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The paper reports the first results of an experiment in northern Finland during winter 2022–2023 that aimed at studying seismic events caused by seasonal freezing in the so-called critical zone (CZ) of the Earth. These events have attracted public attention recently, as multiple reports about them from local inhabitants in Arctic and sub-Arctic areas appeared on social networks. To make an instrumental study of such events, to reveal the relationship between their occurrence and winter weather conditions, and to evaluate the possible hazard, we installed two high-resolution seismic arrays with co-located soil temperature stations at two sites in Finland, one of them being in the city of Oulu in the sub-Arctic area (65.04° N, 25.61° E) and the other one above the polar circle in the municipality of Sodankylä (67.36° N, 26.63° E). The equipment recorded continuous seismic and soil temperature data during November 2022–April 2023. Based on reports from the inhabitants of Talvikangas (Oulu) about the ground shaking and unusual noises on 6 January 2023 and their observations of new fractures on the roads there, we selected the time interval for the identification of frost quakes that originated during that day from continuous seismic records in Talvikangas and in Sodankylä. During the selected time interval, the extremely rapid air temperature drops of about -1.4 °C h -1 in Talvikangas and -0.88 °C h -1 in Sodankylä were observed. We identified and located two types of seismic events – namely, frost quakes with frequencies of about 10–20 Hz, with waveforms like those of tectonic events, and irregularly shaped frost tremors with frequencies of about 120–180 Hz. The sources of frost quakes in Talvikangas are mainly located on irrigated wetland, while in Sodankylä, about 50 % of registered frost quakes were caused by ice fracturing on the Kitinen River. However, several relatively strong events, with their origin in the wetlands, were also recorded. A significant number of sources of frost tremors are confined to wetland areas cut by irrigation channels and to roads cleaned from snow during winter both in Talvikangas and in Sodankylä. We calculated ground accelerations and ground velocities for the strongest events from both groups and compared them to equivalent properties of other seismic signals, like distant local earthquakes in the area, mining production blasts, and cargo train vibration. Our study shows that high-frequency frost tremors corresponding to surface fracture opening in the uppermost frozen surface layer of a thickness of up to 5 cm can directly damage infrastructure objects like roads and basements of buildings. Surface waves, produced by frost quakes and propagating inside the shallow soil layer, have large enough ground accelerations at epicentral distances of hundreds of metres. Therefore, frost quakes should be considered a phenomenon that can potentially damage infrastructures, and they have to be taken into account in seismic hazard assessments. Our research is the first instrumental study of seismic events originating from wetland areas. These events occur as a result of the interaction between the uppermost layer of the solid Earth's CZ and atmosphere processes that deserves further study. [ABSTRACT FROM AUTHOR]

Melting of sea ice in the Arctic has accelerated due to global warming. The Fram Strait (FS) serves as a crucial pathway for sea ice export from the Arctic to the North Atlantic Ocean. Monitoring sea ice drift (SID) in the FS provides insight into how Arctic sea ice responds to the climate change. The SID has been retrieved from Sentinel-1 synthetic aperture radar (SAR), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and further exploration is needed for the retrieval of SID using optical imagery. In this paper, we retrieve SID in the FS using the Chinese HaiYang1-D (HY-1D) satellite equipped with the Coastal Zone Imager (CZI). A multi-template matching technique is employed to calculate the cross-correlation, and subpixel estimation is used to locate displacement vectors from the cross-correlation matrix. The dataset covering March to May 2021 was divided into hourly and daily intervals for analysis, and validation was performed using Copernicus Marine Environment Monitoring Service (CMEMS) SAR-based product and International Arctic Buoy Programme (IABP) buoy. A comparison with the CMEMS SID product revealed a high correlation with the daily interval dataset; however, due to the spatial and temporal variability in the sea ice motion, differences are observed with the hourly interval dataset. Additionally, validation with the IABP buoys yielded a velocity bias of -0.005 m s -1 and RMSE of 0.031 m s -1 for the daily interval dataset, along with a flow direction bias of 0.002 rad and RMSE of 0.009 rad, respectively. For the hourly interval dataset, the velocity bias was negligible (0 m s -1), with a RMSE of 0.036 m s -1 , while the flow direction bias was 0.003 rad, with a RMSE of 0.010 rad. In addition, during the validation with buoys, we found that the accuracy of retrieving the SID flow direction is distinctly interrelated with the sea ice displacement. [ABSTRACT FROM AUTHOR]

To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m 2 /kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m 2 /kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process. [ABSTRACT FROM AUTHOR]

Remote-sensing records over the last 40 years have revealed large year-to-year global and regional variability in Antarctic sea ice extent. Sea ice area and extent are useful climatic indicators of large-scale variability, but they do not allow the quantification of regions of distinct variability in sea ice concentration (SIC). This is particularly relevant in the marginal ice zone (MIZ), which is a transitional region between the open ocean and pack ice, where the exchanges between ocean, sea ice and atmosphere are more intense. The MIZ is circumpolar and broader in the Antarctic than in the Arctic. Its extent is inferred from satellite-derived SIC using the 15 %–80 % range, assumed to be indicative of open drift or partly closed sea ice conditions typical of the ice edge. This proxy has been proven effective in the Arctic, but it is deemed less reliable in the Southern Ocean, where sea ice type is unrelated to the concentration value, since wave penetration and free-drift conditions have been reported with 100 % cover. The aim of this paper is to propose an alternative indicator for detecting MIZ conditions in Antarctic sea ice, which can be used to quantify variability at the climatological scale on the ice-covered Southern Ocean over the seasons, as well as to derive maps of probability of encountering a certain degree of variability in the expected monthly SIC value. The proposed indicator is based on statistical properties of the SIC; it has been tested on the available climate data records to derive maps of the MIZ distribution over the year and compared with the threshold-based MIZ definition. The results present a revised view of the circumpolar MIZ variability and seasonal cycle, with a rapid increase in the extent and saturation in winter, as opposed to the steady increase from summer to spring reported in the literature. It also reconciles the discordant MIZ extent estimates using the SIC threshold from different algorithms. This indicator complements the use of the MIZ extent and fraction, allowing the derivation of the climatological probability of exceeding a certain threshold of SIC variability, which can be used for planning observational networks and navigation routes, as well as for detecting changes in the variability when using climatological baselines for different periods. [ABSTRACT FROM AUTHOR]

Small land-terminating mountain glaciers are a widespread and important element of Arctic ecosystems, influencing local hydrology, microclimate, and ecology. Due to their relatively small ice volumes, this class of ice mass is particularly sensitive to the significant ongoing climate warming in the European sector of the Arctic, i.e. in the Barents Sea area. Archipelagos surrounding the Barents Sea, i.e. Svalbard (SV), Novaya Zemlya (NZ), and Franz Josef Land (FJ), host numerous populations of mountain glaciers, but their response to recent strong warming remains understudied in most locations. This paper aims to obtain a snapshot of their state by utilizing high-resolution elevation data (ArcticDEM) to investigate the recent (ca. 2011–2017) elevation and volume changes of 382 small glaciers across SV, NZ, and FJ. The study concludes that many mountain glacier sites across the Barents Sea have been in a critical imbalance with the recent climate and might melt away within the coming several decades. However, deviations from the general trend exist; e.g. a cluster of small glaciers in north SV has been experiencing thickening. The findings reveal that near-stagnant glaciers might exhibit contrasting behaviours (fast thinning vs. thickening) over relatively short distances, which is a challenge for glacier mass balance models but also an opportunity to test their reliability. [ABSTRACT FROM AUTHOR]

