Since decades, even centuries, blanket and raised bogs in the central-northern United Kingdom have been exposed to climatic and anthropogenic stressors like drainage, land management, evapotranspiration, peat extraction and/or atmospheric pollution. As a result, those landscapes undergo substantial mass changes and soil compaction, but the quantification of these processes remains a challenge: In-situ observations are cost-extensive and/or often biased due to an unstable local measurement reference.To overcome this, we analyze remotely-sensed surface uplift rates from the European Ground Motion Service (EGMS) that were extracted from four years (2019-2023) of radar-interferometric (InSAR) time-series. We also consulted point-wise uplift rates from two decades of positioning (GNSS) measurements to reference ongoing bedrock uplift. We validate these observations with analytical models that mimic a the load response while also accounting for a remaining glacial isostatic adjustment.The surface rate maps show ~50 km-wide uplift bulges that correlate with land classified as heathlands and bogs. Maximum uplift surrounding the heather/bogs reaches 2-7 mm/yr. The bogs/heathlands themselves, however, exhibit distinct subsidence due to mass loss (carbon, water) of up to 10 mm/yr, which is already corrected for the simultaneous bedrock uplift due to unloading. Based on these observations, we can reproduce the spatial bedrock uplift pattern with our load model. The explanation of the signal amplitudes requires further fine-tuning of the model parameters and a better understanding of the in-situ bio-chemical processes. This approach will enable us to quantify the amount of water-vs.-carbon loss in this particular landscape.

Many users of time-variable satellite gravimetry data from the GRACE and GRACE-FO missions employ gridded Level-3 data for various applications in, e.g., hydrology, glaciology, oceanography, and the climate sciences. Operational Level-3 data products are provided, for example, via GravIS (www.gravis.gfz.de) maintained by GFZ based on Level-2 spherical harmonic solutions, or by the three mascon producers JPL, CSR, and GSFC. Users of these products are, however, limited to the processing choices defined by the data providers.In order to make GRACE/-FO data even more accessible, the open-source Python package geogravL3 enables users to generate Level-3 products from spherical harmonic coefficients using user-defined processing settings. The software supports the generation of domain-specific products for land (terrestrial water storage), oceans (ocean bottom pressure), and the Greenland and Antarctic ice sheets (ice mass change). Implemented processing steps include filtering of spherical harmonic coefficients (Gauss, DDK, and VDK), replacement of low-degree harmonics, and correction for glacial isostatic adjustment (GIA). For land and ocean applications, spherical harmonic coefficients are transformed into surface mass distributions using spherical harmonic synthesis under the thin-layer assumption. Ice mass changes over Greenland and Antarctica are estimated using a sensitivity-kernel approach, which is conceptually similar to a Level-2-based mascon method.The geogravL3 package is openly available via GitLab (https://git.gfz.de/grace_l3/geogravl3) and can be installed through PyPI or conda-forge.

The El Niño–Southern Oscillation (ENSO) is a major driver of interannual climate variability, influencing terrestrial water storage (TWS) via atmospheric teleconnections and thereby affecting the Earth’s rotation through hydrological angular momentum (HAM) variations. To assess the relevance of these hydrological signals, we construct excitation budgets of the axial effective angular momentum and compare them with geodetic excitation from the IERS EOP 20 C04 series. On interannual time scales, the HAM contribution remains small due to the dominance of atmospheric angular momentum and partial compensation by barystatic ocean adjustments. Nevertheless, noticeable differences between the tested hydrological data sets are found. The hydrological model Open Source (OS) LISFLOOD slightly improves the budget closure, while the Land Surface Discharge Model (LSDM) reduces the agreement with geodetic excitation. We, therefore, revisit regional ENSO–TWS relationships and their contribution to axial HAM variability by comparing model-based TWS (OS LISFLOOD and LSDM) with respect to the satellite-based TWS observations from the GRACE/GRACE-FO missions. Lagged cross-correlation analyses identify regions with significant and temporally coherent responses to ENSO, from which basin-integrated HAM functions are derived. An EOF decomposition is used to quantify their contribution to hydrologically excited Length-of-Day (LOD) variability. The results reveal a robust ENSO-related signal in interannual HAM variability, dominated by a small number of tropical basins, with the Amazon basin emerging as the most influential contributor across all data sets. This indicates a low effective spatial dimensionality of the ENSO–HAM coupling. Among the data sets, OS LISFLOOD provides the best agreement with interannual variability, likely reflecting its more detailed representation of hydrological processes, whereas the older LSDM exhibits artificial variability linked to changes in atmospheric forcing of ECMWF operational data and the outdated ERA40 and ERA-Interim reanalyses. GRACE/GRACE-FO confirms the large-scale ENSO signal but is limited by the relatively short observational record.

