Despite the key role of the Arctic in the global Earth system, year-round in-situ atmospheric composition observations within the Arctic are sparse and mostly rely on measurements at ground-based coastal stations. Measurements of a suite of in-situ trace gases were performed in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. These observations give a comprehensive picture of year-round near-surface atmospheric abundances of key greenhouse and trace gases, i.e., carbon dioxide, methane, nitrous oxide, ozone, carbon monoxide, dimethylsulfide, sulfur dioxide, elemental mercury, and selected volatile organic compounds (VOCs). Redundancy in certain measurements supported continuity and permitted cross-evaluation and validation of the data. This paper gives an overview of the trace gas measurements conducted during MOSAiC and highlights the high quality of the monitoring activities. In addition, in the case of redundant measurements, merged datasets are provided and recommended for further use by the scientific community.
atmospheric ozone • atmospheric carbon dioxide • atmospheric methane • atmospheric carbon monoxide • atmospheric nitrous oxide • atmospheric dimethylsulfide • atmospheric sulfur dioxide • atmospheric gaseous elemental mercury • volatile organic compounds
ozone analyzer • carbon dioxide analyzer • methane analyzer • carbon monoxide analyzer • nitrous oxide analyzer • dimethylsulfide analyzer • sulfur dioxide analyzer • gaseous elemental mercury analyzer • gas chromatography-mass spectrometry
Sample Characteristic - Environment
Sample Characteristic - Location
Background & Summary
The Arctic has warmed three times more rapidly than the rest of the planet, and this warming is happening faster than predicted1. The effects of climate change are thus more pronounced in the Arctic than in other climate zones, leading to e.g., large temperature increase, sea ice decline2,3, loss of permafrost4, and changes in Arctic ecology5,6. In addition, discovery of new petroleum and mineral resources, the opening of shipping routes in the Arctic Ocean, and geopolitical interests are posing ever-increasing pressure on the Arctic and further environmental impacts are becoming evident7,8,9,10. These profound regional changes might have significant impacts on mid-latitude climate variability11,12, highlighting the central role of the Arctic in the global Earth system.
In its Special Report on Global Warming of 1.5 °C, the Intergovernmental Panel on Climate Change (IPCC) identified human activities and associated greenhouse gas (GHG) emissions as the root cause of global warming13. The direct impact of short-lived climate forcers (e.g., methane (CH4), ozone (O3)) persists from a few days to a decade at most. However, due to long atmospheric lifetimes, emissions of GHGs such as carbon dioxide (CO2) and nitrous oxide (N2O) have long-lasting impacts (centuries) on radiative forcing14. Therefore, long-term observations of GHG atmospheric abundances are essential to evaluate the effectiveness of mitigation policies and to identify potential climate feedback processes15.
Monitoring GHG atmospheric abundances in the Arctic is, however, challenging because it is a remote and harsh environment with a sparsity of locations with appropriate infrastructure. As a consequence, observations have mostly been performed at ground-based coastal stations or during short-term aircraft or ship-based campaigns16. In that context, the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC; https://mosaic-expedition.org/) expedition offered an unprecedented opportunity to monitor the year-round atmospheric composition of the central Arctic. The backbone of MOSAiC was the year-round operation of the Research Vessel Polarstern which drifted with the sea ice across the central Arctic from October 2019 to September 2020. In-situ observations addressing key aspects of the coupled Arctic climate system were set up on-board Polarstern and on the surrounding sea ice. A general overview of the expedition and a description of observations carried out by the “Atmosphere” science team and the drift track can be found in Shupe et al.17.
In addition to monitoring GHGs, the expedition provided a unique platform to study the wider Arctic atmospheric chemical composition. The latest Arctic Monitoring & Assessment Programme (AMAP) report on the impacts of short-lived climate forcers on Arctic Climate18 highlighted the climate-relevance of other compounds such as sulfur dioxide (SO2; precursor of sulfate aerosols). In the period 1990–2015, the Arctic warming attributed to declining SO2 emissions was of similar magnitude to the warming driven by increasing CO2 emissions (~0.29 °C per decade). The year-long expedition also provided a platform to investigate seasonal variations. During winter and spring, the combination of increased long-range transport from mid-latitudes and of relatively weak removal processes leads to the build-up of air pollution, the so-called Arctic haze19,20,21,22. Previous studies have also shown that the Arctic atmosphere features a number of complex chemical and physical processes at the onset of spring23,24,25. These transformations can, for example, result in the formation or depletion of gases at rates and magnitudes not observed in other environments26,27,28. These findings have drawn a generation of researchers to study this unique air-sea-ice environment and prompted us to expand the array of atmospheric trace gases monitored during the expedition.
