Alessandro Di Bella

Snow on sea ice is an essential climate variable due to its high albedo and insulating capability. In addition, snow thickness estimates are needed to calculate sea ice freeboard and thickness (SIT) from radar altimetry measurements and uncertainties in snow depth contribute significantly to the resulting SIT uncertainty. In recent years, progress has been made observing or estimating winter snow depth. However, very few snow depth products exist year-round, i.e. including the summer melt season, despite considerable snow remaining on the ice in early summer. This study is the first to assess snow depth estimates year-round with a focus on the so far unexplored melt season. We intercompare estimates (a) based on in situ data from the Warren climatology and (b) two modified Warren climatologies using either ice age data or ice charts, (c) modeled snow depth from SnowModel-LG using two different forcings, (d) a climatology of SnowModel-LG, (e) a combination of the modified Warren climatology and the SnowModel-LG climatology, and (f) finally we also, for the first time, explore the use of dual-frequency altimetry (KuLa) to derive snow depth estimates year-round. We compare the snow depth products against buoy observations of snow depth and airborne measurements of SIT, as well as mooring measurements of sea ice draft together with CryoSat-2 freeboard data. We find that the MERRA-2 forced version of SnowModel-LG agrees better with validation data during the melt season, because the ERA-5 forced version melts the snow much faster than any other product. When a Warren climatology is used, we recommend using the modified version and to strictly only use it in the Central Arctic, where it is constrained by measurements. For KuLa we find that realistic results extend into summer. However, some regions retain snow throughout summer, which likely indicates residual negative biases in CryoSat-2 freeboards and/or positive biases in the ICESat-2 freeboard data.

Radar altimetry provides sea ice thickness estimates for polar region. However, uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, improving our knowledge on electromagnetic waves scattering with the snowpack and ice is necessary to retrieve sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low-resolution altimeter waveform echo from snow-covered sea ice, using in-situ measurements as input. In-situ measurements from four field campaigns in three distinct Canadian Arctic regions includes temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to optimize the snow and sea ice roughness. In addition, simulations of backscatter in low-resolution mode in preparation for the European Space Agency’s CRISTAL mission indicated that the dominant return comes from the ice surface at Ku-band and from the snow surface at Ka-band for smooth first-year ice. However, for rougher multi-year ice, the main scattering comes from the snow surface for both Ku and Ka-band. These findings depend on the parameterisation of the roughness. This work offers insight into the dominant surface return for Ku and Ka and paves the way towards a physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.

Radar altimetry provides sea ice thickness estimates for polar region. However, uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, improving our knowledge on electromagnetic waves scattering with the snowpack and ice is necessary to retrieve sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low-resolution altimeter waveform echo from snow-covered sea ice, using in-situ measurements as input. In-situ measurements from four field campaigns in three distinct Canadian Arctic regions includes temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to optimize the snow and sea ice roughness. In addition, simulations of backscatter in low-resolution mode in preparation for the European Space Agency’s CRISTAL mission indicated that the dominant return comes from the ice surface at Ku-band and from the snow surface at Ka-band for smooth first-year ice. However, for rougher multi-year ice, the main scattering comes from the snow surface for both Ku and Ka-band. These findings depend on the parameterisation of the roughness. This work offers insight into the dominant surface return for Ku and Ka and paves the way towards a physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.

Radar altimetry provides sea ice thickness estimates for polar region. However, uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, improving our knowledge on electromagnetic waves scattering with the snowpack and ice is necessary to retrieve sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low-resolution altimeter waveform echo from snow-covered sea ice, using in-situ measurements as input. In-situ measurements from four field campaigns in three distinct Canadian Arctic regions includes temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to optimize the snow and sea ice roughness. In addition, simulations of backscatter in low-resolution mode in preparation for the European Space Agency’s CRISTAL mission indicated that the dominant return comes from the ice surface at Ku-band and from the snow surface at Ka-band for smooth first-year ice. However, for rougher multi-year ice, the main scattering comes from the snow surface for both Ku and Ka-band. These findings depend on the parameterisation of the roughness. This work offers insight into the dominant surface return for Ku and Ka and paves the way towards a physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.