The ESA Earth System Model (ESA ESM; Dobslaw et al., 2015) is widely applied as a source model in end-to-end simulation studies for future gravity missions but has been also utilised to study novel gravity observing concepts on the ground. The model provides a synthetic time-variable global gravity field that includes realistic mass variations in the atmosphere, oceans, terrestrial water storage, continental ice sheets and the solid Earth for a variety of spatial and temporal frequencies. With the continuous development of the next gravity missions such as GRACE-C and NGGM / MAGIC, the ESM should provide a wide range of signals at spatial scales which might not have been reliably observed by currently active missions.In this contribution, we present the first steps towards version 3.0 of the ESA ESM. The projected changes include an update of the atmosphere and ocean components, a small ensemble of co- and post-seismic earthquake signals, an updated GIA model, and additional mass balance signals from previously not considered Arctic glaciers. Extreme hydrometeorological events as well as climate-driven and anthropogenic impacts on continental water storage will be represented through an update of the hydrological component. Additionally, the ESM will separately include ocean bottom pressure variations along the western slope of the Atlantic, representing variations in the meridional overturning circulation. ESA ESM 3.0 will be available with 6 hourly resolution from January 2007 until December 2020. It will be also augmented with synthetic error time-series to facilitate stochastical modelling of residual background model errors.Dobslaw, H., Bergmann-Wolf, I., Dill, R., Forootan, E., Klemann, V., Kusche, J., & Sasgen, I. (2015). The updated ESA Earth System Model for future gravity mission simulation studies. Journal of Geodesy, 89(5), 505–513. https://doi.org/10.1007/s00190-014-0787-8

Surface deformations due to changes in the rotation of the Earth are significantly impacted by glacial isostatic adjustment (GIA). The long-term trend of polar motion contributes to global observations like that of the current satellite gravity mission GRACE-FO. The theory and how to apply this contribution to correct GRACE observational data is well understood and goes back to the concise studies of Mitrovica et al. (2005) and Wahr et al. (2015), respectively. According to the International Earth Rotation Service (IERS), a standard correction method is suggested, where the observed long-term trend of the polar motion is considered to originate from GIA. Recent studies show that the modelled GIA contribution to polar motion strongly depends on structural features of the Earth's interior as well as on the glacial history. Other processes like mantle convection or more recent climatic processes are attributed to contribute as well (Adhikari et al. 2018). In this presentation we focus on the impact of the Earth's viscosity structure on the modelled polar motion. In addition to its radial stratification, we discuss the influence of lateral variability. We apply the numerical 3D viscoelastic lithosphere and mantle model VILMA, which solves the gravitationally self-consistent field equations in a spherical geometry, and which considers the rotational feedback and the sea-level equation. The theory of Martinec and Hagedoorn (2014) applied here is not based on the normal mode theory, but solves the field equations in the time domain. We show the consistency of the chosen approach and rate the influence of lateral changes in viscosity against the impact of radial viscosity stratification. The study was motivated by the ESA Third Party Mission 'GRACE-FO' and contributes to the German Climate Modelling Initiative 'PalMod'. Lit:Adhikari, S, Caron L, Steinberger, B, ..., Ivins, ER (2018). What drives 20th century polar motion? Earth Planet. Sci. Lett. doi:10.1016/j.epsl.2018.08.059Martinec, Z, Hagedoorn, JM (2014). The rotational feedback on linear-momentum balance in glacial isostatic adjustment. Geophys. J. Int. doi:10.1093/gji/ggu369Mitrovica, JX, Wahr, J, Matsuyama, I, Paulson, A (2005). The rotational stability of an ice-age earth. Geophys. J. Int. doi:10.1111/j.1365-246X.2005.02609.xWahr, J, Nerem, RS, Bettadpur, SV (2015). The pole tide and its effect on GRACE time-variable gravity measurements: Implications for estimates of surface mass variations. J. Geophys. Res. Solid Earth. doi:10.1002/2015JB01198

