CoAuthors

Lionel Zawadzki (CLS, France); Jean-Christophe Poisson (CLS, France); Pierre Thibaut (CLS, France); Michaël Ablain (CLS, France); Graham Quartly (PML, United Kingdom); Jérôme Benveniste (ESA/ESRIN, Italy); Nicolas Picot (CNES, France)

The Arctic Ocean sea level remains largely unobserved by satellite altimetry missions, either due to orbit constraints (e. g. for Jason missions) or because ice coverage hinders the ability of conventional radar altimeters to retrieve sea surface height. Over the last few years, several efforts have been made to improve the observability of the Arctic Ocean and generate tailored sea level products (Prandi et al, 2012 & Andersen et al., 2015). These products remain based on the processing of 1Hz measurements, with dedicated editing and choice of geophysical corrections.
In this study, we take advantage of the waveform classification developed for the Envisat (CCI project) and SARAL/AltiKa missions (PEACHI project) that allows discriminating echo returns from leads in the ice pack, ice floes and open ocean. All echoes are retracked using the same adaptive algorithm. After editing and correction the measurements are used to build cycle-wise grids of sea level anomaly in the Arctic Ocean with unprecedented data availability in ice covered areas.
We present the methodology used to build this new dataset, its validation and the new insights on Arctic Ocean sea level variability that can be derived from it.