Here, we present the comprehensive suite of in-situ surface trace gas measurements during the MOSAiC expedition, including CO2, CH4, N2O, O3, carbon monoxide (CO), dimethylsulfide (DMS), SO2, elemental mercury (Hg(0)), and selected volatile organic compounds (VOCs). Redundancy in certain measurements improved continuity and permitted cross-evaluation and validation of the measurements. We present the results of this intercomparison in an effort to demonstrate the quality of these individual datasets and of the overall trace gas monitoring activities during the expedition. In addition, we provide merged datasets (which combine redundant individual datasets and limit gaps in time series) for further use by the community.
Anchored to an ice floe, the research icebreaker Polarstern drifted for an entire year over the central Arctic Ocean. The vessel departed from Tromsø, Norway on September 20, 2019. A suitable floe was found on October 4, 2019, at 85°N, 134°E, where the drift began. Due to logistical constraints related to the COVID-19 pandemic, Polarstern left the MOSAiC floe from mid-May to mid-June, 2020. Most measurements continued as the ship transited to Svalbard and back. From mid-June to the end of July, Polarstern was again attached to the MOSAiC floe. After the disintegration by melting and breakup of the original floe, the vessel transited to a new location close to the North Pole and drifted again from late August to late September, 2020. Most measurements continued as the ship transited back to Svalbard at the end of the expedition.
As summarized in Fig. 1, trace gas measurements described hereafter were performed on-board Polarstern in three distinct sea-container laboratories, and on the sea ice itself from a 10-meter flux tower at Met City (meteorological station housing numerous atmospheric measurements located 300–600 m away from Polarstern17). While the Atmospheric Radiation Measurement (ARM) Program and Swiss containers were located on the foredeck (D-deck) with sampling inlets pointing upwards (inlet height of approximately 18 and 15 m above sea level (asl) in the ARM and Swiss containers, respectively), the University of Colorado (CU) container was installed below deck in the forward cargo hold. Sampling lines (roughly 50 m long) were deployed from the CU container to the bow crane to allow measurements forward of the vessel (Teflon lines for all instruments except a stainless-steel line for VOCs). The inlet height on the bow crane for slow trace gas measurements (Hg(0), O3, VOCs) was 15 m asl while inlets for the fast flux measurements (DMS, CO2, CH4) were at 20 m asl from October 2019 to May 2020, and 18 m asl for the rest of the expedition. Losses along the sampling lines are expected to be minimal for CO2, CH4, O3, and Hg(0). DMS losses are usually negligible but were accounted for by injecting internal standards at the inlet tip (see below). The temperature inside the different containers was kept constant at approximately 20 °C. Low ambient dew point temperatures (−20 to 0 °C) combined with the use of Nafion dryers limited the effect of water vapor on the measurements. The ARM container was operated as part of the United States Department of Energy (US DOE) Aerosol Observing System (AOS). As described by Uin et al.29, AOSs are designed as standardized platforms for atmospheric aerosol and trace gas measurements. The here reported trace gas measurements performed in the Swiss container were considered ancillary as the main objective was to monitor characteristics of aerosols and their precursors (see Fig. A3 in Beck et al.30 for a description of the full setup in this container). The comprehensive suite of in-situ trace gas measurements performed in the various containers during the expedition is summarized in Fig. 1 and Table 1. Figure 2 gives the number of operating instruments per day during the expedition.
