Intercomparison of large‐eddy simulations of Arctic mixed‐phase clouds: Importance of ice size distribution assumptions

Ovchinnikov, Mikhail; Ackerman, Andrew S.; Avramov, Alexander; Cheng, Anning; Fan, Jiwen; Fridlind, A. M.; Ghan, Steven J.; Harrington, Jerry Y.; Hoose, Corinna; Korolev, Alexei; McFarquhar, Greg M.; Morrison, Hugh; Paukert, Marco; Savre, Julien; Shipway, Ben J.; Shupe, Matthew D.; Solomon, Amy; Sulia, Kara J.

doi:10.1002/2013ms000282

Cited by 142 publications

(247 citation statements)

References 56 publications

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“…A TKE (turbulent kinetic energy)-based surface transfer scheme describes the transport through the surface layer (Schättler et al, 2015). Some basic aspects of our simulations follow the model setup of Ovchinnikov et al (2014), such as fixed number concentrations of both CDNC and ice crystal concentration, large-scale subsidence, and a 2 h spin-up period before ice crystal formation. The advantage of this simplified approach, having fixed number concentrations of CDNC, is that the microphysical processes are constrained and can be easily varied in sensitivity experiments.…”

Section: Model Description and Setupmentioning

confidence: 99%

Modelling micro- and macrophysical contributors to the dissipation of an Arctic mixed-phase cloud during the Arctic Summer Cloud Ocean Study (ASCOS)

et al. 2017

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Abstract. The Arctic climate is changing; temperature changes in the Arctic are greater than at midlatitudes, and changing atmospheric conditions influence Arctic mixedphase clouds, which are important for the Arctic surface energy budget. These low-level clouds are frequently observed across the Arctic. They impact the turbulent and radiative heating of the open water, snow, and sea-ice-covered surfaces and influence the boundary layer structure. Therefore the processes that affect mixed-phase cloud life cycles are extremely important, yet relatively poorly understood. In this study, we present sensitivity studies using semi-idealized large eddy simulations (LESs) to identify processes contributing to the dissipation of Arctic mixed-phase clouds. We found that one potential main contributor to the dissipation of an observed Arctic mixed-phase cloud, during the Arctic Summer Cloud Ocean Study (ASCOS) field campaign, was a low cloud droplet number concentration (CDNC) of about 2 cm −3 . Introducing a high ice crystal concentration of 10 L −1 also resulted in cloud dissipation, but such high ice crystal concentrations were deemed unlikely for the present case. Sensitivity studies simulating the advection of dry air above the boundary layer inversion, as well as a modest increase in ice crystal concentration of 1 L −1 , did not lead to cloud dissipation. As a requirement for small droplet numbers, pristine aerosol conditions in the Arctic environment are therefore considered an important factor determining the lifetime of Arctic mixed-phase clouds.

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Section: Model Description and Setupmentioning

confidence: 99%

Modelling micro- and macrophysical contributors to the dissipation of an Arctic mixed-phase cloud during the Arctic Summer Cloud Ocean Study (ASCOS)

et al. 2017

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“…Our default model resolution (Dx, Dz) 5 (100, 50) m has allowed us to run a large number of simulations across different microphysics schemes and temperatures but may be insufficient to fully resolve the liquid layer at the top of Arctic mixed-phase clouds (e.g., Ovchinnikov et al 2014) and the sharpness of inversions that form near stratus cloud tops (e.g., Blossey et al 2013). Sensitivity tests with varied resolution using the Lin-Purdue scheme at T 2 (0) of 08 and 208C show that altering the horizontal resolution by a factor of 2 typically leads to a change of a few watts per square meter in surface cloud forcing and ;18C in time-mean surface air temperature and that decreasing the resolution for a cold initial state can completely suppress the low-level turbulence (Table 2).…”

Section: Treatment Of the Surface And Other Limitationsmentioning

confidence: 99%

Suppression of Arctic Air Formation with Climate Warming: Investigation with a Two-Dimensional Cloud-Resolving Model

