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Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.

Operation Icebridge (OIB) 2016 level 1b Airborne Ku-band Synthetic Aperture Radar waveforms, concurrent with both OIB lidar data and Environment and Climate Change Canada in situ data, over snow on arctic sea ice, near Eureka, Canada. Footprint-scale (~16 m × 4.5 m) snow depths are estimated and compared to in situ measurements. Relative return powers and energies of the air-snow and snow-ice interface are estimated, as well as footprint-scale roughness and slope. Quasi-specular scattering (higher return powers/energies) are observed for smoother, flatter footprints. In addition to this, satellite data is discussed, including Cryosat-2 SARIn.

Recent work has demonstrated how Gaussian Process Regression (GPR) can be used to interpolate Pan-Arctic radar freeboard of sea ice as measured by satellites. Sea ice freeboard is crucial to measuring sea ice thickness, and thus sea ice volume, which can play an important role in climate models. Similarly sea surface heights from altimetry are essential to determine the geostrophic currents from space. Using GPR can be computationally burdensome for modest dataset sizes and prohibitive for large datasets. To avoid having to deal with a large dataset the raw satellite observations were binned (averaged) onto a regularly spaced grid. We look at how these calculations can be reduced in terms of run time by utilising a Graphical Processing Unit (GPU), a dedicated GPR python package and by making practical adjustments to the methodology. We find by adopting these changes the overall run time of a single day’s interpolation can be greatly reduced by a factor of over 60, making it practical to run such calculations on an environment with a GPU. We then extend the method to use raw satellite observation data (no binning), which greatly increases the number of training points, requiring the use a sparse method for GPR. We conclude with recommendations for further work on this subject as it has the potential for widespread use in remote sensing applications.

The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution radar altimetry data over the polar regions up to 81 degrees North. The combination of synthetic aperture radar (SAR) mode altimetry from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise these datasets to validate existing surface classification algorithms, in addition to investigating novel applications of deep learning to classify sea-ice from leads. This is important for estimating sea-ice thickness and to predict future changes in the Arctic and Antarctic regions. In particular, we propose the use of Vision Transformers (ViT) for this task and demonstrate their effectiveness, with accuracy reaching above 92%. We compare our automated results with human classification using the software IRIS.

The Arctic is warming at almost four times the global average rate. Here we reframe this amplified Arctic warming in terms of global climate ambition to show that it causes a breach of the Paris Agreement’s 1.5 °C and 2 °C limits 5 and 8 years earlier, respectively. This outsized influence on global climate targets highlights the need for better modelling and monitoring of Arctic change.

The indirect effect of winter Arctic Oscillation (AO) events on the proceeding summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer time scales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in northern-hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the Adjusted Rand Index – a method for comparing spatial patterns of variability, and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability of the AO relatively well, although over-estimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean, and under-estimate the variability over the north Africa and southern Europe. They also under-estimate the importance of regions such as the Beaufort, East Siberian and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

The effect that sea ice topography has on the momentum transfer between ice and atmosphere is not fully quantified due to the vast extent of the Arctic and limitations of current measurement techniques. Here we present a method to estimate pan-Arctic momentum transfer via a parameterization which links sea ice–atmosphere form drag coefficients with surface feature height and spacing. We measure these sea ice surface feature parameters using the Cloud and land Elevation Satellite-2 (ICESat-2) which, though it cannot resolve as well airborne surveys, has a higher along-track spatial resolution than other contemporary altimeter satellites. As some narrow obstacles are effectively smoothed out by the ICESat-2 ATL07 spatial resolution, we use near-coincident high-resolution Airborne Topographic Mapper (ATM) elevation data from NASA's Operation IceBridge (OIB) mission to scale up the regional ICESat-2 drag estimates. By also incorporating drag due to open water, floe edges and sea ice skin drag, we produced a time series of average total pan-Arctic neutral atmospheric drag coefficient estimates from October 2018 to May 2022. Here we have observed its temporal evolution to be unique and not directly tied to sea ice extent. By also mapping 3-month aggregates for the years 2019, 2020 and 2021 for better regional analysis, we found the thick multiyear ice area directly north of the Canadian Archipelago and Greenland to be consistently above 2.0 · 10−3 with rough ice ∼ 1.5 · 10−3 typically filling the full multiyear ice portion of the Arctic each spring.

