Moist Convection in the Giant Planet Atmospheres

Abstract

The outer planets of our Solar System display a myriad of interesting cloud features, of different colors and sizes. The differences between the types of observed clouds suggest a complex interplay between the dynamics and chemistry at play in these atmospheres. Particularly, the stark difference between the banded structures of Jupiter and Saturn vs. the sporadic clouds on the ice giants highlights the varieties in dynamic, chemical and thermal processes that shape these atmospheres. Since the early explorations of these planets by spacecrafts, such as Voyager and Voyager 2, there are many outstanding questions about the long-term stability of the observed features. One hypothesis is that the internal heat generated during the formation of these planets is transported to the upper atmosphere through latent heat release from convecting clouds (i.e., moist convection). In this review, we present evidence of moist convective activity in the gas giant atmospheres of our Solar System from remote sensing data, both from ground- and space-based observations. We detail the processes that drive moist convective activity, both in terms of the dynamics as well as the microphysical processes that shape the resulting clouds. Finally, we also discuss the effects of moist convection on shaping the large-scale dynamics (such as jet structures on these planets).

Heat-flux-limited Cloud Activity and Vertical Mixing in Giant Planet Atmospheres with an Application to Uranus and Neptune

“…The microphysics of cloud particles and precipitating condensates are controlled by three constant parameters in simulations, autoconversion rate (one over the autoconversion timescale, τ c→p ), evaporation rate, and settling velocity of precipitations. We exclude the possibility of supersaturation in simulations by assuming the supersaturated moisture condenses in one numerical time step (about 10 s) since the condensation timescale is generally less than 10 s to form submicron particles (Carlson et al 1987;Palotai et al 2022). We set the autoconversion rate as 10 −4 s −1 .…”

“…We set the autoconversion rate as 10 −4 s −1 . This number is inferred from previous cloud microphysics models (Carlson et al 1987;Palotai et al 2022) and observations (Sromovsky & Fry 2005;de Pater et al 2015). We have presented the reasons for choosing this number in Section 2.1.…”

“…The autoconversion timescale can be constrained by observations and previous microphysics works on ice giants. Cloud microphysics studies suggest the faster timescale among coalescence and coagulation processes is about ∼10 2 to 10 4 s for cloud particles with the size ranging from submicrons to submeters (Carlson et al 1987;Palotai et al 2022). Indeed, these timescales are sensitive to the collision rate of particles inside the cloud and, therefore, sensitive to the cloud density itself (Equations ( 3)-(8) in Carlson et al 1987).…”

“…Indeed, these timescales are sensitive to the collision rate of particles inside the cloud and, therefore, sensitive to the cloud density itself (Equations ( 3)-(8) in Carlson et al 1987). With a reduced cloud density, the coagulation-coalescence timescale suggested by Carlson et al (1987), Palotai et al (2022) should be longer. From the observational side, high albedo clouds can last for about 10 days (Sromovsky & Fry 2005;de Pater et al 2015;Molter et al 2019).…”

“…Observations also suggest that there are high-altitude CH 4 clouds near the tropopause at 0.1 bar on ice giants, but they are more commonly seen and active on Neptune than Uranus (e.g., Gibbard et al 2003;Irwin et al 2022;Chavez et al 2023). Summaries of cloud activities and storm events can be found in recent review papers (e.g., Hueso & Sánchez-Lavega 2019; Hueso et al 2020;Fletcher 2021;Palotai et al 2022).…”