Sea Ice Deformation Research Articles

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.

On the sensitivity of sea ice deformation statistics to plastic damage

Abstract. We implement a plastic damage parametrization, distinct from the elastic damage in the elasto-brittle framework, in the standard viscous–plastic (VP) sea ice model to disentangle its effect from resolved model physics (visco-plastic with and without damage) on its ability to reproduce observed scaling laws of deformation. To this end, we compare scaling properties and multifractality of simulated divergence and shear strain rate, as proposed in the Sea Ice Rheology Experiment (SIREx) studies, with those derived from the RADARSAT Geophysical Processor System (RGPS). Results show that including a plastic damage parametrization in the standard viscous–plastic model increases the spatial but decreases the temporal localization of simulated linear kinematic features (LKFs) and brings all spatial deformation rate statistics in line with observations from RGPS without the need to increase the mechanical shear strength of sea ice as recently proposed for lower-resolution viscous–plastic sea ice models. In fact, including damage with a healing timescale of th=30 d and an increased mechanical strength unveils multifractal behavior that does not fit the theory. Therefore, a plastic damage parametrization is a powerful tuning knob affecting the deformation statistics of viscous–plastic sea ice.

ПРИМЕНЕНИЕ СВЕРТОЧНОЙ НЕЙРОННОЙ СЕТИ ДЛЯ ОБНАРУЖЕНИЯ РАЗВОДИЙ В МОРЕ ЛАПТЕВЫХ ПО СНИМКАМ СПУТНИКА “LANDSAT-8

A method for detecting leads in the ice of the Arctic seas from satellite images of the visible range is presented. It is shown that sea ice leads are formed under the influence of dynamic processes in the ice cover, such as convergence, drift, and deformation of sea ice, as well as during the interaction of drifting ice with icebergs that have gone aground. The method for identifying sea ice leads is based on the use of artificial intelligence. To analyze the Landsat-8 satellite imagery, a convolutional neural network (U-Net architecture) was used. The method was tested using the satellite images of the visible spectral range that were obtained for the Laptev Sea. The results showed that the lead detection accuracy was above 80%. The method of the minimum rotated rectangle surrounding the polygon was used to determine the geometric parameters of the leads (length, width, inflection points).

Evaluation of Antarctic sea ice thickness and volume during 2003–2014 in CMIP6 using Envisat and CryoSat-2 observations

Sea ice thickness (SIT), which is a crucial and sensitive indicator of climate change in the Antarctic, has a substantial impact on atmosphere-sea-ice-ocean interactions. Despite the slight thinning in SIT and reduction in sea ice volume (SIV) in the Antarctic in the recent decade, challenges remain in quantifying their changes, primarily because of the limited availability of high-quality long-term observational data. Therefore, it is crucial to accurately simulate Antarctic SIT and to assess the SIT simulation capability of state-of-the-art climate models. In this study, we evaluated historical simulations of SIT by 51 climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Envisat (ES) and CryoSat-2 (CS2) observations. Results revealed that most models can capture the seasonal cycles in SIV and that the CMIP6 multimodel mean (MMM) can reproduce the increasing and decreasing trends in the SIV anomaly based on ES and CS2 data, although the magnitudes of the trends in the SIV anomaly are underestimated. Additionally, the intermodel spread in simulations of SIT and SIV was found to be reduced (by 43%) from CMIP5 to CMIP6. Nevertheless, based on the CMIP6 MMM, substantial underestimations in SIV of 57.52% and 59.66% were found compared to those derived from ES and CS2 observations, respectively. The most notable underestimation in SIT was located in the sea ice deformation zone surrounding the northwestern Weddell Sea, coastal areas of the Bellingshausen and Amundsen seas, and the eastern Ross Sea. The substantial bias in the simulated SIT might result from deficiencies in simulating critical physical processes such as ocean heat transport, dynamic sea ice processes, and sea ice-ocean interactions. Therefore, increasing the model resolution and improving the representation of sea ice dynamics and the physical processes controlling sea ice-ocean interactions are essential for improving the accuracy of Antarctic sea ice simulation.

