Stress and deformation characteristics of sea ice in a high-resolution, anisotropic sea ice model

The drift and deformation of sea ice floating on the polar oceans is caused by the applied wind and ocean currents. Over ocean basin length scales the internal stresses and boundary conditions of the sea ice pack result in observable deformation patterns. Cracks and leads can be observed in satellite images and within the velocity fields generated from floe tracking. In a climate sea ice model the deformation of sea ice over ocean basin length scales is modelled using a rheology that represents the relationship between stresses and deformation within the sea ice cover. Here we investigate the link between emergent deformation characteristics and the underlying internal sea ice stresses using the Los Alamos numerical sea ice climate model. We have developed an idealized square domain, focusing on the role of sea ice rheologies in producing deformation at spatial resolutions of up to 500 m. We use the elastic anisotropic plastic (EAP) and elastic viscous plastic (EVP) rheologies, comparing their stability, with the EAP rheology producing sharper deformation features than EVP at all space and time resolutions. Sea ice within the domain is forced by idealized winds, allowing for the emergence of five distinct deformation types. Two for a low confinement ratio: convergent and expansive stresses. Two about a critical confinement ratio: isotropic and anisotropic conditions. One for a high confinement ratio and isotropic sea ice. Using the EAP rheology and through the modification of initial conditions and forcing, we show the emergence of the power law of strain rate, in accordance with observations.This article is part of the theme issue 'Modelling of sea-ice phenomena'

Cryosat-2 significant wave height in polar oceans derived using a semi-analytical model of synthetic aperture radar 2011-2019

This paper documents the retrieval of significant ocean surface wave heights in the Arctic Ocean from CryoSat-2 data. We use a semi-analytical model for an idealised synthetic aperture satellite radar or pulse-limited radar altimeter echo power. We develop a processing methodology that specifically considers both the Synthetic Aperture and Pulse Limited modes of the radar that change close to the sea ice edge within the Arctic Ocean. All CryoSat-2 echoes to date were matched by our idealised echo revealing wave heights over the period 2011–2019. Our retrieved data were contrasted to existing processing of CryoSat-2 data and wave model data, showing the improved fidelity and accuracy of the semi-analytical echo power model and the newly developed processing methods. We contrasted our data to in situ wave buoy measurements, showing improved data retrievals in seasonal sea ice covered seas. We have shown the importance of directly considering the correct satellite mode of operation in the Arctic Ocean where SAR is the dominant operating mode. Our new data are of specific use for wave model validation close to the sea ice edge and is available at the link in the data availability statement

Summer extreme cyclone impacts on arctic sea ice

In this study the impact of extreme cyclones on Arctic sea ice in summer is investigated. Examined in particular are relative thermodynamic and dynamic contributions to sea ice volume budgets in the vicinity of Arctic summer cyclones in 2012 and 2016. Results from this investigation illustrate that sea ice loss in the vicinity of the cyclone trajectories during each year was associated with different dominant processes: thermodynamic processes (melting) in the Pacific sector of the Arctic in 2012, and both thermodynamic and dynamic processes in the Pacific sector of the Arctic in 2016. Comparison of both years further suggests that the Arctic minimum sea ice extent is influenced by not only the strength of the cyclone, but also by the timing and location relative to the sea ice edge. Located near the sea ice edge in early August in 2012, and over the central Arctic later in August in 2016, extreme cyclones contributed to comparable sea ice area (SIA) loss, yet enhanced sea ice volume loss in 2012 relative to 2016. Central to a characterization of extreme cyclone impacts on Arctic sea ice from the perspective of thermodynamic and dynamic processes, we present an index describing relative thermodynamic and dynamic contributions to sea ice volume changes. This index helps to quantify and improve our understanding of initial sea ice state and dynamical responses to cyclones in a rapidly warming Arctic, with implications for seasonal ice forecasting, marine navigation, coastal community infrastructure, and designation of protected and ecologically sensitive marine zones

A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional ‘winter’ processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness

Phased response of the subpolar Southern Ocean to changes in circumpolar winds

The response of the subpolar Southern Ocean (sSO) to wind forcing is assessed using satellite radar altimetry. sSO sea level exhibits a phased, zonally coherent, bimodal adjustment to circumpolar wind changes, involving comparable seasonal and interannual variations. The adjustment is effected via a quasi-instantaneous exchange of mass between the Antarctic continental shelf and the sSO to the north, and a 2-month-delayed transfer of mass between the wider Southern Ocean and the subtropics. Both adjustment modes are consistent with an Ekman-mediated response to variations in surface stress. Only the fast mode projects significantly onto the surface geostrophic flow of the sSO; thus, the regional circulation varies in phase with the leading edge of sSO sea level variability. The surface forcing of changes in the sSO system is partly associated with variations of surface winds linked to the Southern Annular Mode and is modulated by sea ice cover near Antarctica

Animated version of figure 8 from Stress and deformation characteristics of sea ice in a high-resolution, anisotropic sea ice model

Animation of 72 hours of winds with a confinemnt ratio of -0.4. The winds rotated about the centre of the grid by 45 degrees every 12 hours

A year-round satellite sea-ice thickness record from CryoSat-2

Arctic sea ice is diminishing with climate warming1 at a rate unmatched for at least 1,000 years2. As the receding ice pack raises commercial interest in the Arctic3, it has become more variable and mobile4, which increases safety risks to maritime users5. Satellite observations of sea-ice thickness are currently unavailable during the crucial melt period from May to September, when they would be most valuable for applications such as seasonal forecasting6, owing to major challenges in the processing of altimetry data7. Here we use deep learning and numerical simulations of the CryoSat-2 radar altimeter response to overcome these challenges and generate a pan-Arctic sea-ice thickness dataset for the Arctic melt period. CryoSat-2 observations capture the spatial and the temporal patterns of ice melting rates recorded by independent sensors and match the time series of sea-ice volume modelled by the Pan-Arctic Ice Ocean Modelling and Assimilation System reanalysis8. Between 2011 and 2020, Arctic sea-ice thickness was 1.87 ± 0.10 m at the start of the melting season in May and 0.82 ± 0.11 m by the end of the melting season in August. Our year-round sea-ice thickness record unlocks opportunities for understanding Arctic climate feedbacks on different timescales. For instance, sea-ice volume observations from the early summer may extend the lead time of skilful August-October sea-ice forecasts by several months, at the peak of the Arctic shipping season

A year-round satellite sea-ice thickness record from CryoSat-2

Arctic sea ice is diminishing with climate warming1 at a rate unmatched for at least 1,000 years2. As the receding ice pack raises commercial interest in the Arctic3, it has become more variable and mobile4, which increases safety risks to maritime users5. Satellite observations of sea-ice thickness are currently unavailable during the crucial melt period from May to September, when they would be most valuable for applications such as seasonal forecasting6, owing to major challenges in the processing of altimetry data7. Here we use deep learning and numerical simulations of the CryoSat-2 radar altimeter response to overcome these challenges and generate a pan-Arctic sea-ice thickness dataset for the Arctic melt period. CryoSat-2 observations capture the spatial and the temporal patterns of ice melting rates recorded by independent sensors and match the time series of sea-ice volume modelled by the Pan-Arctic Ice Ocean Modelling and Assimilation System reanalysis8. Between 2011 and 2020, Arctic sea-ice thickness was 1.87 ± 0.10 m at the start of the melting season in May and 0.82 ± 0.11 m by the end of the melting season in August. Our year-round sea-ice thickness record unlocks opportunities for understanding Arctic climate feedbacks on different timescales. For instance, sea-ice volume observations from the early summer may extend the lead time of skilful August–October sea-ice forecasts by several months, at the peak of the Arctic shipping season

Modelling Sea Ice and Melt Ponds Evolution: Sensitivity to Microscale Heat Transfer Mechanisms

We present a mathematical model describing the evolution of sea ice and meltwater during summer. The system is described by two coupled partial differential equations for the ice thickness h and pond depth w fields. We test the sensitivity of the model to variations of parameters controlling fluid-dynamic processes at the pond level, namely the variation of turbulent heat flux with pond depth and the lateral melting of ice enclosing a pond. We observe that different heat flux scalings determine different rates of total surface ablations, while the system is relatively robust in terms of probability distributions of pond surface areas. Finally, we study pond morphology in terms of fractal dimensions, showing that the role of lateral melting is minor, whereas there is evidence of an impact from the initial sea ice topography