Simulated terrestrial water storage (TWS) data from global hydrological models are indispensable for various geodetic applications, e.g., for simulating Earth orientation parameters, deriving time series of deformations of the Earth’s surface needed for the realization of global reference systems, or de-aliasing purposes of GRACE/-FO gravity products. So far, the Land Surface Discharge Model (LSDM) has been routinely used for such tasks at the GFZ. However, the current standard experiment of LSDM is already several years old, and many limitations are known, in particular a limited spatial resolution of 0.5°, which limits the accuracy of crustal deformation predictions close to rivers and lakes. In this contribution, we evaluate the suitability of LISFLOOD (https://ec-jrc.github.io/lisflood/), an open source, high-resolution hydrological rainfall-runoff-routing model, for geodetic purposes. We compare the performance of various global LISFLOOD model runs for the time period 2000 – 2022 against the current LSDM configuration. In addition to two LISFLOOD model generations, which differ in their spatial resolution (0.1° and 0.05°) and their input land surface parameter data set, we also explore a number of high-resolution (0.05°) model runs with respect to the influence of the soil depth on simulated TWS. Model results are validated against mass anomalies from the satellite gravimetry missions GRACE and GRACE-FO on different spatial and temporal scales. Furthermore, to demonstrate the benefit of the higher spatial resolution of LISFLOOD, we utilize data from selected ground based GNSS stations to validate the models’ performance regarding mass-induced loading. We find that LISFLOOD significantly outperforms LSDM in many regions, especially on interannual time scales, in terms of various validation metrics (i.e., correlation, root mean squared deviation, and explained variance). Analyzing the different LISFLOOD runs reveals advantages of the new (0.05°) over the old (0.1°) model version, and a large impact of the choice of soil depth on simulated TWS.

Accurately quantifying global mass changes at the Earth's surface is essential for understanding climate system dynamics and their evolution. Satellite gravimetry, as realized with the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, is the only currently operative remote sensing technique that can track large-scale mass variations, making it a unique monitoring opportunity for various geoscientific disciplines. To facilitate easy accessibility of GRACE and GRACE-FO (GRACE/-FO in the following) results (also beyond the geodetic community), the Helmholtz Centre for Geosciences (GFZ) developed the Gravity Information Service (GravIS) portal (https://gravis.gfz.de, last access: 21 January 2025). This work aims to introduce the user-friendly mass anomaly products provided at GravIS that are specifically processed for hydrology, glaciology, and oceanography applications. These mass change data, available in both a gridded representation and as time series for predefined regions, are routinely updated when new monthly GRACE/-FO gravity field models become available. The associated GravIS web portal visualizes and describes the products, demonstrating their usefulness for various studies and applications in the geosciences. Together with GFZ's complementary information portal https://www.globalwaterstorage.info/ (last access: 21 January 2025), GravIS supports widening the dissemination of knowledge about satellite gravimetry in science and society and highlights the significance and contributions of the GRACE/-FO missions for understanding changes in the climate system. The GravIS products, divided into several data sets corresponding to their specific application, are available at https://doi.org/10.5880/GFZ.GRAVIS_06_L2B (Dahle and Murböck, 2019), https://doi.org/10.5880/COST-G.GRAVIS_01_L2B (Dahle and Murböck, 2020), https://doi.org/10.5880/GFZ.GRAVIS_06_L3_ICE (Sasgen et al., 2019), https://doi.org/10.5880/COST-G.GRAVIS.5880/GFZ.GRAVIS_01_L3_ICE (Sasgen et al., 2020), https://doi.org/10.5880/GFZ.GRAVIS_06_L3_TWS (Boergens et al., 2019), https://doi.org/10.5880/COST-G.GRAVIS_01_L3_TWS (Boergens et al., 2020a), https://doi.org/10.5880/GFZ.GRAVIS_06_L3_OBP (Dobslaw et al., 2019), and https://doi.org/10.5880/COST-G.GRAVIS_01_L3_OBP (Dobslaw et al., 2020a).

