Sea Ice Thickness Research Articles

An Examination of the Wrangel Island Sea Ice Thickness Dipole

AbstractThe Beaufort Sea High is a high‐pressure system located in the Beaufort Sea that influences ocean circulation in the western Arctic known as the Beaufort Gyre. Wrangel Island, located in the western Chukchi Sea, typically experiences easterly sea ice motion due to the Beaufort Gyre. We find that under these climatological conditions, moving ice is blocked by the island and accumulates on its eastern side, while ice on its western side continues to drift leaving an area of open water and reduced ice thickness along the western side of the island. This results in an ice thickness dipole across the island. A reversal in the atmospheric circulation across the western Arctic results in a dipole with the opposing sign. We find the dipole is present throughout the year and is strongest in January when the difference in ice thickness between the eastern and western sides of the island is approximately 1 m. During the spring, it is associated with the transient opening of a polynya to the west of the island. The dipole is the result of opposing ice divergence and convergence across the western Arctic and may impact ocean circulation and ecosystems within the Chukchi Sea.

Recent Thickening of the Barents Sea Ice Cover

AbstractThe Arctic sea ice cover has decreased rapidly over the last few decades both in extent and thickness. Here we present multi‐year (2013–2022) observations of sea ice thickness in the northwestern Barents Sea based on Upward Looking Sonar measurements and show that the winter sea ice has become thicker over the last decade. Sea ice thickness from the Pan‐Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) reproduces both the observed variability and recent 10‐year trend and shows that this thickening (0.24 m decade−1) has not been seen since the 1990s. Using PIOMAS we find that the recent increase in sea ice thickness can be explained by increased sea ice freezing as a result of lower temperatures in the ocean and in the atmosphere. The recent thickening is set in the context of a long‐term thinning trend, with PIOMAS showing much thinner ice now than in the 1980s.

Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology

Abstract. In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice concentration, thickness and velocity, but also its level of damage, internal stress and cohesion. Specifically, we estimate the air drag coefficient and the so-called damage parameter of the MEB model. Mimicking the realistic observation network with different combinations of observations, we demonstrate that various issues can potentially arise in a complex sea-ice model, especially in instances for which the external forcing dominates the model forecast error growth. Even though further investigation will be needed using an operational (a coupled dynamics–thermodynamics) sea-ice model, we show that, with the current observation network, it is possible to improve both the observed and the unobserved model state forecast and parameter accuracy.

Arctic phytoplankton microdiversity across the marginal ice zone: Subspecies vulnerability to sea-ice loss

Seasonal phytoplankton blooms are important Arctic phenomena, contributing to global primary production and biogeochemical cycling. The decline in sea-ice extent and thickness favors a longer open-water period with impacts on phytoplankton dynamics. Arctic net productivity is influenced by microalgae living associated with sea ice, with distinct species thought to be favored by ice-covered and ice-free waters. In this study, we investigated the phytoplankton community structure in Baffin Bay, a semi-enclosed sea where Arctic and North Atlantic water masses interact. We compared communities from the ice-free Atlantic-influenced eastern, the marginal ice zone, and the ice-covered Arctic-influenced western Baffin Bay. The community was characterized using 18S rRNA high-throughput amplicon sequencing and flow cytometry cell counting, and compared to environmental data collected during the Green Edge campaign. We sampled 16 stations grouped by sectors according to sea-ice cover. In the sectors associated with sea ice, phytoplankton formed a highly diverse community of smaller taxa, which contrasted with a low-diversity community in ice-free sectors, dominated by larger centric diatoms and Phaeocystis pouchetii adapted to high light/low nutrient conditions. Several phytoplankton species were flagged as indicators for the under-ice and marginal ice zone sectors, including ice-associated taxa such as the diatoms Melosira arctica and Pseudo-nitzschia seriata, but also subspecies representatives of the early-blooming alga Micromonas polaris and the cryptophyte Baffinella frigidus. The strong association of certain taxa with under-ice and marginal ice zone sectors, including Pterosperma sp., Chrysochromulina sp., Micromonas polaris, and B. frigidus, suggest that they might be indicators of diversity loss due to ongoing sea-ice changes in Baffin Bay. We report new intra-species variability of Micromonas polaris suggesting that seasonal specialists could wax and wane over the bloom and non-bloom periods, highlighting the need for detailed year-long studies and the importance of microdiversity when assessing the diversity and distribution of polar phytoplankton.

