Abstract's details
Uncertainties Affecting Regional Sea Level Trends
CoAuthors
Event: 2015 Ocean Surface Topography Science Team Meeting
Session: Quantifying Errors and Uncertainties in Altimetry data
Presentation type: Type Oral
Contribution: PDF file
Abstract:
Satellite altimetry missions now provide a more than 20 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 derive confidence intervals for regional sea level trends in order to build a map of sea level trends uncertainties than can be associated with the map of the sea level trends.
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 account for the impact of natural ocean variability on the reliability of trend estimates.
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 derive confidence intervals for regional sea level trends in order to build a map of sea level trends uncertainties than can be associated with the map of the sea level trends.
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 account for the impact of natural ocean variability on the reliability of trend estimates.