Effective angular momentum (EAM) forecasts are widely used as an important input for predicting both polar motion and dUT1. So far, model predictions for atmosphere, ocean, and terrestrial hydrosphere utilized in Earth rotation research reach only 6-days into the future. GFZ’s oceanic and land-surface model forecasts are forced with operational 6-day high-resolution deterministic numerical weather predictions provided by the European Centre for Medium-range Weather Forecasts. Those atmospheric forecasts extend also further into the future with a reduced sampling rate of just 6 h but the prediction skill decreases rapidly after roughly one week. To decide about publishing 10-day instead of 6-day model-based EAM forecasts, we generated a test set of 454 individual 10-day forecasts and used it with GFZ’s EAM Predictor method to calculate Earth rotation predictions. Using 10-day instead of 6-day EAM forecasts leads to slight improvements in y-pole and dUT1 predictions for 10 to 30 days ahead. By introducing additional neural network models trained on the errors of the EAM forecasts when compared to their subsequently available analysis runs, Earth rotation prediction can be enhanced even further. A reduction of the mean absolute errors for polar motion and length-of-day prediction at a forecast horizon of 10 days of 26.8 Δ LOD is achieved. This test application successfully demonstrates the potential of the extended EAM forecasts for Earth rotation prediction although the success rate has to be further improved to arrive at robust routine predictions. GFZ publishes from October 2024 onwards raw uncorrected 10-day instead of 6-day EAM forecasts at www.gfz-potsdam.de/en/esmdata for the individual contributions of atmosphere, ocean, and terrestrial hydrosphere. Users interested in the summarized effect of all subsystems are advised to use the 90-day combined EAM forecast product that also makes use of the presented corrections to the EAM forecasts.

Accurate knowledge of the Earth’s orientation and rotation in space is essential for a broad variety of scientific and societal applications such as near-Earth and deep space navigation, global positioning and satellite orbit determination as well as monitoring geodynamics and climate change phenomena. Consequently, an essential basis for any of the above-mentioned applications is the accurate determination and prediction of Earth Orientation Parameters (EOP), which describe the instantaneous relation between reference frames fixed to the Earth and to inertial space at any epoch. High-precision EOP are determined by combining the observations of four different geodetic space techniques. Due to different standards for the processing of the observations – most notably the parameterisation of the EOP in the observation-type-specific processing softwares – current EOP combination approaches lack consistency. Such inconsistencies represent a major limiting factor of today’s accuracy of determined EOP as well as of short-term EOP predictions, which are an essential prerequisite for any application in (near-)real time. This study identifies current deficiencies that limit the consistency and achievable accuracy of combined EOP series. It provides recommendations on how to improve the parameterisation and processing standards of the technique-specific input, and it outlines strategies to achieve a more consistent and accurate EOP combination. The proposed processing strategies shall serve as a basis for improved EOP prediction algorithms in a later stage of the study.

