The science guiding the EUREC⁴A campaign and its measurements is presented. EUREC⁴A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC⁴A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC⁴A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC⁴A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

The African Monsoon Multidisciplinary Analysis (AMMA) is a major international campaign investigating far‐reaching aspects of the African monsoon, climate and the hydrological cycle. A special observing period was established for the dry season (SOP0) with a focus on aerosol and radiation measurements. SOP0 took place during January and February 2006 and involved several ground‐based measurement sites across west Africa. These were augmented by aircraft measurements made by the Facility for Airborne Atmospheric Measurements (FAAM) aircraft during the Dust and Biomass‐burning Experiment (DABEX), measurements from an ultralight aircraft, and dedicated modeling efforts. We provide an overview of these measurement and modeling studies together with an analysis of the meteorological conditions that determined the aerosol transport and link the results together to provide a balanced synthesis. The biomass burning aerosol was significantly more absorbing than that measured in other areas and, unlike industrial areas, the ratio of excess carbon monoxide to organic carbon was invariant, which may be owing to interaction between the organic carbon and mineral dust aerosol. The mineral dust aerosol in situ filter measurements close to Niamey reveals very little absorption, while other measurements and remote sensing inversions suggest significantly more absorption. The influence of both mineral dust and biomass burning aerosol on the radiation budget is significant throughout the period, implying that meteorological models should include their radiative effects for accurate weather forecasts and climate simulations. Generally, the operational meteorological models that simulate the production and transport of mineral dust show skill at lead times of 5 days or more. Climate models that need to accurately simulate the vertical profiles of both anthropogenic and natural aerosols to accurately represent the direct and indirect effects of aerosols appear to do a reasonable job, although the magnitude of the aerosol scattering is strongly dependent upon the emission data set.

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization.

Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a d... Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a d...