420 research outputs found

    On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest

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    We present an evaluation of the regional chemical transport model (CTM) WRF-CHIMERE (v2020r2) for the formation of biogenic secondary organic aerosol (BSOA) with a focus over the Finnish boreal forest. Formation processes of biogenic aerosols are still affected by different sources of uncertainties, and model predictions vary greatly depending on the levels of details of the adopted chemical and emissions schemes. In this study, air quality simulations were conducted for the summer of 2019 using different organic aerosol (OA) schemes (as currently available in the literature) to treat the formation of BSOA. First, we performed a set of simulations in the framework of the volatility basis set (VBS) scheme carrying different assumptions for the treatment of the aging processes of BSOA. The results of the model were compared against high-resolution (i.e., 1 h) organic aerosol mass and size distribution measurements performed at the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR-II) site located in Hyytiälä, in addition to other gas-phase species such as ozone (O3), nitrogen oxides (NOx), and biogenic volatile organic compound (BVOC) measurements of isoprene (C5H10) and monoterpenes. We show that WRF-CHIMERE could reproduce well the diurnal variation of the measured OA concentrations for all the investigated scenarios (along with the standard meteorological parameters) as well as the increase in concentrations during specific heat wave episodes. However, the modeled OA concentrations varied greatly between the schemes used to describe the aging processes of BSOA, as also confirmed by an additional evaluation using organic carbon (OC) measurement data retrieved from the EBAS European databases. Comparisons with isoprene and monoterpene air concentrations revealed that the model captured the observed monoterpene concentrations, but isoprene was largely overestimated, a feature that was mainly attributed to the overstated biogenic emissions of isoprene. We investigated the potential consequences of such an overestimation by inhibiting isoprene emissions from the modeling system. Results indicated that the modeled BSOA concentrations increased in the northern regions of the domain (e.g., Finland) compared to southern European countries, possibly due to a shift in the reactions of monoterpene compounds against available radicals, as further suggested by the reduction in α-pinene modeled air concentrations. Finally, we briefly analyze the differences in the modeled cloud liquid water content (clwc) among the simulations carrying different chemical schemes for the treatment of the aging processes of BSOA. The results of the model indicated an increase in clwc values at the SMEAR-II site, for simulations with higher biogenic organic aerosol loads, most likely as a result of the increased number of biogenic aerosol particles capable of activating cloud droplets.</p

    Assessing potential indicators of aerosol wet scavenging during long-range transport

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    As one of the dominant sinks of aerosol particles, wet scavenging greatly influences aerosol lifetime and interactions with clouds, precipitation, and radiation. However, wet scavenging remains highly uncertain in models, hindering accurate predictions of aerosol spatiotemporal distributions and downstream interactions. In this study, we present a flexible, computationally inexpensive method to identify meteorological variables relevant for estimating wet scavenging using a combination of aircraft, satellite, and reanalysis data augmented by trajectory modeling to account for air mass history. We assess the capabilities of an array of meteorological variables to predict the transport efficiency of black carbon (TEBC) using a combination of nonlinear regression, curve fitting, and k-fold cross-validation. We find that accumulated precipitation along trajectories (APT) – treated as a wet scavenging indicator across multiple studies – does poorly when predicting TEBC. Among different precipitation characteristics (amount, frequency, intensity), precipitation intensity was the most effective at estimating TEBC but required longer trajectories (&gt;48 h) and including only intensely precipitating grid cells. This points to the contribution of intense precipitation to aerosol scavenging and the importance of accounting for air mass history. Predictors that were most able to predict TEBC were related to the distribution of relative humidity (RH) or the frequency of humid conditions along trajectories, suggesting that RH is a more robust way to estimate TEBC than APT. We recommend the following alternatives to APT when estimating aerosol scavenging: (1) the 90th percentile of RH along trajectories, (2) the fraction of hours along trajectories with either water vapor mixing ratios &gt;15 g kg−1 or RH &gt;95 %, and (3) precipitation intensity along trajectories at least 48 h along and filtered for grid cells with precipitation &gt;0.2 mm h−1. Future scavenging parameterizations should consider these meteorological variables along air mass histories. This method can be repeated for different regions to identify region-specific factors influencing wet scavenging.</p

    Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties

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    Aerosol effects on cloud properties are notoriously difficult to disentangle from variations driven by meteorological factors. Here, a machine learning model is trained on reanalysis data and satellite retrievals to predict cloud microphysical properties, as a way to illustrate the relative importance of meteorology and aerosol, respectively, on cloud properties. It is found that cloud droplet effective radius can be predicted with some skill from only meteorological information, including estimated air mass origin and cloud top height. For ten geographical regions the mean coefficient of determination is 0.41 and normalised root-mean square error 24%. The machine learning model thereby performs better than a reference linear regression model, and a model predicting the climatological mean. A gradient boosting regression performs on par with a neural network regression model. Adding aerosol information as input to the model improves its skill somewhat, but the difference is small and the direction of the influence of changing aerosol burden on cloud droplet effective radius is not consistent across regions, and thereby also not always consistent with what is expected from cloud brightening

    Statistical methods to understand and visualise the complex behaviour of clouds in the climate system

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    Uncertainty surrounding cloud responses to changes in their environments contributes majorly to uncertainty in the radiative effects of aerosol and predictions of future climates. Stratocumulus clouds exert a strong net cooling effect due to their high albedo and large horizontal extent, yet their behaviour in the climate system is particularly uncertain due to their high sensitivity to surroundings. High-resolution modelling is crucial for studying stratocumulus behaviours, which are made up of many complex interacting processes, on many scales from large-scale dynamics to the microphysical responses to aerosol. However, many studies perturb cloud-controlling factors one at a time, which makes it challenging to identify interactions with other factors and how they jointly affect cloud properties. To understand the complex behaviour of marine stratocumulus clouds, this thesis uses two statistical methods: perturbed parameter ensembles and Gaussian process emulation. Perturbed parameter ensembles perturb multiple factors simultaneously so that their joint effects can be analysed. Furthermore, these ensembles can be used as training data for Gaussian process emulation, which is used to create statistical representations of the relationships between multiple cloud-controlling factors and cloud properties of interest. The emulators are used to generate the values of cloud properties for many new combinations of factor values, which allows the joint effects of parameters to be analysed and parameter contributions to the variances in the cloud properties to be quantified. Firstly, two properties of the free troposphere are perturbed from simulations of a homogeneous, nocturnal stratocumulus cloud to analyse cloud behaviour around the break-up threshold for cloud-top entrainment instability. Dense sampling using emulators of liquid water path and cloud fraction showed that there were non-linear interactions between the two perturbed factors and two behavioural regimes. Additionally, a method for approximating the natural variability of the cloud and accounting for it in the emulator build was demonstrated. Secondly, the stratocumulus-to-cumulus transition was simulated to study the roles of aerosol and drizzle in the context of other cloud-controlling factors. From the base simulation, one model parameter and five cloud-controlling factors were perturbed across reasonable ranges. Analysis of the perturbed parameter ensemble showed that the fastest transitions occurred in low-aerosol environments combined with deep boundary layers, high autoconversion rates and dry temperature inversions. When the ensemble was split into high- and low-drizzle environments, the inversion strength was found to have a strong control on transition time, via entrainment, in low-drizzle environments. Thirdly, the ensemble of stratocumulus-to-cumulus transitions was used as training data for Gaussian process emulation, which allowed the joint effects of parameters in transition properties to be fully visualised and quantified. Emulation revealed that there was a low-aerosol regime, where aerosol concentration strongly controlled the transition time, but outside that regime, the transition time was largely dependent on the inversion strength. The transition time was found to be a complex process that was influenced by multiple interacting parameters, whereas the rain water path is controlled by individual parameters

    Towards a better characterization of submicron aerosol in the Mediterranean basin

