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New insight into the spatiotemporal variability and source apportionments of C1–C4 alkyl nitrates in Hong Kong

C1–C4 alkyl nitrates (RONO2) were measured concurrently at a mountain site, Tai Mo Shan (TMS), and an urban site, Tsuen Wan (TW), at the base of the same mountain in Hong Kong from September to November 2010. Although the levels of parent hydrocarbons were much lower at TMS (p  <  0.05), similar alkyl nitrate levels were found at both sites regardless of the elevation difference, suggesting various source contributions of alkyl nitrates at the two sites. Prior to using a positive matrix factorization (PMF) model, the data at TW were divided into "meso" and "non-meso" scenarios for the investigation of source apportionments with the influence of mesoscale circulation and regional transport, respectively. Secondary formation was the prominent contributor of alkyl nitrates in the meso scenario (60 ± 2 %, 60.2 ± 1.2 pptv), followed by biomass burning and oceanic emissions, while biomass burning and secondary formation made comparable contributions to alkyl nitrates in the non-meso scenario, highlighting the strong emissions of biomass burning in the inland Pearl River delta (PRD) region. In contrast to TW, the alkyl nitrate levels measured at TMS mainly resulted from the photooxidation of the parent hydrocarbons at TW during mesoscale circulation, i.e., valley breezes, corresponding to 52–86 % of the alkyl nitrate levels at TMS. Furthermore, regional transport from the inland PRD region made significant contributions to the levels of alkyl nitrates (∼  58–82 %) at TMS in the non-meso scenario, resulting in similar levels of alkyl nitrates observed at the two sites. The simulation of secondary formation pathways using a photochemical box model found that the reaction of alkyl peroxy radicals (RO2) with nitric oxide (NO) dominated the formation of RONO2 at both sites, and the formation of alkyl nitrates contributed negatively to O3 production, with average reduction rates of 4.1 and 4.7 pptv pptv−1 at TMS and TW, respectively.
The millennium water vapour drop in chemistry–climate model simulations

This study investigates the abrupt and severe water vapour decline in the stratosphere beginning in the year 2000 (the "millennium water vapour drop") and other similarly strong stratospheric water vapour reductions by means of various simulations with the state-of-the-art Chemistry-Climate Model (CCM) EMAC (ECHAM/MESSy Atmospheric Chemistry Model). The model simulations differ with respect to the prescribed sea surface temperatures (SSTs) and whether nudging is applied or not. The CCM EMAC is able to most closely reproduce the signature and pattern of the water vapour drop in agreement with those derived from satellite observations if the model is nudged. Model results confirm that this extraordinary water vapour decline is particularly obvious in the tropical lower stratosphere and is related to a large decrease in cold point temperature. The drop signal propagates under dilution to the higher stratosphere and to the poles via the Brewer–Dobson circulation (BDC). We found that the driving forces for this significant decline in water vapour mixing ratios are tropical sea surface temperature (SST) changes due to a coincidence with a preceding strong El Niño–Southern Oscillation event (1997/1998) followed by a strong La Niña event (1999/2000) and supported by the change of the westerly to the easterly phase of the equatorial stratospheric quasi-biennial oscillation (QBO) in 2000. Correct (observed) SSTs are important for triggering the strong decline in water vapour. There are indications that, at least partly, SSTs contribute to the long period of low water vapour values from 2001 to 2006. For this period, the specific dynamical state of the atmosphere (overall atmospheric large-scale wind and temperature distribution) is important as well, as it causes the observed persistent low cold point temperatures. These are induced by a period of increased upwelling, which, however, has no corresponding pronounced signature in SSTs anomalies in the tropics. Our free-running simulations do not capture the drop as observed, because a) the cold point temperature has a low bias and thus the water vapour variability is reduced and b) because they do not simulate the appropriate dynamical state. Large negative water vapour declines are also found in other years and seem to be a feature which can be found after strong combined El Niño/La Niña events if the QBO west phase during La Niña changes to the east phase.
Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model

