Abstract. Mixed-phase clouds over the Southern Ocean profoundly influence Earth's radiative balance, yet climate models persistently exhibit a positive surface shortwave radiation bias driven by over-glaciation in mixed-phase clouds. Addressing this bias requires accurate representation of ice-nucleating particles (INPs). This study presents the first online implementation of aerosol-aware marine INP parametrizations derived interactively from sea spray and marine organic aerosols within the high-resolution Unified Model. Using CAPRICORN-2 shipborne observations, we demonstrate that the default INP scheme overestimates INP concentrations by up to four orders of magnitude, causing a severely underestimated liquid water path. In contrast, empirical Antarctic and deterministic marine INP schemes reproduce the low INP concentrations typical of the pristine Southern Ocean, improving cloud and radiative properties. Crucially, microphysical and radiative responses to these INP reductions are strongly regime-dependent. In deep, pre-frontal mixed-phase clouds, suppressing INP concentrations effectively inhibits cloud glaciation, significantly enhancing supercooled liquid water and largely mitigating the surface shortwave radiation bias. However, in shallow, post-frontal stratocumulus clouds, altering INP parametrization yields negligible improvements. Radiosonde evaluations reveal this insensitivity is driven by several model deficits, including systematically smoothed boundary layer inversions leading to excessive dry air entrainment, exacerbated by underestimated cyclonic moisture transport. Consequently, these shallow clouds are thermodynamically starved of water vapor, rendering microphysical INP adjustments ineffective. Ultimately, fully resolving Southern Ocean cloud-radiation biases requires synergistic advancements in representing aerosols, boundary layer physics and large-scale meteorological forcings.

The impact that biogenic emissions have on aerosol-cloud interactions across the Southern Ocean is poorly quantified. Here we use satellite and ship observations during austral summer to study these interactions. We present observational evidence that biogenic aerosols increase cloud condensation nuclei and cloud droplet number concentrations over the Southern Ocean off East Antarctica, coinciding with very low concentrations of ice-nucleating particles and higher occurrences of supercooled liquid-containing low-level clouds.

Global storm resolving models (GSRMs) represent the next generation of global climate models. One of them is a 5‐km Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Its high resolution means that parameterizations of convection and clouds, including subgrid‐scale clouds, are omitted, relying on explicit simulation but necessarily utilizing microphysics and turbulence parameterizations. Standard‐resolution (10–100 km) models, which use convection and cloud parameterizations, have substantial cloud biases over the Southern Ocean (SO), adversely affecting radiation and sea surface temperature. The SO is dominated by low clouds, which cannot be observed accurately from space due to overlapping clouds, attenuation, and ground clutter. We evaluated SO clouds in ICON and the ERA5 and MERRA‐2 reanalyzes using approximately 2400 days of lidar observations and 2300 radiosonde profiles from 31 voyages and a Macquarie Island station during 2010–2021, compared to the model and reanalyzes using a ground‐based lidar simulator. We found that ICON and the reanalyzes underestimate the total cloud fraction by about 10% and 20%, respectively. ICON and ERA5 overestimate the cloud occurrence peak at about 500 m, associated with underestimated lower tropospheric stability and overestimated lifting condensation level. The reanalyzes strongly underestimate fog and very low‐level clouds, and MERRA‐2 underestimates cloud occurrence at almost all heights. Outgoing shortwave radiation is overestimated in MERRA‐2, implying a “too few, too bright” cloud problem. SO cloud and fog biases are a substantial issue in the analyzed model and reanalyzes and result in shortwave and longwave radiation biases.

The interaction of natural marine aerosol with clouds and radiation is a significant source of climate model uncertainty. The Southern Ocean represents a key area to understand these interactions, and a region where significant model biases exist. Here we provide an evaluation of the Australian Community Climate and Earth System Simulator atmosphere model which includes a double-moment aerosol scheme. We evaluate against measurements of condensation nuclei (N10) and cloud condensation nuclei (CCN) number from seven ship campaigns and three terrestrial locations, spanning the years 2015-2019. We find that N10 is heavily underestimated in the model across all regions and seasons by more than 50 % and in some cases by over 80 % at higher latitudes. CCN is also strongly underestimated over marine and Antarctic regions, often by more than 50 %. We then perform seven sensitivity tests to explore different aerosol configurations. We find that updating the dimethyl sulfide climatology and turning on the primary marine organic aerosol flux marginally improves marine CCN by between 4 %-9 %. N10 was reduced by between 3 %-9 %. The Southern Ocean radiative bias is also reduced by this combination of changes, with limited adverse effects. We also test altering the sea spray flux to use wind gust instead of mean wind speed. This significantly improved CCN in the marine regions, but resulted in detrimental impacts on the region's radiation budget, indicating that drastically improving the Southern Ocean's CCN budget may lead to poorer simulations of the global climate.

