Description: National commitments under the Paris Agreement on climate change interact with other global environmental objectives, such as those of the Minamata Convention on Mercury. We assess how mercury emissions and deposition reductions from national climate policy in China under the Paris Agreement could contribute to the country's commitments under the Minamata Convention. We examine emissions under climate policy scenarios developed using a computable general equilibrium model of China's economy, end-of-pipe control scenarios that meet China's commitments under the Minamata Convention, and these policies in combination, and evaluate deposition using a global atmospheric transport model. We find climate policy in China can provide mercury benefits when implemented with Minamata policy, achieving in the year 2030 approximately 5\% additional reduction in mercury emissions and deposition in China when climate policy achieves a 5% reduction per year in carbon intensity (CO2 emissions 9.7 Gt in 2030). This corresponds to 63 Mg additional mercury emissions reductions in 2030 when implemented with Minamata Convention policy, compared to Minamata policy implemented alone. Climate policy provides emissions reductions in sectors not considered under the Minamata Convention, such as residential combustion. This changes the combination of sectors that contribute to emissions reductions. This data submission includes scripts to project China's 2012 mercury emissions from the Emissions Database for Global Atmospheric Research (EDGAR) and prepare them for input to the global chemical transport model, GEOS-Chem. It also includes scripts to plot projected emissions and plot deposition results (with required raw results from GEOS-Chem) for the figures included in the Environmental Science and Technology article.

Description: This dataset contains a pollution flag in 1 min time resolution. It is derived by the pollution detection algorithm (PDA) based on the corrected particle number concentration data (doi:10.1594/PANGAEA.941886) measured during the year long MOSAiC expedition from October 2019 to September 2020. With pollution, we refer to emission from the exhaust of the ship stack, snow groomers, diesel generators, ship vents, helicopters and other. Pollution hence reflects locally emitted particles and trace gases, which are not representative of the central Arctic ambient concentrations. The PDA identifies and flags periods of polluted data in the particle number concentration dataset five steps. The first and most important step identifies polluted periods based on the gradient (time-derivative) of a concentration over time. If this gradient exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median and a sparse data filter (optional). The detailed methodology of the derivation of the pollution flag is described in Beck et al. (2022). A description and download link to the used particle number concentration dataset can be found here: doi:10.1594/PANGAEA.941886. The code of the PDA can be found on Zenodo (Beck et al., 2021; doi:10.5281/zenodo.5761101). Participation of the Swiss Container was co-financed by the Swiss Polar Institute and University of Helsinki.

Description: This dataset contains carbon dioxide dry air mole fractions measured during the year-long MOSAiC expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern. Data were collected by cavity ring-down spectroscopy using a commercial Picarro instrument (model G2401). The minute-averaged dry air mole fractions were adjusted after cross-evaluation against discrete whole air samples collected for post-cruise analysis at the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory. Pollution spikes due to local anthropogenic pollution sources (e.g., exhaust by the vessel's engine and vents, skidoos, helicopters, on-ice diesel generators) were identified and flagged as follows. For each data point, the gradient (time derivative) was calculated (Beck et al., 2022). Data points corresponding to an abnormally high gradient (〉 1.5 times the interquartile range) and neighboring points were discarded. The function “despike” from R package oce (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 and k = 61 (~ 1 hour). The data columns include the Date and Time in Coordinated Universal Time (UTC), the latitude and longitude of Research Vessel Polarstern, the MOSAiC event label, the original carbon dioxide dry air mole fraction in µmol/mol, the adjusted carbon dioxide dry air mole fraction in µmol/mol after cross-evaluation, and a pollution flag where 'yes' means that local pollution was detected.

Description: This dataset contains hourly-averaged methane dry air mole fractions measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. This is a merged dataset that combines cross-evaluated measurements performed in the University of Colorado (CU) and Swiss containers on Research Vessel Polarstern, along with cross-evaluated measurements performed on sea ice at Met City, and discrete whole air samples collected for post-cruise analysis at the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML). The data columns include the Date and Time in Coordinated Universal Time (UTC), the latitude and longitude of the Research Vessel Polarstern, the methane dry air mole fraction in nmol/mol, and the sampling location.

