We use the land-ice surface height data product (ATL06 release 5) from NASA’s latest satellite laser altimetry, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to compute surface elevation changes (SEC) from October 2018 to September 2021 over both Antarctica and Greenland. To convert the SEC to mass change we need to remove the non-ice related SEC processes. To remove the signal from the firn compaction, we use an offline surface energy and firn model. The model is driven by outputs from the atmospheric regional climate model HIRHAM5, forced with reanalysis dataset ERA5, and it simulates the physics of the firn pack. The vertical bedrock movement also creates non-ice related signals, the glacial isostatic adjustment has been computed using the ICE-7G model and SELEN4, and the elastic rebound has been computed using a modified version of the REAR code. 

When the SEC are corrected for signals that are not associated with a change in snow or ice mass, we convert to mass change by multiplying the height change with an appropriate density.  The corrected SEC can result from a change in either melt, snow accumulation, or dynamical behavior, this means that the appropriate density depends on which physical processes are driving the observed SEC. In this study, we have made a new density parametrization to convert the volume change into mass change. The density parametrization determines if one should multiply with snow densities (250-350 kg/m³) or ice density (917 kg/m³) based on a number of criteria; the sign of SEC, ice flow velocity, and the altitude of the area.
With our new density parametrization, we get that the Greenland Ice Sheet has lost 237.5±10.3 Gt/year and the grounded Antarctic Ice Sheet has lost -137.6±27.2 Gt/year in the period. These results are in agreement with other mass balance estimates derived with different methods.

How to cite: Hansen, N., Sørensen, L. S., Spada, G., Melini, D., Forsberg, R., Mottram, R., and Simonsen, S. B.: ICESat-2 Ice Sheet Mass balance: Going below the surface, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12349, https://doi.org/10.5194/egusphere-egu23-12349, 2023.

We developed a methodology for deriving automated and continuous specific surface mass balance time series for fast moving parts of ice sheets and shelves (>10m/a) by an accurate and simultaneous estimation of continuous in-situ snow density, snow water equivalent (SWE), and snow deposition and erosion, averaged over an area of several square meters and independent on weather conditions. Reliable in-situ surface mass balance estimates are scarce due to limited spatial and temporal data availability. While surface accumulation can be obtained in various ways, conversion to mass requires knowledge of the snow density, which is more difficult to obtain.

A combined Global Navigation Satellite Systems reflectometry and refractometry (GNSS-RR) approach based on in-situ refracted and reflected GNSS signals is developed. The individual GNSS-RR methods have already been successfully applied on stationary grounds and seasonal snowpacks and are now combined and transferred to moving surfaces like ice sheets. We installed a combined GNSS-RR system in November 2021 on the fast moving (~150m/d), high latitude Ekström ice shelf in the vicinity of the Neumayer III station in Antarctica. Continuous snow accumulation reference data is provided by a laser distance sensor at the same test site and manual density observations. Refracted and reflected GNSS observations from site are post-processed for SWE, snow accumulation, and snow density estimation with a sub-daily temporal resolution. Preliminary results of the first year of data show a high level of agreement with reference observations, calculated from snow accumulation data collected by the laser distance sensor and linearly interpolated monthly snow density observations of the uppermost layer equivalent to the height of snow above the buried antenna.

The deployed devices are geared towards prototype applications for reliable low-cost applications, which will allow large-scale retrieval of surface mass balance for general cryospheric applications, not only on ice sheets or shelves, but also sea ice. Regional climate models, snow modelling, and extensive remote sensing data products will profit from calibration and validation based on the derived field measurements, once such sensors can be deployed on larger scales.

How to cite: Steiner, L., Schmitthüsen, H., Wickert, J., and Eisen, O.: Combined GNSS Reflectometry/Refractometry for Continuous In Situ Surface Mass Balance Estimation on an Antarctic Ice Shelf, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6919, https://doi.org/10.5194/egusphere-egu23-6919, 2023.

