Abstract
Abstract. PollyXT Raman polarization lidar observations were performed at the Remote Sensing Laboratory (RS-Lab) in Warsaw (52.2109∘ N, 20.9826∘ E), Poland, in the framework of the European Aerosol Research Lidar Network (EARLINET) and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) projects. Data collected in July, August, and September of 2013, 2015, and 2016 were analysed using the classical Raman approach. In total, 246 sets of intact profiles, each set comprising particle extinction (α) and backscatter coefficients (β) as well as linear particle depolarization ratios (δ) at 355 nm and 532 nm, were derived for statistical investigations and stored in the EARLINET/ACTRIS database. The main analysis was focused on intensive optical properties obtained within the atmospheric boundary layer (ABL). Their interrelations were discussed for different periods: the entire day; nighttime, with respect to the nocturnal boundary layer (NL) and the residual boundary layer (RL); at sunrise, with respect to the morning transition boundary layer (MTL); and from late afternoon until sunset, with respect to the well-mixed boundary layer (WML). Within the boundary layer, the lidar-derived optical properties (entire day, 246 sets) revealed a mean aerosol optical depth (AODABL) of 0.20±0.10 at 355 nm and 0.11±0.06 at 532 nm; a mean Ångström exponent (ÅEABL) of 1.54±0.37; a mean lidar ratio (LRABL) of 48±17 sr at 355 nm and 41±15 sr at 532 nm; a mean linear particle depolarization ratio (δABL) of 0.02±0.01 at 355 nm and 0.05±0.01 at 532 nm; and a mean water vapour mixing ratio (WVABL) of 8.28±2.46 g kg−1. In addition, the lidar-derived daytime boundary layer optical properties (for the MTL and WML) were compared with the corresponding daytime columnar aerosol properties derived from the multi-filter rotating shadowband radiometer (MFR-7) measuring within the National Aerosol Research Network (PolandAOD-NET) and the CE318 sun photometer of the Aerosol Robotic NETwork (AERONET). A high linear correlation of the columnar aerosol optical depth values from the two latter instruments was obtained in Warsaw (a correlation coefficient of 0.98 with a standard deviation of 0.02). The contribution of the aerosol load in the summer and early-autumn free troposphere can result in an AODCL value that is twice as high as the AODABL over Warsaw. The occurrence of a turbulence-driven aerosol burst from the boundary layer into the free troposphere can further increase this difference. Aerosol within the ABL and in the free troposphere was interpreted based on comparisons of the properties derived at different altitudes with values reported in the literature, which were characteristic for different aerosol types, in combination with backward trajectory calculations, satellite data, and model outputs. Within the boundary layer, the aerosol consisted of either urban anthropogenic pollution (∼ 61 %) or mixtures of anthropogenic aerosol with biomass-burning aerosol (< 14 %), local pollen (< 7 %), or Arctic marine particles (< 5 %). No significant contribution of mineral dust was found in the boundary layer. The lidar-derived atmospheric boundary layer height (ABLH) and the AODABL exhibited a positive correlation (R of 0.76), associated with the local anthropogenic pollution (most pronounced for the RL and WML). A positive correlation of the AODABL and LRABL and a negative correlation of the ÅEABL and LRABL, as well as the expected negative trends for the WVABL (and surface relative humidity, RH) and δABL, were observed. Relations of the lidar-derived aerosol properties within the ABL and the surface in situ measurements of particulate matter with an aerodynamic diameter less than 10 µm (PM10) and less than 2.5 µm (PM2.5) measured by the Warsaw Regional Inspectorate for Environmental Protection (WIOS) network, and the fine-to-coarse mass ratio (FCMR) were investigated. The FCMR and surface RH showed a positive correlation even at nighttime (R of 0.71 for the MTL, 0.63 for the WML, and 0.6 for the NL), which generally lacked statistically significant relations. A weak negative correlation of the FCMR and δABL (more pronounced at 532 nm at nighttime) and no casual relation between the FCMR and ÅEABL were found. Most interestingly, distinct differences were observed for the morning transition layer (MTL) and the well-mixed layer (WML). The MTL ranged up to 0.6–1 km, and was characterized by a lower AODABL(<0.12), wetter conditions (RH 50–80 %), smaller particles (ÅEABL of 1–2.2; FCMR from 0.5 to 3), and a low LRABL of between 20 and 40 sr. The WML ranged up to 1–2.5 km and exhibited a higher AODABL (reaching up to 0.45), drier conditions (RH 25–60 %), larger particles (ÅEABL of 0.8–1.7; FCMR of 0.2–1.5), and a higher LRABL of up to 90 sr.
Highlights
Atmospheric aerosol can impact climate (e.g. Feingold et al, 2016; Seinfeld et al, 2016), weather (e.g. Fan et al, 2016; Gayatri et al, 2017), air quality (e.g. Fuzzi et al, 2015), and human health (e.g. Zheng et al, 2015; Trippetta et al, 2016)
The results reported in the current paper will enrich the state of knowledge on boundary layer aerosol optical properties by building a seasonal climatology over Warsaw and, providing a reference for comparative studies with the other EARLINET (e.g. Papayannins et al, 2008) and Aerosol Robotic NETwork (AERONET) sites (e.g. Siomos et al, 2018)
The mean values of the αABL, AODABL, and LRABL calculated at the two wavelengths in the transition time are higher during the sunset period (WML) and lower during the sunrise period (MTL), the latter being similar to the nocturnal boundary layer (NL)
Summary
Atmospheric aerosol can impact climate (e.g. Feingold et al, 2016; Seinfeld et al, 2016), weather (e.g. Fan et al, 2016; Gayatri et al, 2017), air quality (e.g. Fuzzi et al, 2015), and human health (e.g. Zheng et al, 2015; Trippetta et al, 2016). Size, and composition are important for aerosol–cloud–radiation interaction studies (Seinfeld et al, 2016) and for radiative transfer modelling (Lolli et al, 2018). The Intergovernmental Panel on Climate Change (IPCC) reported that the sparse and/or poorly-known information on the aerosol temporal and spatial variability causes high uncertainty in the assessment of their influence on the global radiation budget (Stocker et al, 2013). The latest IPCC report indicates that the reduction of uncertainties is still necessary, as it can improve the ability to accurately forecast global climate change (Masson-Delmotte et al, 2018). Seinfeld et al (2016) provided strategies for improving estimates of aerosol–cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing the model uncertainties Several past studies have been dedicated to investigating and improving the above-mentioned uncertainties. Pan et al (2015) identified the major discrepancies between the state-of-theart global aerosol models and observations with respect to simulating aerosol loading over South Asia, thereby providing directions for future model improvements in this important region. Ghan et al (2016) found that uncertainty regarding anthropogenic aerosol effects on cloud radiative forcing arises from uncertainty in several relationships, including the choice of parameter values and numerical integration methods. Koffi et al (2016) provided further spatial and temporal details on the state-of-the-art AeroCom II global aerosol models and investigated the reasons for the model discrepancies and diversity, which provided a good foundation for further evaluation of the models’ performance at a global scale. Kipling et al (2016) investigated the impact of a wide range of processes (emission, transport, deposition, and microphysical and chemical processes) on aerosol vertical distribution in the aerosol–climate model via a series of limiting case process-based sensitivity tests; they showed that the processes that have the greatest impact on the vertical distribution vary both between different aerosol components and over the particle size spectrum. Seinfeld et al (2016) provided strategies for improving estimates of aerosol–cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing the model uncertainties
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