The Amazon Basin, which plays a critical role in the carbon and water cycle, is under stress due to changes in climate, agricultural practices, and deforestation. The effects of thermodynamic and microphysical forcing on the strength of thunderstorms in the Basin (75-45° W, 0-15° S) were examined during the pre-monsoon season (mid-August through mid-December), a period with large variations in aerosols, intense convective storms, and plentiful flashes. The analysis used measurements of radar reflectivity, ice water content (IWC), and aerosol type from instruments aboard the CloudSat and CALIPSO satellites, flash rates from the ground-based STARNET network, and total aerosol optical depth (AOD) from a surface network and a meteorological re-analysis. After controlling for convective available potential energy (CAPE), it was found that thunderstorms that developed under dirty (high-AOD) conditions were 1.5 km deeper, had 50% more IWC, and more than two times as many flashes as storms that developed under clean conditions. The sensitivity of flashes to AOD was largest for low values of CAPE where increases of more than a factor of three were observed. The additional ice water indicated that these deeper systems had higher vertical velocities and more condensation nuclei capable of sustaining higher concentrations of water and large hydrometeors in the upper troposphere. Flash rates were also found to be larger during periods when smoke rather than dust was common in the lower troposphere, likely because smoky periods were less stable due to higher values of CAPE and AOD and lower values of mid-tropospheric relative humidity.

Surface latent heat flux (LHF) has been deemed as the determinant driver of the stratocumulus-to-cumulus transition (SCT). The distinct signature of the LHF in driving the SCT, however, has not been found in observations. This motivates us to ask: how determinant the LHF is to SCT? To answer it, we conduct large-eddy simulations in a Lagrangian setup in which the sea-surface temperature increases over time to mimic a low-level cold air advection. To isolate the role of LHF, we conduct a mechanism-denial experiment in which the LHF adjustment is turned off to evaluate the response of SCT. The simulations confirm the indispensable roles of LHF in sustaining (although not initiating) the boundary layer decoupling (first stage of SCT) and driving the cloud regime transition (second stage of SCT). Specifically, we found that decoupling can happen without the need for LHF to increase as long as the capping inversion is weak enough to ensure high entrainment efficiency. The decoupled state, however, cannot sustain without the help of LHF adjustment, leading to the recoupling of the boundary layer. In the coupled boundary layer, the stratocumulus sheet thins over time due to the lack of moisture supply, eventually leading to a cloud-free boundary layer. Interestingly, the stratocumulus sheet sustains longer without LHF adjustment. The mechanisms underlying the findings are explained from the perspectives of cloud-layer budgets of energy (first stage) and liquid water path (second stage). Lastly, we develop a new model diagnostic that offers a physically robust conceptualization of boundary layer decoupling.

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.

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.

Ozone (O3) is an important trace and greenhouse gas in the atmosphere yet, and it threatens the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct measurements whose accuracy and density vary considerably. To overcome these limitations, we employed the ensemble learning method of the extremely randomized trees model by utilizing the spatiotemporal information of a large number of input variables from ground-based observations, remote sensing, atmospheric reanalysis, and model simulation products to estimate ground-level O3. This method yields uniform, long-term and continuous spatiotemporal information of daily maximum eight-hour average (MDA8) O3 over China (called ChinaHighO3) from 2013 to 2020 at a 10 km resolution without any missing values (spatial coverage = 100%). Evaluation against observations indicates that our O3 estimations and predictions are reliable with an average out-of-sample (out-of-station) coefficient of determination (CV-R2) of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 [units here are at standard conditions (273K, 1013hPa)], and are also robust at varying spatial and temporal scales in China. This high-quality and full-coverage O3 dataset allows us to investigate the exposure and trends in O3 pollution at both long- and short-term scales. Trends in O3 concentrations varied substantially but showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020 in China. Most areas show an increasing trend since 2015, especially in summer ozone over the North China Plain. Our dataset accurately captured a recent national and regional O3 pollution event from 23 April to 8 May in 2020. Rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.