CoAuthors

Ole Baltazar Andersen (DTU Space, Elektrovej, 328 DK-2800 , Denmark); Sinead L. Farrell (NOAA Center for Weather and Climate Prediction, College Park, MD, USA, USA); Stefan Hendricks (DTU Space, Elektrovej 328 , Germany); Thomas W. K. Armitage (CPOM University College London, UK, UK); Ridout Andy (CPOM University College London, UK, UK); Haas Christian (York University, Toronto, Ontario, Canada, Canada); Baker Steven (Mullard Space Science Laboratory, University College London, UK, UK)

State-of-the-art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high frequency domain, primarily due to7 unresolved gravity features, can result in errors in the estimated along-track freeboard. These8 errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer-term, multi-sensor oceanographic time-series of sea level change in the Arctic. This study focuses on an assessment of five state-of-the-art Arctic MSS models (UCL04/13, DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, inter- satellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat-2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03-0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimetres in parts of the Arctic.