Since more than 15 years, GFZ routinely provides daily updated hydrological effective angular momentum functions (HAM) and its 6 days-long forecasts for the study of Earth orientation parameter variability and for improving polar motion and UT1-UTC predictions. GFZ’s HAM time series are part of a consistent set of effective angular momentum functions (EAM) covering the Earth’s major subsystems atmosphere, oceans, and the terrestrial hydrosphere. In addition, all EAM products are consistent with the GRACE/GRACE-FO atmosphere-ocean dealiasing product AOD1B release 06 which is used for gravity field processing and precise orbit determination. For the new HAM data set we switch our hydrological model setup from the Land Surface Discharge Model (LSDM) to the open source, high-resolution hydrological rainfall-runoff-routing model LISFLOOD (https://ec-jrc.github.io/lisflood/). We slightly adapted the latest LISFLOOD 0.05° version for geodetic applications by modifying the snow melting parameterization, the soil depth parameterization, and implementing a seasonal snow storage model for Antarctica to optimize the agreement of simulated terrestrial water storage with mass anomalies from the satellite gravimetry missions GRACE and GRACE-FO on different spatial and temporal scales. Due to (I) up-to-date surface parameter maps; (ii) increased temporal resolution of 3 hours; (iii) enhanced parameterization of hydrological processes such as evapotranspiration, soil infiltration, snow accumulation and dynamic river routing; and (iv) a much more extensive set of atmospheric forcing parameters from ECMWF’s latest global atmospheric reanalysis ERA5, the derived HAM time series could be substantially improved in terms of long-term stability, seasonal amplitudes, sub-seasonal and episodic variations, and short-term forecasts. Furthermore, the new HAM data set is consistent with the new AOD1B release 07.

The global hydrological model LISFLOOD (https://ec-jrc.github.io/lisflood/) is in operational use for, e.g., the Global Flood Awareness System (GloFAS) of the Copernicus Emergency Management Service. Due to its continuous development and open source availability, it is also a valuable tool for other geoscientific applications, like the assessment of terrestrial water storage (TWS) variations that can be also observed with geodetic techniques. Since TWS is understood as the sum of all hydrological storages from the surface to the deepest aquifers, it is sensitive to various aspects of the terrestrial water cycle, including surface water dynamics, soil infiltration, and groundwater flow. The current global configuration of LISFLOOD (GloFAS v4.0) has a spatial resolution of 0.05° (~5km), and utilizes a set of implementation maps that is based on various remote sensing products describing morphological conditions, soil physics, and land use characteristics. Here we investigate the influence of the soil depth parameterization on the LISFLOOD model results. We perform different model runs (for the time period 2000 – 2022) by exchanging the input soil depth map, and evaluate modeled discharge and TWS on different time scales (long-term trend, interannual and subseasonal signal) against observations. As a reference for TWS we use satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO), which provides monthly global maps of TWS since 2002. Due to the relatively coarse resolution of the GRACE/-FO observation method, we perform the comparison at basin scale for some of the World’s largest river basins. Discharge is compared with data from gauging stations at the corresponding model grid cells. Results indicate an overall good match between modelled and satellite based TWS. Furthermore, we demonstrate the significant impact of soil depth on TWS simulations. When running the model with the standard soil map, long-term trends and interannual signals deviate from observations more strongly compared to using an adjusted soil map which is limited by the water table depth. Such findings may be valuable also for the parameterization of other hydrological models.

In current official low-latency Earth Rotation Parameter (ERP) products of the International Earth Rotation and Reference Systems Service (IERS), only geodetic data is used. For ERP predictions, deterministic signals and long-term trends of geodetic time series are combined with geophysical (Effective angular momentum; EAM) data. Consequently, the transition between the combined (geodetic) and predicted ERPs (prediction day zero) is connected to an abrupt change in input data yielding inconsistencies between the two parts of the time series. Most notably, the gradients of the predicted ERPs differ with respect to the geodetic ERPs.In our study, carried out within the framework of a DFG-funded project between DGFI-TUM, GFZ and TUM named PROGRESS (Pro- and Retrospective highly accurate and consistent Earth Orientation parameters for Geodetic Research within the Earth System Sciences), we developed an alternative approach that directly combines geodetic and geophysical ERP information to achieve a consistent and continuously differentiable time series. To achieve this goal, the last days of the ERP combination include EAM-based ERP information with increasing relative weight with respect to the geodetic ERP information (and vice-versa). To improve the information provided by space-geodetic techniques, systematic biases are studied in detail such as the impact of different satellite constellations and solar radiation pressure models on the determination of GNSS LOD biases.

