Abstract. The correct representation of Antarctic clouds in atmospheric models is crucial for accurate projections of the future Antarctic climate. This is particularly true for summer clouds which play a critical role in the surface melting of the ice-shelf in the vicinity of Weddell Sea. However these clouds are often poorly represented, as ice crystal number concentrations (ICNCs) are undepredicted by atmospheric models, even when primary ice formation is constrained with aerosol measurements. Rime-splintering, thought to be the dominant secondary ice production (SIP) mechanism at temperatures between −8 and −3 °C, is also very weak in summer Antarctic conditions. Including a parameterization for SIP due to break-up (BR) from collisions between ice particles in the Weather and Research Forecasting model bridges the gap between observations and simulations, suggesting that BR could account for the enhanced ICNCs in the pristine Antarctic atmosphere. These results are insensitive to uncertainties in primary ice production. The BR mechanism is currently not represented in most weather prediction and climate models; including this process can have a significant impact on the Antarctic radiation budget and thus in projections of the future regional climate.

<p><strong>Abstract.</strong> This study uses large eddy simulations to test the sensitivity of single-layer mixed-phase stratocumulus to primary ice number concentrations in the European Arctic. Observations from the Aerosol-Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) campaign are considered for comparison with cloud microphysics modelled using the Large Eddy Model (LEM, UK Met. Office). We find that cloud structure is very sensitive to ice number concentrations, N_ice , and small increases can cause persisting mixed-phase clouds to glaciate and break up. <br><br> Three key sensitivities are identified with comparison to in situ cloud observations over the sea ice pack, marginal ice zone (MIZ), and ocean. Over sea ice, we find deposition-condensation ice formation rates are overestimated, leading to cloud glaciation. When ice formation is limited to water-saturated conditions, we find microphysics comparable to the aircraft observations over all surfaces considered. We show that warm supercooled (−13 °C) mixed-phase clouds over the MIZ are simulated to reasonable accuracy when using both the DeMott et al. (2010) and Cooper (1986) parameterisations. Over the ocean, we find a strong sensitivity of Arctic stratus to ice number concentrations. Cooper (1986) performs poorly at the lower ambient temperatures, leading to comparatively higher ice number concentrations (2.43 L<sup>−1</sup> at the cloud top temperature, approximately −20 °C) and cloud glaciation. A small decrease in the predicted N<sub>ice</sub> (2.07 L<sup>−1</sup> at −20 °C), using the DeMott et al. (2010) parameterisation, causes mixed-phase conditions to persist for 24 h over the ocean. However, this representation leads to the formation of convective structures which reduce the cloud liquid water through snow precipitation, promoting cloud break up. Decreasing the ice crystal number concentration further (0.54 L<sup>−1</sup>, using a relationship derived from ACCACIA observations) allows mixed-phase conditions to be maintained for at least 24 h with more stability in the liquid and ice water paths. Sensitivity to N<sub>ice</sub> is also evident at low number concentrations, where 0.1×N<sub>ice</sub> predicted by the DeMott et al. (2010) parameterisation results in the formation of rainbands within the model; rainbands which also act to deplete the liquid water in the cloud and promote break up.</p>

