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A field campaign at Siple Dome in West Antarctica during the austral summer 2019/20 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-powered equipment, includes surface energy balance measurements, meteorology, and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model’s snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-μm atmospheric window reveal cloud phase transitions associated with cloud life cycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations.

Antarctica is a critical region for understanding climate change and sea level rise, as the great ice sheets and the ice shelves are subject to increasing risk as global climate warms. Climate models have difficulties over Antarctica, particularly with simulation of cloud properties that regulate snow surface melting or refreezing. Atmospheric and climate-related field work has significant challenges in the Antarctic, due to the small number of research stations that can support state-of-the-art equipment. Here we present new data from a suite of transportable and solar-powered instruments that can be deployed to remote Antarctic sites, including regions where ice shelves are most at risk, and we demonstrate how key components of climate model simulations can be evaluated against these data.

 

A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka‐band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12‐h increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in‐cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid‐containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), that is, high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid‐containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. This diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein.

 

Abstract. Due to its remote location and extreme weather conditions, atmospheric in situmeasurements are rare in the Southern Ocean. As a result, aerosol–cloudinteractions in this region are poorly understood and remain a major source ofuncertainty in climate models. This, in turn, contributes substantially topersistent biases in climate model simulations such as the well-known positiveshortwave radiation bias at the surface, as well as biases in numericalweather prediction models and reanalyses. It has been shown in previousstudies that in situ and ground-based remote sensing measurements across theSouthern Ocean are critical for complementing satellite data sets due to theimportance of boundary layer and low-level cloud processes. These processesare poorly sampled by satellite-based measurements and are often obscured bymultiple overlying cloud layers. Satellite measurements also do not constrainthe aerosol–cloud processes very well with imprecise estimation of cloudcondensation nuclei. In this work, we present a comprehensive set of ship-basedaerosol and meteorological observations collected on the 6-weekSouthern Ocean Ross Sea Marine Ecosystem and Environment voyage(TAN1802) voyage of RV Tangaroa across the Southern Ocean, from Wellington, New Zealand, tothe Ross Sea, Antarctica. The voyage was carried out from 8 February to21 March 2018. Many distinct, but contemporaneous, data sets were collectedthroughout the voyage. The compiled data sets include measurements from arange of instruments, such as (i) meteorological conditions at the sea surfaceand profile measurements; (ii) the size and concentration of particles; (iii)trace gases dissolved in the ocean surface such as dimethyl sulfide andcarbonyl sulfide; (iv) and remotely sensed observations of low clouds. Here,we describe the voyage, the instruments, and data processing, and provide a briefoverview of some of the data products available. We encourage the scientificcommunity to use these measurements for further analysis and model evaluationstudies, in particular, for studies of Southern Ocean clouds, aerosol, andtheir interaction. The data sets presented in this study are publiclyavailable at https://doi.org/10.5281/zenodo.4060237 (Kremser et al., 2020). 

Supercooled water is common in the clouds near coastal Antarctica and occasionally occurs at temperatures at or below −30°C. Yet the ice physics in most regional and global numerical models will glaciate out these clouds. This presents a challenge for the simulation of highly supercooled clouds that were observed at McMurdo, Antarctica during the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) project during 2015–2017. The polar optimized version of the Weather Research and Forecasting model (Polar WRF) with the recently developed two‐moment P3 microphysics scheme was used to simulate observed supercooled liquid water cases during March and November 2016. Nudging of the simulations to observed rawinsonde profiles and Antarctic automatic weather station observations provided increased realism and much greater cloud water amounts. Sensitivity tests that adjust the ice physics for extremely low ice nucleating particle (INP) concentrations decrease cloud ice and increases the cloud liquid water closer to observed amounts. In these tests, a liquid layer near cloud top is simulated, in agreement with observations. Accurate representation of INP concentrations appears to be critical for the simulation of coastal Antarctic clouds.

 

We present an analysis of long‐term data collected at Utqiaġvik, Alaska, to explore the impacts of cloud processes on the probability of finding supercooled water given cloud temperature,P(L|T), in the topmost unseeded liquid‐bearing layers.P(L|T) has local minima at temperatures around −6°C and −15°C. Simulations using habit‐evolving ice microphysics models suggest that these minima are the result of efficient vapor growth by non‐isometric habits found at these temperatures. We conclude that habit‐dependent vapor growth of ice crystals modulates the macrophysical occurrence of supercooled water in polar clouds, the effect of which should be included in model parametrizations to avoid biases and/or error compensation. Our methodology is adaptable for spherical ice treatments implemented in models (example parametrizations provided), amenable for use with satellite measurements to give global impartial observational targets for model evaluations, and may allow empirical characterization of bulk responses to seeding and possibly secondary ice effects.

 

Different cloud types are generated over Antarctica as a result of various synoptic conditions. The cloud characteristics affect their impact on the surface energy budget. In this study, the dominating synoptic regimes over Antarctica (centered on the Ross Ice Shelf) are classified using self‐organizing map analysis, applied over long‐term ERA‐Interim 700‐hPa geopotential height data. The corresponding cloud properties over McMurdo Station (measured as part of the AWARE campaign) are described and discussed with respect to the synoptic settings and sea‐ice extent conditions. Cloud radiative forcing calculations are performed as well, and a particular focus is given to the net longwave “radiatively cloudy/opaque” (RO) regime. These results are compared with measurements performed at the West Antarctic Ice Sheet (WAIS) Divide to examine their variability and applicability to other Antarctic locations. It is found that the McMurdo cloud properties are strongly affected by the regional flow patterns and mesoscale cyclonic activity, which often moderates the larger‐scale synoptic regime influence. In contrast, the WAIS clouds are more susceptible to the varying synoptic settings. It is suggested that the positive trend in the (frequent) cyclonic activity near the Antarctic coastal regions makes ice clouds an increasingly prominent contributor for the RO cases, especially during freezeup and maximum sea‐ice conditions.

 

The surface downwelling longwave radiation component (LW↓) is crucial for the determination of the surface energy budget and has significant implications for the resilience of ice surfaces in the polar regions. Accurate model evaluation of this radiation component requires knowledge about the phase, vertical distribution, and associated temperature of water in the atmosphere, all of which control the LW↓ signal measured at the surface. In this study, we examine the LW↓ model errors found in the Antarctic Mesoscale Prediction System (AMPS) operational forecast model and the ERA5 model relative to observations from the ARM West Antarctic Radiation Experiment (AWARE) campaign at McMurdo Station and the West Antarctic Ice Sheet (WAIS) Divide. The errors are calculated separately for observed clear-sky conditions, ice-cloud occurrences, and liquid-bearing cloud-layer (LBCL) occurrences. The analysis results show a tendency in both models at each site to underestimate the LW↓ during clear-sky conditions, high error variability (standard deviations > 20 W m−2) during any type of cloud occurrence, and negative LW↓ biases when LBCLs are observed (bias magnitudes >15 W m−2 in tenuous LBCL cases and >43 W m−2 in optically thick/opaque LBCLs instances). We suggest that a generally dry and liquid-deficient atmosphere responsible for the identified LW↓ biases in both models is the result of excessive ice formation and growth, which could stem from the model initial and lateral boundary conditions, microphysics scheme, aerosol representation, and/or limited vertical resolution.