolution of about 3 m and 22 m in slant range and azimuth, respectively, with a swath width of 250 km. Sentinel1 is the main source for regular and comprehensive monitoring of land ice motion.
Algorithm name and version
The ENVEO software package (ESP v2.1) is a state-of-the-art IV retrieval algorithm designed for various SAR sensors (e.g. Sentinel-1, TerraSAR-X, ALOS PALSAR, Cosmo-SkyMed). The processor has been tested rigorously through intercomparisons with other packages and extensive validation efforts. The ESP-IV processing system runs on common Linux operating systems and has successfully been connected to cluster systems utilising several hundreds of cores. This is especially of interest for campaign processing of big data sets as for Greenland. The existing system for annual IV production for Greenland is fully operational. Further improvements of the software are planned and discussed in section 1.4.
Auxiliary data
Auxiliary data needed for input in the IV processor are a digital elevation model (DEM) and polygon shapefiles of the ice sheet boundary.
DEM
A DEM is needed for geometric co-registration of repeat pass SAR data and geocoding of the final products. This requires an accurate DEM without artefacts, as spurious jumps in the derived velocity fields can occur otherwise. For the IV maps produced in the Greenland Ice Sheet CCI, the Greenland Ice sheet Mapping Project (GIMP) DEM (Howat et al., 2014) was used. For C3S a new DEM was compiled and implemented based on the recently released 90 m TanDEM-X Global DEM (Rizzoli et al., 2017). Known issues relating to processing artefacts, outliers and gaps, are filled in using a gap interpolation method. The extent of the DEM is equal to the IV product.
Ice sheet boundary
The ice sheet and glacier boundaries are based on the latest version of the Randolph Glacier Inventory (RGI 6.0, RGI Consortium, 2017) with updated glacier fronts. The inventory has been compiled from more than 70 Landsat scenes (mostly acquired between 1999 and 2002) using semi-automated glacier mapping techniques (Rastner et al., 2012).
Antarctic surface elevation change
The product provides estimates of surface elevation change over the Antarctic ice sheets and ice shelves, over a long period, using level 2 radar altimeter data from six satellite missions: ERS-1, ERS-2, EnviSat, CryoSat-2, Sentinel-3A and Sentinel-3B. Its algorithms and processing scheme are based on previous work for the ESA Antarctic Ice Sheet Climate Change Initiative and are guided by the GCOS (Global Climate Observing System) targets for the Ice Sheets Land ECV (Essential Climate Variable).
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The product is distributed in NetCDF4 format according to the C3S Common Data Model conventions. The main ECV and its uncertainties are accompanied by a map of surface type, i.e. ice sheet, ice shelf or island/ice rise, and a set of flags denoting regions of high surface slope. 
Figure 2: Example Antarctic SEC product showing the rates of change derived for the period from 01-07-2007 to 01-07-2012. This merges data from EnviSat and CryoSat-2. In this case the data extends only as far south as the EnviSat southern orbit limitAnchor Figure 2 Figure 2
Instruments
The instruments used are the ERS-1 RA, ERS-2 RA, EnviSat RA2, CryoSat-2 SIRAL and Sentinel-3A and B SRAL. The data products used are the ERS-1 and ERS-2 Reaper L2, the EnviSat L2 GDR_v3, the CryoSat-2 L2i LRM (Low Rate Mode) and SIN (Synthetic aperture radar INterferometer), both baseline D, and the Sentinel-3A and B L2 which is currently optimised for ocean studies.
Algorithm name and version
The software package has been assembled and tailored to the C3S requirements from previous work on IMBIE (the Ice sheet Mass Balance Intercomparison Exercise) and various ESA Climate Change Initiative projects. The second-year version is called C3S_Ant_Sec_ops_v3.0. The results have been tested against datasets from the previous projects mentioned and validated against the multi-year IceBridge airborne laser altimetry campaigns. The underlying processing system runs on common Linux operating systems.
Auxiliary data
Four auxiliary datasets are needed.
