The relatively stable conditions of the sea ice cover in the Antarctic, observed for almost 40 years, seem to be changing recently. Therefore, it is essential to provide sea ice thickness (SIT) and volume (SIV) estimates in order to anticipate potential multi-scale changes in the Antarctic sea ice. For that purpose, the main objectives of this work are: (1) to assess a new sea ice freeboard, thickness and volume altimetry dataset over 2003–2020 and (2) to identify first order impacts of the sea ice recent conditions. To produce these series, we use a neuronal network to calibrate Envisat radar freeboards onto CryoSat-2 (CS2). This method addresses the impacts of surface roughness on Low Resolution Mode (LRM) measurements. During the 2011 common flight period, we found a mean deviation between Envisat and CryoSat-2 radar freeboards by about 0.5 cm. Using the Advanced Microwave Scanning Radiometer (AMSR) and the dual-frequency Altimetric Snow Depth (ASD) data, our solutions are compared with the Upward looking sonar (ULS) draft data, some in-situ measurement of the SIMBA campaign, the total freeboards of 6 Operation Ice Bridge (OIB) missions and ICESat-2 total freeboards. Over 2003–2020, the global mean radar freeboard decreased by about −14% per decade and the SIT and SIV by about −10% per decade (considering a snow depth climatology). This is marked by a slight increase through 2015, which is directly followed by a strong decrease in 2016. Thereafter, freeboards generally remained low and even continued to decrease in some regions such as the Weddell sea. Considering the 2013–2020 period, for which the ASD data are available, radar freeboards and SIT decreased by about −40% per decade. The SIV decreased by about −60% per decade. After 2016, the low SIT values contrast with the sea ice extent that has rather increased again, reaching near-average values in winter 2020. The regional analysis underlines that such thinning (from 2016) occurs in all regions except the Amundsen-Bellingshausen sea sector. Meanwhile, we observed a reversal of the main regional trends from 2016, which may be the signature of significant ongoing changes in the Antarctic sea ice.

Sea ice and snow volume are essential variables for polar predictions, but operational systems still struggle to accurately capture their evolution. Satellite measurements now provide estimates of sea ice freeboard and snow depth. The combined assimilation of sea ice concentration (SIC), along-track altimetry radar freeboard data from Cryosat-2 and observations of snow depth from Cryosat-2 and SARAL is implemented in a multivariate approach in a global $\mathrm{1}/\mathrm{4}$ ° ocean/sea ice coupled NEMO4.2/SI3 model. A multivariate experiment, performed on two full seasonal cycles 2017–2018, is compared to a free (no assimilation) and a SIC-only assimilation simulations. The multivariate technique increases the sea ice volume, even in the absence of freeboard and snow measurements during summer, and rapidly changes the spatial patterns of ice and snow thicknesses in both hemispheres, in accordance with the assimilated observations. The sea ice volume from the multivariate approach compares better with independent (not assimilated) estimates from ICESat-2 and CS2SMOS or SMOS in both hemispheres. The multivariate system performs better in the Arctic than in Antarctica where the ice and ocean separate analyses are not designed to handle properly the strong interactions between upper oceanic layers and sea ice cover in the Southern Ocean. These results also confirm the importance of using variable snow and ice densities in a freeboard assimilation context. This study shows promising results for enhancing the capacity of assimilation systems to monitor the volume of sea ice and snow and paves the way for future satellite missions.

Sea ice and snow volume are essential variables for polar predictions, but operational systems still struggle to accurately capture their evolution. Satellite measurements now provide estimates of sea ice freeboard and snow depth. The combined assimilation of sea ice concentration (SIC), along-track altimetry radar freeboard data from Cryosat-2 and observations of snow depth from Cryosat-2 and SARAL is implemented in a multivariate approach in a global ¼° ocean/sea ice coupled NEMO4.2/SI3 model. A multivariate experiment, performed on two full seasonal cycles 2017–2018, is compared to a free (no assimilation) and a SIC-only assimilation simulations. The multivariate technique increases the sea ice volume, even in the absence of freeboard and snow measurements during summer, and rapidly changes the spatial patterns of ice and snow thicknesses in both hemispheres, in accordance with the assimilated observations. The sea ice volume from the multivariate approach compares better with independent (not assimilated) estimates from IceSat-2 and CS2SMOS or SMOS in both hemispheres. The multivariate system performs better in the Arctic than in Antarctica where the ice and ocean separate analyses seem not designed to consider the strong interactions between upper oceanic layers and sea ice cover in the Southern Ocean and to prevent localised degradations. These results also confirm the importance of using variable snow and ice densities in a freeboard assimilation context. This study shows promising results for enhancing the capacity of assimilation systems to monitor the volume of sea ice and snow and paves the way for future satellite missions.

