Eddies in the Marginal Ice Zone of Fram Strait and Svalbard from Spaceborne SAR Observations in Winter

Here we investigate the intensity of eddy generation and their properties in the marginal ice zone (MIZ) regions of Fram Strait and around Svalbard using spaceborne synthetic aperture radar (SAR) data from Envisat ASAR and Sentinel-1 in winter 2007 and 2018. Analysis of 2039 SAR images allowed identifying 4619 eddy signatures. The number of eddies detected per image per kilometer of MIZ length is similar for both years. Submesoscale and small mesoscale eddies dominate with cyclones detected twice more frequently than anticyclones. Eddy diameters range from 1 to 68 km with mean values of 6 km and 12 km over shallow and deep water, respectively. Mean eddy size grows with increasing ice concentration in the MIZ, yet most eddies are detected at the ice edge and where the ice concentration is below 20%. The fraction of sea ice trapped in cyclones (53%) is slightly higher than that in anticyclones (48%). The amount of sea ice trapped by a single ‘mean’ eddy is about 40 km2, while the average horizontal retreat of the ice edge due to eddy-induced ice melt is about 0.2–0.5 km·d–1 ± 0.02 km·d–1. Relation of eddy occurrence to background currents and winds is also discussed.

Variability in spring phytoplankton blooms associated with ice retreat timing in the Pacific Arctic from 2003–2019

The Arctic is experiencing rapid changes in sea-ice seasonality and extent, with significant consequences for primary production. With the importance of accurate monitoring of spring phytoplankton dynamics in a changing Arctic, this study further examines the previously established critical relationship between spring phytoplankton bloom types and timing of the sea-ice retreat for broader temporal and spatial coverages, with a particular focus on the Pacific Arctic for 2003–2019. To this end, time-series of satellite-retrieved phytoplankton biomass were modeled using a parametric Gaussian function, as an effective approach to capture the development and decay of phytoplankton blooms. Our sensitivity analysis demonstrated accurate estimates of timing and presence/absence of peaks in phytoplankton biomass even with some missing values, suggesting the parametric Gaussian function is a powerful tool for capturing the development and decay of phytoplankton blooms. Based on the timing and presence/absence of a peak in phytoplankton biomass and following the classification developed by the previous exploratory work, spring bloom types are classified into three groups (under-ice blooms, probable under-ice blooms, and marginal ice zone blooms). Our results showed that the proportion of under-ice blooms was higher in the Chukchi Sea than in the Bering Sea. The probable under-ice blooms registered as the dominant bloom types in a wide area of the Pacific Arctic, whereas the marginal ice zone bloom was a relatively minor bloom type across the Pacific Arctic. Associated with a shift of sea-ice retreat timing toward earlier dates, we confirmed previous findings from the Chukchi Sea of recent shifts in phytoplankton bloom types from under-ice blooms to marginal ice zone blooms and demonstrated that this pattern holds for the broader Pacific Arctic sector for the time period 2003–2019. Overall, the present study provided additional evidence of the changing sea-ice retreat timing that can drive variations in phytoplankton bloom dynamics, which contributes to addressing the detection and consistent monitoring of the biophysical responses to the changing environments in the Pacific Arctic.

Altimetric observation of wave attenuation through the Antarctic marginal ice zone using ICESat-2

The Antarctic marginal ice zone (MIZ) is a highly dynamic region where sea ice interacts with ocean surface waves generated in ice-free areas of the Southern Ocean. Improved large-scale (satellite-based) estimates of MIZ width and variability are crucial for understanding atmosphere-ice-ocean interactions and biological processes, and detection of change therein. Legacy methods for defining the MIZ width are typically based on sea ice concentration thresholds, and do not directly relate to the fundamental physical processes driving MIZ variability. To address this, new techniques have been developed to determine MIZ width based on the detection of waves and calculation of significant wave height attenuation from variations in ICESat-2 surface heights. The poleward MIZ limit (boundary) is defined as the location where significant wave height attenuation equals the estimated satellite height error. Extensive automated and manual acceptance/rejection criteria are employed to ensure confidence in MIZ width estimates, due to significant cloud contamination of ICESat-2 data or where wave attenuation was not observed. Analysis of 304 MIZ width estimates retrieved from four months of 2019 (February, May, September and December) revealed that sea ice concentration-derived MIZ width estimates were far narrower (by a factor of ~7) than those from the new techniques presented here. These results suggest that indirect methods of MIZ estimation based on sea ice concentration are insufficient for representing physical processes that define the MIZ. Improved measurements of MIZ width based on wave attenuation will play an important role in increasing our understanding of this complex sea ice zone.

Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Knowledge of transport in the marginal ice zone (MIZ) is critical for operations in the Arctic and associated emergency response applications, for example, the transport of pollutants, such as oil, as well as predicting drift associated with search and rescue operations. This paper proposes a general transport equation for the MIZ that can be used for operational purposes in the MIZ. This equation is designed to use a mean velocity of the ice and water velocity, which is weighted by the ice concentration. A key component is the introduction of a leeway coefficient for both the ocean and ice components. These leeway coefficients are determined by minimizing the velocity error between the transport model and observed drifter velocity in the MIZ. These leeway values are found to be 3 % of the wind for the water leeway and 2 % and 30° to the right of the wind for the ice leeway, which are consistent with "rule of thumb" values for surface drifters and sea ice respectively. This general transport model is compared with other transport models and the error is reduced by a factor of 2 compared with traditional transport models for 48 hour lead times. The inclusion of a leeway coefficient in the ice is the key component to reduce trajectory errors in the MIZ.

Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice floes is identified using a mathematical morphology method. Hausdorff distance is used to measure the similarity of segmented ice floes. It is tolerant to perturbations and deficiencies of floe shapes, which enhances the density of retrieved sea ice motion vectors. The PHD algorithm can be applied to sequential images from different sensors, and was tested on two combined image mosaics consisting of Sentinel-1 and Sentinel-2 data acquired over the Fram Strait; the PHD algorithm successfully produced pairs of matched ice floes. The matching result has been verified using shape and surface texture similarity of the ice floes. Moreover, the present method can naturally be extended to the problem of multi-source sea ice image registration.

A statistical definition of the Antarctic marginal ice zone

The marginal ice zone (MIZ) is a transitional region between the open ocean and pack ice. This region is circumpolar in the Antarctic, with different sea ice types depending on the season and the sector of the Southern Ocean. The MIZ extent have traditionally been inferred from satellite-derived sea-ice concentration (SIC, one of the essential climate variables), using the 15–80 % range as indicative of sea ice with MIZ characteristics. This proxy has been proven effective in the Arctic, where there is a good correspondence between sea-ice type and sea-ice cover. It is less reliable in the Southern Ocean, where sea-ice type is less linked to the concentration value, since wave penetration and free drift conditions have been reported with 100 % cover. I propose an alternative definition of the MIZ that is based on statistical properties of the SIC and its spatial and temporal variability. The indicator is derived from the standard deviation of daily SIC anomalies, which is often employed in the climate sciences. The use of a monthly climatological mean as the baseline allows to capture changes due to both the seasonal advancement/retreat and the local weather-driven variability typical of less consolidated sea-ice conditions. This method has been tested on the available climate data records to derive maps of the MIZ distribution over the year. It reconciles the discordant seasonal extent estimates using the SIC threshold, which is now independent of the used algorithm. This indicator also allows to derive the climatological probability of exceeding a certain threshold of SIC variability, which can be used for ship navigation, design of observational networks and for testing the skills of sea-ice models in forecasting or climate mode.

Eddy generation and variability of the marginal ice zone in the Fram Strait according to satellite radar measurements

Based on analysis of spaceborne synthetic aperture data (SAR), acquired in summer of 2007 over Fram Strait and around Svalbard, we investigate spatial and temporal variability of the ice edge and generation of eddies in the marginal ice zone. During the season, the ice-water boundary nonuniformly moves along its entire length with the overall width of the ice edge displacement ranging from 30 to 220 km. The ice edge movement is often accompanied by generation of eddies and filaments peaking in August. Analysis of the data serves to find out over 2000 distinct MIZ eddies with a clear dominance of cyclones (78%). In July the detected eddies are predominantly formed along the ice edge, in August most of them are generated inside the MIZ, while in September their numbers along the ice edge and within the MIZ are similar. Larger eddies (10-20 km in diameter) are found over deep Fram Strait and the Greenland Sea shelf, while smaller eddies (~5 km) are observed in coastal regions around Svalbard.

WIFF1.0: A hybrid machine-learning-based parameterization of Wave-Induced sea-ice Floe Fracture

Ocean surface waves play an important role in maintaining the marginal ice zone, a heterogenous region occupied by sea ice floes with variable horizontal sizes. The location, width, and evolution of the marginal ice zone is determined by the mutual interaction of ocean waves and floes, as waves propagate into the ice, bend it, and fracture it. In previous work, we developed a one-dimensional “superparameterized” scheme to simulate the interaction between the stochastic ocean surface wave field and sea ice. As this method is computationally expensive and not bitwise reproducible, here we use a pair of neural networks to accelerate this parameterization, delivering an adaptable, computationally-inexpensive, reproducible approach for simulating stochastic wave-ice interactions. Implemented in the sea ice model CICE, this accelerated code reproduces global statistics resulting from the full wave fracture code without increasing computational overheads. The combined model, Wave-Induced Floe Fracture (WIFF v1.0) is publicly available and may be incorporated into climate models that seek to represent the effect of waves fracturing sea ice.