Sahra Kacimi

We present our first estimates of Arctic sea ice freeboard, sea surface height (SSH) from leads, and sea ice thickness from SWOT wide-swath altimetry, which span two growth seasons between October 2023 and April 2025. An incidence-angle-dependent classification scheme separates returns from leads and ice floes for freeboard retrievals. Open water leads, for freeboard calculations, are characterized by high radar backscatter, and low surface heights. Separation of returns from ice floes and leads yields, as expected, spatially decoupled fields of sea surface height and freeboard. Comparisons of area-averaged ICESat-2 and SWOT estimates of lead SSH anomalies over two Arctic growth seasons show strong agreement (R² = 0.95, slope = 0.91), with mean differences ranging between −8.33 ± 8.0 cm and 2.62 ± 10.1 cm. The area-averaged freeboard estimates from ICESat-2 are consistently higher than those from SWOT, with differences that trend from a low of −0.78 ± 4.22 cm in early fall, to a high of 11.3 ± 5.79 cm in early spring. Regression analysis of the two freeboard records shows consistent seasonal variability, explained largely by the expected growth of the sea ice cover (R² = 0.96). This seasonal behavior of ICESat-2 and SWOT freeboard differences strongly mirrors altimeter-derived snow depths (R = 0.99 and 0.97 for the 2023–2024 and 2024–2025 seasons), suggesting a time-invariant Ka-band penetration depth into the snow cover. Comparisons between ICESat-2 and SWOT sea ice thicknesses—using SWOT total freeboard—reveal close overall agreement, with a mean difference of 0.02 ± 0.17 m for the two seasons.

We present our first estimates of Arctic sea ice freeboard, sea surface height (SSH) from leads, and sea ice thickness from SWOT wide-swath altimetry, which span two growth seasons between October 2023 and April 2025. An incidence-angle-dependent classification scheme separates returns from leads and ice floes for freeboard retrievals. Open water leads, for freeboard calculations, are characterized by high radar backscatter, and low surface heights. Separation of returns from ice floes and leads yields, as expected, spatially decoupled fields of sea surface height and freeboard. Comparisons of area-averaged ICESat-2 and SWOT estimates of lead SSH anomalies over two Arctic growth seasons show strong agreement (R² = 0.95, slope = 0.91), with mean differences ranging between −8.33 ± 8.0 cm and 2.62 ± 10.1 cm. The area-averaged freeboard estimates from ICESat-2 are consistently higher than those from SWOT, with differences that trend from a low of −0.78 ± 4.22 cm in early fall, to a high of 11.3 ± 5.79 cm in early spring. Regression analysis of the two freeboard records shows consistent seasonal variability, explained largely by the expected growth of the sea ice cover (R² = 0.96). This seasonal behavior of ICESat-2 and SWOT freeboard differences strongly mirrors altimeter-derived snow depths (R = 0.99 and 0.97 for the 2023–2024 and 2024–2025 seasons), suggesting a time-invariant Ka-band penetration depth into the snow cover. Comparisons between ICESat-2 and SWOT sea ice thicknesses—using SWOT total freeboard—reveal close overall agreement, with a mean difference of 0.02 ± 0.17 m for the two seasons.

The seasonal cycle of Antarctic sea ice growth, redistribution, and melt is the dominant process controlling the strength and structure of the Southern Ocean's overturning circulation through surface water-mass transformation (WMT). Changes in sea ice extent (SIE) modify both the surface buoyancy forcing magnitude and the density classes into which the freshwater is transported, thereby impacting the patterns of WMT. We use ECCO Version 4, release 5 to diagnose WMT rates from 1992 to 2024, which show a modest sensitivity to SIE variability. Anti-correlation between anomalies in the density outcrop area and surface buoyancy flux provides a compensating effect on overall WMT rates during extreme SIE anomalies. This compensation is further reinforced by offsetting variations in heat and sea ice freshwater fluxes in regions of sea ice melt. These results highlight a complex interplay, both spatially and temporally, between surface forcing and surface hydrographic properties following sea ice changes.

In recent years, the Antarctic has seen a notable shift in sea ice trends: Where sea ice extent and volume were increasing on average, they have shown an unprecedented decline since 2016. Understanding the drivers of this shift is crucial to determining the impacts of climate change on sea ice and improving modeling of Antarctic sea ice evolution. In this study, we introduce the CryoSat‐2 Antarctic Ocean (CSAO) Sea Ice Thickness (SIT) product. We find there is good agreement between CSAO sea ice thickness estimates with the airborne measurements from Operation IceBridge and with ASPeCt shipborne observations, with mean biases of −0.68 and −0.25 m, attributed to an underestimation in snow depth in the CSAO SIT. We use this new SIT product along with satellite sea ice drift and concentration data to create the first observational budget of pan‐Antarctic sea ice volume (SIV). We present a budget decomposition of seasonal dynamic and thermodynamic changes in SIV across the pan‐Antarctic from 2014 to 2020. Our budget indicates a sharp 27% decrease in September SIV between September 2014 and September 2016, consistent with previous literature, with the largest seasonal decline occurring between 2015 and 2016. Thermodynamic growth is a critical determinant of the winter SIV, showing consistent decline since 2016, most notably and consistently in the Weddell Sea, while dynamic processes maintain the winter ice edge. The 2014–2016 decline in SIV also shows significant spatial variability across the Antarctic.

