Observed impact of meso-scale vertical motion on cloudiness

Abstract

We use estimates of meso-scale vertical velocity and co-located cloud measurements from the second Next-Generation Aircraft Remote Sensing for Validation campaign (NARVAL2) in the tropical North Atlantic to show the observed impact of meso-scale vertical motion on tropical clouds. Our results not only confirm previously untested hypotheses about the role of dynamics being non-negligible in determining cloudiness, but go further to show that at the meso-scale, the dynamics has a more dominant control on cloudiness variability than thermodynamics. A simple mass-flux estimate reveals that meso-scale vertical velocity at the sub-cloud layer top explains much of the variations in peak shallow cumulus cloud fraction. In contrast, we find that thermodynamic cloud-controlling factors, such as humidity and stability, are unable to explain the variations in cloudiness at the meso-scale. Thus, capturing the observed variability of cloudiness may require not only a consideration of thermodynamic factors, but also dynamic ones such as the meso-scale vertical velocity.

Non‐Local Parameterization of Atmospheric Subgrid Processes With Neural Networks

“…The large‐scale horizontal wind and moisture divergence and the vertical velocity are both a cause and a consequence of convection, and there is debate as to whether these features should be included as an input to convection and cloud parameterizations (Emanuel et al., 1994; George et al., 2021). Detailed testing would be needed to determine how including these inputs in an ML parameterization affects simulation of the general circulation and transient disturbances, and whether their use in an ML parameterization may affect the robustness or numerical stability of the simulations.…”

“…Some conventional convection schemes rely on closures in terms of the vertical velocity (Ooyama, 1969) or the moisture convergence which is closely related to the vertical velocity (Kuo, 1974), although this is less common in climate models compared 10.1029/2022MS002984 3 of 14 to closures based on measures of instability (see Table 2 of Pathak et al (2019)). Whether variables such as the vertical velocity or moisture convergence should be included as inputs to convection and cloud parameterizations is the subject of debate (Emanuel et al, 1994;George et al, 2021). In particular, using convergence as an input may lead to reverse causation since convergence is both a cause and a consequence of convection (Back & Bretherton, 2009), and this is potentially an issue in the context of ML parameterizations which can be unstable as a result of learning non-causal relations between inputs and outputs (Brenowitz et al, 2020).…”

JOANNE: Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments

“…JOANNE's immediate usefulness lies in aiding the calibration of or processing the data from remote-sensing instruments on board HALO as well as creation of derived products, e.g., a dataset of radiative profiles from EUREC 4 A soundings (Albright et al, 2021). Furthermore, the dataset potentially has applications in furthering the understanding of processes in the trades, e.g., the influence of mesoscale circulation on clouds (George et al, 2021a) or the changes in atmospheric properties within a cold pool . The vertical profiles and histogram of flight altitude for dropsonde launches shown in Fig.…”

Non-precipitating shallow cumulus convection is intrinsically unstable to length-scale growth

“…This is particularly pertinent because approaches such as those taken by Myers et al (2021); Cesana and Del Genio (2021) essentially assume large-scale cloud-controlling variables set the cloud fraction. Recent observations lend credence to this approach, suggesting that it is (presumably externally induced) variability in vertical velocity at larger-scales which raises the subcloud layer and thus gives rise to stronger cloud-base mass fluxes and cloud fractions (Bony and Stevens, 2019;Vogel et al, 2020a;George et al, 2021a).…”