Africa’s coastal regions face accelerating sea level rise compounded by climate variability. Here we analyze satellite altimetry data from 1993 to 2024 across African marine domains. The 2023–2024 El Niño produced the largest detrended sea level anomaly on record (27 mm), exceeding even the stronger 1997–1998 event. This exceptional response arose from ocean preconditioning: record-positive Indian Ocean Dipole, Atlantic Niño, and Tropical North Atlantic indices preceded and amplified El Niño forcing, while anomalous winds suppressed upwelling and record stratification trapped surface heat, quadrupling ocean heat content. Thermal expansion accounted for over 70% of this event’s total anomaly, reaching nearly 30 mm across the African marine domain. Sea levels rose 11.26 cm since 1993, accelerating at 0.14 mm.yr-2, exceeding the global average. These findings reveal compound threats to low-lying deltas and Small Island Developing States from flooding, ongoing land subsidence, and declining marine productivity.

Major applications of the GRACE and GRACE-FO satellite gravimetry missions address mass redistribution of water and ice on the Earth’s surface, often with a direct link to climate research. Such applications are commonly based on global gridded datasets of mass distribution anomalies. Such Level 3 data products from satellite gravimetry are derived by individual institutions either directly from Level-1 observations or from Level-2 gravity field solutions. Different Level-3 products use different methodologies and therefore yield different results. This contribution examines and compares, both qualitatively and quantitatively, the Level-1-based Mascon data products from the Jet Propulsion Laboratory (JPL), the Goddard Space Flight Center (GSFC) and the Center for Space Research (CSR), as well as a data product based on Level-2 data from the Technische Universität Dresden (TUD). Various components of temporal changes are considered, such as mean linear change, seasonal signals, and non-linear and non-seasonal components. Furthermore, the integrated mass changes of selected regions are analysed and compared. These include the Antarctic and Greenland ice sheets, as well as ocean areas adjacent to the ice sheets. The results show that the four products are largely consistent in terms of the temporally mean rate of mass change. Significant discrepancies occur primarily over the ice sheets and cover less than 3% of the Earth’s surface. A comparison of the annual signal reveals that the TUD product differs from the other products in certain locally confined coastal regions. Furthermore, spatially coherent phase differences in the seasonal component of ocean mass changes are observed, which can be attributed to differences in the treatment of the atmospheric background model. Small-scale differences resulting from different grid size definitions are observed between all products. The results of the integrated mass change over the Antarctic and Greenland ice sheets show that all products show ice-sheet mass loss and that the linear trend varies by a maximum of 20 Gt. However, when considering the ocean areas adjacent to the ice sheets, clear differences become apparent. Here, the various products show trends that are, in some cases, contradictory. This can be explained by the different strategies employed to avoid leakage effects. It highlights the need for further research into method comparison and method development.

Under the assumption that a warming climate leads to an intensification of the global water cycle, it is hypothesized that also the occurrence frequency and severity of extreme events such as droughts and floods will increase in the upcoming decades. GRACE/-FO observations of terrestrial water storage (TWS) have been used in the past to identify and analyse extreme events both on a global and regional scale. However, these analyses are restricted by the limited spatial and temporal resolution of current satellite gravimetry observations. Especially, flooding events tend to occur very locally and with short temporal (sub-monthly) extent, thus capturing them is challenging. Future satellite gravimetry missions, particularly the double-pair constellation MAGIC, are expected to significantly enhance the spatial and temporal resolution. In this study, we globally investigate the benefit MAGIC can achieve to detect wet extreme events using long-term (50 years) end-to-end simulations of GRACE-C and MAGIC. The simulation environment is based on the acceleration approach and considers tidal and non-tidal background model errors as well as instrument noise of the acceleration and ranging instruments following the current MAGIC mission design studies. As input and reference, we use the daily output of a climate model (GFDL-CM4) from the CMIP6 archive that has been identified as a realistic representation of water storage evolution in previous studies. To explore the improved temporal and spatial resolution expected from the MAGIC constellation, we (i) compare extreme values derived from 5-daily gravity field simulations to those from monthly fields, and (ii) show how the weaker spatial filtering required for MAGIC has a positive influence on the detectability of extremes. For the analysis two different approaches are exploited: One method focuses solely on the stochastic characteristics of the time series in terms of extreme value theory, evaluating the magnitude-frequency relationship of large TWS values by calculating expected return levels of wet extremes. The other approach builds on the fact that a 50-years simulation time series allows to derive statistically meaningful conclusions from directly comparing reference and simulation output on a time series level. We evaluate the time of occurrence of wet extremes on the basis of classification scores assessing correctly and incorrectly identified extreme events.

