Cloud Top Phase Distributions of Simulated Deep Convective Clouds

Abstract

Space‐based observations of the thermodynamic cloud phase are frequently used for the analysis of aerosol indirect effects and other regional and temporal trends of cloud properties; yet they are mostly limited to the cloud top layers. This study addresses the information content in cloud top phase distributions of deep convective clouds during their growing stage. A cloud‐resolving model with grid spacings of 300 m and lower is used in two different setups, simulating idealized and semiidealized isolated convective clouds of different strengths. It is found that the cloud top phase distribution is systematically shifted to higher temperatures compared to the in‐cloud phase distribution due to lower vertical velocities and a resultingly stronger Wegener‐Bergeron‐Findeisen process at the cloud top. Sensitivity studies show that heterogeneous freezing can modify the cloud top glaciation temperature (where the ice pixel fraction reaches 50%) and ice multiplication via rime splintering is visible in an early ice onset at temperatures around −10°C. However, if the analyses are repeated with a coarsened horizontal resolution (above 1 km, similar to many satellite data sets), a significant part of this signal is lost, which limits the detectability of these microphysical fingerprints in the observable cloud top phase distribution. In addition, variation in the cloud dynamics also impacts the cloud phase distribution but cannot be quantified easily.

Analysis of the Thermodynamic Phase Transition of Tracked Convective Clouds Based on Geostationary Satellite Observations

“…They found T g between −15 • C and −38 • C, which is in agreement with values retrieved in Table 1. It is not straightforward to compare passive satellite measurements with models: Hoose et al (2018) showed that some processes such as rime splittering at −10 • C are not detectable by passive satellites due to spatial resolution coarser than 1 km. Also, the cloud top phase transition temperature is more likely to be shifted to higher temperatures than in cloud due to a stronger Wegner-Bergeron-Findeisen process at cloud top (Hoose et al, 2018;Korolev et al, 2017).…”

“…It is not straightforward to compare passive satellite measurements with models: Hoose et al (2018) showed that some processes such as rime splittering at −10 • C are not detectable by passive satellites due to spatial resolution coarser than 1 km. Also, the cloud top phase transition temperature is more likely to be shifted to higher temperatures than in cloud due to a stronger Wegner-Bergeron-Findeisen process at cloud top (Hoose et al, 2018;Korolev et al, 2017). Therefore, when comparing cloud top information from passive satellite with vertical distribution from active satellite, the temperature transition is more likely to be higher for passive satellites.…”

Secondary Ice Production in Simulated Deep Convective Clouds: A Sensitivity Study

“…A binary cloud phase assumption for cloudy pixels is derived following earlier studies (Hoose et al 2018;Han et al 2023) to mimic satellite products of the cloud-top phase. A cloudy pixel having a liquid mass fraction larger than 0.5 is recognized as a liquid pixel.…”

“…Cloud phase distribution of mixed-phase clouds impacts cloud radiative properties, cloud dynamics, and cloud lifetime. A lot of works have focused on the partitioning between ice and liquid and the phase transition from liquid to ice in mixed-phase clouds using numerical simulations and remote sensing retrievals (McCoy et al 2016;Hoose et al 2018;Coopman et al 2020;Bruno et al 2021;Coopman et al 2021). Observations and simulations reveal that cloud phase distributions depend on not only temperature but also cloud type and SIP processes (Rosenfeld et al 2011;Hoose et al 2018).…”

“…A lot of works have focused on the partitioning between ice and liquid and the phase transition from liquid to ice in mixed-phase clouds using numerical simulations and remote sensing retrievals (McCoy et al 2016;Hoose et al 2018;Coopman et al 2020;Bruno et al 2021;Coopman et al 2021). Observations and simulations reveal that cloud phase distributions depend on not only temperature but also cloud type and SIP processes (Rosenfeld et al 2011;Hoose et al 2018). Unfortunately, physical processes that modulate cloud phase distribution in mixed-phase clouds are still poorly understood.…”