The Arctic Ocean double estuary is a "three-legged" overturning system in which inflowing waters are converted into both lighter and denser waters before being exported equatorwards. As the northern terminus of the Atlantic Meridional Overturning Circulation (MOC), it thus both affects, and is affected by, the Atlantic MOC. Here we quantify the magnitudes of the two overturning cells in density space, and then decompose the water mass transformation rates into net pan-Arctic contributions from surface forcing and diapycnal mixing. We use a high-resolution, quasi-synoptic ice and ocean hydrographic data set spanning the four main Arctic Ocean gateways-Fram, Davis and Bering Straits, and the Barents Sea Opening. Two surface flux reanalyses and a hydrographic climatology are used to generate estimates of surface water mass transformation rates by density class. A box model then determines the profiles of turbulent mixing transformation rates, and associated turbulent diffusivities. We show that turbulent mixing and surface forcing drive transformations of similar magnitudes, while mixing dominates in the upper cell and surface fluxes in the lower cell. Consideration of uncertainties and timescales leads to the tentative suggestion that our results might be representative of recent decades. We discuss the possible significance of tides and sea ice brine rejection as energy sources driving turbulent mixing. Finally, we speculate as to whether water mass transformation rates may change in future as ocean heat transport into the Arctic increases. As sea ice declines and the efficiency of atmosphere-to-ocean momentum transfer increases, the Arctic Ocean is expected to "spin up," causing more intense turbulent mixing, with uncertain consequences.

Sea ice floe size can impact several processes that determine the evolution of the Arctic sea ice, including lateral melt volume, momentum exchange, and rheology. Floe size distribution (FSD) models are applied within continuum sea ice models to capture the evolution of the FSD through parameterisations of the processes that modify floe size such as lateral melting and wave break-up of floes. FSD models do not yet adequately resolve in-plane fragmentation processes of floes such as the breakup of floes under wind forcing, through interactions between neighbouring floes, or through thermal weakening. It is challenging to characterise and therefore parameterise these in-plane floe breakup processes due to limited availability of in-situ observations. Discrete element models (DEMs) offer an alternative way to understand the different mechanisms of floe fragmentation. By resolving relevant properties such as shear and normal stress and sea ice strength at the sub-floe scale, it is possible to use DEMs as a virtual laboratory and directly simulate the break-up of floes into smaller fragments. In this study, we describe how in-situ observations of sea ice can be combined with output from sea ice DEMs to develop parameterisations of in-plane breakup of floes that can then be applied in continuum models. We then discuss the necessary model developments in order to apply a sea ice DEM to floe fragmentation at smaller scales. We will also present results from a series of DEM simulations used to model the fracture of sea ice under different forcing conditions and with varying sea ice states to identify the important sea ice parameters and processes in determining the size of the floes that form from in-plane breakup events.

