Improved Quantification of Global Mean Ocean Mass Change Using GRACE Satellite Gravimetry Measurements

Chen, Jianli; Tapley, Byron D; Seo, Ki‐Weon; Wilson, Clark R.; Ries, John

doi:10.1029/2019gl085519

Cited by 35 publications

(51 citation statements)

References 21 publications

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“…The differences between each other among the GMOM rates from CSR, GFZ, and JPL solutions reached up to 0.11 mm/year (i.e., the difference between the GFZ and JPL rates before seismic correction), which was right within the estimated uncertainty range. Our estimated uncertainty ± 0.11 mm/year was close to the one provided in Chen et al [14], in which they obtained a ± 0.14 mm/year uncertainty level (see their Table 1) by fitting the GMOM time series from January 2005 to December 2015. Our uncertainty estimate was slightly smaller because we used a longer time span (2003-2015) of GRACE data in the time series fitting.…”

Section: Uncertainty Estimate Of Gmom Change Ratesupporting

confidence: 89%

“…If one assumes that the GMOM rate 2003.01-2015.12 by Altimetry minus Argo is close to that for the period 2004.01-2015.12, neither of our GRACE estimated GMOM rates before and after seismic correction (2.12 ± 0.30 mm/year and 2.05 ± 0.30 mm/year) agrees with the trend rate from Altimetry and Argo (2.35 ± 0.14 mm/year). The GRACE estimate of GMOM rate is noticeably smaller than that obtained from Altimetry and Argo, which is the same situation as in the previous studies Chen et al [14] and Dieng et al [10] (mentioned above). The discrepancy might attribute to the low-degree term errors as well as data filtering uncertainties associated with GRACE observations, model uncertainties in the GIA effect correction, Argo measurement uncertainties (especially for the deep ocean), etc.…”

Section: Comparison With Estimates From Altimery and Argosupporting

confidence: 85%

“…To estimate the uncertainty of the GMOM change rate obtained in this study, we followed the method used in Chen et al [9,14] based on least squares fitting. We applied the forward modeling (FM) leakage correction (with 100 iterations, as done for the global trend rate field in Section 3.2) to the monthly global EWH change fields (142 months in total, as stated in Section 2), and obtained the GMOM change time series from January 2003 to December 2015.…”

Section: Uncertainty Estimate Of Gmom Change Ratementioning

confidence: 99%

“…Due to the spatial coverage limitation of the Argo floats during its early observation period, the sea level change under a global scale is usually investigated with Argo data after 2005. Recently, Chen et al [14] obtained a GMOM rate of 2.68 ± 0.15 mm/year for the period 2005.01-2015.12, with 3.79 mm/year (from Altimetry) minus 1.11 mm/year (from Argo). In an earlier study Dieng et al [10] estimated the GMOM rate (2004.01-2015.12) as 2.35 ± 0.14 mm/year, with 3.49 mm/year (Altimetry) minus 1.14 mm/year (Argo).…”

Section: Comparison With Estimates From Altimery and Argomentioning

confidence: 99%

“…Satellite gravimetry, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO), provides a unique and large-scale measurement to quantify the global mean ocean mass (GMOM) changes. However, precise quantification of the GMOM change using GRACE/GFO is challenges including the land-ocean leakage errors (e.g., [8][9][10][11]), the significant low-degree term errors (e.g., [12][13][14][15]), and the large glacial isostatic adjustment (GIA) model uncertainties (e.g., [16][17][18][19]). Besides, the gravity effect linked with large offshore earthquakes should also affect the estimation of the GMOM change, and has not been taken into account in previous studies.…”

Section: Introductionmentioning

confidence: 99%

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Seismic Impact of Large Earthquakes on Estimating Global Mean Ocean Mass Change from GRACE

Tang

Chen

et al. 2020

Remote Sensing

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We analyze the impact of large earthquakes on the estimation of the global mean ocean mass (GMOM) change rate over the 13-year period (January 2003 to December 2015) using the Gravity Recovery and Climate Experiment (GRACE) Release-06 (RL06) monthly gravity solutions released by the Center for Space Research (CSR). We take into account the effects of the December 2004 Mw9.1 and April 2012 Mw8.6 Sumatra earthquakes, the March 2011 Mw9.0 Tohoku-Oki earthquake, and the February 2010 Mw8.8 Chile earthquake. After removing the co- and post-seismic effects of these earthquakes in the oceanic areas by least squares fitting, we estimate the GMOM rate from GRACE monthly observations. Results show that GRACE-observed GMOM rate before the seismic correction is 2.12 ± 0.30 mm/year, while after correction the rate is 2.05 ± 0.30 mm/year. Even though the −0.07 ± 0.02 mm/year seismic influence on GRACE GMOM rate is small on a global scale, it is a systematic bias and should be considered for improved quantification and understanding of the global sea level change.

