Abstract. We have analyzed recent GRACE RL04 monthly gravity solutions, using a new decorrelating post-processing approach. We find very good agreement with mass anomalies derived from a global hydrological model (WGHM). The post-processed GRACE solutions exhibit only little amplitude damping and an almost negligeable phase shift and period distortion for relevant hydrological basins. Furthermore, these post-processed GRACE solutions have been inspected in terms of data fit with respect to the original inter-satellite ranging and to SLR and GPS observations. This kind of comparison is new. We find variations of the data fit due to solution postprocessing only within very narrow limits. This confirms our suspicion that GRACE data does not firmly 'pinpoint' the standard unconstrained solutions. Regarding the original Kusche (2007) decorrelation and smoothing method, a simplified (order-convolution) approach has been developed. This simplified approach allows to realize a higher resolution -as necessary e.g. for generating computed GRACE observations -and needs far less coefficients to be stored.

Dividing the sea-level budget into contributions from ice sheets and glaciers, the water cycle, steric expansion, and crustal movement is challenging, especially on regional scales. Here, Gravity Recovery And Climate Experiment (GRACE) gravity observations and sea-level anomalies from altimetry are used in a joint inversion, ensuring a consistent decomposition of the global and regional sea-level rise budget. Over the years 2002-2014, we find a global mean steric trend of 1.38 ± 0.16 mm/y, compared with a total trend of 2.74 ± 0.58 mm/y. This is significantly larger than steric trends derived from in situ temperature/salinity profiles and models which range from 0.66 ± 0.2 to 0.94 ± 0.1 mm/y. Mass contributions from ice sheets and glaciers (1.37 ± 0.09 mm/y, accelerating with 0.03 ± 0.02 mm/y 2 ) are offset by a negative hydrological component (−0.29 ± 0.26 mm/y). The combined mass rate (1.08 ± 0.3 mm/y) is smaller than previous GRACE estimates (up to 2 mm/y), but it is consistent with the sum of individual contributions (ice sheets, glaciers, and hydrology) found in literature. The altimetric sea-level budget is closed by coestimating a remaining component of 0.22 ± 0.26 mm/y. Well above average sea-level rise is found regionally near the Philippines (14.7 ± 4.39 mm/y) and Indonesia (8.3 ± 4.7 mm/y) which is dominated by steric components (11.2 ± 3.58 mm/y and 6.4 ± 3.18 mm/y, respectively). In contrast, in the central and Eastern part of the Pacific, negative steric trends (down to −2.8 ± 1.53 mm/y) are detected. Significant regional components are found, up to 5.3 ± 2.6 mm/y in the northwest Atlantic, which are likely due to ocean bottom pressure variations.lobal sea-level rise has been identified as one of the major threats associated with global climate change (1, 2). However, from the perspective of assessment-and decision-making, regional estimates of sea-level rise are even more important to formulate meaningful adaptation plans on a national or international level. Besides the magnitude of the total sea-level rise itself, identifying dominant drivers, and their corresponding uncertainties, may also prove beneficial for projection studies.Historical records from tide gauges indicate a sea-level rate of about 1.7 mm/y over the period 1900-2009, where it must be noted that tide gauges indicate an acceleration (0.009-0.017 mm/y 2 ) over the last century (3-5). Besides the steric expansion of sea water due to temperature changes, the ongoing melting and ablation of ice sheets in Greenland and Antarctica and other land glaciers cause the sea level to rise. Hydrological mass variability on land and reservoir construction have been found to cause a negative trend (6-9). Furthermore, meltwater, precipitation, or evaporation result in regional salinity changes, leaving steric signatures in sea level once the barotropic component has been compensated (10). For an observer at the coast, crustal movement, caused by glacial isostatic adjustment (GIA), tectonics, or local subsidence may also significantly affect the r...

The purpose of this paper is to assess the mass changes of the Greenland Ice Sheet (GrIS), Ice Sheets over Antarctica, and Land glaciers and Ice Caps with a global mascon method that yields monthly mass variations at 10,242 mascons. Input for this method are level 2 data from the Gravity Recovery and Climate Experiment (GRACE) system collected between February 2003 and June 2013 to which a number of corrections are made. With glacial isostatic adjustment (GIA) corrections from an ensemble of models based on different ice histories and rheologic Earth model parameters, we find for Greenland a mass loss of −278 ± 19 Gt/yr. Whereas the mass balances for the GrIS appear to be less sensitive to GIA modeling uncertainties, this is not the case with the mass balance of Antarctica. Ice history models for Antarctica were recently improved, and updated historic ice height data sets and GPS time series have been used to generate new GIA models. We investigated the effect of two new GIA models for Antarctica and found −92 ± 26 Gt/yr which is half of what is obtained with ICE-5G-based GIA models, where the largest GIA model differences occur on East Antarctica. The mass balance of land glaciers and ice caps currently stands at −162 ± 10 Gt/yr. With the help of new GIA models for Antarctica, we assess the mass contribution to the mean sea level at 1.47 ± 0.09 mm/yr or 532 ± 34 Gt/yr which is roughly half of the global sea level rise signal obtained from tide gauges and satellite altimetry.