We present our first estimates of Arctic sea ice freeboard, sea surface height (SSH) from leads, and sea ice thickness from SWOT wide-swath altimetry, which span two growth seasons between October 2023 and April 2025. An incidence-angle-dependent classification scheme separates returns from leads and ice floes for freeboard retrievals. Open water leads, for freeboard calculations, are characterized by high radar backscatter, and low surface heights. Separation of returns from ice floes and leads yields, as expected, spatially decoupled fields of sea surface height and freeboard. Comparisons of area-averaged ICESat-2 and SWOT estimates of lead SSH anomalies over two Arctic growth seasons show strong agreement (R2 = 0.95, slope = 0.91), with mean differences ranging between −8.33 ± 8.0 cm and 2.62 ± 10.1 cm. The area-averaged freeboard estimates from ICESat-2 are consistently higher than those from SWOT, with differences that trend from a low of −0.78 ± 4.22 cm in early fall, to a high of 11.3 ± 5.79 cm in early spring. Regression analysis of the two freeboard records shows consistent seasonal variability, explained largely by the expected growth of the sea ice cover (R2 = 0.96). This seasonal behavior of ICESat-2 and SWOT freeboard differences strongly mirrors altimeter-derived snow depths (R = 0.99 and 0.97 for the 2023–2024 and 2024–2025 seasons), suggesting a time-invariant Ka-band penetration depth into the snow cover. Comparisons between ICESat-2 and SWOT sea ice thicknesses—using SWOT total freeboard—reveal close overall agreement, with a mean difference of 0.02 ± 0.17 m for the two seasons.

ICESat‐2 has demonstrated solid performance in retrieving sea ice freeboard during winter, when sea ice surface types are well‐defined and leads provide reliable sea surface height references. Summer retrievals are challenging because melt ponds are not explicitly distinguished in the ATL07 surface classification and may be identified as ice or leads. Here we assess ICESat‐2 summer sea ice observations using airborne LiDAR and imagery collected along near‐coincident ground tracks during a July 2022 calibration and validation campaign. Over a ∼50‐km Arctic sea ice transect, we compare ICESat‐2 surface classifications, elevations, and freeboards with high‐resolution airborne measurements. Melt ponds are generally not selected as sea surface height references in the ICESat‐2 freeboard retrieval. ICESat‐2 and airborne surface heights agree well (R 0.9) and there are minimal freeboard differences (−0.05 0.12 m to 0.01 0.10 m). ICESat‐2 summer freeboard retrievals are largely unaffected by melt pond misclassification, supporting the potential for year‐round sea ice thickness monitoring.

In recent years, the Antarctic has seen a notable shift in sea ice trends: Where sea ice extent and volume were increasing on average, they have shown an unprecedented decline since 2016. Understanding the drivers of this shift is crucial to determining the impacts of climate change on sea ice and improving modelling of Antarctic sea ice evolution. In this study, we introduce the CryoSat-2 Antarctic Ocean (CSAO) Sea Ice Thickness (SIT) product. We find there is good agreement between CSAO sea ice thickness estimates with the airborne measurements from Operation IceBridge and with ASPeCt shipborne observations, with mean biases of -0.68 and -0.25 m, attributed to an underestimation in snow depth in the CSAO SIT. We use this new SIT product along with satellite sea ice drift and concentration data to create the first observational budget of pan-Antarctic sea ice volume (SIV). We present a budget decomposition of seasonal dynamic and thermodynamic changes in SIV across the pan-Antarctic from 2014-2020. Our budget indicates a sharp SIV decrease of 29% between September 2014 and September 2016, consistent with previous literature. The largest seasonal decline of SIV was observed in 2015. Thermodynamic growth is a critical determinant of the winter SIV, showing consistent decline since 2016, most notably and consistently in the Weddell Sea, while dynamic processes maintain the winter ice edge. The 2014-2016 decline in SIV also shows significant spatial variability across the Antarctic.

We present a first examination of observations from the surface water and ocean topography (SWOT) mission over the polar oceans. In SWOT, bright, quasi‐specular leads are associated with lower heights relative to the surrounding ice cover. Surface heights and freeboard from SWOT and ICESat‐2 are compared in two near‐coincident segments in the Beaufort and the Weddell Seas. Correlations and mean differences between these 30‐km ICESat‐2 and SWOT height segments are 0.91 (−0.02 ± 0.08 m) in the Beaufort and 0.65 (0.01 ± 0.09 m) in the Weddell. Differences in freeboards using minimum height as a reference sea surface are 0.04 ± 0.08 m and −0.01 ± 0.09 m; minimum heights are localized within the same lead features of the two segments. The SWOT wide‐swath radar altimeter can, for the first time, provide two‐dimensional mappings of the spatial variability of freeboard and ice thickness — crucial information that will enhance our understanding of the physical mechanisms driving the decline of sea ice covers.

Antarctic snow on sea ice can contain slush, snow ice, and stratified layers, complicating satellite retrieval processes for snow depth, ice thickness, and sea ice concentration. The presence of moist and brine-wetted snow alters microwave snow emissions and modifies the energy and mass balance of sea ice. This study assesses the impact of brine-wetted snow and slush layers on L-band surface brightness temperatures (TBs) by synergizing a snow stratigraphy model (SNOWPACK) driven by atmospheric reanalysis data and the RAdiative transfer model Developed for Ice and Snow in the L-band (RADIS-L) v1.0 The updated RADIS-L v1.1 further introduces parameterizations for brine-wetted snow and slush layers over Antarctic sea ice. Our findings highlight the importance of including both brine-wetted snow and slush layers in order to accurately simulate L-band brightness temperatures, laying the groundwork for improved satellite retrievals of snow depth and ice thickness using satellite sensors such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP). However, biases in modelled and observed L-band brightness temperatures persist, which we attribute to small-scale sea ice heterogeneity and snow stratigraphy. Given the scarcity of comprehensive in situ snow and ice data in the Southern Ocean, ramping up observational initiatives is imperative to not only provide satellite validation datasets but also improve process-level understanding that can scale up to improving the precision of satellite snow and ice thickness retrievals.