Carbon dioxide, methane, carbon monoxide, and nitrous oxide
Atmospheric abundances, reported in dry air mole fractions, were monitored by cavity ring-down spectroscopy (CRDS) at Met City and in the CU and Swiss containers using commercial Picarro instruments (model G2311-f at Met City and in the CU container, model G2401 in the Swiss container; see Table 1). The Picarro instruments allow for simultaneous and continuous measurements of atmospheric trace gases along with water vapor. Dry air mole fractions were automatically obtained by applying water vapor correction factors31. The two G2311-f instruments were operated in 10 Hz flux mode during the expedition, with a manufacturer-specified precision <200 nmol/mol (parts per billion; ppb) for CO2 and <3 ppb for CH4. The G2401 instrument provided simultaneous measurements of CO2, CH4, and CO ambient air mole fractions, with a manufacturer-specified precision at 5 sec and 5 min of <50 ppb and 20 ppb for CO2, <1 ppb and 0.5 ppb for CH4, and <15 ppb and 1.5 ppb for CO. Simultaneous measurements of N2O, CO, and water vapor ambient air mole fractions were performed in the ARM container with an off-axis integrated cavity output spectroscopy instrument (OA-ICOS; Los Gatos Research model 098-0014) with a precision of 0.1 ppb for CO and 0.2 ppb for N2O32. Similarly to the Picarro instruments, the OA-ICOS instrument automatically corrects the measurements to dry conditions. Regular in-cruise calibrations were carried out for CO2, CH4, and CO to ensure the stability and accuracy of the response of the various instruments. The Picarro instrument in the Swiss container was calibrated using working standards that were characterized at the Swiss Federal Laboratories for Materials Science and Technology (EMPA) before the expedition. These working standards were directly calibrated against three standards traceable to the following calibration scales: WMO-X2007 for CO233, WMO-X2004A for CH434, and WMO-X2014A for CO35. The standards used at Met City and in the ARM and CU containers were working standards obtained from the Lawrence Berkley National Laboratory (ARM) and Airgas (Met City/CU). Due to logistical constraints before and after the expedition, these standards were not independently calibrated and are thus not traceable to the WMO calibration scales. Note that the OA-ICOS instrument was not calibrated for N2O as these measurements are considered ancillary by the US DOE AOS. Here, we report and compare minute-averaged ambient air mole fractions for all instruments.
O3 ambient air mole fractions were monitored in the three afore-mentioned sea-container laboratories using commercial instruments (Thermo Fisher Scientific model 49i in the ARM container, Thermo Environmental Instruments model 49c in the CU container, and 2B Technologies model 205 in the Swiss container; see Table 1). These instruments have manufacturer-specified precisions of 1.0 ppb for 20-s averages. As described in detail in the instrument handbook36, the ARM instrument was checked twice a day for zero and span checks. Measurements during the zero measurement periods were used to calculate the instrument baseline with a 3–6 week moving average. This instrument baseline was then subtracted from the ambient air measurements. Measurements during zero and span checks were assessed for possible drifts. Note that measurements in the first 105 seconds after a zero and in the first 30 seconds after each span check were discarded. In addition, a linear calibration coefficient (determined from a five-point span check at the New York State Department of Environmental Conservation standards laboratory) was applied to O3 values. This final, quality checked, minute-averaged O3 dataset was used as reference to adjust O3 mole fractions from the Swiss and CU instruments. This is further discussed below in the sub-section “cross-evaluation of redundant measurements and merged datasets”. Note that zero and flow rate checks were performed every 2 weeks in the Swiss container.
Continuous DMS measurements were performed using an Atmospheric Pressure Ionization Mass Spectrometer with an Isotopically Labeled Standard (APIMS-ILS). The description of this custom-built instrument can be found in Appendix A of Blomquist et al.37. Briefly, the APIMS-ILS monitors the DMS mole fraction of a dried sample air stream at 10 Hz for analysis of the eddy correlation turbulent DMS flux. The air sample was drawn from an inlet at the top of the Polarstern bow sampling tower (Fig. 1), adjacent to a sonic anemometer. A known concentration of isotopically labeled DMS (d3-DMS, mass 65) was continuously injected at the inlet tip. The DMS mole fraction was computed from the signal intensity ratio of the protonated ambient and standard isotopomers (masses 63 and 66) and the gas flow rates. Note that the use of a continuous internal standard compensates for calibration drift and variable sensitivity. Averaged to 10 seconds, the APIMS-ILS detection limit is typically < 5 ppt. The d3-DMS compressed gas standard was calibrated with respect to a permeation tube device as the primary standard.
A commercial pulsed fluorescence instrument (Thermo Fisher Scientific model 43i) was used during the expedition with a flow rate of 0.5 L/min. Biweekly zero measurements were performed with a scrubber and we used an external permeation source to periodically check the calibration of the instrument during the expedition. This permeation source was characterized using a certified SO2 standard at the end of the expedition at EMPA. The instrument has a manufacturer-specified lower detectable limit of 1 ppb for a 1-minute averaging time.