Wind extraction from stratospheric ozone (O3) assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar) shallow water model (SWM) system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z), while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members), with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF). This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2–0.5 for small ensembles to 0.5–1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from ∼  35 % with 25 and 50 members to ∼  50 % with 1518 members. For the smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like) region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members), the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.
Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.
Numerical Analysis of the Role of Snowpack in the Ozone Depletion Events during the Arctic Spring

The tropospheric ozone depletion events (ODEs) and the related enhancement of reactive bromine in the boundary layer were observed in the springtime of Arctic almost 40 years ago. It is found that various substrates in polar regions such as the snowpack are able to release bromine, which is responsible for the consumption of ozone in the boundary layer. In the present simulation, a snowpack module which represents the mass transfer between the ambient air and the snowpack is implemented in a box model, aiming to clarify the influences of the snowpack on ODEs and the associated bromine explosion in the ambient air as well as in the interstitial air of the snowpack. In the snowpack module, the processes including the deposition of bromine containing compounds onto the snowpack, the mass exchange between the snow interstitial air and snow particles, and the release of Br2 from the snowpack to the ambient air are parameterized by estimating the transfer resistances which an air parcel experiences when being transported through the boundary layer into the snowpack. The present model successfully captures the complete removal of ozone both in the boundary layer and in the snow interstitial air. The temporal and spatial distributions of bromine species such as Br2 are shown and compared with observations. By changing the properties of the snowpack, it is found that the size of snow grains, volume fraction of the liquid-like layer (LLL), and the rate of the mass exchange between the snow interstitial air and the snow particles are the critical parameters which determine the occurrence of ODEs. The simulation results show that a smaller size of the snow grains considerably accelerates the ozone depletion process. Moreover, the decrease of LLL volume fraction in snow grains is found to slow down the scavenging process of HOBr by the snow particles, which prohibits the occurrence of ODEs in the snowpack. In addition, according to the simulations with the modification of the snowpack thickness, the depletion of ozone in the ambient air is shown to be influenced more heavily by the bromine explosion occurring in the surface snow layers instead of the deep snow layers. The importance of each step in the mass transfer processes occurring between the boundary layer and the snowpack is identified by conducting a local concentration sensitivity analysis. It is shown that the snow chemistry occurring in the surface snow layers has a relatively larger impact on the depletion of ozone in the ambient air compared to that within the deep snow layers. Besides, during the period of the ozone depletion, the mixing ratio of ozone in the boundary layer is mostly influenced by the deposition of HOBr onto the surface snow layers and the release of Br2 from the snow layers close to the ground surface. In contrast to that, in the interstitial air of the surface snow layer, the uptake of HOBr by snow particles is indicated as the most dominant step for the ODE.
Abundance of fluorescent biological aerosol particles at temperatures conducive to the formation of mixed-phase and cirrus clouds

Some types of biological particles are known to nucleate ice at warmer temperatures than mineral dust, with the potential to influence cloud microphysical properties and climate. However, the prevalence of these particle types above the atmospheric boundary layer is not well known. Many types of biological particles fluoresce when exposed to ultraviolet light, and the Wideband Integrated Bioaerosol Sensor takes advantage of this characteristic to perform real-time measurements of fluorescent biological aerosol particles (FBAPs). This instrument was flown on the National Center for Atmospheric Research Gulfstream V aircraft to measure concentrations of fluorescent biological particles from different potential sources and at various altitudes over the US western plains in early autumn. Clear-air number concentrations of FBAPs between 0.8 and 12 µm diameter usually decreased with height and generally were about 10–100 L−1 in the continental boundary layer but always much lower at temperatures colder than 255 K in the free troposphere. At intermediate temperatures where biological ice-nucleating particles may influence mixed-phase cloud formation (255 K  ≤ T ≤  270 K), concentrations of fluorescent particles were the most variable and were occasionally near boundary-layer concentrations. Predicted vertical distributions of ice-nucleating particle concentrations based on FBAP measurements in this temperature regime sometimes reached typical concentrations of primary ice in clouds but were often much lower. If convection was assumed to lift boundary-layer FBAPs without losses to the free troposphere, better agreement between predicted ice-nucleating particle concentrations and typical ice crystal concentrations was achieved. Ice-nucleating particle concentrations were also measured during one flight and showed a decrease with height, and concentrations were consistent with a relationship to FBAPs established previously at the forested surface site below. The vertical distributions of FBAPs measured on five flights were also compared with those for bacteria, fungal spores, and pollen predicted from the EMAC global chemistry–climate model for the same geographic region.
New insights into the atmospheric mercury cycling in central Antarctica and implications on a continental scale

