Abstract
Abstract. 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, Nice, and small increases can cause persisting mixed-phase clouds to glaciate and break up.Three key dependencies on Nice are identified from sensitivity simulations and comparisons with observations made 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 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) primary ice nucleation parameterisations. Over the ocean, we find a strong sensitivity of Arctic stratus to Nice. The Cooper(1986) parameterisation performs poorly at the lower ambient temperatures, leading to a comparatively higher Nice (2.43 L−1 at the cloud-top temperature, approximately −20 °C) and cloud glaciation. A small decrease in the predicted Nice (2.07 L−1 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 through a depleted liquid phase. Decreasing the Nice further (0.54 L−1, 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 Nice is also evident at low number concentrations, where 0.1 × Nice 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.
Highlights
The significant uncertainties associated with global climate model (GCM) predictions may be largely attributed to the inadequate treatment of sub-grid-scale parameterisations (Boucher et al, 2013)
This cloud glaciation is tied to the number of ice crystals produced: over the temperature range shown in Fig. 1, D10 × 10 and C86 typically produce the most ice; rapid ice formation is simulated once the onset thresholds are reached
We have used large eddy simulations to investigate the microphysical sensitivity of Arctic mixed-phase clouds to primary ice number concentrations and surface conditions
Summary
The significant uncertainties associated with global climate model (GCM) predictions may be largely attributed to the inadequate treatment of sub-grid-scale (such as cloud microphysical) parameterisations (Boucher et al, 2013). These uncertainties are predicted to enhance discrepancies in temperature forecasts at the polar regions of our planet (ACIA, 2005; Serreze and Barry, 2011; Stocker et al, 2013). The accuracy of these forecasts can be improved by developing the modelled representation of the physical processes involved through comparisons with in situ observations (Curry et al, 1996). Young et al.: Mixed-phase Arctic cloud sensitivity to ice number concentrations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