Rapid melting of ice sheets and glaciers drives a unique geometry, or fingerprint, of sea-level change, including a sea-level fall in the vicinity of the ice sheet that is an order of magnitude greater than the associated global mean sea-level rise of the melt event. The detection of individual fingerprints has been challenging due to sparse sea surface height measurements at high latitudes and the difficulty of disentangling ocean dynamic variability from the signal. Efforts to date have analyzed sea level records outside the zone of major sea-level fall, where the gradients and amplitudes of the fingerprint signal are significantly lower. We predict the fingerprint of Greenland Ice Sheet (GrIS) melt using new ice mass loss estimates from radar altimetry data and model reconstructions of nearby glaciers, and compare this prediction to an independent, altimetry-derived sea surface height trend corrected for ocean dynamic variability in the region adjacent to the ice sheet. The two fields show consistent gradients across the region, with the expected strong drawdown of the sea surface toward GrIS. A statistically significant correlation between the two fields (p < 0.001) provides the first unambiguous observational detection of the near-field sea level fingerprint of recent GrIS melting in our warming world. This detection provides a robust map of the impact of ice mass flux on global oceans since the early 1990s, and validates theoretical and numerical developments in the sea level modelling community.

How to cite: Coulson, S., Dangendorf, S., Mitrovica, J. X., Tamisiea, M., Pan, L., and Sandwell, D.: A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4554, https://doi.org/10.5194/egusphere-egu23-4554, 2023.

In comparison with the number of tide gauges measuring in-situ sea-level change along the Northern Hermisphere coastlines, the Southern Hemisphere has a poor spatial distribution of stations. For example, along the South American Atlantic coastline, only 12 tide gauges are registered at the Permanent Service for Mean Sea-level (PSMSL), of which only two have been updated in the last three years. While satellite altimetry can be used to provide data in locations where there is no in-situ data, estimating coastal sea-level change using altimetry data is challenging due to the distortion of the satellite signal close to the land. Consequently, sea-level change along the South American Atlantic coastline is still poorly understood. Here, we fill this gap by using coastal altimetry products together with a new network of tide gauges deployed along the coast of Brazil (by the SIMCosta project). Via a sea-level budget analysis, we look at the regional drivers of sea-level change along the coast.

Recently, a large effort has been put towards developing algorithms that improve the accuracy of standard radar altimetry in coastal regions. Here, we compare both a coastal altimetry product (XTRACT/ALES) and a standard altimetry product (from CMEMS) to the local tide gauges. Previous studies have shown that, for some regions, coastal sea level is driven by open ocean sea-level change ( e.g., Dangendorf et al, 2021). Following this approach, we use clusters of coherent sea-level variability (Camargo et al., 2022), extracted with a network detection algorithm (delta-Maps), that extend to the open ocean, as proxies of the drivers of sea-level change along the coast.  The northern part of the study region, covering the Amazon Plateau, has a good match between the coastal altimetry-observed sea-level change and the sum of the drivers. The sum of the drivers and coastal altimetry trends also match, considering the uncertainty bars, for the most southern part, covering the Patagonian Shelf. For the other regions, we find a large difference between the coastal altimetry-observed sea-level change and the sum of the drivers. Thus, it is possible that these regions cover large-scale features, which are not strongly correlated with coastal sea level.

References

Camargo, C. M. L., Riva, R. E. M., Hermans, T. H. J., Schütt, E. M., Marcos, M., Hernandez-Carrasco, I., and Slangen, A. B. A.: Regionalizing the Sea-level Budget With Machine Learning Techniques, EGUsphere [preprint, accepted], https://doi.org/10.5194/egusphere-2022-876, 2022.

How to cite: M.L. Camargo, C., Gerkema, T., Okta Andrawina, Y., and B.A. Slangen, A.: Sea-level change along the South American Atlantic coastline, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16296, https://doi.org/10.5194/egusphere-egu23-16296, 2023.

For 25 years, geodesists have inferred that the displacement of the "geocenter" estimated from (SLR) satellite laser ranging represents fluctuation of Earth's fluid envelope relative to solid Earth.  However, SLR determines the displacement of the (CN) center of network of geodetic sites relative to the (CM) center of mass of Earth, consisting of solid Earth, the oceans, the atmosphere, and continental water, snow, and ice. Because solid Earth's surface is deforming in elastic response to the changing load of continental water, atmosphere and oceans, CN only roughly approximates the (CE) center of mass of solid Earth.  In this study, estimate the velocity of CM relative to the (CE) center of mass of Earth by first correcting SLR site displacements (estimated by the International Laser Ranging Service 2020) for their elastic response relative to CE produced by fluctuations of continental water, atmosphere and oceans.  We maintain that by correcting for loading displacements relative to CE, we arrive at an estimate of the displacement of CE.  We find that transforming the SLR series from CN to CE reduces the discrepancy between the seasonal oscillation of Earth's fluid envelope estimated by SLR and that assumed by GRACE (using the technique of Sun et al. 2017) by 40 per cent.  In both SLR and GRACE, a total of 0.5 x 10¹⁶ kg of mass moves between hemispheres from southern oceans in August to snow-covered areas in North America and Europe (in particular in Canada and Siberia).  The primary remaining difference between the two techniques is that mass in the northern hemisphere is maximum on February 5 in SLR, 20 days before it is maximum on Feb 25 in GRACE.  Knowing the total transfer of the mass of between hemispheres places a boundary constraint on global models of circulation of water on land and in the oceans and atmospheres (that may be applied to forecasting extreme events such as flooding and drought).