Under the framework of the GMOS project (Global Mercury Observation System) atmospheric mercury monitoring has been implemented at Concordia Station on the high-altitude Antarctic plateau (75°06′ S, 123°20′ E, 3220 m above sea level). We report here the first year-round measurements of gaseous elemental mercury (Hg(0)) in the atmosphere and in snowpack interstitial air on the East Antarctic ice sheet. This unique data set shows evidence of an intense oxidation of atmospheric Hg(0) in summer (24-hour daylight) due to the high oxidative capacity of the Antarctic plateau atmosphere in this period of the year. Summertime Hg(0) concentrations exhibited a pronounced daily cycle in ambient air with maximal concentrations around midday. Photochemical reactions and chemical exchange at the air–snow interface were prominent, highlighting the role of the snowpack on the atmospheric mercury cycle. Our observations reveal a 20 to 30 % decrease of atmospheric Hg(0) concentrations from May to mid-August (winter, 24 h darkness). This phenomenon has not been reported elsewhere and possibly results from the dry deposition of Hg(0) onto the snowpack. We also reveal the occurrence of multi-day to weeklong atmospheric Hg(0) depletion events in summer, not associated with depletions of ozone, and likely due to a stagnation of air masses above the plateau triggering an accumulation of oxidants within the shallow boundary layer. Our observations suggest that the inland atmospheric reservoir is depleted in Hg(0) in summer. Due to katabatic winds flowing out from the Antarctic plateau down the steep vertical drops along the coast and according to observations at coastal Antarctic stations, the striking reactivity observed on the plateau most likely influences the cycle of atmospheric mercury on a continental scale.
Inventory of African desert dust events in the north-central Iberian Peninsula in 2003–2014 based on sun-photometer–AERONET and particulate-mass–EMEP data

A reliable identification of desert dust (DD) episodes over north-central Spain is carried out based on the AErosol RObotic NETwork (AERONET) columnar aerosol sun photometer (aerosol optical depth, AOD, and Ångström exponent, α) and European Monitoring and Evaluation Programme (EMEP) surface particulate-mass concentration (PMx, x = 10, 2.5, and 2.5–10 µm) as the main core data. The impact of DD on background aerosol conditions is detectable by means of aerosol load thresholds and complementary information provided by HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) air mass back trajectories, MODIS (Moderate Resolution Imaging Spectroradiometer) images, forecast aerosol models, and synoptic maps, which have been carefully reviewed by a human observer for each day included in the DD inventory. This identification method allows the detection of low and moderate DD intrusions and also of mixtures of mineral dust with other aerosol types by means of the analysis of α. During the period studied (2003–2014), a total of 152 DD episodes composed of 418 days are identified. Overall, this means ∼ 13 episodes and ∼ 35 days per year with DD intrusion, representing 9.5 % days year−1. During the identified DD intrusions, 19 daily exceedances over 50 µg m−3 are reported at the surface. The occurrence of DD event days during the year peaks in March and June, with a marked minimum in April and lowest occurrence in winter. A large interannual variability is observed showing a statistically significant temporal decreasing trend of ∼ 3 days year−1. The DD impact on the aerosol climatology is addressed by evaluating the DD contribution in magnitude and percent (in brackets) for AOD, PM10, PM2.5, and PM2.5 − 10, obtaining mean values of 0.015 (11.5 %), 1.3 µg m−3 (11.8 %), 0.55 µg m−3 (8.5 %) and 0.79 µg m−3 (16.1 %), respectively. Annual cycles of the DD contribution for AOD and PM10 present two maxima – one in summer (0.03 and 2.4 µg m−3 for AOD in June and PM10 in August) and another in March (0.02 for AOD and 2.2 µg m−3 for PM10) – both displaying a similar evolution with exceptions in July and September. The seasonal cycle of the DD contribution to AOD does not follow the pattern of the total AOD (close to a bell shape), whereas both PM10 cycles (total and DD contribution) are more similar to each other in shape, with an exception in September. The interannual evolution of the DD contribution to AOD and PM10 has evidenced a progressive decrease. This decline in the levels of mineral dust aerosols can explain up to 30 % of the total aerosol load decrease observed in the study area during the period 2003–2014. The relationship between columnar and surface DD contribution shows a correlation coefficient of 0.81 for the interannual averages. Finally, synoptic conditions during DD events are also analysed, observing that the north African thermal low causes most of the events ( ∼  53 %). The results presented in this study highlight the relevance of the area studied since it can be considered representative of the clean background in the western Mediterranean Basin where DD events have a high impact on aerosol load levels.
Eddy covariance measurements of the net turbulent methane flux in the city centre – results of 2-year campaign in Łódź, Poland

