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During polar winter, refreezing of exposed ocean areas results in the rejection of brine, i.e., salt-enriched plumes of water, a source of available potential energy that can drive ocean instabilities. As this process is highly localized, and driven by sea ice physics, not gradients in oceanic or atmospheric buoyancy, it is not currently captured in modern climate models. This study aims to understand the energetics and lateral transfer of density at a semi-infinite, instantaneously-opened and continuously re-freezing sea ice edge through a series of high resolution model experiments. We show that kilometer-scale submesoscale eddies grow from baroclinic instabilities via an inverse energy cascade. These eddies meander along the ice edge and propagate laterally. The lateral transfer of buoyancy by eddies is not explained by existing theories. We isolate the fundamental forcing-independent quantities driving lateral mixing, and discuss the implications for the overall strength of submesoscale activity in the Arctic Ocean.

 

Abstract. 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 are 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. 

Abstract. In sea-ice-covered areas, the sea ice floe size distribution (FSD) plays an important role in many processes affecting the coupled sea–ice–ocean–atmosphere system. Observations of the FSD are sparse – traditionally taken via a painstaking analysis of ice surface photography – and the seasonal and inter-annual evolution of floe size regionally and globally is largely unknown. Frequently, measured FSDs are assessed using a single number, the scaling exponent of the closest power-law fit to the observed floe size data, although in the absence of adequate datasets there have been limited tests of this “power-law hypothesis”. Here we derive and explain a mathematical technique for deriving statistics of the sea ice FSD from polar-orbiting altimeters, satellites with sub-daily return times to polar regions with high along-track resolutions. Applied to the CryoSat-2 radar altimetric record, covering the period from 2010 to 2018, and incorporating 11 million individual floe samples, we produce the first pan-Arctic climatology and seasonal cycle of sea ice floe size statistics. We then perform the first pan-Arctic test of the power-law hypothesis, finding limited support in the range of floe sizes typically analyzed in photographic observational studies. We compare the seasonal variability in observed floe size to fully coupled climate model simulations including a prognostic floe size and thickness distribution and coupled wave model, finding good agreement in regions where modeled ocean surface waves cause sea ice fracture. 

Recent field programs have highlighted the importance of the composite nature of the sea ice mosaic to the climate system. Accordingly, we previously developed a process‐based prognostic model that captures key characteristics of the sea ice floe size distribution and its evolution subject to melting, freezing, new ice formation, welding, and fracture by ocean surface waves. Here we build upon this earlier work, demonstrating a new coupling between the sea ice model and ocean surface waves and a new physically based parameterization for new ice formation in open water. The experiments presented here are the first to include two‐way interactions between prognostically evolving waves and sea ice on a global domain. The simulated area‐average floe perimeter has a similar magnitude to existing observations in the Arctic and exhibits plausible spatial variability. During the melt season, wave fracture is the dominant FSD process driving changes in floe perimeter per unit sea ice area—the quantity that determines the concentration change due to lateral melt—highlighting the importance of wave‐ice interactions for marginal ice zone thermodynamics. We additionally interpret the results to target spatial scales and processes for which floe size observations can most effectively improve model fidelity.

 

We have derived an analytic form of the thickness redistribution function, Ψ, and compressive strength of sea ice using variational principles. By using the technique of coarse‐graining vertical sea ice deformation, or ridging, in the momentum equation of the pack, we isolate frictional energy loss from potential energy gain in the collision of floes. The method accounts for macroporosity of ridge rubble,ϕ_R, and by including this in the state space of the pack, we expand the sea ice thickness distribution,g(h), to a bivariate distribution,g(h,ϕ_R). The effect of macroporosity is for the first time included in the large‐scale mass conservation and momentum equations of frozen oceans. We make assumptions that have simplified the problem, such as treating sea ice as a granular material in ridges, and assuming that bending moments associated with ridging are perturbations around an isostatic state. Regardless of these simplifications, the coarse‐grained ridge model is highly predictive of macroporosity and ridge shape. By ensuring that vertical sea ice deformation observes a variational principle both at the scale of individual ridges and over the pack as a whole, we can predict distributions of ridge shapes using equations that can be solved in Earth system models. Our method also offers the possibility of more accurate derivations of sea ice thickness from ice freeboard measured by space‐borne altimeters over polar oceans.