The Arctic region is responding heavily to climate change, and yet, the air temperature of ice-covered areas in the Arctic is heavily under-sampled when it comes to in situ measurements, resulting in large uncertainties in existing weather and reanalysis products. This paper presents a method for estimating daily mean clear-sky 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model, which has been tuned against in situ observations to estimate daily mean T2m based on clear-sky satellite ice surface skin temperatures. The daily satellite-derived T2m product includes estimated uncertainties and covers the Arctic sea ice and the Greenland Ice Sheet during clear skies for the period 2000–2009, provided on a 0.25 ∘ regular latitude–longitude grid. Comparisons with independent in situ measured T2m show average biases of 0.30 and 0.35 ∘ C and average root-mean-square errors of 3.47 and 3.20 ∘C for land ice and sea ice, respectively. The associated uncertainties are verified to be very realistic for both land ice and sea ice, using in situ observations. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic and is independent of numerical weather prediction model input. Therefore, it provides an important supplement to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison of T2m derived from satellite and ERA-Interim/ERA5 estimates shows that the satellite-derived T2m validates similar to or better than ERA-Interim/ERA5 against in situ measurements in the Arctic. [ABSTRACT FROM AUTHOR]

The Robeson Channel is a narrow sea water passage between Greenland and Ellesmere Island in the Arctic. It is a pathway of sea ice from the central Arctic and out to Baffin Bay. In this study, we used a set of daily synthetic aperture radar (SAR) images from the Sentinel-1A/1B satellites, acquired between September 2016 and April 2017, to study the kinematics of individual ice floes as they approach and then drift through the Robeson Channel. The tracking of 39 selected ice floes was visually performed in the image sequence, and their speed was calculated and linked to the reanalysis 10 m wind from ERA5. The results show that the drift of ice floes is very slow in the compact ice regime upstream of the Robeson Channel, unless the ice floe is surrounded by water or thin ice. In this case, the wind has more influence on the drift. On the other hand, the ice floe drift is found to be about 4–5 times faster in the open-drift regime within the Robeson Channel and is clearly influenced by wind. A linear trend is found between the change in wind and the change in ice drift speed components, along the length of the channel. Case studies are presented to reveal the role of wind in ice floe drift. This paper also addresses the development of the ice arch at the entry of the Robeson Channel, which started development on 24 January and matured on 1 February 2017. Details of the development, obtained using the sequential SAR images, are presented. It is found that the arch's shape continued to adjust by rupturing ice pieces at the locations of cracks under the influence of the southward wind (and hence the contour kept displacing northward). The findings of this study highlight the advantage of using the high-resolution daily SAR coverage in monitoring aspects of sea ice cover in narrow water passages where the ice cover is highly dynamic. The information will be particularly interesting for the possible applications of SAR constellation systems. [ABSTRACT FROM AUTHOR]

Permafrost is a ubiquitous phenomenon in the Arctic. Its future evolution is likely to control changes in northern high-latitude hydrology and biogeochemistry. Here we evaluate the permafrost dynamics in the global models participating in the Coupled Model Intercomparison Project (present generation – CMIP6; previous generation – CMIP5) along with the sensitivity of permafrost to climate change. Whilst the northern high-latitude air temperatures are relatively well simulated by the climate models, they do introduce a bias into any subsequent model estimate of permafrost. Therefore evaluation metrics are defined in relation to the air temperature. This paper shows that the climate, snow and permafrost physics of the CMIP6 multi-model ensemble is very similar to that of the CMIP5 multi-model ensemble. The main differences are that a small number of models have demonstrably better snow insulation in CMIP6 than in CMIP5 and a small number have a deeper soil profile. These changes lead to a small overall improvement in the representation of the permafrost extent. There is little improvement in the simulation of maximum summer thaw depth between CMIP5 and CMIP6. We suggest that more models should include a better-resolved and deeper soil profile as a first step towards addressing this. We use the annual mean thawed volume of the top 2 m of the soil defined from the model soil profiles for the permafrost region to quantify changes in permafrost dynamics. The CMIP6 models project that the annual mean frozen volume in the top 2 m of the soil could decrease by 10 % –40 %∘C-1 of global mean surface air temperature increase. [ABSTRACT FROM AUTHOR]

The Greenland ice sheet is one of the largest contributors to global mean sea-level rise today and is expected to continue to lose mass as the Arctic continues to warm. The two predominant mass loss mechanisms are increased surface meltwater run-off and mass loss associated with the retreat of marine-terminating outlet glaciers. In this paper we use a large ensemble of Greenland ice sheet models forced by output from a representative subset of the Coupled Model Intercomparison Project (CMIP5) global climate models to project ice sheet changes and sea-level rise contributions over the 21st century. The simulations are part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We estimate the sea-level contribution together with uncertainties due to future climate forcing, ice sheet model formulations and ocean forcing for the two greenhouse gas concentration scenarios RCP8.5 and RCP2.6. The results indicate that the Greenland ice sheet will continue to lose mass in both scenarios until 2100, with contributions of 90±50 and 32±17 mm to sea-level rise for RCP8.5 and RCP2.6, respectively. The largest mass loss is expected from the south-west of Greenland, which is governed by surface mass balance changes, continuing what is already observed today. Because the contributions are calculated against an unforced control experiment, these numbers do not include any committed mass loss, i.e. mass loss that would occur over the coming century if the climate forcing remained constant. Under RCP8.5 forcing, ice sheet model uncertainty explains an ensemble spread of 40 mm, while climate model uncertainty and ocean forcing uncertainty account for a spread of 36 and 19 mm, respectively. Apart from those formally derived uncertainty ranges, the largest gap in our knowledge is about the physical understanding and implementation of the calving process, i.e. the interaction of the ice sheet with the ocean. [ABSTRACT FROM AUTHOR]

The Arctic is warming at a faster rate compared to the globe on average, a phenomenon commonly referred to as Arctic amplification. Sea ice has been linked to Arctic amplification and has gathered attention recently due to the decline in summer sea ice extent. Data assimilation (DA) is the act of combining observations with prior forecasts to obtain a more accurate model state. Sea ice poses a unique challenge for DA because sea ice variables have bounded distributions, leading to non-Gaussian distributions. The non-Gaussian nature violates the Gaussian assumptions built into DA algorithms. This study presents different observing system simulation experiments (OSSEs), which will provide a data assimilating testing framework through experimental observation networks and synthetic observations. The OSSE framework will help determine the best data assimilation configuration for assimilating sea ice and snow observations. Findings indicate that assimilating both sea ice thickness and snow depth observations while omitting sea ice concentration observations produced the best sea ice and snow forecasts in our idealized experimental setup. A simplified DA experiment helped demonstrate that the DA solution is biased when assimilating sea ice concentration observations. The biased DA solution is related to the observation error distribution being a truncated normal distribution, and the assumed observation likelihood is normal for the DA method. Additional OSSEs show that using a non-Gaussian DA method does not alleviate the non-Gaussian effects of sea ice concentration observations, and assimilating sea ice surface temperatures has a positive impact on snow updates. Finally, it is shown that the perturbed sea ice model parameters used to create additional ensemble spread in the free forecasts lead to a year-long negative snow volume bias. [ABSTRACT FROM AUTHOR]

