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The Southern Ocean is a critical region for global climate, yet large cloud and solar radiation biases over the Southern Ocean are a long-standing problem in climate models and are poorly understood, leading to biases in simulated sea surface temperatures. This study shows that supercooled liquid clouds are central to understanding and simulating the Southern Ocean environment. A combination of satellite observational data and detailed radiative transfer calculations is used to quantify the impact of cloud phase and cloud vertical structure on the reflected solar radiation in the Southern Hemisphere summer. It is found that clouds with supercooled liquid tops dominate the population of liquid clouds. The observations show that clouds with supercooled liquid tops contribute between 27% and 38% to the total reflected solar radiation between 40° and 70°S, and climate models are found to poorly simulate these clouds. The results quantify the importance of supercooled liquid clouds in the Southern Ocean environment and highlight the need to improve understanding of the physical processes that control these clouds in order to improve their simulation in numerical models. This is not only important for improving the simulation of present-day climate and climate variability, but also relevant for increasing confidence in climate feedback processes and future climate projections.

The authors study the role of clouds in the persistent bias of surface downwelling shortwave radiation (SDSR) in the Southern Ocean in the atmosphere-only version of the Met Office model. The reduction of this bias in the atmosphere-only version is important to minimize sea surface temperature biases when the atmosphere model is coupled to a dynamic ocean. The authors use cloud properties and radiative fluxes estimates from the International Satellite Cloud Climatology Project (ISCCP) and apply a clustering technique to classify clouds into different regimes over the Southern Ocean. Then, they composite the cloud regimes around cyclone centers, which allows them to study the role of each cloud regime in a mean composite cyclone. Low- and midlevel clouds in the cold-air sector of the cyclones are responsible for most of the bias. Based on this analysis, the authors develop and test a new diagnosis of shear-dominated boundary layers. This change improves the simulation of the SDSR through a better simulation of the frequency of occurrence of the cloud regimes in the cyclone composite. Substantial biases in the radiative properties of the midtop and stratocumulus regimes are still present, which suggests the need to increase the optical depth of the low-level cloud with moderate optical depth and cloud with tops at midlevels.

Shattering presents a serious obstacle to current airborne in situ methods of characterizing the microphysical properties of ice clouds. Small shattered fragments result from the impact of natural ice crystals with the forward parts of aircraft-mounted measurement probes. The presence of these shattered fragments may result in a significant overestimation of the measured concentration of small ice crystals, contaminating the measurement of the ice particle size distribution (PSD). One method of identifying shattered particles is to use an inter-arrival time algorithm. This method is based on the assumption that shattered fragments form spatial clusters that have short inter-arrival times between particles, relative to natural particles, when they pass through the sample volume of the probe. The inter-arrival time algorithm is a successful technique for the classification of shattering artifacts and natural particles. This study assesses the limitations and efficiency of the inter-arrival time algorithm. The analysis has been performed using simultaneous measurements of two-dimensional (2-D) optical array probes with the standard and antishattering \"K-tips\" collected during the Airborne Icing Instrumentation Experiment (AIIE). It is shown that the efficiency of the algorithm depends on ice particle size, concentration and habit. Additional numerical simulations indicate that the effectiveness of the inter-arrival time algorithm to eliminate shattering artifacts can be significantly restricted in some cases. Improvements to the inter-arrival time algorithm are discussed. It is demonstrated that blind application of the inter-arrival time algorithm cannot filter out all shattered aggregates. To mitigate against the effects of shattering, the inter-arrival time algorithm should be used together with other means, such as antishattering tips and specially designed algorithms for segregation of shattered artifacts and natural particles.

