Sea Ice Motion Research Articles

Exploring the Use of Fengyun-3 Meteorological Satellite for Monitoring Sea Ice to Provide Services for Polar Navigation

This paper explores the feasibility and effectiveness of using the Fengyun-3 meteorological satellite to monitor sea ice, providing services for ships navigating in polar regions. Firstly, it analyzes the impact of Arctic sea ice changes on ship navigation and the importance of sea ice monitoring in route planning. Next, it provides a detailed introduction to the data sources and processing methods of the Fengyun-3 satellite, including radiometric calibration, geometric correction, image registration, and cropping. Subsequently, it discusses the characteristics of sea ice in the visible spectrum and successfully extracts sea ice information using MERSI-II data with land, cloud, and seawater masking techniques. The study indicates that the comprehensive use of multi-spectral data and other observation methods can significantly enhance sea ice monitoring capabilities. In the future, integrating more advanced technologies is expected to achieve refined identification and short-term prediction of sea ice movement, thereby providing more scientific and efficient support for ships navigating in polar regions, enhancing navigation safety and efficiency, and offering a scientific basis for the development of Arctic shipping routes.

Capacity of a set of CMIP6 models to simulate Arctic sea ice drift

Abstract Evaluating CMIP6 model performance helps to improve the prediction of future changes in Arctic sea ice. We analyze the seasonal cycles, distribution, and evolution of sea ice in different regions from 1979 to 2014. We compare the output from selected CMIP6 models with reference data for sea ice motion. We also discuss the correlations between sea ice motion(SIM) and sea ice thickness (SIT) in reference data, and how CMIP6 models explain them. We select EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, MPI-ESM1-2-HR, and NorESM2-LM for CMIP6 study. We compare outputs with reference data: Sea ice extent (SIE) from NSIDC; SIT from PIOMAS; and SIM from the IABP buoy data. Analytical techniques include Theil-Sen and Ordinary least squares (OLS) regression. Most selected CMIP6 models have seasonal cycles of SIM lagging behind IABP observations by 1-2 month and overestimate central Arctic SIM magnitude, with MPI-ESM1-2-HR having the highest discrepancy and NorESM2-LM lowest. The models show better simulation of SIM in the ice melting season than in the growing season. Models perform worse at capturing regional differences in SIM evolution and are overly conservative when simulating the increasing trend in ice motion, especially in coastal Arctic seas during summer. There is significant negative correlation between SIT and SIM in October.

Analysis of Dynamic Changes in Sea Ice Concentration in Northeast Passage during Navigation Period

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage.

Thermodynamic and Dynamic Variations in Sea Ice Thickness of the Ross Sea, Antarctica, Driven by Atmospheric Circulation

AbstractAtmospheric circulation has significant impacts on sea ice drifting patterns and mass balance, as wind drag induces pressure ridges and leads on the sea ice surface. In this study, the spatiotemporal distributions of these dynamic sea ice deformation features in the Ross Sea are examined using ICESat‐2 (IS2) ATL10 freeboard data (2019–2022). The temporal variation of the modal sea ice thickness (SIT), caused by thermodynamic ice growth and sea ice advection, varies from 0.7–1.0 m in April to 1.0–1.6 m in July–September and decreases thereafter in the northwest (NW) and northeast (NE) sectors. This temporal variation of modal SIT agrees with the air temperature (correlation coefficients >0.5). The southwest (SW) sector shows a consistently low modal SIT (<1.0 m) because of the production of new ice in polynyas and continuous northward sea ice drift. Meanwhile, the southeast (SE) sector shows the thickest ice in Octobers 2019 and 2020 because of the advection of thick ice from the Amundsen Sea, which was reduced in 2021 and 2022. In terms of dynamic sea ice deformation, the SE sector shows the largest deformation because of the wind‐driven convergence of sea ice movement. However, such intense deformation in the SE sector diminished in 2021 and 2022 due to the dominance of strong southerly wind associated with the Amundsen Sea Low (ASL). This study emphasizes the potential of IS2 sea ice products to assess the role of atmospheric driving forces on thermodynamic and dynamic sea ice changes.

