The AErosol RObotic NETwork (AERONET) is a federated ground-based sun photometer network with over 600 active sites across the globe. AERONET's Cimel photometers measure sun and sky radiance at several wavelengths, ranging from the near-ultraviolet to the near-infrared. While the exact set of bands depend on the model, all Cimel configurations include 440, 670, 870, and 1020 nm bands. All the sites used here also included 380 and 500 nm bands. The network has been providing high-accuracy daytime measurements of aerosol optical properties since the 1990s (Holben et al., 1998, 2001). Cloud-screened and quality-assured Version 3 Level 2 AERONET data (Giles et al., 2019) are used in this study.

Fine-mode (FM) and coarse-mode (CM) AOD at 550 nm are derived based on the spectral deconvolution algorithm (SDA) of O'Neill et al. (2001, 2003) and averaged over 6 h time bins. The same 10 AERONET sites employed in Part 1, were selected (Fig. 1 of this paper) for this study. Those sites had been chosen based on their regional representativeness as well as the availability of data records between January 2003 and December 2019 period of study.

Optically thin clouds, mostly cirrus, occasionally contaminate CM aerosol retrievals in Level 2, Version 3 AERONET data (Ranjbar et al., 2022). Data were manually inspected and retrievals screened using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery at visible wavelengths from NASA Worldview (https://worldview.earthdata.nasa.gov/, last access: 15 May 2022) and by comparing the 6-hourly U.S. Navy Aerosol Analysis and Prediction System ReAnalysis version 1 (NAAPS-RA v1) with AERONET AODs. This step is likely an incomplete one, given the likely lower sensitivity of MODIS imagers to thin clouds (Marquis et al., 2017). As such, CM AODs that deviate by more than the 3σ level from the background climatological mean were also removed (as per AboEl-Fetouh et al., 2020).

The Marine Aerosol Network (MAN) is part of the broader AERONET global network: in this case however, it is limited to AODs collected over open water. Handheld Microtops sun photometers are deployed during research cruises of opportunity (Smirnov et al., 2009, 2011). Data processing is similar to that of AERONET with product nomenclature similar to AERONET. Level 2 data acquired above 70∘ N in the 2003–2019 period are used in this study. FM and CM AOD at 550 nm are derived using the SDA and averaged over 6 h time bins.

The Navy Aerosol Analysis and Prediction System (NAAPS) AOD reanalysis (NAAPS-RA) v1 was developed at the U.S. Naval Research Laboratory. It provides speciated AOD and concentrations at a global scale with 1∘ × 1∘ latitude–longitude and 6 h resolution for 2003–2019 (Lynch et al., 2016). NAAPS-RA is driven by the Navy Operational Global Analysis and Prediction System (NOGAPS; Hogan and Rosmond, 1991), with satellite precipitation applied within the tropics to mitigate model precipitation errors (Xian et al., 2009). NAAPS-RA features the assimilation of quality-controlled AOD retrievals from MODIS and Multi-angle Imaging SpectroRadiometer (MISR) (Zhang and Reid, 2006; Hyer et al., 2011; Shi et al., 2011, 2013) with the Navy Atmospheric Variational Data Assimilation System for AOD (NAVDAS-AOD, Zhang et al., 2008). A first-order approximation of secondary organic aerosol (SOA) processes is adopted. Production of SOA from its precursors is assumed to be instantaneous and is included with the original anthropogenic species to form a combined anthropogenic and biogenic fine (ABF) species. In other words, ABF is a mixture of sulfate, BC, organic aerosols, and secondary organic aerosols from non-BB sources. Monthly anthropogenic emissions come from a 2000–2010 average of the ECMWF MACC inventory (e.g., Granier et al., 2011). BB smoke is derived from the Fire Locating and Modeling of Burning Emissions inventory (FLAMBE, Reid et al., 2009). This version of FLAMBE uses MODIS near-real-time satellite-based thermal anomaly data to initialize the smoke source where corrections that minimize the impact of inter-orbit variations are applied to the MODIS data (Lynch et al., 2016). FLAMBE processing is applied consistently through the reanalysis time period while a smoke particle emission climatology and its spring and summertime trends (both north of 50 and 60∘ N) are provided in Fig. 12 of Part 1 (Xian et al., 2022). Dust is emitted dynamically and is a function of modeled friction velocity to the fourth power, surface wetness, and surface erodibility. In this model run, erodibility is adopted from Ginoux et al. (2001) with regional tuning. Sea salt modeling is the same as Witek et al. (2007), and sea salt emission is driven dynamically by sea surface wind.