We examine the past and projected changes in Arctic sea ice properties in six climate models participating in the High-Resolution Model Intercomparison Project (HighResMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Within HighResMIP, each of the experiments is run using a reference resolution configuration (consistent with typical CMIP6 runs) and using higher-resolution configurations. The role of horizontal grid resolution in both the atmosphere model component and the ocean model component in reproducing past and future changes in the Arctic sea ice cover is analysed. Model outputs from the coupled historical (hist-1950) and future (highres-future) runs are used to describe the multi-model, multi-resolution representation of the Arctic sea ice and to evaluate the systematic differences (if any) that resolution enhancement causes. Our results indicate that there is not a strong relationship between the representation of sea ice cover and the ocean/atmosphere grids; the impact of horizontal resolution depends rather on the sea ice characteristic examined and the model used. However, the refinement of the ocean grid has a more prominent effect compared to the refinement of the atmospheric one, with eddy-permitting ocean configurations generally providing more realistic representations of sea ice area and sea ice edges. All models project substantial sea ice shrinking: the Arctic loses nearly 95 % of sea ice volume from 1950 to 2050. The model selection based on historical performance potentially improves the accuracy of the model projections and predicts that the Arctic will turn ice-free as early as 2047. Along with the overall sea ice loss, changes in the spatial structure of the total sea ice and its partition in ice classes are noticed: the marginal ice zone (MIZ) will dominate the ice cover by 2050, suggesting a shift to a new sea ice regime much closer to the current Antarctic sea ice conditions. The MIZ-dominated Arctic might drive development and modification of model physics and parameterizations in the new generation of general circulation models (GCMs). [ABSTRACT FROM AUTHOR]

The thermal regime in the sediment column below shallow bodies of water in Arctic permafrost controls benthic habitats and permafrost stability. We present a robust, portable device that measures detailed temperature–depth profiles of the near-surface sediments in less than 1 h. Test campaigns in the Canadian Arctic and on Svalbard have demonstrated its utility in a range of environments during winter and summer. Measured temperatures were spatially heterogeneous, even within single bodies of water. We observed the broadest temperature range in water less than 1 m deep, a zone that is not captured by single measurements in deeper water. [ABSTRACT FROM AUTHOR]

In recent decades, the Arctic Ocean has undergone changes associated with enhanced poleward inflow of Atlantic and Pacific waters and increased heat flux exchange with the atmosphere in seasonally ice-free regions. The associated changes in upper-ocean heat content can alter the exchange of energy at the ocean–ice interface. Yet, the role of ocean heat content in modulating Arctic sea ice variability at sub-seasonal timescales is still poorly documented. We analyze ocean heat transports and surface heat fluxes between 1980–2021 using two eddy-permitting global ocean reanalyses, C-GLORSv5 and ORAS5, to assess the surface energy budget of the Arctic Ocean and its regional seas. We then assess the role of upper-ocean heat content, computed in the surface mixed layer (Qml) and in the 0–300 m layer (Q300), as a sub-seasonal precursor of sea ice variability by means of lag correlations. Our results reveal that in the Pacific Arctic regions, sea ice variability in autumn is linked with Qml anomalies leading by 1 to 3 months, and this relationship has strengthened in the Laptev and East Siberian seas during 2001–2021 relative to 1980–2000, primarily due to reduced surface heat loss since the mid-2000s. Q300 anomalies act as a precursor for wintertime sea ice variability in the Barents and Kara seas, with considerable strengthening and expansion of this link from 1980–2000 and 2001–2021 in both reanalyses. Our results highlight the role played by upper-ocean heat content in modulating the interannual variability of Arctic sea ice at sub-seasonal timescales. Heat stored in the ocean has important implications for the predictability of sea ice, calling for improvements in forecast initialization and a focus upon regional predictions in the Arctic region. [ABSTRACT FROM AUTHOR]

Reanalysis products provide spatially homogeneous coverage for a variety of climate variables in regions such as the Arctic where observational data are limited. Soil temperatures are an important control of many land–atmosphere exchanges and hydrological processes, and permafrost soils are thawing as the climate warms. However, very little validation of reanalysis soil temperatures in the Arctic has been performed to date, because widespread in situ reference observations have historically been limited there. Here we validate pan-Arctic soil temperatures from eight reanalysis and land data assimilation system products, using a newly assembled database of in situ observations from diverse measurement networks across Eurasia and North America. We examine product performance across the extratropical Northern Hemisphere between 1982 and 2018, and find that most products have soil temperatures that are biased cold by 1–5 K, with an RMSE of 2–9 K, and that biases and RMSE are generally largest in the cold season. Monthly mean values from most products correlate well with in situ data (r>0.9) in the warm season but show lower correlations (r=0.55 –0.85) in the cold season. Similarly, the magnitude of monthly variability in soil temperatures is well captured in summer but overestimated by 20 %–50 % for several products in winter. The suggestion is that soil temperatures in reanalysis products are subject to much higher uncertainty when the soil is frozen and/or when the ground is snow covered, suggesting that the representation of processes controlling snow cover in reanalysis systems should be urgently studied. We also validate the ensemble mean of all available products and find that, when all seasons and metrics are considered, the ensemble mean generally outperforms any individual product, in terms of its correlation and variability, while maintaining relatively low biases. As such, we recommend the ensemble mean soil temperature product for a wide range of applications, such as the validation of soil temperatures in climate models, and to inform models that require soil temperature inputs, such as hydrological models. [ABSTRACT FROM AUTHOR]

A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks. [ABSTRACT FROM AUTHOR]

Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation. [ABSTRACT FROM AUTHOR]

Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite L-band observations for obtaining sea ice concentration had not yet been explored. In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100% sea ice and 100% seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate. After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness .0.6 m), the method underestimates the actual sea ice concentration. Our results open the way for a systematic exploitation of SMOS data for monitoring sea ice concentration, at least for specific seasons. Additionally, SMOS data can be synergistically combined with data from other sensors to monitor pan- Arctic sea ice conditions. [ABSTRACT FROM AUTHOR]

Quantitative parameters characterizing the sea ice surface topography are needed in geophysical investigations such as studies on atmosphere-ice interactions or sea ice mechanics. Recently, the use of space-borne single-pass interferometric synthetic aperture radar (InSAR) for retrieving the ice surface topography has attracted notice among geophysicists. In this paper the potential of InSAR measurements is examined for several satellite configurations and radar frequencies, considering statistics of heights and widths of ice ridges as well as possible magnitudes of ice drift. It is shown that, theoretically, surface height variations can be retrieved with relative errors ≤0.5 m. In practice, however, the sea ice drift and open water leads may contribute significantly to the measured interferometric phase. Another essential factor is the dependence of the achievable interferometric baseline on the satellite orbit configurations. Possibilities to assess the influence of different factors on the measurement accuracy are demonstrated: signal-to-noise ratio, presence of a snow layer, and the penetration depth into the ice. Practical examples of sea surface height retrievals from bistatic SAR images collected during the TanDEM-X Science Phase are presented. [ABSTRACT FROM AUTHOR]

Rapid decline in Arctic sea ice has created more open water for ocean wave development and highlighted the importance of wave–ice interactions in the Arctic. Some studies have made contributions to our understanding of the potential role of the prognostic floe size distribution (FSD) in sea ice changes. However, these efforts do not represent the full interactions across atmosphere, ocean, wave, and sea ice. In this study, we implement a modified joint floe size and thickness distribution (FSTD) in a newly developed regional atmosphere–ocean–wave–sea ice coupled model and conduct a series of pan-Arctic simulations with different physical configurations related to FSD changes, including FSD-fixed, FSD-varied, lateral melting rate, wave-fracturing formulation, and wave attenuation rate. Firstly, our atmosphere–ocean–wave–sea ice coupled simulations show that the prognostic FSD leads to reduced ice area due to enhanced ice–ocean heat fluxes, but the feedbacks from the atmosphere and the ocean partially offset the reduced ice area induced by the prognostic FSD. Secondly, lateral melting rate formulations do not change the simulated FSD significantly, but they influence the flux exchanges across atmosphere, ocean, and sea ice and thus sea ice responses. Thirdly, the changes in FSD are sensitive to the simulated wave height, wavelength, and wave period associated with different wave-fracturing formulations and wave attenuation rates, and the limited oceanic energy imposes a strong constraint on the response of sea ice to FSD changes. Finally, our results also demonstrate that wave-related physical processes can have impacts on sea ice changes with the constant FSD, suggesting the indirect influences of ocean waves on sea ice through the atmosphere and the ocean. [ABSTRACT FROM AUTHOR]

