Evaluating GRACE Mass Change Time Series for the Antarctic and Greenland Ice Sheet—Methods and Results

Satellite gravimetry data acquired by the Gravity Recovery and Climate Experiment (GRACE) allows to derive the temporal evolution in ice mass for both the Antarctic Ice Sheet (AIS) and the Greenland Ice Sheet (GIS). Various algorithms have been used in a wide range of studies to generate Gravimetric Mass Balance (GMB) products. Results from different studies may be affected by substantial differences in the processing, including the applied algorithm, the utilised background models and the time period under consideration. This study gives a detailed description of an assessment of the performance of GMB algorithms using actual GRACE monthly solutions for a prescribed period as well as synthetic data sets. The inter-comparison exercise was conducted in the scope of the European Space Agency’s Climate Change Initiative (CCI) project for the AIS and GIS, and was, for the first time, open to everyone. GMB products generated by different groups could be evaluated and directly compared against each other. For the period from 2003-02 to 2013-12, estimated linear trends in ice mass vary between −99 Gt/yr and −108 Gt/yr for the AIS and between −252 Gt/yr and −274 Gt/yr for the GIS, respectively. The spread between the solutions is larger if smaller drainage basins or gridded GMB products are considered. Finally, findings from the exercise formed the basis to select the algorithms used for the GMB product generation within the AIS and GIS CCI project

Mass balance of the Greenland Ice Sheet from 1992 to 2018

In recent decades, the Greenland Ice Sheet has been a major contributor to global sea-level rise1,2, and it is expected to be so in the future3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the degree and trajectory of today’s imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. Although the ice sheet was close to a state of balance in the 1990s, annual losses have risen since then, peaking at 335 ± 62 billion tonnes per year in 2011. In all, Greenland lost 3,800 ± 339 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.6 ± 0.9 millimetres. Using three regional climate models, we show that reduced surface mass balance has driven 1,971 ± 555 billion tonnes (52%) of the ice loss owing to increased meltwater runoff. The remaining 1,827 ± 538 billion tonnes (48%) of ice loss was due to increased glacier discharge, which rose from 41 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. Between 2013 and 2017, the total rate of ice loss slowed to 217 ± 32 billion tonnes per year, on average, as atmospheric circulation favoured cooler conditions15 and as ocean temperatures fell at the terminus of Jakobshavn Isbræ16. Cumulative ice losses from Greenland as a whole have been close to the IPCC’s predicted rates for their high-end climate warming scenario17, which forecast an additional 50 to 120 millimetres of global sea-level rise by 2100 when compared to their central estimate

Preface: New results from DORIS for science and society

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Astrodynamics & Space Mission

Update on CryoSat-2 long-term ocean data analysis and validation

ESA’s Earth Explorer CryoSat-2 precisely measures the changes in the thickness of marine ice floating on the polar oceans and variations in the thickness of the vast ice sheets that overlie Greenland and Antarctica. The data delivered by the CryoSat-2 mission completes the picture to determine and understand the ice role in the Earth system in general and climate change in particular. For this, the quality of the satellite orbit, the measurements of the altimeter, and all required corrections have to meet the highest performance; not only over the ice caps and sea-ice surface but also over the oceans. As Cryosat-2 ocean products continuously evolve they need to be quality controlled and thoroughly validated via science-oriented diagnostics based on multi-platform in situ data, models and other (altimeter) satellite missions. The rationale for this is based on the new CryoSat-2 scientific roadmap, which specifically addresses the key technical and scientific challenges related to the long-term monitoring of sea-level and ocean circulation changes in the context of Global Warming. This also involves opportunities for synergy with missions like ICESAT-2 and the upcoming Copernicus CRISTAL mission.In this context, the objective of our research is the long-term monitoring of the level-2 CryoSat-2 Geophysical Ocean Product (GOP), by evaluating the stability of the measurement system and identifying potential biases, trends and drifts over the ocean, through calibration and comparisons with concurrent ocean altimeter data, supported by the Radar Altimeter Database System (RADS). Independently, we also address this by comparing the GOP geophysical parameters with external models and in situ measurements such as the ones from selected sets of tide gauges. The very precise determination of the orbital height is part of the research activity but dealt with in a separate paper.For our activity we persistently monitor, analyze and identify systematic errors in the observations, estimated (trends in) biases in range, significant wave height, backscatter, wind speed and sea state bias, and timing biases. An important finding is that GOP CryoSat-2 Baseline C data seem to have a range bias of -2.82 cm and no apparent drift w.r.t. altimeter (Jason) reference missions (< 0.1 mm/yr). The comparison with tide gauges is based on monthly averaged sea level from the PSMSL archive, for which we conclude that GOP data has a correlation of better than 0.84 with a selected set of 185 PSMSL tide gauges, a mean standard deviation better than 5.8 cm, and an average drift of -0.19 mm/yr, which translates to an overall drift of +0.11 mm/yr when taking a global GIA correction of +0.3 mm/yr into account. We conclude that Cryosat-2 GOP represents a (long-term) stable measurement

