A full year of aerosol size distribution data from the central Arctic under an extreme positive Arctic Oscillation : insights from the Multidisciplinarydrifting Observatory for the Study of Arctic Climate (MOSAiC) expedition

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k-means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019-2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January-March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies.Peer reviewe

Climatology of atmospheric boundary layer height over Switzerland

Determining the height of the planetary boundary layer (PBL) is of crucial importance as it is a key parameter in air-quality modelling and weather forecasting. Continuous remote sensing measurements allow to estimate this parameter based on temperature, humidity, turbulence, or aerosol backscatter profiles. In this study, measurements from radio-sounding (RS), lidars, microwave radiometers (MWR) and wind profilers (WP) were coupled to various detection methods (parcel method (PM), bulk-Richardson number method (bR), surface-based temperature inversion (SBI), potential temperature gradients (SBLpt), aerosol scattering ratio (ASR) and signal-to-noise (SNR) ratio gradients) for day-time and night-time detection of the PBL height. An inter-comparison of the results from each set of instrument and method, for a period of 5 years (2016-2020), was performed taking RS with PM as reference. The Raman lidar (RALMO) and the COSMO model showed very good agreements with RS, while MWR underestimated the PBL height, mostly in summer, probably due to an overheating of the instrument by the sun. This study notably exposed the great perspective of using temperature and humidity profiles retrieved with RALMO to estimate the PBL height. WP showed more scattered, and overall underestimated, results as the measured maximum of turbulence did not always correspond to the PBL height. A 5-year climatology resulted in clear seasonal and diurnal cycles, with maximum height attained in summer, during the day between 12:00 and 14:00, and a minimum in wintertime. Clear and cloudy sky differentiation showed a negative correlation between the PBL height and cloudiness. A decrease of the PBL height in June was observed with all instruments and methods in the three stations of interest, with no clear explanation of the phenomenon. During the night, the bR method has been invalidated due to its tendency to detect layers, almost constantly, just above ground with RS and KENDA. For RALMO and MWR, the use of wind speed measurements from WP in the bR method resulted in a positive bias of the results. This was attributed to a large amount of missing WP data points near ground due to ground clutter and weak nightly turbulences bellow the detection threshold. The growth rate of the convective layer during the day showed similar seasonality, with a maximum in summer and a minimum in autumn when using RALMO and KENDA. An under-estimation of the growth rate was observed using MWR, as a consequence of the underestimated convective boundary layer height. The analysis of a 30-year long-term PBL height trend, using RS, resulted in a small positive trend using PM and negative using bR method, with weak statistical significance. Trends with larger magnitude were observed with shorter data sets from 10 to 20 years, suggesting that stronger variations are observed on the decadal time scale, due to climate oscillations. Finally, the restrictions for each instrument and method, due to weather conditions, vertical resolution and accuracy have been exposed and discussed in this study

CSR communication in the outdoor sports industry: a content analysis of CSR disclosures by seven international corporate brands

International audienc

Temperature and relative humidity in 10 min time resolution measured in the interstitial inlet of the Swiss container during MOSAiC 2019/2020

This dataset contains the temperature and relative humidity in a bypass to the interstitial inlet, measured during the MOSAiC expedition from October 2019 to September 2020. The measurements were performed in the bypass of the interstitial inlet of the Swiss container on the D-deck of Research Vessel Polarstern, using a commercial hygrometer model HC2 (Rotronic AG, Bassersdorf, Switzerland). The interstitial inlet had a flow of > 17 L/min and was equipped with a 1 µm cyclone, for sampling interstitial particles only, as opposed to the total inlet (flow of > 15 L/min) which sampled all particles and hydrometeors up to 40 µm in diameter. The total and interstitial inlets were located 3 meters apart and connected to a valve that switched hourly between the two inlets for a targeted set of instruments. The complete instrumental setup inside the Swiss container is presented in Heutte et al. (Submitted), Dada et al. (2022), and Beck et al. (2022). Inside both inlets, the temperature was kept constant around 20 °C and the relative humidity was maintained below 40 %, with a heating system following Global Atmosphere Watch (GAW) standards for aerosol sampling (WMO, 2016). This dataset contains measurements of temperature and relative humidity inside the bypass to the interstitial inlet, averaged to 10 min time resolution (native resolution was 5 min), along with the position of the switching valve (1 = total inlet, 0 = interstitial inlet)

Temperature and relative humidity in 10 min time resolution measured in the total inlet of the Swiss container during MOSAiC 2019/2020