The Covid-19 Pandemic affects not only populations around the world but also the environment and natural resources. Lockdowns and restricted new lifestyles have had wide-ranging impacts on the environment (e.g., air quality in cities). Although hygiene and disinfection procedures and precautions are effective ways to protect people from Covid-19, they have significant consequences for water usage and resources especially given the increasing impacts of climate change on rainfall patterns, water use and resources. Climate change and public health issues may compound one another, and so we used a DPSIR Framework (no previous use to examine the actual and potential impacts of Covid-19 and climate change on water consumption and resources) to scope the main factors that may interact to affect water use and resources (in the form of reservoirs) using evidence from Istanbul, Turkiye, with some discussion of the comparative situation in elsewhere. We modified initial views on the framework to account for the regional, city and community level experiences. We noted that water consumption in Istanbul has been increasing over the last two decades (except, it appears, in times of very low rainfall/drought); that there were increases in water consumption in the early stages of the Covid-19 pandemic; and, despite some increase in rainfall, water levels in reservoirs appeared to decrease during lockdowns (for a range of reasons). Through a new simple way of visualising the data, we also noted that a low resource capacity might be recurring every 6 or 7 years in Istanbul (same finding from Thames Reservoir in London). We made no attempt in this paper to quantify the relative contribution that climate change, population growth etc. are making to water consumption and reservoir levels as we were focused here on scoping those social, environmental and economic factors that appear to play a role in potential water stress and on developing a DPSIR Framework that could aid both subsequent quantitative studies and the development of policy and adaptive management options for Istanbul and other large complex conurbations. If there are periodic water resource issues and temperatures rise as expected in climate projections with an accompanying increase in the duration of hot spells, the subsequent additional stress on water systems might make managing future public health emergencies, such as a pandemic, even more difficult.

Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an R2 (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, r = 0.58, p < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, rs = 0.66, p < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas.

The volume of Arctic sea ice is in decline but exhibits high interannual variability, which is driven primarily by atmospheric circulation. Through analysis of satellite-derived ice products and atmospheric reanalysis data, we show that winter 2020/21 was characterised by anomalously high sea-level pressure over the central Arctic Ocean, which resulted in unprecedented anticyclonic winds over the sea ice. This atmospheric circulation pattern drove older sea ice from the central Arctic Ocean into the lower-latitude Beaufort Sea, where it is more vulnerable to melting in the coming warm season. We suggest that this unusual atmospheric circulation may potentially lead to unusually high summer losses of the Arctic’s remaining store of old ice. In the winter 2020-2021, unusual winds pushed old sea ice from the central Arctic Ocean to lower latitudes, where the ice may melt more easily in the coming warm season, suggests an analysis of sea ice satellite data and ERA5 reanalysis data.

In February 2016 and April 2018 the European Space Agency launched the Sentinel-3A and 3B satellites respectively, as part of the European Commission’s multi-satellite Copernicus Programme. Here we process Sentinel-3A waveform data to estimate Arctic sea level anomaly and radar freeboard from November 2017 to April 2018. We compare our results to those from the CryoSat-2 satellite, and find an intermission bias on sea-level anomaly of 2 cm. We also find a mean radar freeboard difference of 1 cm, which we attribute to the use of empirical retrackers to retrieve lead and floe elevations. Ahead of Sentinel-3B waveform data being made available, we use orbit files to estimate the improvement in sampling resolution afforded by the addition of Sentinel-3A and 3B data to the CryoSat-2 dataset. By combining data from the three satellites, grid resolution or time-sampling can be almost tripled compared with using CryoSat-2 data alone.