Sea-ice thickness is a key component of the Arctic climate system, but yet a comprehensive assessment across observations and numerical models is still missing. Previous studies have either compared only a small ensemble of sea-ice thickness products, focused on a short time scale, or both. We use an ensemble of 23 harmonised large-scale satellite, model, reanalysis and multi-product data ranging partly from 1995 to 2024, but mostly from 2010 to 2023. The products are compared against reference data derived from upward-looking sonar measurements in the Beaufort Sea. We find that biases are typically in the range of −0.2 m to 0.3 m, root mean square deviations are usually between 0.25 m and 0.5 m, and correlation coefficients mostly fall between 0.7 and 0.85, although larger deviations occur in some cases. Satellite and multi-product data mostly have lower biases and lower root mean square deviations (RMSD), but similar correlation coefficients compared to models and reanalyses. We examine the reliability of the uncertainties stated by the providers of twelve products and find that, while individual products tend to state uncertainties that are either too small or too large relative to their actual difference towards reference data, the ensemble as a whole shows comparable magnitudes of uncertainties and difference towards reference data. Subsequently, we do a pairwise comparison between decadal averages of large-scale products. We find biases largely between 0.2 and 0.4 m, RMSDs largely between 0.4 and 0.9 m and correlation coefficients largely between 0.5 and 0.8. Our study concludes with a time-series analysis of sea-ice thickness for each category (model/reanalysis, satellite, multi-product) in November and March for 2010–2023 and 1995–2023, with the second period being limited to the region south of 81.5° N. For the first period, we find no significant trend in any category for both months. For the second period, we find that sea-ice thickness has declined by roughly 0.5–0.6 m in November and 0.3–0.4 m in March, with stronger trends for models/reanalyses and multi-product data than for satellite products.

Increased anthropogenic stressors (e.g., warming, acidification, wildfires, and other extreme events) present complex observational challenges for Earth science, and no one sensor can “do it all”. While many remote sensing technologies are available at present, scientific disciplines are often trained to use only a specific subset, greatly limiting scientific advancements. Here we present open‐source software (icepyx) that lowers the barrier for entry for two remote platforms offering vertically‐resolved information about the ocean's subsurface: ICESat‐2 (Ice, Cloud, and land Elevation Satellite 2) and Argo floats. icepyx provides object‐oriented code for querying and downloading ICESat‐2 and Argo data within a single analysis workflow. icepyx natively handles ICESat‐2 data access and read‐in; here we introduce the Query, Unify, Explore SpatioTemporal (QUEST) module as a framework for adapting icepyx to easily access and ingest other datasets and present Argo data as the initial use case. Seamless retrieval of coincident data from ICESat‐2 and Argo enables improved targeted and exploratory studies across the cryosphere and open ocean realms. We close with recommendations for future work, discussion of the value of open science, relevance of our work to upcoming satellite missions, and an invitation to join our programming community. Link to repository: https://github.com/icesat2py/icepyx/tree/main. Link to documentation: https://icepyx.readthedocs.io/en/latest/.

Launched in 2010, the European Space Agency’s (ESA) CryoSat mission was the first polar-orbiting satellite flying a SAR Interferometric altimeter dedicated to the cryosphere, with the objectives to monitor precise changes in the thickness of polar ice sheets and floating sea ice. After 14 years in orbit, CryoSat remains one of the most innovative radar altimeters in space and continues to deliver high-quality data, providing unique contributions to several Earth Science and application domains. The mission has been extended until the end of 2025 with the scope to achieve important scientific objectives and to extend the synergy with other missions by further strengthening international cooperation.Routine CalVal activities are fundamental to evaluate the accuracy of CryoSat measurements, to monitor the long-term stability of the altimeter, and to characterise uncertainties on the final geophysical retrievals. In this talk, we present the CryoSat mission status and show results from some of the several CalVal activities currently in place, e.g., acquisition over transponders, comparison of sea level at tide gauges and exploitation of data collected during polar field campaigns. We also highlight the importance of international cooperation in CalVal and Science activities from the perspective of the ESA-NASA CRYO2ICE campaign, aligning CryoSat orbit to the one of ICESat-2, and the Sea Ice Thickness Intercomparison Exercise (SIN’XS) project, aiming to provide reconciled sea ice thickness estimates in both hemispheres. Finally, we discuss how current and future CryoSat activities are crucial to prepare for the upcoming Copernicus CRISTAL mission which will provide coincident measurements at Ka and Ku bands.