DEM
Radar altimetry over regions of very high slope is generally of poor quality due to confusion over echo provenance. The digital elevation model is used to remove data from areas extremely high slope, i.e. greater than 10°, from the input measurements. It is also used to provide a grid of flags ranking the slope angle in each cell. The model in use is the Slater et al. model based on CryoSat-2 data.
Ice extent
The processing area consists of all of the Antarctic ice sheets, ice shelves and associated ice rises and island. Its boundaries are based on the IceSAT MODIS (Moderate Resolution Imaging Spectroradiometer) 1km resolution mask, produced for the IMBIE2 project by Zwally et al.
Glacial isostatic adjustment
Movements of the surface related to glacial isostasy are corrected for using the Ivins et al. model IJ05.
Tides
Due to the poor resolution of the satellites' land masks in processing Antarctic coastal regions, it is necessary to remove the tides supplied in the L2 products and replace them with a consistent set. The replacements are generated using the Padman et al. CATS 2008a tide model.
Greenland surface elevation change
The Greenland surface elevation change closely follows the Antarctic SEC (see section 1.2.2). The main algorithms are based on previous work for the Greenland Ice Sheet CCI and are guided by the GCOS (Global Climate Observing System) targets for the Ice Sheets Land ECV. A full description of the processing approaches and algorithms are found in Sørensen et al. (2018) and Simonsen and Sørensen (2017, LSM5).
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The C3S-SEC product is distributed in NetCDF4 format according to the C3S Common Data Model conventions, at 25 by 25 km grid resolution. The grid is an equal area grid as defined by the NSIDC North Polar Stereographic projection with latitude of true scale at 70°N and central meridian at 45°W (EPSG: 3413). This projection is the same as used for the Ice velocity product. In addition to the gridded solution of SEC, the following fields are also available: cartesian x-coordinate ( x ) , cartesian y-coordinate ( y ), geographical longitude and latitude (lon, lat), grid area (accounting for projection errors), relative elevation change since 1992 (dh), start and end times for the altimeter data used (start_time, stop_time), distance from grid cell centre to observation location, and a number of different accuracy fields for the different parameters.
Instruments
The instruments used are the ERS-1 RA, ERS-2 RA, EnviSat RA2, CryoSat-2 SIRAL and Sentinel-3A and B SRAL. The data products used are the ERS-1 and ERS-2 Reaper L2, the EnviSat L2 GDR_v3, CryoSat-2 L2i LRM (Low Rate Mode) and SIN (Synthetic aperture radar INterferometer) both baseline D and the Sentinel-3A/B L2 which is currently optimised for ocean studies.
Algorithm name and version
The software package has been assembled and tailored to the C3S requirements from previous work in ESA Climate Change Initiative projects and is evolved with an annual iteration, with the current version being provided as Version 3. The results have been validated against the multi-year NASA Operation IceBridge airborne laser altimetry campaigns, see section 1.4.3.3 and Simonsen et al (2017).
The underlying processing system runs on a common Linux operating system. For the older missions (ERS-1, ERS-2 and ENVISAT), the processing is implemented using a combination of repeat-track and plane-fitting algorithms as documented in Sørensen et al. (2018). This method has been independently validated and inter-compared with state-of-the-art methods in Levinsen et al. (2015). A 5-year running mean window is used to derive an annual SEC solution. The final monthly solution provided for the C3S-product is derived by a temporal-weighted mean of all solutions covering a given month. For CryoSat-2 and Sentinel3 A/B, the plane-fitting algorithm (LSM5, Simonsen and Sørensen (2017)) has been tailored to the requirements of the C3S product and the inclusion of Sentinel-3. The monthly solution is derived in in a similar fashion as for the older satellites, but the running-mean window has been shortened to 3 years. This is possible due to the more favourable orbit of CryoSat-2, which still ensures a stable plane-fit solution at the same time as it limits the imprint of interannual weather in the SEC product and predict climatic signals.
Auxiliary data
The processing approach for the Greenland SEC are in less degree in need of auxiliary data. However, to provide consistent documentation, a full description of the same auxiliary data as in the Antarctic SEC is provided here. If not used, the reason for not considering them is provided.
