Abstract. 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.

Sea ice altimetry currently remains the primary method for estimating sea ice thickness from space, however, time series of such satellite-derived estimates are of limited use without having been quality-controlled against reference measurements. Such reference measurements (a term encapsulating in situ observations and remotely sensed measurements from ground, air, and below the ice) for validation of altimetry measurements over sea ice in the polar regions are sparse and rarely presented in a manner where the time-space averaging matches that of the satellite-derived products. Here, an approach to a published comprehensive collection of sea ice reference measurements repurposed for satellite altimetry observations over sea ice is presented, which includes estimates of freeboard, thickness, draft and snow depth from sea ice-covered regions in the Northern Hemisphere (NH) and the Southern Hemisphere (SH), all of which are relevant for comparison with altimetry estimates. The measurements have been collected using airborne sensors, autonomous drifting buoys, moored and submarine-mounted upward-looking sonars, and visual observations. The data package has been prepared to match the spatial (25 km for NH and 50 km for SH) and temporal (monthly) resolutions of conventional satellite altimetry-derived sea ice thickness data products for a direct evaluation of these, and the code is publicly available and distributed for users to modify depending on their aim. This data package, also known as the Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP), was produced within the ESA CCI Sea Ice project. The current version of the CCI SIT RRDP covers the polar satellite altimetry era (1993-2024) and has ongoing efforts aimed at continuously updating the datasets. The CCI SIT RRDP has been collocated with satellite-derived sea ice thickness products from CryoSat-2, Envisat, and ERS-1/2 produced within the ESA CCI and the Fundamental Data Records for Altimetry (FDR4ALT) projects to demonstrate the overlap and inter-comparison between the reference measurements and satellite-derived products. Here, the CCI SIT RRDP is introduced along with examples of its use as a validation source for satellite altimetry products, where the averaging, collocation and uncertainty methodology is presented, and advantages and limitations are discussed. The CCI SIT RRDP dataset is available at 10.11583/DTU.24787341 .

Slush from flooding of sea ice contributes significantly to the sea ice mass balance in the Arctic and Antarctic and poses significant hazards for Arctic communities, affecting the safe use of sea ice for travel, hunting, and other activities. This study demonstrates the effectiveness of multi-frequency electromagnetic (EM) induction sounding for the joint retrieval of slush and ice thicknesses. For the multi-frequency GEM-2 instrument, we identified optimal frequency combinations, for example 5, 10, 20, 30, and 93 kHz, through inversion of synthetic data with realistic noise to achieve minimal mean absolute errors (MAE) of less than 5 cm for slush as thick as 60 cm.Field EM surveys, validated with coincident drill hole data, demonstrated reliable performance of the method under practical field conditions for slush layers up to 20 cm thick. Instrument calibration was robust but faced challenges at sites where snow and ice conditions deviated from the ideal one-layer model for snow and ice. The inclusion of varying sea ice conductivities in the calibration process enhanced reliability, and we show that a single instrument calibration remains stable for over a week for this instrument of the newest GEM-2 generation.The method’s transferability to airborne applications, such as drone-mounted surveys, offers the potential to eliminate operator risks associated with ground-based measurements on thin ice with thick slush. Overall, multi-frequency EM induction sounding provides a time-efficient and accurate tool for mapping the separate thicknesses of slush and of snow-plus-ice thicknesses.

L-band satellite radiometry has emerged as an important tool for monitoring Earth's essential climate variables (ECVs). It relies on spaceborne radiometers operating in a protected band (1.4-1.427 GHz) to measure Earth's surface thermal microwave emission in brightness temperatures. Observations at this band experience minimal atmospheric attenuation and radio-frequency interference (RFI), and can be acquired continuously from day to night, ensuring a short global revisit time. Moreover, L-band radiation can partially penetrate natural materials, allowing for the assessment of subsurface state parameters. These features make L-band radiometry satellites valuable for continuous global climate monitoring, offering unique advantages in tracking specific ECVs that are difficult to measure using other remote sensing techniques. This article reviews recent advances in satellite microwave radiometry at L-band, with a focus on the contributions of key missions-SMOS, Aquarius and SMAP-in retrieving six critical ECVs: 1) surface soil moisture (SM), 2) soil freeze/thaw (FT) status, 3) vegetation aboveground biomass (AGB), 4) sea surface salinity (SSS), 5) sea surface wind (SSW) speed, and 6) sea ice thickness (SIT). It summarizes the rationale behind satellite microwave radiometry and its role in understanding of the spatiotemporal dynamics of these ECVs on a global scale based on more than 16 years of continuous data. Furthermore, it identifies the state-of-the-art status of the discussed ECV products, highlights current challenges, and outlines future directions for the application of microwave radiometry in monitoring ECVs.