Sea ice thickness is essential for climate studies and numerical weather prediction. Radar altimetry has provided sea ice thickness measurement since the launch of ERS-1 and currently through CryoSat-2, Sentinel-3 and Altika but uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, modelling the scattering of the electromagnetic waves with the snowpack and ice is necessary to retrieve the sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low resolution altimeter waveform echo from the snow-covered sea ice, using in-situ measurements as input. Measurement from four field campaigns were used: Cambridge Bay, Eureka Sound and near Alert, Nunavut, Canada in April 2022 in the cold and later winter condition when snow and ice thickness are neat their seasonal maxima prior to melt. In-situ measurements included snow temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to adjust sea ice roughness. The retrieved roughness (root-mean-square height) in Cambridge Bay was 2.1 mm and 1.6 mm in Eureka, which was close to the observed value of 1.4 mm for flat sea ice. In addition, simulations of backscatter in preparation for the European Space Agency's CRISTAL mission demonstrated the dominance of scattering from the snow surface at Ku and Ka-band. However, these findings depend on the parameterisation of the roughness. The scattering from the snow surface dominates when roughness is high, but the interface return dominates if the roughness is low ( < 2.5 mm). This is the first study to consider scattering within the snow and demonstrate the origin of CryoSat-2 signals. This work paved the way to a new physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.

Sea ice thickness is essential for climate studies and numerical weather prediction. Radar altimetry has provided sea ice thickness measurement since the launch of ERS-1 and currently through CryoSat-2, Sentinel-3 and Altika but uncertainty in the scattering horizon used to retrieve sea ice thickness arises from interactions between the emitted signal and snow cover on the ice surface. Therefore, modelling the scattering of the electromagnetic waves with the snowpack and ice is necessary to retrieve the sea ice thickness accurately. The Snow Microwave Radiative Transfer (SMRT) model was used to simulate the low resolution altimeter waveform echo from the snow-covered sea ice, using in-situ measurements as input. Measurement from four field campaigns were used: Cambridge Bay, Eureka Sound and near Alert, Nunavut, Canada in April 2022 in the cold and later winter condition when snow and ice thickness are neat their seasonal maxima prior to melt. In-situ measurements included snow temperature, salinity, density, specific surface area, microstructure from X-ray tomography and surface roughness measurements using structure from motion photogrammetry. Evaluation of SMRT in altimeter mode was performed against CryoSat-2 waveform data in pseudo-low-resolution mode. Simulated and observed waveforms showed good agreement, although it was necessary to adjust sea ice roughness. The retrieved roughness (root-mean-square height) in Cambridge Bay was 2.1 mm and 1.6 mm in Eureka, which was close to the observed value of 1.4 mm for flat sea ice. In addition, simulations of backscatter in preparation for the European Space Agency's CRISTAL mission demonstrated the dominance of scattering from the snow surface at Ku and Ka-band. However, these findings depend on the parameterisation of the roughness. The scattering from the snow surface dominates when roughness is high, but the interface return dominates if the roughness is low ( < 2.5 mm). This is the first study to consider scattering within the snow and demonstrate the origin of CryoSat-2 signals. This work paved the way to a new physical retracker using SMRT to retrieve snow depth and sea ice thickness for radar altimeter missions.