Abstract
Abstract. The formation of ice in clouds is an important processes in mixed-phase and ice-phase clouds. Yet, the representation of ice formation in numerical models is highly uncertain. In the last decade, several new parameterizations for heterogeneous freezing have been proposed. However, it is currently unclear what the effect of choosing one parameterization over another is in the context of numerical weather prediction. We conducted high-resolution simulations (Δx=250 m) of moderately deep convective clouds (cloud top ∼-18 ∘C) over the southwestern United Kingdom using several formulations of ice formation and compared the resulting changes in cloud field properties to the spread of an initial condition ensemble for the same case. The strongest impact of altering the ice formation representation is found in the hydrometeor number concentration and mass mixing ratio profiles. While changes in accumulated precipitation are around 10 %, high precipitation rates (95th percentile) vary by 20 %. Using different ice formation representations changes the outgoing short-wave radiation by about 2.9 W m−2 averaged over daylight hours. The choice of a particular representation for ice formation always has a smaller impact then omitting heterogeneous ice formation completely. Excluding the representation of the Hallett–Mossop process or altering the heterogeneous freezing parameterization has an impact of similar magnitude on most cloud macro- and microphysical variables with the exception of the frozen hydrometeor mass mixing ratios and number concentrations. A comparison to the spread of cloud properties in a 10-member high-resolution initial condition ensemble shows that the sensitivity of hydrometeor profiles to the formulation of ice formation processes is larger than sensitivity to initial conditions. In particular, excluding the Hallett–Mossop representation results in profiles clearly different from any in the ensemble. In contrast, the ensemble spread clearly exceeds the changes introduced by using different ice formation representations in accumulated precipitation, precipitation rates, condensed water path, cloud fraction, and outgoing radiation fluxes.
Highlights
Clouds consisting of a mixture of liquid and solid particles clouds play an important role in weather and climate at all latitudes
Accumulated surface precipitation varies by about 8 % between formation representations (FSENS) experiments (Fig. 4a)
At least for the investigated case, forecast uncertainty is dominated by initial condition uncertainty for all cloud field variables, whereas uncertainty intrinsic to the representation of ice formation only plays a dominant role for the detailed cloud microphysical structure
Summary
Clouds consisting of a mixture of liquid and solid particles (mixed-phase) clouds play an important role in weather and climate at all latitudes. A recent publication by Hawker et al (2020) suggested that the increase in the ice-nucleating particle (INP) number concentration per unit decrease in temperature, i.e. the slope of the parameterization, plays a key role in determining the impact of a specific parameterization on the simulated tropical deep convective cloud field. To assess the importance of the identified sensitivity in the context of model development and improvements for numerical weather prediction, it is vital to compare the sensitivity to changes in one parameterization to the uncertainty of the prediction due to other deficiencies in the model formulation and the overall predictability of the considered case The latter is important for convective situations with a small intrinsic predictability, as any small perturbation may rapidly amplify under these conditions
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