Geodetic observations for constraining mantle processes in Antarctica

Geodynamic processes in Antarctica such as glacial isostatic adjustment (GIA) and post-seismic deformation are measured by geo-detic observations such as global navigation satellite systems (GNSS) and satellite gravimetry. GNSS measurements have comprised both continuous measurements and episodic measurements since the mid-1990s. The estimated velocities typically reach an accuracy of 1 mm a−1 for horizontal velocities and 2 mm a−1 for vertical velocities. However, the elastic deformation due to present-day ice-load change needs to be considered accordingly. Space gravimetry derives mass changes from small variations in the inter-satellite distance of a pair of satellites, starting with the GRACE (Gravity Recovery and Climate Experiment) satellite mission in 2002 and continuing with the GRACE-FO (GRACE Follow-On) mission launched in 2018. The spatial resolution of the measurements is low (about 300 km) but the measurement error is homogeneous across Ant-arctica. The estimated trends contain signals from ice-mass change, and local and global GIA signals. To combine the strengths of the individual datasets, statistical combinations of GNSS, GRACE and satellite altimetry data have been developed. These combinations rely on realistic error estimates and assumptions of snow density. Nevertheless, they capture signals that are missing from geodynamic forward models such as the large uplift in the Amundsen Sea sector caused by a low-viscous response to century-scale ice-mass changes.</p

Update on CryoSat-2 long-term ocean data analysis and validation

ESA’s Earth Explorer CryoSat-2 precisely measures the changes in the thickness of marine ice floating on the polar oceans and variations in the thickness of the vast ice sheets that overlie Greenland and Antarctica. The data delivered by the CryoSat-2 mission completes the picture to determine and understand the ice role in the Earth system in general and climate change in particular. For this, the quality of the satellite orbit, the measurements of the altimeter, and all required corrections have to meet the highest performance; not only over the ice caps and sea-ice surface but also over the oceans. As Cryosat-2 ocean products continuously evolve they need to be quality controlled and thoroughly validated via science-oriented diagnostics based on multi-platform in situ data, models and other (altimeter) satellite missions. The rationale for this is based on the new CryoSat-2 scientific roadmap, which specifically addresses the key technical and scientific challenges related to the long-term monitoring of sea-level and ocean circulation changes in the context of Global Warming. This also involves opportunities for synergy with missions like ICESAT-2 and the upcoming Copernicus CRISTAL mission.In this context, the objective of our research is the long-term monitoring of the level-2 CryoSat-2 Geophysical Ocean Product (GOP), by evaluating the stability of the measurement system and identifying potential biases, trends and drifts over the ocean, through calibration and comparisons with concurrent ocean altimeter data, supported by the Radar Altimeter Database System (RADS). Independently, we also address this by comparing the GOP geophysical parameters with external models and in situ measurements such as the ones from selected sets of tide gauges. The very precise determination of the orbital height is part of the research activity but dealt with in a separate paper.For our activity we persistently monitor, analyze and identify systematic errors in the observations, estimated (trends in) biases in range, significant wave height, backscatter, wind speed and sea state bias, and timing biases. An important finding is that GOP CryoSat-2 Baseline C data seem to have a range bias of -2.82 cm and no apparent drift w.r.t. altimeter (Jason) reference missions (&lt; 0.1 mm/yr). The comparison with tide gauges is based on monthly averaged sea level from the PSMSL archive, for which we conclude that GOP data has a correlation of better than 0.84 with a selected set of 185 PSMSL tide gauges, a mean standard deviation better than 5.8 cm, and an average drift of -0.19 mm/yr, which translates to an overall drift of +0.11 mm/yr when taking a global GIA correction of +0.3 mm/yr into account. We conclude that Cryosat-2 GOP represents a (long-term) stable measurement.Astrodynamics & Space Mission

Signal and noise in Gravity Recovery and Climate Experiment (GRACE) observed surface mass variations