CryoRad is a candidate satellite mission concept equipped with a broadband low-frequency microwave radiometer operating between 0.4 and 2 GHz with continuous frequency coverage. Its primary objective is to advance cryosphere science by delivering new geophysical variables critical for ocean and climate studies. CryoRad will provide temperature profiles of the Antarctic and Greenland ice sheets from surface to bedrock, previously accessible only through sparse borehole observations, and will address long-standing limitations of L-band radiometers in retrieving sea surface salinity (SSS) in cold waters. Furthermore, the mission aims to significantly improve estimates of sea ice thickness, volume, and salinity, thereby enabling a more comprehensive characterization of the polar ocean-sea ice system. To quantify the scientific return and support the definition of radiometer performance requirements, we combine forward modeling and sensitivity experiments with data from the ECMWF ORAS6 ocean reanalysis. ORAS6 includes a multicategory sea ice model with prognostic ice salinity, providing spatially and temporally consistent fields of sea ice concentration, thickness, volume, temperature, and salinity. These parameters serve as a physically consistent baseline for simulating CryoRad brightness temperatures using simplified one-layer emissivity models. Sensitivity analyses reveal the frequency-dependent impact of ice thickness, salinity, and ocean surface properties on measured brightness temperatures.

Abstract. Arctic sea ice thickness has declined rapidly over recent decades, yet the relative roles of thermodynamic growth and dynamic redistribution in driving this change remain poorly constrained at basin scale. We quantify thermodynamic and dynamic contributions to sea-ice thickness change together with their uncertainties across the Arctic from 2002 to 2020 by combining satellite-derived thickness with sea-ice model simulations (Icepack) along trajectories. Separating dynamical thickening (30%), dynamical thinning (−24%), and lead-ice growth (10.2%) shows that dynamic processes contribute nearly as much to the average winter ice growth of 0.21 m per month as thermodynamic processes (35.8%). Regional, seasonal, and thickness-dependent variability is consistent with large-scale dynamic patterns and the ice-growth feedback. We quantify the effects of the overly smooth deformation forcing, which leads to an underestimation of large dynamic events and a substantial noise floor during dynamically quiet periods, and relate their magnitude to other sources of uncertainty. Analyzing the long-term trend from 2002–2020, we resolve a weak increase in median sea ice deformation (1.5% per year) and net dynamic thickness change (12% per year) within the limits of our study setup. Overall, our results suggest that increasing deformation in the Arctic enhances net dynamic thickness change and acts as a negative feedback in the pan-Arctic winter thickness budget.

Abstract. Since 2016, Antarctic sea-ice extent has undergone an abrupt regime shift, with a sequence of record low years not observed in the preceding satellite record. These recent anomalies further underline the need for improved characterization of sea‑ice and snow thickness. We primarily use radar (CryoSat-2 and Sentinel-3) and laser (ICESat-2) satellite altimetry to estimate radar and total freeboard. The difference between the two freeboard retrievals provides an estimate of snow depth. However, snow on Antarctic sea ice is very complex, affecting elevation distribution of radar backscattering targets, leading to higher uncertainties than in the Arctic. Monthly gridded datasets are produced by averaging along‑track altimetry data, but sea-ice drift can affect spatial consistency, especially when combining measurements from different satellites. To address this, we combine altimetry with satellite-derived sea-ice drift retrievals to generate drift‑aware daily freeboard, thickness, and snow-depth estimates. Within the ESA “Sea Ice Mass Balance Assessment: Southern Ocean” (SO-SIMBA) project, we advect along‑track CryoSat‑2, Sentinel‑3, and ICESat‑2 measurements over a ± 15‑day window to preserve spatial structures, reduce temporal smearing, and improve the co‑location of radar and laser freeboards. Merging several altimeters increases the sampling density and enables an effective spatial resolution of 12.5 km for the sea-ice thickness dataset. We also track uncertainties from the raw satellite measurements to the final gridded products, combining measurement uncertainties and drift‑related propagation errors. Here we present a nearly 7-year long dataset (October 2018 to August 2025) of year-round daily updated, monthly sea‑ice thickness and snow depth along with uncertainties. Initial results show coherent and realistic spatial patterns, consistent with features visible in SAR imagery. Comparisons with independent airborne measurements, and available in-situ observations, indicate improved spatial fidelity and internal consistency compared to standard monthly gridded approaches, especially if only one sensor is used.

