ALBERT

Description: Absolute radiometric calibrations are important for measurements of the atmospheric spectral radiance. Such measurements can be used to determine actinic fluxes, the properties of aerosols and clouds and the short wave energy budget. Conventional calibration methods in the laboratory are based on calibrated light sources and reflectors and are expensive, time consuming and subject to relatively large uncertainties. Also, the calibrated instruments might change during transport from the laboratory to the measurement sites. Here we present a new calibration method for UV/vis instruments that measure the spectrally resolved sky radiance, like for example zenith sky Differential Optical Absorption Spectroscopy (DOAS-) instruments or Multi-AXis (MAX-) DOAS instruments. Our method is based on the comparison of the solar zenith angle dependence of the measured zenith sky radiance with radiative transfer simulations. For the application of our method clear sky measurements during periods with almost constant aerosol optical depth are needed. The radiative transfer simulations have to take polarisation into account. We show that the calibration results are almost independent from the knowledge of the aerosol optical properties and surface albedo, which causes a rather small uncertainty of about

Description: Multi-Axis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterise their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g. clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been modified, in particular in order to account for smaller solar zenith angles (SZA). Instrumental degradation is accounted for to avoid artificial trends of the cloud classification. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby AERONET station and from MODIS, visibility derived from a visibility meter; and various cloud parameters from different satellite instruments (MODIS, OMI, and GOME-2). The most important findings from these comparisons are: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective AOD ranges obtained by AERONET and MODIS, (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite indicate that the cloud classification scheme is sensitive to cloud (optical) properties, (3) separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds, (4) some clear sky conditions, especially with high aerosol load, classified from MAX-DOAS observations corresponding to the optically thin and low clouds derived by satellite observations probably indicate that the satellite cloud products contain valuable information on aerosols.

Description: We compare tropospheric column densities (vertically integrated concentrations) of NO2 from three data sets for the metropolitan area of Paris during two extensive measurement campaigns (25 days in summer 2009 and 29 days in winter 2010) within the European research project MEGAPOLI. The selected data sets comprise a regional chemical transport model (CHIMERE) as well as two observational data sets: ground based mobile Multi-AXis-Differential Optical Absorption Spectroscopy (car-MAX-DOAS) measurements and satellite measurements from the Ozone Monitoring Instrument (OMI). On most days, car-MAX-DOAS measurements were carried out along large circles (diameter ~35 km) around Paris. The car-MAX-DOAS results are compared to coincident data from CHIMERE and OMI. All three data sets have their specific strengths and weaknesses, especially with respect to their spatio-temporal resolution and coverage as well as their uncertainties. Thus we compare them in two different ways: first, we simply consider the original data sets. Second, we compare modified versions making synergistic use of the complementary information from different data sets. For example, profile information from the regional model is used to improve the satellite data, observations of the horizontal trace gas distribution are used to adjust the respective spatial patterns of the model simulations, or the model is used as a transfer tool to bridge the spatial scales between car-MAX-DOAS and satellite observations. Using the modified versions of the data sets, the comparison results substantially improve compared to the original versions. In general, good agreement between the data sets is found outside the emission plume, but inside the emission plumes the tropospheric NO2 VCDs are systematically underestimated by the CHIMERE model and the satellite observations (compared to the car-MAX-DOAS observations). One major result from our study is that for satellite validation close to strong emission sources (like power plants or megacities) detailed information about the intra-pixel heterogeneity is essential. Such information may be gained from simultaneous car-MAX-DOAS measurements using multiple instruments or by combining (car-) MAX-DOAS measurements with results from regional model simulations.

Description: We present a new method to quantify NOx emissions and corresponding atmospheric lifetimes from OMI NO2 observations together with ECMWF wind fields without further model input for sources located in polluted background. NO2 patterns under calm wind conditions are used as proxy for the spatial patterns of NOx emissions, and the effective atmospheric NOx lifetime is determined from the change of spatial patterns measured at larger wind speeds. Emissions are subsequently derived from the NO2 mass above background integrated around the source of interest. Lifetimes and emissions are estimated for 17 power plants and 53 cities located in non-mountainous regions across China and the US. The derived lifetimes for non-mountainous sites are 3.8 ± 1.0 h on average with ranges of 1.8 to 7.5 h. The derived NOx emissions show generally good agreement with bottom-up inventories for power plants and cities. Global inventory significantly underestimated NOx emissions in Chinese cities, most likely due to uncertainties associated with downscaling approaches.

Description: A study was carried out to understand the contributions of soil biogenic NO emissions from managed (fertilized and irrigated) hyper-arid ecosystem in NW-China to the regional NO2 emissions during growing season. Soil biogenic NO emissions were quantified by laboratory incubation of corresponding soil samples. We have developed the Geoscience General Tool Package (GGTP) to obtain soil temperature, soil moisture and biogenic soil NO emission at oasis scale. Bottom-up anthropogenic NO2 emissions have been scaled down from annual to monthly values to compare mean monthly soil biogenic NO2 emissions. The top-down emission estimates have been derived from satellite observations compared then with the bottom-up emission estimates (anthropogenic and biogenic). The results show that the soil biogenic emissions of NO2 during the growing period are (at least) equal until twofold of the related anthropogenic sources. We found that the grape soils are the main summertime contributor to the biogenic NO emissions of study area, followed by cotton soils. The top-down and bottom-up emission estimates were shown to be useful methods to estimate the monthly/seasonal cycle of the total regional NO2 emissions. The resulting total NO2 emissions show a strong peak in winter and a secondary peak in summer, providing confidence in the method. These findings provide strong evidence that biogenic emissions from soils of managed drylands (irrigated and fertilized) in the growing period can be much more important contributors to the regional NO2 budget (hence to regional photochemistry) of dryland regions than thought before.