An accurate representation of the annual evolution of surface albedo of the Arctic Ocean, especially during the melting period, is crucial to obtain reliable climate model predictions in the Arctic. Therefore, the output of the surface albedo scheme of a coupled regional climate model (HIRHAM–NAOSIM) was evaluated against airborne and ground-based measurements. The observations were conducted during five aircraft campaigns in the European Arctic at different times of the year between 2017 and 2022; one of them was part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020. We applied two approaches for the evaluation: (a) relying on measured input parameters of surface type fraction and surface skin temperature (offline) and (b) using HIRHAM–NAOSIM simulations independently of observational data (online). From the offline method we found a seasonally dependent bias between measured and modeled surface albedo. In spring, the cloud effect on surface broadband albedo was overestimated by the surface albedo parametrization (mean albedo bias of 0.06), while the surface albedo scheme for cloudless cases reproduced the measured surface albedo distributions for all seasons. The online evaluation revealed an overestimation of the modeled surface albedo resulting from an overestimation of the modeled cloud cover. Furthermore, it was shown that the surface type parametrization contributes significantly to the bias in albedo, especially in summer (after the drainage of melt ponds) and autumn (onset of refreezing). The lack of an adequate model representation of the surface scattering layer, which usually forms on bare ice in summer, contributed to the underestimation of surface albedo during that period. The difference between modeled and measured net irradiances for selected flights during the five airborne campaigns was derived to estimate the impact of the model bias for the solar radiative energy budget at the surface. We revealed a negative bias between modeled and measured net irradiances (median: -6.4 W m -2) for optically thin clouds, while the median value of only 0.1 W m -2 was determined for optically thicker clouds. [ABSTRACT FROM AUTHOR]

The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part of the European Arctic including substantial amounts of ice-covered areas. Sea ice in CARRA is modelled by means of a one-dimensional thermodynamic sea ice parameterisation scheme, which also explicitly resolves the evolution of the snow layer over sea ice. In the present study, we assess the representation of sea ice cover in CARRA and validate it against a wide set of satellite products and observations from ice mass balance buoys. We show that CARRA adequately represents general interannual trends towards thinner and warmer ice in the Arctic. Compared to ERA5, sea ice in CARRA shows a reduced warm bias in the ice surface temperature. The strongest improvement was observed for winter months over the central Arctic and the Greenland and Barents seas where a 4.91 °C median ice surface temperature error in ERA5 is reduced to 1.88 °C in CARRA on average. Over Baffin Bay, intercomparisons suggest the presence of a cold winter-time ice surface temperature bias in CARRA. No improvement over ERA5 was found in the ice surface albedo with spring-time errors in CARRA being up to 0.08 higher on average than those in ERA5 when computed against the CLARA-A2 satellite retrieval product. Summer-time ice surface albedos are comparable in CARRA and ERA5. Sea ice thickness and snow depth in CARRA adequately resolve the annual cycle of sea ice cover in the Arctic and bring added value compared to ERA5. However, limitations of CARRA indicate potential benefits of utilising more advanced approaches for representing sea ice cover in next-generation reanalyses. [ABSTRACT FROM AUTHOR]

Anthropogenic warming in the Arctic is causing hydrological cycle intensification and permafrost thaw, with implications for flows of water, carbon, and energy from terrestrial biomes to coastal zones. To better understand the likely impacts of these changes, we used a hydrology model driven by meteorological data from atmospheric reanalysis and two global climate models for the period 1980–2100. The hydrology model accounts for soil freeze–thaw processes and was applied across the pan-Arctic drainage basin. The simulations point to greater changes over northernmost areas of the basin underlain by permafrost and to the western Arctic. An acceleration of simulated river discharge over the recent past is commensurate with trends drawn from observations and reported in other studies. Between early-century (2000–2019) and late-century (2080–2099) periods, the model simulations indicate an increase in annual total runoff of 17 %–25 %, while the proportion of runoff emanating from subsurface pathways is projected to increase by 13 %–30 %, with the largest changes noted in summer and autumn and across areas with permafrost. Most notably, runoff contributions to river discharge shift to northern parts of the Arctic Basin that contain greater amounts of soil carbon. Each season sees an increase in subsurface runoff; spring is the only season where surface runoff dominates the rise in total runoff, and summer experiences a decline in total runoff despite an increase in the subsurface component. The greater changes that are seen in areas where permafrost exists support the notion that increased soil thaw is shifting hydrological contributions to more subsurface flow. The manifestations of warming, hydrological cycle intensification, and permafrost thaw will impact Arctic terrestrial and coastal environments through altered river flows and the materials they transport. [ABSTRACT FROM AUTHOR]

A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 Wm-2 for the latent heat flux and over 800 Wm-2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 Wm-2 for the latent heat flux and 600 Wm-2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes. [ABSTRACT FROM AUTHOR]

Understanding the future evolution of permafrost requires a better understanding of its climatological past. This requires permafrost models to efficiently simulate the thermal dynamics of permafrost over the past centuries to millennia, taking into account highly uncertain soil and snow properties. In this study, we present a computationally efficient numerical permafrost model which satisfactorily reproduces the current ground temperatures and active layer thicknesses of permafrost in the Arctic and their trends over recent centuries. The performed simulations provide insights into the evolution of permafrost since the 18th century and show that permafrost on the North American continent is subject to early degradation, while permafrost on the Eurasian continent is relatively stable over the investigated 300-year period. Permafrost warming since industrialization has occurred primarily in three "hotspot" regions in northeastern Canada, northern Alaska, and, to a lesser extent, western Siberia. We find that the extent of areas with a high probability (p3m>0.9) of near-surface permafrost (i.e., 3 m of permafrost within the upper 10 m of the subsurface) has declined substantially since the early 19th century, with loss accelerating during the last 50 years. Our simulations further indicate that short-term climate cooling due to large volcanic eruptions in the Northern Hemisphere in some cases favors permafrost aggradation within the uppermost 10 m of the ground, but the effect only lasts for a relatively short period of a few decades. Despite some limitations, e.g., with respect to the representation of vegetation, the presented model shows great potential for further investigation of the climatological past of permafrost, especially in conjunction with paleoclimate modeling. [ABSTRACT FROM AUTHOR]

Variations in Arctic sea ice are apparent not only in its extent and thickness but also in its internal properties under global warming. The microstructure of summer Arctic sea ice changes due to varying external forces, ice age, and extended melting seasons, which affect its optical properties. Sea ice cores sampled in the Pacific sector of the Arctic obtained by the Chinese National Arctic Research Expedition (CHINARE) during the summers of 2008 to 2016 were used to estimate the variations in the microstructures and inherent optical properties (IOPs) of ice and determine the radiation budget of sea ice based on a radiative transfer model. The variations in the volume fraction of gas bubbles (Va) of the ice top layer were not significant, and the Va of the ice interior layer was significant. Compared with 2008, the mean Va of interior ice in 2016 decreased by 9.1 %. Meanwhile, the volume fraction of brine pockets increased clearly during 2008–2016. The changing microstructure resulted in the scattering coefficient of the interior ice decreasing by 38.4 % from 2008 to 2016, while no clear variations can be seen in the scattering coefficient of the ice top layer. These estimated ice IOPs fell within the range of other observations. Furthermore, we found that variations in interior ice were significantly related to the interannual changes in ice ages. At the Arctic basin scale, the changing IOPs of interior ice greatly changed the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness is assumed, especially for thin ice in marginal zones, implying the presence of different sea ice bottom melt processes. These findings revealed the important role of the changing microstructure and IOPs of ice in affecting the radiation transfer of Arctic sea ice. [ABSTRACT FROM AUTHOR]

Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25 ∘ × 0.25 ∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration. [ABSTRACT FROM AUTHOR]

Grinnell and Terra Nivea Ice Caps are located on the southern Baffin Island, Nunavut, in the Canadian Arctic Archipelago. These relatively small ice caps have received little attention compared to the much larger ice masses further north. Their evolution can, however, give valuable information about the impact of the recent Arctic warming at lower latitudes (i.e. ~62.5° N). In this paper, we measure or estimate historical and recent changes of area, elevation and mass of both ice caps using in situ, airborne and spaceborne data sets, including imagery from the Pléiades satellites. The area of Terra Nivea Ice Cap has decreased by 34% since the late 1950s, while that of Grinnell Ice Cap has decreased by 20% since 1952. For both ice caps, the areal reduction accelerated at the beginning of the 21st century. The estimated glacier-wide mass balance was -0.37±0.21ma-1 water equivalent (w.e.) over Grinnell Ice Cap for the 1952-2014 period, and -0.47±0.16ma-1 w.e. over Terra Nivea Ice Cap for the 1958/59-2014 period. Terra Nivea Ice Cap has experienced an accelerated rate of mass loss of -1.77±0.36ma-1 w.e. between 2007 and 2014. This rate is 5.9 times as negative when compared to the 1958/59-2007 period (-0.30±0.19ma-1 w.e.) and 2 times as negative when compared to the mass balance of other glaciers in the southern parts of Baffin Island over the 2003-2009 period. A similar acceleration in mass loss is suspected for the Grinnell Ice Cap, given the calculated elevation changes and the proximity to Terra Nivea Ice Cap. The recent increase in mass loss rates for these two ice caps is linked to a strong near-surface regional warming and a lengthening of the melt season into the autumn that may be indirectly strengthened by a later freezing of sea ice in the Hudson Strait sector. On a methodological level, our study illustrates the strong potential of Pléiades satellite data to unlock the under-exploited archive of old aerial photographs. [ABSTRACT FROM AUTHOR]

The paper presents a case study of the regional (≈150 km) morphological and optical properties of a relatively thin, 70–90 cm modal thickness, first-year Arctic sea ice pack in an advanced stage of melt. The study combines in situ broadband albedo measurements representative of the four main surface types (bare ice, dark melt ponds, bright melt ponds and open water) and images acquired by a helicopter-borne camera system during ice-survey flights. The data were collected during the 8-day ICE12 drift experiment carried out by the Norwegian Polar Institute in the Arctic, north of Svalbard at 82.3°N, from 26 July to 3 August 2012. A set of>10 000 classified images covering about 28 km2 revealed a homogeneous melt across the study area with melt-pond coverage of ≈0.29 and open-water fraction of ≈0.11. A decrease in pond fractions observed in the 30 km marginal ice zone (MIZ) occurred in parallel with an increase in open-water coverage. The moving block bootstrap technique applied to sequences of classified sea-ice images and albedo of the four surface types yielded a regional albedo estimate of 0.37 (0.35; 0.40) and regional sea-ice albedo of 0.44 (0.42; 0.46). Random sampling from the set of classified images allowed assessment of the aggregate scale of at least 0.7 km2 for the study area. For the current setup configuration it implies a minimum set of 300 images to process in order to gain adequate statistics on the state of the ice cover. Variance analysis also emphasized the importance of longer series of in situ albedo measurements conducted for each surface type when performing regional upscaling. The uncertainty in the mean estimates of surface type albedo from in situ measurements contributed up to 95% of the variance of the estimated regional albedo, with the remaining variance resulting from the spatial inhomogeneity of sea-ice cover. [ABSTRACT FROM AUTHOR]

Global warming has already substantially altered the Arctic cryosphere. Due to the Arctic warming amplification, the temperature is increasing more strongly, leading to pervasive changes in this area. Recent years were notably marked by melt records over the Greenland Ice Sheet, while other regions such as Svalbard seem to remain less influenced. This raises the question of the current state of the Greenland Ice Sheet and the various ice caps in the Arctic for which few studies are available. Here, we run the regional climate model (RCM) Modèle Atmosphérique Régional (MAR) at a resolution of 6 km over four different domains covering all Arctic land ice to produce a unified surface mass balance product from 1950 to the present day. We also compare our results to large-scale indices to better understand the heterogeneity of the evolutions across the Arctic and their links to recent climate change. We find a sharp decrease of surface mass balance (SMB) over the western Arctic (Canada and Greenland) in relationship with the atmospheric blocking situations that have become more frequent in summer, resulting in a 41 % increase of the melt rate since 1950. This increase is not seen over the Russian Arctic permanent ice areas, where melt rates have increased by only 3 % on average, illustrating a heterogeneity in the Arctic SMB response to global warming. [ABSTRACT FROM AUTHOR]

In the warming Arctic, retrogressive thaw slumping (RTS) has emerged as the primary thermokarst modifier of ice-rich permafrost slopes, raising urgency to investigate the distribution and intensification of disturbances and the cascade of effects. Tracking RTS is challenging due to the constraints of remote sensing products and a narrow understanding of complex, thaw-driven landforms; however, high-resolution elevation models provide new insights into geomorphic change. Structural traits, such as RTS depth of thaw or volume, can be obtained through allometric scaling. To address fundamental knowledge gaps related to area–volume scaling of RTS, a suitable surface interpolation technique was first needed to model pre-disturbance topography upon which volume estimates could be based. Among eight methods with 32 parameterizations, natural neighbour surface interpolation achieved the best precision in reconstructing pre-disturbed slope topography (90th percentile root mean square difference ±1.0 m). An inverse association between RTS volume and relative volumetric error was observed, with uncertainties < 10 % for large slumps and < 20 % for small to medium slumps. Second, a multisource slump inventory (MSI) for two study areas in the Beaufort Delta (Canada) region was developed to characterize the diverse range of disturbance morphologies and activity levels, which provided consistent characterization of thaw-slump-affected slopes between regions and through time. The MSI delineation of high-resolution hillshade digital elevation models (DEMs) for three time periods (airborne stereo-imagery, lidar, ArcticDEM) revealed temporal and spatial trends in these chronic mass-wasting features. For example, in the Tuktoyaktuk Coastlands, a + 38 % increase in active RTS counts and + 69 % increase in total active surface area were observed between 2004 and 2016. However, the total disturbance area of RTS-affected terrain did not change considerably (+ 3.5 %) because the vast majority of active thaw slumping processes occurred in association with past disturbances. Interpretation of thaw-driven change is thus dependent on how active RTS is defined to support disturbance inventories. Our results highlight that active RTS is tightly linked to past disturbances, underscoring the importance of inventorying inactive scar areas. Third, the pre-disturbance topographies, MSI digitizations, and DEMs were integrated to explore allometric scaling relationships between RTS area and eroded volume. The power-law model indicated non-linearity in the rates of RTS expansion and intensification across scales (adj- R2 of 0.85, n= 1522) but also revealed that elongated, shoreline RTS reflects outliers poorly represented by the modelling. These results indicate that variation in the allometric scaling of RTS populations is based on morphometry, terrain position, and complexity of the disturbance area, as well as the method and ontology by which slumps are inventoried. This study highlights the importance of linking field-based knowledge to feature identification and the utility of high-resolution DEMs in quantifying rates of RTS erosion beyond tracking changes in the planimetric area. [ABSTRACT FROM AUTHOR]