The North Atlantic Climate System Integrated Study (ACSIS) was a large multidisciplinary research programme funded by the UK's Natural Environment Research Council (NERC). ACSIS ran from 2016 to 2022 and brought together around 80 scientists from seven leading UK-based environmental research institutes to deliver major advances in the understanding of North Atlantic climate variability and extremes. Here, we present an overview of the data generated by the ACSIS programme. The datasets described cover the North Atlantic Ocean, the atmosphere above it (including its composition), and Arctic sea ice. Atmospheric composition datasets include measurements from seven aircraft campaigns (45 flights in total, 0–10 km altitude range) in the northeastern Atlantic (∼ 15–55° N, ∼ 40° W–5° E) made at intervals of 6 months to 2 years between February 2017 and May 2022. The flights measured chemical species (including greenhouse gases; ozone precursors; and volatile organic compounds – VOCs) and aerosols (organic aerosol – OA; SO4; NH4; NO3; and non-sea salt chloride – nss-Cl) (https://doi.org/10.5285/6285564c34a246fc9ba5ce053d85e5e7, FAAM et al., 2024). Ground-based stations at the Cape Verde Atmospheric Observatory (CVAO), Penlee Point Atmospheric Observatory (PPAO), and Plymouth Marine Laboratory (PML) recorded ozone, ozone precursors, halocarbons, greenhouse gases (CO2 and methane), SO2, and photolysis rates (CVAO; http://catalogue.ceda.ac.uk/uuid/81693aad69409100b1b9a247b9ae75d5, National Centre for Atmospheric Science et al., 2010); O3 and CH4 (PPAO, https://catalogue.ceda.ac.uk/uuid/8f1ff8ea77534e08b03983685990a9b0 (Plymouth Marine Laboratory and Yang, 2017); and aerosols (PML, https://doi.org/10.5285/e74491c96ef24df29a9342a3d57b5939, Smyth, 2024), respectively. Complementary model simulations of atmospheric composition were performed with the UK Earth System Model (UKESM1) for the period from 1982 to 2020 using Coupled Model Intercomparison Project Phase 6 (CMIP6) historical forcing up to 2014 and Shared Socioeconomic Pathways (SSP) scenario SSP3-7.0 from 2015 to 2020. Model temperature and winds were relaxed towards ERA reanalysis. Monthly mean model data for ozone, NO, NO2, CO, methane, stratospheric ozone tracers, and 30 regionally emitted tracers are available for download (https://data.ceda.ac.uk/badc/acsis/UKESM1-hindcasts, Abraham, 2024). ACSIS also generated new ocean heat content diagnostics (https://doi.org/10/g6wm, https://doi.org/10/g8g2, Moat et al., 2021a–b) and gridded temperature and salinity based on objectively mapped Argo measurements (https://doi.org/10.5285/fe8e524d-7f04-41f3-e053-6c86abc04d51 King, 2023). An ensemble of atmosphere-forced global-ocean sea-ice simulations using the NEMO-CICE model was performed with horizontal resolutions of 1/4 and 1/12° covering the period from 1958 to 2020 using several different atmosphere-reanalysis-based surface forcing datasets, supplemented by additional global simulations and stand-alone sea-ice model simulations with advanced sea-ice physics using the CICE model (http://catalogue.ceda.ac.uk/uuid/770a885a8bc34d51ad71e87ef346d6a8, Megann et al., 2021e). Output is stored as monthly averages and includes 3D potential temperature, salinity, zonal velocity, meridional velocity, and vertical velocity; 2D sea-surface height, mixed-layer depth, surface heat, and freshwater fluxes; ice concentration and thickness; and a wide variety of other variables. In addition to the data presented here, we provide a very brief overview of several other datasets that were generated during ACSIS and have been described previously in the literature.

As the summer Arctic sea ice extent has retreated, the marginal ice zone (MIZ) has been widening. The MIZ is defined as the region of the ice cover that is influenced by waves and for convenience here is defined as the region of the ice cover between sea ice concentrations (SIC) of 15 % to 80 %. The MIZ is projected to become a larger percentage of the summer ice cover, as the Arctic transitions to ice-free summers. Using numerical simulations, we explicitly compare, for the first time, individual processes of ice volume gain and loss in the ice pack (SIC > 80 %) to those in the MIZ to establish and contrast their relative importance and examine how these processes change as the summer MIZ fraction increases over time. We use an atmosphere-forced, physics-rich, sea-ice-mixed layer model based on CICE, that includes a joint prognostic floe size and ice thickness distribution (FSTD) model including brittle fracture and form drag. We demonstrate that this model is realistic using satellite observations of sea ice extent and PIOMAS (the Pan-Arctic Ice Ocean Modeling and Assimilation System) estimates of thickness. A comparable setup has also been compared to floe size distribution (FSD) observations in prior studies. The MIZ fraction of the July sea ice cover, when the MIZ is at its maximum extent, increases by a factor of 2 to 3, from 14 % (20 %) in the 1980s to 46 % (50 %) in the 2010s in NCEP (HadGEM2-ES) atmosphere-forced simulations. In a HadGEM2-ES forced projection, the July sea ice cover is almost entirely MIZ (93 %) in the 2040s. Basal melting accounts for the largest proportion of melt in regions of pack ice and MIZ for all time periods. During the historical period, top melt is the next largest melt term in pack ice, but in the MIZ, top melt and lateral melt are comparable. This is due to a relative increase of lateral melting and a relative reduction of top melting by a factor of 2 in the MIZ compared to the pack ice. The volume fluxes due to dynamic processes decrease due to the reduction in ice volume in both the MIZ and pack ice. For areas of sea ice that transition to being MIZ in summer, we find an earlier melt season: in the region that was pack ice in the 1980s and became MIZ in the 2010s, the peak in the total melt volume flux occurs 20(12) d earlier. This continues in the projection where melting in the region that becomes MIZ in the 2040s shifts 14 d earlier compared to the 2010s. Our analysis shows that a different balance of processes controls the volume budget of the MIZ versus the pack ice. We also find that the balance of processes is different for the MIZ in the 2040s compared to the 1980s, and conclude that we cannot understand the disposition between basal, lateral and top melt in a future Arctic solely based on increased MIZ fraction, since changes in surface energy balance remain a strong control on these behaviours.

