Long-term monitoring of black carbon across Germany

Lately, black carbon (BC) has received significant attention due to its climate-warming properties and adverse health effects. Nevertheless, long-term observations in urban areas are scarce, most likely because BC monitoring is not required by environmental legislation. This, however, handicaps the evaluation of air quality models which can be used to assess the effectiveness of policy measures which aim to reduce BC concentrations. Here, we present a new dataset of atmospheric BC measurements from Germany constructed from over six million measurements at over 170 stations. Data covering the period between 1994 and 2014 were collected from twelve German Federal States and the Federal Environment Agency, quality checked and harmonized into a database with comprehensive metadata. The final data in original time resolution are available for download (https://doi.org/10.1594/PANGAEA.881173). Though assembled in a consistent way, the dataset is characterized by differences in (a) measurement methodologies for determining evolved carbon and optical absorption, (b) covered time periods, and (c) temporal resolutions that ranged from half hourly to measurements every 6th day. Usage and interpretation of this dataset thus requires a careful consideration of these differences. Our analysis focuses on 2009, the year with the largest data coverage with one single methodology, as well as on the relative changes in long-term trends over ten years. For 2009, we find that BC concentrations at traffic sites were at least twice as high as at urban background, industrial and rural sites. Weekly cycles are most prominent at traffic stations, however, the presence of differences in concentrations during the week and on weekends at other station types suggests that traffic plays an important role throughout the full network. Generally higher concentrations and weaker weekly cycles during the winter months point towards the influence of other sources such as domestic heating. Regarding the long-term trends, advanced statistical techniques allow us to account for instrumentation changes and to separate seasonal and long-term changes in our dataset. Analysis shows a downward trend in BC at nearly all locations and in all conditions, with a high level of confidence for the period of 2005–2014. In depth analysis indicates that background BC is decreasing slowly, while the occurrences of high concentrations are decreasing more rapidly. In summary, legislation – both in Europe and locally – to reduce particulate emissions and indirectly BC appear to be working, based on this analysis. Adverse human health and climate impacts are likely to be diminished because of the improvements in air quality

Observing the Central Arctic Atmosphere and Surface with University of Colorado uncrewed aircraft systems

AbstractOver a five-month time window between March and July 2020, scientists deployed two small uncrewed aircraft systems (sUAS) to the central Arctic Ocean as part of legs three and four of the MOSAiC expedition. These sUAS were flown to measure the thermodynamic and kinematic state of the lower atmosphere, including collecting information on temperature, pressure, humidity and winds between the surface and 1 km, as well as to document ice properties, including albedo, melt pond fraction, and open water amounts. The atmospheric state flights were primarily conducted by the DataHawk2 sUAS, which was operated primarily in a profiling manner, while the surface property flights were conducted using the HELiX sUAS, which flew grid patterns, profiles, and hover flights. In total, over 120 flights were conducted and over 48 flight hours of data were collected, sampling conditions that included temperatures as low as −35 °C and as warm as 15 °C, spanning the summer melt season.</jats:p

Aerobiology over the Southern Ocean – implications for bacterial colonization of Antarctica

The Antarctic is experiencing drastic ecosystem change due to rapid and record warming. Understanding dispersal processes is paramount to assess the risks of microbiological invasions. Here, we analyze bacterial biodiversity in the circumpolar air above the Southern Ocean. This diversity had both local and global origins and presented from very low to very high Shannon biodiversity indices with consistent low richness, compatible with a scenario whereby samples are composed of a suite different species in very low abundances. However, only 4 of ASVs were identified in both Polar and non-Polar air masses, suggesting that the air mass over the Southern Ocean can act as a selective dispersal filter. Furthermore, air microbial diversity varied significantly with meteorological data, suggesting that regional bacterial biodiversity could be sensitive to changes in weather patterns. Future changes in weather patterns could, therefore, potentially alter the existing pattern of microbial deposition in the Antarctic

The value of remote marine aerosol measurements for constraining radiative forcing uncertainty

Aerosol measurements over the Southern Ocean are used to constrain aerosol-cloud interaction radiative forcing (RFaci) uncertainty in a global climate model. Forcing uncertainty is quantified using 1 million climate model variants that sample the uncertainty in nearly 30 model parameters. Measurements of cloud condensation nuclei and other aerosol properties from an Antarctic circumnavigation expedition strongly constrain natural aerosol emissions: default sea spray emissions need to be increased by around a factor of 3 to be consistent with measurements. Forcing uncertainty is reduced by around 7% using this set of several hundred measurements, which is comparable to the 8% reduction achieved using a diverse and extensive set of over 9000 predominantly Northern Hemisphere measurements. When Southern Ocean and Northern Hemisphere measurements are combined, uncertainty in RFaci is reduced by 21 %, and the strongest 20% of forcing values are ruled out as implausible. In this combined constraint, observationally plausible RFaci is around 0.17Wm-2 weaker (less negative) with 95% credible values ranging from-2:51 to-1:17Wm-2 (standard deviation of-2:18 to-1:46Wm-2). The Southern Ocean and Northern Hemisphere measurement datasets are complementary because they constrain different processes. These results highlight the value of remote marine aerosol measurements. © 2020 Laser Institute of America. All rights reserved

Automated identification of local contamination in remote atmospheric composition time series

Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be "tuned" to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).Peer reviewe

Overview of the MOSAiC expedition-Atmosphere INTRODUCTION

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 crosscutting 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.Peer reviewe

Annual cycle observations of aerosols capable of ice formation in central Arctic clouds

The Arctic is warming faster than anywhere else on Earth, prompting glacial melt, permafrost thaw, and sea ice decline. These severe consequences induce feedbacks that contribute to amplified warming, affecting weather and climate globally. Aerosols and clouds play a critical role in regulating radiation reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic where they impact the energy balance over the sea ice. Specifically, aerosols called ice nucleating particles (INPs) remain understudied yet are necessary for cloud ice production and subsequent changes in cloud lifetime, radiative effects, and precipitation. Here, we report observations of INPs in the central Arctic over a full year, spanning the entire sea ice growth and decline cycle. Further, these observations are size-resolved, affording valuable information on INP sources. Our results reveal a strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer melt, likely from marine biological production in local open waters. This comprehensive characterization of INPs will ultimately help inform cloud parameterizations in models of all scales