Arctic Thin Ice Detection Using AMSR2 and FY-3C MWRI Radiometer Data

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature (TB) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath TB datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of TB signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks.

GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies-Sea Ice Thickness Inversion in Beaufort Sea.

Sea ice, as an important component of the Earth's ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model's generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data.

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

The Bohai Sea and its surrounding areas are rich in oil and natural gas and play an important role in industry, agriculture and the economy. However, the Bohai Sea suffers severely from sea ice in the winter. While previous research has predominantly focused on methods for retrieving sea ice parameters in the Bohai Sea, analyses of their long-term statistical patterns have been limited. The Geostationary Ocean Color Imager (GOCI) is the first geostationary satellite for ocean color remote sensing, offering high spatial and temporal resolution, which greatly facilitates the extraction of Bohai Sea ice parameters. Utilizing GOCI data, we systematically extracted relevant sea ice parameters for the Bohai Sea region from 2011 to 2021. These parameters include sea ice concentration, sea ice thickness, and sea ice drift. We conducted a comprehensive statistical analysis of the long-term sea ice changes in the Bohai Sea and found that the development process of winter sea ice area is different from the sea ice thickness, and the direction of sea ice drift is basically unchanged. Then we developed statistical models linking sea ice parameters with ocean dynamic factors such as temperature, wind, and drift currents. Among them, the correlation coefficient between the predicted value and the measured value of the sea ice area model is the highest, reaching 0.8382. Furthermore, we examined the previously unexplored relationship between daily sea ice area, sea ice thickness, and accumulated temperature with their respective starting temperatures and accumulation periods. This study provides critical data to support Bohai Sea ice monitoring and marine environmental research. The results of this study contribute to a better understanding of sea ice change trends in the Bohai Sea and inform the development of disaster prevention and mitigation measures.

Effects of Ice-Microstructure-Based Inherent Optical Properties Parameterization in the CICE Model

The constant inherent optical properties (IOPs) for sea ice currently applied in sea ice models do not realistically represent the dividing of shortwave radiative fluxes in sea ice and the ocean below it. Here we implement a parameterization of variable IOPs based on ice microstructures in the Los Alamos sea ice model, version 6.0 (CICE6) and investigate its effects on the simulation of the dividing of shortwave radiation and sea ice in the Arctic. Our sensitivity experiments indicate that variable IOP parameterization results in strong seasonal variation for the IOP parameters, typically reaching the seasonal maximum in the boreal summer. With such large differences, variable IOP parameterization leads to increased absorbed solar radiation at the surface and in the interior of Arctic sea ice relative to constant IOPs, up to ~3 W/m2, but decreased solar radiation penetrating into the ocean, up to ~5–6 W/m2. The changes in the dividing of shortwave fluxes in sea ice and the ocean below it induced by the variable IOPs have significant influence on Arctic sea ice thickness by modulating surface and bottom melting and frazil ice formation (increasing surface melting by ~16% and reducing bottom melting by ~11% in summer).

Wave attenuation by three-dimensional circular floating sea ice: Regular and irregular waves

The evolution of ice conditions and wave patterns in polar water areas is complex, and studying the hydrodynamic response between sea ice and waves is crucial. In this study, the hydrodynamic response of sea ice in infinitely deep water is investigated by a sea ice scattering model based on linear potential flow and thin plate approximation theory. Building upon regular wave and sea ice scattering models, an irregular wave model was established and analyzed. This paper discusses the factors that influence sea ice resonance in a three-dimensional scattering model. It categorizes resonance phenomena into strong and weak based on mass and elasticity coefficients and provides a predictive function for identifying strong resonance points. Additionally, the study examines sea ice attenuation from an energy perspective. Research on sea ice in irregular waves indicates a negative correlation between the directional spreading parameter and wave attenuation. An increase in directional spreading parameter weakens the inhibitory effect on the high-frequency part of the waves. Increasing sea ice thickness enhances wave attenuation, exerting an inhibitory effect on waves in the high-frequency region.

Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC

AbstractModels struggle to accurately simulate observed sea ice thickness changes, which could be partially due to inadequate representation of thermodynamic processes. We analyzed co‐located winter observations of the Arctic sea ice from the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate for evaluating and improving thermodynamic processes in sea ice models, aiming to enable more accurate predictions of the warming climate system. We model the sea ice and snow heat conduction for observed transects forced by realistic boundary conditions to understand the impact of the non‐resolved meter‐scale snow and sea ice thickness heterogeneity on horizontal heat conduction. Neglecting horizontal processes causes underestimating the conductive heat flux of 10% or more. Furthermore, comparing model results to independent temperature observations reveals a ∼5 K surface temperature overestimation over ice thinner than 1 m, attributed to shortcomings in parameterizing surface turbulent and radiative fluxes rather than the conduction. Assessing the model deficiencies and parameterizing these unresolved processes is required for improved sea ice representation.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

Multisensor data fusion of operational sea ice observations

Multisensor data fusion (MDF) is a process/technique of combining observations from multiple sensors to provide a more robust, accurate and complete description of the concerned object, environment or process. In this paper we introduce a new MDF method, multisensor optimal data fusion (MODF), to fuse different operational sea ice observations around Svalbard. The overall MODF includes regridding, univariate multisensor optimal data merging (MODM), multivariate check of consistency, and generation of new variables. For MODF of operational sea ice observations around Svalbard, the AMSR2 sea ice concentration (SIC) is firstly merged with the Norwegian Meteorological Institute ice chart. Then the daily SMOS sea ice thickness (SIT) is merged with the weekly CS2SMOS SIT to form a daily CS2SMOS SIT, which is further refined to be consistent with the SIC through consistency check. Finally sea ice volume (SIV) and its uncertainty are calculated based on the merged SIC and fused SIT. The fused products provide an improved, united, consistent and multifaceted description for the operational sea ice observations, they also provide consistent descriptions of sea ice edge and marginal ice zone. We note that uncertainties may vary during the regridding process, and therefore correct determination of the observation uncertainties is critically important for MDF. This study provides a basic framework for managing multivariate multisensor observations.

Predictability of the Minimum Sea Ice Extent from Winter Fram Strait Ice Area Export: Model versus Observations

Abstract Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.

Monitoring climate change in the marine Arctic

Changes in the temperature regime of the marine Arctic and the influencing factors are considered based on the current knowledge of the causes of climate change and the use of new data sources. In the Arctic, the warming is developing due to such factors as the increase in the transfer of heat and moisture from the low latitudes. This, in turn, drives the feedbacks in the Arctic climate system, increasing the flow of long-wave radiation to the surface due to rising atmospheric water vapor concentrations and slowing down the growth of the sea ice thickness in winter. The increase in the atmospheric heat transfer to the Arctic is associated with changes in atmospheric circulation, in particular, under the influence of ocean surface temperature anomalies, especially in the low latitudes since the bulk of the heat influx from the from solar radiation and anthropogenic forcing is accumulated here. Analyzing the causes of warming in the Arctic in the 1930s and 40s led researchers to the conclusion that the water influx from the North Atlantic is a factor to consider. Therefore, the influx of warm and salty water is also an important influence on the formation of the climate of the marine Arctic today, which should be taken into account when monitoring the temperature and ice regime of this area. Based on the analysis of the characteristics of climate variability in the marine Arctic and its causes, the article examines representative indicators of climate change in the temperature and ice regime of the marine Arctic and the factors influencing them in the present period.

Retrieval of sea ice thickness from FY-3E data using Random Forest method

In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT) using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method relies on four input parameters: incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). In addition, FY-3E can receive both Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) reflected signals. Evaluation for the Arctic region based on data spanning from October 2022 to April 2023 reveals that the proposed models trained on GPS and BDS signals from FY-3E achieve high consistency and low error. Take GPS signals as an example, coefficients of determination are 0.97 and 0.91 and mean absolute errors are 0.019 m and 0.032 m for the training and test sets, respectively. In general, SIT inversion based on GPS signals slightly exhibits a higher accuracy than that based on BDS signals, but both approaches display high performances. The areas with the highest accuracy of SIT estimation based on GPS and BDS signals are the Shelikhov Bay and the Okhotsk Sea, followed by the Bering Sea and the Bering Strait. We conclude that machine learning and data fusion are effective for SIT estimation.

