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The estimation of ice forces are critical to Dynamic Positioning (DP) operations in Arctic waters. Ice conditions are important for the analysis of ice-structure interaction in an ice field. To monitor sea-ice conditions, cameras are used as field observation sensors on mobile sensor platforms in Arctic. Various image processing techniques, such as Otsu thresholding, k-means clustering, distance transform, Gradient Vector Flow (GVF) Snake, mathematical morphology, are then applied to obtain ice concentration, ice types, and floe size distribution from sea-ice images to ensure safe operations of structures in ice covered regions. Those techniques yield acceptable results, and their effectiveness are demonstrated in case studies.

Large‐eddy simulations of mixed‐phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi‐Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, Ni, exerts significant influence on the cloud structure. Increasing Ni leads to a substantial reduction in liquid water path (LWP), in agreement with earlier studies. In contrast to previous intercomparison studies, all models here use the same ice particle properties (i.e., mass‐size, mass‐fall speed, and mass‐capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSDs) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass‐weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case. Sensitivity tests indicate LWP and IWP are much closer to the bin model simulations when a modified shape factor which is similar to that predicted by bin model simulation is used in bulk scheme. These results demonstrate the importance of representation of ice PSD in determining the partitioning of liquid and ice and the longevity of mixed‐phase clouds. Key Points Constrained LES of mixed‐phase Arctic clouds from 11 models are analyzed Ice water path differences are attributed to assumed ice size distributions Bulk schemes with gamma size distributions agree better with bin schemes

NRC publication: Yes

Recent studies on low aromatic fuels have shown that lower soot number emissions may reduce contrail ice particle number concentrations (N ice). Here we implemented, in a sophisticated radiative transfer model, two ice particle size distribution schemes in order to estimate the contrail radiative forcing’s (RFs) dependence on these prospective N ice reductions resulting from the introduction of sustainable aviation fuels. The results show that an 85% contrail N ice reduction produces a 35% smaller contrail RF, while neglecting all non-radiative effects. This estimate of an RF reduction only considers the effects of the N ice change assumed here, and neglects other potentially important microphysical mechanisms that may change the relationship between soot number emissions and N ice. A comparison of our results with previous published estimates from full climate model simulations, shows similar RF reductions to those which also take into account non-radiative mechanisms, evidencing the need for more studies in order to allocate the contribution from radiative and non-radiative changes, as this would guide possible mitigation implementations. Despite these modeled contrail RF reductions being largely independent of the assumed ice water content (IWC), it is only through simultaneous improvement of the IWC and N ice representation in models that contrail RF estimates can be better constrained. This is because our calculated RF varied by a factor of 3 when assuming a ±30% IWC range; and by a factor of 5 if a, still conservative, ±60% IWC range was prescribed, suggesting that the differences in the prescribed IWC and N ice values in different models may explain the large discrepancies amongst published RF estimates. Recent estimates of higher N ice values, and lower IWCs found in contrails even after several hours, compared to surrounding cirrus under similar atmospheric conditions, were assessed to conclude that it is mainly the differences in IWC that make young contrails have a smaller RF, and to reduce our previous estimate for linear contrail RF for year 2006 by 65%.

The ice cloud estimates in current global models exhibit significant inconsistency, resulting in a significant amount of uncertainties in climate forecasting. Vertically resolved ice water content (IWC) is recently available from new satellite products, such as CloudSat, providing important observational constraints for evaluating the global models. To account for the varied nature of the model parameterization schemes, it is valuable to develop methods to distinguish the cloud versus precipitating ice components from the remotely sensed estimates in order to carry out meaningful model‐data comparisons. The present study develops a new technique that partitions CloudSat total IWC into small and large ice hydrometeors, using the ice particle size distribution (PSD) parameters provided by the retrieval algorithm. The global statistics of CloudSat‐retrieved PSD are analyzed for the filtered subsets on the basis of convection and precipitation flags to identify appropriate particle size separation. Results are compared with previous partitioning estimates and suggest that the small particles contribute to ∼25–45% of the global mean total IWC in the upper to middle troposphere. Sensitivity measures with respect to the PSD parameters and the retrieval algorithm are presented. The current estimates are applied to evaluate the IWC estimates from the European Centre for Medium‐Range Weather Forecasts model and the finite‐volume multiscale modeling framework model, pointing to specific areas of potential model improvements. These results are discussed in terms of applications to model diagnostics, providing implications for reducing the uncertainty in the model representation of cloud feedback and precipitation. Key Points A new method is developed to partition CloudSat IWC on the basis of particle size Data are compared to model output, pointing to areas for model improvements Data are crucial for evaluating and reducing uncertainty of climate models