The aggregation of ice crystals and its temperature dependence is studied in the laboratory using a large ice cloud chamber. This process is important to the evolution of ice clouds in earth's atmosphere, yet there have been relatively few laboratory studies quantifying this parameter and its dependence on temperature. A detailed microphysical model is used to interpret the results from the experiments and derive best estimates for the aggregation efficiency as well as error bars. Our best estimates for the aggregation efficiency, at temperatures other than −15 °C, (in the interval −30≤T≤5 °C) are mostly in agreement with previous findings, which were derived using a very different approach to that described here. While the errors associated with such experiments are reasonably large, statistically, at temperatures other than −15, we are able to rule out aggregation efficiencies larger than 0.5 at the 75th percentile and rule out non-zero values at −15 °C, whereas at −15 °C we can rule out values higher than 0.85 and values lower than 0.35. The values of the aggregation efficiency shown here may be used in model studies of aggregation, but care must be taken that they only apply for the initial stages of aggregate growth, with humidities at or close to water saturation, and for particles up to a maximum size of ~500 μm. They may therefore find useful application for modelling supercooled mid-level layer clouds that contain ice crystals, which are known to be important radiatively.

We show that the seasonal cycles of clouds over the mid‐latitude oceans in the Northern Hemisphere are predictors of the responses of clouds to increasing sea‐surface temperatures globally. These regions are therefore “natural laboratories” in which the processes responsible for low‐cloud feedbacks on global scales are observed as seasonal changes in local cloud properties. We use an ensemble of configurations of a global‐climate model to show that the sensitivities of cloud‐radiative anomalies to surface temperature and lower‐tropospheric stability in the “laboratory” regions predict the models' global cloud‐radiative feedbacks. Models with greater changes in low‐clouds between seasons are shown to have stronger negative feedbacks in the mid‐latitudes, and stronger positive feedbacks from the subtropical stratocumulus. The biases in the simulated seasonal cycles, compared to observations, imply that both feedbacks are too weak in the model. The consequences of this for configuring our model to have a lower climate sensitivity are discussed. Plain Language Summary As the Earth's climate warms, feedbacks from changes in clouds are crucial for determining the rate of warming. In some climate models, positive cloud feedbacks are known to contribute to projected temperature increases which fall outside the range that is considered plausible given our current understanding of the Earth's climate. Methods are needed which directly relate present‐day model errors to projected cloud feedbacks so that we can understand how to improve models and simultaneously obtain plausible feedback strengths. We show that in the Met Office Hadley Centres' climate model, the seasonal cycles of clouds in a few key regions, specifically the poleward ocean basins in the Northern Hemisphere, can be used to predict cloud feedbacks, globally, across a wide range of configurations of the model. These regions, particularly the North‐west Pacific, are “natural laboratories” in which the same processes that are responsible for global, cloud‐feedbacks onto global‐warming rates, occur annually as part of the local seasonal cycle. Studying clouds in these regions and model biases in predictions of these clouds will allow us to directly improve estimates of global climate change. Key Points Seasonal cycles of clouds constrain cloud feedbacks The northern ocean basins are natural laboratories for low‐cloud feedbacks The hot‐model problem in HadGEM3 will not easily be solved by tuning

We analyze the atmospheric processes that explain the large changes in radiative feed-backs between the two latest climate configurations of the Hadley Centre Global Environmental model. We use a large set of atmosphere-only climate-change simulations (amip and amip-p4K) to separate the contributions to the differences in feedback parameter from all the atmospheric model developments between the two latest model configurations. We show that the differences are mostly driven by changes in the shortwave cloud radiative feedback in the midlatitudes, mainly over the Southern Ocean. Two new schemes explain most of the differences: the introduction of a new aerosol scheme; and the development of a new mixed-phase cloud scheme. Both schemes reduce the strength of the pre-existing shortwave negative cloud feedback in the midlatitudes. The new aerosol scheme dampens a strong aerosol-cloud interaction, and it also suppresses a negative clear-sky shortwave feedback. The mixed-phase scheme increases the amount of cloud liquid water path (LWP) in the present-day, thereby reducing the radiative effciency of the increase of LWP in the warmer climate. It also enhances a strong, pre-existing, positive cloud fraction feedback. We assess the realism of the changes by comparing present-day simulations against observations, and discuss avenues that could help constrain the relevant processes.