This dataset contains the temperature and relative humidity in a bypass to the total inlet, measured during the MOSAiC expedition from October 2019 to September 2020. The measurements were performed in the bypass interstitial inlet of the Swiss container on the D-deck of Research Vessel Polarstern, using a commercial hygrometer model HC2 (Rotronic AG, Bassersdorf, Switzerland). The interstitial inlet had a flow of > 17 L/min and was equipped with a 1 µm cyclone, for sampling interstitial particles only, as opposed to the total inlet (flow of > 15 L/min) which sampled all particles and hydrometeors up to 40 µm in diameter. The total and interstitial inlets were located 3 meters apart and connected to a valve that switched hourly between the two inlets for a targeted set of instruments. The complete instrumental setup inside the Swiss container is presented in Heutte et al. (Submitted), Dada et al. (2022), and Beck et al. (2022). Inside both inlets, the temperature was kept constant around 20 °C and the relative humidity was maintained below 40 %, with a heating system following Global Atmosphere Watch (GAW) standards for aerosol sampling (WMO, 2016). This dataset contains measurements of temperature and relative humidity inside the bypass to the total inlet, averaged to 10 min time resolution (native resolution was 5 min), along with the position of the switching valve (1 = total inlet, 0 = interstitial inlet)

Equivalent black carbon concentration in 10 minutes time resolution, measured in the Swiss container during MOSAiC 2019/2020

This dataset contains equivalent black carbon (eBC) concentrations, averaged to 10 min time resolution, 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, using a commercial aethalometer (model AE33, Magee Scientific, Berkeley, USA). 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. The inlet flow, of 2 liters per minute, was verified biweekly. The dual spot technology of the instrument allowed for a real-time compensation of what is known as the loading effect (Drinovec et al., 2015). Optical absorption was measured at 7 different wavelengths simultaneously, with a 1 second time resolution. We used the absorption at 880 nm (channel 6) to derive eBC, using a mass absorption cross-section value of 7.77 m2g-1. The switching valve caused concentration spikes to be observed at the full hours, hence data points within ± 2 minutes of the full hours are removed. The dataset was averaged to 1 minute time resolution (original time resolution is 1 second) to reduce the largest part of the instrument's noise, and outliers of more than 3 times the standard deviation of an hourly moving window were removed from the 1 minute averaged dataset. During some times for which the switching valve mechanism was on, varying patterns of increased mean and standard deviation of the measurements were observed, due to a pressure drop in the inlet lines. We corrected it by taking the arithmetic means of the data points during interstitial inlet measurements and the two adjacent hours of total inlet measurements, subtracting these two values and adding this difference to the data points of the interstitial inlet measurements. Finally, the data were averaged to 10 minutes time resolution. Based on a visual inspection of the entire dataset, we removed periods of strong noise and intense negative spikes. These artifacts may have emerged from the averaging of the initially noisy 1 second time resolution dataset and/or from the dual spot compensation which may lead to the presence of strong negative outliers right after a large positive outlier. Data collected between June 3rd and June 9th were discarded as Polarstern was within Svalbard's 12 nautical miles zone. The aethalometer dataset was further cleaned for disturbing pollution emissions from local research activities (e.g., exhaust by Polarstern's engine and vents, skidoos, on-ice diesel generators) using a preexisting pollution mask developed by Beck et al. (2022a), where a multi-step pollution detection algorithm was applied on the interstitial CPC dataset at 1 minute time resolution (Beck et al., 2022b). This pollution mask was converted to 10 minutes time resolution by setting a condition where, if more than 1 data point is polluted in a 10 minutes moving window, the entire 10 minutes period is defined as polluted. The resulting flag “Flag_pollution” should be equal to 0 to retain un-polluted data points only

Aerosol optical absorption coefficients at seven wavelengths in 10 min resolution measured in the Swiss container during MOSAiC 2019/2020

This dataset contains aerosol optical absorption coefficients at seven different wavelengths, averaged to 10 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 a commercial aethalometer (model AE33, Magee Scientific, Berkeley, USA)

Cloud Condensation Nuclei (CCN) concentrations at 0.15% supersaturation level measured in the Swiss container during MOSAiC 2019/2020

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)

Cloud Condensation Nuclei (CCN) concentrations measured in the Swiss container during MOSAiC 2019/2020

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)

Cloud Condensation Nuclei (CCN) concentrations at 0.2% supersaturation level measured in the Swiss container during MOSAiC 2019/2020

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)