Towards improving short-term sea ice predictability using deformation observations

Abstract. Short-term sea ice predictability is challenging despite recent advancements in sea ice modelling and new observations of sea ice deformation that capture small-scale features (open leads and ridges) at the kilometre scale. A new method for assimilation of satellite-derived sea ice deformation into numerical sea ice models is presented. Ice deformation provided by the Copernicus Marine Service is computed from sea ice drift derived from synthetic aperture radar at a high spatio-temporal resolution. We show that high values of ice deformation can be interpreted as reduced ice concentration or increased ice damage – i.e. scalar variables responsible for ice strength in brittle or visco-plastic sea ice dynamical models. This method is tested as a proof of concept with the neXt-generation Sea Ice Model (neXtSIM), where the assimilation scheme uses a data insertion approach and forecasting with one member. We obtain statistics of assimilation impact over a long test period with many realisations starting from different initial times. Assimilation and forecasting experiments are run on synthetic and real observations in January 2021 and show increased accuracy of deformation prediction for the first 3–4 d. Similar conclusions are obtained using both brittle and visco-plastic rheologies implemented in neXtSIM. Thus, the forecasts improve due to the update of sea ice mechanical properties rather than the exact rheological formulation. It is demonstrated that the assimilated information can be extrapolated in space – gaps in spatially discontinuous satellite observations of deformation are filled with a realistic pattern of ice cracks, confirmed by later satellite observations. The limitations and usefulness of the proposed assimilation approach are discussed in a context of ensemble forecasts. Pathways to estimate intrinsic predictability of sea ice deformation are proposed.

Deformation lines in Arctic sea ice: intersection angle distribution and mechanical properties

Abstract. Despite its relevance for the Arctic climate and ecosystem, modeling sea-ice deformation, i.e., the opening, shearing, and ridging of sea ice, along linear kinematic features (LKFs) remains challenging, as the mechanical properties of sea ice are not yet fully understood. The intersection angles between LKFs provide valuable information on the internal mechanical properties, as they are linked to them. Currently, the LKFs emerging from sea-ice rheological models do not reproduce the observed LKF intersection angles, pointing to a gap in the model physics. We aim to obtain an intersection angle distribution (IAD) from observational data to serve as a reference for high-resolution sea-ice models and to infer the mechanical properties of the sea-ice cover. We use the sea-ice vorticity to discriminate between acute and obtuse LKF intersection angles within two sea-ice deformation datasets: the RADARSAT Geophysical Processor System (RGPS) and a new dataset from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment. Acute angles dominate the IAD, with single peaks at 48∘±2 and 45∘±7. The IAD agrees well between both datasets, despite the difference in scale, time period, and geographical location. The divergence and shear rates of the LKFs also have the same distribution. The dilatancy angle (the ratio of shear and divergence) is not correlated with the intersection angle. Using the IAD, we infer two important mechanical properties of the sea ice: we found an internal angle of friction in sea ice of μI=0.66±0.02 and μI=0.75±0.05. The shape of the yield curve or the plastic potential derived from the observed IAD resembles a teardrop or a Mohr–Coulomb shape. With these new insights, sea-ice rheologies used in models can be adapted or redesigned to improve the representation of sea-ice deformation.

Disintegration and Buttressing Effect of the Landfast Sea Ice in the Larsen B Embayment, Antarctic Peninsula

AbstractThe speed‐up of glaciers following ice shelf collapse can accelerate ice mass loss dramatically. Investigating the deformation of landfast sea ice enables studying its resistive (buttressing) stresses and mechanisms driving ice collapse. Here, we apply offset tracking to Sentinel‐1A/B synthetic aperture radar data to obtain a 2014–2022 time‐series of horizontal velocity and strain rate fields of landfast ice filling the embayment formerly covered by the Larsen B Ice Shelf, Antarctic Peninsula until 2002. The landfast ice disintegrated in 2022, and we find that it was precipitated by a few large opening rifts. Grounded glaciers did not accelerate instantaneously after the collapse, which implies little buttressing effect from landfast ice, a conclusion also supported by the near‐zero correlation between glacier velocity and landfast ice area. Our observations suggest that buttressing stresses are unlikely to be recovered by landfast sea ice over sub‐decadal timescales following the collapse of an ice shelf.