Recent years have seen a rapid reduction in the summer Arctic sea ice extent. To both understand this trend and project the future evolution of the summer Arctic sea ice, a better understanding of the physical processes that drive the seasonal loss of sea ice is required. The marginal ice zone, here defined as regions with between 15 % and 80 % sea ice cover, is the region separating pack ice from the open ocean. Accurate modelling of this region is important to understand the dominant mechanisms involved in seasonal sea ice loss. Evolution of the marginal ice zone is determined by complex interactions between the atmosphere, sea ice, ocean, and ocean surface waves. Therefore, this region presents a significant modelling challenge. Sea ice floes span a range of sizes but sea ice models within climate models assume they adopt a constant size. Floe size influences the lateral melt rate of sea ice and momentum transfer between atmosphere, sea ice, and ocean, all important processes within the marginal ice zone. In this study, the floe size distribution is represented as a power law defined by an upper floe size cut-off, lower floe size cut-off, and power-law exponent. This distribution is also defined by a new tracer that varies in response to lateral melting, wave-induced break-up, freezing conditions, and advection. This distribution is implemented within a sea ice model coupled to a prognostic ocean mixed-layer model. We present results to show that the use of a power-law floe size distribution has a spatially and temporally dependent impact on the sea ice, in particular increasing the role of the marginal ice zone in seasonal sea ice loss. This feature is important in correcting existing biases within sea ice models. In addition, we show a much stronger model sensitivity to floe size distribution parameters than other parameters used to calculate lateral melt, justifying the focus on floe size distribution in model development. We also find that the attenuation rate of waves propagating under the sea ice cover modulates the impact of wave break-up on the floe size distribution. It is finally concluded that the model approach presented here is a flexible tool for assessing the importance of a floe size distribution in the evolution of sea ice and is a useful stepping stone for future development of floe size modelling.

Sea ice dynamics exhibit granular behavior as individual floes and fracture networks become particularly evident at length scales O(10–100) km and smaller. However, climate models do not resolve floes and represent sea ice as a continuum, while existing floe‐scale sea ice models tend to oversimplify floes using discrete elements of predefined simple shapes. The idealized nature of climate and discrete element sea ice models presents a challenge of comparing the model output with floe‐scale sea ice observations. Here we present SubZero, a conceptually new sea ice model geared to explicitly simulate the life cycles of individual floes by using complex discrete elements with time‐evolving shapes. This unique model uses parameterizations of floe‐scale processes, such as collisions, fractures, ridging, and welding, to simulate a wide range of evolving floe shapes and sizes. We demonstrate the novel capabilities of the SubZero model in idealized experiments, including uniaxial compression, the summer‐time sea ice flow through the Nares Strait, and winter‐time sea ice growth. The model naturally reproduces the statistical behavior of the observed sea ice, such as the power‐law appearance of the floe size distribution and the long‐tailed ice thickness distribution. The SubZero model could provide a valuable alternative to existing discrete element and continuous sea ice models for simulations of floe interactions. Plain Language Summary Sea ice is an inherent part of our climate system that responds rapidly to climate change. It is commonly conceptualized as a collection of many strongly interacting floes (sea ice fragments). However, climate models treat sea ice as a continuum, as resolving the complexity of floe‐scale mechanical and thermodynamical processes is challenging. Here we present a conceptually new sea ice model that can explicitly simulate the life cycle of individual sea ice floes, including collisions, fractures, ridging and rafting, welding, and growth. We demonstrate the novel capabilities of SubZero in idealized experiments, including simulations of summer‐time sea ice flow through a narrow strait and winter‐time sea ice growth. Both experiments were successful in reproducing the statistical behavior of the observed sea ice, specifically the distribution of floe sizes and thicknesses. The unique SubZero capabilities may improve the realism of sea ice modeling. Key Points A prototype of a conceptually new sea ice model is developed for explicit simulation of floe‐scale dynamics Floes are modeled as polygons with evolving boundaries subject to parameterized mechanical and thermodynamic processes A set of idealized process studies demonstrated that the new model can mimic the observed floe size and ice thickness distributions