Verification of monthly binned NAAPS-RA total AODs at 550 nm using monthly binned AERONET data from 10 Arctic sites (Tables 2–4 of Part 1, Xian et al., 2022) shows that NAAPS-RA is able to capture the AOD interannual variability. The spatial distributions and magnitudes of climatological and seasonal AOD averages and their trends for 2003–2019 are also consistent with those derived from MODIS, MISR, and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) (Part 1). NAAPS-RA is also shown to be able to capture biomass burning smoke transport events into the polar regions in various case studies (e.g., Markowicz et al., 2016, 2017; Khan et al., 2017, 2018, 2019; Ranjbar et al., 2019; AboEl-Fetouh et al., 2022).

Our study period is January 2003 to December 2019, the same principal study period as used in Part 1. We define extreme events as those corresponding to AOD exceeding the 95th percentile mark in 6 h or daily AOD data at a specific location or across a given region (the region north of 70∘ N for example). We employ 6 h AERONET AODs as well as speciated daily and 6 h NAAPS-RA AOD to depict the frequency and magnitude of the large FM AOD events. Pairwise data are used for verification. “Pairwise” refers to those NAAPS-RA AODs that correspond to a resampled AERONET or MAN AOD whose ±3 h bin contains at least one AERONET/MAN retrieval. Three independent aerosol reanalysis products were used in Part 1 of the study. For this study, the NAAPS-RA reanalysis was chosen given its slightly better performance in terms of FM and total-AOD bias, RMSE, and r2 scores (Part 1) as well as its capability of separating BB smoke from other aerosol species. To simplify some of the discussion below, we frequently employed the symbol “AODn” to represent the AOD associated with the nth percentile of its cumulative (histogram) distribution. One important application of this AODn formulation is to employ a particular value (AOD95) as a threshold for the definition of extreme events (see Sect. 3.1 below). AOD75, AOD90, AOD99, AOD99.5, and maximum AOD are also calculated to show AOD gradients for high AODs. A local extreme total-AOD event for the NAAPS-RA means AOD >AOD95 for the model grid cell of 1∘ × 1∘ (latitude–longitude). Again, we define the Arctic and the high Arctic as regions north of 60 and 70∘ N, respectively. To reference source influences from lower latitudes, the area of 50–90∘ N is included for context.

The reanalysis performance for 6 h time bins was evaluated in order to study extreme events. Our choice of AOD95 as an extreme-event threshold was influenced by the fact that it was an upper-limit cumulative probability indicator that was robust. We reasoned, at the same time, that it should be comparable with the analog parameter derived from NAAPS-RA. Figure 1 displays NAAPS-RA AOD95 overplotted with those from the 10 selected AERONET sites for spring and summertime 2003–2019. NAAPS-RA appears to successfully capture the AOD95 amplitude and spatial pattern as well as those of FM AOD95 and CM AOD95. It also shows that FM is the main contributor to AOD95 in the Arctic.

Detailed geographical coordinates of the 10 AERONET sites employed in our study are included in Table 1 as well as the simulation performance indicators of NAAPS-RA 550 nm total, FM, and CM AOD. These AERONET parameters are an analog to parameters used in the first part of the study and its Table 1 statistics, except that the averaging period extends across both the spring and summer seasons, as the averaging period is mostly confined to the April–August time frame. NAAPS-RA performance indicators relative to MAN data are shown in Figs. S1 and S2.