Arctic sea ice is diminishing with climate warming1 at a rate unmatched for at least 1,000 years2. As the receding ice pack raises commercial interest in the Arctic3, it has become more variable and mobile4, which increases safety risks to maritime users5. Satellite observations of sea-ice thickness are currently unavailable during the crucial melt period from May to September, when they would be most valuable for applications such as seasonal forecasting6, owing to major challenges in the processing of altimetry data7. Here we use deep learning and numerical simulations of the CryoSat-2 radar altimeter response to overcome these challenges and generate a pan-Arctic sea-ice thickness dataset for the Arctic melt period. CryoSat-2 observations capture the spatial and the temporal patterns of ice melting rates recorded by independent sensors and match the time series of sea-ice volume modelled by the Pan-Arctic Ice Ocean Modelling and Assimilation System reanalysis8. Between 2011 and 2020, Arctic sea-ice thickness was 1.87 ± 0.10 m at the start of the melting season in May and 0.82 ± 0.11 m by the end of the melting season in August. Our year-round sea-ice thickness record unlocks opportunities for understanding Arctic climate feedbacks on different timescales. For instance, sea-ice volume observations from the early summer may extend the lead time of skilful August–October sea-ice forecasts by several months, at the peak of the Arctic shipping season.

The anti-cyclonic Beaufort Gyre is the dominant circulation of the Canada Basin and the largest freshwater reservoir in the Arctic Ocean. Using an extensive historical hydrographic dataset together with updated satellite dynamic ocean topography data, we find that the Beaufort Gyre has transitioned to a new state in which the increase in sea surface height of the gyre has slowed and the freshwater content has stabilized. In addition, the cold halocline layer has thinned significantly due to less input of cold and salty water stemming from the Pacific Ocean and the Chukchi Sea shelf, together with greater entrainment of lighter water from the eastern Beaufort Sea. The new state of the Beaufort Gyre is due to a southwestward shift in its location as a result of variation in the regional wind forcing. Our results imply that continued thinning of the cold halocline layer could disrupt the present stable state, allowing for a significant freshwater release. This in turn could freshen the subpolar North Atlantic, impacting the Atlantic Meridional Overturning Circulation.

Wind transport alters the snow topography and microstructure on sea ice through snow redistribution controlled by deposition and erosion. The impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution on Arctic sea ice from Ka- and Ku-band radar signatures. Measurements were obtained during two wind events in November 2019 during the MOSAiC expedition. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coincident with surface height changes measured from a terrestrial laser scanner are observed. At both frequencies, snow redistribution events increased the dominance of the air/snow interface at nadir as the dominant radar scattering surface, due to wind densifying the snow surface and uppermost layers. The radar waveform data also detect the presence of previous air/snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air/snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite radar altimeters. The relative scattering contribution of the air/snow interface decreases, and the snow/sea ice interface increases with increasing incidence angles. Relative to pre-wind conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Binned backscatter within 5° azimuth bins reveals substantial backscatter variability in the radar footprint at all incidence angles and polarizations. The sensitivity of the co-polarized phase difference is linked to changes in snow settling and temperature-gradient induced grain metamorphism, demonstrating the potential of the radar to discriminate between newly deposited and older snow on sea ice. Our results reveal the importance of wind, through its geophysical impact on Ka- and Ku-band radar signatures of snow on sea ice and has implications for reliable interpretation of airborne and satellite radar measurements of snow-covered sea ice.

Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

We have recently applied an objective mapping type approach to merge observations from multiple altimeters, both for enhancing the temporal/spatial resolution of freeboard samples and for analyzing crossovers between satellites (Gregory et al, 2021). This mapping provides optimal interpolation of proximal observations to a location in space and time based on the covariance of the observations and a priori understanding of their spatiotemporal correlation length scales. This offers a best linear estimator and error field for the observation (radar freeboard or snow depth), which can be used to better constrain pan-Arctic uncertainties. In addition we will explore here a newly developed inverse modelling framework to synchronously retrieve the snow and ice thickness from bias corrected or calibrated radar freeboards from multiple satellite retrievals. The radar equations expressed in section can be rearranged to formulate the joint forward model at gridded level relating measured radar freeboards from multiple satellites (and airborne data) to the underlying snow and ice thickness. In doing so we have also introduced a penetration factor correction term for OIB radar freeboard measurements. To solve this inverse model problem for and we use the following two methodologies inspired from Earth Sciences applications (i.e. seismology): Space ‘uncorrelated’ inverse modelling. The method is called `space uncorrelated' inverse modelling as the algorithm is applied locally, for small distinct regions in the Arctic Ocean, multiple times, until the entire Arctic ocean is covered. To sample the parameter space we use the publicly available Neighbourhoud Algorithm (NA) developed originally for seismic tomography of Earth’s interior and recently by us to a sea ice dynamic inversion problem (Hoerton et al, 2019). Space ‘correlated inverse modelling. For the second method of inverse modelling, we used what we call a `space correlated' approach. Here the main algorithm is applied over the entire Arctic region, aiming to retrieve the desired parameters at once. In contrast with the previous approach, in this method we take into account positional correlations for the physical parameters when we are solving the inverse problem, the output being a map of the Arctic composed of a dynamically generated a tiling in terms of Voronoi cells. In that way, regions with less accurate observations will be more coarsely resolved while highly sampled regions will be provided on a finer grid with a smaller uncertainty. The main algorithm used here to calculate the posterior solution is called `reverse jump Monte Carlo Markov Chain' (hereafter referred to as rj-MCMC) and its concept was designed by Peter Green in 1999 (Green, 1995). Bodin and Sambridge (2009) adapted this algorithm for seismic inversion, which is the basis of the algorithm used in this study.

Sea ice albedo is a key climate variable that affects the Earth’s radiation budget. Spatio-temporal variation of sea ice albedo can be retrieved from pre existing satellite observation processing chains such as the CLARA2-SAL product. However, currently there is only one albedo product which is derived from instantaneous multi-angle measurements and that is from MISR [1]. The accuracy of surface albedo products is usually affected by error accumulation from atmospheric corrections to the top-of-atmosphere bi-directional reflectance factor (BRF) and the modelling of bottom of the atmosphere BRF and subsequent modelling to bi-directional reflectance distribution function (BRDF) using these BRFs. Sea ice surfaces being both anisotropic and dynamic have satellite product accuracies that also depend on the length of deployed time window, thus requiring sufficient numbers of observations over a short period of time. In this study, we present a data fusion method using the high accuracy near simultaneous sampling of the Multiangle Imaging SpectroRadiometer (MISR) generated at the Langley Research Center applying a Rayleigh atmospheric correction, with the MOD35 cloud mask which is part of the MOD29 Surface Temperature and Ice Extent product derived from the Moderate Imaging Spetroradiometer (MODIS), both onboard the Terra satellite. We assume that the MISR bi-hemispherical reflectance (BHR) albedo is independent of solar angle, a crucial condition for instantaneous albedo products. As the accuracy of MOD29 cloud mask is assessed at >90% [1], this synergistic method can retrieve an improved BHR of the Arctic sea ice between April and September of each year from 2000 to 2019, and of the Antarctic sea ice between September and March of each year from 2000 to 2019. This study is a follow-on from Kharbouche and Muller (2018), that developed this method and focused on the Arctic region for the time span between March and September from 2000 to 2016. For both polar regions, we create four daily sea ice products consisting of different averaging time window (±1 day, ±3 days, ±7 days and ±15 days), each containing the number of samples, mean and standard deviation. For all four MISR cloud-free daily sea ice products, we derive 1km, 5km and 25km spatial resolutions. We perform an assessment of the day-of-year trend of sea ice BHR between 2000 and 2019 for the Arctic, and between 2000 and 2019 for Antarctic, confirming a continuing decline of sea ice shortwave albedo in the Arctic depending on the day of year and length of observed time window, and providing a novel sea ice shortwave albedo product analysis for Antarctica.Acknowledgements. This work was supported by the QA4ECV project www.QA4ECV.eu, of the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement number 607405. We thank our colleagues at JPL and NASA LaRC for processing the MISR data, especially Sebastian Val and Steve Protack and Jeff Walter, respectively and Richard Frey and Steve Ackerman at CIMMS, SSEC, University of Madison, WI for the analysis of the MOD35 cloud mask using CALIPSO shown in [1].[1] https://doi.org/10.3390/rs11010009

