CoAuthors

Lionel Zawadzki (CLS, France); Jean-Christophe Poisson (CLS, France); Vincent Debout (CLS, France); Pierre Thibaut (CLS, France); Michael Ablain (CLS, France); Graham Quartly (PML, England); Jérôme Benveniste (ESA, 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 prevents the sea surface height retrieval. However the Arctic Ocean is a crucial region for the global climate which is undergoing rapid changes. Recent papers have been dedicated to observing SSH in the Arctic Ocean: Giles et al., 2012; Prandi et al., 2012; Cheng et al., 2015; Armitage et al., 2016.
In this study, we use recent results from Poisson et al. (2016, in prep.) regarding waveform classification and retracking for ENVISAT and SARAL/AltiKa missions to estimate a new dataset over the Arctic Ocean.
The outputs of a neural network waveform classification are used to identify echoes from leads in the ice pack which are retracked using an adaptive retracker able to fit Brownian and specular echoes.. After applying geophysical corrections and editing the data, SLA measurements are used to build monthly 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, validation results against previously available datasets (Cheng et al., 2015), and try to extract ocean features from the data.