Satellite radar altimeters provide crucial insights into polar oceans and their sea ice cover, enabling the estimation of sea level, sea ice freeboard, and thickness. These retrieval algorithms depend on accurate discrimination between radar altimeter waveforms from sea ice and ocean surfaces in heterogeneous and dynamic surface conditions. A further and less mature step is classifying different sea ice types in addition to the ice/ocean discrimination. We aim to develop new methods for a novel multi-category sea ice and ocean surface classification directly from satellite radar altimeter data to improve sea ice climate data records. Traditional waveform representations are limited to a small set of parameters, leading to information loss. Moreover, machine learning models for sea ice classification often depend on supervised training, which is vulnerable to uncertainties in labeled data, especially in polar regions. To address these limitations, we explore self-supervised learning methods to optimize waveform representations, which can capture more detailed information for a classification with finer granularity. Furthermore, they do not require labeled data, which is not available at the spatial coverage and resolution of radar altimeter waveforms. We apply these techniques to SRAL data from the Sentinel-3 mission. We show that the information preserved in the latent space of an auto-encoder enhances the feature space of traditional waveform parameters, improving the subsequent classification process, when comparing our results to available sea ice charts and other remote sensing products. Our results demonstrate better generalization compared to supervised approaches.

Arctic sea ice thickness impacts various physical and biogeochemical processes at the air-ice-ocean interface. For example, it determines how much sunlight reaches the base of the ice – a key parameter for primary production. It is also an essential variable for sea ice forecasts, shipping and other marine activities. During the summer months (May-September) melt ponds complicate the retrieval of sea ice thickness compared to winter. On the other hand, summer sea ice thickness observations are particularly important as this is when most shipping and biological production happen. Summer sea ice thickness estimates are also crucial to extend predictions by many months. In this study, we present the novel summer sea ice Cryo-TEMPO product. Cryo-TEMPO is a set of easily accessible thematic products derived from CryoSat data targeted at both expert and non-expert users. The summer sea ice product contains freeboard measurements and smoothed freeboard, which are calculated at each lead position along the altimetry tracks. The product covers the whole Arctic and the full CryoSat lifetime since 2010. It will be updated operationally from summer 2024 onwards. Comparisons against various airborne and mooring data were conducted for validation and will be presented, too.

The standard approach to deriving gridded sea ice thickness (SIT) from satellite altimeters is to aggregate the original along-track SIT estimates over a 1-month period to achieve sufficient coverage across the Arctic. However, this approach neglects processes like sea ice advection, deformation, and thermodynamic growth that occur within the aggregation period. To address these limitations, we propose a drift-aware method that accounts for sea ice motion and SIT changes due to dynamics and thermodynamics in monthly SIT products. We present a method to derive daily drift-aware sea ice thickness (DA-SIT) maps for the Arctic based on Envisat and CryoSat-2 along-track data. The approach is validated against buoys, airborne SIT surveys, and moored upward-looking sonar (ULS) measurements. DA-SIT demonstrates the ability to register sea ice thickness anomalies, which are also observed by daily ULS SIT averages but are overlooked by the conventional gridded SIT data. Comparative analysis reveals that drift awareness reduces orbit track patterns in the gridded SIT and improves consistency in regions with significant ice drift, such as the transpolar drift. The drift awareness facilitates detailed studies of regional sea ice dynamics and fluxes, while improving co-registration of multi-mission satellite data. However, when considering pan-Arctic estimates of ice volume, we do not expect significant changes in time series and trends compared to in existing studies.