In a context of global warming, it is key to estimate the evolution of the atmospheric hydrological cycle and temperature in the polar regions. Since records are only available from satellite data for the last 40 years, one of the best ways to access longer records is to use climate proxies in firn cores. The water isotopic composition of firn cores is widely used to reconstruct past temperature variations. However, both temperature and atmospheric water cycle (origin of the precipitation, deposition and post-deposition effects) influence the isotopic composition of snow. We present a 2-year long time series of vapor and precipitation isotopic composition measurement at Dumont D’Urville (DDU), a coastal station in Adélie Land. This unique data set is first used to study the link between hydrological cycle and weather regimes at DDU. It is found that both continental and oceanic air masses impact the signal. Then, this record is used to evaluate the Global Climate Model ECHAM6-wiso equipped with water stable isotopes which is able to reproduce the observed isotopic signal. This result permits further use of ECHAM6-wiso to interpret water isotopic profiles on short firn cores. Using this methodology, we evaluate ECHAM6-wiso atmospheric outputs at two other East Antarctic coastal sites: Davis and Neumayer stations.

Global climate models and reanalysis products have revealed large, persistent downwelling shortwave radiation biases over the Southern Ocean and coastal Antarctica, likely caused by the incapability of models to accurately simulate frequent low‐level mixed‐phase clouds in these regions. In this study, we use the ground‐based observations collected at Davis, Antarctica during the Precipitation over Land and The Southern Ocean field campaign in austral summer of 2019 to assess the capability of the high‐resolution regional Unified Model (UM) to reproduce precipitating clouds off coastal Antarctica. We test the new UM RAL3 (Regional Atmosphere and Land 3) configuration with double‐moment Cloud AeroSol Interacting Microphysics scheme and bimodal cloud fraction scheme, running at the spatial resolution of 1.5‐km. We compare it to the previous RA2M configuration with a single‐moment cloud microphysics scheme and unimodal cloud fraction scheme. The RAL3 exhibits marginally degraded meteorological conditions relative to RA2M compared with observations. For cloud properties, the UM regional models can generally simulate the phase, vertical structure and timing of events during the sublimation and precipitation periods. Nevertheless, overestimated ice water path and potentially underestimated liquid water path (LWP) contribute to positive surface shortwave biases and negative longwave biases. The RA2M simulates more LWP, though we suggest for the wrong reasons due to its ice nucleating parameterization. Our results suggest that the new double‐moment cloud microphysics combined with bimodal cloud fraction parameterizations, while having reduced performance in some respects, has large potential to better represent low‐level mixed phase clouds for this region.

Abstract. The interaction of natural marine aerosol with clouds and radiation is a significant source of climate model uncertainty. The Southern Ocean represents a key area to understand these interactions, and a region where significant model biases exist. Here we provide an evaluation of the Australian Community Climate and Earth System Simulator atmosphere model which includes a double-moment aerosol scheme. We evaluate against condensation nuclei (N10) and cloud condensation nuclei (CCN) from seven ship campaigns and three terrestrial locations, spanning the years 2015–2019. We find that N10 is heavily underestimated in the model across all regions and seasons by more than 50 % and in some cases by over 80 % at higher latitudes. CCN is also strongly underestimated over marine and Antarctic regions, often by more than 50 %. We then perform seven sensitivity tests to explore different aerosol configurations. We find that updating the dimethyl sulfide climatology and turning on the primary marine organic aerosol flux marginally improves marine CCN by between 4–9 %. N10 however was reduced by between 3–9 %, resulting in worse model performance. The Southern Ocean radiative bias is also reduced by this combination of changes, with limited adverse effects. We also test altering the sea spray flux to use wind gust instead of mean wind speed, which significantly improved CCN in the marine regions, but resulted in detrimental impacts on the radiation budget. Our results indicate significant problems in the model’s microphysical processes and with over tuning. We suggest this needs to be addressed in a holistic way.