Sea-ice floating upon the Arctic ocean is a constantly moving, growing and melting surface. The seasonal cycle of sea ice volume has an average change of 10 000 Km$^3$ or 9 billion tonnes of sea ice. The role of dynamic redistribution of sea ice, the process by which it flows and deforms when blown by winds and floating upon ocean currents, has been observable during winter growth by the incorporation of satellite remote sensing of ice thickness and drift. CMIP6 models contain dynamic sea-ice components that simulate the drift and mass balance of Arctic sea ice.We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide maps of ice growth, melt and dynamic redistribution. Winter growth and summer melt seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. We reveal key circulation patterns that contribute to summer melt and minimum sea ice volume and extent. Specifically, we show the importance of ice drift to the interannual variability in Arctic sea-ice volume, and the regional distribution of sea ice growth and melt rates. When comparing these observations to CMIP6 models long term trends are revealed. We show how the divergence and mechanical redistribution of sea ice is a key component in the resilience of central Arctic ice volume to anthropogenic climate change.

Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age and thickness. At a local scale, roughness in the form of ridges, hummocks, rafted ice can slow down and hinder safe transport on the ice as well as be a hazard for ice strengthened vessels and structures. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extend over multi-decadal time-scales. The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident roughness from coincident operation IceBridge (OIB) airborne laser data is generated. Surface roughness, defined as the standard deviation of the within-pixel lidar elevations to a best-fit plane, is modelled using several techniques and Support Vector Regression with a Radial Basis Function kernel selected. Hyperparameters are tuned using grid optimisation, model performance is assessed using blocked k-fold cross-validation. We present a derived sea ice roughness product at 1.1km resolution over the period of operation (April 2000 – 2020) and a corresponding time series analysis. To demonstrate the validity of the derived product, we first evaluate our roughness product against independent LiDAR characterisations of surface roughness consistent with our training data. We also evaluate our derived roughness product with known proxies of surface roughness on a pan-Arctic basis (AWISMOS CS2-SMOS sea ice thickness.) Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable capacity in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.

Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional ‘winter’ processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.

A growing number of studies are concluding that the resilience of the Arctic sea ice cover in a warming climate is essentially controlled by its thickness. Satellite radar and laser altimeters have allowed us to routinely monitor sea ice thickness across most of the Arctic Ocean for several decades. However, a key uncertainty remaining in the sea ice thickness retrieval is the error on the sea surface height (SSH) which is conventionally interpolated at ice floes from a limited number of lead observations along the altimeter's orbital track. Here, we use an objective mapping approach to determine sea surface height from all proximal lead samples located on the orbital track and from adjacent tracks within a neighborhood of 30–220 (mean 105) km. The patterns of the SSH signal's zonal, meridional, and temporal decorrelation length scales are obtained by analyzing the covariance of historic CryoSat-2 Arctic lead observations, which match the scales obtained from an equivalent analysis of high-resolution sea ice-ocean model fields. We use these length scales to determine an optimal SSH and error estimate for each sea ice floe location. By exploiting leads from adjacent tracks, we can increase the sea ice radar freeboard precision estimated at orbital crossovers by up to 20%. In regions of high SSH uncertainty, biases in CryoSat-2 radar freeboard can be reduced by 25% with respect to coincident airborne validation data. The new method is not restricted to a particular sensor or mode, so it can be generalized to all present and historic polar altimetry missions.