Sea-ice thickness is a crucial parameter for a variety of scientific disciplines, including climate science, oceanography, and ecology. It plays a vital role in regulating exchanges of heat, moisture and momentum between the polar oceans and the atmosphere, influencing ocean currents, and affecting local cloud cover and precipitation. The ESA-funded project SIN’XS, led by NOVELTIS, AWI, LEGOS, and UCL, aims to comprehensively assess available sea-ice thickness and snow thickness products and their uncertainties. We are building up a database of large-scale datasets (satellite-based and models) as well as reference datasets (in-situ, airborne, moorings, etc.) to better understand the variability and change in observed ice thickness in both hemispheres. A web portal enables users to interactively explore and analyse data. In the talk, we will introduce the project and database and present the first results. We will also encourage potential collaborators to contribute to the project by submitting data to our website. We look forward to collaborating with the scientific community to better understand the complexities of sea-ice thickness and its impact on our planet. The ultimate goal of SIN’XS is to provide a reconciled and comprehensive sea-ice thickness estimate.

Pressure ridges, formed by sea ice deformation, affect momentum transfer in the Arctic Ocean and support a larger biomass than the surrounding-level ice. Although trends in Arctic sea ice thickness and concentration are well documented, changes in ridge morphology remain unclear. This study provides airborne-based evidence of a shift towards a smoother ice surface, with fewer pressure ridges and reduced surface drag, attributed to the loss of old ice. Furthermore, an increase in seasonal ice cover enhances overall deformation in the Arctic and acts as a negative feedback mechanism on pan-Arctic ridge morphology: the greater the proportion of seasonal ice, the higher the pan-Arctic mean ridge rate, dampening an overall decline in ridges with age. While thinner and less frequent ridges benefit industries such as shipping, these changes are likely to have profound impacts on the energy and mass balance and the ecosystem of the Arctic Ocean. Pressure ridges, a characteristic feature of Arctic sea ice, play an important role in the ecosystem but pose challenges to shipping. Here the authors use aircraft measurements to document a decline in both the frequency and height of these pressure ridges in recent decades.

This paper presents the Copernicus Sentinel-3 level-2 altimetry Hydro-Cryo “Thematic Products”, operationally generated by ESA since September 2023. In comparison to previous product versions, the level-2 processing is now performed through three independent chains, generating three families of “Thematic Products”, for measurements acquired over hydrological, sea ice and land ice areas, respectively. Prior to the operational deployment, a full mission reprocessing was achieved with the thematic processors, providing a complete and harmonised dataset to the users. In this paper, the new architecture of the ground segment processing is presented, along with the major algorithmic developments. The main data content of the Thematic Products is also described, to indicate the key product variables for the end users. The Thematic Products have been evaluated by the Sentinel-3 Mission Performance Cluster (MPC) experts. The major results and outcomes are presented, showing the significant performance improvement achieved in comparison to previous processing versions.

Leads and fractures in sea ice play a crucial role in the heat and gas exchange between the ocean and atmosphere, impacting atmospheric, ecological, and oceanic processes. We estimated lead fractions from high-resolution divergence obtained from satellite synthetic aperture radar (SAR) data and evaluated them against existing lead products. We derived two new lead fraction products from divergence with a spatial resolution of 700 m calculated from daily Sentinel-1 images. For the first lead product, we advected and accumulated the lead fractions of individual time instances. With those accumulated divergence-derived lead fractions, we comprehensively described the presence of up to 10 d old leads and analyzed their deformation history. For the second lead product, we used only divergence pixels that were identified as part of linear kinematic features (LKFs). Both new lead products accurately captured the formation of new leads with widths of up to a few hundred meters. We presented a Lagrangian time series of the divergence-based lead fractions along the drift of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic Ocean during winter 2019-2020. Lead activity was high in fall and spring, consistent with wind forcing and ice pack consolidation. At larger scales of 50-150 km around the MOSAiC expedition, lead activity on all scales was similar, but differences emerged at smaller scales (10 km). We compared our lead products with six others from satellite and airborne sources, including classified SAR, thermal infrared, microwave radiometer, and altimeter data. We found that the mean lead fractions varied by 1 order of magnitude across different lead products due to different physical lead and sea ice properties observed by the sensors and methodological factors such as spatial resolution. Thus, the choice of lead product should align with the specific application.

