Abstract. Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12 % and 27 % and between 1 and 11×1014 molec cm−2, respectively. The median values among all stations are 13 % and 2.9×1014 molec cm−2 for the total systematic and random uncertainties.This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013 molec cm−2) to highly polluted levels (7×1016 molec cm−2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions.
Abstract. Among the more than twenty ground-based FTIR (Fourier Transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total columns time-series until now. Although several independent studies have shown that the FTIR measurements can provide accurate and precise formaldehyde total columns, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50% can be observed in the HCHO total columns depending on these 5 retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.For the present work, the HCHO retrieval settings have been optimized based on experience gained from the past studies and have been applied consistently at the 21 participating stations, most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC), or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the systematic and random uncertainties of an 10
Isoprene is the dominant nonmethane volatile organic compound (VOC) emitted to the atmosphere (Guenther et al., 2012). Produced mainly in the leaves of woody plants, isoprene is highly reactive and drives ozone and aerosol production (Lin et al., 2013;Paulot et al., 2012), modulates atmospheric oxidation (Bates & Jacob, 2019), and affects the global nitrogen cycle (Mao et al., 2013;Paulot et al., 2013). Accurate flux estimates are critical for assessing and predicting these impacts; however, bottom-up isoprene inventories are highly uncertain as they (a) rely on emission factors extrapolated from limited point measurements and (b) are sensitive to model assumptions for land cover, meteorology, and plant canopy structure (Arneth et al., 2011;Ganzeveld et al., 2002;Messina et al., 2016). Particular uncertainties have been identified in the world's isoprene hotspots such as Amazonia,
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