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We estimate the uncertainty of satellite-retrieved Arctic sea-ice thickness, sea-ice volume, and their trends stemming from the lack of reliable snow-thickness observations. To do so, we simulate a Cryosat2-type ice-thickness retrieval in an ocean-model simulation forced by atmospheric reanalysis, pretending that only freeboard is known as model output. We then convert freeboard to sea-ice thickness using different snow climatologies and compare the resulting sea-ice thickness retrievals to each other and to the real sea-ice thickness of the reanalysis-forced simulation. We find that different snow climatologies cause significant differences in the obtained ice thickness and ice volume. In addition, we show that Arctic ice-volume trends derived from ice-thickness retrievals using any snow-depth climatology are highly unreliable because the estimated trend in ice volume can strongly be influenced by the neglected interannual variability in snow volume.

Plain Language Summary In our study, we show that the lack of snow-thickness information can cause substantial uncertainties in estimated sea-ice thickness and pan-Arctic sea-ice volume from satellites such as Cryosat2. In particular, even the sign of short-term trends can be erroneous when changes in snow thickness are ignored. The satellites measure how much the sea ice extends above the ocean surface. Current algorithms interpret any change in this quantity from one year to the next to be entirely caused by a change in the associated sea-ice thickness. This is because existing algorithms assume that the snow thickness on the sea ice is constant from one year to the next. However, in reality, the snow thickness can vary substantially, which in turn influences how much the ice extends above the ocean surface for a given sea-ice thickness. Our findings suggest that better estimates and inclusion of realistic snow depths are crucial for reliable ice-thickness retrievals from satellite altimeter data. A possible step forward is the usage of accumulated snow depth from reanalysis as a source for interannually varying snow depth data.