John Backman

Primary sources control the variability of aerosol optical properties in the Antarctic Peninsula

Abstract Aerosol particle optical properties were measured continuously between years 2013–2015 at the Marambio station in the Antarctic Peninsula. Annual cycles of particle scattering and absorption were studied and explained using measured particle chemical composition and the analysis of air mass transport patterns. The particle scattering was found elevated during the winter but the absorption did not show any clear annual cycle. The aerosol single scattering albedo at nm was on average 0.96 0.10, with a median of 0.99. Aerosol scattering Ångström exponent increased during summer, indicating an increasing fraction of fine mode particles. The aerosol was mainly composed of sea salt, sulphate and crustal soil minerals, and most of the particle mass were in the coarse mode. Both the particle absorption and scattering were increased during high wind speeds. This was explained by the dominance of the primary marine sea-spray and wind-blown soil dust sources. In contrast, the back-trajectory analysis suggested that long-range transport has only a minor role as a source of absorbing aerosol at the peninsula.

Anthropogenic fine aerosols dominate the wintertime regime over the northern Indian Ocean

Abstract This study presents and evaluates the most comprehensive set to date of chemical, physical and optical properties of aerosols in the outflow from South Asia covering a full winter (Nov. 2014 – March 2015), here intercepted at the Indian Ocean receptor site of the Maldives Climate Observatory in Hanimaadhoo (MCOH). Cluster analysis of air-mass back trajectories for MCOH, combined with AOD and meteorological data, demonstrate that the wintertime northern Indian Ocean is strongly influenced by aerosols transported from source regions with three major wind regimes, originating from the Indo-Gangetic Plain (IGP), the Bay of Bengal (BoB) and the Arabian Sea (AS). As much as 97 ± 3% of elemental carbon (EC) in the PM10 was also found in the fine mode (PM2.5). Other mainly anthropogenic constituents such as organic carbon (OC), non-sea-salt (nss) -K+, nss-SO42− and NH4+ were also predominantly in the fine mode (70–95%), particularly in the air masses from IGP. The combination at this large-footprint receptor observatory of consistently low OC/EC ratio (2.0 ± 0.5), strong linear relationships between EC and OC as well as between nss-K+ and both OC and EC, suggest a predominance of primary sources, with a large biomass burning contribution. The particle number-size distributions for the air masses from IGP and BoB exhibited clear bimodal shapes within the fine fraction with distinct accumulation (0.1 μm < d < 1 μm) and Aitken (0.025 μm < d < 0.10 μm) modes. This study also supports that IGP is a key source region for the wider South Asia and nearby oceans, as defined by the criteria that anthropogenic AODs exceed 0.3 and absorption AOD > 0.03. Taken together, the aerosol pollution over the northern Indian Ocean in the dry season is dominated by a well-mixed long-range transported regime of the fine-mode aerosols largely from primary combustion origin.

Sensitivity analysis of the meteorological pre-processor MPP-FMI 3.0 usingalgorithmic differentiation

The meteorological input parameters for urban and local scale dispersion models can be evaluated by pre-processing meteorological observations, using a boundary-layer parametrization model. This study presents a sensitivity analysis of a meteorological pre-processor model (MPPFMI) that utilises readily available meteorological data as input. The sensitivity of the pre-processor to meteorological input was analysed using algorithmic differentiation (AD). The AD tool used was TAPENADE. The AD method numerically evaluates the partial derivatives of functions that are implemented in a computer program. In this study, we focus on the evaluation of vertical fluxes in the atmosphere, and in particular on the sensitivity of the predicted inverse Obukhov length and friction velocity on the model input parameters. The study shows that the estimated inverse Obukhov length and friction velocity are most sensitive to wind speed, and second most sensitive to solar irradiation. The dependency on wind speed is most pronounced at low wind speeds. The presented results have implications for improving the meteorological pre-processing models. AD is shown to be an efficient tool for studying the ranges of sensitivities of the predicted parameters on the model input values quantitatively. A wider use of such advanced sensitivity analysis methods could potentially be very useful in analysing and improving the models used in atmospheric sciences.

The Sensitivity of the Predictions of a Roadside Dispersion Model to Meteorological Variables: Evaluation Using Algorithmic Differentiation

Dispersion and transformation of air pollution originated from a network of vehicular sources can be evaluated using the CAR-FMI model, combined with a meteorological pre-processor, MPP-FMI. The aim of this study is to analyse the sensitivities of both the meteorological pre-processor and the roadside dispersion model to the variations of model input values, taking especially into account the meteorological variables. Comprehensive and systematic analyses of the sensitivities of atmospheric dispersion models have been scarce in the literature. Such sensitivity analyses can be used in the refinement of both categories of models. The sensitivity analyses have been performed using an algorithmic differentiation (AD) tool called TAPENADE. We present selected illustrative results on the sensitivities of the meteorological pre-processing model MPP-FMI and the roadside dispersion model CAR-FMI on the model input variables. However, the AD method in general could also be applied for analysing the sensitivities of any other atmospheric modelling system.

New aerosol particle formation in Amazonia

Particle nucleation in Amazonia has been an enigma throughout decades of active scrutiny of natural nucleation processes; however, measurements have so far been thought to fail capturing an actual new particle formation (NPF) event. In this study we have analyzed latest measurements of ultra-fine particle size distributions alongside with air ion spectra and revealed a diurnal pattern of ultra-fine particle apparent growth. The revealed growth pattern is preceded by diurnal precipitation probability maxima, and simultaneous abundant ion production as detected by Neutral cluster and Air Ion Spectrometer (NAIS) data. Thus, we claim that by implementing statistical analysis of scanning mobility particle sizer (SMPS) data and combining with independent observations from Neutral cluster and Air Ion Spectrometer (NAIS) we can observe a consistent signal of NPF events in Amazonia.