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    Programa de Doctorat en Física / Tesi realitzada a l'Institut de Diagnòstic Ambiental i Estudis de l'Aigua (IDAEA - CSIC)[eng] Atmospheric aerosol is an ensemble of atmospheric pollutants with a severe impact on human health and Earth climate. Particulate Matter (PM) effects vary depending on the composition and size. However, current air quality guidelines (AQG) from WHO and from the EU Commission only define threshold standards for bulk PM10 and PM2.5 concentrations. In terms of health, the smaller the particles, the deeper they penetrate into the respiratory system, and they can even diffuse into the bloodstream and circulate to other parts of the organism. The adverse effects on the tissues in which they are deposited depend on the PM composition. Regarding climate, PM size and composition relevantly affect many aerosol-radiation direct and indirect interactions, which regulate troposphere temperature. Consequently, the consideration of these properties is relevant for present and future climate descriptions. Barcelona, the city where this study focuses, is located in the Mediterranean basin, a region with high complexity in terms of air pollution, hence, the nature of this enclave requires thorough monitoring of PM in order to protect the population from exposition accurately. This dissertation focuses on submicronic aerosol evolution over the last decade, with a more detailed study for May 2014-May 2015 and September 2017-October 2018. The objective of this thesis is to describe the PM1 sources in Barcelona by means of source apportionment (SA) techniques. The Positive Matrix Factorisation (PMF) algorithm is one of the most widely used approaches for SA and is the tool used in this dissertation. A secondary aim of this study is the improvement of the SA methodology itself. This is accomplished by testing the outcomes of new methodologies involving the more automatic analysis and dataset junction with several approaches. A field-deployed aerosol mass spectrometer was used at the Barcelona site for continuous PM1 measurements, and SA was performed on Organic Aerosol (OA). First, SA was tackled from a conventional methodology, the seasonal PMF, then, the novel rolling PMF methodology was tested and compared to the fore one. Finally, a comprehensive PM1 SA was performed based on an ensemble of different datasets coming from a variety of measurement techniques. These steps enabled a progressive aerosol composition understanding and acknowledgement of subsequent aerosol trends. A PM1 concentrations decrease was found in the 2014-2018 period, a trend confirmed by other studies at the site. Its relative composition changed significantly; a decrease was found for SO42-, BC, and NH4+, while NO3- increased and OA levels were found stable. The OA SA revealed that its sources were: secondary OA (SOA, >55-70%), road traffic OA (12-19%), cooking-like OA (14-18%), and biomass burning OA (4-6%). These sources are similar to those reported in other sites across the Mediterranean region. In this study, all the primary OA sources were found in a clear decrease from 2014 to 2018. An increasing SOA proportion and SOA oxidation state were also observed. These increments could be explained by a likely increase in the oxidation capacity of the atmosphere, related to the accumulation of oxidative radicals reported in many cities. In order to bridge the possible inter-annual variability in that period, the time period was elongated (2014-2021) detecting the same underlying trends. With the aim of further climate and health impact aerosol impact assessment, this thesis provides mid-term PM1 sources diagnosis. SOA is especially concerning in terms of health effects, hence this pollutant is to be continuously monitored to deeply understand its precursors and formation mechanisms to design effective abatement policies.[cat] L’aerosol atmosfèric és un conjunt de contaminants atmosfèrics amb un impacte sever sobre la salut humana i el clima. Els efectes del material particulat (PM) varien depenent de la seva composició i mida. Barcelona, la ciutat en què es centra aquest estudi, està localitzada a la conca mediterrània, una regió d’alta complexitat en termes de contaminació de l’aire. Per tant, requereix un monitoratge conscienciós del PM per tal de protegir la població acuradament de la seva exposició. Aquesta tesi es focalitza en l’evolució del PM submicrònic (PM1) al llarg de l’última dècada, amb un estudi més detallat per als períodes maig 2014 - maig 2015 i setembre 2017 - octubre 2018. L'objectiu d'aquesta tesi és descriure les fonts de PM1 a Barcelona per mitjà de tècniques de contribució de fonts (SA). L'algoritme de Factorització de Matriu Positiva (PMF) és un dels enfocaments més utilitzats per al SA o és l'eina emprada per a aquesta dissertació. Un objectiu secundari d'aquesta tesi és la millora de la pròpia metodologia del SA. Això és executat per mitjà del testatge a través de diferents metodologies. Les mesures de PM1 es van dur a terme a través d’un espectròmetre de masses instal·lat a l'estació de Barcelona. El SA va ser executat per a l'Aerosol Orgànic (OA) submicrònic mesurat a partir d’aquest instrument, aplicant diferents metodologies per a avaluar-ne l’exactitud. Finalment, un estudi detallat del SA de PM1 va ser executat basat en un conjunt de dades provinents de diferents tècniques de mesura. Aquests passos van permetre la comprensió progressiva de la composició i el reconeixement de les tendències subjacents de l'aerosol. Un dels resultats més rellevants consisteix en la detecció d'una tendència creixent del SOA independentment de la disminució del PM1, relacionat amb el decreixement de l'OA primari. A més, pel que fa al SA, aquesta tesi proposa diverses modificacions del protocol. El SOA és especialment preocupant pels seus efectes en la salut, per tant, aquest contaminant ha de ser contínuament monitorejat amb tècniques de SA per tal d'entendre els seus precursors i mecanismes de formació amb l'objectiu de dissenyar mesures de mitigació efectives

