Abstract's details
Estimating Trend and Acceleration Uncertainties of Global Mean Sea Level Evolution over the 25-Year Altimetry Era
CoAuthors
Event: 2018 Ocean Surface Topography Science Team Meeting
Session: Quantifying Errors and Uncertainties in Altimetry data
Presentation type: Type Poster
Contribution: not provided
Abstract:
Satellite altimetry missions now provide a more than 25 years record of continuous measurements of sea level along the reference ground track of TOPEX/Poseidon. These measurements are used by different groups to build the mean sea level rise record, which is an essential climate change indicator. Estimating a realistic uncertainty on the sea level rise rate deduced from satellite is of crucial importance for climate studies such as sea level budget closure.
Ablain et al., 2015 estimated the GMSL trend uncertainty 0.5 mm/yr (90% confidence interval) by a careful study of the differences between altimeter standards. In this study we update this study over the 25-year altimetry era, tuning the modelling of altimetry errors. We use a generalized least squares approach, based on the a priori knowledge of the error variance-covariance matrix. Three types of errors that can affect altimetry are modeled (drifts, biases, noise) and combined to derive realistic confidence intervals on local sea level trend estimates. The approach is extended to estimates the GMSL trend uncertainties over the 25-year period, but also for any altimeter periods between 1993 and 2017 years. Furthermore, a confidence envelop of the GMSL time series is inferred from the error variance-covariance matrix.
Several recent studies (e.g. Nerem et al., 2018) have also shown an acceleration close to 0.08-0.10 mm/yr² of the GMSL evolution over the 25-year period. Thanks to the previous error budget approach previously described, we have also estimated the uncertainty of such an acceleration.
Ablain et al., 2015 estimated the GMSL trend uncertainty 0.5 mm/yr (90% confidence interval) by a careful study of the differences between altimeter standards. In this study we update this study over the 25-year altimetry era, tuning the modelling of altimetry errors. We use a generalized least squares approach, based on the a priori knowledge of the error variance-covariance matrix. Three types of errors that can affect altimetry are modeled (drifts, biases, noise) and combined to derive realistic confidence intervals on local sea level trend estimates. The approach is extended to estimates the GMSL trend uncertainties over the 25-year period, but also for any altimeter periods between 1993 and 2017 years. Furthermore, a confidence envelop of the GMSL time series is inferred from the error variance-covariance matrix.
Several recent studies (e.g. Nerem et al., 2018) have also shown an acceleration close to 0.08-0.10 mm/yr² of the GMSL evolution over the 25-year period. Thanks to the previous error budget approach previously described, we have also estimated the uncertainty of such an acceleration.