NAAPS-RA performance for this large averaging period is reasonable for FM and total AOD, though it is less skillful at predicting CM AOD. The FM AOD exhibits an average (Table 1) bias over all stations of -0.01, a root-mean-square error (RMSE) of 0.08, and a coefficient of determination (r2) of 0.66. RMSE values for total and FM AOD are generally large for sites vulnerable to strong smoke influence, e.g., Bonanza Creek, Barrow, Tiksi, and Yakutsk. Total-AOD r2 values are mostly between 0.5–0.7 except for Kangerlussuaq and Ittoqqortoormiit. FM AOD r2 values exceed those of the total AOD for all sites except Kangerlussuaq. The 6 h binned Table 1 total-AOD bias is similar to the monthly binned NAAPS-RA bias results of Table 2 in Part 1 (Xian et al., 2022). This is due to the numerous 6 h samples included in the averaging process. In contrast, the Table 1 RMSE values are roughly doubled, and the r2 values drop by about 20 % relative to those of Tables 3 and 4 of Part 1 (Xian et al., 2022). This suggests Table 1 model shortcomings in capturing finer temporal-scale AERONET-AOD variations. This is also consistent with model performance for regions other than the Arctic and is generally a common result for numerical aerosol models (Lynch et al., 2016; Yumimoto et al., 2017)

The lower CM vs. FM skill of the NAAPS-RA might be a reflection of AERONET limitations as one approaches typical instrumental errors ∼0.01 in total AOD or they could be a reflection of simulation and/or reanalysis limitations as one approaches very small values of CM AOD. The lack of model representation of CM smoke and possible soil particles associated with severe burning events may also contribute. At the same time, it must be recognized that residual cloud contamination in AERONET and MAN data cannot be ruled out as a “false” indicator of poor simulation skill. Cloud screening issues aside, a lower CM vs. FM correlation skill is a common feature of both the Table 1 and 4 (Part 1, Xian et al., 2022) reanalyses. However, modeled monthly CM AOD correlation is slightly more skillful than the averages derived from 6 h data (Table 4 in Part 1, Xian et al., 2022, vs. Table 1 in this paper) inasmuch as the seasonal CM signal associated with dust and sea salt aerosols is apparently better resolved in the former case. The better model skill in seasonal CM simulation is likely due to the relative insensitivity of the model to the higher-frequency components of the reference data in the latter case. It is also noted that the NAAPS-RA is generally less skillful in the Arctic region relative to global reanalyses (see Fig. 7 in Lynch et al., 2016). This is understandable given that there are little satellite-based AOD data available to constrain the model through assimilation in the Arctic compared to lower latitudes. We note however that Zhang et al. (2021) attempted to address this problem with the assimilation of the Ozone Monitoring Instrument (OMI) aerosol index. To date, no remedy for aerosol data assimilation has yet been implemented in a larger reanalysis quality study.

Shown in Fig. 1 are NAAPS-RA and AERONET AOD95 values for the March–August time frame and the 2003–2019 period. The values of AOD95 are high (0.4–0.55) over Siberia and Alaska (and over the Yakutsk and Bonanza Creek AERONET stations) due to strong BB smoke influence. North of 70∘ N, the values are mostly between 0.15 to 0.25, with the exception of Greenland where they are largely below 0.15 (weak values that are attributable to the high terrain). It is also shown that (FM AOD)95 has a similar spatial distribution and magnitude as AOD95, suggesting the dominant contribution of FM to AOD95. The contribution of CM is relatively larger over the North Atlantic and European Arctic, though (CM AOD)95 and (FM AOD)95 are comparable in these regions.

The site-by-site, total, and FM AOD ranges are also shown in Fig. 2 from the 6 h AERONET data for all 550 nm retrievals acquired between 2003–2019. In general, the NAAPS-RA largely captures the AERONET FM and total-AOD range. This includes, for example, the AERONET AOD5 to AOD95 values (0.02 to >0.10 for most sites), and the larger 0.02 to 0.4–0.6 range of sites with a known strong BB influence (notably Bonanza Creek and Yakutsk). Mean and median AODs are also comparable to AERONET values. Maximum AERONET FM AODs vary between 0.5 (Ittoqqortoormiit) to <2.0 for most sites and around 3.0 for sites with strong BB smoke influence (see also Table 2). Maximum NAAPS-RA AOD values are often biased low, which is a common challenge for global aerosol models (e.g., Sessions et al., 2015; Xian et al., 2019).