We present an overview of the UK's Global Sea Ice model configuration version 9 (GSI9), the sea ice component of the latest Met Office Global Coupled model, GC5. The GC5 configuration will, amongst other uses, form the physical basis for the HadGEM3 (Hadley Centre Global Environment Model version 3) climate model and UKESM2 (UK Earth System Model version 2) Earth system model that will provide the Met Office Hadley Centre/UK model contributions to CMIP7 (Coupled Model Intercomparison Project Phase 7). Although UK ocean model configurations have been developed for many years around the NEMO (Nucleus for European Modelling of the Ocean) ocean modelling framework, the GSI9 configuration is the first UK sea ice model configuration to use the new native NEMO sea ice model, SI3 (Sea Ice modelling Integrated Initiative). This replaces the CICE (Community Ice CodE) model used in previous configuration versions. In this paper we document the physical and technical options used within the GSI9 sea ice configuration. We provide details of the implementation of SI3 into the Met Office coupled model and the adaptations required to work with our “conductivity coupling” approach and provide a thorough description of the GC5 coupling methodology. A brief evaluation of sea ice simulated by the GC5 model is included, with results compared to observational references and a previous Global Coupled model version (GC3.1) used for CMIP6, to demonstrate the scientific credibility of the results.

A parameterization of the impact of internal waves on momentum transfer at the sea-ice-ocean interface based on previous work by McPhee has been implemented in a sea-ice model for the first time. The ice-ocean drag from internal waves is relevant for shallow mixed layer depth and the presence of a density jump at the pycnocline and is also a function of the strength of the stratification beneath the ocean mixed layer and geometry of the ice interface. We present results from a coupled sea-ice-ocean model where the parameterization of internal wave drag has been implemented. We conducted simulations spanning the years from 2000 to 2017. We find a deceleration of ice drift by 5-8% in both winter and summer, but with significant spatial and temporal variation reaching seasonal average values of similar to 10%. The spatial variation of ice transport leads to local impacts on deformed ice of magnitude similar to 0.05 m (2-5%), and reductions in ocean-to-ice heat fluxes of similar to 1 W m(-2), and a decrease in bottom melt of similar to 0.02-0.04 cm d(-1). There is an increase of up to 15% in thickness and ice concentration in the Canadian Arctic and a 10% overall impact on the total sea-ice volume.

The efficiency of air-sea momentum exchange depends on top and bottom sea ice surface roughness which varies with ice types and conditions, but constants are applied in most climate models. Future sea ice reduction is expected to lead to an increase in efficiency of air-sea momentum transfer. Accurate representation of momentum transfer processes will be a requirement for realistic model predictions. Within the CANARI project (Climate change in the Arctic-North Atlantic Region and Impacts on the UK) we have implemented the CICE form drag scheme into the sea ice model SI3. Based on parameters of the ice cover such as ice concentration, size, and frequency of the sails and keels, freeboard and floe draft, and size of floes and melt pond fraction, the total form drag can be computed as a sum of form drag from sails and keels, form drag from floe edges, form drag from melt pond edges, and a reduced skin drag due to a sheltering effect. Ocean – sea ice simulations reveal that the inclusion of form drag has a significant impact by reducing sea ice drift and near surface ocean currents by more than 20% in the marginal sea ice regions. However, results depend on the poorly know input variables which are parameterised from the volume of ridged ice. We apply a new surface topography data set which has been derived from the ICESat-2 ATL03 global geolocated photon height data product. We use the continuous data sets of surface roughness, sail heights and frequency of pressure ridges across the Arctic to calibrate the form drag parameterization and present new simulation results.

