Antarctic sea ice expanded for the first 35 years of the satellite record but in the past 10 years has dropped to record lows. Recent studies argue that the recent decline brings climate models into better agreement with observed Antarctic sea ice trends. Here, we assess this claim by considering Antarctic sea ice area and concentration across a large number of Coupled Model Intercomparison Project phase 6 (CMIP6) simulations, and we reach a different conclusion. Even with many models that sample a large range of internal variability, we find that very few capture the observed multidecadal expansion or the large decline after 2014. Further analysis reveals that models with trends similar to observations typically have poor simulation of climatological sea ice, its variability, and its regional patterns. The continued inability of CMIP-class models to simulate Antarctic sea ice and its observed trends severely limits our ability to understand past changes and to confidently project its evolution in the coming decades.

Models from the Coupled Model Intercomparison Project phase 6 (CMIP6) typically struggle to reproduce observed Antarctic sea ice trends, a bias that is substantially alleviated when constraining winds. We use wind-nudged simulations from two CMIP models to investigate the influence of clouds on sea ice area (SIA). We find that nudging model winds in coupled simulations toward reanalysis, in addition to improving SIA variability, is crucial to reproduce realistic anomalies in cloud radiative effect (CRE) and cloud cover. Biases in the variability of cloud properties at sea ice edge-characterized by CRE anomalies-help explain the remaining discrepancies between simulated and observed SIA; a bias of 1 Wm-2 ${\text{Wm}}<^>{-2}$ in the CRE anomaly corresponds to a negative bias of 0.43 106km2 ${10}<^>{6}{\text{km}}<^>{2}$ in SIA anomaly. Finally, we find that most CMIP6 models show positive trends in CRE anomaly biases, which should contribute to enhanced SIA decline, a long-standing bias in CMIP models.

Representing the interactions between ice sheets and climate is essential for more accurate prediction of climate change and sea level rise. Ice sheets interact with the overlying atmosphere via the accumulation of snow and its compaction into firn, then ice, as well as the melting of surface snow and ice and the creation of runoff water. Getting an adequate representation of heat transfer, compaction, and melting processes is essential for an accurate representation of snow on land ice in global climate models. We are implementing an improved snow model on top of land ice as part of an effort to couple the NASA GISS climate model with the PISM ice sheet model. The new snow model includes additional layers and processes that are not currently incorporated (e.g., liquid water retention, percolation and refreezing, and snow densification), and mass and energy transfer methods that are consistent with both static ice sheets (with implicit iceberg fluxes) and interactive ice sheets (with explicit dynamics). We are tuning the densification scheme of this snow model with temperature and density data from common FirnCover and SumUp observations at locations in the accumulation zone of Greenland, and we compare the resulting density profiles to other SumUp density profiles in Greenland and Antarctica. We will assess the impact of this new snow model in climate model simulations with a static ice sheet compared with the previous (simpler) 2-layer snow model. Finally, we aim to use the non-coupled simulations as a baseline to assess the impact of dynamic coupling with an interactive ice sheet model.

Antarctic sea ice extent was the lowest on record in 2023, with an annual mean of 9.81 million km2, beating the previous minimum of 2022. Arctic sea ice extent was also low, with an annual mean of 10.49 million km2, but did not break any records.

The seasonal sea ice zone encompasses the region between the winter maximum and summer minimum sea ice extent. In both the Arctic and Antarctic, the majority of the ice cover can now be classified as seasonal. Here, we review the sea ice physics that governs the evolution of seasonal sea ice in the Arctic and Antarctic, spanning sea ice growth, melt, and dynamics and including interactions with ocean surface waves as well as other coupled processes. The advent of coupled wave–ice modeling and discrete-element modeling, together with improved and expanded satellite observations and field campaigns, has yielded advances in process understanding. Many topics remain in need of further investigation, including rheologies appropriate for seasonal sea ice, wave-induced sea ice fracture, welding for sea ice freeze-up, and the distribution of snow on seasonal sea ice. Future research should aim to redress biases (such as disparities in focus between the Arctic and Antarctic and between summer and winter processes) and connect observations to modeling across spatial scales.