The cumulative probability distributions of 6 h total, FM, and CM AODs are shown in Fig. 3 for AERONET and pairwise NAAPS-RA total and modal AODs and speciated AODs. The median AOD for all AERONET sites in the Arctic (all sites north of 60∘ N) for 2003–2019 is 0.07, while the AOD95 extreme-event threshold is 0.24 with a dominant FM contribution (see also Table 2). The CM AOD median for all measurements is 0.01, with a (CM AOD)95 threshold of only 0.07. Due to a relatively large positive bias in CM AOD of 0.01 below the 95 % threshold, NAAPS-RA total-AOD bias is slightly positive (<0.01) for all sites north of 60∘ N and for the 20 %–80 % cumulative probability range (a positive bias that is generally evident in Table 1).

The negative bias found at the largest CM AOD values could conceivably be associated with an underestimation of the CM AOD generated by sea salt aerosols or local dust in the presence of strong winds or CM smoke and soil particles associated with severe burnings. We should, however, reemphasize this caveat: despite the quality control measures taken to filter out cloud-contaminated AERONET data, the impact of CM residual clouds may still influence estimates of CM AOD.

It is worth noting that BB smoke plays a dominant role compared to other aerosol species above our AOD95 extreme-event threshold (see Fig. 3c and d in particular and note that Fig. 3a and b show the expected dominance of FM AOD). Even for sites distant from BB source regions, including Resolute Bay, Kangerlussuaq, Thule, Andenes, Hornsund, and Ittoqqortoormiit, BB smoke is the principal driver of AOD variations above the AOD95 threshold. To some extent, Barrow can be categorized as being a site that is distant from BB emissions. However, it is also relatively close to the region of Alaska fires, depending on dominant upstream winds and trajectories (see Eck et al., 2009, for details).

The modal and total-AOD values at different percentile levels for the AERONET sites and MAN data collected north of 70∘ N are provided in Table 2. For sites closer to BB sources, including Bonanza Creek, Yakutsk, and Tiksi, the AOD99 and (FM AOD)99 values are around or larger than 1.0, while the maximum values are between 1.4–3.3. For the more distant sites, the AOD99 and (FM AOD)99 values vary between 0.23–0.46, while the maximum values are between 0.45–3.0 (1.5 for Resolute Bay and 3.0 for Barrow). The FM event occurrences for the extreme total-AOD events range from 60 %–99 %, with an average of 86 %, and accordingly dominate CM events statistically. Sites closer to the BB source regions show relative occurrences over 95 %.

Large particles like ash and soil components emitted from vigorous burning during extreme BB smoke events (Reid et al., 2005; Schlosser et al., 2017) can likely be detected as AERONET CM AOD (see, for example, the correlation between the FM and “weak” CM particle size distributions for Bonanza Creek in Fig. 9a of Eck et al., 2009). The extreme AOD events described above are likely dominated by smoke. For example, (FM AOD)99 is 1.64 at Bonanza Creek and 0.92 at Tiksi in Table 2. For events with FM AOD greater than (FM AOD)99, the associated CM AOD means at the two sites showed significantly larger values of 0.05 and 0.03 (significantly larger relative to, for example, the CM AOD means in Table 1). The coherency of the associated CM AOD mean increase with the FM AOD mean increase suggests the presence of detectable CM smoke and/or soil particles induced by severe burning. The inability of the model to simulate potential CM smoke or soil components associated with severe burning could be a contributing reason as to why it performs less well in predicting CM AOD near BB sites.

