The cloud droplet number concentration (N_d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N_d, but it can be inferred from retrievals of cloud optical depth (τ_c) cloud droplet effective radius (r_e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N_d uncertainty is dominated by errors in r_e, and therefore, improvements in r_e retrievals would greatly improve the quality of the N_d retrievals. Recommendations are made for how this might be achieved. Some existing N_d data sets are compared and discussed, and best practices for the use of N_d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N_d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined.

With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC)³ project was established in 2016 (www.ac3-tr.de/). It comprises modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, shipborne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data. For example, a distinct atmospheric moistening, an increase of regional storm activities, an amplified winter warming in the Svalbard and North Pole regions, and a decrease of sea ice thickness in the Fram Strait and of snow depth on sea ice have been identified. A positive trend of tropospheric bromine monoxide (BrO) column densities during polar spring was verified. Local marine/biogenic sources for cloud condensation nuclei and ice nucleating particles were found. Atmospheric–ocean and radiative transfer models were advanced by applying new parameterizations of surface albedo, cloud droplet activation, convective plumes and related processes over leads, and turbulent transfer coefficients for stable surface layers. Four modes of the surface radiative energy budget were explored and reproduced by simulations. To advance the future synthesis of the results, cross-cutting activities are being developed aiming to answer key questions in four focus areas: lapse rate feedback, surface processes, Arctic mixed-phase clouds, and airmass transport and transformation.

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin for 2 years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from the Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the introduction to the special issue of GoAmazon2014/5, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G-1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the 2-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. The G-1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year-round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This introduction also organizes information relevant to many papers in the special issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5 as presented in this introduction, research articles including thematic overview articles are anticipated in this special issue to describe the detailed results and findings of the GoAmazon2014/5 Experiment.