Description: This dataset contains the bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols (NR-PM1) measured during the MOSAiC expedition from October 2019 to July 2020. These include the mass concentrations of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), chloride (Chl), and organics (Org). The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using a commercial High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS, Aerodyne Research, Inc.). One can refer to existing literature (e.g., DeCarlo et al. (2006) and Canagaratna et al. (2007)) for detailed description, functioning principles and field deployment procedures of the AMS. The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al. (Submitted), Beck et al. (2022), and Dada et al. (2022)). Ambient air was hence sampled alternately every hour from the total and interstitial inlets into an aerodynamic lens with a 1 µm critical orifice and a flow of 0.07 L/min. All data were processed using SQUIRREL v1.65B and PIKA v1.25B within the IGOR Pro v9.00 software. This was done separately for the three distinct periods of available measurements, October to December 2019, March to May, and June to July 2020, as the instrument was each time in a different state (after long down times related to turbo pump failures). Regular on-site calibrations using monodisperse, number concentration-defined, ammonium nitrate (NH4NO3) and ammonium sulfate ((NH4)2SO4) particles were performed to determine the ionization efficiency of NO3- and relative ionization efficiencies of NH4+ and SO42- (Jimenez et al. (2003) and Allan et al. (2003)). An airbeam correction factor was applied to the dataset, along with a time and composition-dependent collection efficiency (CDCE, Middlebrook et al. (2012)). Several times per month, zero measurements were performed using High-Efficiency Particulate Absorbing (HEPA) filters. These were used to (1) adapt the AMS fragmentation table for air fragmentation patterns (in post processing), and (2) compute the detection limits (DL) for the main aerosol chemical species (SO42-, NO3-, NH4+, Chl, and Org) as 3 times the standard deviation from the mean species concentration during filter measurements. At the instrument's native time resolution of 90 s, the DL are equal to (for Oct-Dec, Mar-May, and Jun-Jul, respectively) 0.017, 0.102, and 0.084 µg/m3 for SO42-, 0.011, 0.069 and 0.152 µg/m3 for NO3-, 0.001, 0.027 and 0.261 µg/m3 for NH4+, 0.055, 0.055 and 0.071 µg/m3 for Chl, and 0.284, 0.718 and 1.029 µg/m3 for Org. A mass closure analysis was performed between the total NR-PM1 calculated from the AMS and aethalometer and the one approximated from the mobility diameter (dm) measured by the Scanning Mobility Particle Sizer (SMPS) located in a neighboring container. The mass closure analysis was performed independently for the three periods (Oct-Dec, Mar-May and Jun-Jul) and yielded the following scaling factors: 3.77, 0.65 and 0.35 for the three respective periods. These scaling factors are not applied by default on the AMS timeseries, and the user is left with the decision to apply them or compute new ones. The following periods were removed from the dataset: when the airbeam correction factor was larger than 2 or smaller than 0, outliers (defined as more than 3 times the standard deviation of half an hour moving average, that constitute 〈 1 % of the whole dataset), all calibrations, periods when a HEPA filter was placed in front of the instrument, and data non-representative of ambient conditions (e.g. container air). We applied two corrections. First, the switching valve caused data distortion, observed at every full hour (i.e., when the valve turns and the ambient sampling changes from one inlet to another, there is a brief moment with under-pressure in the inlet lines). Consequently, all data points within ± 2 min of the full hours were removed. Second, during some periods when the inlet switching valve was activated, we observed a difference pattern of mean and standard deviation of the measurements between even and odd hours, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. The 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements. This dataset contains a pollution flag ("Flag_pollution") to flag datapoints that were identified as directly influenced by fresh local pollution (e.g., Polarstern exhaust, on-ice diesel generators, skidoos), where a flag equal to 0 indicates clean data and 1 indicates polluted data. The identification method, based on the cosine similarity of the measured mass spectra with a known reference polluted spectrum, is described in Dada et al. (2022). Additionally, a sparse filter with a moving window spanning 60 datapoints (approx. 1h30) was applied to define as entirely polluted periods where more than 60% of the points were already classified as polluted by the cosine similarity method. Finally, the dataset also contains a quality flag for the ammonium timeseries ("Flag_NH4"), as turbo pump failures rendered ammonium measurements very noisy. A value of 1 indicates a quality assured measurement, while a value of 0 indicates a "bad" measure of NH4+ (basically all data after May 24th 2020).