Description: Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broad-band effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g. AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

Description: We present elemental, lipid biomarker and compound-specific isotope (δ13C, δ2H) data for soils and leaf litter collected in the catchment of Lake Ohrid (Albania, Macedonia), as well as macrophytes, particulate organic matter and sediments from the lake itself. Lake Ohrid provides an outstanding archive of continental environmental change of at least 1.2 M years and the purpose of our study is to ground truth organic geochemical proxies that we developed in order to study past changes in the terrestrial biome. We show that soils dominate the lipid signal of the lake sediments rather than the vegetation or aquatic biomass, while compound-specific isotopes (δ13C, δ2H) determined for n-alkanoic acids confirm a dominant terrestrial source of organic matter to the lake. There is a strong imprint of suberin monomers on the composition of total lipid extracts and chain-length distributions of n-alkanoic acids, n-alcohols, ω-hydroxy acids and α,ω-dicarboxylic acids. Our end-member survey identifies that ratios of mid-chain length suberin-derived to long-chain length cuticular-derived alkyl compounds as well as their average chain length distributions can be used as new molecular proxies of organic matter sources to the lake. We tested these for the 8.2 ka event, a pronounced and widespread Holocene climate fluctuation. In SE Europe climate became drier and cooler in response to the event, as is clearly recognizable in the carbonate and organic carbon records of Lake Ohrid sediments. Our new proxies indicate biome modification in response to hydrological changes, identifying two phases of increased soil OM supply, first from topsoils and then from mineral soils. Our study demonstrates that geochemical fingerprinting of terrestrial OM should focus on the main lipid sources, rather than the living biomass. Both can exhibit climate-controlled variability, but are generally not identical.

Description: We compare tropospheric column densities (vertically integrated concentrations) of NO2 from three data sets for the metropolitan area of Paris during two extensive measurement campaigns (25 days in summer 2009 and 29 days in winter 2010) within the European research project MEGAPOLI. The selected data sets comprise a regional chemical transport model (CHIMERE) as well as two observational data sets: ground-based mobile Multi-AXis-Differential Optical Absorption Spectroscopy (car-MAX-DOAS) measurements and satellite measurements from the Ozone Monitoring Instrument (OMI). On most days, car-MAX-DOAS measurements were carried out along large circles (diameter ~ 35 km) around Paris. The car-MAX-DOAS results are compared to coincident data from CHIMERE and OMI. All three data sets have their specific strengths and weaknesses, especially with respect to their spatiotemporal resolution and coverage as well as their uncertainties. Thus we compare them in two different ways: first, we simply consider the original data sets. Second, we compare modified versions making synergistic use of the complementary information from different data sets. For example, profile information from the regional model is used to improve the satellite data, observations of the horizontal trace gas distribution are used to adjust the respective spatial patterns of the model simulations, or the model is used as a transfer tool to bridge the spatial scales between car-MAX-DOAS and satellite observations. Using the modified versions of the data sets, the comparison results substantially improve compared to the original versions. In general, good agreement between the data sets is found outside the emission plume, but inside the emission plumes the tropospheric NO2 vertical column densities (VCDs). are systematically underestimated by the CHIMERE model and the satellite observations (compared to the car-MAX-DOAS observations). One major result from our study is that for satellite validation close to strong emission sources (like power plants or megacities), detailed information about the intra-pixel heterogeneity is essential. Such information may be gained from simultaneous car-MAX-DOAS measurements using multiple instruments or by combining (car-) MAX-DOAS measurements with results from regional model simulations.

Description: Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterize their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g., clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long-term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been adapted to the characteristics of the Wuxi instrument, and extended towards smaller solar zenith angles (SZA). Moreover, a method for the determination and correction of instrumental degradation is developed to avoid artificial trends of the cloud classification results. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby Aerosol Robotic Network (AERONET) station and from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, visibility derived from a visibility meter and various cloud parameters from different satellite instruments (MODIS, the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment (GOME-2)). Here it should be noted that no quantitative comparison between the MAX-DOAS results and the independent data sets is possible, because (a) not exactly the same quantities are measured, and (b) the spatial and temporal sampling is quite different. Thus our comparison is performed in a semi-quantitative way: the MAX-DOAS cloud classification results are studied as a function of the external quantities. The most important findings from these comparisons are as follows: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective aerosol optical depth (AOD) ranges obtained by AERONET and MODIS; (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite confirm that the MAX-DOAS cloud classification scheme is sensitive to cloud (optical) properties; (3) the separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds; (4) for some cloud-free conditions, especially with high aerosol load, the coincident satellite observations indicated optically thin and low clouds. This finding indicates that the satellite cloud products contain valuable information on aerosols.