A quasi-objective single-buoy approach for understanding Lagrangian coherent structures and sea ice dynamics

Abstract. Sea ice drift and deformation, namely sea ice dynamics, play a significant role in atmosphere–ice–ocean coupling. Deformation patterns in sea ice can be observed over a wide range of spatial and temporal scales, though high-resolution objective quantification of these features remains difficult. In an effort to better understand local deformation of sea ice, we adapt the trajectory-stretching exponents (TSEs), quasi-objective measures of Lagrangian stretching in continuous media, to sea ice buoy data and develop a temporal analysis of TSE time series. Our work expands on previous ocean current studies that have shown TSEs provide an approximation of Lagrangian coherent structure diagnostics when only sparse trajectory data are available. As TSEs do not require multiple buoys, we find they have an expanded range of use when compared with traditional Eulerian buoy-array deformation metrics and provide local-stretching information below the length scales possible when averaging over buoy arrays. We verify the ability of TSEs to temporally and spatially identify dynamic features for three different sea ice datasets. The ability of TSEs to quantify trajectory stretching is verified by concurrent ice fracture in buoy neighborhoods ranging from tens to hundreds of kilometers in diameter, as well as the temporal concurrence of significant storm events.

Distinct Role of a Spring Atmospheric Circulation Mode in the Arctic Sea Ice Decline in Summer

AbstractA distinguishing feature of Arctic sea ice is the rapid declining in the Pacific Arctic Sector (PAS) in recent years. The atmospheric variability during spring (March‐May) plays a critical role in determining the sea ice extent (SIE) during the following summer. The summertime (June‐August) SIE reduction in the eastern part of PAS, including the East Siberian Sea and the Laptev Sea, is strongly related to the first two leading atmospheric circulation modes in spring, the Arctic Oscillation and the Arctic Dipole, retrieved from empirical orthogonal function analysis. Here, we show that the third leading mode of atmospheric circulation in spring, namely the Barents‐Beaufort Oscillation (BBO), can also change the summer SIE, especially on the western regime of the PAS across the Beaufort and Chukchi Seas. We find that a positive phase of springtime BBO (i.e., BBO+), relative to the BBO−, acts to decrease sea ice thickness via advection and generate sea ice deformation through divergence and shear processes, thereby preconditioning for a rapid sea ice loss within the PAS in the following summer. Furthermore, the occurrence of BBO is state‐dependent and potentially modulated by the phase shift of the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). Since the mid‐1990s, the AMO (PDO) index has changed from a dominant negative (positive) to a positive (negative) phase. These phase shifts in AMO and PDO would have contributed to the subsequently increased appearance of anomalous BBO+ after 2000 and enhanced the summertime sea ice loss in the western regions of the PAS in recent years.