How to cite: Argus, D., Landerer, F., Wiese, D., and Blewitt, G.: Resolving the discrepancy betweenthe seasonal oscillation of Earth's fluid envelope estimated with SLR and that assumed in GRACE, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10758, https://doi.org/10.5194/egusphere-egu23-10758, 2023.

Accurate representation of the time-variable atmospheric state is achieved by assimilating numerous and disparse observations into numerical weather models (NWM). The four-dimensional atmospheric density distribution, a derivative of essential meteorological variables, affect among else how electromagnetic signals propagate through Earth’s atmosphere and how satellites orbit through Earth’s gravity field. Atmospheric refraction to which microwave signals are subjected as they traverse the electrically neutral atmosphere is quantified e.g., during the GNSS data analysis, and holds valuable information about the water vapor distribution in the vicinity of the ground stations. Satellite gravimetry as realized by the GRACE and GRACE-FO missions is sensitive to mass redistribution within Earth’s fluid envelope, including but not limited to the atmosphere and the terrestrail water storage, and also to high-frequency variations stemming from the time-integrated effect of precipitation and evapotranspiration. In this contribution we employ two state-of-the-art meso-beta scale NWM (ECMWF’s latest reanalysis ERA5 and DWD’s operational model ICON-global) as well as ERA5‘s ensemble members to demonstrate that tropospheric mosture distribution and net atmospheric freshwater fluxes are quite uncertain in modern NWM in comparison to other quantities such as hydrostatic atmospheric mass and that certain space geodetic observing systems such as GNSS and GRACE-FO are appropriate tools to monitor them, thus enhancing the accuracy of weather prediction.

How to cite: Balidakis, K., Dobslaw, H., Zus, F., Eicker, A., Dill, R., and Wickert, J.: Prospects of Space Geodesy to Monitor Atmospheric Moisture and Atmospheric Net-Water Fluxes, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13048, https://doi.org/10.5194/egusphere-egu23-13048, 2023.

Global Navigation Satellite System (GNSS) is now an established observing system for atmospheric water vapour with high spatiotemporal resolution. Water vapour is under-sampled in the current climate-observing systems and obtaining and exploiting more high-quality humidity observations is essential for climate monitoring.

The Global Climate Observing System (GCOS), supported by the World Meteorological Organization (WMO), is establishing a reference climate observation network, the GCOS Reference Upper Air Network (GRUAN). Currently, this network comprises 30 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity in the upper atmosphere. GRUAN observations are required to be of reference quality, with known biases removed and with an associated uncertainty value, based on thorough characterization of all sources of measurement. In support of these goals, GNSS precipitable water (GNSS-PW) measurement has been included as a priority one measurement of the essential climate variable water vapor. The GNSS-PW program produces a nearly continuous reference measurement of PW and is therefore a substantial part of GRUAN.

GFZ contributes to GRUAN with its expertise in processing of ground-based GNSS network data to generate precise PW products. GFZ hosts a central processing facility for the GNSS data and is responsible for the installation of GNSS hardware, data transfer, processing and archiving, as well as derivation of GNSS-PW products according to GRUAN requirements including PW uncertainty estimation. Currently half of the GRUAN sites are equipped with GNSS receivers. GNSS-PW products for GRUAN and the results of validation studies will be presented.

How to cite: Dick, G., Zus, F., Wickert, J., Männel, B., and Bradke, M.: GNSS-derived Precipitable Water Vapor for Climate Monitoring, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7836, https://doi.org/10.5194/egusphere-egu23-7836, 2023.

