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The identification of spikes (i.e., short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, inversion experiments devoted to the top-down quantification of GHG surface–atmosphere fluxes, the characterization of local emissions, or the quality control of GHG measurements. In this work, we analyzed the results provided by two automatic spike identification methods (i.e., the standard deviation of the background (SD) and the robust extraction of baseline signal (REBS)) for a 2-year dataset of 1 min in situ observations of CO 2 , CH 4 and CO at 10 different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification by the site principal investigators (PIs). The study showed that, for CO 2 and CH 4 , REBS identified a larger number of spikes than SD and it was less "site-sensitive" than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO 2 and CH 4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events, and the temporal variabilities in CO 2 , CH 4 and CO were more consistent with those of the original datasets. Further activities are needed for better consolidating the fitness for purpose of the two proposed methods and to compare them with other spike detection techniques. [ABSTRACT FROM AUTHOR]

The year 2022 saw record breaking temperatures in Europe during both summer and fall. Similar to the recent 2018 drought, close to 30% (3.0 million km2) of the European continent was under severe summer drought. In 2022, the drought was located in central and southeastern Europe, contrasting the Northern-centered 2018 drought. We show, using multiple sets of observations, a reduction of net biospheric carbon uptake in summer (56-62 TgC) over the drought area. Specific sites in France even showed a widespread summertime carbon release by forests, additional to wildfires. Partial compensation (32%) for the decreased carbon uptake due to drought was offered by a warm autumn with prolonged biospheric carbon uptake. The severity of this second drought event in 5 years suggests drought-induced reduced carbon uptake to no longer be exceptional, and important to factor into Europe's developing plans for net-zero greenhouse gas emissions that rely on carbon uptake by forests. Heat and moisture stress can reduce carbon uptake by forests. Here, the authors quantify this effect for the extreme 2022 European summer drought. The widespread reduction of photosynthesis exceeded the large local carbon release by intense fires. [ABSTRACT FROM AUTHOR]

The identification of spikes (i.e. short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, the inversion experiments devoted to the top-down quantification of GHG surface-atmosphere fluxes, the characterization of local emissions or the quality control of GHG measurements. In this work, we analysed the results provided by two automatic spike identification methods (i.e. the standard deviation of the background - SD and the robust extraction of baseline signal - REBS) for a 2-year dataset of 1-minute in-situ observations of CO2, CH4 and CO at ten different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification of the site Principal Investigators (PIs). The study showed that, for CO2 and CH4, REBS identified a larger number of spikes than SD and it was less "site-sensitive" than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO2 and CH4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events and the temporal variabilities of CO2, CH4 and CO were more consistent with that of the original datasets. Further activities are needed for better consolidating the fitness for purposes of the two proposed methods and to compare them with other spike detection techniques. [ABSTRACT FROM AUTHOR]

We present a statistical framework to identify regional signals in station-based CO 2 time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO 2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally adjusted noise component, equal to 2 standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which surpass this ±2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale atmospheric transport events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts. [ABSTRACT FROM AUTHOR]

The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high-precision scientific data on the carbon cycle and the greenhouse gas budget. All stations have to undergo a rigorous assessment before being labeled, i.e., receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmosphere network through the 23 stations that were labeled between November 2017 and November 2019. We describe the labeling steps, as well as the quality controls, used to verify that the ICOS data (CO 2 , CH 4 , CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the greenhouse gas data, three to four calibration gases and two target gases are measured: one target two to three times a day, the other gases twice a month. The data are verified on a weekly basis, and tests on the station sampling lines are performed twice a year. From these high-quality data, we conclude that regular calibrations of the CO 2 , CH 4 and CO analyzers used here (twice a month) are important in particular for carbon monoxide (CO) due to the analyzer's variability and that reducing the number of calibration injections (from four to three) in a calibration sequence is possible, saving gas and extending the calibration gas lifespan. We also show that currently, the on-site water vapor correction test does not deliver quantitative results possibly due to environmental factors. Thus the use of a drying system is strongly recommended. Finally, the mandatory regular intake line tests are shown to be useful in detecting artifacts and leaks, as shown here via three different examples at the stations. [ABSTRACT FROM AUTHOR]

The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high precision scientific data on the carbon cycle and the greenhouse gas (GHG) budget. All stations have to undergo a rigorous assessment before being labeled, i.e. receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmospheric network through the 23 stations that have been labeled between November 2017 and November 2019. We describe the labeling steps as well as the quality controls used to verify that the ICOS data (CO2, CH4, CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the GHG data, three to four calibration gases and two target gases, one measured two to three times a day, the other with the calibration gases (twice a month) are measured. The data are controlled on a weekly basis and tests on the station sampling lines are performed twice a year. From these high-quality data, we conclude that regular calibrations of the CO2, CH4 and CO analyzers used here (twice a month) are important in particular for carbon monoxide (CO) due to the analyzer's variability and that reducing the number of calibration injections (from four to three) in a calibration sequence is possible and permits saving gas and extend the calibration gas lifespan. We also show that currently, the on-site water vapor correction test does not deliver quantitative results possibly due to environmental factors. Thus the use of a drying system is strongly recommended. Finally, the mandatory regular intake line tests are shown to be useful to detect artifacts and leaks as shown here via three different examples at the stations. [ABSTRACT FROM AUTHOR]

Quantitative understanding of the sources and sinks of greenhouse gases is essential for predicting greenhouse-gas–climate feedback processes and their impacts on climate variability and change. Australia plays a significant role in driving variability in global carbon cycling, but the budgets of carbon gases in Australia remain highly uncertain. Here, shipborne Fourier transform infrared spectrometer measurements collected around Australia are used together with a global chemical transport model (GEOS-Chem) to analyse the variability of three direct and indirect carbon greenhouse gases: carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO). Using these measurements, we provide an updated distribution of these gases. From the model, we quantify their sources and sinks, and we exploit the benefits of multi-species analysis to explore co-variations to constrain relevant processes. We find that for all three gases, the eastern Australian coast is largely influenced by local anthropogenic sources, while the southern, western and northern coasts are characterised by a mixture of anthropogenic and natural sources. Comparing coincident and co-located enhancements in the three carbon gases highlighted several common sources from the Australian continent. We found evidence for 17 events with similar enhancement patterns indicative of co-emission and calculated enhancement ratios and modelled source contributions for each event. We found that anthropogenic co-enhancement events are common along the eastern coast, while co-enhancement events in the tropics primarily derive from biomass burning sources. While the GEOS-Chem model generally reproduced the timing of co-enhancement events, it was less able to reproduce the magnitude of enhancements. We used these differences to identify underestimated, overestimated and missing processes in the model. We found model overestimates of CH4 from coal burning and underestimates of all three gases from biomass burning. We identified missing sources from fossil fuel, biofuel, oil, gas, coal, livestock, biomass burning and the biosphere in the model, pointing to the need to further develop and evaluate greenhouse-gas emission inventories for the Australian continent. [ABSTRACT FROM AUTHOR]