Showing papers by "Christoph Dahle published in 2020"

Return to rapid ice loss in Greenland and record loss in 2019 detected by the GRACE-FO satellites

Abstract: Between 2003-2016, the Greenland ice sheet (GrIS) was one of the largest contributors to sea level rise, as it lost about 255 Gt of ice per year. This mass loss slowed in 2017 and 2018 to about 100 Gt yr−1. Here we examine further changes in rate of GrIS mass loss, by analyzing data from the GRACE-FO (Gravity Recovery and Climate Experiment – Follow On) satellite mission, launched in May 2018. Using simulations with regional climate models we show that the mass losses observed in 2017 and 2018 by the GRACE and GRACE-FO missions are lower than in any other two year period between 2003 and 2019, the combined period of the two missions. We find that this reduced ice loss results from two anomalous cold summers in western Greenland, compounded by snow-rich autumn and winter conditions in the east. For 2019, GRACE-FO reveals a return to high melt rates leading to a mass loss of 223 ± 12 Gt month−1 during the month of July alone, and a record annual mass loss of 532 ± 58 Gt yr−1. Mass loss from the Greenland ice sheet returned to record levels in 2019, following unusually small loss in 2017-18, according to an analysis of satellite data from GRACE and its follow-on mission GRACE-FO.

Gravitationally Consistent Mean Barystatic Sea Level Rise From Leakage-Corrected Monthly GRACE Data

Abstract: Gravitationally consistent solutions of the Sea Level Equation from leakage-corrected monthly-mean GFZ RL06 Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) Stokes coefficients reveal that barystatic sea level averaged over the whole global ocean was rising by 1.72 mm a−1 during the period April 2002 until August 2016. This rate refers to a truely global ocean averaging domain that includes all polar and semienclosed seas. The result corresponds to 2.02 mm a−1 mean barystatic sea level rise in the open ocean with a 1,000 km coastal buffer zone as obtained from a direct spatial integration of monthly GRACE data. The bias of +0.3 mm a−1 is caused by below-average barystatic sea level rise in close proximity to coastal mass losses induced by the smaller gravitational attraction of the remaining continental ice and water masses. Alternative spherical harmonics solutions from CSR, JPL, and TU Graz reveal open-ocean rates between 1.94 and 2.08 mm a−1, thereby demonstrating that systematic differences among the processing centers are much reduced in the latest release. We introduce in this paper a new method to approximate spatial leakage from the differences of two differently filtered global gravity fields. A globally constant and time-invariant scale factor required to obtain full leakage from those filter differences is found to be 3.9 for GFZ RL06 when filtered with DDK3, and lies between 3.9 and 4.4 for other processing centers. Spatial leakage is estimated for every month in terms of global grids, thereby providing also valuable information of intrabasin leakage that is potentially relevant for hydrologic and hydrometeorological applications. Plain Language Summary Satellite gravimetry as realized with the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions is measuring tiny variations in the Earth's gravity field that are directly caused by divergent horizontal mass transports such as the melting of ice sheets and the corresponding discharge of melt water into the ocean basins. Between April 2002 and August 2016, this mass inflow caused sea level to rise by 1.72 mm each year as quantified from the latest GRACE reprocessing performed at our institute. The indirect observation principle of GRACE limits the spatial resolution so that highly localized mass loss signals are smeared out into the larger surrounding area, and possibly even from land into the ocean. We propose here a new method to quantify this so-called spatial leakage from the difference of gravity fields smoothed with slightly different spatial filters. A scale factor is obtained from exploiting the availability of two independent methods to estimate the mass component of sea level rise: The first method spatially integrates over the global gravity fields in all regions away from the coasts, and the second method utilizes a (leakage-corrected) mass distribution over the continents to calculate the gravitationally consistent distribution of water masses in all ocean basins. We estimate this scale factor as 3.9.

International Combination Service for Time-Variable Gravity Fields (COST-G) : Start of Operational Phase and Future Perspectives

Abstract: The International Combination Service for Time-variable Gravity Fields (COST-G) is a new Product Center of IAG’s International Gravity Field Service (IGFS). COST-G provides consolidated monthly global gravity fields in terms of spherical harmonic coefficients and thereof derived grids of surface mass changes by combining existing solutions or normal equations from COST-G analysis centers (ACs) and partner analysis centers (PCs). The COST-G ACs adopt different analysis methods but apply agreed-upon consistent processing standards to deliver time-variable gravity field models, e.g. from GRACE/GRACE-FO low-low satellite-to-satellite tracking (ll-SST), GPS high-low satellite-to-satellite tracking (hl-SST) and Satellite Laser Ranging (SLR). The organizational structure of COST-G and results from the first release of combined monthly GRACE solutions covering the entire GRACE time period are discussed in this article. It is shown that by combining solutions and normal equations from different sources COST-G is taking advantage of the statistical properties of the various solutions, which results in a reduced noise level compared to the individual input solutions.

