This study quantified the effect of snow-ice formation on SnowModel-LG snow depth and density products. We coupled SnowModel-LG, a modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. We compared snow depth and density from the coupled product (SnowModel-LG_HS) to the original outputs of SnowModel-LG. In SnowModel-LG_HS, domain average snow depth decreased by 22%, and snow density increased by 2% when compared to SnowModel-LG. The differences were much larger in the Atlantic sector. Our simulations suggest that when snow-on-sea-ice models account for snow-ice formation, snow depth can be remarkably reduced. Sea ice thickness retrievals from CryoSat-2 were guided by both snow products. Averaged across the CryoSat-2 era (2011-2022), domain average sea ice thickness retrievals decreased by 10% when snow-ice was accounted for.