This study investigates the reasons for the decrease in the water level of Beysehir Lake and the shrinkage in the lake's surface area in recent years. For this purpose, the lake water level was determined from multi-mission satellite altimeter data, and the lake area was calculated using high-resolution optical satellite images. Data from Copernicus Global Land Service was used for multi-mission satellite altimeter data, and the lake level trend between 1993-2022 was calculated with the least squares method. European Space Agency's (ESA) Sentinel-2 high-resolution optical images were used to determine the change in the lake surface area between 2015 and 2020. These high-resolution optical images were processed with The Sentinel Application Platform (SNAP) software. The Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) were calculated based on processed optical images, and these indexes reflect the changes in water surface area. From the satellite altimeter data, a decreasing trend of 2.5 ± 0.5 cm/yr in the lake water level in the last ten years and shrinkage of approximately 8 km² in the last 6 years from the satellite images were determined. The possibility of one of the most important reasons being drought was emphasized, and monthly average air temperature data and monthly average precipitation data were obtained from the Turkish General Directorate of Meteorology. With these data, 3- and 12-month Standardized Precipitation Evapotranspiration Index (SPEI) were calculated. Regarding these calculated drought indexes, moderate, extreme, and severe hydrological drought has been determined in the region. According to the analysis, drought is thought to be the most important reason for the decrease in the lake water level and shrinkage in the lake surface area.

Keywords : Geodesy for Climate, Lake Water Level, Satellite Altimetry, In-situ observation, Sentinel-2

How to cite: Erkoç, M. H.: Examination of Causes for Decrease in the Water Level of Beysehir Lake and Shrinkage in the Lake's Surface Area., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-258, https://doi.org/10.5194/egusphere-egu23-258, 2023.

Time variable satellite gravimetry, realized with the missions GRACE and GRACE-FO, allows for the only global observation of total water storage (TWS) changes. These observations are inherently smoothed due to the upward continuation of the gravity field at the satellite orbits. Additionally, the correlated errors seen as north-south stripes in global maps require further filtering to separate signal from noise. This causes the signal at any region to be biased by signal at neighboring regions, better known as leakage effect. Various methods have been proposed to mitigate leakage and to spatially assign TWS changes at smaller spatial scales than the satellite data is available by using auxiliary information. Unfortunately, there is a large spatio-temporally variable degree of discrepancy in the agreement or the disagreement within these methods, leaving the non-geodetic users of GRACE TWS changes with the complex question of choosing an appropriate method. The scaling factor approach and the Data-Driven Correction (DDC) approach are the most widely used methods. The scaling factor approach uses a numerical model output of TWS changes, whereas the DDC approach uses only GRACE observations to account for leakage.
Tripathi et al., 2022 (10.5194/hess-26-4515-2022) found for the Indus basin, that a newly proposed variant of the scaling factor method, called Frequency-Dependent scaling, using the WaterGAP (Water Global Assessment and Prognosis) hydrology model (WGHM v2.2d), produced results with a striking agreement against the results from the DDC approach. Therefore, this contribution extends the comparison of Frequency-Dependent scaling using WGHM v2.2d against the DDC method for 189 global hydrological basins. We achieved an agreement between the results from both methods well within the uncertainties of GRACE TWS observations for almost 85-90% of the global hydrological basins. Such an agreement can bring a much-needed consolidation in the treatment of leakage effect across the user community. The disagreement in the rest of the basins varies across time scales, such as long-term trends and periodic signals, and is being further analysed.

How to cite: Tripathi, V., Vishwakarma, B. D., and Horwath, M.: Data-Driven and Scaling Factor methods of GRACE leakage correction: Can they be reconciled?, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12485, https://doi.org/10.5194/egusphere-egu23-12485, 2023.