We must prioritize diversity, scientific communication and team-based science to keep up with rapid Antarctic ice and climate change.

&lt;p&gt;While current-generation CMIP and OMIP models have clear biases in their upper Arctic Ocean hydrography, it is less clear how these biases impact the models&#039; ability to simulate the observed Arctic sea ice mean state and trends. In this study we seek to quantify cross-relationship between sea ice and ocean states in CMIP6 historical simulations and identify common model behaviors. Multi-model mean (MMM) simulations exhibit accelerated changes in the ice and ocean system since the late 20th century. Underlying the MMM is strong inter-model variation in the simulated ice and ocean mean states and their temporal variability including trends. Despite such inter-model differences, all models show a similar ratio between sea ice reduction and upper ocean warming such that models with higher ocean warming also have higher SIE reduction and vice versa. Our results also highlight the urgent needs of reliable Arctic Ocean observations or data products in order to better contextualize modeling results.&lt;/p&gt;

Abstract Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS‐E2.1‐G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. There are detectable differences in zonal‐mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model's representation of historical changes, including reversing the forced trends in Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that model groups should strive to include more accurate estimates of these drivers in all‐forcing historical simulations in future coupled model intercomparisons.

&lt;p&gt;The seasonal cycle of the Antarctic sea ice extent is largely controlled by the evolution of the insolation received at the top of the atmosphere. However, sea ice processes and feedbacks with the ocean and the atmosphere can modulate this seasonal cycle. Here, the atmospheric feedbacks are quantified in a series of idealized sensitivity experiments performed with an eddy-permitting (1/4&amp;#176;) NEMO-LIM3 Southern Ocean configuration, including a representation of ice shelf cavities, in which the model was either driven by an atmospheric reanalysis or coupled to the COSMO-CLM&lt;sup&gt;2&lt;/sup&gt; regional atmospheric model. In these experiments, sea ice thermodynamics and dynamics as well as the exchanges with the ocean and atmosphere are strongly perturbed. This perturbation is achieved by modifying snow and ice thermal conductivities, the vertical mixing in the ocean top layers, the effect of freshwater uptake/release upon sea ice growth/melt, ice dynamics and surface albedo. We show that the changes in surface heat fluxes are very different between the configurations driven by the reanalysis and those coupled to the atmosphere. Atmospheric feedbacks enhance the response of the modelled winter ice extent to any of the perturbed processes, and the enhancement is strongest when the albedo is modified. The response of sea ice volume and extent to changes in entrainment of subsurface warm waters to the ocean surface is also greatly amplified by the coupling with the atmosphere. By contrast, the atmospheric feedbacks can damp the impact of the perturbations affecting the heat conductivity through sea ice.&lt;/p&gt;&lt;div&gt;&lt;/div&gt;

Abstract At most latitudes, the seasonal cycle of zonal‐mean surface air temperature is notably asymmetric: the length of the warming season is not equal to the length of the cooling season. The asymmetry varies spatially, with the cooling season being ∼40 days shorter than the warming season in the subtropics and the warming season being ∼100 days shorter than the cooling season at the poles. Furthermore, the asymmetry differs between the Northern Hemisphere and the Southern Hemisphere. Here, we show that these observed features are broadly captured in a simple model for the evolution of temperature forced by realistic insolation. The model suggests that Earth's orbital eccentricity largely determines the hemispheric contrast, and obliquity broadly dictates the meridional structure. Clouds, atmospheric heat flux convergence, and time‐invariant effective surface heat capacity have minimal impacts on seasonal asymmetry. This simple, first‐order picture has been absent from previous discussions of the surface temperature seasonal cycle.