<p><strong>Abstract.</strong> We present measurements of boundary layer aerosol concentration, size and composition from a series of research flights performed over the southwest peninsula of the UK during the COnvective Precipitation Experiment (COPE) of summer 2013. We place emphasis on periods of southwesterly winds, which locally are most conducive to convective cloud formation, when marine air from the Atlantic reached the peninsula. Accumulation mode mass loadings were typically 2–3 μg m<sup>−3</sup>, the majority of which was sulphuric acid over the sea, or ammonium sulphate inland, as terrestrial ammonia sources neutralised the aerosol. The cloud condensation nuclei (CCN) concentrations in these conditions were ~150–280 cm<sup>−3</sup> at 0.1 % and 400–500 cm<sup>−3</sup> at 0.9 % supersaturation (SST), which are in good agreement with previous Atlantic measurements, and the cloud drop concentrations at cloud base ranged from 100–500 cm<sup>−3</sup>. The concentration of CCN at 0.1 % SST was well correlated with non-sea-salt sulphate, meaning marine sulphate formation was likely the main source of CCN. Marine organic aerosol (OA) had a similar mass spectrum to sea spray, and was poorly correlated with CCN. In one case study that was significantly different to the rest, polluted anthropogenic emissions from the southern and central UK advected to the peninsula, with significant enhancements of OA, ammonium nitrate and sulphate, and black carbon. The CCN concentrations here were around six times higher than in the clean cases, and the cloud drop number concentrations were 3–4 times higher. Sources of ice nuclei (IN) were assessed by comparing different parameterisations of the nucleation of ice, using measured aerosol concentrations as input. The parameterisations based on total aerosol produced IN concentrations that agreed within an order of magnitude with measured first ice concentrations at cloud temperatures as low as −12 °C. Composition-specific parameterisations for mineral dust, fluorescent particles and sea spray OA were 3–4 orders of magnitude lower, meaning either a source of IN was present that was not characterised by our measurements, and/or one or more of the compositionspecific parameterisations greatly underestimated IN in this environment.</p>

Abstract. By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a 4-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited-area model (LAM) configuration of the UM – two regionally operational single-moment schemes (UM_RA2M and UM_RA2T) and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS). Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with >90 % mean cloud occurrence simulated between 0.15 and 1 km in all the model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC. Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud-top radiative cooling from these persistent modelled cloud layers and were consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the global model, driving a cause–effect relationship between the excessive low-altitude cloudiness and the coincident cold bias. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration while improving cloud microphysical structure does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model.

(2015) and Young et al. (2016). GDAS reanalysis meteorology (Global Data Assimilation System; NOAA Air Resources Laboratory, Boulder, CO, USA) was used to simulate the 3D wind fields; however, turbulent motions are not resolved by the model and therefore some uncertainty is attached to the modelled trajectories (Fleming et al. 2012). A sample of trajectories modelled over the sea ice (red), MIZ (magenta) and ocean (blue) are shown. Sea ice concentration from the NSIDC is shown in colour, with the ocean and sea ice depicted in blue and yellow respectively. Sea ice data at the north pole are not included.

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The influence of immersion-and contact-freezing within the Morrison et al. (2005) microphysics scheme was tested to quantify their contribution to N ice. Simulations with contact-freezing (Meyers et al. 1992-hereafter, M92) and immersion-freezing (Bigg 1953-hereafter, B53) switched either on or off are shown in Fig. S1. The addition of B53 and M92 produces a significantly larger ice crystal number concentration (up to 3 L −1 , 1.5 L −1 , and 10 L −1 in cases 1, 2, and 3 respectively) than the mean observed (0.47 ± 0.86 L −1 , 0.35 ± 0.20 L −1 , and 0.55 ± 0.95 L −1 respectively, Table 2). Modelled ice number concentrations with and without B53 and M92 active are similar in case 1. Both representations cause glaciation, and liquid water is not modelled at any point during the simulations. No improvement can be seen in the liquid water mixing ratio when both the B53 and M92 nucleation mechanisms are disabled. Modelled ice number concentrations for case 2 peak at ∼1.5 L −1 and ∼0.8 L −1 with and without both B53 immersion-and M92 contact-freezing nucleation active. Both scenarios allow for liquid water to form in the cloud, with ∼0.2 g kg −1 modelled. When B53 and M92 are active in case 3, high ice number concentrations are rapidly simulated at approximately 12 h-14 h. This event causes the evaporation of all simulated liquid water, and the region of high ice number concentration dissipates back to the original sustained concentration of ∼2 L −1 afterwards. This event is not simulated when B53 and M92 are disabled, suggesting these additional sources of ice number are the cause of this phenomenon.

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