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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).

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 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4g€¯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 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.

Research infrastructures play a key role in launching a new generation of integrated long-Term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO 2 , CH 4 , N 2 O, H 2 O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-Access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.

Until now, micrometeorological measurements of surface/ atmosphere exchange fluxes of submicron aerosol chemical components such as nitrate, sulfate or organics could only be made with gradient techniques. This article describes a novel setup to measure speciated aerosol fluxes by the more direct eddy covariance technique. The system is based on the Aerodyne quadrupole-based Aerosol Mass Spectrometer (Q-AMS), providing a quantitative measurement of aerosol constituents of environmental concern at a time resolution sufficient for eddy-covariance. The Q-AMS control software was modified to maximize duty cycle and statistics and enable fast data acquisition, synchronized with that of an ultrasonic anemometer. The detection limit of the Q-AMS based system for flux measurements ranges from 0.2 for NO3- to 15 ng m-2 s-1 for hydrocarbon-like organic aerosol (HOA), with an estimated precision of around 6 ng m-2 s-1, depending on aerosol loading. At common ambient concentrations the system is capable of resolving deposition velocity values < 1 mm s-1, sufficient for measurements of dry deposition to vegetation. First tests of the system in the urban environment (6 to 20 June 2003) in Boulder, CO, USA, reveal clear diurnal, presumably traffic related, patterns in the emission of HOA and NO3-, with indication of fast production of moderately oxygenated organic aerosol below the measurement height, averaging about 15% of the HOA emission. The average emission factor for HOA was 0.5 g (kg fuel)-1, similar to those found in previous studies. For NO3- an emission factor of 0.09 g (kg fuel)-1 was estimated, implying oxidation of 0.5% of the traffic derived NOx below the measurement height of 45 m. By contrast, SO42- fluxes were on average downward, with deposition velocities that increase with friction velocity from 0.4 to 4 mm s-1. Copyright © American Association for Aerosol Research.

Measurements of aerosol size distribution, chemical composition, and cloud condensation nuclei (CCN) concentration were performed during the Chemical Emission, Loss, Transformation, and Interactions with Canopies (CELTIC) field program at Duke Forest in North Carolina. A kinetic model of the cloud activation of ambient aerosol in the chamber of the CCN instrument was used to perform an aerosol-CCN closure study. This study advances prior investigations by employing a novel fitting algorithm that was used to integrate scanning mobility particle sizer (SMPS) measurements of aerosol number size distribution and aerosol mass spectrometer (AMS) measurements of the mass size distribution for sulfate, nitrate, ammonium, and organics into a single, coherent description of the ambient aerosol in the size range critical to aerosol activation (around 100-nm diameter). Three lognormal aerosol size modes, each with a unique internally mixed composition, were used as input into the kinetic model. For the two smaller size modes, which control CCN number concentration, organic aerosol mass fractions for the defined cases were between 58% and 77%. This study is also unique in that the water vapor accommodation coefficient was estimated based on comparing the initial timing for CCN activation in the instrument chamber with the activation predicted by the kinetic model. The kinetic model overestimated measured CCN concentrations, especially under polluted conditions. Prior studies have attributed a positive model bias to an incomplete understanding of the aerosol composition, especially the role of organics in the activation process. This study shows that including measured organic mass fractions with an assumed organic aerosol speciation profile (pinic acid, fulvic acid, and levoglucosan) and an assumed organic aerosol solubility of 0.02 kg kg-1 still resulted in a significant model positive bias for polluted case study periods. The slope and y intercept for the CCN predicted versus CCN observed regression was found to be 1.9 and -180 cm-3, respectively. The overprediction generally does not exceed uncertainty limits but is indicative that a bias exists in the measurements or application of model. From this study, uncertainties in the particle number and mass size distributions as the cause for the model bias can be ruled out. The authors are also confident that the model is including the effects of growth kinetics on predicted activated number. However, one cannot rule out uncertainties associated with poorly characterized CCN measurement biases, uncertainties in assumed organic solubility, and uncertainties in aerosol mixing state. Sensitivity simulations suggest that assuming either an insoluble organic fraction or external aerosol mixing were both sufficient to reconcile the model bias. © 2007 American Meteorological Society.