Enhanced interannual variability of the May North Atlantic Oscillation and its impact on summer sea ice in the North Atlantic after the mid-2000s

Based on data diagnosis and numerical experiments, this study investigated the changes in the interannual properties of the May North Atlantic Oscillation (NAO) and their impact on summer (June–July) sea ice in the North Atlantic during 1979–2021. Results showed statistically significant increase in the interannual variability of the May NAO after the mid-2000s, which had remarkably enhanced impact on summer sea ice in the eastern Hudson Bay (EHB) and the western Labrador Sea (WLS). During 2005–2021, corresponding to a positive phase of the May NAO, anomalous surface westerly or northwesterly winds prevailed over the Hudson Bay and Labrador Sea in May. This led to statistically significant increase in sea ice in both the EHB and the WLS in May via dynamic processes (favoring southeastward movement of the sea ice) and thermal processes (changing surface turbulent heating and shortwave radiation). In comparison with the situation in May, the increase in sea ice in the EHB developed further during summer mainly via thermal processes (positive feedback between the increased sea ice and shortwave radiation). In contrast, amplitude of the increased sea ice in the WLS was comparable between May and summer. Dynamic processes (southeastward movement of sea ice), which was induced by a barotropic anomalous high in the troposphere centered over the Labrador Peninsula, favored the increase in sea ice in summer in the WLS. The tripole sea surface temperature anomalies in the North Atlantic and increased snowpack on the Labrador Peninsula in May, triggered by the positive phase of the May NAO, played an important role in the formation of the anomalous high. During 1979–2004, the surface wind, snowpack, and tripole sea surface temperature anomalies in May, triggered by the May NAO, were relatively weak, leading to statistically insignificant changes in summer sea ice in the EHB and WLS.

Influence of the Atlantic and Pacific Multidecadal Variability on Arctic Sea Ice in Pacemaker Simulations during 1920–2013

Abstract The Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) can influence Arctic sea ice and modulate its trend, but to what extent the AMV and PMV can affect Arctic sea ice and which processes are dominant are not well understood. Here, we analyze the Community Earth System Model, version 1, idealized and time-varying pacemaker ensemble simulations to investigate these issues. These experiments show that the sea ice concentration varies mainly over the marginal Arctic Ocean, while the sea ice thickness variations occur over the entire Arctic Ocean. The internal components of AMV and PMV can enhance or weaken the decadal sea ice loss rates over the marginal Arctic Ocean by more than 50%. The AMV- or PMV-induced anomalous atmospheric energy transport and downward longwave radiation related to low clouds (thermodynamical processes) and sea ice motion (dynamical processes) contribute to the Arctic surface air temperature and sea ice concentration and thickness changes. Anomalous oceanic heat flux is mainly a response to rather than a cause of sea ice variations. The dynamic processes contribute to the winter Arctic sea ice variations as much as the thermodynamic processes, but they contribute less (more) to the summer Arctic sea ice variability than the thermodynamic processes over the marginal Arctic Ocean (parts of the central Arctic Ocean). Sea ice loss enhances air–sea heat fluxes, which cause oceanic heat convergence and warm near-surface air and the lower troposphere, which in turn melt more sea ice.

Bathymetry-constrained ocean geostrophic currents play a key role in shaping the sea ice circulation in the Canada Basin, Arctic Ocean

Abstract Satellite-based observations and a pan-Arctic coupled sea ice-ocean model are utilized to study the effect of ocean geostrophic currents on large-scale sea ice circulation in the Canada Basin, Arctic Ocean. We find that surface winds primarily drive sea ice drifts in the west-east direction, while the geostrophic currents in the Beaufort Gyre promote north-south ice drifts. Wind fluctuations can create variable ice drifts, yet geostrophic currents respond more slowly due to their larger vertical scale, serving as a slowly-evolving conveyor belt for maintaining the anticyclonic ice circulation. It is further demonstrated that the bathymetry can regulate the movement of sea ice via constraining the expansion of ocean circulation. This mechanism is indirect in the sense that the ice is far from the seafloor. Our research underscores the necessity of considering the bathymetry-constrained geostrophic currents in understanding Arctic sea ice dynamics. With the rapid retreat of Arctic sea ice, the multi-scale interactions between ice drifts and ocean currents may have significant implications for the Arctic ecosystem, climate, and shipping corridors.