Future projections of Arctic and Antarctic sea ice suffer from uncertainties largely associated with inter-model spread. Ocean heat transport has been hypothesised as a source of this uncertainty, based on correlations with sea ice extent across climate models. However, a physical explanation of what sets the sea ice sensitivity to ocean heat transport remains to be uncovered. Here, we derive a simple equation using an idealised energy-balance model that captures the emergent relationship between ocean heat transport and sea ice in climate models. Inter-model spread of Arctic sea ice loss depends strongly on the spread in ocean heat transport, with a sensitivity set by compensation of atmospheric heat transport and radiative feedbacks. Southern Ocean heat transport exhibits a comparatively weak relationship with Antarctic sea ice and plays a passive role secondary to atmospheric heat transport. Our results suggest that addressing ocean model biases will substantially reduce uncertainty in projections of Arctic sea ice. Large-scale energy budgets can explain the correlation between sea ice extent and ocean heat transport, providing fresh insight into accurate projection of Arctic and Antarctic sea ice, according to a simple equation for computing sea ice edge change from surface temperature and ocean heat transport.

<p>Very rapid ice loss events (VRILEs) are extreme, local reductions in Arctic sea ice extent on timescales of days to weeks. They are poorly captured in operational forecasts that are used, for instance, to inform shipping through the Arctic Ocean. A better understanding of the drivers and underlying processes is thus critical to a range of stakeholders. We analyse summertime (May–September) VRILEs occurring in a simulation (1980–2022) with the sea ice model CICE forced by atmospheric reanalyses. Our configuration includes novel marginal ice physics such as a prognostic floe size distribution and an explicit form drag scheme. Most VRILEs are dominated by thermodynamic processes. However, many events occurring near the start or end of the melt season are driven by advective redistribution, often associated with the presence of a cyclone. We illustrate this with key case studies and generalise the results to all simulated VRILEs using simple metrics quantifying the dominant contributions to the sea ice concentration tendencies and atmospheric conditions in each event. Finally, a suite of parameter sensitivity studies highlights factors with potential to improve forecasting of VRILEs.</p>

There have been several recent efforts to develop parameterisations of the sea ice floe size distribution (FSD) for use in sea ice models such as CICE and SI3. These models aim to capture the key processes that determine the evolution of floe sizes, including melting at the edges of floes, welding together of floes, and break-up of floes by waves. However, several fragmentation processes are not yet accounted for in these models. For example, in-plane brittle fracture events can have a direct impact on the size of larger floes and potentially also smaller floes. Plausible indirect mechanisms also exist. It has been observed that thermodynamic weakening of cracks and other linear features in the sea ice cover can in some cases drive the break-up of sea ice in the central Arctic. These observations imply that linear features in the sea ice that form in winter from in-plane brittle fracture before freezing up can then determine the fragmentation of sea ice in summer as it thins and weakens. Here we will present results from sea ice simulations including a prognostic model of sea ice FSD to show that the inclusion of brittle fracture-derived impacts on floe size improves the performance of the FSD model in simulating observed FSD shape for mid-sized floes. We will use these results to motivate the development of a more physically derived parameterisation of floe breakup via thermal weakening of floes along existing linear features. Finally, we will discuss how we can combine novel observations and recent advancements in modelling techniques such as discrete element methods applied to sea ice to aid in the development of parameterisations of these floe-scale processes for subsequent application in continuum models.

<p><span data-ogsc="rgb(36, 36, 36)" data-ogsb="white">Over the last decade, there have been a number of new observational records of Arctic sea ice thickness produced, with improved spatiotemporal coverage, that can allow researchers to better understand the Arctic sea ice system. In this study we use a newly developed sea ice data assimilation system CICE-PDAF, to reanalyse the Arctic sea ice cover over the satellite era. In various combinations we assimilate a year-round sea ice thickness cover and a sub-grid scale sea ice thickness distribution alongside sea ice concentration observations. </span><span data-ogsc="rgb(36, 36, 36)" data-ogsb="white">The assimilation of year-round sea ice thickness provides substantial improvements to the modelled sea ice thickness in comparison to independent observations. The assimilation also has significant consequences on the modelled distribution of the ice thickness across the Arctic, particularly in regions of multi-year ice, and regional model biases are reduced in the reanalyses.</span></p>

In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand thestate of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time andspace. In this study the first results of a new sea ice data assimilation system are presented. Observations assimilated (in various combinations)are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from CryoSat-2 and NASA daily Bootstrap sea iceconcentration. This system couples the Centre for Polar Observation and Modelling's (CPOM) version of the Los Alamos Sea Ice Model (CICE) to thelocalised ensemble transform Kalman filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating asub-grid-scale sea ice thickness distribution is of particular novelty. The sub-grid-scale sea ice thickness distribution is a fundamental componentof sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. This study finds that assimilating CryoSat-2 products for the mean thickness and the sub-grid-scale thickness distribution can have significantconsequences for the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. Theassimilation of sea ice concentration, mean sea ice thickness and sub-grid-scale sea ice thickness distribution together performed best whencompared to a subset of CryoSat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in theCanadian Arctic Archipelago (CAA) is decreased, but the thickness of the ice in the central Arctic is increased. When comparing the assimilation ofmean thickness with the assimilation of sub-grid-scale thickness distribution, it is found that the latter leads to a significant change in thevolume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid-scale thickness distributionalongside mean thickness.