From 2015 to 2020, using the spectral gradient radiometric method, the possibility of the frozen/thawed (FT) state identification of tundra soil was investigated based on Soil Moisture Active Passive (SMAP) and Global Change Observation Mission – Water Satellite 1 (GCOM-W1) satellite observations of 10 test sites located in the Arctic regions of Canada, Finland, Russia, and the USA. It is shown that the spectral gradients of brightness temperature and reflectivity (measured in the frequency range from 1.4 to 36.5 GHz with horizontal polarization, a determination coefficient from 0.775 to 0.834, a root-mean-square error from 6.6 to 10.7 d and a bias from - 3.4 to + 6.5 d) make it possible to identify the FT state of the tundra topsoil. The spectral gradient method has a higher accuracy with respect to the identification of the FT state of tundra soils than single-frequency methods based on the calculation of polarization index. [ABSTRACT FROM AUTHOR]

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 that links sea ice–atmosphere form drag coefficients with surface feature height and spacing. We measure these sea ice surface feature parameters using the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). Though ICESat-2 is unable to resolve as well as airborne surveys, it 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 November 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 , while most of the multiyear ice portion of the Arctic is typically around ∼1.5×10-3. [ABSTRACT FROM AUTHOR]

The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments. [ABSTRACT FROM AUTHOR]

Investigating North Pacific climate variability during warm intervals prior to the Common Era can improve our understanding of the behavior of ocean–atmosphere teleconnections between low latitudes and the Arctic under future warming scenarios. However, most of the existing ice core records from the Alaskan and Yukon region only allow access to climate information covering the last few centuries. Here we present a surface-to-bedrock age scale for a 210 m long ice core recovered in 2013 from the summit plateau of Begguya (Mt. Hunter; Denali National Park, Central Alaska). Combining dating by annual layer counting with absolute dates from micro-radiocarbon dating, a continuous chronology for the entire ice core archive was established using an ice flow model. Calibrated 14 C ages from the deepest section (209.1 m, 7.7 to 9.0 ka cal BP) indicate that basal ice on Begguya is at least of early Holocene origin. A series of samples from a shallower depth interval (199.8 to 206.6 m) were dated with near-uniform 14 C ages (3 to 5 ka cal BP). Our results suggest this may be related to an increase in annual net snow accumulation rates over this period following the Northern Hemisphere Holocene Climate Optimum (around 8 to 5 kyr BP). With absolute dates constraining the timescale for the last >8 kyr BP, this paleo-archive will allow future investigations of Holocene climate and the regional evolution of spatial and temporal changes in atmospheric circulation and hydroclimate in the North Pacific. [ABSTRACT FROM AUTHOR]

In this study, a new method to assimilate freeboard (FB) derived from satellite radar altimetry is presented with the goal of improving the initial state of sea ice thickness predictions in the Arctic. In order to quantify the improvement in sea ice thickness gained by assimilating FB, we compare three different model runs: one reference run (refRun), one that assimilates only sea ice concentration (SIC) (sicRun), and one that assimilates both SIC and FB (fbRun). It is shown that estimates for both SIC and FB can be improved by assimilation, but only fbRun improved the FB. The resulting sea ice thickness is evaluated by comparing sea ice draft measurements from the Beaufort Gyre Exploration Project (BGEP) and sea ice thickness measurements from 19 ice mass balance (IMB) buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The sea ice thickness of fbRun compares better than refRun and sicRun to the longer BGEP observations more poorly to the shorter MOSAiC observations. Further, the three model runs are compared to the Alfred Wegener Institute (AWI) weekly CryoSat-2 sea ice thickness, which is based on the same FB observations as those that were assimilated in this study. It is shown that the FB and sea ice thickness from fbRun are closer to the AWI CryoSat-2 values than the ones from refRun or sicRun. Finally, comparisons of the abovementioned observations and both the fbRun sea ice thickness and the AWI weekly CryoSat-2 sea ice thickness were performed. At the BGEP locations, both fbRun and the AWI CryoSat-2 sea ice thickness perform equally. The total root-mean-square error (RMSE) at the BGEP locations equals 30 cm for both sea ice thickness products. At the MOSAiC locations, fbRun's sea ice thickness performs significantly better, with a total 11 cm lower RMSE. [ABSTRACT FROM AUTHOR]

Size distribution of sea ice floes is an important component for sea ice thermodynamic and dynamic processes, particularly in the marginal ice zone. Recently processes related to the floe size distribution (FSD) have been incorporated into sea ice models, but the sparsity of existing observations limits the evaluation of FSD models, thus hindering model improvements. In this study, perimeter density has been applied to characterise the floe size distribution for evaluating three FSD models – the Waves-in-Ice module and Power law Floe Size Distribution (WIPoFSD) model and two branches of a fully prognostic floe size-thickness distribution model: CPOM-FSD and FSDv2-WAVE. These models are evaluated against a new FSD dataset derived from high-resolution satellite imagery in the Arctic. The evaluation shows an overall overestimation of floe perimeter density by the models against the observations. Comparison of the floe perimeter density distribution with the observations shows that the models exhibit a much larger proportion for small floes (radius <10 –30 m) but a much smaller proportion for large floes (radius >30 –50 m). Observations and the WIPoFSD model both show a negative correlation between sea ice concentration and the floe perimeter density, but the two prognostic models (CPOM-FSD and FSDv2-WAVE) show the opposite pattern. These differences between models and the observations may be attributed to limitations in the observations (e.g. the image resolution is not sufficient to detect small floes) or limitations in the model parameterisations, including the use of a global power-law exponent in the WIPoFSD model as well as too weak a floe welding and enhanced wave fracture in the prognostic models. [ABSTRACT FROM AUTHOR]

This study presents a long-term winter sea ice thickness proxy product for the Canadian Arctic based on a random forest regression model – applied to ice charts and scatterometer data, trained on CryoSat-2 observations, and applying an ice type–sea ice thickness correction using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) – that provides 25 years of sea ice thickness in the Beaufort Sea, Baffin Bay, and, for the first time, the Canadian Arctic Archipelago. An evaluation of the product with in situ sea ice thickness measurements shows that the presented sea ice thickness proxy product correctly estimates the magnitudes of the ice thickness and accurately captures spatial and temporal variability. The product estimates sea ice thickness within 30 to 50 cm uncertainty from the model. The sea ice thickness proxy product shows that sea ice is thinning over most of the Canadian Arctic, with a mean trend of - 0.82 cm yr -1 in April over the whole study area (corresponding to 21 cm thinning over the 25-year record), but that trends vary locally. The Beaufort Sea and Baffin Bay show significant negative trends during all months, though with peaks in November (- 2.8 cm yr -1) and April (- 1.5 cm yr -1), respectively. The Parry Channel, which is part of the Northwest Passage and relevant for shipping, shows significant thinning in autumn. The sea ice thickness proxy product provides, for the first time, the opportunity to study long-term trends and variability in sea ice thickness in the Canadian Arctic, including the narrow channels in the Canadian Arctic Archipelago. [ABSTRACT FROM AUTHOR]