DEM
The Greenland SEC applies the official level-2 data solutions provided by ESA. When this level-2 product is generated by ESA, a DEM is applied in the geolocation of LRM data. For more information refer to the mission specific documentation for the specific DEM used in the geolocation of the echo. No DEM are used for the combined cross-over and repeat-track solutions, however a DEM is used as an initial parameter for the LSM5-method applied for CryoSat-2 and Sentinel-3. The resulting solution from LSM5 is an update to the DEM. In the CryoSat-2 processing the Greenland Ice sheet Mapping Project (GIMP) DEM version 1 is used (Howat, Negrete, and Smith 2017).
Ice extent
In the original version the processing was done for all Greenlandic grid-cells with an ice-cover of more than 95%, as given by the PROMICE ice-cover product (Citterio and Ahlstrøm 2013). From version 2 and onwards, the processing is done for all ice-covered grid-cells in accordance to the ESA glaciers CCI ice-cover product for the Greenland ice sheet and strongly connected peripheral glaciers and ice caps (Rastner et al. 2012, file version: glaciers_cci_gi_rgi05_TMETM_19942009_v170525.zip).
GIA
No glacial isostatic adjustment is applied to the dataset, due to the large discrepancy in the model GIA signal in Greenland, and the limited bias in the resulting SEC.
Tides
As the extent of floating ice shelves is limited in Greenland, no tidal adjustment is added to the product.
Gravimetric mass balance
The Gravimetric mass balance (GMB) relies solely on data from the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on mission (GRACE-FO, until recently only available for the Greenland Ice Sheet). A GRACE-type mission consists of two twin satellites, which measure satellite-to-satellite distance. The gravity field of the Earth can then be derived from the change in the distance between the satellites. This precise evaluation of the gravity-field enables monthly solutions of Earth's gravity field anomalies from the first GRACE mission from March 2002 to October 2017 and GRACE-FO, only available for the Greenland ice sheet, from the end of 2018 to present. The GRACE mass-con solution from both the Greenland and the Antarctic ice sheet CCI+ projects are brokered for the C3S-product and provided for the major ice sheet basins. See Barletta, Sørensen and Forsberg (2013) and Groth and Horwath (2016) for the description of the derivation of GMB from the initial level-2, c20, 1-degree GRACE-data. A GIA model and land ice mask are used as auxiliary data, along with the drainage basin definitions.
Ice Sheets User requirements
The overall requirements for ice sheet related ECVs, as listed by the Global Climate Observing System (GCOS Implementation Plan, 2016) are given in Table 1 below.
Table 1: GCOS target requirements for ice sheet related ECVs (source: GCOS Implementation Plan, 2016)Anchor Table 1 Table 1
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***It should be noted that the grounding line location is currently not included as an ECV within C3S but is already developed and implemented within the Greenland and Antarctic Ice Sheet CCI projects.
Ice velocity
The primary GCOS requirements for ice velocity are listed in Table 1. In addition, as part of Ice Sheets CCI Phase 1, user requirements were identified through an extensive user survey within the community. The User Requirements Document (URD) from the Ice Sheets CCI Phase 1 project contains a full description of the results from this survey (Hvidberg et al., 2012). The user requirements for ice velocity are summarised in Table 2.
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Requirement | Minimum | Optimal |
Spatial Resolution | 100m-1km | 50m-100m |
Temporal Resolution | annual | monthly |
Accuracy | 30-100 m/year | 10-30 m/year |
Time of Observations | All year |
Surface elevation change
The primary GCOS requirements for surface elevation change are listed in Table 1. In addition, as part of the Ice Sheets Greenland/Antarctica CCI Phase 1, user requirements were identified through an extensive user survey within the community. The User Requirements Document (URD) generated contains a full description of the results from this survey (Hvidberg et al., 2012, Shepherd et al., 2018) and its first requirement1 matches the GCOS table (Table 1).