Below‐cloud scavenging (BS) is often underestimated in chemical transport models (CTMs) due to inaccurate parameterizations of BS coefficient for fine particle (Λ) caused by a shortage of high‐time resolution field observations. Rainfall ions and related air pollutants were measured hourly in Central China (CC) during 2019. BS contributed to 37%–68% of wet deposition for SO42– ${\text{SO}}_{4}^{2\mbox{--}}$, NO3– ${\text{NO}}_{3}^{\mbox{--}}$, and NH4+ ${\text{NH}}_{4}^{+}$ (SNA). By a bulk method coupled with brute‐force search, the Λ (10−2–10 hr−1) was parameterized for SNA in PM2.5, which was 1–3 orders of magnitudes higher than theoretical calculations in CTMs. These chemical‐specific Λ parameterizations were validated by EMEP model. Compared to baselines, updated simulations for annual SNA wet deposition increased by 3.3%–20.4% and for mean PM2.5 SNA concentrations reduced by 1.2%–40%, capturing measurements better. The contributions of scavenged gases to wet deposition below cloud were calculated as 9%–73%, exhibiting discrepancies (2%–17% for HNO3 and 19%–90% for SO2) with previous modeling results as different Λ schemes adopted in CTMs. The nonlinearity between Λ and precipitation intensity causes frequency exerting stronger impact on aerosol burden than intensity and duration. Periodic light rain with a precipitation amount of 1–10 mm per event can eliminate 60% of SNA in PM2.5 and is suggested as a routine procedure to improve local air quality. Analyzing a typical washout process after a haze event in CC, BS could reduce PM2.5 SNA concentrations by 44%–54% derived from improved parameterizations. Plain Language Summary: The below‐cloud scavenging (BS) is a key process for removing atmospheric aerosols. An underestimation of BS for fine particle (PM2.5) is common in chemical transport models (CTMs) and biases simulations. A comprehensive observation was conducted for 1 year to improve parameterizations of BS coefficient for PM2.5 compounds (Λ). These parameterizations were assessed with a CTM. The updates increased the modeled wet deposition fluxes and decreased aerosol concentrations. These results mimicked observations better than baselines. The diversity of BS parameterizations in CTMs enhanced uncertainties of numerical simulation evaluations. The nonlinearity pattern of Λ between precipitation intensity implies frequency imposing stronger impact on aerosol burden than intensity and duration. This principle determines periodic light rain more suitable for cleaning air pollutants routinely. Utilizing the observational parameterizations, the importance of washout on clearing haze was revealed. This work can improve numerical simulations and air pollution control policies. Key Points: The parameterizations of below‐cloud scavenging coefficients for fine particle compounds were improvedThe reliability of chemical‐specific parameterizations was verified by a chemical transport modelThe implications of improved parameterizations for numerical simulations and air pollution control were elucidated [ABSTRACT FROM AUTHOR]

A one-dimensional canopy model was used to quantify the impact of photochemistry in modifying biosphere-atmosphere exchange of trace gases. Canopy escape efficiencies, defined as the fraction of emission that escapes into the well-mixed boundary layer, were calculated for reactive terpene species. The modeled processes of emission, photochemistry, diffusive transport, and deposition were highly constrained based on intensive observations collected in a Loblolly Pine plantation at Duke Forest, North Carolina, during the CELTIC field study. Canopy top fluxes for isoprene and α,β-pinene were not significantly altered by photochemistry as calculated escape efficiencies were greater than 0.90 for both species, β-caryophyllene emission and photochemistry were added to the canopy model as a surrogate for the reactive sesquiterpene class of species, β-caryopyllene escape efficiencies of 0.30 were calculated for midday summertime conditions. Urbanization scenarios were also performed to assess the impact of pollution on modifying biosphere-atmosphere exchange. Modest changes in escape efficiencies were calculated for a wide range of anthropogenic hydrocarbon and NOx mixing ratios suggesting a simple parameterization of escape efficiency in terms of grid cell NOx may be possible for incorporating the impact of canopy scale photochemistry within biogenic emission processing systems for regional air quality and climate models. The inferred magnitude of sesquiterpene ozonolysis reactions has important implications on both daytime and nighttime radical formation in the canopy. Copyright 2005 by the American Geophysical Union.