Nonprecipitating Shallow Cumulus Convection Is Intrinsically Unstable to Length Scale Growth

Abstract

Condensation in cumulus clouds plays a key role in structuring the mean, non-precipitating trade-wind boundary layer. Here, we summarise how this role also explains the spontaneous growth of mesoscale (> О(10) km) fluctuations in clouds and moisture around the mean state in a minimal-physics, large-eddy simulation of the undisturbed period during BOMEX on a large (О(100) km) domain. Small, spatial anomalies in latent heating in cumulus clouds, which form on top of small moisture fluctuations, give rise to circulations that transport moisture, but not heat, from dry to moist regions, and thus reinforce the latent heating anomaly. We frame this positive feedback as a linear instability in mesoscale moisture fluctuations, whose time scale depends only on i) a vertical velocity scale and ii) the mean environment’s vertical structure. In our minimal-physics setting, we show both ingredients are provided by the shallow cumulus convection itself: It is intrinsically unstable to length scale growth. The upshot is that energy released by clouds at kilometre scales may play a more profound and direct role in shaping the mesoscale trade-wind environment than is generally appreciated, motivating further research into the mechanism’s relevance.

The Time Scale of Shallow Convective Self‐Aggregation in Large‐Eddy Simulations Is Sensitive to Numerics

“…Hence, in the absence of radiative heterogeneity, we immediately recognize that $${S}_{{\chi }_{m}}^{\prime }=0$$. We will additionally assume that the direct effects of horizontal transport, subsidence and SFS diffusion on the $${\chi }_{m}^{\prime }$$ budget are small (Figures S1 and S2 in Supporting Information , Janssens et al., 2022).…”

“…To explain why, we begin at Figure 1a, which shows a progressing contrast in $${q}_{{t}_{m}}^{\prime }$$ between moist (blue) and dry (red) regions near the inversion base. Upon vertically integrating Equation , the resulting increase in $$\langle {q}_{{t}_{m}}^{\prime }\rangle $$ can primarily be attributed to the “gradient production” term (Janssens et al., 2022, Figure 8) (Bretherton & Blossey, 2017, Figure 13), that is, $$$\frac{\partial \langle {q}_{{t}_{m}}^{\prime }\rangle }{\partial t}\approx -\langle {w}_{m}^{\prime }{{\Gamma }}_{{q}_{t}}\rangle .$$$ …”

“…This minimal model is rather accurate for describing the evolution of $$\langle {q}_{{t}_{m}}^{\prime }\rangle $$ in simulation D1 (Janssens et al., 2022); here we will use it to illustrate how the mechanism is sensitive to discretization and modeling error.…”

“…Janssens et al. (2022) show that the required development of $$\partial /\partial z\left({{\Gamma }}_{{q}_{t}}/{{\Gamma }}_{{\theta }_{lv}}\right)$$ relies only on slab‐averaged heat and moisture fluxes; so does the approximation Equation . Therefore, we pause for a moment to demonstrate which scales of motion control these fluxes.…”

“…Guided by this question, we revisit a classical case of non-precipitating shallow cumulus convection and simulate it on a mesoscale domain in several numerical configurations (Section 2). We then summarize the feedback mechanism discussed by Bretherton and Blossey (2017), Janssens et al (2022) that drives the self-aggregation in these simulations (Section 3). Next, we demonstrate the multiscale nature of the feedback: Small, cumulus-scale processes drive moisture variability at scales an order of magnitude larger (Section 4).…”