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1 The User Requirements reported by the ESA CCI Antarctic Ice Sheets Project provided the requirements to produce SEC product with a minimum spatial resolution of 1-5km or an optimum spatial resolution of <500m (Shepherd et al., 2018) |
Gravimetric mass balance
The GCOS requirements regarding ice mass change do not adequately follow glaciological considerations, as there is a difference between ice sheet volume change (units: km3/year) and mass change (units: Gt/year). It has been assumed that the requirements should be given in water equivalent volumes, hence the conversion of 1-to-1 from volume to mass in Table 1.
Ice Sheets Gap analysis
Ice velocity
Description of past, current and future satellite coverage
The primary source dataset for the Greenland Ice Sheet (GIS) ice velocity product comprises Sentinel-1 (S1) single look complex (SLC) SAR data acquired in Interferometric Wide (IW) swath mode. One of the unique aspects of the S1 mission is the systematic acquisition planning of polar regions, designed to cover the entire GIS margin and large sections of the Antarctic coast continuously. The ongoing acquisition of ice sheet margins in Greenland is augmented by a dedicated annual ice sheet-wide campaign.
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The constellation is currently the primary source for year-round monitoring of ice velocity. In 2019, further expansion of the continuous coverage in Greenland commenced including also the interior ice sheet. This provides an opportunity to produce Greenland wide velocity maps at sub-annual, and even monthly, intervals. The Sentinel-1 constellation will continue to operate well into the next decade with two more satellites (Sentinel-1C and -1D) already in development. This, in combination with other new and planned SAR missions (e.g. SAOCOM, NASA-ISRO NISAR), ensures the long-term sustainability of the CDR.
Development of processing algorithms
The existing system (ESP v2.1) at ENVEO for annual IV production for Greenland is fully operational. ESP is a state-of-the-art IV retrieval algorithm suited to accommodate the ongoing evolution of the Copernicus Sentinel-1 mission data. The primary processor will continue to be developed and updated to accommodate new sensors and requirements. Further technical development activities, ongoing and planned, are described in sections 1.4.1.4 and 1.4.1.5.
Methods for estimating uncertainties
The error prediction framework described in Mohr and Merryman-Boncori (2008) is applied to derive estimates of the error standard deviation of slant-range and azimuth velocity measurements. The input to the framework consists of the location of the GCPs used for velocity calibration, and in models for the covariance function (or equivalently the structure function) of all error sources, including noise and atmospheric propagation. For a mathematical formulation, the reader is referred to Mohr and Merryman-Boncori (2008).
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QA-IV-5 Another internal QA method widely applied for quality assessment of velocity products is the analysis of stable ground where no velocity is expected. This gives a good overall indication for the bias introduced by the end-to-end velocity retrieval including co-registration of images, velocity retrieval, etc. After performing the matching for the entire region covered by the image pair, the results for the ice covered (moving) area are separated from ice-free (stable) ground. The masking is done using a polygon of the glacier/rock outline. The quality metrics of this test provides Mean and RMSD of the velocity over stable terrain; mean values should be close to 0.
Opportunities to improve quality and fitness-for-purpose of the CDRs
The current ice velocity (IV) CDR constitutes an annually averaged Greenland Ice Sheet velocity map, based on offset tracking, derived from all Sentinel-1 repeat acquisitions within a year (6- and 12-day repeats). These data have now been further exploited to assemble and merge IV maps at a higher spatial resolution (250m) and temporal frequency (monthly). This permits high-resolution comprehensive monitoring of the full Greenland Ice Sheet on a monthly basis for studying long term trends and short-term fluctuations. Monthly velocity maps are now routinely produced and are ready to be included as a product improvement (Figure 3).