Model-derived terrestrial water storage (TWS) and its individual storage compartments soil moisture, groundwater, surface water, and snow are widely used in the geodetic community for, e.g., the evaluation and improvement of satellite gravimetry products, the correction of GNSS-based coordinate time-series, and simulation studies for future satellite gravity missions. We employ the open-source, high-resolution hydrological rainfall-runoff-routing model OS LISFLOOD to generate global daily water storage time series in 1/20° resolution over the time period 2000 – 2023.The most recent OS LISFLOOD run performed at the GFZ benefits from several model improvements and adjustments to arrive at a highly realistic TWS simulation. These adjustments include an optimized soil depth definition; an improved model initialization; a modified snow routine; and the inclusion of anthropogenic water abstraction used for irrigation and industrial, domestic, energy (cooling), and livestock demands. A particular challenge in hydrological modeling is the representation of surface water variability. While the most recent version of OS LISFLOOD already explicitly simulates the dynamics of 463 lakes and 667 reservoirs, endorheic lakes (i.e. lakes without an outlet like the Caspian Sea, Lake Balkhash, or Lake Chad) have not been so far accounted for. Since 18% of the land surface drains into endorheic lakes, their consideration is a big step towards improved storage estimates. For the verification of the simulated lake levels we utilize time series from satellite altimetry, and even report on first experiments with altimetry data for the calibration of lake parameters in OS LISFLOOD.With respect to both GRACE-based TWS estimates and GNSS station displacements, TWS from OS LISFLOOD has been shown before to be superior to results from the Land Surface Discharge Model (LSDM), which has been routinely used for many years at GFZ for geodetic applications. In this contribution we further extend the quality assessment of OS LISFLOOD by utilizing additional TWS data sets from alternative hydrological models (e.g., WGHM, GLDAS, W3RA) that provides insights into the specific strengths and weaknesses of those models regarding their ability to represent TWS at a wide range of spatial and temporal scales.

Model-based information about the global water cycle, in particular the redistribution of terrestrial water masses, is highly relevant for the understanding of Earth system dynamics. In many geodetic applications, hydrological model results play an important role by augmenting observations with a higher spatiotemporal resolution and gapless coverage. Here we demonstrate the feasibility of the high-resolution, open-source hydrological model OS LISFLOOD to simulate terrestrial water storage (TWS) variations with a spatial sampling of up to about 5 km (0.05$<^>{\circ }$). Validation against data from satellite gravimetry reveals that the choice of the maximum soil depth has a significant impact on long-term trends in TWS, mainly in the deepest soil layer. We find that refining the soil depth definition effectively reduces spurious TWS trends, while preserving accuracy in modelled river discharge. Using the modified model set-up, we show that in many regions TWS from OS LISFLOOD fits better to observations than TWS from the Land Surface Discharge Model routinely operated at the GFZ and used in geodetic applications worldwide. The advantage of the high spatial resolution of the OS LISFLOOD implementation is shown by comparing vertical surface displacements to GNSS observations in a global network of stations. The data set presented here is the first application of OS LISFLOOD to generate quasi-global (regions south of 60$<^>{\circ }$S excluded) daily 0.05$<^>{\circ }$ TWS fields for a 23-yr period (2000-2022).