Cloud particle size distributions (PSDs) are crucial in determining the clouds physical and optical properties and hence their radiative feedback to the climate. Here we present unprecedented occurrence patterns of cloud particles derived from 270 h of cloud measurements (≈ 975 000 PSDs). The focus of the analysis is on cirrus clouds, but liquid and mixed-phase clouds are also shown. In particular, cirrus PSDs for cold to warm cirrus temperatures and microphysically thin to thick cirrus clouds are provided in a novel presentation as heat maps. The observations are accompanied by simulations of ice crystal growth in in situ-origin cirrus, showing that the maximum size to which the cirrus ice crystals can grow increases from approx. 60 µm@T < 200 K to 230 µm@T > 220 K. Crystals larger than this size are most likely of liquid-origin. The combined evaluation of observations and simulations allows the attribution of processes shaping the PSDs. Important results are that, with increasing temperature and cirrus thickness, the most frequent ice particles change from smaller and fewer crystals of in situ-origin to larger and more crystals of both in situ and liquid-origin i.e. the cirrus type changes from in situ- to liquid-origin. In addition, three characteristic ice crystal size ranges are identified. The nucleation/evaporation size interval (∼ 3–20 µm), most frequent in the coldest, thinnest in situ-origin cirrus; the – most common – overlap size interval (∼ 20–230 µm), where both in situ-origin liquid-origin cirrus occur and the uplift/sedimenation size interval (>∼ 230 µm), which consists mostly of liquid-origin ice crystals.

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.

Soot particles, acting as ice nucleating particles (INPs), can contribute to cirrus cloud formation, which has an important influence on climate. Aviation activities emitting soot particles into the upper troposphere can potentially impact ice nucleation (IN) in cirrus clouds. Pore condensation and freezing (PCF) is an important ice formation pathway for soot particles in the cirrus regime, which requires the soot INP to have specific morphological properties, i.e., mesopore structures. In this study, the morphology and pore size distribution of two kinds of soot samples were modified by a physical agitation method without any chemical modification by which more compacted soot sample aggregates could be produced compared to the unmodified sample. The IN activities of both fresh and compacted soot particles with different sizes, 60, 100, 200 and 400 nm, were systematically tested by the Horizontal Ice Nucleation Chamber (HINC) under mixed-phase and cirrus-cloud-relevant temperatures (T). Our results show that soot particles are unable to form ice crystals at T>235 K (homogeneous nucleation temperature, HNT), but IN is observed for compacted and larger-sized soot aggregates (>200 nm) well below the homogeneous freezing relative humidity (RHhom) for T< HNT, demonstrating PCF as the dominating mechanism for soot IN. We also observed that mechanically compacted soot particles can reach a higher particle activation fraction (AF) value for the same T and RH condition compared to the same aggregate size fresh soot particles. The results also reveal a clear size dependence for the IN activity of soot particles with the same degree of compaction, showing that compacted soot particles with large sizes (200 and 400 nm) are more active INPs and can convey the single importance of soot aggregate morphology for the IN ability. In order to understand the role of soot aggregate morphology for its IN activity, both fresh and compacted soot samples were characterized systematically using particle mass and size measurements, comparisons from TEM (transmission electron microscopy) images, soot porosity characteristics from argon (Ar) and nitrogen (N2) physisorption measurements, as well as soot–water interaction results from DVS (dynamic vapor sorption) measurements. Considering the soot particle physical properties along with its IN activities, the enhanced IN abilities of compacted soot particles are attributed to decreasing mesopore width and increasing mesopore occurrence probability due to the compaction process.