Validation of MPI-ESM Decadal Hindcast Experiments with Terrestrial Water Storage Variations as Observed by the GRACE Satellite Mission

Time-variations in the gravity field as observed by the GRACE mission provide for the first time quantitative estimates of the terrestrial water storage (TWS) at monthly resolution over one decade (2002–2011). TWS from GRACE is applied here to validate three different ensemble sets of decadal hindcasts performed with the coupled climate model MPI-ESM within the German research project MiKlip. Those experiments differ in terms of the applied low (LR) and medium (MR) spatial resolution configuration of MPI-ESM, as well as by the applied ensemble initialization strategy, where ocean-only (b0) is replaced by atmosphere and ocean (b1) anomaly initialization. Moderately positive skill scores of the initialized hindcasts are obtained both with respect to the zero anomaly forecast and the uninitialized projections in particular for lead year 1 in moderate to high latitudes of the Northern Hemisphere. Skill scores gradually increase when moving from b0-LR to b1-LR, and less prominent also for b1-LR to b1-MR, thereby documenting improvements of the MPI-ESM decadal climate prediction system during the most recent years

Atmospheric Contributions to Global Ocean Tides for Satellite Gravimetry

To mitigate temporal aliasing effects in monthly mean global gravity fields from the GRACE and GRACE‐FO satellite tandem missions, both tidal and non‐tidal background models describing high‐frequency mass variability in atmosphere and oceans are needed. To quantify tides in the atmosphere, we exploit the higher spatial (31 km) and temporal (1 hr) resolution provided by the latest atmospheric ECMWF reanalysis, ERA5. The oceanic response to atmospheric tides is subsequently modeled with the general ocean circulation model MPIOM (in a recently revised TP10L40 configuration that includes the feedback of self‐attraction and loading to the momentum equations and has an improved bathymetry around Antarctica) as well as the shallow water model TiME (employing a much higher spatial resolution and more elaborate tidal dissipation than MPIOM). Both ocean models consider jointly the effects of atmospheric pressure variations and surface wind stress. We present the characteristics of 16 waves beating at frequencies in the 1–6 cpd band and find that TiME typically outperforms the corresponding results from MPIOM and also FES2014b as measured from comparisons with tide gauge data. Moreover, we note improvements in GRACE‐FO laser ranging interferometer range‐acceleration pre‐fit residuals when employing the ocean tide solutions from TiME, in particular, for the S1 spectral line with most notable improvements around Australia, India, and the northern part of South America

In-Orbit Performance of the GRACE Accelerometers and Microwave Ranging Instrument

The Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided global long-term observations of mass transport in the Earth system with applications in numerous geophysical fields. In this paper, we targeted the in-orbit performance of the GRACE key instruments, the ACCelerometers (ACC) and the MicroWave ranging Instrument (MWI). For the ACC data, we followed a transplant approach analyzing the residual accelerations from transplanted accelerations of one of the two satellites to the other. For the MWI data, we analyzed the post-fit residuals of the monthly GFZ GRACE RL06 solutions with a focus on stationarity. Based on the analyses for the two test years 2007 and 2014, we derived stochastic models for the two instruments and a combined ACC+MWI stochastic model. While all three ACC axes showed worse performance than their preflight specifications, in 2007, a better ACC performance than in 2014 was observed by a factor of 3.6 due to switched-off satellite thermal control. The GRACE MWI noise showed white noise behavior for frequencies above 10 mHz around the level of (Formula presented.). In the combined ACC+MWI noise model, the ACC part dominated the frequencies below 10 mHz, while the MWI part dominated above 10 mHz. We applied the combined ACC+MWI stochastic models for 2007 and 2014 to the monthly GFZ GRACE RL06 processing. This improved the formal errors and resulted in a comparable noise level of the estimated gravity field parameters. Furthermore, the need for co-estimating empirical parameters was reduced

Modelling spatial covariances for terrestrial water storage variations verified with synthetic GRACE-FO data

Gridded terrestrial water storage (TWS) variations observed by GRACE or GRACE-FO typically show a spatial correlation structure that is both anisotropic (direction-dependent) and non-homogeneous (latitude-dependent). We introduce a new correlation model to represent this structure. This correlation model allows GRACE and GRACE-FO data users to get realistic correlations of the TWS grids without the need to derive them from the formal spherical harmonic uncertainties. Further, we found that the modelled correlations fit the spatial structure of uncertainties to a greater extent in a simulation environment. The model is based on a direction-dependent Bessel function of the first kind which allows to model the longer correlation lengths in the longitudinal direction via a shape parameter, and also to account for residual GRACE striping errors that might remain after spatial filtering. The global scale and shape parameters vary with latitude by means of even Legendre polynomials. The correlation between two points transformed to covariance by scaling with the standard deviations of each point. The covariance model is valid on the sphere which is empirically verified with a Monte-Carlo approach. The covariance model is subsequently applied to 5 years of simulated GRACE-FO data which allow for immediate validation with true uncertainties from the differences between the input mass signal and the recovered gravity fields. Four different realisations of the point standard deviations were tested: two based on the formal errors provided with the simulated Stokes coefficients, and two based on empirical standard deviations, where the first is spatially variant and temporally invariant, and the second spatially invariant and temporally variant. These four different covariance models are applied to compute TWS time series uncertainties for both the fifty largest discharge basins and regular grid cells over the continents. These four models are compared with the true uncertainties available in the simulations. The two empirically-based covariance models provide more realistic TWS uncertainties than the ones based on the formal errors. Especially, the empirically-based covariance models are better in reflecting the spatial pattern of the uncertainties of the simulated GRACE-FO data including their latitude dependence. However, these modelled uncertainties are in general too large. But with only one global scaling factor, a statistical test confirms the equivalence between the empirically-based covariance model with temporally variable point standard deviations and the true uncertainties. Thus at the end, this covariance model represents the closest fit in the simulation environment. The simulated GRACE-FO data are assumed to be very realistic which is why we recommend the new covariance model to be further investigated for the characterisation of real GRACE and GRACE-FO terrestrial water storage data