The Southern Ocean radiation bias has been a multigenerational problem in climate and weather models. This radiation bias, where too much sunlight is allowed to reach the surface, has been attributed to the incorrect simulation of cloud properties, particularly their phase. Allowing too much sunlight to reach the surface of the Southern Ocean has significant effects both locally (e.g. sea surface temperatures) and globally. Traditional model evaluation techniques of the Southern Ocean often focus on one particular cloud type or synoptic situation or have been limited temporally or spatially. Additionally, it is difficult to quantify non-linear relationships with more traditional evaluation techniques. In this work, we present an evaluation of the Southern Ocean cloud-radiation bias using an XGBoost model and SHapley Additive exPlanations feature importance analysis. We propose that this method can provide as much information as traditional evaluation techniques and more by handling large data sets and non-linear relationships. We find that our XGBoost model can explain 55% of the summertime daily cloud radiative bias of the entire Southern Ocean (1x1˚ grid) over 5 years, where biases in cloud properties are used as predictors. Using SHAP feature importance we find that the bias in cloud liquid water path is the most important predictor, though this varies by cloud type and latitude, and that the SHAP feature analysis can identify and quantify important complex relationships. We also show that this method can be useful in evaluating model perturbations, in particular with respect to how changes in a model may impact different clouds or regions in a different way. Overall, we suggest that this method can be used to evaluate large and complex systems in a holistic way, streamlining model evaluation and development testing.

Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30 degrees-40 degrees C temperature anomalies across the ice sheet. This record-shattering event saw numerous monthly temperature records being broken including a new all-time temperature record of -9.4 degrees C on 18 March at Concordia Station despite March typically being a transition month to the Antarctic coreless winter. The driver for these temperature extremes was an intense atmospheric river advecting subtropical/midlatitude heat and moisture deep into the Antarctic interior. The scope of the temperature records spurred a large, diverse collaborative effort to study the heat wave's meteorological drivers, impacts, and historical climate context. Here we focus on describing those temperature records along with the intricate meteorological drivers that led to the most intense atmospheric river observed over East Antarctica. These efforts describe the Rossby wave activity forced from intense tropical convection over the Indian Ocean. This led to an atmospheric river and warm conveyor belt intensification near the coastline, which reinforced atmospheric blocking deep into East Antarctica. The resulting moisture flux and upper-level warm-air advection eroded the typical surface temperature inversions over the ice sheet. At the peak of the heat wave, an area of 3.3 million km(2) in East Antarctica exceeded previous March monthly temperature records. Despite a temperature anomaly return time of about 100 years, a closer recurrence of such an event is possible under future climate projections. In Part II we describe the various impacts this extreme event had on the East Antarctic cryosphere. SIGNIFICANCE STATEMENT: In March 2022, a heat wave and atmospheric river caused some of the highest temperature anomalies ever observed globally and captured the attention of the Antarctic science community. Using our diverse collective expertise, we explored the causes of the event and have placed it within a historical climate context. One key takeaway is that Antarctic climate extremes are highly sensitive to perturbations in the midlatitudes and subtropics. This heat wave redefined our expectations of the Antarctic climate. Despite the rare chance of occurrence based on past climate, a future temperature extreme event of similar magnitude is possible, especially given anthropogenic climate change.

The evaluation and quantification of Southern Ocean cloud–radiation interactions simulated by climate models are essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud-type conditions that do not consider the entirety of the Southern Ocean's cloud regimes at once. Furthermore, it is difficult to directly quantify the complex and non-linear role that different cloud properties have on modulating cloud radiative effect. In this study, we present a new method of model evaluation, using machine learning that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost (eXtreme Gradient Boosting) model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator – Atmosphere-only model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55 % of the radiative bias from these cloud properties alone. We then apply SHAP (SHapley Additive exPlanations) feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in the liquid water path are the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors.

Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30 degrees-40 degrees C temperature anomalies across the ice sheet. In Part I, we assessed the meteorological drivers that generated an intense atmospheric river (AR) that caused these record-shattering temperature anomalies. Here, we continue our large collaborative study by analyzing the widespread and diverse impacts driven by the AR landfall. These impacts included widespread rain and surface melt that was recorded along coastal areas, but this was outweighed by widespread high snowfall accumulations resulting in a largely positive surface mass balance contribution to the East Antarctic region. An analysis of the surface energy budget indicated that widespread downward longwave radiation anomalies caused by large cloudliquid water contents along with some scattered solar radiation produced intense surface warming. Isotope measurements of the moisture were highly elevated, likely imprinting a strong signal for past climate reconstructions. The AR event attenuated cosmic ray measurements at Concordia, something previously never observed. Last, an extratropical cyclone west of the AR landfall likely triggered the final collapse of the critically unstable Conger Ice Shelf while further reducing an already record low sea ice extent.

The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) held seven targeted observing periods (TOPs) during the 2022 austral winter to enhance atmospheric predictability over the Southern Ocean and Antarctica. The TOPs of 5-10-day duration each featured the release of additional radiosonde balloons, more than doubling the routine sounding program at the 24 participating stations run by 14 nations, together with process-oriented observations at selected sites. These extra sounding data are evaluated for their impact on forecast skill via data denial experiments with the goal of refining the observing system to improve numerical weather prediction for winter conditions. Extensive observations focusing on clouds and precipitation primarily during atmospheric river (AR) events are being applied to refine model microphysical parameterizations for the ubiquitous mixed-phase clouds that frequently impact coastal Antarctica. Process studies are being facilitated by high-time-resolution series of observations and forecast model output via the YOPP Model Intercomparison and Improvement Project (YOPPsiteMIIP). Parallel investigations are broadening the scope and impact of the YOPP-SH winter TOPs. Studies of the Antarctic tourist industry's use of weather services show the scope for much greater awareness of the availability of forecast products and the skill they exhibit. The Sea Ice Prediction Network South (SIPN South) analysis of predictions of the sea ice growth period reveals that the forecast skill is superior to the sea ice retreat phase.

Significant variability in climate predictions originates from the simulated cloud cover over the Southern Ocean. Historically, Southern Ocean cloud and aerosol properties have been less studied than their northern hemisphere counterparts, and cloud-sea-ice interactions over the Southern Ocean also remain largely unexamined. We used data from combined radar, lidar, radiometer, radiosonde, and ERA5 reanalysis profiles to investigate cloud property relationships to cloud temperature, sea-ice concentration, and boundary layer stability. Our findings show correlations between both cloud macrophysical properties and radiative effects and sea-ice concentration, and that the marine atmospheric boundary layer is more stable over higher sea-ice concentrations. Mixed-phase cloud frequency of occurrence was highest over the sea-ice zone at 15%, three times higher than over cold water south of the Antarctic Polar Front. For temperatures greater than -15 degrees C, low-level, single-layer clouds were more likely to precipitate ice if they were coupled to cold-water or sea-ice surfaces than if they were decoupled from these surfaces, with the highest percentage of clouds precipitating ice observed over sea ice. These findings suggest a surface source of ice-nucleating particles at high southern latitudes that increases cloud glaciation probability. We discuss the implications of our results for future studies into the relationship between cloud properties, aerosols, sea ice, and boundary layer stability at high latitudes over the Southern Ocean. The atmosphere above the Southern Ocean remains difficult for most modern climate models to simulate well. Clouds containing liquid water below 0 degrees C, also known as supercooled liquid water, are likely responsible for much of the simulation difficulties. In this study we investigated observations of the atmosphere made by several different instruments during a shipborne campaign from 2017 to 2018, as well as satellite observations of sea ice. We found that clouds containing supercooled liquid water were more likely to occur above sea ice than above open ocean at high latitudes, and that the lowest layer of the atmosphere is more stable above sea ice than above open ocean. We also found that, over sea ice, clouds containing supercooled liquid water were less likely to contain and precipitate ice when these clouds were separated from the lowest layer of the atmosphere. Colder, drier air over Antarctic sea ice is associated with increased inversion strength and stability of the atmospheric boundary layer The prevalence of mixed-phase clouds at high southerly latitudes is greatest over sea ice For temperatures greater than -10 degrees C, clouds coupled to the surface are more likely to contain ice