Elemental mercury measurements
A Tekran 2537B mercury analyzer, commonly used at monitoring sites around the world38,39,40, was deployed in the CU container to monitor ambient air concentrations of Hg(0) during the expedition (see Fig. 1). To avoid potential bias in the default integration of the signal41,42,43, integrated samples were analyzed every 15 minutes. Millipore 0.45 µm polyether sulfone cation-exchange membranes were used to remove potential divalent Hg species, and thus, only Hg(0) was collected and analyzed here44,45,46. The instrument was automatically calibrated every 25 hours using an internal Hg permeation source. The accuracy of the permeation source was checked before the beginning of the expedition against manual injections of saturated Hg vapor using a Tekran 2505 Hg vapor calibration unit47. Screening criteria for data validation/invalidation were inspired by standard operative protocols used by the Canadian Atmospheric Mercury Measurement Network (CAMNet), the US Atmospheric Mercury Network (AMNet), and the Global Mercury Observation System (GMOS) network48,49. The average systematic uncertainty for Hg(0) measurements is of approximately 10% based on experimental evidence50.
Volatile organic compounds
An automated gas chromatography and mass spectrometry with flame ionization detector (GC-MS/FID) system was used for continuous measurements of selected VOCs at a 3-hr time resolution. Ambient air, pulled from the inlet on the bow crane, passed through a u-shaped SilcosteelTM (stainless steel treated) moisture trap cooled with thermoelectric coolers to dry the air to a dew point of −45 °C, and through a sodium thiosulfate-coated O3 scrubber to prevent sampling losses and artifacts51. Analytes were concentrated on a Peltier-cooled (−40 °C) multistage micro-adsorbent trap (Carboxen 569 and Carboxen 1000). Analysis was performed by thermal desorption and injection for cryogen-free GC using a Porabond-Q column (50 m × 320 µm × 5 µm) and helium as a carrier gas. Blanks and calibration standards were regularly injected from a manifold. In order to monitor and correct for trends in the detection system (i.e., detector drift, decreasing performance of the adsorbent trap), we used peak areas of long-lived chlorofluorocarbons (CFCs) that were monitored in the air samples together with VOCs as an internal reference standard52,53. Table 2 gives the full list of compounds included in the selected ion-monitoring (SIM) mode target list. We only report here mole fractions for a subset of compounds (propane, isobutane, n-butane, and isoprene); We welcome enquiries regarding the quantification of other compounds listed in Table 2 or the identification of compounds not listed here (SCAN mode chromatograms). Please note that the raw chromatograms are also publicly available on the Arctic Data Center repository (see Data Records section). Propane, isobutane, n-butane, and isoprene were identified and quantified using the MS in SIM mode and a UK-National Physical Laboratory (NPL) calibration standard. The repeatability of these measurements was estimated to 5–6% based on the repeated analysis (n = 54) of the NPL standard throughout the expedition. Chromatograms were analyzed using the TERN (Thermal desorption aerosol gas chromatography ExploreR and iNtegration package) peak fitting tool54.
Global Greenhouse Gas Reference Network
Whole air samples were collected ~weekly following established protocols of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) Carbon Cycle Cooperative Global Air Sampling Network (https://gml.noaa.gov/ccgg/). Samples were collected in pairs, in background air, upwind from local emissions, in 2.5 L borosilicate flasks with two glass-piston stopcocks sealed with Teflon O-rings. Flasks were flushed in series for 5 minutes then pressurized to ~1.2 atm with a portable sampling system55,56. Samples were transported back to the NOAA GML facility in Colorado, United States and analyzed post-cruise following well-established protocols. CO2 and CH4 were analyzed by cavity ring-down spectroscopy while N2O and CO were analyzed using a Tunable Infrared Laser Direct Absorption Spectroscopy (TILDAS) method. The analyzers are routinely calibrated off-line once a month with a suite of standards. The repeatability of the measurements is estimated to be 0.02 μmol/mol (parts per million; ppm) for CO2, 0.2 ppb for CH4, 0.02 ppb for N2O, and 0.1 ppb for CO based on the repeated analysis of air from a high-pressure cylinder. All measurements were referenced to the corresponding NOAA calibration scales (https://gml.noaa.gov/ccl/scales.html), i.e., the World Meteorological Organisation (WMO) X2019 CO2 scale57, the WMO-X2004A CH4 standard scale34, the NOAA-2006A N2O standard scale58, and the WMO-X2014A CO scale35. In addition to CO2, CH4, CO, and N2O, samples were also analyzed for other hydrocarbons under the umbrella of the NOAA GML Halocarbons and other Atmospheric Trace Species (HATS) network (https://gml.noaa.gov/hats/) and for stable isotopes of CO2 and CH4 at the University of Colorado Institute for Arctic and Alpine Research59,60. Table 3 gives the full list of measurements performed on the discrete whole air samples collected during MOSAiC.
