Impact of self‐attraction and loading on the annual cycle in sea level

Tamisiea, M. E.; Hill, Emma M.; Ponte, Rui M.; Davis, J. L.; Velicogna, I.; Vinogradova, Nadya

doi:10.1029/2009jc005687

Cited by 79 publications

(99 citation statements)

References 44 publications

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“…The rotational feedback is included following ref. 42. As for TWS, we only considered the geoid response to the loading, which determines the regional deviations from the global mean.…”

Section: Methodsmentioning

confidence: 99%

Reassessment of 20th century global mean sea level rise

Dangendorf

Marcos

Wöppelmann

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

308

237

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The rate at which global mean sea level (GMSL) rose during the 20th century is uncertain, with little consensus between various reconstructions that indicate rates of rise ranging from 1.3 to 2 mm·y −1 . Here we present a 20th-century GMSL reconstruction computed using an area-weighting technique for averaging tide gauge records that both incorporates up-to-date observations of vertical land motion (VLM) and corrections for local geoid changes resulting from ice melting and terrestrial freshwater storage and allows for the identification of possible differences compared with earlier attempts. Our reconstructed GMSL trend of 1.1 ± 0.3 mm·y −1 (1σ) before 1990 falls below previous estimates, whereas our estimate of 3.1 ± 1.4 mm·y −1 from 1993 to 2012 is consistent with independent estimates from satellite altimetry, leading to overall acceleration larger than previously suggested. This feature is geographically dominated by the Indian Ocean-Southern Pacific region, marking a transition from lower-than-average rates before 1990 toward unprecedented high rates in recent decades. We demonstrate that VLM corrections, area weighting, and our use of a common reference datum for tide gauges may explain the lower rates compared with earlier GMSL estimates in approximately equal proportion. The trends and multidecadal variability of our GMSL curve also compare well to the sum of individual

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“…The rotational feedback is included following ref. 42. As for TWS, we only considered the geoid response to the loading, which determines the regional deviations from the global mean.…”

Section: Methodsmentioning

confidence: 99%

Reassessment of 20th century global mean sea level rise

Dangendorf

Marcos

Wöppelmann

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

308

237

View full text Add to dashboard Cite

show abstract

“…Local sea-level measurements are influenced by VLM due to Glacial Isostatic Adjustment (GIA), tectonics, subsidence, sedimentation and self-attraction and loading (SAL; Tamisiea et al 2010) which can contaminate the trends (Calafat and Gomis 2009) and affect the estimate of reliable sea-level rates purely associated with the ocean dynamics.…”

Section: Tide Gauge Datamentioning

confidence: 99%

Sea-level variability in the Mediterranean Sea from altimetry and tide gauges

et al. 2016

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mean sea-level and the coastal stations. From 1993 to 2012, the mean sea-level trend (2.44 ± 0.5 mm year −1 ) was found to be affected by the positive anomalies of 2010 and 2011, which were observed in all the cases analysed and were mainly distributed in the eastern part of the basin. Ensemble empirical mode decomposition showed that these events were related to the processes that have dominant periodicities of ∼10 years, and positive residual sea-level trend were generally observed in both data-sets. In terms of mean sea-level trends, a significant positive sea-level trend (>95 %) in the Mediterranean Sea was found on the basis of at least 15 years of data.

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“…Here, we estimate the total ocean mass variations by integrating GRACE-derived mass anomalies over oceans. Then we account for SAL effects and determine the fingerprints in ocean waters (Mitrovica et al 2001) by solving the sea level equation (Farrell & Clark 1976;Tamisiea et al 2010). It is worth noting that using GRACE to estimate the total ocean mass variation requires a complete GRACE solution including degree-1 and C 20 coefficients in the CF frame.…”

Section: Oceanic Data and Their Noise Covariance Matricesmentioning

confidence: 99%

Statistically optimal estimation of degree-1 and C20 coefficients based on GRACE data and an ocean bottom pressure model

Sun¹,

Ditmar²,

Riva³

2017

Geophysical Journal International

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S U M M A R YIn this study, we develop a methodology to estimate monthly variations in degree-1 and C 20 coefficients by combing Gravity Recovery and Climate Experiment (GRACE) data with oceanic mass anomalies (combination approach). With respect to the method by Swenson et al., the proposed approach exploits noise covariance information of both input data sets and thus produces stochastically optimal solutions supplied with realistic error information. Numerical simulations show that the quality of degree-1 and -2 coefficients may be increased in this way by about 30 per cent in terms of RMS error. We also proved that the proposed approach can be reduced to the approach of Sun et al. provided that the GRACE data are noise-free and noise in oceanic data is white. Subsequently, we evaluate the quality of the resulting degree-1 and C 20 coefficients by estimating mass anomaly time-series within carefully selected validation areas, where mass transport is small. Our validation shows that, compared to selected Satellite Laser Ranging (SLR) and joint inversion degree-1 solutions, the proposed combination approach better complements GRACE solutions. The annual amplitude of the SLR-based C 10 is probably overestimated by about 1 mm. The performance of the C 20 coefficients, on the other hand, is similar to that of traditionally used solution from the SLR technique.

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Impact of self‐attraction and loading on the annual cycle in sea level

Cited by 79 publications

References 44 publications

Reassessment of 20th century global mean sea level rise

Reassessment of 20th century global mean sea level rise

Sea-level variability in the Mediterranean Sea from altimetry and tide gauges

Statistically optimal estimation of degree-1 and C20 coefficients based on GRACE data and an ocean bottom pressure model

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