As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties. Overall, we find an overestimation of +9.5±33.5 W m−2 for downwelling surface shortwave radiation fluxes and an underestimation of -2.3±13.5 W m−2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of +25.0±48.0 W m−2 occurring in summer. CERES presents an overestimation of +8.0±18.0 W m−2 for the shortwave and an underestimation of -12.1±12.2 W m−2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of +4.8±28.0 W m−2 in ACCESS-AM2 and an underestimation of -7.9±20.9 W m−2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

Ice-nucleating particles (INPs) in the Southern Ocean (SO) atmosphere have significant impacts on cloud radiative and microphysical properties. Yet, INP prediction skill in climate models remains poorly understood, in part because of the lack of long-term measurements. Here we show, for the first time, how model-simulated INP concentrations compare with year-round INP measurements during the Macquarie Island Cloud Radiation Experiment (MICRE) campaign from 2017–2018. We simulate immersion-mode INP concentrations using the Energy Exascale Earth System Model version 1 (E3SMv1) by combining simulated aerosols with recently developed deterministic INP parameterizations and the native classical nucleation theory (CNT) for mineral dust in E3SMv1. Because MICRE did not collect aerosol measurements of super-micron particles, which are more effective ice nucleators, we evaluate the model's aerosol fields at other high-latitude sites using long-term in situ observations of dust and sea spray aerosol. We find that the model underestimates dust and overestimates sea spray aerosol concentrations by 1 to 2 orders of magnitude for most of the high-latitude sites in the Southern Hemisphere. We next compare predicted INP concentrations with concentrations of INPs collected on filter samples (typically for 2 or 3 d) and processed offline using the Colorado State University ice spectrometer (IS) in immersion freezing mode. We find that when deterministic parameterizations for both dust and sea spray INPs are used, simulated INPs are within a factor of 10 of observed INPs more than 60 % of the time during summer. Our results also indicate that the E3SM's current treatment of mineral dust immersion freezing in the SO is impacted by compensating biases – an underprediction of dust amount was compensated by an overprediction of its effectiveness as INPs. We also perform idealized droplet freezing experiments to quantify the implications of the time-dependent behavior assumed by the E3SM's CNT-parameterization and compare with the ice spectrometer observations. We find that the E3SM CNT 10 s diagnostic used in this study is a reasonable approximation of the exact formulation of CNT, when applied to ice spectrometer measurements in low-INP conditions similar to Macquarie Island. However, the linearized 10 s diagnostic underestimates the exact formula by an order of magnitude or more in places with high-INP conditions like the Sahara. Overall, our findings suggest that it is important to correct the biases in E3SM's simulated dust life cycle and update E3SM's INP parameterizations. INP prediction errors of 2 to 3 orders of magnitude can have considerable impacts on the simulated cloud and radiative properties in global climate models. On comparing INP concentrations during MICRE against ship-based campaigns, Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) and Antarctic Circumnavigation Expedition (ACE), we find that INPs from the latter are significantly higher only in regions closer to Macquarie Island. This alludes to the fact that physical, chemical and biological processes affecting INP concentrations as stimulated by the island could be partly responsible for the high INP concentrations observed at Macquarie Island during the MICRE campaign. Therefore, improvements to both aerosol simulation and INP parameterizations are required to adequately simulate INPs and their cloud impacts in E3SM. It will be helpful to include a parallel measurement of the size-resolved aerosol composition and explore opportunities for long-term measurement platforms in future field campaigns studying INP sources in remote marine regions.

Antarctic precipitation remains poorly characterized and understood, especially within the boundary layer. This is due in part to a still‐limited amount of surface‐based remote sensing observations. A suite of cloud and precipitation remote‐sensing instruments including a W‐band cloud radar and a K‐band Micro Rain Radar (MRR) were used to characterize snowfall over Davis (69°S, 78°E). Surface snowfall events occurred when boundary layer wind speeds were weaker, temperatures were warmer, and relative humidity over ice higher than when virga were present. The presence of virga is associated with Föhn winds due to the location of Davis in the lee of an ice ridgeline. Dual wavelength ratio values from the summer indicate particle aggregation at temperatures of −14° to −10°C, consistent with observations made elsewhere, including in the Arctic. Riming frequency increases for temperatures above −10°C and reaches 6.5% at −3°C. No temperature dependence of rime mass fraction was found. Sublimation of snowfall mass aloft was 50% between the snow peak at 1.2 km and 205 m altitude, which occurs within CloudSat’s “blind zone.” Given the common prevailing wind direction and numerous ice ridgelines along much of the East Antarctic coastline, these Davis results can be used as a basis to further understand snowfall across the Antarctic region.