A distinct class of extreme smoke cases comes from pyrocumulonimbus (pyroCb) events induced by intense biomass burning sources: these events inject smoke high into the troposphere or even well into the stratosphere (Fromm et al., 2010; Peterson et al., 2017). A significant pyroCb smoke event that occurred over British Columbia in August 2017 led to substantial increases in various optical measures of aerosol concentration in the lower Canadian and European Arctic (Peterson et al., 2018; Torres et al., 2020; Das et al., 2021). Ranjbar et al. (2019) showed that a specific 19 August 2017 smoke event over the high-Arctic PEARL observatory at Eureka, Nunavut, was induced by the British Colombia pyroCb fires and that it was a statistically significant extreme FM AOD event. More recent eastern Siberian fires in June–August 2021 induced more than a dozen cases of elevated smoke intrusion into the high Arctic with some smoke plumes reaching the North Pole and/or its vicinity. For example, on 5 August 2021, operational NAAPS (common chemistry, physics, and BB emission sources with the NAAPS-RA) resolved a smoke plume north of 80∘ N (Fig. 4) with AOD values of 2–3. Smoke AOD over the source region was also 2 to >3 with a similar amplitude to AODs measured at Yakutsk. CALIOP data suggested a 1–6 km high smoke layer in the source region.

Other extreme or near-extreme smoke events in the Arctic have been reported. A series of intense fires originating in North America led to strong AOD peaks in the summer of 2015 over Svalbard (Markowicz et al., 2016; Lisok et al., 2018). Agricultural fires in eastern Europe in the spring of 2006 caused record-high AODs and pollution levels in the European Arctic (Stohl et al., 2007). The North American boreal fires in the summer of 2004 led to large-amplitude AOD peaks in Alaska and enhanced AODs on a pan-Arctic scale (Stohl et al., 2006).

The NAAPS-RA total-AOD map at different percentile levels locally for March–August 2003–2019 is shown in Fig. 5. We separated the study period into early (2003–2009) and late (2010–2019) subperiods. The end year of the first period was chosen as 2009 given the drop in ABF/sulfate emissions due to the civil Clean Air Acts enacted across the US (e.g., Tosca et al., 2017; Kaku et al., 2018) as well as Europe and China and the attendant decrease in ABF/sulfate AOD in these countries/regions (e.g., Zhang et al., 2017). This ABF/sulfate AOD decrease was also observed in the Arctic, as shown in Fig. 13 of Part 1 (Xian et al., 2022). The median Arctic AOD (less than 0.1 as compared with 0.07 for the AERONET sites from Fig. 3 and Table 2) is an order of magnitude smaller than the maximum AODs. Clear BB smoke features in the North American and Asian boreal burning regions start to emerge in the AOD95 maps (see also Fig. 1). The maximum AOD is high (greater than 2.0) while being relatively low over the Arctic Ocean (∼0.3–1.0) and the North Atlantic, with the lowest values over the generally high-elevation Greenland landmass. The maximum AOD is associated with peak burning activities and generally occurs in July and August. The exception is the Norwegian Sea area, where the maximum AODs occur in March–May. This is possibly associated with a combined high AOD level from anthropogenic pollution, marine aerosols, and springtime agriculture fires.

The occurrence of different aerosol species relative to the occurrence of total AOD for total-AOD extreme events (March–August time frame) are shown in Fig. 6. The occurrence maps accordingly indicate which aerosol species are numerically dominant for extreme AOD events. As expected, BB smoke is the prevailing extreme-event contributor over the North American and Asian Arctic, especially near the boreal source regions and associated transport pathways, as well as most of the Arctic Ocean (except the Barents Sea and the Norwegian Sea). ABF occurrence dominates the low European Arctic. Sea salt particles and, to a lesser extent, ABF are the most significant occurrence contributors in the North Atlantic and the Norwegian Sea. Dust occurrences to extreme AOD events are very small (0 %–10 %) except over the predominantly high-elevation region of Greenland where the relative occurrence of high-altitude African dust dominates the relative occurrence of the other species.

In terms of AOD amplitudes for total-AOD extreme events (Fig. 7), BB smoke AOD shows dominant contributions, especially in the areas near the boreal source regions and transport pathways, including most areas of the high Arctic. ABF and sea salt show slightly higher extreme-event AODs than BB smoke over the North Atlantic and European Arctic. The regional extreme AODs are not, however, as large as the extreme AODs in the BB smoke-dominant regions.