Sea ice is a critical component of the polar and global climate that demands reliable future projections. Yet, such projections continue to suffer from uncertainties largely associated with inter-model spread. Global ocean heat transport (OHT) has been hypothesised as a source of this uncertainty, as simulations with greater OHT into the polar regions tend to exhibit greater historical and future sea ice loss. Previous studies have identified such intuitive correlations but stop short of a physical explanation needed to attribute model spread in sea ice to the ocean. Here, we explain the sign and size of the emergent sensitivity of sea ice to OHT using a simple equation that we derive from an idealised energy-balance model. This equation reveals underlying mechanisms involving the atmosphere and captures the OHT–sea ice relationship in climate models. Future Arctic sea ice loss is regulated by changes in OHT, the impact of which is set by compensation of atmospheric heat transport and radiative feedbacks. Our results suggest that addressing ocean biases in climate models will substantially reduce uncertainty in projections of sea ice and the global climate state.

Abstract. As summer Arctic sea ice extent has retreated, the marginal ice zone (MIZ) has been widening and making up an increasing percentage of the summer sea ice. The MIZ is defined as the region of the ice cover that is influenced by waves, and for convenience here is defined as the region of the ice cover between ice concentrations (area fractions) A of 15 to 80 %. The MIZ is projected to become a larger percentage of the summer ice cover, as the Arctic transitions to ice free summers. We compare individual processes of ice volume gain and loss in the ice pack (A>80 %) to those in the MIZ to establish and contrast their relative importance and examine how these processes change as the summer MIZ fraction and amplitude of the seasonal sea ice growth/melt cycle increases over decadal timescales. We use an atmosphere-forced, physics-rich sea ice-mixed layer model that includes a prognostic floe size distribution (FSD) model including brittle fracture and form drag. The model has been compared to FSD observations, satellite observation of sea ice extent and PIOMAS. The MIZ fraction of the July sea ice cover, when the MIZ is at its maximum extent, increases by a factor of 2 to 3, from 14 % (20 %) in the 1980s to 46 % (50 %) in the 2010s in NCEP (HadGEM2-ES) atmosphere-forced simulations. In a HadGEM2-ES forced projection the July sea ice cover is almost entirely MIZ (93 %) in the 2040s. Basal melting accounted for the largest proportion of melt in regions of pack ice and MIZ for all time periods. During the historical period, top melt was the next largest melt term in pack ice, but in the MIZ top melt and lateral melt were comparable. This is due to a relative increase of lateral melting and a relative reduction of top melting by a factor of 2 in the MIZ compared to the pack ice. The volume fluxes due to dynamic processes decreases due to the reduction in ice volume in both the MIZ and pack ice. As the ice cover becomes marginal (MIZ), it melts earlier: in the region that was pack ice in the 1980s and became marginal in the 2010s, peak melting starts 20/12 days earlier (NCEP/HadGEM2-ES). This continues in the projection where melting in the region that becomes MIZ in the 2040s shifts 14 days earlier.

Sea ice is not homogenous and is instead made up of individual pieces of ice that are called floes. Observations show that these floes range in size from just metres to tens of kilometres. Sea ice and climate models have historically assumed a fixed floe size, if there is an explicit representation of floe size at all. There have been several recent efforts to include a treatment of variable floe size within sea ice models. These models have included several processes thought to be important in floe size evolution including break-up of sea ice by waves, lateral melt and growth, welding together of floes, and brittle fracture processes. Floe size can have a direct impact on sea ice evolution via several mechanisms including lateral melt rate, momentum exchange between the sea ice, ocean, and atmosphere, and the ice rheology. Floe size distribution (FSD) models have so far been used within sea ice models to primarily explore the direct impact of floe size on the sea ice cover, and there has been little exploration of the possible resulting feedback processes. In this study we consider a prognostic approach to modelling the FSD within the CICE sea ice model where the shape of the FSD is an emergent characteristic. We consider results from both standalone sea ice simulations and fully coupled climate simulations. These results are used to explore whether an improved representation of sea ice-ocean and sea ice-atmosphere feedbacks modifies the impact of floe size on the sea ice concentration and thickness over both pan-Arctic and localised scales. We will focus on feedbacks that result from changes to the lateral melt rate, considering in particular whether there is a significant impact from the ice-ocean albedo feedback mechanism. Finally, we will discuss the necessary conditions for there to be significant feedbacks resulting from the inclusion of floe size distribution models in sea ice and climate models.