To complement the APIMS-ILS measurements, ambient DMS mole fractions were intermittently measured using an automated gas chromatography and flame photometric detector (GC/FPD) system. DMS in air pulled from the inlet on the bow crane was automatically sampled over a period of 45 minutes at a flow rate of 0.200 L/min, and concentrated on adsorbent tubes containing a mixture of Carboxen 1016 and Carboxen 1000, held at 30 °C. An automatic thermal desorption system (PerkinElmer ATD 400) was used to transfer samples to a GC (Shimadzu GC8/FPD) with a Chromosil 330 packed column (4 m × 2.1 mm), using helium as the carrier gas. Calibration made use of the same DMS standard (d3-DMS, 576 ppb) as used as the internal standard in the APIMS-ILS system.
Local pollution screening procedures
While frozen into the pack ice, it was not possible to maneuver the ship’s bow into the prevailing wind for clean air sampling, so all measurements were episodically influenced by local anthropogenic pollution sources (e.g., exhaust by the vessel’s engine and vents, skidoos, helicopters, on-ice diesel generators). Different screening strategies were employed in the three sea-container laboratories to identify and/or mitigate these influences. Sampling of polluted air was prevented in the CU container by automatically backflushing the inlet stack with zero-air during unfavorable wind conditions, empirically determined to be a relative wind direction more than ± 130° from the bow. Similarly, for the Met City measurements, true wind direction within ± 10° of the compass bearing from the tower to the ship was excluded. The ARM container was equipped with a purge blower set up to trigger based on ambient CO mole fractions. As CO turned out not to be an ideal tracer for local pollution from the ship stack of Polarstern during MOSAiC30, the purge blower was turned on manually when the container was exposed to local pollution for extended periods, as identified by local operators. The purge blower only affected the O3 data, as the CO analyzer was sampling from a separate inlet line collocated with the main AOS inlet. Lastly, sampling was uninterrupted in the Swiss container. As a result, all datasets were carefully screened for local pollution during post processing as described hereafter.
As described in Beck et al.30, local pollution typically leads to rapid fluctuations in measurements of many parameters. Local pollution in such a remote environment can often be detected based on the time derivative of the ambient air mole fraction. For each data point, the time derivative was calculated. Data points corresponding to an abnormally high derivative (>1.5 times the interquartile range) and neighboring points were discarded. The function “despike” from R package oce61 (version 1.3-0) was then applied to the time-series to remove any remaining local pollution spikes. Briefly, this function first linearly interpolates across any gaps (missing values). Then, it calculates a running median spanning k elements. The result of these two steps is the “reference” time-series. The standard deviation of the difference between values and the reference is then calculated. Values that differ from the reference by more than n times this standard deviation are considered to be spikes and eliminated. The function was applied once with n = 1 (n = 3 for CO62) and k = 61 (~1 hour). This procedure was applied to all datasets, unless mentioned otherwise below.
As the time derivative method is better suited for primary pollutants30, O3 time-series were cleaned from local pollution influence using the above-mentioned “despike” function only. The function was applied twice using different k values (k = 1439 (~1 day) and k = 61 (~1 hour)) and n = 3, which satisfactorily eliminated negative O3 spikes due to local nitric oxide emissions.
In parallel, CO2 and CH4 time-series collected in the CU container and at Met City exclude all measurements that were not in the clean wind sector (see above), eliminating the majority of local pollution events. Selected instances of emission spikes from equipment operations on-ice during working hours (e.g., skidoos) were identified and removed manually.
