CoAuthors

Rémi Jugier (CLS, France); Michaël Ablain (CLS, France); Florence Birol (LEGOS, France); Benoît Meyssignac (LEGOS, France); Anny Cazenave (LEGOS, France)

Sea level is a key indicator of global climate change as it integrates changes of several components of the climate system in response to anthropogenic forcing and other forcing factors related to natural sources and internal climate variability. Since the early 1990s, sea level variations have been routinely measured by high-precision altimeter satellites. Most recent results indicate a rate of global mean sea level rise of 3.3 mm/yr since 1993 with an uncertainty of 0.4 mm/yr within a confidence interval of 90% (Ablain et al., 2018). The sea level data also shows important regional variability as illustrated by the large-scale spatial trend patterns in sea level. In coastal regions, sea level variations result from a combination of different processes that act at different spatial and temporal scales. In addition to the global mean rise and superimposed large-scale regional variability, small-scale ocean processes (waves, meso-scale currents) and dynamical atmospheric forcing-induced sea level also affect sea level in coastal regions. It will be important to evaluate the relative importance of each of these processes causing coastal sea level variability at different time-scales. As these processes impact the coast differently, evaluating their relative importance is essential for the assessment of the local coastline vulnerability. As discussed by Cipollini et al. (2017a), the answer to the question “is coastal sea level rising at the same rate as open ocean sea level?” is still unknown. This largely results from the lack of long tide gauge records in many coastal regions (e.g. Africa). However, the adapted processing of multi-mission altimetry opens a new perspective on this important topic.

Therefore, in this study, we propose to estimate the evolution of the altimeter Mean Sea Level (MSL) trends uncertainty, from the open ocean to the coast. Our approach is based on an “ensemble” approach where a set of potential equivalent altimeter standards (e.g. geophysical and atmospheric corrections, retracking, etc…) is applied to calculate sea-level height and along-track MSL trends. MSL trend uncertainties are then basically inferred from the dispersion of the along-track MSL trend dataset and are calculated versus the coastal distance. The results of this study could be very useful to determine the confidence of MSL measurements in coastal areas and contribute to a better knowledge of sea-level processes in these areas.