Optical array probes are one of the most important tools for determining the microphysical structure of clouds. It has been known for some time that the shattering of ice crystals on the housing of these probes can lead to incorrect measurements of particle size distributions and subsequently derived microphysical properties if the resulting spurious particles are not rejected. In this paper it is shown that the interarrival times of particles measured by these probes can be bimodal—the “cloud” probes are more affected than the “precipitation” probes. The long interarrival time mode represents real cloud structure while the short interarrival time mode results from fragments of shattered ice particles. It is demonstrated for the flights considered here that if the fragmented particles are filtered using an interarrival time threshold of 2 × 10−4 s in three of the four cases and 1 × 10−5 s in the other, then the measured total concentration can be affected by up to a factor of 4 in situations where large particles are present as determined by the mass-weighted mean size exceeding 1 mm, or the exponential slope parameter falling below 30 cm−1. When the size distribution is narrow (mass weighted mean size <1 mm), ice water contents can be overestimated by 20%–30% for the cases presented here.

A physically based method for parameterizing the role of subgrid-scale turbulence in the production and maintenance of supercooled liquid water and mixed-phase clouds is presented. The approach used is to simplify the dynamics of supersaturation fluctuations to a stochastic differential equation that can be solved analytically, giving increments to the prognostic liquid cloud fraction and liquid water content fields in a general circulation model (GCM). Elsewhere, it has been demonstrated that the approach captures the properties of decameter-resolution large-eddy simulations of a turbulent mixed-phase environment. In this paper, it is shown that it can be implemented in a GCM, and the effects that this has on Southern Ocean biases and on Arctic stratus are investigated.

Heterogeneous ice nucleation is a source of uncertainty in models that represent ice clouds. The primary goal of the Ice in Clouds Experiment–Layer Clouds (ICE-L) field campaign was to determine if a link can be demonstrated between ice concentrations and the physical and chemical characteristics of the ambient aerosol. This study combines a 1D kinematic framework with lee wave cloud observations to infer ice nuclei (IN) concentrations that were compared to IN observations from the same flights. About 30 cloud penetrations from six flights were modeled. The temperature range of the observations was −16° to −32°C. Of the three simplified ice nucleation representations tested (deposition, evaporation freezing, and condensation/immersion droplet freezing), condensation/immersion freezing reproduced the lee wave cloud observations best. IN concentrations derived from the modeling ranged from 0.1 to 13 L−1 compared to 0.4 to 6 L−1 from an IN counter. A better correlation was found between temperature and the ratio of IN concentration to the concentration of large aerosol (>500 nm) than between IN concentration and the large aerosol concentration or temperature alone.

Identical composite analysis of midlatitude cyclones over oceanic regions has been carried out on both output from the NCAR Community Atmosphere Model, version 3 (CAM3) and multisensor satellite data. By focusing on mean fields associated with a single phenomenon, the ability of the CAM3 to reproduce realistic midlatitude cyclones is critically appraised. A number of perturbations to the control model were tested against observations, including a candidate new microphysics package for the CAM. The new microphysics removes the temperature-dependent phase determination of the old scheme and introduces representations of microphysical processes to convert from one phase to another and from cloud to precipitation species. By subsampling composite cyclones based on systemwide mean strength (mean wind speed) and systemwide mean moisture the authors believe they are able to make meaningful like-with-like comparisons between observations and model output. All variations of the CAM tested overestimate the optical thickness of high-topped clouds in regions of precipitation. Over a system as a whole, the model can both over- and underestimate total high-topped cloud amounts. However, systemwide mean rainfall rates and composite structure appear to be in broad agreement with satellite estimates. When cyclone strength is taken into account, changes in moisture and rainfall rates from both satellite-derived observations and model output as a function of changes in sea surface temperature are in accordance with the Clausius–Clapeyron equation. The authors find that the proposed new microphysics package shows improvement to composite liquid water path fields and cloud amounts.