Description: This dataset contains CCN concentrations at five supersaturation levels, averaged to 1 min time resolution, measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using the model CCN-100 from Droplet Measurement Technologies (DMT, Boulder, USA). Detailed description of the measurement principle can be found in e.g. Roberts & Nenes (2005). The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al., Submitted; Beck et al., 2022; Dada et al., 2022). The measurements were performed in 1-h cycles, with a 0.5 L/min sample flow and a 2 L/min make up flow, where the supersaturations 0.15, 0.2, 0.3, 0.5 and 1.0 % were measured. The supersaturation of 0.15 % is measured for 20 min, as it takes longer to equilibrate, and the remaining supersaturations were measured for 10 min each. The instrument was calibrated in July 2019 before the campaign, and in March and April 2020 during the campaign. Based on the inter-variability of the calculated supersaturation levels during these calibrations, we can expect values ranging from 0.15-0.20, 0.20-0.25, 0.29-0.33, 0.43-0.5, 0.78-1.0 % for the nominal supersaturations of 0.15, 0.2, 0.3, 0.5 and 1.0 %, respectively. The counting error for the CCNC is associated with the error in the optical counting of particles and is about 10 %. Data were removed during the cooling cycle (i.e., the time when the measurement cycle starts again and the temperature is cooled to set the lowest supersaturation), which corresponds roughly to the first 10 min of each hour (so 50 % of the 0.15 % supersaturation period). Additionally, the first minute of the transition between supersaturations was removed before averaging the data to 1 min time resolution. During some time periods, a difference pattern of mean and standard deviation of the measurements between even and odd hours was observed, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. For correction, the 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements. The dataset contains a pollution mask for local pollution (predominantly exhaust from the Research Vessel Polarstern) with 0 indicating clean, and 1 indicating polluted periods (Beck et al., 2022; Beck et al., 2022).

Description: This dataset contains minute-averaged water vapor mole fractions measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern. Data were collected by cavity ring-down spectroscopy using a commercial Picarro instrument (model G2401). The data columns include the Date and Time in Coordinated Universal Time (UTC), the latitude and longitude of Research Vessel Polarstern, the MOSAiC event label, and the water vapor mole fraction in µmol/mol.

Description: This dataset contains CCN concentrations at five supersaturation levels, averaged to 1 min time resolution, measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using the model CCN-100 from Droplet Measurement Technologies (DMT, Boulder, USA). Detailed description of the measurement principle can be found in e.g. Roberts & Nenes (2005). The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al., Submitted; Beck et al., 2022; Dada et al., 2022). The measurements were performed in 1-h cycles, with a 0.5 L/min sample flow and a 2 L/min make up flow, where the supersaturations 0.15, 0.2, 0.3, 0.5 and 1.0 % were measured. The supersaturation of 0.15 % is measured for 20 min, as it takes longer to equilibrate, and the remaining supersaturations were measured for 10 min each. The instrument was calibrated in July 2019 before the campaign, and in March and April 2020 during the campaign. Based on the inter-variability of the calculated supersaturation levels during these calibrations, we can expect values ranging from 0.15-0.20, 0.20-0.25, 0.29-0.33, 0.43-0.5, 0.78-1.0 % for the nominal supersaturations of 0.15, 0.2, 0.3, 0.5 and 1.0 %, respectively. The counting error for the CCNC is associated with the error in the optical counting of particles and is about 10 %. Data were removed during the cooling cycle (i.e., the time when the measurement cycle starts again and the temperature is cooled to set the lowest supersaturation), which corresponds roughly to the first 10 min of each hour (so 50 % of the 0.15 % supersaturation period). Additionally, the first minute of the transition between supersaturations was removed before averaging the data to 1 min time resolution. During some time periods, a difference pattern of mean and standard deviation of the measurements between even and odd hours was observed, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. For correction, the 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements. The dataset contains a pollution mask for local pollution (predominantly exhaust from the Research Vessel Polarstern) with 0 indicating clean, and 1 indicating polluted periods (Beck et al., 2022; Beck et al., 2022).

Description: This dataset contains minute-averaged carbon monoxide dry air mole fractions measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern. Data were collected by cavity ring-down spectroscopy using a commercial Picarro instrument (model G2401). The minute-averaged dry air mole fractions were adjusted after cross-evaluation against discrete whole air samples collected for post-cruise analysis at the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory. Pollution spikes due to local anthropogenic pollution sources (e.g., exhaust by the vessel's engine and vents, skidoos, helicopters, on-ice diesel generators) were identified and flagged as follows. For each data point, the gradient (time derivative) was calculated (Beck et al., 2022). Data points corresponding to an abnormally high gradient (〉 1.5 times the interquartile range) and neighboring points were discarded. The function “despike” from R package oce (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 = 3 and k = 61 (~ 1 hour). The data columns include the Date and Time in Coordinated Universal Time (UTC), the latitude and longitude of Research Vessel Polarstern, the MOSAiC event label, the original carbon monoxide dry air mole fraction in nmol/mol, the adjusted carbon monoxide dry air mole fraction in nmol/mol after cross-evaluation, and a pollution flag where 'yes' means that local pollution was detected.

Description: This dataset contains hourly-averaged ozone dry air mole fractions measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. This is a merged dataset that combines cross-evaluated measurements performed in the University of Colorado (CU), the Atmospheric Radiation Measurement (ARM) Program, and Swiss containers onboard Research Vessel Polarstern. The data columns include the Date and Time in Coordinated Universal Time (UTC), the latitude and longitude of the Research Vessel Polarstern, the ozone dry air mole fraction in nmol/mol, and the sampling location.