The uncertainty of climate projection is significantly contributed by warm cloud feedback, which involves a complex interplay of various mechanisms. However, it is hard to unentangle temperature’s impact on a single cloud with experiments, since the cloud dynamics always covaries with environmental thermodynamical conditions. In this study, we investigate a simulated single shallow cumulus cloud’s response to temperature using two perturbation methods, namely “uniform” and “buoyancy-fixed”, the latter of which keeps the buoyancy profile unchanged in temperature perturbation. High-resolution large eddy simulation shows that uniform warming significantly increases cloud buoyancy, reducing cloud adiabaticity. If buoyancy is fixed, warming only reduces cloud area, leaving adiabatic fraction almost unchanged. Such response can be explained by Clausius-Clapeyron effect with an idealized 1D diffusion model, showing that warming increases the cloud-environment absolute humidity difference more than the increase in cloud liquid water content, resulting in a faster loss in both cloud coverage and total liquid water solely by lateral mixing. The responses of cloud coverage and total liquid water counteract, making adiabatic fraction insensitive to temperature change. Our works shows that cloud adiabatic fraction’s response to temperature is sensitive to the perturbed structure of the boundary layer, and the cloud coverage reduction by diffusion acts as positive cloud feedback mechanism in addition to the adjustment processes of the boundary layer.

A one-year’s worth of global (except poleward of 65 º N/S) marine shallow single-layer cloud-top radiative cooling (CTRC) is derived from a radiative transfer model with inputs from the satellite cloud retrievals and reanalysis sounding. The mean cloud-top radiative flux divergence is 61 Wm-2, decomposed into the longwave and shortwave components of 73 and -11 W m-2, respectively. The CTRC is largely a reflection of free-atmospheric specific humidity distribution: a dry atmosphere enhances CTRC by reducing downward thermal radiation. Consequently, the cooling minimizes in the “wet” tropics and maximizes in the “dry” eastern subtropics. Poleward of 30 º N/S, the CTRC decreases slightly due to the colder clouds that emit less effectively. The CTRC exhibits distinctive seasonal cycles with stronger cooling in the winter and has amplitudes of order 10~20 Wm-2 in stratocumulus-rich regions. The datasets were used to train a machine-learning model that substantially speeds up the retrieval.

A new methodology for satellite retrieval of cloud condensation nuclei (CCN) in shallow marine boundary layer clouds is developed and validated in this study. The methodology is based on retrieving cloud base drop concentrations (Nd) and updrafts (Wb), and calculating the supersaturation (S) based on that. Then Nd is the CCN at S. 50 The accuracy of the satellite retrievals was validated against ship borne measurements of CCN done in recent campaigns in the Southern Oceans (ACE-SPACE, MARCUS & PEGASO [2015-2018]) and in the subtropics (MAGIC [2012-2013]). The satellite retrieve Nd and S at cloud base was related to the actually measured CCN(S) at sea surface. The main findings show that: (a) coupled clouds have good agreement 55 between satellite retrievals and ship measurements of CCN(S); (b) the best agreement is achieved when using the brightest 10% of the clouds, and accounting for their adiabatic fraction, as measured by aircrafts; (c) most of the decoupled clouds had much lower CCN(S) than at the underlying surface. This means that most CCN originate from the surface and not from the free troposphere. This validates the satellite retrievals and 60 allows us to further quantify the relationships between CCN(S) and cloud microphysical properties.

Sub-cloud turbulent kinetic energy has been used to parameterize the cloud-base updraft velocity (wb) in cumulus parameterizations. The validity of this idea has never been proved in observations. Instead, it was challenged by recent Doppler lidar observations showing a poor correlation between the two. We argue that the low correlation is likely caused by the difficulty of a fixed-point lidar to measure ensemble properties of cumulus fields. Taking advantage of the stationarity and ergodicity of early-afternoon convection, we developed a lidar sampling methodology to measure wb of a shallow cumulus (ShCu) ensemble (not a single ShCu). By analyzing 128 ShCu ensembles over the Southern Great Plains, we show that the ensemble properties of sub-cloud turbulence explain nearly half of the variability in ensemble-mean wb, demonstrating the ability of sub-cloud turbulence to dictate wb. The derived empirical formulas will be useful for developing cumulus parameterizations and satellite inference of wb.