We present preliminary results of ultra-short-term prediction (10-day forecast horizon) of UT1-UTC and LOD. Forecast procedure is based on Dynamic Mode Decomposition (DMD) and uses IERS EOP 14 C04 as a reference as well as Atmospheric Angular Momentum (AAM) as auxiliary data (AAM come from GFZ Potsdam and ETH Zurich). Two main prediction experiments (using different types of input data) were conducted: the ideal and the operational case. The ideal one was based on final (historical) data (both C04 and AAM); predictions performed within one year time span starting on a random MJD with 7 day step between subsequent 10 day forecasts, each yearly time span includes 51 10-day predictions. The operational case was based on operational data, covers the period of the 2nd Earth Orientation Parameters Prediction Comparison Campaign, each variant of this case includes 69 10-day predictions. Within each experiment and for each considered EOP we prepared some additional analysis. In case of LOD, we conducted predictions using 2 types of input LOD time series: directly on published IERS EOP 14 C04 time series and computed as a derivative of IERS EOP 14 C04 UT1-UTC time series. The final UT1-UTC predictions vary depending on the method of determining the constant of integration restoring the proper scale of UT1-UTC. In the ideal case the mean absolute prediction errors for UT1-UTC vary from 0.009 ms – 0.036 ms for the 1st day and 0.224 ms – 0.292 ms for the 10th day of prediction, whilst those values vary from 0.016 ms – 0.028 ms and 0.045 ms – 0.063 ms for LOD prediction. Corresponding values in the operational case are within the range of 0.058 ms – 0.065 ms and 0.438 ms – 0.463 ms for UT1-UTC, whilst for LOD these values are 0.032 ms – 0.040 ms and 0.093 ms – 0.099 ms. In operational settings of UT1-UTC prediction, we can observe that our results are slightly worse than the accuracy of IERS predictions (Bulletin A), while comparable to the accuracy of the forecast of methods from 2nd EOPPCC. The results demonstrate that the proposed techniques can efficiently forecast UT1-UTC and LOD. Nevertheless, a deeper analysis is needed on efficient incorporation of Effective Angular Momentum Functions information to improve presented prediction procedure.

Mass redistribution within Earth’s atmosphere and oceans affects gravity time series recorded by precise superconducting and quantum gravimeters at a multitude of temporal scales. While the largest component of the systematic disturbances is attributed to tides mainly in the oceans, the solid Earth, and to a smaller extent also the atmosphere, synoptic weather features also cause non-negligible gravity anomalies. The accurate description of these effects requires a high-resolution representation of certain components of the instantaneous Earth system state, namely the 3D atmospheric density and the ocean bottom pressure distribution. In this contribution, we calculate gravity anomalies induced by the Newtonian attraction of the mass anomalies and the loading effect they exert on Earth’s crust, employing the state-of-the-art meso-beta scale numerical weather model ERA5 reanalysis from ECMWF. We compare the ERA5-derived gravity anomalies to those provided by ATMACS, a service that features weather-driven gravity anomaly corrections for most superconducting gravimeter sites based on the operational model ICON-global, from the German Weather Service. In this work, we place our focus on non-tidal contributions only, while tidal signatures are estimated based on the gravity anomaly time series. The ocean state is based on a recent MPIOM simulations forced consistently from ERA5 which is also the basis of the latest GRACE/GRACE-FO non-tidal atmosphere-ocean dealiasing product AOD1B RL07. To assess the effectiveness of the modelling strategy as well as the quality of the mass anomaly fields, we apply the ERA5 and ATMACS-retrieved models to a few selected superconducting gravimeter time series with a focus on sites with unusual orography such as on the small island of Helgoland located in the North Sea, and assess the band-pass filtered residuals to assess the quality of the various correction models available.