Causes and evolution of winter polynyas north of Greenland

Abstract. During the 42-year period (1979–2020) of satellite measurements, four major winter (December–March) polynyas have been observed north of Greenland: one in December 1986 and three in the last decade, i.e., February of 2011, 2017, and 2018. The 2018 polynya was unparalleled in its magnitude and duration compared to the three previous events. Given the apparent recent increase in the occurrence of these extreme events, this study aims to examine their evolution and causality, in terms of forced versus natural variability. The limited weather station and remotely sensed sea ice data are analyzed combining with output from the fully coupled Regional Arctic System Model (RASM), including one hindcast and two ensemble simulations. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact (i.e., no significant ice melting) on the evolution of the observed winter open-water episodes in the region. Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss in all four polynyas. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds greater than 10 m s−1) blowing persistently over the study region for at least 2 d or more were required over the study region to mechanically redistribute some of the thickest Arctic sea ice out of the region and thus to create open-water areas (i.e., a latent heat polynya). To assess the role of internal variability versus external forcing of such events, we carried out and examined results from the two RASM ensembles dynamically downscaled with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles (initialized 30 years apart: one in December 1985 and another in December 2015), 17 and 16 winter polynyas were produced north of Greenland, respectively. The frequency of polynya occurrence had no apparent sensitivity to the initial sea ice thickness in the study area pointing to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations, and probabilistic forecasts of Arctic events, such as the ones being investigated here and elsewhere.

North Pole ice-resistant self-propelled platform as an innovative complex for research in the Arctic

The Russian presence in the Arctic Region and the development of the Arctic is one of the most important geopolitical interests of Russia. At the beginning of the 21st century, the problem of using innovative methods in Arctic research came to the fore. Based on the analysis of the unique work experience of drifting stations “Severniy Polus” (“North Pole”) (1937-2015), Arctic and Antarctic Research Institute specialists have concluded that the stations should be replaced by a modern scientific complex, capable of solving a wider range of problems. As a result, it was proposed to create an ice-resistant self-propelled platform (IRSPP) – an engineering structure for permanent basing of scientific observatories. The IRSPP is designed to conduct year-round comprehensive scientific research in the high latitudes of the Arctic Ocean and should make a long drift of at least one year together with the surrounding ice massive. The scientific complex of the IRSPP includes 16 different laboratories, including an ice load monitoring laboratory. A unique ice load monitoring system (ILMS) has been developed for the IRSPP. The ILMS will be an essential part of the system of safe operation and persistence of the IRSPP and will provide the platform hull with a measurement tool for studying the mechanics of deformation and destruction of sea ice in its interaction with the engineering structures. In all respects, the IRSPP is unparalleled in the world. Its use can open a new chapter in the exploration of the Russian Arctic and in international collaboration aimed at studying the northern latitudes. The platform was put into operation in 2022.

Thermodynamical and Dynamical Impacts of an Intense Cyclone on Arctic Sea Ice

AbstractThis study investigates the thermodynamical and dynamical influences of the intense cyclone in the Arctic Ocean in August 2012 on the synoptic‐scale sea ice evolution, using the Arctic Ice Ocean Prediction System (ArcIOPS). As it is hard to fully isolate sea ice loss owing to extreme cyclone from that owing to background atmospheric state in most previous studies on this topic, this study introduces a newly developed algorithm to remove the cyclone component in atmospheric forcing, and conducts sea ice and heat budget analyses in two simulations driven by atmospheric forcing with and without the cyclone. Strong impact of the intense cyclone on sea ice locates on the east side of the cyclone's path, that is, Pacific Arctic for this case. The cyclone affects sea ice in two ways. First, cyclone‐induced enhancement in ice‐ocean interaction leads to increased sea ice basal melt in the Pacific Arctic and part of the Atlantic Arctic, which induces strong sea ice area and volume loss when the cyclone's intensity peaks. Second, as the cyclone strengthens, the increases in air temperature, humidity and wind speed accelerate turbulent heat exchange at the air‐ocean and air‐ice interfaces, leading to enhanced local sea ice surface melt in the Chukchi Sea and northern Beaufort Sea. The cyclone‐induced strong winds stir sea ice leading to enhanced gradients in sea ice velocity field and thus increased sea ice deformation, which further induces strong sea ice area loss. This study also demonstrates a precise atmospheric forcing field is essential for sea ice modeling.