Sea ice retreat and opening of large, previously ice-covered areas of the Arctic Ocean to wind and ocean waves is leading to large changes in the sea ice state. The Arctic sea ice cover is becoming more fragmented and mobile, with large regions of ice cover projected to evolve into a marginal ice zone (MIZ). Fragmented sea ice has different dynamics, necessitating changes in sea ice model rheology. The objective of this study is to improve sea ice dynamics in models for forecasting and climate projections. We introduce granular behaviour in the ice dynamics and assess the impact on sea ice behaviour. For this purpose we have implemented a seamless rheology for MIZ and pack ice in an idealised sea ice and ocean model. The study compares the effect of the combined rheology with that of the standard elastic-viscous-plastic (EVP) rheology. The main effect of granular behaviour in ice rheology is on internal ice pressure. The jostling of the floes causes divergence of the sea ice cover. Sea ice viscosities are only weakly impacted. In idealised simulations the new sea ice rheology results in widening of the MIZ and a more diffuse ice edge in a stand-alone set-up. Oceanic feedbacks counteract and can undo this effect. The resolution of the simulation modifies the effect of the rheology: a rheology that accounts for granular effects offers better convergence of the solution than the standard EVP. In conclusion, granular sea ice rheology affects the sea ice in the MIZ in some cases, but the importance of the effect in general is not clear.

Perspectives are discussed on future directions for the field of marginal ice zone (MIZ) dynamics, based on the extraordinary progress made over the past decade in its theory, modelling and observations. Research themes are proposed that would shift the field's focus towards the broader implications of MIZ dynamics in the climate system. In particular, pathways are recommended for research that highlights the impacts of trends in the MIZ on the responses of Arctic and Antarctic sea ice to climate change. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'.

Understanding drivers of Arctic and Antarctic sea ice on multidecadal timescales is key to reducing uncertainties in long-term climate projections. Here we investigate the impact of ocean heat transport (OHT) on sea ice, using pre-industrial control simulations of 20 models participating in the latest Coupled Model Intercomparison Project (CMIP6). In all models and in both hemispheres, sea ice extent is negatively correlated with poleward OHT. However, the similarity of the correlations in both hemispheres hides radically different underlying mechanisms. In the northern hemisphere, positive OHT anomalies primarily result in increased ocean heat convergence along the Atlantic sea ice edge, where most of the ice loss occurs. Such strong, localised heat fluxes ( $$\sim {}100~\text {W}~\text {m}^{-2}$$ ) also drive increased atmospheric moist-static energy convergence at higher latitudes, resulting in a pan-Arctic reduction in sea ice thickness. In the southern hemisphere, increased OHT is released relatively uniformly under the Antarctic ice pack, so that associated sea ice loss is driven by basal melt with no direct atmospheric role. These results are qualitatively robust across models and strengthen the case for a substantial contribution of ocean forcing to sea ice uncertainty, and biases relative to observations, in climate models.

The marginal ice zone (MIZ) is the dynamic interface between the open ocean and sea ice-covered ocean. It is characterized by interactions between surface gravity waves and granular ice covers consisting of relatively small, thin chunks of sea ice known as floes. This structure gives the MIZ markedly different properties to the thicker, quasi-continuous ice cover of the inner pack that waves do not reach, strongly influencing various atmosphere-ocean fluxes, especially the heat flux. The MIZ is a significant component of contemporary sea ice covers in both the Antarctic, where the ice cover is surrounded by the Southern Ocean and its fierce storms, and the Arctic, where the MIZ now occupies vast expanses in areas that were perennial only a decade or two ago. The trend towards the MIZ is set to accelerate, as it reinforces positive feedbacks weakening the ice cover. Therefore, understanding the complex, multiple-scale dynamics of the MIZ is essential to understanding how sea ice is evolving and to predicting its future. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'.