Sea ice and snow volume are essential variables for polar predictions, but operational systems still struggle to accurately capture their evolution. Satellite measurements now provide estimates of sea ice freeboard and snow depth. The combined assimilation of sea ice concentration (SIC), along-track altimetry radar freeboard data from Cryosat-2 and observations of snow depth from Cryosat-2 and SARAL is implemented in a multivariate approach in a global ¼° ocean/sea ice coupled NEMO4.2/SI3 model. A multivariate experiment, performed on two full seasonal cycles 2017–2018, is compared to a free (no assimilation) and a SIC-only assimilation simulations. The multivariate technique increases the sea ice volume, even in the absence of freeboard and snow measurements during summer, and rapidly changes the spatial patterns of ice and snow thicknesses in both hemispheres, in accordance with the assimilated observations. The sea ice volume from the multivariate approach compares better with independent (not assimilated) estimates from IceSat-2 and CS2SMOS or SMOS in both hemispheres. The multivariate system performs better in the Arctic than in Antarctica where the ice and ocean separate analyses seem not designed to consider the strong interactions between upper oceanic layers and sea ice cover in the Southern Ocean and to prevent localised degradations. These results also confirm the importance of using variable snow and ice densities in a freeboard assimilation context. This study shows promising results for enhancing the capacity of assimilation systems to monitor the volume of sea ice and snow and paves the way for future satellite missions.

Sea ice and snow volume are essential variables for polar predictions, but operational systems still struggle to accurately capture their evolution. Satellite measurements now provide estimates of sea ice freeboard and snow depth. The combined assimilation of sea ice concentration (SIC), along-track altimetry radar freeboard data from Cryosat-2 and observations of snow depth from Cryosat-2 and SARAL is implemented in a multivariate approach in a global ¼° ocean/sea ice coupled NEMO4.2/SI3 model. A multivariate experiment, performed on two full seasonal cycles 2017–2018, is compared to a free (no assimilation) and a SIC-only assimilation simulations. The multivariate technique increases the sea ice volume, even in the absence of freeboard and snow measurements during summer, and rapidly changes the spatial patterns of ice and snow thicknesses in both hemispheres, in accordance with the assimilated observations. The sea ice volume from the multivariate approach compares better with independent (not assimilated) estimates from IceSat-2 and CS2SMOS or SMOS in both hemispheres. The multivariate system performs better in the Arctic than in Antarctica where the ice and ocean separate analyses seem not designed to consider the strong interactions between upper oceanic layers and sea ice cover in the Southern Ocean and to prevent localised degradations. These results also confirm the importance of using variable snow and ice densities in a freeboard assimilation context. This study shows promising results for enhancing the capacity of assimilation systems to monitor the volume of sea ice and snow and paves the way for future satellite missions.

Sea ice and snow volume are essential variables for polar predictions, but operational systems still struggle to accurately capture their evolution. Satellite measurements now provide estimates of sea ice freeboard and snow depth. The combined assimilation of sea ice concentration (SIC), along-track altimetry radar freeboard data from Cryosat-2 and observations of snow depth from Cryosat-2 and SARAL is implemented in a multivariate approach in a global ¼° ocean/sea ice coupled NEMO4.2/SI3 model. A multivariate experiment, performed on two full seasonal cycles 2017–2018, is compared to a free (no assimilation) and a SIC-only assimilation simulations. The multivariate technique increases the sea ice volume, even in the absence of freeboard and snow measurements during summer, and rapidly changes the spatial patterns of ice and snow thicknesses in both hemispheres, in accordance with the assimilated observations. The sea ice volume from the multivariate approach compares better with independent (not assimilated) estimates from IceSat-2 and CS2SMOS or SMOS in both hemispheres. The multivariate system performs better in the Arctic than in Antarctica where the ice and ocean separate analyses seem not designed to consider the strong interactions between upper oceanic layers and sea ice cover in the Southern Ocean and to prevent localised degradations. These results also confirm the importance of using variable snow and ice densities in a freeboard assimilation context. This study shows promising results for enhancing the capacity of assimilation systems to monitor the volume of sea ice and snow and paves the way for future satellite missions.