Extracting large scale water storage (WS) patterns is essential for understanding the hydrological cycle and improving the water resource management of Iran, a country that is facing challenges of limited water resources. The Gravity Recovery and Climate Experiment (GRACE) mission offers a unique possibility of monitoring total water storage (TWS) changes. An accurate estimation of terrestrial and surface WS changes from GRACE-TWS products, however, requires a proper signal separation procedure. To perform this separation, this study proposes a statistical approach that uses a priori spatial patterns of terrestrial and surface WS changes from a hydrological model and altimetry data. The patterns are then adjusted to GRACE-TWS products using a least squares adjustment (LSA) procedure, thereby making the best use of the available data. For the period of October 2002 to March 2011, monthly GRACE-TWS changes were derived over a broad region encompassing Iran. A priori patterns were derived by decomposing the following auxiliary data into statistically independent components: (i) terrestrial WS change outputs of the Global Land Data Assimilation System (GLDAS); (ii) steric-corrected surface WS changes of the Caspian Sea; (iii) that of the Persian and Oman Gulfs; (iv) WS changes of the Aral Sea; and (v) that of small lakes of the selected region. Finally, the patterns of (i) to (v) were adjusted to GRACE-TWS maps so that their contributions were estimated and GRACE-TWS signals separated. After separation, our re

The Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow-On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE-type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate-related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data-driven methods and identifying the more robust alternatives for predicting GRACE-like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1°r esolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino-Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long-period TWSC fields globally when combined with statistical decomposition techniques.

Accurate estimates of ocean mass change are necessary to infer steric sea level change from sea level changes measured with satellite altimeters. Published studies using the Gravity Recovery and Climate Experiment (GRACE) satellite mission indicated a large range in trends (∼1–2 mm/year) with reported standard errors of 0.1–0.3 mm/year. Here we show that a large part of this discrepancy (up to 0.6 mm/year) can be explained by which model is used to account for the effect of glacial isostatic adjustment (GIA). The second largest contribution (0.3–0.4 mm/year) is related to the way how different studies have restored atmospheric and oceanic signals which have been removed during the GRACE gravity estimation process. Here two processing strategies, which previously resulted in differing ocean mass trends, are considered. The “direct” method uses the standard GRACE Stokes coefficients, while the “inverse” method applies a joint inversion of data from GRACE and altimetry. After accounting for differences in processing corrections, global mean ocean mass estimates from the direct, the mascon, and inverse approach agree with each other on global scales within less than 0.1 mm/year. Using the A et al. (2013; https://doi.org/10.1093/gji/ggs030) GIA model, we provide a reconciled monthly time series of global mean ocean mass, which suggests that ocean mass has increased by 1.43 mm/year over 2002.6–2014.5, with an amplified rate of 1.75 mm/year over 2002.6–2016.5 which covers almost the complete GRACE time span. However, we note that estimates as low as 1.05 mm/year cannot be ruled out when other published GIA corrections with lower mass‐equivalent signals over Antarctica are used.

No abstract

[1] We derive changes in ocean bottom pressure (OBP) and ocean mass by combining modeled ocean bottom pressure, weekly GRACE-derived models of gravity change, and large-scale deformation patterns sensed by a global network of GPS stations in a joint least squares inversion. The weekly combination allows a consistent estimation of geocenter motion, loading mass harmonics up to degree 30, and a spatially uniform mass correction term, which serves as a correction for forcing of the ocean model. We provide maps and time series of ocean mass and bottom pressure variations. Furthermore, we discuss the estimated geocenter motion and the estimated model correction. Our results indicate that the total ocean mass change is predominantly annual, with a maximum amplitude corresponding to 7.4 mm in October, which is in line with earlier work. The mean ocean bottom pressure (i.e., ocean plus atmospheric mass) shows an annual amplitude of 8.7 mm and is shifted forward by about 1.5 months. In addition, the solution exhibits typical autocorrelation times of about 2 weeks. A comparison with in situ bottom pressure time series in the southern Indian Ocean shows a good agreement, with correlations of 0.7-0.8. Based on these comparisons, we see that our results monitor realistic submonthly variations, which are strongest at high latitudes. The addition of GRACE data in the inversion is found to improve these high-latitude variations and enables better separability of the geocenter motion from other unknowns. Increasing the OBP model error from 3 cm to 4.8 cm affects mainly the higher-degree coefficients.