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In recent years, the Southern Ocean has experienced extremely low sea ice cover in multiple summers. These low events were preceded by a multidecadal positive trend that culminated in record high ice coverage in 2014. This abrupt transition has led some authors to suggest that Antarctic sea ice has undergone a regime shift. In this study we analyze the satellite sea ice record and atmospheric reanalyses to assess the evidence for such a shift. We find that the standard deviation of the summer sea ice record has doubled from 0.31 million km 2 in 1979–2006 to 0.76 million km 2 for 2007–22. This increased variance is accompanied by a longer season-to-season sea ice memory. The atmosphere is the primary driver of Antarctic sea ice variability, but using a linear predictive model we show that sea ice changes cannot be explained by the atmosphere alone. Identifying whether a regime shift has occurred is difficult without a complete understanding of the physical mechanism of change. However, the statistical changes that we demonstrate (i.e., increased variance and autocorrelation, and a changed response to atmospheric forcing), as well as the increased spatial coherence noted by previous research, are indicators based on dynamical systems theory of an abrupt critical transition. Thus, our analysis is further evidence in support of a changed Antarctic sea ice system.

This paper examines the trajectories followed by intense intrusions of moist air into the Arctic polar region during autumn and winter and their impact on local temperature and sea ice concentration. It is found that the vertical structure of the warming associated with moist intrusions is bottom amplified, corresponding to a transition of local conditions from a “cold clear” state with a strong inversion to a “warm opaque” state with a weaker inversion. In the marginal sea ice zone of the Barents Sea, the passage of an intrusion also causes a retreat of the ice margin, which persists for many days after the intrusion has passed. The authors find that there is a positive trend in the number of intrusion events crossing 70°N during December and January that can explain roughly 45% of the surface air temperature and 30% of the sea ice concentration trends observed in the Barents Sea during the past two decades.

In Part I of this study, the impact of Ural blocking (UB) on the warm Arctic–cold Eurasian (WACE) pattern associated with the winter (DJF) arctic sea ice loss during 1979–2013 is examined by dividing the arctic sea ice reduction region into two dominant subregions: the Barents and Kara Seas (BKS) and the North American high-latitude (NAH) region (Baffin and Hudson Bay, Davis Strait, and Labrador Sea). It is found that atmospheric response to arctic sea ice loss resembles a negative Arctic response oscillation with a dominant positive height anomaly over the Eurasian subarctic region. Regression analyses of the two subregions further show that the sea ice loss over the BKS corresponds to the UB pattern together with a positive North Atlantic Oscillation (NAO⁺) and is followed by a WACE anomaly, while the sea ice reduction in the NAH region corresponds to a negative NAO (NAO⁻) pattern with a cold anomaly over northern Eurasia. Further analyses reveal that the UB pattern is more persistent during the period 2000–13 (P2) than 1979–99 (P1) because of the reduced middle-to-high-latitude mean westerly winds over Eurasia associated with the intense BKS warming. During P2 the establishment of the UB becomes a slow process because of the role of the BKS warming, while its decay is slightly rapid. In the presence of the long-lived UB that often occurs with the NAO⁺, the BKS-warming-induced DJF-mean anticyclonic anomaly is intensified and widened and then expands southward during P2 to amplify the WACE pattern and induce the southward displacement of its cold anomaly and the further loss of the BKS sea ice. Thus, midlatitude Eurasian cold events should be more frequent as the sea ice loss continues over the BKS.

Ice habit diagrams published prior to 2009—and many since—do not accurately describe in situ observations of ice shapes as a function of temperature and moisture. Laboratory studies and analysis of field observations by Bailey and Hallett in a series of papers in 2002, 2004, and 2009 corrected several errors from earlier studies, but their work has not been widely disseminated. We present a new, simplified diagram based on Bailey and Hallett’s work that focuses on several ice growth forms arising from the underlying surface processes by which mass is added to a crystal: tabular, columnar, branched, side branched, two types of polycrystalline forms, and a multiple growth regime at low ice supersaturations. To aid interpretation for a variety of applications, versions of the ice growth diagram are presented in terms of relative humidity with respect to water as well as the traditional formats of relative humidity with respect to ice and vapor density excess. These diagrams are intended to be understandable and useful in classroom settings at the sophomore undergraduate level and above. The myriad shapes of pristine snow crystals can be described as the result of either a single growth form or a sequence of growth forms. Overlays of data from upper-air soundings on the ice growth diagrams aid interpretation of expected physical properties and processes in conditions of ice growth.

This study investigates the stratospheric response to Arctic sea ice loss and subsequent near-surface impacts by analyzing 200-member coupled experiments using the Whole Atmosphere Community Climate Model version 6 (WACCM6) with preindustrial, present-day, and future sea ice conditions specified following the protocol of the Polar Amplification Model Intercomparison Project. The stratospheric polar vortex weakens significantly in response to the prescribed sea ice loss, with a larger response to greater ice loss (i.e., future minus preindustrial) than to smaller ice loss (i.e., future minus present-day). Following the weakening of the stratospheric circulation in early boreal winter, the coupled stratosphere–troposphere response to ice loss strengthens in late winter and early spring, projecting onto a negative North Atlantic Oscillation–like pattern in the lower troposphere. To investigate whether the stratospheric response to sea ice loss and subsequent surface impacts depend on the background oceanic state, ensemble members are initialized by a combination of varying phases of Atlantic multidecadal variability (AMV) and interdecadal Pacific variability (IPV). Different AMV and IPV states combined, indeed, can modulate the stratosphere–troposphere responses to sea ice loss, particularly in the North Atlantic sector. Similar experiments with another climate model show that, although strong sea ice forcing also leads to tighter stratosphere–troposphere coupling than weak sea ice forcing, the timing of the response differs from that in WACCM6. Our findings suggest that Arctic sea ice loss can affect the stratospheric circulation and subsequent tropospheric variability on seasonal time scales, but modulation by the background oceanic state and model dependence need to be taken into account.

