Evaluation of Release-05 GRACE time-variable gravity coefficients over the ocean

Chambers, D. P.; Bonin, J. A.

doi:10.5194/os-8-859-2012

Cited by 165 publications

(181 citation statements)

References 30 publications

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“…Values are from Release-05 data processed by the three main science data system centers at the University of Texas at Austin Center for Space Research (CSR;Bettadpur, 2012), the Jet Propulsion Laboratory (JPL; Watkins and Yuan, 2012), and the GeoForschungsZentrum Potsdam (GFZ; Dahle, 2013). These data are then postprocessed by Don P. Chambers at the University of South Florida following the methods detailed in Chambers and Bonin (2012) and Johnson and Chambers (2013). We consider the ensemble mean across the estimates, deriving an estimate of the standard error according to variances in the differences between series (Ponte and Dorandeu, 2003).…”

Section: Gravimetric Retrievalsmentioning

confidence: 99%

El Niño, La Niña, and the global sea level budget

Piecuch

Quinn

2016

Ocean Sci.

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Abstract. Previous studies show that nonseasonal variations in global-mean sea level (GMSL) are significantly correlated with El Niño-Southern Oscillation (ENSO). However, it has remained unclear to what extent these ENSO-related GMSL fluctuations correspond to steric (i.e., density) or barystatic (mass) effects. Here we diagnose the GMSL budget for ENSO events observationally using data from profiling floats, satellite gravimetry, and radar altimetry during 2005-2015. Steric and barystatic effects make comparable contributions to the GMSL budget during ENSO, in contrast to previous interpretations based largely on hydrological models, which emphasize the barystatic component. The steric contributions reflect changes in global ocean heat content, centered on the Pacific. Distributions of ocean heat storage in the Pacific arise from a mix of diabatic and adiabatic effects. Results have implications for understanding the surface warming slowdown and demonstrate the usefulness of the Global Ocean Observing System for constraining Earth's hydrological cycle and radiation imbalance.

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Section: Gravimetric Retrievalsmentioning

confidence: 99%

El Niño, La Niña, and the global sea level budget

Piecuch

Quinn

2016

Ocean Sci.

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“…We study the mean equivalent water height (MEWH) within selected river basins to assess the signal content and the RMS of the non-seasonal variability over the oceans to assess the noise levels of the monthly gravity fields. The C 20 coefficient is excluded from the analysis because it is degraded in most of the GRACE gravity fields (Chen et al 2005) and normally replaced by SLR-derived values (e.g., Chambers and Bonin 2012;King et al 2012 andVelicogna andWahr 2013). …”

Section: Comparison Of Individual Time Seriesmentioning

confidence: 99%

“…Different processing strategies were applied, resulting in various noise characteristics of the individual solutions. Numerous studies compare subsets of the available monthly gravity fields in various geophysical applications such as ocean bottom pressure, ice mass change, and glacial isostatic adjustment (e.g., Chambers and Bonin 2012;Johnson and Chambers 2013;Sasgen et al 2007a;Steffen et al 2009). Sasgen et al (2007a) and Sakumura et al (2014) compared and combined the three official SDS and the GRGS gravity fields.…”

Section: Introductionmentioning

confidence: 99%

Combination of GRACE monthly gravity field solutions from different processing strategies

Jean

Meyer

Jäggi

2018

J Geod

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We combine the publicly available GRACE monthly gravity field time series to produce gravity fields with reduced systematic errors. We first compare the monthly gravity fields in the spatial domain in terms of signal and noise. Then, we combine the individual gravity fields with comparable signal content, but diverse noise characteristics. We test five different weighting schemes: equal weights, non-iterative coefficient-wise, order-wise, or field-wise weights, and iterative field-wise weights applying variance component estimation (VCE). The combined solutions are evaluated in terms of signal and noise in the spectral and spatial domains. Compared to the individual contributions, they in general show lower noise. In case the noise characteristics of the individual solutions differ significantly, the weighted means are less noisy, compared to the arithmetic mean: The non-seasonal variability over the oceans is reduced by up to 7.7% and the root mean square (RMS) of the residuals of mass change estimates within Antarctic drainage basins is reduced by 18.1% on average. The field-wise weighting schemes in general show better performance, compared to the order-or coefficient-wise weighting schemes. The combination of the full set of considered time series results in lower noise levels, compared to the combination of a subset consisting of the official GRACE Science Data System gravity fields only: The RMS of coefficient-wise anomalies is smaller by up to 22.4% and the non-seasonal variability over the oceans by 25.4%. This study was performed in the frame of the European Gravity Service for Improved Emergency Management (EGSIEM; http://www.egsiem.eu) project. The gravity fields provided by the EGSIEM scientific combination service (ftp://ftp.aiub.unibe.ch/EGSIEM/) are combined, based on the weights derived by VCE as described in this article.

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“…The three products (coded CSR, JPL and GFZ; release 05) each had a nominal 1 • and monthly resolution. The land and ocean mass retrievals (Chambers and Bonin, 2012) were combined. The land retrievals had been "de-striped" and smoothed with a 200 km half-width spherical Gaussian filter (Swenson et al, 2008;Swenson and Wahr, 2006), whereas the ocean retrievals had been smoothed with a 500 km filter (Chambers and Bonin, 2012).…”

Section: Data Sourcesmentioning

confidence: 99%

A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

Dijk

Renzullo

Wada

et al. 2014

Hydrol. Earth Syst. Sci.

132

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Abstract. We present a global water cycle reanalysis that merges water balance estimates derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission, satellite water level altimetry and off-line estimates from several hydrological models. Error estimates for the sequential data assimilation scheme were derived from available uncertainty information and the triple collocation technique. Errors in four GRACE storage products were estimated to be 11-12 mm over land areas, while errors in monthly storage changes derived from five global hydrological models were estimated to be 17-28 mm. Prior and posterior water storage estimates were evaluated against independent observations of river water level and discharge, snow water storage and glacier mass loss. Data assimilation improved or maintained agreement overall, although results varied regionally. Uncertainties were greatest in regions where glacier mass loss and subsurface storage decline are both plausible but poorly constrained. We calculated a global water budget for [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012]. The main changes were a net loss of polar ice caps (−342 Gt yr −1 ) and mountain glaciers (−230 Gt yr −1 ), with an additional decrease in seasonal snowpack (−18 Gt yr −1 ). Storage increased due to new impoundments (+16 Gt yr −1 ), but this was compensated by decreases in other surface water bodies (−10 Gt yr −1 ). If the effect of groundwater depletion (−92 Gt yr −1 ) is considered separately, subsurface water storage increased by +202 Gt yr −1 due particularly to increased wetness in northern temperate regions and in the seasonally wet tropics of South America and southern Africa. The reanalysis results are publicly available via www.wenfo.org/wald/.

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Evaluation of Release-05 GRACE time-variable gravity coefficients over the ocean

Cited by 165 publications

References 30 publications

El Niño, La Niña, and the global sea level budget

El Niño, La Niña, and the global sea level budget

Combination of GRACE monthly gravity field solutions from different processing strategies

A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble

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