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Figure 4: Ice velocity map of Nioghalvfjerdsbrae/79Fjord-Glacier and Zachariae Isbræ from Sentinel-1 only (left) and merged product based on Sentinel-1 and Sentinel-2 (right). Anchor Figure 4 Figure 4
Scientific research needs
A clear and foremost scientific research need is the expansion of the Ice Velocity service to include also the Antarctic Ice Sheet. The current C3S service foresees only the production of an ice velocity map for the Greenland Ice Sheet. While Greenland is a main factor for current sea level rise, the largest unknown for future sea level rise is caused by uncertainty in the predicted response of the Antarctic Ice Sheet to global warming. Refining these predictions requires accurate knowledge of the past and current ice mass imbalance of Antarctica and its main driving forces. Detailed homogenized ice velocity maps and velocity time series are hereby essential, as primary input for studies on dynamic processes, ice discharge, iceberg calving and possible spatial and interannual variations herein. This is of major importance in order to establish how short-term fluctuations relate to longer multiyear trends and to identify the principal driving mechanisms. The system for producing monthly and annual ice velocity mosaics, covering the Antarctic Ice Sheet margins, is already in place and implemented. Monthly and annual ice velocity maps are currently produced and can readily be included as an extension of the ice velocity CDR within the Ice Sheets and Ice Shelves service. Figure 5 shows monthly ice velocity maps for Antarctica since January 2015, derived from Sentinel-1.
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Another research need required for improving the processing algorithm is reduction of the effects of differential ionospheric path delay and removal of ionospheric stripes. These stripes are clearly evident as streaks in the retrieved velocity (particularly over northern Greenland) that are aligned slightly oblique to the LOS direction. Ionospheric disturbances are one of the main sources of error in the IV maps and hinder applications. As the repeat cycle for S1 is short, the potential impact of ionosphere-induced noise on the velocity is high. A way to compensate the ionospheric effects is the implementation of the split-spectrum method in the processor, which permits separating the ionospheric and the non-dispersive phase terms.
Opportunities from exploiting the Sentinels and any other relevant satellite
As mentioned in section 1.1, further expansion of continuous acquisition coverage in Greenland of Sentinel-1 provides an opportunity to produce Greenland-wide velocity maps at high temporal resolution. Additionally, the increased temporal coverage in the interior could reduce the error in the annual maps and facilitate the removal of ionospheric stripes.
An opportunity that arises from the reduced revisit time and increased coverage provided by the dual satellite constellation is the application of InSAR for improving the accuracy and resolution of velocity retrievals. A further expansion in coverage of Sentinel-1 crossing-orbit pairs, as well as a further reduction in revisit time to potentially one day, when Sentinel-1C is launched, would be advantageous.
Antarctic surface elevation change
Description of past, current and future satellite coverage
The Antarctic SEC data initially came from four satellite missions. One more was added in the evolution to the v2 system, and another in the evolution to the v3 system.
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In the data product there are no temporal data gaps. At each timestamp a varying pattern of grid cells contain no data. Estimation of the missing data values may be undertaken with care, considering the underlying geophysics of the Antarctic.
Development of processing algorithms
The original system, C3S_Ant_Sec_ops_v1.0, was used to make the initial data product. Its modular layout allowed it to be upgraded to the v2 system with minimal alteration. Sentinel-3A data was first included in v2, and the multi-mission cross-calibration algorithm was improved. Processing of CryoSat-2 data in v2 stopped when the baseline C data stream was halted by ESA. For v3, the system, now called C3S_Ant_Sec_ops_v3.0, was configured to process new releases of data. The EnviSat GDRv2.1 dataset was replaced in its entirety by GDRv3. The CryoSat-2 baseline D data stream is incorporated from the start of the mission, replacing baseline C entirely, and continues to update. The data stream from Sentinel-3B is newly incorporated.
Methods for estimating uncertainties
The uncertainty in each surface elevation change rate is calculated from three components summed in quadrature. These components are independent of each other and independent in all grid cells. They are:
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Figure 9: Validation against Operation IceBridge of the v3 dataset.Anchor Figure 9 Figure 9
Opportunities to improve quality and fitness-for-purpose of the CDRs
The input data used in the product comes from data streams that are constantly being upgraded and refined. Incorporation of CryoSat-2 baseline D data will add coverage to the region around the south pole, and extra data density elsewhere. Incorporation of Sentinel-3B will also add data density. The EnviSat GDRv3 dataset will replace the current v2.1 in the product. When available, the Sentinel-3A and B land ice processor data products will fill the current gaps left where the orbital track transition from ocean to land is not handled properly.