In Antarctica, sub-ice platelet layers (SIPL) accumulate beneath sea ice where ice crystals emerge from adjacent ice shelf cavities, serving as a unique habitat and indicator of ice-ocean interaction. Atka Bay in the eastern Weddell Sea, close to the German overwintering base Neumayer Station III, is well known for hosting a SIPL linked to ice shelf water outflow from beneath the Ekstrom Ice Shelf. This study presents a comprehensive analysis of an extensive multi-frequency electromagnetic (EM) induction sounding dataset in Atka Bay. Employing an open-source inversion scheme, the dataset was inverted to determine fast ice and platelet layer thicknesses along with their electrical conductivities. From electrical conductivity of the SIPL, we derive the SIPL solid fraction. Our results demonstrate the capability of obtaining high-resolution maps of SIPL thickness over extensive areas, providing unprecedented insights into accumulation patterns and identifying regions of ice-shelf water outflow in Atka Bay. Calibration in a zero-conductivity environment on the ice shelf proves effective, reducing logistical efforts for correcting electronic offsets and drift. Moreover, we demonstrate that both instrument noise and motion noise are sufficiently low to accurately determine SIPL thickness, with uncertainties within the decimeter range. Notably, this investigation is the first to cover the entirety of Atka Bay, including ice shelf fringes, overcoming limitations of prior studies. Our approach represents a significant advancement in studying ocean/ice-shelf interactions using non-destructive EM methods, emphasizing the potential to assess future changes in sub-ice shelf processes. In the future, the adaptation of this method to airborne multi-frequency EM measurements using drones or aircraft has the potential to further extend spatial coverage.

Abstract. Sea ice altimetry currently remains the primary method for estimating sea ice thickness from space, however time-series of sea ice thickness estimates are of limited use without having been quality-controlled against reference measurements. Such reference observations for sea ice thickness validation in the polar regions are sparse and rarely presented in a format matching the satellite-derived products. Here, the first published comprehensive collection of sea ice reference observations including freeboard, thickness, draft and snow depth from sea ice-covered regions in the Northern Hemisphere (NH) and the Southern Hemisphere (SH) is presented. The observations have been collected using airborne sensors, autonomous drifting buoys, moored and submarine-mounted upward-looking sonars, and visual observations. The data package has been prepared to match the spatial (25 km for NH and 50 km for SH) and temporal (monthly) resolutions of conventional satellite altimetry-derived sea ice thickness data products for a direct evaluation of these. This data package, also known as the Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP) was produced within the ESA CCI sea ice project. The current version of the CCI SIT RRDP covers the polar satellite altimetry era (1993–2021) and is part of ongoing efforts to keep the dataset updated. The CCI SIT RRDP has been collocated to satellite-derived sea ice thickness products from CryoSat-2, Envisat and ERS-1/2 produced within ESA CCI and the Fundamental Data Records for Altimetry (FDR4ALT) project to demonstrate the overlap and inter-comparison between the reference observations and satellite-derived products. Here, the CCI SIT RRDP is introduced along with examples of its use as a validation source for satellite altimetry products, where the averaging, collocation and uncertainty methodology is presented and their advantages and limitations are discussed.

This paper presents a practical step-by-step approach to Frequency Modulated Continuous Wave (FMCW) radar nonlinearity correction (deconvolution), utilizing surface-based Ku- and Ka-band radar data collected over nilas ice within a newly-opened sea ice lead during the 2019/2020 MOSAiC expedition. Two performance metrics are introduced to evaluate deconvolution effectiveness: the spurious free dynamic range (SFDR), which quantifies sidelobe suppression, and the leading edge width (LEW), which quantifies the improvement in surface return clarity. The impact of deconvolution waveforms on different survey dates, radar polarizations, and surface types is examined using echograms and quantitative metrics. Deconvolution results in a maximum SFDR increase of 28 dB, with a maximum 3 dB decline in deconvolution performance observed over an 8-day period and a maximum decline of 15 dB observed over a 71-day period. The LEW values indicate that the effectiveness of deconvolution in enhancing interface clarity depends on the combination of pre-deconvolution sidelobe shape, prominence of the surface return, the influence of snowpack returns, as well as a time-dependent reduction in deconvolution performance. Deconvolution significantly improves surface return clarity for cross-polarized radar data, where weak surface returns are obscured by returns from within the snowpack. The results demonstrate that deconvolution performance is most effective shortly after deconvolution waveform characterization. Therefore, it is recommended to perform at least weekly calibrations using a large metal sheet and ideally calibration before/after data collection to ensure optimal deconvolution performance and effective sidelobe suppression.