The GFZ GRACE RL06 Monthly Gravity Field Time Series: Processing Details and Quality Assessment

Time-variable gravity field models derived from observations of the Gravity Recovery and Climate Experiment (GRACE) mission, whose science operations phase ended in June 2017 after more than 15 years, enabled a multitude of studies of Earth’s surface mass transport processes and climate change. The German Research Centre for Geosciences (GFZ), routinely processing such monthly gravity fields as part of the GRACE Science Data System, has reprocessed the complete GRACE mission and released an improved GFZ GRACE RL06 monthly gravity field time series. This study provides an insight into the processing strategy of GFZ RL06 which has been considerably changed with respect to previous GFZ GRACE releases, and modifications relative to the precursor GFZ RL05a are described. The quality of the RL06 gravity field models is analyzed and discussed both in the spectral and spatial domain in comparison to the RL05a time series. All results indicate significant improvements of about 40% in terms of reduced noise. It is also shown that the GFZ RL06 time series is a step forward in terms of consistency, and that errors of the gravity field coefficients are more realistic. These findings are confirmed as well by independent validation of the monthly GRACE models, as done in this work by means of ocean bottom pressure in situ observations and orbit tests with the GOCE satellite. Thus, the GFZ GRACE RL06 time series allows for a better quantification of mass changes in the Earth system.DFG, FOR 2736, New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV)BMBF, 03F0654A, GRACE-FO - Projektmanagement, Aufbau eines wissenschaftlichen Auswertesystems und Aufbau eines GRACE-FO Projektbüro

Gravitationally Consistent Mean Barystatic Sea Level Rise From Leakage‐Corrected Monthly GRACE Data

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

What happens when we remove GRACE or Ocean Bottom pressure from a GRACE+GPS+OBP joint inversion? Roelof Rietbroek, Sandra-Esther Brunnabend, Madlen Gebler, Mathias Fritsche, Jürgen Kusche, Christoph Dahle, Frank Flechtner, Schröter Jens, and Dietrich Reinhard

The movement of large masses, originating from hydrological and oceanographic variations, causes detectable variations in gravity and surface deformation. These may be detected by satellite gravimetry and a network of permanent GPS stations respectively. Alternatively, additional information on ocean bottom pressure(OBP) variations may be retrieved from simulations. Joint inversions offer a way to combine different data sources in order to obtain improved estimates of surface loading. This technique can be used to compensate for weaknesses in one dataset, by the strengths of the others. But what happens when one datasets is taken out of the equation? Here, we compute a joint inversion using a GPS+GRACE+OBP combination. Additionally, we purposely deteriorate the solution by removing either data from GRACE or OBP. The accuracy and resolution of the solutions is discussed. Furthermore, regions are identified where the restricted inversion is consistent with the full inversion, and where the results show strong hydrological signals

Satellite Gravity Field Recovery Using Variance-Covariance Information From Ocean Tide Models

Monthly gravity field recovery using data from the GRACE and GRACE Follow-On missions includes errors limiting the spatial and temporal resolution of the estimated gravity fields. The major error contributions, besides the noise of the accelerometer instruments, arise from temporal aliasing errors due to imperfections in the non-tidal atmospheric and oceanic de-aliasing models and ocean tide models. We derive uncertainty information for the eight major tidal constituents from five different ocean tide models and introduce it into the gravity field recovery process in terms of a constrained normal equation system while expanding the parameter space by additional tidal parameters to be adjusted. We prove the effectiveness of the ocean tide variance-covariance information through realistic simulations and we assess its potential based on microwave and laser interferometer observations from the GRACE Follow-On mission. We show that errors are reduced by more than 20% ocean wRMS for a Gaussian filter radius of 300 km if uncertainty information for ocean tides is considered and stochastic modeling of instrument errors is applied, compared to the latest GFZ release 6.1. Our results also show the limited visibility of the effectiveness of the ocean tide variance-covariance information due to the dominating error contribution of non-tidal atmospheric and oceanic mass variations. Additionally, we investigate the option of estimating ocean tide parameters over a 1-year period while including ocean tide uncertainty information in order to improve ocean tide background modeling