Cross-evaluation of redundant measurements and creation of merged datasets
Redundant measurements were cross-evaluated using i) weekly discrete samples collected for post-cruise analysis at NOAA GML as calibration reference for CO2, CH4, and CO, and ii) ARM measurements as calibration reference for O3. Ambient air mole fractions were adjusted with respect to reference measurements using the slope and intercept of the correlation with initial (non-adjusted) values (Eq. 1).
Time-dependent correction factors (slope and intercept) were used to account for drifts in differences between instruments. Correction factors can be found in Tables 4 and 5. The cross-evaluated adjusted mole fractions were then used to generate hourly-averaged merged datasets in order to limit gaps in the time series and facilitate use by the scientific community. Figure 3 shows the order of priority given to the different cross-evaluated individual datasets for the creation of the hourly-averaged merged datasets. Priority was given to continuous measurements over discrete samples, and to instruments with the highest precision. As summarized in Table 1, the cavity ring-down instruments used in the CU container and at Met City are designed for flux measurements and were operated in 10 Hz mode. This resulted in higher precision minute-averaged measurements of CO2 and CH4 as compared to measurements in the Swiss container. Priority was thus given to these two individual datasets for the creation of the merged datasets. Following a similar approach, priority was given to CO measurements performed in the ARM container (manufacturer-specified precision at 1 sec of 0.1 ppb for the OA-ICOS instrument) over measurements performed in the Swiss container (manufacturer-specified precision at 5 sec < 15 ppb for the G2401 instrument). Finally, priority was given to the O3 measurements performed in the CU container over measurements performed in the ARM and Swiss containers (Fig. 3d). Figure 3 also highlights the very good agreement between the different adjusted time-series, reflecting the high quality of the monitoring activities during the expedition. This is further discussed in the “Technical Validation” section.
Table 6 summarizes data records associated with this work, including the repositories where data are stored. Tables 7 and 8 summarize the list of attributes for datasets collected in the Swiss container and for merged datasets, respectively, both archived on PANGAEA (https://www.pangaea.de/). Datasets collected in the CU container and at Met City are archived on the Arctic Data Center (https://arcticdata.io/); the list of attributes can be found in Table 9. Datasets collected in the ARM container are archived on the ARM Data Archive (https://www.arm.gov/data/); the list of attributes can be found in Tables 10 and 11. Finally, data inferred from discrete whole air sampling and post-cruise analysis at NOAA GML are available on a dedicated webpage (https://gml.noaa.gov/ccgg/arc/?id=157); the list of attributes can be found in Table 12. Please note that one needs to register to access datasets archived on the ARM Data Archive and at NOAA GML and that contact information will be sent to contributing data providers.
The comparison of redundant measurements before and after cross-evaluation are presented in Fig. 4. Redundant measurements were performed using completely independent setups (inlet, instrument, calibration standards) and biases are thus expected. The CU CO2 and CH4 measurements were for instance biased low (median relative difference to the NOAA GML reference of −0.50%) and high (+0.38%), respectively. The Swiss CO and O3 time series were biased low (−17.5% and −19.0%, respectively, relative to CO discrete samples and ARM O3 data) while the CU O3 time series was biased low by −6.7% relative to ARM O3 data. The median relative differences between redundant measurements of CO2, CH4, and CO were relatively large (larger than the WMO compatibility guidelines, see below). These differences can largely be explained by the use of different working standards that were not all traceable to the same calibration scale (see Methods section). The cross-evaluation step allows for correction of these calibration biases. In addition, the use of time-dependent correction factors (see above) removes biases associated with the potential drift of instruments. After the cross-evaluation step, redundant measurements are now fully consistent. For instance, on average over the full period, the difference between hourly-averaged continuous measurements is 0.08 ppm for CO2 and 0.06 ppb for CH4 after cross-evaluation, i.e., below the Global Atmosphere Watch (GAW) Programme of the World Meteorological Organization (WMO) compatibility goals (0.1 ppm for CO2 and 2 ppb for CH463). The final merged datasets are referenced to the corresponding NOAA calibration scales, i.e., the WMO-X2019 CO2 scale57, the WMO-X2004A CH4 standard scale34, and the WMO-X2014A CO scale35.