How well can hydrological models capture changes in Earth’s water storage in data-scarce regions such as the Congo River Basin? In this presentation, Terrestrial Water Storage (TWS) dynamics and their individual storage compartments (i.e., soil moisture, groundwater, water in lakes, rivers, and reservoirs) are explored using outputs from OS LISFLOOD, an open-source spatially distributed water resources model developed by the Joint Research Centre of the European Commission. Simulations forced with ERA5 atmospheric reanalysis data are evaluated against GRACE satellite gravimetry observations and compared with simulations driven by alternative meteorological forcing datasets as well as other hydrological models. The evaluation focuses on different temporal TWS signal components and is performed using statistical measures such as correlation, Root Mean Squared Differences, and Kling-Gupta Efficiency. Special attention is given to the low performance of OS LISFLOOD in the Congo Basin in the low-frequency parts of the signal when forced with ERA5 data. By analyzing differences between meteorological forcing datasets, model sensitivity to different atmospheric forcings, and the influence of calibration, this study aims to identify potential sources of uncertainty in large-scale hydrological simulations.

GNSS coordinate time series are widely used to monitor climate-driven surface deformation caused by atmospheric, hydrological, and oceanic loading. However, these products may contain constellation-specific artefacts related to orbit modelling, observation geometry, and processing strategies. Understanding the consistency of climate-related signals across individual GNSS constellations is therefore important for geodetic climate studies. This study investigates system-specific coordinate time series derived from GPS, GLONASS, Galileo, BeiDou, and combined multi-GNSS solutions produced by the European Space Agency (ESA). The dataset includes stations with at least four years of observations from 2020 to 2025. The GNSS-derived displacements are compared with atmospheric, hydrological, and oceanic loading models. Correlations, amplitude differences, and regional variability are analysed to assess the consistency between individual constellations and geophysical loading signals. Preliminary results reveal clear constellation-specific spectral signatures associated with orbital periods and their aliases, particularly for GLONASS and BeiDou. Relative annual amplitude differences reveal generally good agreement between GPS, Galileo, and multi-GNSS solutions, with median differences of 8–9%, whereas larger discrepancies are observed for BeiDou (11%) and especially GLONASS, whose annual amplitudes are typically 60% smaller than those derived from GPS. While individual systems exhibit distinct artifacts, multi-GNSS solutions substantially reduce long-period orbital signals and show the best agreement with loading models. The analysis also indicates regional differences in the magnitude of system-specific effects. The results provide new insights into the suitability of individual GNSS constellations for climate applications and highlight the benefits of multi-GNSS integration for monitoring environmentally driven surface deformation.

Atmospheric water vapor is the most significant greenhouse gas and a vital Essential Climate Variable (ECV). This variable plays a key role in monitoring the long-term effects of global climate change. Traditional techniques such as water vapor radiometers and radiosondes measure essential meteorological data. However, over the last two decades Global Navigation Satellite System (GNSS) technology has emerged as a cost-effective tool for continuous atmospheric monitoring in all weather conditions. To exploit this technique for robust climate monitoring, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) was established as an international reference observing network. Within this network, GFZ functions as a key processing center, offering a comprehensive source of tropospheric products, including Precipitable Water Vapor (PWV). With over 10 years of consistently processed data now available, the network provides a unique opportunity for long-term climate studies. In this work, 10-year PWV time series across four different climatological regions are investigated. The primary focus is to characterize the long-term stability and traceable uncertainty of the GNSS-derived products. For this purpose, we assess the precision of the estimated GNSS-PWV and validate these products through a comparison with ERA5 reanalysis data. Additionally, the GNSS-PWV time series are analyzed to evaluate the impact of global change on atmospheric moisture distribution.

River water storage variations produce elastic loading deformation that can, in principle, be detected by Global Navigation Satellite System (GNSS) observations. However, transient river-stage-induced loading signals are usually small, localized, and difficult to separate from broader hydrological loading and common-mode errors. Here, we present the first GNSS observation of transient river water storage variations along the Rhine River in northwestern Europe, using a dense regional GNSS network and daily river-stage observations. After removing large-scale hydrological loading and regional common-mode signals, we identify coherent vertical land motion signals associated with transient river-stage changes. The detected signals have amplitudes of approximately 1 mm and persist over time scales of 14–60 days. Spatially, the response is concentrated along the Rhine corridor, with sensitivity extending up to approximately 300 km from the river. Two representative events, in October 2011 and July 2021, show clear transient deformation patterns related to river-stage variations. In particular, the October 2011 event exhibits a downstream migration of GNSS-observed uplift following river-level recession, with an apparent propagation speed slower than that of the river-stage wave. The GNSS-derived deformation is approximately five times larger than predicted by PREM-based purely elastic loading models driven by river-stage observations, and its propagation speed is about half that of the river-stage wave. This discrepancy suggests that the observed signals reflect not only direct river-water loading but also delayed bank storage and groundwater responses in the river corridor, as supported by groundwater model simulations and well observations. This study highlights the potential of GNSS as a complementary observation system for monitoring transient terrestrial water storage changes at regional scales.