Abstract Southern Ocean surface cooling and Antarctic sea ice expansion from 1979 through 2015 have been linked both to changing atmospheric circulation and melting of Antarctica's grounded ice and ice shelves. However, climate models have largely been unable to reproduce this behavior. Here we examine the contribution of observed wind variability and Antarctic meltwater to Southern Ocean sea surface temperature (SST) and Antarctic sea ice. The free‐running, CMIP6‐class GISS‐E2.1‐G climate model can simulate regional cooling and neutral sea ice trends due to internal variability, but they are unlikely. Constraining the model to observed winds and meltwater fluxes from 1990 through 2021 gives SST variability and trends consistent with observations. Meltwater and winds contribute a similar amount to the SST trend, and winds contribute more to the sea ice trend than meltwater. However, while the constrained model captures much of the observed sea ice variability, it only partially captures the post‐2015 sea ice reduction.

The seasonal cycle of the Antarctic sea ice extent is strongly asymmetric, with a relatively slow increase after the summer minimum followed by a more rapid decrease after the winter maximum. This cycle is intimately linked to the seasonal cycle of the insolation received at the top of the atmosphere, but sea ice processes as well as the exchanges with the atmosphere and ocean may also play a role. To quantify these contributions, a series of idealized sensitivity experiments have been performed with an eddy-permitting (1/4∘) NEMO-LIM3 (Nucleus for European Modelling of the Ocean–Louvain-la-Neuve sea ice model version 3) Southern Ocean configuration, including a representation of ice shelf cavities, in which the model was either driven by an atmospheric reanalysis or coupled to the COSMO-CLM2 regional atmospheric model. In those experiments, sea ice thermodynamics and dynamics as well as the exchanges with the ocean and atmosphere are strongly perturbed. This perturbation is achieved by modifying snow and ice thermal conductivities, the vertical mixing in the ocean top layers, the effect of freshwater uptake and release upon sea ice growth and melt, ice dynamics, and surface albedo. We find that the evolution of sea ice extent during the ice advance season is largely independent of the direct effect of the perturbation and appears thus mainly controlled by initial state in summer and subsequent insolation changes. In contrast, the melting rate varies strongly between the experiments during the retreat, in particular if the surface albedo or sea ice transport are modified, demonstrating a strong contribution of those elements to the evolution of ice coverage through spring and summer. As with the advance phase, the retreat is also influenced by conditions at the beginning of the melt season in September. Atmospheric feedbacks enhance the model winter ice extent response to any of the perturbed processes, and the enhancement is strongest when the albedo is modified. The response of sea ice volume and extent to changes in entrainment of subsurface warm waters to the ocean surface is also greatly amplified by the coupling with the atmosphere.

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.

The retreat of Arctic sea ice is enabling increased ocean wave activity at the sea ice edge, yet the interactions between surface waves and sea ice are not fully understood. Here, we examine in situ observations of wave spectra spanning 2012-2021 in the western Arctic marginal ice zone (MIZ). Swells exceeding 30 cm are rarely observed beyond 100 km inside the MIZ. However, local wind waves are observed in patches of open water amid partial ice cover during the summer. These local waves remain fetch-limited between ice floes with heights less than 1 m. To investigate these waves at climate scales, we conduct experiments varying wave attenuation and generation in ice with a global model including coupled interactions between waves and sea ice. A weak high-frequency attenuation rate is required to simulate the local waves in observations. The choices of attenuation scheme and wind input in ice have a remarkable impact on the extent of wave activity across ice-covered oceans, particularly in the Antarctic. As well as demonstrating the need for stronger constraints on wave attenuation, our results suggest that further attention should be directed towards locally generated wind waves and their role in sea ice evolution. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'.

Arctic sea ice has declined strikingly over the past 40 years under climate change. The physical processes driving these changes and the relative roles of external forcing versus natural variability remain under debate, partly due to lack of confidence in the simulation of Arctic sea ice by coupled climate models. Here, we nudge a widely‐used CMIP5‐class model (CESM1) toward observed winds above the boundary layer. These simulations reproduce observed Arctic trend patterns and Arctic‐average interannual fluctuations in near‐surface temperature. We find that atmospheric circulation plays a crucial role in September Arctic sea ice loss and sea ice interannual variability. Trends in the observed atmospheric circulation that are not part of the modeled forced response cause approximately 20%–25% of the modeled September sea ice decline, which is in close agreement with the observed trend. However, our simulations do not capture sea ice loss in other seasons, pointing to important model biases in winter and spring.