Determining Variability in Arctic Sea Ice Pressure Ridge Topography With ICESat‐2

AbstractWe investigate the characteristics and distribution of pressure ridges in Arctic sea ice using a novel algorithm applied to ICESat‐2 surface heights. We derive the frequency and height of individual pressure ridges and map surface roughness and ridging intensity at the basin scale over three winters between 2019 and 2021. Comparisons with near‐coincident airborne lidar data show that not only can we detect individual ridges 5.6 m wide, but also measure sail height more accurately than the existing ICESat‐2 sea ice height product. We find large regional variability in ridge morphology not only related to parent ice type but also geographic location. Ridge sails are best represented by log‐normal distributions while surface roughness is well fit by an exponential normal function. Our results reveal that high‐resolution satellite altimetry is valuable for characterizing sea ice deformation at short length‐scales and delivers observations that will advance ridging parameterizations in sea ice models.

Observations of Stress‐Strain in Drifting Sea Ice at Floe Scale

AbstractThe mechanical deformation of sea ice has substantial influence over large‐scale (e.g., >10 km) ice properties, such as the ice thickness distribution, as well as small‐scale (e.g., <50 m) features, including leads and ridges. The conditions leading to sea ice fracture are frequently studied in the context of a uniform ice sheet. Natural sea ice, however, is highly heterogeneous and riddled with flaws. Failure occurs primarily as brittle fracture localized in space and time where stresses, and strain rates, locally exceed failure criteria. Here we seek to better understand the mechanical deformation and fracture of sea ice under such typical field conditions. In particular, we aim to characterize how forces propagate across an approximately 1 km2 heterogeneous domain by observing the stress‐strain field in an ice floe at resolutions required to capture pre‐fracture elastic strains. The combination of instruments deployed allow a detailed view of the formation, propagation, parting, and subsequent shearing of a fracture in natural sea ice, providing field evidence of modes of failure in compressive shear. The ability of this system to capture strain concentration zones and to detect initial fracture hours prior to lead formation indicates the potential for predicting areas at high risk for fracture in an on‐ice operational setting.

Arctic sea ice anomalies during the MOSAiC winter 2019/20

Abstract. During the winter of 2019/2020, as the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project started its work, the Arctic Oscillation (AO) experienced some of its largest shifts, ranging from a highly negative index in November 2019 to an extremely positive index during January–February–March (JFM) 2020. The permanent positive AO phase for the 3 months of JFM 2020 was accompanied by a prevailing positive phase of the Arctic Dipole (AD) pattern. Here we analyze the sea ice thickness (SIT) distribution based on CryoSat-2/SMOS satellite-derived data augmented with results from the hindcast simulation by the fully coupled Regional Arctic System Model (RASM) from November 2019 through March 2020. A notable result of the positive AO phase during JFM 2020 was large SIT anomalies of up to 1.3 m that emerged in the Barents Sea (BS), along the northeastern Canadian coast and in parts of the central Arctic Ocean. These anomalies appear to be driven by nonlinear interactions between thermodynamic and dynamic processes. In particular, in the Barents and Kara seas (BKS), they are a result of enhanced ice growth connected with low-temperature anomalies and the consequence of intensified atmospherically driven sea ice transport and deformations (i.e., ice divergence and shear) in this area. The Davies Strait, the east coast of Greenland and the BS regions are characterized by convergence and divergence changes connected with thinner sea ice at the ice borders along with an enhanced impact of atmospheric wind forcing. Low-pressure anomalies that developed over the eastern Arctic during JFM 2020 increased northerly winds from the cold Arctic Ocean to the BS and accelerated the southward drift of the MOSAiC ice floe. The satellite-derived and simulated sea ice velocity anomalies, which compared well during JFM 2020, indicate a strong acceleration of the Transpolar Drift relative to the mean for the past decade, with intensified speeds of up to 6 km d−1. As a consequence, sea ice transport and deformations driven by atmospheric surface wind forcing accounted for the bulk of the SIT anomalies, especially in January 2020 and February 2020. RASM intra-annual ensemble forecast simulations with 30 ensemble members forced with different atmospheric boundary conditions from 1 November 2019 through 30 April 2020 show a pronounced internal variability in the sea ice volume, driven by thermodynamic ice-growth and ice-melt processes and the impact of dynamic surface winds on sea ice formation and deformation. A comparison of the respective SIT distributions and turbulent heat fluxes during the positive AO phase in JFM 2020 and the negative AO phase in JFM 2010 corroborates the conclusion that winter sea ice conditions in the Arctic Ocean can be significantly altered by AO variability.

Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity

Abstract. Wide-swath C-band synthetic aperture radar (SAR) has been used for sea ice classification and estimates of sea ice drift and deformation since it first became widely available in the 1990s. Here, we examine the potential to distinguish surface features created by sea ice deformation using ice type classification of SAR data. Also, we investigate the cross-platform transferability between training sets derived from Sentinel-1 Extra Wide (S1 EW) and RADARSAT-2 (RS2) ScanSAR Wide A (SCWA) and fine quad-polarimetric (FQ) data, as the same radiometrically calibrated backscatter coefficients are expected from the two C-band sensors. We use a novel sea ice classification method developed based on Arctic-wide S1 EW training, which considers per-ice-type incident angle (IA) dependency of backscatter intensity. This study focuses on the region near Fram Strait north of Svalbard to utilize expert knowledge of ice conditions during the Norwegian young sea ICE (N-ICE2015) expedition. Manually drawn polygons of different ice types for S1 EW, RS2 SCWA and RS2 FQ data are used to retrain the classifier. Different training sets yield similar classification results and IA slopes, with the exception of leads with calm open water, nilas or newly formed ice (the “leads” class). This is caused by different noise floor configurations of S1 and RS2 data, which interact differently with leads, necessitating dataset-specific retraining for this class. SAR scenes are then classified based on the classifier retrained for each dataset, with the classification scheme altered to separate level from deformed ice to enable direct comparison with independently derived sea ice deformation maps. The comparisons show that the classification of C-band SAR can be used to distinguish areas of ice divergence occupied by leads, young ice and level first-year ice (LFYI). However, it has limited capacity in delineating areas of ice deformation due to ambiguities between ice types with higher backscatter intensities. This study provides reference to future studies seeking cross-platform application of training sets so they are fully utilized, and we expect further development of the classifier and the inclusion of other SAR datasets to enable image-classification-based ice deformation detection using only satellite SAR.

An Assessment of Sea Ice Motion Products in the Robeson Channel Using Daily Sentinel-1 Images

Sea ice motion is an essential parameter when determining sea ice deformation, regional advection, and the outflow of ice from the Arctic Ocean. The Robeson Channel, which is located between Ellesmere Island and northwest Greenland, is a narrow but crucial channel for ice outflow. Only three Eulerian sea ice motion products derived from ocean/sea ice reanalysis are available: GLORYS12V1, PSY4V3, and TOPAZ4. In this study, we used Lagrangian ice motion in the Robeson Channel derived from Sentinel-1 images to assess GLORYS12V1, PSY4V3, and TOPAZ4. The influence of the presence of ice arches, and wind and tidal forcing on the accuracies of the reanalysis products was also investigated. The results show that the PSY4V3 product performs the best as it underestimates the motion the least, whereas TOPAZ4 grossly underestimates the motion. This is particularly true in regimes of free drift after the formation of the northern arch. In areas with slow ice motion or grounded ice floes, the GLORYS12V1 and TOPAZ4 products offer a better estimation. The spatial distribution of the deviation between the products and ice floe drift is also presented and shows a better agreement in the Robeson Channel compared to the packed ice regime north of the Robeson Channel.

