Found 2533 publications. Showing page 66 of 254:
A satellite-based estimate of combustion aerosol cloud microphysical effects over the Arctic Ocean
Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol–cloud interactions. Here, we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify large-scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (∼ 10Wm−2) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain the majority of this signal. For example, combustion aerosols explain at most 40% of the CF differences between the full dataset and the clean-condition subset, compared to between 57% and 91% of the differences that can be predicted by co-varying meteorology. After normalizing for meteorological regime, aerosol microphysical effects have small but significant impacts on CF, CP, and precipitation frequency on an Arctic-wide scale. These effects indicate that dominant aerosol–cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic.
2018
Curating scientific information in knowledge infrastructures
Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Ubiquity Press
2018
Assessing the Relocation Robustness of on Field Calibrations for Air Quality Monitoring Devices
Springer
2018
2018
Atmospheric measurements show an increase in CH4 from the 1980s to 1998 followed by a period of near‐zero growth until 2007. However, from 2007, CH4 has increased again. Understanding the variability in CH4 is critical for climate prediction and climate change mitigation. We examine the role of CH4 sources and the dominant CH4 sink, oxidation by the hydroxyl radical (OH), in atmospheric CH4 variability over the past three decades using observations of CH4, C2H6, and δ13CCH4 in an inversion. From 2006 to 2014, microbial and fossil fuel emissions increased by 36 ± 12 and 15 ± 8 Tg y−1, respectively. Emission increases were partially offset by a decrease in biomass burning of 3 ± 2 Tg y−1 and increase in soil oxidation of 5 ± 6 Tg y−1. A change in the atmospheric sink did not appear to be a significant factor in the recent growth of CH4.
American Geophysical Union (AGU)
2018
Recent Arctic ozone depletion: Is there an impact of climate change?
After the well-reported record loss of Arctic stratospheric ozone of up to 38% in the winter 2010–2011, further large depletion of 27% occurred in the winter 2015–2016. Record low winter polar vortex temperatures, below the threshold for ice polar stratospheric cloud (PSC) formation, persisted for one month in January 2016. This is the first observation of such an event and resulted in unprecedented dehydration/denitrification of the polar vortex. Although chemistry–climate models (CCMs) generally predict further cooling of the lower stratosphere with the increasing atmospheric concentrations of greenhouse gases (GHGs), significant differences are found between model results indicating relatively large uncertainties in the predictions. The link between stratospheric temperature and ozone loss is well understood and the observed relationship is well captured by chemical transport models (CTMs). However, the strong dynamical variability in the Arctic means that large ozone depletion events like those of 2010–2011 and 2015–2016 may still occur until the concentrations of ozone-depleting substances return to their 1960 values. It is thus likely that the stratospheric ozone recovery, currently anticipated for the mid-2030s, might be significantly delayed. Most important in order to predict the future evolution of Arctic ozone and to reduce the uncertainty of the timing for its recovery is to ensure continuation of high-quality ground-based and satellite ozone observations with special focus on monitoring the annual ozone loss during the Arctic winter.
Elsevier
2018
Observation of turbulent dispersion of artificially released SO2 puffs with UV cameras
In atmospheric tracer experiments, a substance is released into the turbulent atmospheric flow to study the dispersion parameters of the atmosphere. That can be done by observing the substance's concentration distribution downwind of the source. Past experiments have suffered from the fact that observations were only made at a few discrete locations and/or at low time resolution. The Comtessa project (Camera Observation and Modelling of 4-D Tracer Dispersion in the Atmosphere) is the first attempt at using ultraviolet (UV) camera observations to sample the three-dimensional (3-D) concentration distribution in the atmospheric boundary layer at high spatial and temporal resolution. For this, during a three-week campaign in Norway in July 2017, sulfur dioxide (SO2), a nearly passive tracer, was artificially released in continuous plumes and nearly instantaneous puffs from a 9m high tower. Column-integrated SO2 concentrations were observed with six UV SO2 cameras with sampling rates of several hertz and a spatial resolution of a few centimetres. The atmospheric flow was characterised by eddy covariance measurements of heat and momentum fluxes at the release mast and two additional towers. By measuring simultaneously with six UV cameras positioned in a half circle around the release point, we could collect a data set of spatially and temporally resolved tracer column densities from six different directions, allowing a tomographic reconstruction of the 3-D concentration field. However, due to unfavourable cloudy conditions on all measurement days and their restrictive effect on the SO2 camera technique, the presented data set is limited to case studies. In this paper, we present a feasibility study demonstrating that the turbulent dispersion parameters can be retrieved from images of artificially released puffs, although the presented data set does not allow for an in-depth analysis of the obtained parameters. The 3-D trajectories of the centre of mass of the puffs were reconstructed enabling both a direct determination of the centre of mass meandering and a scaling of the image pixel dimension to the position of the puff. The latter made it possible to retrieve the temporal evolution of the puff spread projected to the image plane. The puff spread is a direct measure of the relative dispersion process. Combining meandering and relative dispersion, the absolute dispersion could be retrieved. The turbulent dispersion in the vertical is then used to estimate the effective source size, source timescale and the Lagrangian integral time. In principle, the Richardson–Obukhov constant of relative dispersion in the inertial subrange could be also obtained, but the observation time was not sufficiently long in comparison to the source timescale to allow an observation of this dispersion range. While the feasibility of the methodology to measure turbulent dispersion could be demonstrated, a larger data set with a larger number of cloud-free puff releases and longer observation times of each puff will be recorded in future studies to give a solid estimate for the turbulent dispersion under a variety of stability conditions.
2018
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