A novel bulk microphysics scheme that predicts the evolution of ice properties, including aspect ratio (shape), mass, number, size, and density is described, tested, and demonstrated. The scheme is named the Ice-Spheroids Habit Model with Aspect-Ratio Evolution (ISHMAEL). Ice is modeled as spheroids and is nucleated as one of two species depending on nucleation temperature. Microphysical process rates determine how shape and other ice properties evolve. A third aggregate species is also employed, diversifying ice properties in the model. Tests of ice shape evolution during vapor growth and riming are verified against wind tunnel data, revealing that the model captures habit-dependent riming and its effect on fall speed. Lagrangian parcel studies demonstrate that the bulk model captures ice property evolution during riming and melting compared with a bin model. Finally, the capabilities of ISHMAEL are shown in a 2D kinematic framework with a simple updraft. A direct result of predicting ice shape evolution is that various states of ice from unrimed to lightly rimed to densely rimed can be modeled without converting ice mass between predefined ice categories (e.g., snow and graupel). This leads to a different spatial precipitation distribution compared with the traditional method of separating snow and graupel and converting between the two categories, because ice in ISHMAEL sorts in physical space based on the amount of rime, which controls the thickness and therefore fall speed. Predicting these various states of rimed ice leads to a reduction in vapor growth rate and an increase in riming rate in a simple updraft compared with the traditional approach.

The fate of icebergs in the polar oceans plays an important role in Earth’s climate system, yet a detailed understanding of iceberg dynamics has remained elusive. Here, the central physical processes that determine iceberg motion are investigated. This is done through the development and analysis of an idealized model of iceberg drift. The model is forced with high-resolution surface velocity and temperature data from an observational state estimate. It retains much of the most salient physics, while remaining sufficiently simple to allow insight into the details of how icebergs drift. An analytical solution of the model is derived, which highlights how iceberg drift patterns depend on iceberg size, ocean current velocity, and wind velocity. A long-standing rule of thumb for Arctic icebergs estimates their drift velocity to be 2% of the wind velocity relative to the ocean current. Here, this relationship is derived from first principles, and it is shown that the relationship holds in the limit of small icebergs or strong winds, which applies for typical Arctic icebergs. For the opposite limit of large icebergs (length > 12 km) or weak winds, which applies for typical Antarctic tabular icebergs, it is shown that this relationship is not applicable and icebergs move with the ocean current, unaffected by the wind. The latter regime is confirmed through comparisons with observed iceberg trajectories near the Antarctic Peninsula.

Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

In this paper, the lead–lag relationship between the Arctic sea ice variability over the Barents–Kara Sea (BKS) and Ural blocking (UB) in winter (DJF) ranging from 1979/80 to 2011/12 is examined. It is found that in a regressed DJF-mean field an increased UB frequency (days) corresponds to an enhanced sea ice decline over the BKS, while the high sea surface temperature over the BKS is accompanied by a significant Arctic sea ice reduction. Lagged daily regression and correlation reveal that the growth and maintenance of the UB that is related to the positive North Atlantic Oscillation (NAO⁺) through the negative east Atlantic/west Russia (EA/WR⁻) wave train is accompanied by an intensified negative BKS sea ice anomaly, and the BKS sea ice reduction lags the UB pattern by about four days. Because the intensified UB pattern occurs together with enhanced downward infrared radiation (IR) associated with the intensified moisture flux convergence and total column water over the BKS, the UB pattern contributes significantly to the BKS sea ice decrease on a time scale of weeks through intensified positive surface air temperature (SAT) anomalies resulting from enhanced downward IR. It is also found that the BKS sea ice decline can persistently maintain even when the UB has disappeared, thus indicating that the UB pattern is an important amplifier of the BKS sea ice reduction. Moreover, it is demonstrated that the EA/WR⁻ wave train formed by the combined NAO⁺ and UB patterns is closely related to the amplified warming over the BKS through the strengthening (weakening) of mid-to-high-latitude westerly wind in the North Atlantic (Eurasia).

Large-scale modes of atmospheric variability in the southern midlatitudes can influence Antarctic sea ice concentrations (SIC) via diverse processes. For instance, variability in both the Southern Annular Mode (SAM) and zonal wave 3 (ZW3) have been linked to the abrupt 2015/16 sea ice decline. While SIC responses to each of SAM and ZW3 have been examined previously, their interaction and synchronous impact on Antarctic sea ice has not. Here, we investigate SAM/ZW3 interactions and their associated combined impacts on Antarctic sea ice using a 1200-yr simulation from a state-of-the-art climate model. Our results suggest that zonal wind anomalies associated with SAM drive SIC anomalies in the marginal ice-zone via advection of ice normal to the ice edge and Ekman drift. In contrast, meridional wind anomalies associated with ZW3 can have opposing dynamic and thermodynamic effects on SIC. Both SAM- and ZW3-related SIC anomalies propagate eastward, likely by the Antarctic Circumpolar Current. The interaction of SAM and ZW3 leads to interesting regional SIC responses. During negative SAM, ZW3-associated meridional wind anomalies across western Antarctica are closer to the ice edge and have a stronger impact on sea ice overall. ZW3 phase affects meridional wind anomalies across the whole ice edge, whereas it affects SIC anomalies mainly over western Antarctica. In parts of eastern Antarctica, SIC anomalies are less sensitive to ZW3 phase, but are sensitive to SAM, particularly in locations where the ice edge has a prominent angle relative to the SAM-related zonal wind anomalies.