With the intensification of global climate change, droughts have occurred frequently in the Yangtze River Basin (YRB), which has caused significant impacts on human production, life, and socio-economic development. To reduce the damage caused by drought in the YRB, the drought characteristics must be comprehensively detected and quantified. Here, the spatial and temporal variability of precipitation, runoff, soil moisture, terrestrial water storage, and groundwater in the YRB from the Gravity Recovery and Climate Experiment (GRACE), hydrological and in situ observations were comprehensively estimated by decomposing them into seasonal, subseasonal, trend, and interannual observations. The new weighted GRACE drought standardisation index (WGDSI) was reconstructed using the component contribution ratio and compared with the standardised soil moisture index (SSI), standardised precipitation index (SPI06), and standardisation runoff index (SRI). Additionally, the drought characteristics identified based on observations of the water storage deficit, severity, peak, duration, and recovery time were also quantified using the WGDSI over the YRB. The results indicated that changes in soil moisture, terrestrial water storage, and groundwater in the YRB increased from 2003 to 2019 and mainly based on seasonal and interannual signals. The correlation coefficients between the WGDSI and the SSI, SPI06, and SRI were 0.92, 0.62, and 0.79, respectively, which represented increases of 9%, 14%, and 21% compared to that with the unweighted GRACE drought standardisation index, respectively. The interannual variability of the hydrologic variables was more consistent with drought events in the YRB, which was beneficial for detecting drought. Two serious droughts occurred in the YRB from 2003 to 2019. In 2006, a continuous 7-month-long drought occurred, with a peak at -28.974 km³, severity of -174.767 km³∙month, average drought recovery rate of 0.83 km³/month, and recovery time of 30 months, while in 2011, a continuous 5-month-long drought occurred, with a peak at -18.384 km³, severity of -78.106 km³/month, average drought recovery rate of 0.40 km³/month and recovery time of 39 months. The above results indicate that the WGDSI can be used to monitor and quantify drought over the YRB. The index proposed in this study can be applied to generate new datasets and methods for detecting and quantifying global drought.

How to cite: Wan, X., Chao, N., Hu, Y., Wang, J., Liu, Z., and Zou, K.: Reconstructing a new terrestrial water storage deficit index to detect and quantify drought in the Yangtze River Basin, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2734, https://doi.org/10.5194/egusphere-egu23-2734, 2023.

Geophysical interpretation of polar motion (PM) and finding the sources of its excitation is an important but challenging task that takes place on the boundary between geodesy and geophysics. Especially the role of hydrological signals in PM excitation is not yet fully understood, mainly because of the lack of agreement between estimates of hydrological angular momentum (HAM) computed from different data sources (e.g., land surface models, global hydrological models, satellite gravity measurements).

The recently observed climate changes affect the global distribution and transport of continental water mass, which may also influence the HAM. Projections of past and future changes in the physical and chemical properties of the atmosphere, ocean, and hydrosphere caused by climate change are delivered by climate models, which are collected and made available to the public in the frame of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Such models provide many of variables, including variations in soil moisture and snow water storage, which are necessary for HAM computation. However, CMIP6 models differ in terms of initial conditions, physical properties of atmosphere, oceans, hydrosphere, and climate forcing. Such divergences obviously contribute to the differences between various CMIP6-based HAM series.

In this study, we investigate various groups of models according to providing institute, mean of selected models and more sophisticated combinations determined using different methods like e.g., variance components estimation, three cornered hat method. The obtained series are analyzed and evaluated in several spectral bands. The goal of such study is to check whether grouping or combining the models could improve the consistency between CMIP6-based HAM and hydrological signal in geodetically observed PM excitation. To evaluate the combined CMIP6-based HAM series, we compare them with geodetic residuals (GAO) obtained from geodetic angular momentum reduced by atmospheric and oceanic signals, as well as with HAM computed from data from Gravity Recovery and Climate Experiment (GRACE) mission. Generally, the analyses confirm the results obtained from previous studies (Nastula et al. 2022). It is possible to find grouped CMIP6 models that provide HAM series as or more compliant with GAO than HAM determined from GRACE.

How to cite: Nastula, J., Kur, T., Śliwińska, J., Wińska, M., and Partyka, A.: Study on combination approaches for hydrological angular momentum determined from climate data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12215, https://doi.org/10.5194/egusphere-egu23-12215, 2023.

The Earth Surface undergoes continuous deformation due to surface mass variations. These mass variations are primarily caused by the hydrological cycle, snowfall, ice melt and glacial isostatic adjustment (GIA). Modern geodetic sensing techniques like the Global Navigational Satellite System (GNSS) can sense these mass variations with unprecedented accuracy.  Therefore, the GNSS positioning time series provides a unique opportunity to study these mass variations and their causes.

In this study, we have used the GNSS time series from the region of Africa and Antarctica to analyse the mass variations. Conditions like draught and ice melting characterise these two regions. Therefore this current study will look at the signals of these two physical conditions. The results obtained are discussed and analysed.

How to cite: Ray, J. D., Patar, S., and Mahata, R.: Geodetic sensing of mass variations due to climatic conditions, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16692, https://doi.org/10.5194/egusphere-egu23-16692, 2023.