Despite a recent resurgence of observational studies attempting to quantify the ice-induced attenuation of ocean waves in polar oceans, the physical processes governing this wave attenuation phenomenon are still poorly understood. Most analyses have attempted to relate the spatial rate of wave attenuation to wave frequency, but have not considered how this relationship depends on ice, wave and atmospheric conditions. An in-depth analysis of the wave-buoy data collected during the 2017 PIPERS programme in the Ross Sea is conducted. Standard techniques are used to estimate the spatial rate of wave attenuation $\alpha$ and the influence of a number of potential physical drivers on its dependence on wave period $T$ is investigated. A power-law is shown to consistently describe the $\alpha(T)$ relationship, in line with other recent analyses. The two parameters describing this relationship are found to depend significantly on sea ice concentration, mean wave period and wind direction, however. Looking at cross-correlations between these physical drivers, three regimes of ice-induced wave attenuation are identified, which characterise different ice, wave and wind conditions, and very possibly different processes causing this observed attenuation. This analysis suggests that parametrisations of ice-induced wave decay in spectral wave models should be piecewise, so their dependence on local ice, wave and wind conditions is described in some way.

The mean seasonal cycle of Antarctic sea-ice extent is asymmetric, with the period of ice retreat being approximately two months shorter than the period of ice advance. This feature is largely consistent in observations from year to year and across different satellite products. The asymmetry is also broadly reproduced by comprehensive climate models across generations from CMIP3 to CMIP6, with limited impacts from internal variability. Using a range of idealized climate models of varying complexity, we show that the seasonal cycle in top-of-atmosphere incident solar radiation drives the asymmetry. Because insolation in southern high latitudes departs from a sinusoid by having a narrow peak of intense brightness in summer and a long period of low light in winter, there is rapid summer ice retreat and gradual winter ice advance. This simple physical explanation is markedly different from those proposed in previous studies. Solar radiation drives the asymmetry in the seasonal cycle of Antarctic sea ice, according to climate model simulations.

Here we investigate the role of the atmospheric circulation in the Atlantic Meridional Overturning Circulation (AMOC) by comparing a fully‐coupled large ensemble, a forced‐ocean simulation, and new experiments using a fully‐coupled global climate model where winds above the boundary layer are nudged toward reanalysis. When winds are nudged north of 45°N, agreement with RAPID array observations of AMOC at 26.5°N improves across several metrics. The phasing of interannual variability is well‐captured due to the response of the local Ekman component in both wind‐nudging and forced‐ocean simulations, however the variance remains underestimated. The mean AMOC strength is substantially reduced relative to the fully‐coupled model large ensemble, which is biased high, due to the impact of winds on surface buoyancy fluxes over the subpolar gyre. Nudging winds toward observations also reduces the 1979–2016 trend in AMOC, suggesting that improvement in the representation of the high‐latitude atmosphere is important for projecting long‐term AMOC changes.

We characterize high‐frequency variability of sea ice extent (HFVSIE) in observations and climate models. We find that HFVSIE in models is biased low with respect to observations, especially at synoptic timescales (<20 days) in the Arctic year‐round and at monthly timescales (30–60 days) in Antarctica in winter. Models show large spread in HFVSIE, especially in Antarctica. This spread is partly explained by sea ice mean‐state while model biases in sea level pressure (SLP) and wind variability do not appear to play a major role in HFVSIE spread. Extreme sea ice extent (SIE) changes are associated with SLP anomaly dipoles aligned with the sea ice edge and winds directed on‐ice (off‐ice) during SIE loss (gain) events. In observations, these events are also associated with distinct ocean wave states during the cold season, when waves are greater (smaller) and travel toward (away from) the sea ice edge during SIE loss (gain) events.