text: Daily and seasonal medians are computed and used to assess the trends in aerosol optical properties. The seasons are defined based on calendar dates: winter (December–February), spring (March–May), summer (June–August), and autumn (September–November). Daily medians are based on a minimum of 6-hourly data points (in keeping with a 25 % threshold imposed by Collaud Coen et al., 2020). Daily medians reduce the noise related to very low aerosol loadings; any uncertainties in σsp and σbsp are more pronounced in the Arctic region and are further enhanced in the derived intensive properties, b and α. It should be noted that the light scattering properties at ZEP do not show any pronounced daily cycle (not shown).
  When the observatory is in the midst of a cloud, large water droplets taken in by the whole-air inlet may affect the collection efficiency of the inlet. In addition, particles in the surrounding air can be removed due to in-cloud scavenging. This study aims to take this into account, however, for heated whole-air inlets, inside-cloud situations should not affect the measurements significantly (Asmi et al., 2013). Hourly mean values of ambient RH are used as a proxy for the presence of clouds. The ambient RH for ZEP is acquired through EBAS (http://ebas.nilu.no/, last access: 7 January 2020). The ambient RH measurements are operated by the Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department. The data are prescreened to remove periods during which ambient RH exceeded 95 %, whereby it is assumed that cloud conditions were present for some fraction of that hour. The measurements are therefore assumed to be of cloud-free conditions (see Fig. S2 in the Supplement). The chosen threshold is the point at which a significant drop in σsp is observed. The observatory has frequent inside-cloud situations, which can affect the aerosol optical measurements; approximately 10.9 % of the optical data is removed as a result of high ambient RH values, with summer the most affected season; ∼ 21.0 % is removed, as opposed to ∼ 5.9 %, ∼ 12.1 %, and ∼ 6.8 % for spring, autumn, and winter, respectively.
  Each back trajectory is 7 d in length; the number of days is chosen as a compromise between restricting the increasing uncertainties the further back in time they go and capturing the typical lifetime of the aerosol in the atmosphere, and with this the main source regions. The HYSPLIT back trajectories provide a detailed history of the air parcels, including changes in latitude, longitude, altitude, the height of the mixed layer (ML), precipitation, relative humidity, and temperature. The ML forms part of the planetary boundary layer (PBL); it is defined as the height at which substances, such as aerosol, can be vertically dispersed and well mixed (Seinfeld and Pandis, 2006). The back trajectories are classified as being above or below the ML height. In the interest of this study, only data points (time) for which the air parcels reside within the ML are considered to be influenced by surface sources and dominate the observed signal at ZEP.
  Wind speed is a main driver to SSA production and thus highly pertinent to this study. The average wind speeds are calculated as part of the back trajectory analysis based on the change in latitude, longitude, and altitude of each respective hourly data point. The direction from which back trajectories arrive at ZEP is computed by calculating the mean Cartesian-transformed coordinates. The mean coordinate is used to assign each back trajectory a region, namely north-west (NW), north-east (NE), south-east (SE), and south-west (SW). The region the back trajectories are assigned is dependent on the average of the coordinates, so they are not defined based on the origin. It is understood that back trajectories can cross multiple regions; the assigned regions simplify the classification. The four regions are defined relative to ZEP being at the centre. Monthly Special Sensor Microwave/Imager (SSM/I) satellite-derived sea ice concentration (SIC) data from the Version 1.1 Hadley Centre Sea Ice (HadISST1.1) data set (Rayner et al., 2003) are used. The SIC data set is of a 1∘ × 1∘ resolution. SIC data are temporally collocated with the back trajectories. Each data point from the back trajectory is classified into the following: (1) above ML, (2) above land and within ML, (3) above sea ice and within ML, and (4) above open water and within ML. The ice surface type is defined as having a mean SIC above 0.85 (see Fig. S3 in the Supplement), which is commonly used in the literature (e.g. Rayner et al., 2003; Fetterer et al., 2016; Stroeve and Meier, 2003), although lower thresholds have been used as well.
  The Mann–Kendall test (hereafter “MK test”) is one of the most widely used non-parametric tests for determining the statistical significance of trends in environmental data. The test is rank-based with a null hypothesis that the time series has no trend. The alternative hypothesis is that a monotonic trend exists (Gilbert, 1987). The MK test is ideal since it is not affected by missing values. Given that the data set contains gaps and irregular spacing, this is a major advantage. Hirsch et al. (1982) developed the “seasonal” MK test to take seasonality into account. The seasonal MK test works by separating the data into distinct seasons and then comparing like for like.
  The MK test assumes that the data are independent; thus there is no autocorrelation. Kulkarni and von Storch (1995) show that the result of the MK test depends strongly on the autocorrelation; a positive autocorrelation increases the likelihood that the null hypothesis is rejected when there is no trend. The time series is “pre-whitened” (PW) based on the trend-free pre-whitening (TFPW) procedure described in detail in Yue et al. (2002).
  The daily medians provide a sufficient amount of data points; however, they are considerably more plagued by autocorrelation than time series constructed from longer averaging periods (i.e. seasonal medians). The seasonal MK test is performed on the PW daily medians with a two-tailed significance test of 95 %. Medians are used for all the analyses as the variables are non-normally distributed. Medians are also less affected by outliers.
  The Theil–Sen estimator (TS) is used in this study to calculate the slope of the linear trends. The procedure developed by Sen (1968) is useful, as it is relatively less affected by outliers. TS is a non-parametric tool (therefore no type of distribution needs to be assumed for the data) and often accompanies the MK test. TS works by calculating the median of all the slopes of every pair of ordered data points. The slope is unbiased in regards to highly autocorrelated data; however standard errors are affected by autocorrelation. The estimated slopes of the regression lines are multiplied by 365.25 or 4 for daily and seasonal medians respectively. The relative trends (i.e. % yr−1) are obtained by dividing by the median of the data set in question, similar to previous studies (e.g. Asmi et al., 2013).
  The least mean square analysis (LMS) is applied to the logarithm of the dependent variable to calculate the relative trends. The logarithm approximates the data to a normal distribution, allowing for the use of this statistical tool.
  where Xt is the observation at time t, a is the regression parameter representing the intercept, and β is the regression parameter representing the slope, while s is a small scalar. ϵt is the random error term associated with each observation.
  The confidence level is set at 95 %, and the trend is deemed as having statistical significance (ss) if |β/SEβ|>1.960, where SEβ denotes the standard error of the slope, β. For LMS trends not in terms of relative changes, the logarithm is not applied to Xt.
  Section 3.1 focuses on long-term trends; trends in aerosol light scattering properties are presented in Sect. 3.1.1, and the trends in key meteorological parameters from the back trajectory analysis and the changes in source types and regions are presented in Sect. 3.1.2. The trends are calculated and presented based on both seasonal and daily medians. The seasonality of the trends is also included. The final part of the results section, Sect. 3.2, combines the aerosol and back trajectory parameters.