Scientific research needs
In order to identify ice dynamic trends, the main emphasis for scientific research is in a long period of continuous acquisition. Progressive improvements in instrumentation allow for greater accuracy and areal coverage and thus a better focus on interesting regions at the sub-drainage-basin scale.
Opportunities from exploiting the Sentinels and any other relevant satellites
The CryoSat-2 and Sentinel-3 A and B missions continue to extend the product time series, providing greater simultaneous coverage, both geographically and temporally, than has previously been possible.
Greenland Surface elevation Change
Description of past, current and future satellite coverage
The original release of the Greenland ice sheet surface elevation change data utilised four radar-altimeter satellite missions. The evolution to version 2 included Sentinel-3A data, and the newest evolution, version 3, now includes new data from Sentinel-3B, and reprocessed data from ENVISAT and CryoSat-2. The amount of new data has called for a full reprocessing of the elevation change time series for Greenland using an updated processing chain.
The satellite coverage for the GrIS is the same as for Antarctica and is listed in section 1.4.2.1 and Table 4. However, as the north-pole is covered by ocean and not ice sheet, the coverage of the GrIS is more complete than for the Antarctic ice sheet, as only the northernmost part of the ice sheet is not covered by the orbit inclination of ERS-1, ERS-2, ENVISAT, Sentinel-3A and Sentinel-3B. Satellite radar altimetry is more challenging for the GrIS than the Antarctic ice sheet, as a larger proportion of the ice sheet is located in areas with complex topography. The traditional radar altimeters, with the large footprint size, are especially challenged. Here, the principle of observations only at the point-of-closest-approach results in biasing the observations to points at higher elevation. Hence, to retrieve surface elevation change an optimal combination of along-track and plane-fitting methods are used for 5-year or 3-year data-windows advancing in steps of one month. To insure good spatial coverage, the individual methods are averaged at a larger grid (25km by 25km polar stereographic) than their native grid resolution by ordinary kriging. At each timestamp, a varying pattern of grid cells contains no data. Estimation of the missing data values may be undertaken with care, considering the underlying geophysics of the Greenland ice sheet.
Development of processing algorithms
The original system, C3SMontly, had a modular layout in terms of missions. This allows for alterations throughout the processing chain. The version2 system, C3SMontlyVers2, were done without any changes to the main structure of the operational code of the original version. However, a major update to the system was the addition of the ordinary-kriging module, which allows for surface elevation change predictions at all ice sheet grid-cells, and not only at low slope as in the original version.
The version 3 system upgrades, now called C3SV3, included a reprocessing of ERS-1, ERS-2, and ENVISAT, which allowed for the structural code to be revised to include the true-repeat track algorithm in the python environment of version 2 and not being processed separately. This will in the future also allow for a fast transition of the applied method if CryoSat-2 should be decommissioned.
Methods for estimating uncertainties
The uncertainty is given by the combination of the epoch uncertainty (derived from the supplied input data) and the model uncertainty. Figure 10 shows the distributions of the fitting stability and accuracy evaluated for all surface elevation estimates. We see more values closer to the GCOS requirements in version 3 fitting stability compared to version 2. The main improvement in the stability is ascribed to the reprocessing of ENVISAT. There are still a substantial number of values just above the GCOS requirements, which are introduced by the shortening of the data-record used for CryoSat-2 and Sentinel-3, alongside the increased number of observations at coastal locations, where the uncertainty is larger due to the complex topography. This is mainly due to more weather variability introduced by the shortening of the averaging window, but is removed in the accuracy estimate by averaging data on sub-grid-cell level.