Influence of New Parameterization Schemes on Arctic Sea Ice Simulation

Two coupled climate models that participated in the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0), and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3) were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air–ice heat flux, radiation penetration and absorption, melt ponds, and ice–ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis products from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of the Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes in spatial patterns of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have the potential for broad application in climate models for simulation and future climate scenario projection, especially for those with underestimated SIT.

An Improved NLCS Algorithm Based on Series Reversion and Elliptical Model Using Geosynchronous Spaceborne–Airborne UHF UWB Bistatic SAR for Oceanic Scene Imaging

Geosynchronous spaceborne–airborne (GEO-SA) ultra-high-frequency ultra-wideband bistatic synthetic aperture radar (UHF UWB BiSAR) provides high-precision images for marine and polar environments, which are pivotal in glacier monitoring and sea ice thickness measurement for polar ocean mapping and navigation. Contrasting with traditional high-frequency BiSAR, it faces unique challenges, such as the considerable spatial variability, significant range–azimuth coupling, and vast volumes of echo data, which impede high-resolution image reconstruction. This paper presents an improved bistatic nonlinear chirp scaling (NLCS) algorithm for imaging oceanic scenes with GEO-SA UHF UWB BiSAR. This methodology extends the two-dimensional (2-D) spectrum up to the sixth order via the method of series reversion (MSR) to meet accuracy demands and then employs an elliptical model to elucidate the alterations in the azimuth frequency modulation (FM) rate mismatch. Initially, the imaging geometry and signal model are introduced, and then a separation of bistatic slant ranges based on the configuration is proposed. In addition, during range processing, after eliminating linear range cell migration (RCM), the derivation process for the sixth-order 2-D spectrum is detailed and an improved filter is applied to correct the high-order RCM. Finally, during azimuth processing, the causes of the FM rate mismatch are analyzed, a cubic perturbation function derived from the elliptical model is used for FM rate equalization, and a unified sixth-order filter is applied to complete the azimuth compression. Experimental results with point targets and natural oceanic scenes validate the outstanding efficacy of the proposed NLCS algorithm, particularly in imaging quality enhancements for GEO-SA UHF UWB BiSAR.

Результаты определения профиля высот геоида и уклонения отвесной линии по сигналам ГНСС, отраженным от водной поверхности

The method for determining geoid elevations, based on the use of GNSS signals reflected from water surface, is called bistatic one. Its features are small dimensions and weight of the necessary on-board equipment, and that enables mounting them on a nano-satellite, as well as simultaneously determining several dozen geoid height profiles. It corresponds to the number of visible navigation satellites from among GLONASS, GPS, Galileo, BeiDou. Several space projects using the technology under description were implemented abroad, their analysis has shown the possibility of determining geoid heights with an error of 10–20 cm, as well as sea ice thickness. The authors provide a review of some similar projects, an assessment of the simultaneously determined number of geoid height profiles, and calculation of its elevations and deflection of vertical along a single profile based on primary measurements from the project of CYGNSS. To compensate for various interfering parameters when calculating geoid elevations, the following models were used

Limited increases in Arctic offshore oil and gas production with climate change and the implications for energy markets

Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial–temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a “fossil-fueled development” scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8–2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.

CD-type discretization for sea ice dynamics in FESOM version 2

Abstract. Two recently proposed variants of CD-type discretizations of sea ice dynamics on triangular meshes are implemented in the Finite-VolumE Sea ice–Ocean Model (FESOM version 2). The implementations use the finite element method in spherical geometry with longitude–latitude coordinates. Both are based on the edge-based sea ice velocity vectors but differ in the basis functions used to represent the velocities. The first one uses nonconforming linear (Crouzeix–Raviart) basis functions, and the second one uses continuous linear basis functions on sub-triangles obtained by splitting parent triangles into four smaller triangles. Test simulations are run to show how the performance of the new discretizations compares with the A-grid discretization using linear basis functions. Both CD discretizations are found to simulate a finer structure of linear kinematic features (LKFs). Both show some sensitivity to the representation of scalar fields (sea ice concentration and thickness). Cell-based scalars lead to a finer LKF structure for the first CD discretization, but the vertex-based scalars may be advantageous in the second case.