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Section: Uncertainty Estimate Of Gmom Change Ratesupporting

confidence: 89%

Section: Comparison With Estimates From Altimery and Argosupporting

confidence: 85%

Section: Uncertainty Estimate Of Gmom Change Ratementioning

confidence: 99%

Section: Comparison With Estimates From Altimery and Argomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seismic Impact of Large Earthquakes on Estimating Global Mean Ocean Mass Change from GRACE

Tang

Chen

et al. 2020

Remote Sensing

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The sensitivity kernel perspective on GRACE mass change estimates

Döhne

Horwath

Groh

et al. 2023

J Geod

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Mass change inferences from GRACE and GRACE-FO typically involve, first, the preparation of spherical harmonic (SH) datasets on global gravity field changes and, second, their subsequent analysis that leads to mass change estimates. This study addresses the second step, which builds on SH input datasets that comprise the monthly gravity field solutions as well as amendments to low-degree components and subtraction or re-addition of certain modeled geophysical signals. A variety of methods have been developed to estimate mass changes from SH input datasets. It remains a challenge to assess and compare different methods adopted by different studies and to understand the mechanisms by which their results differ. Methods are often distinguished as belonging to either the inverse or direct approach. In the inverse approach, mass changes are estimated using a set of predefined spatial patterns. In the direct approach, surface mass density variations are integrated by using a predefined weight function, or sensitivity kernel. In this paper, we recall that sensitivity kernels are inherent not only to the direct approach. They are also inherent and may be made explicit, for inverse approaches. We prove that certain implementations of the direct and inverse approach have identical sensitivity kernels, and are therefore equivalent, under the condition that they rigorously incorporate the same signal and error covariance information. We present sensitivity kernels for the example of four different methods to estimate Greenland Ice Sheet mass changes. We discuss the sensitivity kernels in relation to the underlying differences in the methods. We propose to use sensitivity kernels as a means of communicating, assessing and comparing methods of mass change estimates. Once the sensitivity kernels associated to a method are made explicit, any user can readily investigate the method in terms of leakage effects, error propagation from the input SH datasets, or effects of the choice of the SH input datasets.

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Determination of the time-variable geopotential by means of orbiting clocks

Giuliani,

Tapley,

Ries

2024

J Geod

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Monitoring the time-variable geopotential identifies the mass redistribution across the Earth and reveals, e.g., climate change and availability of water resources. The features of interest are characterized by spatial and temporal scales accessible only through space missions. Among the most important gravity missions are GRACE (2002–2017), its successor GRACE-FO (since 2018), and GOCE (2009–2013), which all sense the Earth’s gravity field via the geopotential derivatives. We investigate the geopotential estimation through frequency comparisons between orbiting clocks by means of the Doppler-canceling technique, describing the clocks’ behavior in the Earth’s gravitational field via Einstein’s general relativity. The novelty of this approach lies in measuring gravity by sensing the geopotential itself. The proof of principle for the measurement is achieved through an innovative mission scenario: for the first time, the observations are collected by a probing clock in LEO. We show gravity solutions obtained by simulating an estimation problem via our proposed architecture. The results suggest that we can conceivably retrieve the geopotential coefficients with accuracy comparable to the GRACE measurement concept by employing clocks with stabilities of order $${10}^{-18}$$ 10 - 18 . Presently, terrestrial clocks can routinely attain fractional frequency stabilities of $${10}^{-18}$$ 10 - 18 , whereas spaceborne clocks are still at the $${10}^{-15}$$ 10 - 15 level. While our findings are promising, further analysis is needed to obtain more realistic indications on the feasibility of an actual mission, whose realization will be possible when clock technology reaches the required performance. The goal is for the technique investigated in this study to become a future staple for gravity field estimation.

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Improved Quantification of Global Mean Ocean Mass Change Using GRACE Satellite Gravimetry Measurements

Cited by 35 publications

References 21 publications

Seismic Impact of Large Earthquakes on Estimating Global Mean Ocean Mass Change from GRACE

Seismic Impact of Large Earthquakes on Estimating Global Mean Ocean Mass Change from GRACE

The sensitivity kernel perspective on GRACE mass change estimates

Determination of the time-variable geopotential by means of orbiting clocks

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