    Global aviation contrail climate effects from 2019 to 2021

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    The global annual mean radiative forcing (RF) attributable to contrail cirrus is comparable to the RF from aviation’s cumulative CO2 emissions. Here, we simulate the global contrail climate forcing for 2019–2021 using reanalysis weather data and improved engine emission estimates along actual flight trajectories derived from Automatic Dependent Surveillance–Broadcast telemetry. Our 2019 global annual mean contrail net RF (62.1 mW m-2) is 44 % lower than current best estimates for 2018 (111 [33, 189] mW m-2). Regionally, the contrail net RF is largest over Europe (876 mW m-2) and the US (414 mW m-2), while the RF over East Asia (64 mW m-2) and China (62 mW m-2) are close to the global mean value because fewer flights in these regions form contrails as a result of lower cruise altitudes and limited ice supersaturated regions in the subtropics due to the Hadley Circulation. Globally, COVID-19 reduced the flight distance flown and contrail net RF in 2020 (-43 % and -56 % respectively vs. 2019) and 2021 (-31 % and -49 % respectively) with significant regional variation. Around 14 % of all flights form a contrail with a net warming effect, yet only 2 % of all flights account for 80 % of the annual contrail energy forcing. The spatiotemporal patterns of the most strongly warming and cooling contrail segments can be attributed to flight scheduling factors, aircraft–engine particle number emissions, tropopause height, background cloud and radiation fields, and albedo. Our contrail RF estimates are most sensitive to corrections applied to the global humidity fields, followed by assumptions on the aircraft-engine particle number emissions, and is least sensitive to radiative heating effects on the contrail plume and contrail-contrail overlapping. Accounting for the sensitivity analysis, we estimate a 2019 global contrail net RF of 62.1 [34.8, 74.8] mW m-2

    The Center of Excellence in Atmospheric Science (2002–2019) — from molecular and biological processes to the global climate

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    The study of atmospheric processes related to climate requires a multidisciplinary approach, encompassing physics, chemistry, meteorology, forest science, and environmental science. The Academy of Finland Centre of Excellence in atmospheric sciences (CoE ATM) responded to that need for 18 years and produced extensive research and eloquent results, which are summarized in this review. The work in the CoE ATM enhanced our understanding in biogeochemical cycles, ecosystem processes, dynamics of aerosols, ions and neutral clusters in the lower atmosphere, and cloud formation and their interactions and feedbacks. The CoE ATM combined continuous and comprehensive long-term in-situ observations in various environments, ecosystems and platforms, ground- and satellitebased remote sensing, targeted laboratory and field experiments, and advanced multi-scale modeling. This has enabled improved conceptual understanding and quantifications across relevant spatial and temporal scales. Overall, the CoE ATM served as a platform for the multidisciplinary research community to explore the interactions between the biosphere and atmosphere under a common and adaptive framework

    Időjárás 2023

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    Dripping Rainfall Simulators for Soil Research - Performance Review

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    Rainfall simulators represent often used equipment for soil research. Depending of their performance they could be appropriate for some soil research or not. The aim of this research is to provide insight into the capabilities of existing dripping rainfall simulators (DRS) to mimic natural rainfall and frequency of simulated rainfalls of certain characteristics, to facilitate the selection of rainfall simulators that would best meet the needs of soil research and to reach step closer to standardization of rainfall simulators. DRS performance was analysed integrally, for simulators with more than one dripper (DRS>1) and with one dripper (DRS=1). A statistical analysis of the performance of DRS, wetted area, drop size, rainfall intensity, duration and kinetic energy (KE) was performed. The analysis showed that DRS can provide rainfall that resembles natural rainfall, except in terms of drop size distribution and wetted area. However, usually there are more factors that do not correspond to the natural rainfall, such as median drop size, volume and kinetic energy. The sizes of the drops generated by drippers are mostly in the range between 2 and 6 mm, while the number of drops smaller than 2 mm is relatively small. The intensity and duration of the simulated rain can be successfully produced to match natural values, with the most frequently simulated short-term rainfall of high intensity. The majority of the simulations was conducted at a fall height of up to 2 m; the other experiments were conducted at fall heights that increased from 2 m up to a fall height of 5 m. The KE of the majority of simulations (58.6%) occurred in the range between 20–90% of terminal KE, 33.0% in the range 90–100% and only 8.4% was lower than 20%
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