The ESA Earth System Model (ESA ESM) provides a synthetic data-set of the time-variable global gravity field that includes realistic mass variations in atmosphere, oceans, terrestrial water storage, continental ice-sheets, and the solid Earth on a wide set of spatial and temporal frequencies. It was widely applied as a source model in simulations of for gravity missions, but has been also applied to study novel gravity observing concepts on the ground. For that purpose, the ESM needs to include a wide range of signals even at very small spatial scales which might not yet have been reliably observed by any active mission. In this contribution, we present first steps towards an update to the current ESA ESM. We focus in particular on an evolved oceanic component which will newly include (a) deep oceanic transport variations in the Atlantic Overturning Circulation and the associated variation in oceanic bottom pressure along the shelf slope of the Western boundary; (b) an update to the realistically perturbed de-aliasing model and (c) the inclusion of the Sea-Level Equation for spatially variable barystatic sea-level variations and global mass conservation.

With planet Earth being a deformable body, any geodetic marker attached to its crust exhibts slight motions in response to various geophysical forces. Prominent examples are the diurnal and semi-diurnal tides of the solid Earth, but also other periodic and non-periodic forces induced by mass transport divergence in atmosphere, oceans and the terrestrially stored water are deforming the Earth's surface and therefore displace any geodetic instrument attached to it. Based on a suite of different numerical model data-sets, the Earth System Modelling group at GFZ is routinely calculating both tidal and non-tidal surface deformations that can be readily applied as a priori information for the processing of space geodetic data. The model data-sets are publicly available as global grids with 3-hourly temporal sampling covering almost five decades from 1975 until present time. We present results from dedicated geodetic analysis experiments in order to demonstrate potential impact of such prior information on the GNSS-based coordinate estimates. We utilize data from 220 globally distributed IGS stations from 2005 until 2019 and apply the IGS repro3 strategies for the GPS daily precise orbit determination strategy. We apply non-tidal atmospheric and oceanic loading corrections from the ESMGFZ products on the (i) observation, (ii) normal equation, and (iii) parameter levels, and study the impact of such background models on the coordinate time-series of GNSS permanent stations and other associated parameters.

Predicting Earth Orientation Parameters (EOP) is crucial for precise positioning and navigation both on the Earth’s surface and in space. In recent years, many approaches have been developed to forecast EOP, incorporating observed EOP as well as information on the effective angular momentum (EAM) derived from numerical models of the atmosphere, oceans, and land-surface dynamics. The Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) aimed to comprehensively evaluate EOP forecasts from many international participants and identify the most promising prediction methodologies. This paper presents the validation results of predictions for universal time and length-of-day variations submitted during the 2nd EOP PCC, providing an assessment of their accuracy and reliability. We conduct a detailed evaluation of all valid forecasts using the IERS 14 C04 solution provided by the International Earth Rotation and Reference Systems Service (IERS) as a reference and mean absolute error as the quality measure. Our analysis demonstrates that approaches based on machine learning or the combination of least squares and autoregression, with the use of EAM information as an additional input, provide the highest prediction accuracy for both investigated parameters. Utilizing precise EAM data and forecasts emerges as a pivotal factor in enhancing forecasting accuracy. Although several methods show some potential to outperform the IERS forecasts, the current standard predictions disseminated by IERS are highly reliable and can be fully recommended for operational purposes.

AbstractGrowing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. Considering these developments and recently emerged new methodologies, the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) was pursued in 2021–2022. The campaign was led by Centrum Badań Kosmicznych Polskiej Akademii Nauk in cooperation with Deutsches GeoForschungsZentrum and under the auspices of the International Earth Rotation and Reference Systems Service. This paper provides the analysis and evaluation of the polar motion predictions submitted during the 2nd EOP PCC with the prediction horizons between 10 and 30 days. Our analysis shows that predictions are highly reliable with only a few occasional discrepancies identified in the submitted files. We demonstrate the accuracy of EOP predictions by (a) calculating the mean absolute error relative to polar motion observations from September 2021 through December 2022 and (b) assessing the stability of the predictions in time. The analysis shows unequal results for the x and y components of polar motion (PMx and PMy, respectively). Predictions of PMy are usually more accurate and have a smaller spread across all submitted files when compared to PMx. We present an analysis of similarity between the participants to indicate what methods and input data give comparable output. We also prepared the ranking of prediction methods for polar motion summarizing the achievements of the campaign.