Modelling spatial covariances for terrestrial water storage variations verified with synthetic GRACE-FO data

Abstract: Gridded terrestrial water storage (TWS) variations observed by GRACE or GRACE-FO typically show a spatial correlation structure that is both anisotropic (direction-dependent) and non-homogeneous (latitude-dependent). We introduce a new correlation model to represent this structure. This correlation model allows GRACE and GRACE-FO data users to get realistic correlations of the TWS grids without the need to derive them from the formal spherical harmonic uncertainties. Further, we found that the modelled correlations fit the spatial structure of uncertainties to a greater extent in a simulation environment. The model is based on a direction-dependent Bessel function of the first kind which allows to model the longer correlation lengths in the longitudinal direction via a shape parameter, and also to account for residual GRACE striping errors that might remain after spatial filtering. The global scale and shape parameters vary with latitude by means of even Legendre polynomials. The correlation between two points transformed to covariance by scaling with the standard deviations of each point. The covariance model is valid on the sphere which is empirically verified with a Monte-Carlo approach. The covariance model is subsequently applied to 5 years of simulated GRACE-FO data which allow for immediate validation with true uncertainties from the differences between the input mass signal and the recovered gravity fields. Four different realisations of the point standard deviations were tested: two based on the formal errors provided with the simulated Stokes coefficients, and two based on empirical standard deviations, where the first is spatially variant and temporally invariant, and the second spatially invariant and temporally variant. These four different covariance models are applied to compute TWS time series uncertainties for both the fifty largest discharge basins and regular grid cells over the continents. These four models are compared with the true uncertainties available in the simulations. The two empirically-based covariance models provide more realistic TWS uncertainties than the ones based on the formal errors. Especially, the empirically-based covariance models are better in reflecting the spatial pattern of the uncertainties of the simulated GRACE-FO data including their latitude dependence. However, these modelled uncertainties are in general too large. But with only one global scaling factor, a statistical test confirms the equivalence between the empirically-based covariance model with temporally variable point standard deviations and the true uncertainties. Thus at the end, this covariance model represents the closest fit in the simulation environment. The simulated GRACE-FO data are assumed to be very realistic which is why we recommend the new covariance model to be further investigated for the characterisation of real GRACE and GRACE-FO terrestrial water storage data.

NASA and GFZ GRACE Follow-On Mission: Status, Science, Advances

Abstract:

The twin satellites of the Gravity Recovery and Climate Experiment (GRACE) Follow-On mission were successfully launched in May-2018. The primary objective of the mission is to continue the 15-year GRACE (2002-2017) global data record of Earth’s monthly mass changes. These measurements have become an indispensable tool to quantify and track Earth’s water movement and surface mass changes across the planet. Monitoring changes in ice sheets and glaciers, near-surface and underground water storage, the amount of water in large lakes and rivers, as well as changes in sea level and ocean currents provides an integrated global view of how Earth’s water cycle and energy balance are evolving.

In this presentation we will present the current mission status, including instrument and flight system performance, discuss science data quality and performance as well as recent science results from the first two years of observations, and address data continuity from GRACE to GRACE Follow-On.

Benchmark data for verifying background model implementations in orbit and gravity field determination software

Abstract: . In the framework of the COmbination Service for Time-variable Gravity fields (COST-G) gravity field solutions from different analysis centres are combined to provide a consolidated solution of improved quality and robustness to the user. As in many other satellite-related sciences, the correct application of background models plays a crucial role in gravity field determination. Therefore, we publish a set of data of various commonly used forces in orbit and gravity field modelling (Earth's gravity field, tides etc.) evaluated along a one day orbit arc of GRACE, together with auxiliary data to enable easy comparisons. The benchmark data is compiled with the GROOPS software by the Institute of Geodesy (IfG) at Graz University of Technology. It is intended to be used as a reference data set and provides the opportunity to test the implementation of these models at various institutions involved in orbit and gravity field determination from satellite tracking data. In view of the COST-G GRACE and GRACE Follow-On gravity field combinations, we document the outcome of the comparison of the background force models for the Bernese GNSS software from AIUB (Astronomical Institute, University of Bern), the EPOS software of the German Research Centre for Geosciences (GFZ), the GINS software, developed and maintained by the Groupe de Recherche de Geodesie Spatiale (GRGS), the GRACE-SIGMA software of the Leibniz University of Hannover (LUH) and the GRASP software also developed at LUH. We consider differences in the force modelling for GRACE (-FO) which are one order of magnitude smaller than the accelerometer noise of about 10−10 m s−2 to be negligible and formulate this as a benchmark for new analysis centres, which are interested to contribute to the COST-G initiative.

Uncertainties of Terrestrial Water Storage Anomalies for Global Basins – A Comparison Between Modelled and Formal Covariances

Abstract:

The application of GRACE and GRACE-FO observed gridded terrestrial water storage data (TWS) often requires realistic assumptions of the data variances and covariances. Such covariances are, e.g., needed for data assimilation in various models or combinations with other data sets. The formal variance-covariance matrices now provided with the Stokes coefficients can yield such spatial variances and covariances after variance propagating them through the various post-processing steps, including the filtering, and spherical harmonic synthesis. However, a rigorous variance propagation to the TWS grids is beyond the capabilities of most non-geodetic users.

That is why we developed a new spatial covariance model for global TWS grids. This covariance model is non-stationary (time-depending), non-homogeneous (location-depending), and anisotropic (direction-depending). Additionally, it allows latitudinal wave-like correlations caused by residual striping errors. The model is tested for both GFZ RL06 Level-3 TWS data as provided via the GravIS portal (gravis.gfz-potsdam.de) and ITSG-Grace2018 GravIS-like processed Level-3 TWS data. The model parameters are fitted to empirical correlations derived from both TWS fields. Both data sets yield the same model parameters within the uncertainty of the parameter estimation.

Now, the covariance model derived thereof can be used to estimate uncertainties of mean TWS time series of arbitrary regions such as river basins. Here, we use a global basin segmentation covering all continents. At the same time, such regional uncertainties can be derived from formal variance-covariance matrices as well. To this end, the formal ITSG-Grace2018 variance-covariance matrices of the spherical harmonic coefficients are used. Thus, the modelled and formal basin uncertainties can be compared against each other globally, both spatially and temporally. Further, external validation investigates the usefulness of the basin uncertainties for applications such as data assimilation into hydrological models. Our results show a high agreement between the modelled and the formal basin uncertainties proving our approach of modelled covariance to be a suitable surrogate for the formal variance-covariance matrices.