Due to different sampling frequencies (3-hr vs. weekly snapshot samples), a direct cross-evaluation of redundant VOC measurements (propane, i-butane, n-butane) is not possible. Figure 5 shows the comparison of daily averages (n = 36) and highlights the very good agreement between the two datasets (correlation coefficients of 0.98, 0.85, and 0.93 for propane, i-butane, and n-butane, respectively; Spearman correlation test for paired samples). As no redundant measurements are available for DMS, SO2, and Hg(0), a similar cross-evaluation is not possible. Top-notch quality-control procedures were, however, used during the expedition (see Methods section) to ensure validity of the measurements.
The standardized *.txt file format permits easy import into all analysis software commonly used in the atmospheric science community. The files are self-explanatory as they contain all metadata and data. The time series archived on PANGAEA (Swiss container and merged datasets) and the Arctic Data Center (CU container and Met City datasets) are designed such that they can be used without further processing. The CO2, CH4, and CO merged datasets are referenced to the corresponding NOAA calibration scales, i.e., the WMO-X2019 CO2 scale57, the WMO-X2004A CH4 standard scale34, and the WMO-X2014A CO scale35. Most datasets contain a pollution flag indicating when local anthropogenic pollution was detected. Merged datasets do not include a pollution flag because they were created using clean individual time-series. For datasets collected in the CU container and at Met City, and for discrete flask sampling, no measurements were performed when the wind was out of the clean air sector (hence no pollution flag needed). When available, we highly encourage the use of hourly-averaged merged datasets that limit gaps in the time series. It should be noted that the time series available on the ARM user facility archive are not screened for local pollution nor adjusted for calibration bias. The raw chromatograms acquired with the automated GC-MS/FID system during the expedition are available in AIA format (*.CDF), one of the standard formats used for exchanging data among various chromatography systems.
The pollution detection algorithm described in Beck et al.30 to identify and flag periods of primary polluted data in remote atmospheric time series is available at64: https://doi.org/10.5281/zenodo.5761101. The TERN peak fitting tool implemented in Igor Pro used for the analysis of the GC-MS/FID chromatograms is available at https://sites.google.com/site/terninigor/.
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Data reported in this manuscript were produced as part of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition with the tag MOSAiC20192020, with activities supported by Polarstern expedition AWI_PS122_00. The authors would like to thank Lynne E. Gratz for the Tekran 2505 Hg vapor calibration unit used to check the accuracy of the Hg permeation source before the expedition, Andrea Baccarini for his contribution and support with the preparation, calibration and operation of the instruments in the Swiss container before and during the expedition, David Schönenberger for the help with the SO2 permeation source at EMPA, and the teams at the Paul Scherrer Institute, Switzerland, and INAR, Univ. of Helsinki, for their land-based support during the MOSAiC expedition. We also thank all those who contributed to MOSAiC and made this endeavour possible65. Some observations used here were provided by the Atmospheric Radiation Measurement (ARM) Climate Research Facility, a US Department of Energy (DOE) Office of Science User Facility sponsored by the Office of Biological and Environmental Research. We thank the many ARM operators who supported the field observations. This research was funded by the US National Science Foundation (awards OPP 1807496, 1914781, and 1807163), the Swiss National Science Foundation (grant 200021_188478), the Swiss Polar Institute (grant DIRCR-2018-004), the DOE Atmospheric System Research Program (DE-SC0019251), and the US National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. The EMME-CARE project received funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 856612). M.K. acknowledges the following projects: ACCC Flagship funded by the Academy of Finland grant number 337549, Academy professorship funded by the Academy of Finland (grant no. 302958), Academy of Finland projects no. 1325656, 311931, 316114, 332547, and 325647, “Quantifying carbon sink, CarbonSink+ and their interaction with air quality” INAR project funded by Jane and Aatos Erkko Foundation, and European Research Council (ERC) project ATM-GTP contract no. 742206. The analysis of discrete whole air samples was supported by the NOAA Global Monitoring Laboratory. J.S. holds the Ingvar Kamprad chair for extreme environments research, sponsored by Ferring Pharmaceuticals. L.Q. thanks the European research council ERC (GASPARCON – grant 714621). Valuable insights and comments on an earlier manuscript draft were provided by Zoé Brasseur.
The authors declare no competing interests.
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Angot, H., Blomquist, B., Howard, D. et al. Year-round trace gas measurements in the central Arctic during the MOSAiC expedition. Sci Data 9, 723 (2022). https://doi.org/10.1038/s41597-022-01769-6
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