Cronin

Tziperman

2017

Journal of the Atmospheric Sciences

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Arctic climate change in winter is tightly linked to changes in the strength of surface temperature inversions, which occur frequently in the present climate as Arctic air masses form during polar night. Recent work proposed that, in a warmer climate, increasing low-cloud optical thickness of maritime air advected over highlatitude landmasses during polar night could suppress the formation of Arctic air masses, amplifying winter warming over continents and sea ice. But this mechanism was based on single-column simulations that could not assess the role of fractional cloud cover change. This paper presents two-dimensional cloud-resolving model simulations that support the single-column model results: low-cloud optical thickness and duration increase strongly with initial air temperature, slowing the surface cooling rate as the climate is warmed. The cloud-resolving model cools less at the surface than the single-column model, and the sensitivity of its cooling to warmer initial temperatures is also higher, because it produces cloudier atmospheres with stronger lowertropospheric mixing and distributes cloud-top cooling over a deeper atmospheric layer with larger heat capacity. Resolving larger-scale cloud turbulence has the greatest impact on the microphysics schemes that best represent general observed features of mixed-phase clouds, increasing their sensitivity to climate warming. These findings support the hypothesis that increasing insulation of the high-latitude land surface by low clouds in a warmer world could act as a strong positive feedback in future climate change and suggest studying Arctic air formation in a three-dimensional climate model.

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“…However, large-scale models typically do not resolve this vertical structure. Much higher-resolution large-eddy simulations (LES) have successfully been employed to study Arctic mixed-phase clouds, but show considerable intermodel spread with a strong sensitivity of model results to both ice number concentration and particle-size distribution [Ovchinnikov et al, 2014]. The important role of cloud microphysics for the maintenance of Arctic mixedphase stratocumulus clouds was also emphasized by Fridlind et al [2012], who showed that consumption of ice nuclei by cloud processes can limit ice formation.…”

Section: Introductionmentioning

confidence: 99%

Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison

Pithan

Ackerman

Angevine

et al. 2016

J Adv Model Earth Syst

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Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic winter boundary layer. In the cloudy state, cloud liquid water limits surface radiative cooling, and temperature inversions are weak and elevated. In the radiatively clear state, strong surface radiative cooling leads to the build‐up of surface‐based temperature inversions. Many large‐scale models lack the cloudy state, and some substantially underestimate inversion strength in the clear state. Here, the transformation from a moist to a cold dry air mass is modeled using an idealized Lagrangian perspective. The trajectory includes both boundary layer states, and the single‐column experiment is the first Lagrangian Arctic air formation experiment (Larcform 1) organized within GEWEX GASS (Global atmospheric system studies). The intercomparison reproduces the typical biases of large‐scale models: some models lack the cloudy state of the boundary layer due to the representation of mixed‐phase microphysics or to the interaction between micro‐ and macrophysics. In some models, high emissivities of ice clouds or the lack of an insulating snow layer prevent the build‐up of surface‐based inversions in the radiatively clear state. Models substantially disagree on the amount of cloud liquid water in the cloudy state and on turbulent heat fluxes under clear skies. Observations of air mass transformations including both boundary layer states would allow for a tighter constraint of model behavior.

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Intercomparison of large‐eddy simulations of Arctic mixed‐phase clouds: Importance of ice size distribution assumptions

Cited by 142 publications

References 56 publications

Modelling micro- and macrophysical contributors to the dissipation of an Arctic mixed-phase cloud during the Arctic Summer Cloud Ocean Study (ASCOS)

Modelling micro- and macrophysical contributors to the dissipation of an Arctic mixed-phase cloud during the Arctic Summer Cloud Ocean Study (ASCOS)

Suppression of Arctic Air Formation with Climate Warming: Investigation with a Two-Dimensional Cloud-Resolving Model

Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison

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