This dataset contains aerosol optical absorption coefficients at seven different wavelengths (babs(λ)), 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, 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). The instrument reports equivalent black carbon (eBC) mass concentrations at seven different wavelengths (370, 470, 520, 590, 660, 880, and 950 nm), computed from the measured light attenuation at each wavelength on the filter (equation (16) in Drinovec et al., 2015), with a 1 sec time resolution. The data obtained at 880 nm (channel 6: BC6) is the standard for reporting eBC concentrations (Drinovec et al., 2015), and are reported in Heutte et al. (2022). Here, we report the aerosol optical absorption coefficients at all seven wavelengths mentioned above, where the eBC (λ) concentrations were converted to optical absorption coefficients by multiplying them by the default mass absorption cross-section values of 18.47, 14.54, 13.14, 11.58, 10.35, 7.77, and 7.19 m2g-1 for the wavelengths 370, 470, 520, 590, 660, 880, and 950 nm, respectively. These optical absorption coefficients can be used for source apportionment or for the computation of the Absorption Ångström Exponent (AAE, Helin et al., 2021). The switching valve caused concentration spikes to be observed at the full hour, hence data points within ± 2 min of the full hour are removed. The dataset was averaged to 1 min 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 datapoints 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 min 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 min time resolution (Beck et al., 2022b). This pollution mask was converted to 10 min time resolution by setting a condition where, if more than 1 data point is polluted in a 10 min moving window, the entire 10 min period is defined as polluted. The resulting flag "Flag_pollution" should be equal to 0 to retain un-polluted data points only.

We thank the Laboratory for Atmospheric Chemistry at the Paul Scherrer Institute for providing the AE33 instrument.We extracted 10 min time resolution positional data from the following datasets: Rex, M (2020, doi:10.1594/PANGAEA.924669), Haas, C (2020, doi:10.1594/PANGAEA.924672), Kanzow, T (2020, doi:10.1594/PANGAEA.924678), Rex, M (2021, doi:10.1594/PANGAEA.926830) and Rex, M (2021, doi:10.1594/PANGAEA.926911).