To investigate temporal variability of methane (CH4) fluxes in an urban environment, air–surface exchange fluxes of CH4 were continuously measured using eddy covariance techniques at a city-centre site in Łódź, Poland, from July 2013 to August 2015. In the immediate vicinity of the measurement site, potential methane sources include vehicle traffic, dense sewerage infrastructure and natural gas networks. Sensible and latent heat fluxes have also been measured since 2000 and carbon dioxide fluxes since 2007 at this site. Upward CH4 fluxes dominated during the measurement period, indicating that the city centre is a net source of CH4 to the troposphere. The highest monthly fluxes were observed in winter (2.0 to 2.7 g m−2 month−1) and the lowest in summer (0.8 to 1.0 g m−2 month−1). Fluxes on working days were around 6 % higher than on weekends. The cumulative flux indicates that the city centre emitted a net quantity of nearly 18 g m−2 of CH4 in 2014. Stable values of the FCO2∕ FCH4 ratio in months (minimum 2.41 × 10−3, maximum 5.3 × 10−3) and the lack of a clear annual course suggest comparable magnitude of both fluxes.
Predicting abundance and variability of ice nucleating particles in precipitation at the high-altitude observatory Jungfraujoch

Nucleation of ice affects the properties of clouds and the formation of precipitation. Quantitative data on how ice nucleating particles (INPs) determine the distribution, occurrence and intensity of precipitation are still scarce. INPs active at −8 °C (INPs−8) were observed for 2 years in precipitation samples at the High-Altitude Research Station Jungfraujoch (Switzerland) at 3580 m a.s.l. Several environmental parameters were scanned for their capability to predict the observed abundance and variability of INPs−8. Those singularly presenting the best correlations with observed number of INPs−8 (residual fraction of water vapour, wind speed, air temperature, number of particles with diameter larger than 0.5 µm, season, and source region of particles) were implemented as potential predictor variables in statistical multiple linear regression models. These models were calibrated with 84 precipitation samples collected during the first year of observations; their predictive power was successively validated on the set of 15 precipitation samples collected during the second year. The model performing best in calibration and validation explains more than 75 % of the whole variability of INPs−8 in precipitation and indicates that a high abundance of INPs−8 is to be expected whenever high wind speed coincides with air masses having experienced little or no precipitation prior to sampling. Such conditions occur during frontal passages, often accompanied by precipitation. Therefore, the circumstances when INPs−8 could be sufficiently abundant to initiate the ice phase in clouds may frequently coincide with meteorological conditions favourable to the onset of precipitation events.