The Arctic is undergoing increased warming compared to the global mean, which has major implications for freshwater runoff into the oceans from seasonal snow and glaciers. Here, we present high-resolution (2.5 km) simulations of glacier mass balance, runoff, and snow conditions on Svalbard from 1991–2022, one of the fastest warming regions in the world. The simulations are created using the CryoGrid community model forced by Copernicus Arctic Regional ReAnalysis (CARRA) (1991–2021) and AROME-ARCTIC forecasts (2016–2022). Updates to the water percolation and runoff schemes are implemented in the CryoGrid model for the simulations. In situ observations available for Svalbard, including automatic weather station data, stake measurements, and discharge observations, are used to carefully evaluate the quality of the simulations and model forcing. We find a slightly negative climatic mass balance (CMB) over the simulation period of - 0.08 mw.e.yr-1 but with no statistically significant trend. The most negative annual CMB is found for Nordenskiöldland (- 0.73 mw.e.yr-1), with a significant negative trend of - 0.27 mw.e. per decade for the region. Although there is no trend in the annual CMB, we do find a significant increasing trend in the runoff from glaciers of 0.14 mw.e. per decade. The average runoff was found to be 0.8 mw.e.yr-1. We also find a significant negative trend in the refreezing of - 0.13 mw.e. per decade. Using AROME-ARCTIC forcing, we find that 2021/22 has the most negative CMB and highest runoff over the 1991–2022 simulation period investigated in this study. We find the simulated climatic mass balance and runoff using CARRA and AROME-ARCTIC forcing are similar and differ by only 0.1 mw.e.yr-1 in climatic mass balance and by 0.2 mw.e.yr-1 in glacier runoff when averaged over all of Svalbard. There is, however, a clear difference over Nordenskiöldland, where AROME-ARCTIC simulates significantly higher mass balance and significantly lower runoff. This indicates that AROME-ARCTIC may provide similar high-quality predictions of the total mass balance of Svalbard as CARRA, but regional uncertainties should be taken into consideration. The simulations produced for this study are made publicly available at a daily and monthly resolution, and these high-resolution simulations may be re-used in a wide range of applications including studies on glacial runoff, ocean currents, and ecosystems. [ABSTRACT FROM AUTHOR]

Thermodynamic and dynamic sea ice thickness processes are affected by differing mechanisms in a changing climate. Independent observational datasets of each are essential for model validation and accurate projections of future sea ice conditions. Here, we present a monthly, Arctic-basin-wide, and 25 km resolution Eulerian estimation of thermodynamic and dynamic effects on wintertime sea ice thickness from 2010–2021. Estimates of thermodynamic growth rate are determined by coupling passive microwave-retrieved snow–ice interface temperatures to a simple sea ice thermodynamic model, total growth is calculated from a weekly Alfred Wegener Institute (AWI) European Space Agency (ESA) CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) combination product (CS2SMOS), and dynamic effects are calculated as their difference. The dynamic effects are further separated into advection and residual effects using a sea ice motion dataset. Our results show new detail in these fields and, when summed to a basin-wide or regional scale, are in line with previous studies. Across the Arctic, dynamic effects are negative and about one-fourth the magnitude of thermodynamic growth. Thermodynamic growth varies from less than 0.1 m per month in the central Arctic to greater than 0.3 m per month in the seasonal ice zones. High positive dynamic effects of greater than 0.1 m per month, twice that of thermodynamic growth or more in some areas, are found north of the Canadian Arctic Archipelago, where the Transpolar Drift and Beaufort Gyre deposit ice. Strong negative dynamic effects of less than -0.2 m per month are found where the Transpolar Drift originates, nearly equal to and opposite the thermodynamic effects in these regions. Monthly results compare well with a recent study of the dynamic and thermodynamic effects on sea ice thickness along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift track during the winter of 2019–2020. Couplets of deformation and advection effects with opposite signs are common across the Arctic, with positive advection effects and negative deformation effects found in the Beaufort Sea and negative advection effects and positive deformation effects found in most other regions. The seasonal cycle shows residual deformation effects and overall dynamic effects increasing as the winter season progresses. [ABSTRACT FROM AUTHOR]

Sea ice leads play an important role in the heat exchange between the ocean and the overlying atmosphere, particularly narrow leads with widths of less than 100 m. We present a method for detecting sea ice leads in the Arctic using high-resolution infrared images from the Thermal Infrared Spectrometer (TIS) on board the Sustainable Development Science Satellite 1 (SDGSAT-1), with a resolution of 30 m in a swath of 300 km. With the spatial resolution of leads observed by infrared remote sensing increasing to tens of meters, focused on the Beaufort Sea cases in April 2022, the TIS-detected leads achieve good agreement with Sentinel-2 visible images. For the three infrared bands of the TIS, the B2 (10.3–11.3 µm) and B3 (11.5–12.5 µm) bands show similar performance in detecting leads. The B1 band (8.0–10.5 µm) can be usefully complementary to the other two bands, as a result of different temperature measurement sensitivity. Combining the detected results from the three TIS bands, the TIS is able to detect more leads with widths less than hundreds of meters compared to the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results demonstrate that SDGSAT-1 TIS data at 30 m resolution can effectively observe previously unresolvable sea ice leads, providing new insight into the contribution of narrow leads to rapid sea ice changes in the Arctic. [ABSTRACT FROM AUTHOR]

In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from CryoSat-2 and NASA daily Bootstrap sea ice concentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the localised ensemble transform Kalman filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid-scale sea ice thickness distribution is of particular novelty. The sub-grid-scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. This study finds that assimilating CryoSat-2 products for the mean thickness and the sub-grid-scale thickness distribution can have significant consequences for the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid-scale sea ice thickness distribution together performed best when compared to a subset of CryoSat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Arctic Archipelago (CAA) is decreased, but the thickness of the ice in the central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid-scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid-scale thickness distribution alongside mean thickness. [ABSTRACT FROM AUTHOR]

Multiyear sea ice (MYI) cover in the Arctic has been monitored for decades using increasingly sophisticated remote sensing techniques, and these have documented a significant decline in MYI over time. However, such techniques are unable to differentiate between the processes affecting the evolution of the MYI. Further, estimating the thickness and thus the volume of MYI remains challenging. In this study we employ a sea ice–ocean model to investigate the changes to MYI over the period 2000–2018. We exploit the Lagrangian framework of the sea ice model to introduce a new method of tracking MYI area and volume which is based on identifying MYI during freeze onset each autumn. The model is found to successfully reproduce the spatial distribution and evolution of observed MYI extent. We discuss the balance of the processes (melt, ridging, export, and replenishment) linked to the general decline in MYI cover. The model suggests that rather than one process dominating the losses, there is an episodic imbalance between the different sources and sinks of MYI. We identify those key to the significant observed declines in 2007 and 2012; while melt and replenishment are important in 2012, sea ice dynamics play a significant role in 2007. Notably, the model suggests that in years such as 2007, convergence of the ice, through ridging, can result in large reductions in MYI area without a corresponding loss of MYI volume. This highlights the benefit of using models alongside satellite observations to aid interpretation of the observed MYI evolution in the Arctic. [ABSTRACT FROM AUTHOR]

Hydrologically active subglacial lakes can drain large volumes of water and sediment along subglacial pathways, affecting the motion and mass balance of ice masses and impacting downstream sediment dynamics. To date, only eight active lakes have been reported beneath the Greenland Ice Sheet (GrIS), and thus the understanding of their spatial distribution and dynamic processes is still lacking. Here, using ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) ATL11 data, we identify 18 active subglacial lakes, 16 of which have not been previously reported. Multi-temporal ArcticDEM (digital elevation model of the Arctic) strip maps were used to extend the time series to verify lakes and determine their drainage history. The identification of active subglacial lakes beneath the GrIS is complicated by the occurrence of supraglacial lakes, which also fill and drain and are hypothesized to be almost co-located. We therefore used the temporal pattern of ice-surface elevation change to discriminate subglacial lakes and utilized the ability of ICESat-2 to penetrate through surface water to correct the elevation provided by the ATL11 data. A significant localized elevation anomaly (-16.03 –10.30 m yr -1) was measured in all detected subglacial lakes after correction, revealing that six subglacial lakes are twinned with supraglacial lakes. The active subglacial lakes have large upstream hydrological catchments and are located near or below the equilibrium line. Lakes have a median area of 1.20 km 2 , and 12 lakes exhibited positive elevation-change rates during the ICESat-2 period. These observations illustrate the potential for combining ICESat-2 and the ArcticDEM to differentiate small subglacial lakes in the ablation zone and beneath supraglacial lakes. [ABSTRACT FROM AUTHOR]

