The mass balance of the Antarctic Ice Sheet (AIS) can be obtained from time-variable gravity fields from GRACE and GRACE-FO. This is useful to investigate the ice sheet's adaption to a changing climate and its contribution to the global mean sea level. However, the correction for the present-day glacial isostatic adjustment (GIA) in Antarctica is still the largest uncertainty contributor in gravimetric mass balances. Lacks of knowledge of the ice loading history and the solid Earth's rheology lead to a large spread of GIA predictions from forward modelling approaches. We developed a method that allows to spatially resolve GIA, ice mass changes, and firn thickness changes in Antarctica within a globally consistent framework using geodetic satellite data and results of climate modelling. From simulation experiments, we found that the signal separation is possible despite data limitations as long as accounting for spatial error covariances of the input data sets. Here, we present and discuss results from a global inversion of satellite gravimetry data (from GRACE/GRACE-FO), satellite altimetry data (CryoSat-2), regional climate modelling (from RACMO2), and modelled firn thickness changes (from IMAU-FDM) over the time period Nov 2010 to Dec 2020. Furthermore, we will discuss the advantage of implementing this methodology in a global sea level inversion framework.

Estimation of the ice mass change in Antarctica is a critical component of sea level rise determination. Satellite gravimetry measures mass distribution changes and allows to calculate ice mass loss. However, such estimates crucially depend on the correction for glacial isostatic adjustment (GIA), the vertical movement of the solid-Earth due to ice load changes in the past.

We investigate the determination of GIA by the combination of gravimetry, altimetry, and firn densification model (FDM) data products. The investigation encompasses the impact of various processing parameters of the GIA on rate level for the four regions West- and East Antarctic Ice Sheet (WEAIS), Amundsen Sea Embayment (ASE, West Antarctica), Atlantic Sector (ATS, East Antarctica), and Wilkes Land (WIL, East Antarctica). Furthermore, an analysis of GIA is conducted at time series level to reveal the differences in combinations using different altimetry and FDM products.

The consideration of GIA at rate level reveals differences through the utilization of disparate altimetry products, FDM products and time periods. Variations of up to 50 % of the total GIA signal are observed in WEAIS and ASE, 100 % in WIL and 200 % in ATS, respectively. The investigation at time series level unravels limitations of the input data through unphysical seasonal amplitudes, interannual variations, jumps, and trend shifts in the GIA combination result. Deviations from a linear trend in the time series, i.e. temporal variations of the linear trend, are analyzed using a state space appraoch. The mean rate with 2σ uncertainty of the GIA mass effect combination ensemble using state space is 79 ± 29 Gt/a, 45 ± 6 Gt/a, 10 ± 16 Gt/a, and 1 ± 7 Gt/a for the WEAIS, the ASE, the ATS, and the WIL, respectively.

The results indicate the substantial impact of the selection of the utilized input data product, as well as the challenges associated with data-based GIA estimation in regions exhibiting a low signal-to-noise ratio. The determination of the GIA time series offers a benchmark opportunity to assess the performance of various data products, thereby highlighting their respective limitations.

Co-Author: Matthias Willen

The use of low-cost geodetic sensor technology opens up promising prospects for comprehensive and cost-effective monitoring of climate-related coastal risks. This presentation will introduce two types of sensors: First, low-cost GNSS receivers that can provide precise position data in real time. They are ideal for identifying subsidence, coastline displacement or deformations of protective structures along the coast. Secondly, MEMS sensors will be presented, as used in geodetic research, in particular for recording accelerations, inclinations or vibrations. These properties can be effectively utilised in the field of climate-related coastal risks. The fusion of GNSS and MEMS data increases reliability as well as spatial and temporal resolution. In addition, geodetic monitoring data becomes easier to interpret, especially for dynamic objects or in sensitive coastal areas that are difficult to access. Data fusion as a challenge will be discussed together with the topics of calibration and long-term stability. Finally, integration strategies in the context of the Internet of Things (IoT) and integration into monitoring networks will be discussed.

Simulated terrestrial water storage (TWS) is an important source of information for various climate-related geodetic studies, e.g., the evaluation and improvement of satellite gravimetry products, or the correction of GNSS-based coordinate time-series. We employ the open-source hydrological model OS LISFLOOD developed by the Joint Research Centre (JRC) of the European Commission to generate global daily TWS time series, and its individual storage compartments soil moisture, groundwater, surface water, and snow, in a so far unprecedented high spatial resolution of 1/20° over the time period 2000 – 2023. Modeled TWS results from OS LISFLOOD have been shown to be competitive to other state-of-the-art hydrological models, using satellite gravimetric data from GRACE/-FO and GNSS-derived surface displacements for validation.

Recently, a new model version (v5) was developed by the JRC, which is currently being calibrated globally and will be publicly available soon. The new version benefits from several model improvements including an optimized soil depth definition; an improved model initialization; a modified snow routine; and an alternative river routing scheme. Furthermore, the number of lakes and reservoirs explicitly simulated was more than doubled compared to the previous version, and also includes several endorheic lakes now (i.e. lakes without an outlet like the Caspian Sea, Lake Balkhash, or Lake Chad). While endorheic lakes experience considerable water storage variations, they, however, pose a challenge to hydrological modeling as the calibration cannot be done with the classical approach of using river gauges.

In this contribution we show results from a first experiment with the new OS LISFLOOD v5, and evaluate them with respect to previous results, by computing the fit to GRACE/-FO observations on various spatial and temporal scales with different measures (e.g. correlation, RMSD, KGE). We particularly focus on the ability of the new model version to reproduce endorheic lake level changes by utilizing time series from satellite altimetry.

Co-authors:
Robert Dill (1), Stefania Grimaldi (2), and Henryk Dobslaw (1)
(1) GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
(2) European Commission Joint Research Centre, Ispra, Italy

The atmospheric boundary layer is characterized by pervasive turbulent motions across diverse scales, driving the transport and mixing of crucial scalar quantities like temperature and water vapor (WV). Water vapor, a potent greenhouse gas, critically influences weather dynamics, radiative transfer, and numerical weather prediction (NWP). Its variability also introduces significant atmospheric artifacts in remote sensing techniques such as Interferometric Synthetic Aperture Radar (InSAR).

Understanding these WV fluctuations is thus essential not only for correcting measurement distortions in various applications but also for advancing climatological research. This work introduces the PALM model system, a high-resolution Large Eddy Simulation (LES) framework, as a novel tool in geodesy. LES models like PALM enable detailed and controlled simulations of the turbulent atmospheric boundary layer, capturing fine-scale dynamics unresolved by coarser atmospheric models.

To illustrate PALM's potential, we present a focused example: ray tracing within a PALM-simulated atmosphere to estimate the Zenith Wet Delay (ZWD), a critical parameter in GNSS-based positioning. We demonstrate that the spectrum of ZWD fluctuations derived from these simulations aligns well with theoretical predictions, highlighting PALM's capability for accurate turbulent atmospheric characterization.