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Figure 11: Difference in the rate of elevation change between OIB and the C3S product version 2 and 3. As the OIB level 4 data consist of data from all repeats of older flight paths, the years in the figure refer to the first year of observations, e.g. 1993 includes data for all repeats of the 1993 flightpath until 2017. The upper-left panel shows the point-to-point agreement, alongside the one-to-one line. The lower-left panel shows the complete distribution for all years, which is averaged in the right panel to show the spatial distribution.Anchor Figure 11 Figure 11
Opportunities to improve quality and fitness-for-purpose of the CDRs
The input data used in the SEC product comes from data-streams that are constantly being upgraded and refined, as we see with the update of EnviSat GDRv3 and CryoSat-2 baseline-D datasets. In the future the Sentinel-3 land ice processor will become available and will fill the current gaps left where the orbital track transition from ocean to land is not handled properly. When the land-ice processing is operational, the processing chain can be switched to the more optimal Sentinel-3 data product to improve the data quality at the coastal regions with the updated slope model being applied in the product.
Scientific research needs
The scientific research needs for the SEC product over Greenland are the same as for the Antarctic surface elevation change product, section 1.4.2.5.
Opportunities for exploiting the Sentinels and any other relevant satellites
These are the same as for the Antarctic surface elevation change product, section 1.4.2.6.
Gravimetric mass balance
Description of past, current, and future satellite coverage
As the first GRACE mission ended in October 2017, the gravimetric mass balance has a data gap between GRACE and GRACE-Follow-On missions. GRACE-FO was launched on May 22, 2018. Only the Greenland CCI project have released GRACE-FO solutions, therefore Greenland data is the only data brokered for the GRACE-FO period for now. When available the Antarctic counterpart will be brokered, which is expected in the first half of 2021.
Development of processing algorithms and methods for estimating uncertainties
The GRACE solution provided for the major drainage basins are brokered from the Greenland and the Antarctic ice sheet CCI projects. For both processing algorithms and uncertainty estimates we refer to Barletta, Sørensen and Forsberg (2013), and Groth and Horwath (2016).
The primary GCOS requirements for Gravimetric mass balance are met in terms of horizontal resolution (Table 1). If typical ice densities are assumed, the measurement uncertainties are at present about twice the requirement. This emphasises the outstanding scientific question of how to deal with the signal leakages between changing bodies of mass, such as individual drainage basins and peripheral glaciers and ice caps.
Opportunities to improve quality, fitness-for-purpose of the CDRs
In addition to understanding the signal leakage, a major opportunity lies with the R&D activity anticipated in the community in relation to the GRACE-FO mission to fully understand the Antarctic record. In addition to the research and development in resolving the Antarctic issue, an unresolved issue for both hemispheres remains: How should GRACE and GRACE-FO missions be merged/bridged. This will not be solved by the GMB alone but will require inputs from the surface elevation change and the ice velocity records.
Grounding Line Location
Introduction
The grounding line separates the floating part of a glacier/ice shelf from the grounded part. Processes at the grounding lines of floating marine termini of glaciers and ice streams are important for understanding the response of the ice masses to changing boundary conditions and to establish realistic scenarios for the response to climate change and implications for sea level rise. The discharge of an ice sheet is measured at the grounding line and enhanced ice discharge directly affects sea level rise. Furthermore, the migration of the grounding line is a sensitive indicator of ice thickness change and the Grounding Line Location (GLL) is listed as an "important parameter for ice sheets" in the IGOS Cryosphere Theme Report (IGOS, 2007), and listed in the user requirements for ice sheet related ECVs in the GCOS Implementation Plan (GCOS, 2016). Remote sensing observations do not provide direct measurements of the grounding line position but can be used to detect the tidal flexure zone, which is a proxy for the GLL. InSAR provides an excellent tool for directly observing the tidal motion of a marine terminating outlet glacier or ice shelf, as it shows up as distinct fringe patterns in the interferograms.
Description of past, current and future satellite coverage
With the launch of Sentinel-1 in April 2014 a new SAR data set became available for mapping the location of the grounding line. The main acquisition mode of Sentinel-1 is Interferometric Wide Swath Mode, which applies TOPS mode for acquiring the data. Initially, due to the repeat interval of 12 days, coherence was low over fast moving outlet glaciers, complicating the formation of interferograms suitable for GLL delineation. The launch of Sentinel-1B, in April 2016, has reduced the repeat pass period to 6 days providing significant improvements. Sentinel-1 data are now regularly acquired every 6 to 12 days along the margins of the Greenland and Antarctic Ice Sheets, allowing for regular InSAR analysis for determining the grounding line location and its evolution.