When water drops below 0° Celsius, we assume it turns to ice. This is not always true though, especially inside of some clouds over the Southern Ocean. When water exists as a liquid below 0°C, it is known as super-cooled liquid water. Water only stays liquid below 0°C if it is extremely pure. Pollution and dust are two examples of impurities that can help water freeze. The water in Southern Ocean clouds has had very little contact with pollution or dust, so these clouds are often made up of super-cooled liquid water instead of ice. Super-cooled liquid water clouds reflect more sunlight back out into space than ice clouds do. Whether clouds are made of liquid or ice can control how warm the ocean is. This makes Southern Ocean clouds super cool as well as super important for the Earth’s climate!

Southern Ocean (SO) low‐level mixed phase clouds have been a long‐standing challenge for Earth system models to accurately represent. While improvements to the Community Earth System Model version 2 (CESM2) resulted in increased supercooled liquid in SO clouds and improved model radiative biases, simulated SO clouds in CESM2 now contain too little ice. Previous observational studies have indicated that marine particles are major contributor to SO low‐level cloud heterogeneous ice nucleation, a process that initiates a number of cloud processes that govern cloud radiative properties. In this study, we utilize detailed aerosol and ice nucleating particle (INP) measurements from two recent measurement campaigns to assess simulated aerosol abundance, number size distributions, and composition and INP parameterizations for use in CESM2. Our results indicate that CESM2 has a positive bias in simulated surface‐level total aerosol surface area at latitudes north of 58°S. Measured INP populations were dominated by marine INPs and we present evidence of refractory INPs present over the SO assumed here to be mineral dust INPs. Results highlight a critical need to assess simulated mineral dust number and size distributions in CESM2 in order to adequately represent SO INP populations and their response to long‐term changes in atmospheric transport patterns and land use change. We also discuss important cautions and limitations in applying a commonly used mineral dust INP parameterization to remote regions like the pristine SO.

Our understanding of cross-disciplinary connections for Antarctica’s role in the Earth system remains incomplete, especially around its coastal margins. The focus here is on sea-ice, oceanic, and atmospheric drivers in the joint Ross Sea-far East Antarctic Region (RSfEAR)—one which spans a large longitudinal range and connects a number of ice shelves and polynyas promoting sea-ice growth and underpinning a diverse and rich ecosystem. Here, we present a minireview of recent case studies and how these inform the design for a future integrated ocean–sea ice–atmosphere observing system. The review is built around five themes: i) regional setting, ii) recent studies in the region and current strategies, iii) gap analysis, iv) future observing system design, and v) wider implications for stakeholders.

Shallow cloud decks residing in or near the boundary layer cover a large fraction of the Southern Ocean (SO) and play a major role in determining the amount of shortwave radiation reflected back to space from this region. In this article, we examine the macrophysical characteristics and thermodynamic phase of low clouds (tops < 3 km) and precipitation using ground-based ceilometer, depolarization lidar and vertically-pointing W-band radar measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE) from April 2016-March 2017. During MICRE, low clouds occurred ~65% of the time on average (slightly more often in austral winter than summer). About 2/3 of low clouds were cold-topped (temperatures < 0°C); these were thicker and had higher bases on average than warm-topped clouds. 83-88% of cold-topped low clouds were liquid phase at cloud base (depending on the season). The majority of low clouds had precipitation in the vertical range 150 to 250 meters below cloud base, a significant fraction of which did not reach the surface. Phase characterization is limited to the period between April 2016 and November 2016. Small-particle (low-radar-reflectivity) precipitation (which dominates precipitation occurrence) was mostly liquid below-cloud, while large-particle precipitation (which dominates total accumulation) was predominantly mixed/ambiguous or ice phase. Approximately 40% of cold-topped clouds had mixed/ambiguous or ice phase precipitation below (with predominantly liquid phase cloud droplets at cloud base). Below-cloud precipitation with radar reflectivity factors below about -10 dBZ were predominantly liquid, while reflectivity factors above about 0 dBZ were predominantly ice.