Effects of Global Mean Atmospheric Pressure Variations on Mean Sea Level Changes from TOPEX/Poseidon

Dorandeu, J.; Traon, Pierre‐Yves Le

doi:10.1175/1520-0426(1999)016<1279:eogmap>2.0.co;2

Cited by 64 publications

(41 citation statements)

References 5 publications

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“…The inverse barometer effect considered here is the same as Dorandeu and Traon (1999): where ρ is the sea water density, P the Mean Sea Level Pressure (MSLP) from the European Centre for MediumRange Weather Forecasts (ECMWF) Re-analysis dataset (ERA-Interim; Dee et al 2011) and P ref is the MSLP spatial mean over the global ocean computed from 1993 to 2012.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…The effect of the high frequency response to the atmospheric pressure and wind is minimized in our analysis, considering monthly averaged data. The low frequency response to the atmospheric pressure (inverse barometer effect) can be considered as in Dorandeu and Traon (1999), as shown in Eq. (6).…”

Section: Analysis Of Sea-level Componentsmentioning

confidence: 99%

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

et al. 2016

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“…These data are geophysically corrected for tides, inverse barometer, tropospheric, and ionospheric signals (Le Traon and Ogor, 1998;Dorandeu and Le Traon, 1999). The oceanographic signal is less accurate near the coast because of pollution by land and in shallow waters due to inaccuracies of the global tidal model that is used to dealias the along-track altimeter observations.…”

Section: Observationsmentioning

confidence: 99%

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

et al. 2012

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Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ -a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003-2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates -a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.

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“…Account was also taken of the inverse barometer effect with respect to the reference atmospheric pressure assumed showing: a atmospheric pressure, P; mean wind speed, U w ; mean wind direction, h w measured by the M3 and M6 buoys; predicted astronomical tide (Delft Dashboard), h; predicted and measured (M3 and M6 buoys) significant wave height, Hs; predicted mean wave period, Tm10; predicted mean wave direction, h; and predicted wave power, WP 1013.3 mb (e.g. Dorandeu and Le Traon 1999). During the 13-14 December 2008 storm, the P decreased from around 1015 mb on 12 December to a minimum value of 989 mb ( Fig.…”

Section: Metocean Conditions At the Time Of The Breachmentioning

confidence: 99%

Modelling storm responses on a high-energy coastline with XBeach

Williams

Esteves

Rochford

2015

Model. Earth Syst. Environ.

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The XBeach model has been used to simulate the morphological impacts of storms on sandy and gravel beaches. Taking as a case study Rossbeigh Spit located on the high-energy coast of western Ireland, the study reported here tests the capacity of XBeach to reproduce barrier breaching during a storm in December 2008. It demonstrates that predictions of the breaching event agree reasonably well with observations. However, the main focus of the paper is to establish using the model results, sitespecific critical wave and water level conditions giving rise to dune erosion, overwashing and breaching. By deriving simple-to-use expressions to define hydrodynamic thresholds the study advances the ability to predict the impacts of infrequent and rarely observed storm events and is considered to provide useful coastal management tool for assessing the vulnerability of sandy barriers to breaching high-energy during storms.

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Effects of Global Mean Atmospheric Pressure Variations on Mean Sea Level Changes from TOPEX/Poseidon

Cited by 64 publications

References 5 publications

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

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

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Modelling storm responses on a high-energy coastline with XBeach

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