Uncertainty in Satellite estimate of Regional Mean Sea Level trends
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
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.