Abstract
Abstract. A double moment warm rain scheme that includes the effects of turbulence on droplet collision rates has been implemented in a large-eddy model to investigate the impact of turbulence effects on clouds and precipitation. Simulations of shallow cumulus and stratocumulus show that different precipitation-dynamical feedbacks occur in these regimes when the effects of turbulence are included in the microphysical processes. In both cases inclusion of turbulent microphysics increases precipitation due to a more rapid conversion of cloud water to rain. In the shallow convection case, the greater water loading in the upper cloud levels reduces the buoyancy production of turbulent kinetic energy and the entrainment. The stratocumulus case on the other hand shows a weak positive precipitation feedback, with enhanced rainwater producing greater evaporation, stronger circulations and more turbulence. Sensitivity studies in which the cloud droplet number was varied show that greater number concentrations suppress the stratocumulus precipitation leading to larger liquid water paths. This positive second indirect aerosol effect shows no sensitivity to whether or not the effects of turbulence on droplet collision rates are included. While the sign of the second indirect effect is negative in the shallow convection case whether the effects of turbulence are considered or not, the magnitude of the effect is doubled when the turbulent microphysics are used. It is found that for these two different cloud regimes turbulence has a larger effect than cloud droplet number and the use of a different bulk microphysics scheme on producing rainfall in shallow cumuli. However, for the stratocumulus case examined here, the effects of turbulence on rainfall are not statistically significant and instead it is the cloud droplet number concentration or the choice of bulk microphysics scheme that has the largest control on the rain water.
Highlights
Cloud microphysical parameterisations are required in atmospheric models of all scales from large-eddy simulation models through to climate models
For the cloud droplet number concentration (CDNC) values explored here, the non-turbulent microphysics simulations demonstrate that stratocumulus clouds typical of this case study increase the amount of cloud water and reduce the rain water content when there is an increase in cloud droplet number
The microphysics parameterisations that include the effects of turbulence on droplet collision rates had a greater impact on the simulated precipitation rates in the shallow convection case, where the larger dissipation rates of turbulent kinetic energy (TKE) produced a more rapid conversion of cloud water to rain water
Summary
Cloud microphysical parameterisations are required in atmospheric models of all scales from large-eddy simulation models through to climate models. The collision kernels from Franklin et al (2007) were shown to be in good agreement with those of Kunnen et al (2013), who used a novel technique to simulate the turbulent flow field in their DNS These turbulent collision kernels were used in solutions of the stochastic collection equation (SCE) by Franklin (2008) to develop empirical double-moment parameterisations of the effect of autoconversion, accretion and self-collection on the rain and cloud water mixing ratios and the rain and cloud drop number concentrations. Parameterisations using both turbulent and non-turbulent collision kernels were developed.
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