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Temporal variations in the total ocean mass representing the barystatic part of present-day global-mean sea-level rise can be directly inferred from time-series of global gravity fields as provided by the GRACE and GRACE-FO missions. A spatial integration over all ocean regions, however, largely underestimates present-day rates as long as the effects of spatial leakage along the coasts of in particular Antarctica, Greenland, and the various islands of the Canadian Archipelago are not properly considered. Based on the latest release 06 of monthly gravity fields processed at GFZ, we quantify (and subsequently correct) the contribution of spatial leakage to the post-processed mass anomalies of continental water storage and ocean bottom pressure. We find that by utilizing the sea level equation to predict spatially variable ocean mass trends out of the (leakage-corrected) terrrestial mass distributions from GRACE and GRACE-FO consistent results are obtained also from spatial integrations over ocean masks with different coastal buffer zones ranging from 400 to 1000 km. However, the results are critically dependent on coefficients of degree 1, 2 and 3, that are not precisely determined from GRACE data alone and need to be augemented by information from satellite laser ranging. We will particularly discuss the impact of those low-degree harmonics on the secular rates in global barystatic sea-level.

Glacial-isostatic adjustment (GIA) models simulate the viscoelastic response of the solid earth due to loading. During the last glacial maximum, large areas in the northern and southern hemisphere were covered by km-thick ice sheets. Although most of the ice has been melted already 8,000 year ago, the time-delayed response of the viscoelastic earth is still a significant contribution to present-day uplift rates. The implementation of GIA models in global climate models is an essential part of the current research. Hereby, the choice of an appropriate earth structure in the GIA model plays an important role and has to be constrained by observational data. Here, we apply present-day uplift data to constrain a set of GIA models that differ in 3D earth structure. To this end, these different GIA models are validated against GPS uplift rates provided by Schumacher et al. (2019). The GPS stations are globally distributed and not necessarily clustered in regions with strong GIA signal. For validation, regions with the largest gradient present in the GIA signal are most crucial. Thus, we use a weighting scheme, where those GPS stations get a higher weight that are less correlated to all other stations. Additionally, uncertainties in the GPS rates appear due to the length of the GPS time series and due to station specifics such as the used GPS receiver, and are provided together with the rates as standard deviations. Thence, the weighting used for the validation is the sum of the correlation derived weights and the uncertainty derived weights. With this weighting in place, different GIA models can be validated against present day uplift rates by means of root mean square errors or mean absolute error.

Über das vom GFZ Potsdam betriebene Datenportal »Gravity Information Service« (GravIS, gravis.gfz-potsdam.de) werden vorprozessierte Daten der Satellitenmissionen GRACE und GRACE Follow-On (GRACE-FO) bereitgestellt. Die verfügbaren Datensätze umfassen Massenanomalien der Eisschilde Grönlands und der Antarktis, terrestrische Wasserspeicheränderungen sowie ozeanische Bodendruckvariationen, aus denen der masseninduzierte Meeresspiegelanstieg abgeleitet werden kann. Dabei werden die Daten dem Nutzer sowohl regelmäßig gegittert als auch für ausgewählte Regionen als Gebietsmittelwerte zur Verfügung gestellt. Der Nutzer kann sich räumliche und zeitliche Veränderungen sowohl global als auch regional interaktiv anzeigen lassen. Das Portal richtet sich an Anwender aus der Hydrologie, Ozeanographie und Kryosphäre, aber auch an die populärwissenschaftlich interessierte Öffentlichkeit. Alle zugrunde liegenden Daten stehen dem Nutzer auch zum Download für weitere Auswertungen zur Verfügung.

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.

Based on the latest GFZ release 06 of monthly gravity fields from GRACE satellite mission, area-averaged barystatic sea-level is found to rise by 2.02 mm/a during the period April 2002 until August 2016 in the open ocean with a 1000 km coastal buffer zone when low degree coefficients are properly augmented with information from satellite laser ranging. Alternative spherical harmonics solutions from CSR, JPL and TU Graz reveal rates between 1.94 and 2.08 mm/a, thereby demonstrating that systematic differences among the centers are much reduced in the latest release. The results from the direct integration in the open ocean can be aligned to associated solutions of the sea-level equation when fractional leakage derived from two differently filtered global gravity fields is explicitly considered, leading to a global mean sea-level rise of 1.72 mm/a. This result implies that estimates obtained from a 1000 km coastal buffer zone are biased 0.3 mm/a high due the systematic omission of regions with below-average barystatic sea-level rise in regions close to substantial coastal mass losses induced by the reduced gravitational attraction of the remaining continental ice and water masses.

In this study we use CryoSat-2 SAR (delay-Doppler synthetic-aperture radar) data in the Mekong River Basin to estimate water levels. Compared to classical pulse limited radar altimetry, medium- and small-sized inland waters can be observed with CryoSat-2 SAR data with a higher accuracy due to the smaller along track footprint. However, even with this SAR data the estimation of water levels over a medium-sized river (width less than 500 m) is still challenging with only very few consecutive observations over the water. The target identification with land-water masks tends to fail as the river becomes smaller. Therefore, we developed a classification approach to divide the observations into water and land returns based solely on the data. The classification is done with an unsupervised classification algorithm, and it is based on features derived from the SAR and range-integrated power (RIP) waveforms. After the classification, classes representing water and land are identified. Better results are obtained when the Mekong River Basin is divided into different geographical regions: upstream, middle stream, and downstream. The measurements classified as water are used in a next step to estimate water levels for each crossing over a river in the Mekong River network. The resulting water levels are validated and compared to gauge data, Envisat data, and CryoSat-2 water levels derived with a land-water mask. The CryoSat-2 water levels derived with the classification lead to more valid observations with fewer outliers in the upstream region than with a land-water mask (1700 with 2% outliers vs. 1500 with 7% outliers). The median of the annual differences that is used in the validation is in all test regions smaller for the CryoSat-2 classification results than for Envisat or CryoSat-2 land-water mask results (for the entire study area: 0.76 m vs. 0.96 m vs. 0.83 m, respectively). Overall, in the upstream region with small- and medium-sized rivers the classification approach