Ships are projected to navigate whole year-round along the North Sea route by 2100

In the context of global warming, the Arctic sea ice is rapidly receding, and the possibility of navigating the Arctic shipping route continues to increase. Here we combine climate and ship navigability data and use low and medium emission scenarios to analyze sea ice conditions and navigability from 2023 to 2100, focusing on Polar Class 7 and open water vessels. The results show that sea ice motion deteriorates the navigability. Polar Class 7 ships will be able to sail the Arctic passages throughout all seasons except for the spring of 2065, while there is a comparative advantage of the Northern Sea Route for open-water ships. The optimal shipping routes of Polar Class 7 ships from 2065 to 2100 are more distributed toward the Central Arctic. These could considerably reshape the patterns of global shipping networks and international trade among Asia, Europe, and America.

An assessment of the CMIP6 performance in simulating Arctic sea ice volume flux via Fram Strait

Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change. In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.

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.

A large-scale high-resolution numerical model for sea-ice fragmentation dynamics

Abstract. Forecasts of sea-ice motion and fragmentation are of vital importance for all human interactions with sea ice, ranging from those involving indigenous hunters to shipping in polar regions. Sea-ice models are also important for simulating long-term changes in a warming climate. Here, we apply the Helsinki Discrete Element Model (HiDEM), originally developed for glacier calving, to sea-ice breakup and dynamics. The code is highly optimized to utilize high-end supercomputers to achieve an extreme time and space resolution. Simulated fracture patterns and ice motion are compared with satellite images of the Kvarken region of the Baltic Sea from March 2018. A second application of HiDEM involves ice ridge formation in the Gulf of Riga. With a few tens of graphics processing units (GPUs), the code is capable of reproducing observed ice patterns that in nature may take a few days to form; this is done over an area of ∼100km×100km, with an 8 m resolution, in computations lasting ∼10 h. The simulations largely reproduce observed fracture patterns, ice motion, fast-ice regions, floe size distributions, and ridge patterns. The similarities and differences between observed and computed ice dynamics and their relation to initial conditions, boundary conditions, and applied driving forces are discussed in detail. The results reported here indicate that the HiDEM has the potential to be developed into a detailed high-resolution model for sea-ice dynamics at short timescales, which, when combined with large-scale and long-term continuum models, may form an efficient framework for forecasts of sea-ice dynamics.

Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery

This study proposes a dual-branch encoder U-Net (DBU-Net) deep learning model to classify sea ice types based on synthetic aperture radar (SAR) images in the Beaufort Sea. The DBU-Net can segment multi-year ice (MYI), first-year ice (FYI), open water (OW), and leads on SAR images. We design a dual-branch encoder to fuse the polarization and the grey-level co-occurrence matrix (GLCM) information of SAR images to improve the model's classification capability. The model is subsequently fine-tuned using lead samples to identify leads. 24 Sentinel-1 SAR images acquired in the Beaufort Sea are utilized for model training and testing. The accuracy (Acc), mean intersection over union (mIoU), and kappa coefficient (Kappa) are employed as evaluation metrics. Experiments show that DBU-Net achieves 91.83%/0.841/0.849 in Acc/mIoU/Kappa in classifying MYI, FYI, and OW, significantly outperforming three traditional models based on support vector machine, random forest, or convolutional neural network. Compared with the original U-Net, the dual-branch encoder and the GLCMs improve 1.45%/4.4%/2.8% in Acc/mIoU/Kappa in MYI, FYI, and OW. Acc/mIoU/Kappa metrics of leads detection is 99.49%/0.801/0.754. Besides, 454 Sentinel-1 SAR images are fed into the optimal DBU-Net to generate 80 m sea ice products in the Beaufort Sea for winters 2018–2022. As the MYI draws wide attention and the FYI and MYI are complementary in the area during the Winter, we discuss the variation of MYI based on the generated sea ice products and explore the relationship between MYI's variation and the Beaufort High. We found that the MYI export in the 2018/19 winter was due to large summer sea ice remains and the abnormal sea ice motion caused by the southeast shifting Beaufort Atmospheric Pressure High (Beaufort High). The MYI import in the 2020/21 winter was due to a strong northward MYI import caused by the powerful Beaufort High.

Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics

Arctic sea ice leads are linear fractures in pack ice, which provide narrow windows for the enhanced exchange of mass, energy, and momentum between the atmosphere and ocean. However, the parameterisations of the sub-grid (< 1 km) lead distribution and its associated dynamic processes are still challenging for numerical sea ice modelling. This study explores the dynamics governing lead formation in the Arctic Ocean using multiple data sources including Sentinel-1 synthetic aperture radar (SAR) images, buoy array, and atmospheric reanalysis. Random forest (RF) models trained based on the polarisation and texture features of SAR images were compared, and an optimal RF model sensitive to narrow leads was selected with implementation of screening based on lead geometry. As a case study, the lead distribution along the trajectory of buoy array in the central Arctic Ocean during the freezing period of 2018–2019 was obtained. An enhanced lead opening was observed as the sea ice motion swerved from clockwise circulation following the Beaufort Gyre (BG) to meridional advection when assembled into the Transpolar Drift (TPD). Synoptically, active lead-opening events associated with ice divergence were driven by episodic cyclones. We noticed a dramatic change in the backscatter signal over the leads caused by the growth of thin ice and formation of frost flowers. The identification and the evolution of narrow leads during the freezing season given in this study are conducive to increasing our understanding of the response mechanism of sea ice to atmospheric dynamic processes and supporting the numerical simulation of leads.

An improved current retrieval method for ice-edge eddies from multi-sensor remote sensing image sequences

ABSTRACT Eddies in the Marginal Ice Zones (MIZ) contribute to an enhanced melting of sea ice by forcing contact between the sea ice and the warmer water off the ice edge. The horizontal current field can be retrieved by tracking the movement of ice floes between sequential satellite images. However, the traditional method of current retrieval imposes stringent requirements on the time intervals between sequential images, and the performance of the traditional method diminishes rapidly with increasing time intervals. In this paper, an improved method of retrieving currents from multi-sensor remote sensing images is proposed, which can significantly improve the accuracy of current retrieval. The method first extracts feature from remote sensing images based on texture features and a support vector machine model and then extracts prior information about the ice-edge eddy from the first two remote sensing images. Under the guidance of prior information, horizontal currents of the eddy are retrieved by tracking the movement of sea ice from sequential remote sensing images. The proposed method was used to retrieve currents from Sentinel-1 and Sentinel-2 sequential images over the MIZ of the Fram Strait. Comparisons with the traditional method show encouraging results that the proposed method can significantly reduce the number of mismatches and improve the reliability of current vectors.

Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model

Abstract. 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.

Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)

Abstract. 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.

Why is summertime Arctic sea ice drift speed projected to decrease?

Abstract. Alongside declining Arctic sea ice cover during the satellite era, there have also been positive trends in sea ice Arctic average drift speed (AADS) during both winter and summer. This increasing sea ice motion is an important consideration for marine transportation as well as a potential feedback on the rate of sea ice area decline. Earlier studies have shown that nearly all modern global climate models (GCMs) produce positive March (winter) AADS trends for both the historical period and future warming scenarios. However, most GCMs do not produce positive September (summer) AADS trends during the historical period, and nearly all GCMs project decreases in September AADS with future warming. This study seeks to understand the mechanisms driving these projected summertime AADS decreases using output from 17 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) along with 10 runs of the Community Earth System Model version 2 Large Ensemble (CESM2-LE). The CESM2-LE analysis reveals that the projected summertime AADS decreases are due to changes in sea surface height (SSH) and wind stress which act to reduce sea ice motion in the Beaufort Gyre and Transpolar Drift. During March, changes in internal stress and wind stress counteract tilt force changes and produce positive drift speed trends. The simulated wintertime mechanisms are supported by earlier observational studies, which gives confidence that the mechanisms driving summertime projections are likely also at work in the real world. However, the precise strength of these mechanisms is likely not realistic during summer, and additional research is needed to assess whether the simulated summertime internal stress changes are too weak compared to changes in other forces. The projected summertime wind stress changes are associated with reduced sea level pressure north of Greenland, which is expected with the northward shift of the jet streams. The projected summertime SSH changes are primarily due to freshening of the Arctic Ocean (i.e., halosteric expansion), with thermal expansion acting as a secondary contribution. The associated ocean circulation changes lead to additional piling up of water in the Russian shelf regions, which further reinforces the SSH increase. Analysis of CMIP6 output provides preliminary evidence that some combination of wind stress and SSH changes is also responsible for projected AADS decreases in other models, but more work is needed to assess mechanisms in more detail. Altogether, our results motivate additional studies to understand the roles of SSH and wind stress in driving changes in Arctic sea ice motion.

