Abstract. The role of aerosols, clouds and their interactions with radiation remain among the largest unknowns in the climate system. Even though the processes involved are complex, aerosol-cloud interactions are often analyzed by means of bivariate relationships. In this study, 15 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) of monthly satellite-retrieved near-global aerosol products are combined with reanalysis data of various meteorological parameters to predict satellite-derived marine liquidwater cloud occurrence and properties by means of regionspecific artificial neural networks. The statistical models used are shown to be capable of predicting clouds, especially in regions of high cloud variability. On this monthly scale, lower-tropospheric stability is shown to be the main determinant of cloud fraction and droplet size, especially in stratocumulus regions, while boundary layer height controls the liquid-water amount and thus the optical thickness of clouds. While aerosols show the expected impact on clouds, at this scale they are less relevant than some meteorological factors. Global patterns of the derived sensitivities point to regional characteristics of aerosol and cloud processes.

Understanding the processes that determine lowcloud properties and aerosol-cloud interactions (ACIs) is crucial for the estimation of their radiative effects. However, the covariation of meteorology and aerosols complicates the determination of cloud-relevant influences and the quantification of the aerosol-cloud relation.This study identifies and analyzes sensitivities of cloud fraction and cloud droplet effective radius to their meteorological and aerosol environment in the atmospherically stable southeast Atlantic during the biomass-burning season based on an 8-day-averaged data set. The effect of geophysical parameters on clouds is investigated based on a machine learning technique, gradient boosting regression trees (GBRTs), using a combination of satellite and reanalysis data as well as trajectory modeling of air-mass origins. A comprehensive, multivariate analysis of important drivers of cloud occurrence and properties is performed and evaluated.The statistical model reveals marked subregional differences of relevant drivers and processes determining low clouds in the southeast Atlantic. Cloud fraction is sensitive to changes of lower tropospheric stability in the oceanic, southwestern subregion, while in the northeastern subregion it is governed mostly by surface winds. In the pristine, oceanic subregion large-scale dynamics and aerosols seem to be more important for changes of cloud droplet effective radius than in the polluted, near-shore subregion, where free tropospheric temperature is more relevant. This study suggests the necessity to consider distinct ACI regimes in cloud studies in the southeast Atlantic.

This study investigates the impact of air mass origin and dynamics on cloud property changes in the Southeast Atlantic (SEA) during the biomass burning season. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget and thus prominent in climate system research. In this study, the thermodynamically stable SEA stratocumulus cover is observed not only as the result of local environmental conditions but also as connected to large‐scale meteorology by the often neglected but important role of spatial origins of air masses entering this region. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a Hybrid Single‐Particle Lagrangian Integrated Trajectory cluster analysis is conducted linking satellite observations of cloud properties (Spinning‐Enhanced Visible and Infrared Imager), information on aerosol species (Monitoring Atmospheric Composition and Climate), and meteorological context (ERA‐Interim reanalysis) to air mass clusters. It is found that a characteristic pattern of air mass origins connected to distinct synoptical conditions leads to marked cloud property changes in the southern part of the study area. Long‐distance air masses are related to midlatitude weather disturbances that affect the cloud microphysics, especially in the southwestern subdomain of the study area. Changes in cloud effective radius are consistent with a boundary layer deepening and changes in lower tropospheric stability (LTS). In the southeastern subdomain cloud cover is controlled by a generally higher LTS, while air mass origin plays a minor role. This study leads to a better understanding of the dynamical drivers behind observed stratocumulus cloud properties in the SEA and frames potentially interesting conditions for aerosol‐cloud interactions.

An intensive observation period was conducted in September 2017 in the central Namib, Namibia, as part of the project Namib Fog Life Cycle Analysis (NaFoLiCA). The purpose of the field campaign was to investigate the spatial and temporal patterns of the coastal fog that occurs regularly during nighttime and morning hours. The fog is often linked to advection of a marine stratus that intercepts with the terrain up to 100 km inland. Meteorological data, including cloud base height, fog deposition, liquid water path, and vertical profiles of wind speed/direction and temperature, were measured continuously during the campaign. Additionally, profiles of temperature and relative humidity were sampled during five selected nights with stratus/fog at both coastal and inland sites using tethered balloon soundings, drone profiling, and radiosondes. This paper presents an overview of the scientific goals of the field campaign; describes the experimental setup, the measurements carried out, and the meteorological conditions during the intensive observation period; and presents first results with a focus on a single fog event.

Abstract. Air pollution, in particular high concentrations of particulate matter smaller than 1 µm in diameter (PM1), continues to be a major health problem, and meteorology is known to substantially influence atmospheric PM concentrations. However, the scientific understanding of the ways in which complex interactions of meteorological factors lead to high-pollution episodes is inconclusive. In this study, a novel, data-driven approach based on empirical relationships is used to characterize and better understand the meteorology-driven component of PM1 variability. A tree-based machine learning model is set up to reproduce concentrations of speciated PM1 at a suburban site southwest of Paris, France, using meteorological variables as input features. The model is able to capture the majority of occurring variance of mean afternoon total PM1 concentrations (coefficient of determination (R2) of 0.58), with model performance depending on the individual PM1 species predicted. Based on the models, an isolation and quantification of individual, season-specific meteorological influences for process understanding at the measurement site is achieved using SHapley Additive exPlanation (SHAP) regression values. Model results suggest that winter pollution episodes are often driven by a combination of shallow mixed layer heights (MLHs), low temperatures, low wind speeds, or inflow from northeastern wind directions. Contributions of MLHs to the winter pollution episodes are quantified to be on average ∼5 µg/m3 for MLHs below <500 m a.g.l. Temperatures below freezing initiate formation processes and increase local emissions related to residential heating, amounting to a contribution to predicted PM1 concentrations of as much as ∼9 µg/m3. Northeasterly winds are found to contribute ∼5 µg/m3 to predicted PM1 concentrations (combined effects of u- and v-wind components), by advecting particles from source regions, e.g. central Europe or the Paris region. Meteorological drivers of unusually high PM1 concentrations in summer are temperatures above ∼25 ∘C (contributions of up to ∼2.5 µg/m3), dry spells of several days (maximum contributions of ∼1.5 µg/m3), and wind speeds below ∼2 m/s (maximum contributions of ∼3 µg/m3), which cause a lack of dispersion. High-resolution case studies are conducted showing a large variability of processes that can lead to high-pollution episodes. The identification of these meteorological conditions that increase air pollution could help policy makers to adapt policy measures, issue warnings to the public, or assess the effectiveness of air pollution measures.