ALBERT

Description: The "Berlin Air quality and Ecosystem Research: Local and long-range Impact of anthropogenic and Natural hydrocarbons" (BAERLIN2014) campaign was conducted during the 3 summer months (June–August) of 2014. During this measurement campaign, both stationary and mobile measurements were undertaken to address complementary aims. This paper provides an overview of the stationary measurements and results that were focused on characterization of gaseous and particulate pollution, including source attribution, in the Berlin–Potsdam area, and quantification of the role of natural sources in determining levels of ozone and related gaseous pollutants. Results show that biogenic contributions to ozone and particulate matter are substantial. One indicator for ozone formation, the OH reactivity, showed a 31 % (0.82 ± 0.44 s−1) and 75 % (3.7 ± 0.90 s−1) contribution from biogenic non-methane volatile organic compounds (NMVOCs) for urban background (2.6 ± 0.68 s−1) and urban park (4.9 ± 1.0 s−1) location, respectively, emphasizing the importance of such locations as sources of biogenic NMVOCs in urban areas. A comparison to NMVOC measurements made in Berlin approximately 20 years earlier generally show lower levels today for anthropogenic NMVOCs. A substantial contribution of secondary organic and inorganic aerosol to PM10 concentrations was quantified. In addition to secondary aerosols, source apportionment analysis of the organic carbon fraction identified the contribution of biogenic (plant-based) particulate matter, as well as primary contributions from vehicles, with a larger contribution from diesel compared to gasoline vehicles, as well as a relatively small contribution from wood burning, linked to measured levoglucosan.

Description: Tropospheric ozone and particulate matter affect human health and cause vegetation stress, dysfunction and damages. In this study we investigate the effect of increasing urban vegetation i.e. tree species on atmospheric chemistry, a potential urban management strategy to counteract high levels of local pollutants such as ozone, OH and PM10 caused by e.g. traffic. We use an extended version of an atmospheric chemistry box model including detailed gas-phase chemistry, mixing layer height variation and secondary organic aerosol calculations based on observations for Berlin, Germany. It is shown to accurately simulate the observed ozone volume mixing ratios during the intensive measurement period in July 2014 (BAERLIN2014) if basic parameters such as nitrogen oxides, meteorological conditions, PM10 concentrations as well as volatile organic compounds (VOCs) are considered as 1 h resolved datasets. Based on this setup the effects of changing present day vegetation mixture by 24 different relevant tree species and of urban greening is tested to elucidate benefits and drawbacks in order to support future urban planning. While the present day vegetation causes boundary layer ozone to decline slightly at 35 °C, individual tree types alter the ozone production rate and concentration as well as the secondary organic aerosol mass in different ways. Our results suggest that trees intensively emitting isoprene such as black locust, European oak and poplar result in higher ozone and total PM10 concentrations than at present, while tree species emitting primarily monoterpenes such as beech, magnolia and wayfaring trees yield less of both. This is in line with the similar behaviour of OH concentration and new particle formation rates. Thus, for future urban planning including urban greening, consideration of the beneficial and harmful aspects of tree species need to ensure that citizens benefit from and are not being negatively affected by climate adaptation strategies.

Description: The mixing layer height (MLH) is a measure for the vertical turbulent exchange within the boundary layer, which is one of the controlling factors for the dilution of pollutants emitted near the ground. Based on continuous MLH measurements with a Vaisala CL51 ceilometer and measurements from an air quality network, the relationship between MLH and near-surface pollutant concentrations has been investigated. In this context the uncertainty of the MLH retrievals and the representativeness of ground-based in situ measurements are crucial. We have investigated this topic by using data from the BAERLIN2014 campaign in Berlin, Germany, conducted from June to August 2014. To derive the MLH, three versions of the proprietary software BL-VIEW and a novel approach COBOLT were compared. It was found that the overall agreement is reasonable if mean diurnal cycles are considered. The main advantage of COBOLT is the continuous detection of the MLH with a temporal resolution of 10 min and a lower number of cases when the residual layer is misinterpreted as mixing layer. We have calculated correlations between MLH as derived from the different retrievals and concentrations of pollutants (PM10, O3 and NOx) for different locations in the metropolitan area of Berlin. It was found that the correlations with PM10 are quite different for different sites without showing a clear pattern, whereas the correlation with NOx seems to depend on the vicinity of emission sources in main roads. In the case of ozone as a secondary pollutant, a clear correlation was found. We conclude that the effects of the heterogeneity of the emission sources, chemical processing and mixing during transport exceed the differences due to different MLH retrievals. Moreover, it seems to be unrealistic to find correlations between MLH and near-surface pollutant concentrations representative for a city like Berlin (flat terrain), in particular when traffic emissions are dominant. Nevertheless it is worthwhile to use advanced MLH retrievals for ceilometer data, for example as input to dispersion models and for the validation of chemical transport models.

Description: Ceilometers have been used for several meteorological applications, often in the framework of air qualitystudies. Whereas the particle backscatter coefficient can be retrieved in a quantitative way only recently (withthe improvements of the hardware, Wiegner et al., 2014), mixing layer heights (MLH) have been derived formore than two decades. Several approaches are documented in the literature, however, automated proceduresare still prone to errors because of difficulties in the recognition and attribution of discontinuities (“steps”) inthe backscatter profiles. For example, it is often not clear whether a detected change in the vertical distributionof aerosol backscatter indicates the residual layer, the stable boundary layer, or an elevated layer. Thus, anassessment of the reliability and accuracy of MLH-retrievals is relevant for air quality studies: on the one handMLHs are often inversely correlated with ground-based in-situ measurements of particulate matter and gaseouspollutant concentrations, and on the other hand, MLH-retrievals can be used to validate chemistry transport models.To understand the benefit of MLH in the context of air quality, we have compared several retrievals of theMLH from ceilometer measurements during a field-campaign in summer 2014 (Bonn et al., 2016) for backgroundand polluted sites in Berlin. Correlations between the concentrations of several pollutants and MLH are analyzed,and how they are influenced by the uncertainty of the derived MLH.

Description: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Description: This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (〉53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources.

Description: Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

Description: Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.