Numerical simulation for cutoff draft of sea ice ridge keels based on a novel optimal modeling with nonlinear-statistical constraints

<abstract> <p>Optimal identification and numerical models are powerful tools that have been widely used in geoscience research for many years. In this study, we proposed a novel optimal method to simulate a key parameter (cutoff draft) of the ridge keels due to dynamic deformation of sea ice at bottom. The sea ice ridges were measured in the Northwestern Weddell Sea of Antarctic, by a helicopter-borne electromagnetic-induction (EM) system. An optimal model with nonlinear-statistical constraints was developed, by taking deviations between the theoretical and measured keel draft (spacing) distributions as the performance criterion, and cutoff draft as the identified parameter. The properties of the optimal model and the existence of the optimal parameter were demonstrated. We identified that the optimal cutoff draft was 3.78 m via an optimal numerical algorithm, this value was then employed to separate the ridge keels from the ice bottom. Finally, the relationship between the mean keel draft and frequency (number of keels per km) was analyzed, and the result showed that this relationship was modeled well by a logarithmic function with a correlation coefficient of 0.7. The present optimal modeling method will provide a new theoretical reference for separating accurately the ridge keels from undeformed sea ice bottom, and analyzing the relationship between the morphologies of sea ice surface and bottom and the inversions of sea ice bottom draft and ice thickness by the surface height.</p> </abstract>

InSAR Monitoring of Arctic Landfast Sea Ice Deformation Using L-Band ALOS-2, C-Band Radarsat-2 and Sentinel-1

Arctic amplification is accelerating changes in sea ice regimes in the Canadian Arctic with later freeze-up and earlier melt events, adversely affecting Arctic wildlife and communities that depend on the stability of sea ice conditions. To monitor both the rate and impact of such change, there is a need to accurately measure sea ice deformation, an important component for understanding ice motion and polar climate. The objective of this study is to determine the spatial-temporal pattern of deformation over landfast ice in the Arctic using time series SAR imagery. We present Interferometric Synthetic Aperture Radar (InSAR) monitoring of Arctic landfast sea ice deformation using C-band Radarsat-2, Sentinel-1 and L-band ALOS-2 in this paper. The small baseline subset (SBAS) approach was explored to process time series observations for retrieval of temporal deformation changes along a line-of-sight direction (LOS) over the winter. It was found that temporal and spatial patterns of deformation observed from different sensors were generally consistent. Horizontal and vertical deformations were also retrieved by a multi-dimensional SBAS technique using both ascending and descending Sentinel-1 observations. Results showed a horizontal deformation in the range of −95–85 cm, and vertical deformation in the range of −41–63 cm in Cambridge Bay, Nunavut, Canada during February-April 2019. High coherence over ice from C-band was maintained over a shorter time interval of acquisitions than L-band due to temporal decorrelation.

A baseline evaluation of oceanographic and sea ice conditions in the Hudson Bay Complex during 2016–2018

In this paper, we examine sea surface temperatures (SSTs) and sea ice conditions in the Hudson Bay Complex as a baseline evaluation for the BaySys 2016–2018 field program time frame. Investigated in particular are spatiotemporal patterns in SST and sea ice state and dynamics, with rankings of the latter to highlight extreme conditions relative to the examined 1981–2010 climatology. Results from this study show that SSTs in northwestern Hudson Bay from May to July, 2016–2018, are high relative to the climatology for SST (1982–2010). SSTs are also warmer in 2016 and 2017 than in 2018 relative to their climatology. Similarly, unusually low sea ice cover existed from August to December of 2016 and July to September of 2017, while unusually high sea ice cover existed in January, February, and October of 2018. The ice-free season was approximately 20 days longer in 2016 than in 2018. Unusually high ice-drift speeds occurred in April of 2016 and 2017 and in May of 2018, coinciding with strong winds in 2016 and 2018 and following strong winds in March 2017. Strong meridional circulation was observed in spring of 2016 and winter of 2017, while weak meridional circulation existed in 2018. In a case study of an extreme event, a blizzard from 7 to 9 March 2017, evaluated using Lagrangian dispersion statistics, is shown to have suppressed sea ice deformation off the coast of Churchill. These results are relevant to describing and planning for possible future pathways and scenarios under continued climate change and river regulation.