Development of processing algorithms
In the ESA Antarctic Ice Sheet CCI and Greenland Ice Sheet CCI projects, ENVEO was ECV lead and ECV collaborator in developing algorithms for mapping the Grounding Line Location using SAR data, focussed on Sentinel-1. The processing chains have already been developed and implemented and can be rolled out for deriving valuable new climate data records on grounding line positions. The method has the potential to deliver a systematic and continuous record of GLLs and GLL migration around Antarctica and main Greenland outlet glaciers. This will greatly benefit the investigation of environmental forcings on ice discharge and of the current and future sea level rise contribution of the ice sheets. Figure 12shows as example an interferogram and the grounding line of Ryder Glacier in northern Greenland derived from Sentinel-1 data acquired in 2017.
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Figure 12: Geocoded double difference interferogram of the grounding zone of Ryder Glacier derived from repeat pass SAR data of Sentinel-1A and 1B acquired at 6, 12 and 18 January 2017 (background: Google Earth). Thick black lines indicate the lower and upper boundary of the tidal flexure zone. Inset shows location of Ryder Glacier in North Greenland (figure adapted from Mottram et al. , 2019).Anchor Figure 12 Figure 12
Surface Melt Processes
Introduction
The availability of Copernicus Sentinel-1 C-band SAR data since 2014 provides the opportunity for producing a consistent high-resolution climate data record on the presence of liquid water ("melt extent") and properties over Antarctica and Greenland. The area extent and duration of surface melt on ice sheets are important parameters for climate and cryosphere research and key indicators for climate change. Surface melting has a significant impact on the surface energy budget of snow areas, as wet snow has a relatively low albedo in the visible and near-infrared spectral regions. Moreover, enhanced surface meltwater production, raising the internal water pressure and leading to enhanced lubrication at the base which has a strong impact on glacier motion. Surface melt also plays an important role for the stability of marine ice sheets and ice shelves, as the intensification of surface melting as precursor to the break-up of ice shelves in the Antarctic Peninsula has shown.
Description of past, current and future satellite coverage
Passive and active microwave satellite sensors are the main data sources for products on melt extent over Greenland and Antarctica. In particular, low resolution passive microwave data has been widely used to map and monitor melt extent on ice sheets with earlier work focusing on melt zones of the Greenland Ice Sheet. The difficulty in accessing higher resolution SAR data, that existed in the past, has been overcome with the launch of the Copernicus Sentinel-1 (S1) mission, developed and operated by ESA, guaranteeing the availability of regular C-band SAR acquisitions free of charge. S1 SAR data are now regularly acquired every 6 to 12 days in many parts of the world, allowing for time series analysis at a high resolution for investigating the evolution of snow melting and refreezing processes during the season. The dedicated acquisition plan for the polar regions, covering Greenland and Antarctica with short revisit times of 6 to 12 days, enables the production of a dense year-round time-series of high-resolution radar backscatter maps, which form the basis for deriving melt products. Over Greenland S1 IW mode data is collected in co- and cross polarisation, that is, both horizontal-horizontal (HH) and horizontal-vertical (HV). This provides additional benefit for the identification of surface melt and surface refreezing due to different backscatter signatures in HV- and HH-polarized data.
Development of processing algorithms
In the ESA projects 4DAntarctica and 4DGreenland ENVEO is developing algorithms for mapping the Surface Melt Extent from Sentinel-1. For filling in gaps in time and space, that are not covered by Sentinel-1, METOP ASCAT data is used. The final goal is the generation of monthly time series of liquid water presence over Greenland and Antarctica at 200m spatial resolution and covering the duration of the Sentinel-1 mission (2014-onward). Derived products include maps of the start, duration and end of the annual surface melt periods.
References
Ice Sheets
Barletta, V.R.; Sørensen, L.S.; Forsberg, R. Scatter of mass changes estimates at basin scale for Greenland and Antarctica. The Cryosphere 2013, 7, 1411–1432. 757 42.
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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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