In light of the recent climate warming, monitoring of lake ice in Arctic and subarctic regions is becoming increasingly important. Many shallow Arctic lakes and ponds of thermokarst origin freeze to the bed in the winter months, maintaining the underlying permafrost in its frozen state. However, as air temperatures rise and precipitation increases, fewer lakes are expected to develop bedfast ice. In this work, we propose a novel temporal deep-learning approach to lake ice regime mapping from synthetic aperture radar (SAR) and employ it to study lake ice dynamics in the Old Crow Flats (OCF), Yukon, Canada, over the 1992/1993 to 2020/2021 period. We utilized a combination of Sentinel-1, ERS-1 and ERS-2, and RADARSAT-1 to create an extensive annotated dataset of SAR time series labeled as either bedfast ice, floating ice, or land, which was used to train a temporal convolutional neural network (TempCNN). The trained TempCNN, in turn, allowed us to automatically map lake ice regimes. The classified maps aligned well with the available field measurements and ice thickness simulations obtained with a thermodynamic lake ice model. Reaching a mean overall classification accuracy of 95 %, the TempCNN was determined to be suitable for automated lake ice regime classification. The fraction of bedfast ice in the OCF increased by 11 % over the 29-year period of analysis. Findings suggest that the OCF lake ice dynamics are dominated by lake drainage events, brought on by thermokarst processes accelerated by climate warming, and fluctuations in water level and winter snowfall. Catastrophic drainage and lowered water levels cause surface water area and lake depth to decrease and lake ice to often transition from floating to bedfast ice, while a reduction in snowfall allows for the growth of thicker ice. The proposed lake ice regime mapping approach allowed us to assess the combined impacts of warming, drainage, and changing precipitation patterns on transitions between bedfast and floating-ice regimes, which is crucial to understanding evolving permafrost dynamics beneath shallow lakes and drained basins in thermokarst lowlands such as the OCF. [ABSTRACT FROM AUTHOR]

In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness, will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequake waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen Fjord (Svalbard), we observe the spatial clustering of icequake sources along the shoreline of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the 4 weeks of data recording. Comparing the energy of the icequakes with that of artificial seismic sources, we were able to derive a power law of icequake energy and to relate this energy to the size of the cracks that generate the icequakes. [ABSTRACT FROM AUTHOR]

Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snowpack properties are changing, and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Specifically, satellite microwave observations have been shown to provide insight into known events. Only Ku-band radar (scatterometer) has been applied so far across the entire Arctic. Data availability at this frequency is limited, however. The utility of other frequencies from passive and active systems needs to be explored to develop a concept for long-term monitoring. The latter are of specific interest as they can be potentially provided at higher spatial resolution. Radar records have been shown to capture the associated snow structure change based on time-series analyses. This approach is also applicable when data gaps exist and has capabilities to evaluate the impact severity of events. Active as well as passive microwave sensors can also detect wet snow at the timing of an ROS event if an acquisition is available. The wet snow retrieval methodology is, however, rather mature compared to the identification of snow structure change since ambiguous scattering behaviour needs consideration. C-band radar is of special interest due to good data availability including a range of nominal spatial resolutions (10 m–12.5 km). Scatterometer and SAR (synthetic aperture radar) data have therefore been investigated. The temperature dependence of C-band backscatter at VV (V – vertical) polarization observable down to - 40 ∘ C is identified as a major issue for ROS retrieval but can be addressed by a combination with a passive microwave wet snow indicator (demonstrated for Metop ASCAT – Advanced Scatterometer – and SMOS – Soil Moisture and Ocean Salinity). Results were compared to in situ observations (snowpit records, caribou migration data) and Ku-band products. Ice crusts were found in the snowpack after detected events (overall accuracy 82 %). The more crusts (events) there are, the higher the winter season backscatter increase at C-band will be. ROS events captured on the Yamal and Seward peninsulas have had severe impacts on reindeer and caribou, respectively, due to ice crust formation. SAR specifically from Sentinel-1 is promising regarding ice layer identification at better spatial details for all available polarizations. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record, but the consideration of performance differences due to spatial and temporal cover, as well as microwave frequency, is crucial. Retrieval is most robust in the tundra biome, where results are comparable between sensors. Records can be used to identify extremes and to apply the results for impact studies at regional scale. [ABSTRACT FROM AUTHOR]

Polar sea ice is one of the Earth's climate components that has been significantly affected by the recent trend of global warming. While the sea ice area in the Arctic has been decreasing at a rate of about 4 % per decade, the multi-year ice (MYI), also called perennial ice, is decreasing at a faster rate of 10 %–15 % per decade. On the other hand, the sea ice area in the Antarctic region was slowly increasing at a rate of about 1.5 % per decade until 2014, and since then it has fluctuated without a clear trend. However, no data about ice type areas are available from that region, particularly for MYI. Due to differences in the physical and crystalline structural properties of sea ice and snow between the two polar regions, it has become difficult to identify ice types in the Antarctic. Until recently,no satellite retrieval scheme was ready to monitor the distribution and temporal development of Antarctic ice types, particularly MYI, throughout the freezing season and on timescales of several years. In this study, we have adapted a method for retrieving Arctic sea ice types and partial concentrations using microwave satellite observations to fit the Antarctic sea ice conditions. The core of the retrieval method is a mathematical scheme that needs empirical distributions of the microwave brightness temperature and backscatter input parameters for the different ice types. The first circumpolar, long-term time series of Antarctic sea ice types (MYI, first-year ice, and young ice) is being established, and so far covers the years 2013–2021. Qualitative comparison with (a) synthetic aperture radar data, (b) charts of the development stage of the sea ice, and (c) the Antarctic polynya distribution data show that the retrieved ice types, in particular the MYI, are reasonable. Although there are still some shortcomings, the new retrieval allows insight into the interannual evolution and dynamics of Antarctic sea ice types for the first time. The current time series can in principle be extended backwards to start in the year 2002 and can be continued with current and future sensors. [ABSTRACT FROM AUTHOR]

Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozen and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments. [ABSTRACT FROM AUTHOR]

The forest–tundra ecotone is a large circumpolar transition zone between the Arctic tundra and the boreal forest, where snow properties are spatially variable due to changing vegetation. The extent of this biome through all circumpolar regions influences the climate. In the forest–tundra ecotone near Umiujaq in northeastern Canada (56∘33′31′′ N, 76∘28′56′′ W), we contrast the snow properties between two sites, TUNDRA (located in a low-shrub tundra) and FOREST (located in a boreal forest), situated less than 1 km apart. Furthermore, we evaluate the capability of the snow model Crocus, initially developed for alpine snow, to simulate the snow in this subarctic setting. Snow height and density differed considerably between the two sites. At FOREST, snow was about twice as deep as at TUNDRA. The density of snow at FOREST decreased slightly from the ground to the snow surface in a pattern that is somewhat similar to alpine snow. The opposite was observed at TUNDRA, where the pattern of snow density was typical of the Arctic. We demonstrate that upward water vapor transport is the dominant mechanism that shapes the density profile at TUNDRA, while a contribution of compaction due to overburden becomes visible at FOREST. Crocus was not able to reproduce the density profiles at either site using its standard configuration. We therefore implemented some modifications for the density of fresh snow, the effect of vegetation on compaction, and the lateral transport of snow by wind. These adjustments partly compensate for the lack of water vapor transport in the model but may not be applicable at other sites. Furthermore, the challenges using Crocus suggest that the general lack of water vapor transport in the snow routines used in climate models leads to an inadequate representation of the density profiles of even deep and moderately cold snowpacks, with possible major impacts on meteorological forecasts and climate projections. [ABSTRACT FROM AUTHOR]

Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and must therefore define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator, the Arctic Ocean Observation Operator (ARC3O), to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using Community Earth System Model version 2 (CESM2) ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place. [ABSTRACT FROM AUTHOR]