Abstract's details

Uncertainties in sea ice thickness products from altimetry. Towards new methods

garnier florent (LEGOS, France)


sara Fleury (LEGOS, France); Tanguy Jacober (LEGOS, France); Antoine Laforge (LEGOS, France); frédérique rémy (LEGOS, France); benoit meyssignac (LEGOS, France)

Event: 2019 Ocean Surface Topography Science Team Meeting

Session: Quantifying Errors and Uncertainties in Altimetry data

Presentation type: Type Oral

Contribution: PDF file


Sea ice volume is one of the most sensible indicators of climate change and an integrated measure of sea ice energy and freshwater budget. While sea ice extent and concentration are fairly well observed from space since the 70ies, observation of sea ice thickness is quite recent and yet remain unhomogeneously distributed over space and time (usually over 10 year period and only in Arctic). Consequently, operational sea ice reanalysis only integrate observations of sea ice extend and sea ice concentration. However, recent works (e.g Day et al, 2014, Chevallier et al, 2016, Xie et al, 2018, Blockley et al, 2018) show that the assimilation of sea ice concentration only is not sufficient for relevant estimations of sea ice seasonal variations and climate model projections. Also, because of a longer time correlation compare to sea ice concentrations, model initialization from sea ice thickness observations improves sea ice model physics (Schroder et al, 2018). The development of sea ice thickness observation products adapted to model needs is therefore essential. In this context, the purpose of this presentation is to overview our late development of sea ice thickness observation uncertainties to facilitate the synergy between model and observations.

In this presentation, we first describe the methodology used to derive sea ice freeboard from altimetric power echo measurements and construct 2002-2017 Envisat/Cryosat-2 sea ice radar freeboard time series. In the same time, we describe how freeboard and sea ice thickness uncertainties are calculated in actual sea-ice products.
In a second part, in order to understand the difficulties to derive consistent uncertainties of sea ice thickness, we will list the various sources of uncertainties. In particular, we focus on uncertainties inherent to the « waveform to freeboard » process.
Then, we present a new approach to explicitely simulate uncertainties based on random numbers. One advantage is to avoid prior assumptions such as the uncorrelation of errors.
Finally, the relevancy of these freeboard and sea ice thickness uncertainties for modelling applications and data assimilation will be assessed from comparisons with various independant missions such as ICESat and Operation Ice Bridge (OIB).


Guerreiro, K., Fleury, S., Zakharova, E., Rémy, F., & Kouraev, A. (2016). Potential for estimation of snow depth on Arctic sea ice from CryoSat-2 and SARAL/AltiKa missions. Remote Sensing of Environment, 186, 339-349.

Schroder, D., Feltham, D L., Tsamados, M., Ridout, A., Tilling, R., (2018). New insight from CryoSat-2 sea ice thickness for sea ice modelling. The Cryosphere, 13,125-139.

Chevallier, M., Smith, G C., et al., (2016). Intercomparison of the Arctic sea ice cover in global ocean_sea ice reanalyses from the ORA-IP project. Clim Dyn,49, 1107-1136.

Day, J., Hawkins, E., Tietsche, S., Will arctic sea ice thickness initialization improve seasonal forecast skill ? Geophysical Research letters, 41 ; 7566-7575.

Xie, J., Counillon, F., Bertino, L. (2018). Impact of assimilating a merged sea ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis. The Cryosphere, 12, 3671-3691.

Blockley, E.W., Peterson, K.A., (2018). Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness. The Cryosphere, 12, 3419-3438

Oral presentation show times:

Room Start Date End Date
The Monroe Hub Wed, Oct 23 2019,14:00 Wed, Oct 23 2019,14:18
garnier florent