The Gravity Recovery and Climate Experiment (GRACE) product used for this study consists of 43 monthly potential coefficient sets released by the GRACE science team which are used to generate surface mass thickness grids expressed as equivalent water heights (EQWHs). We optimized both the smoothing radius and the level of approximation by empirical orthogonal functions (EOFs) and found that 6.25° and three modes are able to describe more than 73.5% of the variance. The EQWHs obtained by the EOF method describe all known variations in the continental hydrology, present?day ice sheet melting, and global isostatic adjustment. To assess the quality of the estimated grids, we constructed degree error spectra of EQWHs. We conclude that a significant part of the errors in GRACE can be explained by a scaling factor of 0.85 relative to degree error estimates provided by the GGM02C gravity model but that the present?day errors in the GRACE data are a factor 2 to 5 larger than forecasted by tide model differences and atmospheric pressure differences. Comparison to a network of 59 International GNSS Service (IGS) stations confined the filter parameter settings to three EOF modes and 5° or 6.25° smoothing radius. Residuals that remain after the EOF method do exhibit S2 aliasing errors and a semiannual continental hydrology signal contained in the Global Land Data Assimilation Systems (GLDAS) model. Further analysis of the residual EOF signal revealed alternating track correlation patterns that are partially explained by the GRACE covariance matrix and the handling of nuisance parameters in the GRACE data processing.Space EngineeringAerospace Engineerin

Geodetic observations for constraining mantle processes in Antarctica

Geodynamic processes in Antarctica such as glacial isostatic adjustment (GIA) and post-seismic deformation are measured by geo-detic observations such as global navigation satellite systems (GNSS) and satellite gravimetry. GNSS measurements have comprised both continuous measurements and episodic measurements since the mid-1990s. The estimated velocities typically reach an accuracy of 1 mm a−1 for horizontal velocities and 2 mm a−1 for vertical velocities. However, the elastic deformation due to present-day ice-load change needs to be considered accordingly. Space gravimetry derives mass changes from small variations in the inter-satellite distance of a pair of satellites, starting with the GRACE (Gravity Recovery and Climate Experiment) satellite mission in 2002 and continuing with the GRACE-FO (GRACE Follow-On) mission launched in 2018. The spatial resolution of the measurements is low (about 300 km) but the measurement error is homogeneous across Ant-arctica. The estimated trends contain signals from ice-mass change, and local and global GIA signals. To combine the strengths of the individual datasets, statistical combinations of GNSS, GRACE and satellite altimetry data have been developed. These combinations rely on realistic error estimates and assumptions of snow density. Nevertheless, they capture signals that are missing from geodynamic forward models such as the large uplift in the Amundsen Sea sector caused by a low-viscous response to century-scale ice-mass changes.Physical and Space GeodesyAstrodynamics & Space Mission

Towards covariance realism in batch least-squares orbit determination

Regular products within the field of Space Surveillance and Tracking (SST) and Space Traffic Management (STM), such as high-risk collisions, upcoming re-entries or fragmentations, rely both on the estimated state and associated uncertainty of detectable resident space objects (RSOs). Classical orbit determination (OD) algorithms provide the required estimations, assuming that the uncertainty in the state of the object is properly characterized by its state vector covariance and assuming Gaussian processes. However, a common problem of classical orbit determination processes is the misrepresentation of the RSOs uncertainty through the estimated covariance. Ultimately, this causes a great impact in the quality and accuracy of SST products as the estimated covariance is overly optimistic (too small) and the true uncertainty of the object is not captured. One of the causes for the unrealism of the estimated covariance is found in the classical OD approaches, as they fail to consider, or properly characterize, the uncertainty of the dynamical models used to describe the motion of the objects, such as the atmospheric drag force or the solar radiation pressure acting on the orbiting RSOs. Because these models provide a deterministic solution to a stochastic phenomenon, an inherent associated uncertainty should be regarded when used during an orbit determination. The aim of this work is to devise a methodology to improve the covariance realism of common OD processes through the classical theory of consider parameters of batch least squares methods. The methodology uses the classical theory of consider parameter to add to the estimated covariance the contribution coming from the uncertainty of the consider parameters. To do so, the variances of the consider parameters are estimated through another least squares process, with which the propagated covariance best fits a so-called observed covariance, previously derived, in a process named covariance determination. The influence of the main sources of dynamic model uncertainty can be evaluated by examining the resulting covariance correction for each uncertainty source (e.g. atmospheric drag force modelling, sensor calibration parameters or solar radiation prediction). This publication focus on studying the effect of the atmospheric drag force and range bias modelling uncertainty in the correction of an estimated covariance. The proposed methodology has been applied to a simulated realistic scenario of measurements and objects to evaluate the consistency of the corrected covariance via Monte Carlo analysis. Thorough analyses are presented to illustrate the effect of dynamic model errors on covariance realism. Copyright © 2019 by the International Astronautical Federation (IAF). All rights reserved