Abstract's details
Uncertainty in Satellite estimate of Regional Mean Sea Level trends
CoAuthors
Event: 2019 Ocean Surface Topography Science Team Meeting
Session: Science I: Climate data records for understanding the causes of global and regional sea level variability and change
Presentation type: Type Oral
Contribution: PDF file
Abstract:
Satellite altimetry missions now provide more than 25 years of accurate, continuous and quasi-global measurements of sea level along the reference ground track of TOPEX/Poseidon. These measurements are used by different groups to build the Global Mean Sea Level (GMSL) record, an essential climate change indicator. Recently Ablain et al. (2019) derived uncertainty levels for the GMSL record, its trend and acceleration.
The quasi-synoptic view of the global ocean provided by satellite altimetry also provides information about the regional sea level rise distribution. In some regions the altimetry record shows a local sea level rise up to 5 times greater than the global mean rise (i.e >12 mm/yr) since 1993. This very fast sea level rise increases significantly the exposure of the local coastal communities to flooding . Estimating a realistic uncertainty of the regional sea level records is of crucial importance for impact studies.
In this study we use the SL-CCI monthly sea level dataset over 1993-2014 and downscale the approach of Ablain et al. (2019) to build local error variance covariance matrices with a yearly resolution. The error prescription relies on an empirical estimate of the different contributions to the sea level measurement error budget: long term drifts in the orbit solution, long period oscillations in geophysical corrections and the local level of the altimeter noise. We use a least square approach and the error variance-covariance matrix to estimate the local MSL trend uncertainties. Results suggest that local uncertainty levels range between 1.9 and 2.2 mm/yr (at the 90% confidence level). Such uncertainty values imply that the majority (about 60%) of global ocean is rising at a statistically significant rate. A sensitivity analysis shows that the regional uncertainty pattern is robust to changes in the empirical error estimates. Further work aims at providing a description of the spatial structure of the altimetry error covariance and building a full space/time description of the altimeter measurement error at climate scales.
The quasi-synoptic view of the global ocean provided by satellite altimetry also provides information about the regional sea level rise distribution. In some regions the altimetry record shows a local sea level rise up to 5 times greater than the global mean rise (i.e >12 mm/yr) since 1993. This very fast sea level rise increases significantly the exposure of the local coastal communities to flooding . Estimating a realistic uncertainty of the regional sea level records is of crucial importance for impact studies.
In this study we use the SL-CCI monthly sea level dataset over 1993-2014 and downscale the approach of Ablain et al. (2019) to build local error variance covariance matrices with a yearly resolution. The error prescription relies on an empirical estimate of the different contributions to the sea level measurement error budget: long term drifts in the orbit solution, long period oscillations in geophysical corrections and the local level of the altimeter noise. We use a least square approach and the error variance-covariance matrix to estimate the local MSL trend uncertainties. Results suggest that local uncertainty levels range between 1.9 and 2.2 mm/yr (at the 90% confidence level). Such uncertainty values imply that the majority (about 60%) of global ocean is rising at a statistically significant rate. A sensitivity analysis shows that the regional uncertainty pattern is robust to changes in the empirical error estimates. Further work aims at providing a description of the spatial structure of the altimetry error covariance and building a full space/time description of the altimeter measurement error at climate scales.