Improved Arctic sea-ice motion in summer from the brightness temperature of the AMSR2 36GHz channel

ABSTRACT Information on Arctic sea-ice motion in summer is critical for operational sea-ice monitoring and prediction and commercial navigation. Affected by the melting of the sea-ice surface, the accuracy and spatial coverage of the sea-ice velocity (SIV) retrieved by brightness temperature (TB) during the melting period are always lower than in winter. Our research indicates that the lack of ice–water discrimination in summer for the Advanced Microwave Scanning Radiometer 2 (AMSR2) 36 GHz TB is the main reason for the poor quality of its SIV results. We propose a new SIV retrieval scheme based on the polarization difference (P) of the 36 GHz TB. The 36-P SIV has a higher spatial coverage than that derived from 18-H, 36-H and 36-V SIVs and a higher maximum cross-correlation coefficient than 36 GH TB-derived SIVs. Compared with buoy and Synthetic Aperture Radar SIVs, the 36-P SIV’s error is close to that of 18-H SIV and significantly lower than that of 36 TB SIV. Therefore, our results indicate that 36-P is more competitive than the 36 GHz TB in SIV retrieval in summer. Further analysis shows that our proposed scheme is compatible with the two current mainstream SIV tracking algorithms, demonstrating its great potential for future summer SIV retrieval.

Estimating Winter Arctic Sea Ice Motion Based on Random Forest Models

Sea ice motion (SIM) plays a crucial role in setting the distribution of the ice cover in the Arctic. Limited by images’ spatial resolution and tracking algorithms, challenges exist in obtaining coastal sea ice motion (SIM) based on passive microwave satellite sensors. In this study, we developed a method based on random forest (RF) models to obtain Arctic SIM in winter by incorporating wind field and coastal geographic location information. These random forest models were trained using Synthetic Aperture Radar (SAR) SIM data. Our results show good consistency with SIM data retrieved from satellite imagery and buoy observations. With respect to the SAR data, compared with SIM estimated with RF model training using reanalysis surface wind, the results by additional coastal information input had a lower root mean square error (RMSE) and a higher correlation coefficient by 31% and 14% relative improvement, respectively. The latter SIM result also showed a better performance for magnitude, especially within 100 km of the coastline in the north of the Canadian Arctic Archipelago. In addition, the influence of coastline on SIM is quantified through variable importance calculation, at 22% and 28% importance of all RF variables for east and north SIM components, respectively. These results indicate the great potential of RF models for estimating SIM over the whole Arctic Ocean in winter.

Changes in Beaufort High and Their Impact on Sea Ice Motion in the Western Arctic during the Winters of 2001–2020s

Sea ice affects the Earth’s energy balance and ocean circulation and is crucial to the global climate system. However, research on the decadal variations in the mean sea-level pressure patterns in recent winters (2001–2020) and the characteristics of sea ice motion (SIM) in the Western Arctic region is very limited. In this study, we utilized the Empirical Orthogonal Function (EOF) analysis method to investigate the potential impacts of Arctic Oscillation (AO) and Arctic Dipole (AD) on the Beaufort High (BH) during the period 2001–2020 and discuss the changes in SIM intensity in the Western Arctic. The results indicate that the negative phases of AO and AD are connected with (tend to bring about) a higher BH, strengthening anticyclonic circulation in the Arctic region. Conversely, the positive phases of AO and AD led to the collapse of the BH, resulting in a reversal of sea ice movement. Additionally, during the period 2001–2020, the BH consistently explained 67% of the sea ice motion (had the highest explanatory degree for sea ice advection within the region (weighted average 61.71%)). Meanwhile, the sea ice advection has become more sensitive to change in various atmospheric circulations. This study contributes to an in-depth understanding of the response of sea ice motion to atmospheric circulation in the Western Arctic in recent years, offering more explanations for the anomalous movement of sea ice in the Western Arctic.