The NAAPS-RA seasonal cycle of total and speciated AOD is shown in Fig. 8 for daily averages across the area north of 70∘ N (a latitude limit which largely excludes BB source regions). The seasonal cycle of monthly mean total AOD shows relatively higher values in March–April–May (MAM) compared with the lower AODs in June–July–August (JJA) and a minimum in June. The spread of the ABF AOD seasonal values is moderately stable, with a relatively higher mean and median in MAM than JJA. Sea salt AOD and its spread are relatively higher in the earlier months (March and April). Dust AOD and spread are generally stable through the season, with a visibly higher mean and median in April and May. Smoke AOD amplitude and spread exhibit the greatest inter-species seasonal variations with the lowest mean and spread in March, increased means and spreads in April, and significantly higher mean and spread in later months. July and August appear to have the largest mean, spread, and maximum smoke AODs (a smoke importance statement that is generally consistent with the results of Fig. 6). These smoke features significantly contribute to the seasonality of total-AOD extremes. It is also noted that the MAM total and smoke AOD means approximately equal their medians but that the JJA means are greater than their medians (and that this is especially true for August). The greater number of smoke AOD extremes in the later season and the attendant consequence of greater positive histogram skewness would explain those relative increases in the mean.

There is, as shown in Part 1 of the study, a multi-year decreasing MAM trend and an increasing JJA trend for total AOD in the Arctic over the 2003–2019 sampling period. This was attributed to an overall decrease in MAM and JJA sulfate/ABF AOD coupled with a negative trend in MAM and a strong positive trend in JJA for biomass burning smoke AOD. In terms of extreme-event trends, AOD95 (Fig. 5) and the average AOD above AOD95 (Fig. 6) generally increased over the boreal continents from the 2003–2009 to 2010–2019 period (with the notable exception of Alaska and northeastern Siberia in 2010–2019). This is consistent with the positive BB emission trends in JJA north of 50 and 60∘ N (for which the JJA trend dominated the MAM trend inasmuch as JJA was associated with much higher BB emissions; Part 1).

The negligible or slight decrease in high-Arctic AOD50, AOD75, and AOD95 values from the 2003–2009 to the 2010–2019 period (Fig. 5) is likely associated with the generally weak ABF decrease seen in Fig. 7. However, the increase in the maximum AOD value (Fig. 5) and the contribution of BB smoke to AOD extreme events (Fig. 7) in the latter period is an indication of stronger extreme BB smoke influence in more recent years. It is also noted that the maximum high-Arctic AOD occurred later in the season (mostly August) in 2010–2019 compared with the more balanced variation occurring in March through August in 2003–2009. This is likely attributable to overall lower ABF levels in the 2010–2019 period (especially in MAM) and a shift in extreme smoke events to later in the season (see discussion below).

The time series of high-Arctic-averaged daily mean BB smoke and total AOD from March to September for all years between 2003–2019 is shown in Fig. 9. The extreme total-AOD variation is largely dictated by BB smoke. There is also a discernible 2003–2009 to 2010–2019 springtime reduction in extreme total AOD: this, as discussed in the previous paragraph, is likely due to an overall reduction in ABF AOD. The occurrence of extreme smoke events tended to be more equally distributed over all months (April–August) during the 2003–2009 period while being more concentrated in the late season (July–August) during the 2010–2019 period. The extreme smoke and total-AOD trends resembled the extreme smoke occurrence trends: more seasonally balanced during the 2003–2009 period and summertime dominance during the 2010–2019 period. Specific counts of extreme BB smoke days for different months and years and yearly cumulative extreme AODs also support the seasonal shift of extreme smoke events (Table S1).

The occurrence of extreme high-Arctic smoke events thus demonstrates a clear smoke and total-AOD shift from a more balanced spring and summer to the late season (notably the months of July and August; see also Table S1). This is consistent with the temporal shift of fire activity to a later time in Siberia over 2003–2018 (Liu et al., 2021) and the projection of emerging pan-Arctic fire regimes marked by increases in the likelihood of extreme fires later in the growing season (McCarty et al., 2021). An earlier fire season in the boreal region normally suggests a better-managed forest/land with fewer large and destructive fires, while a later fire season indicates the opposite.

The shift of boreal fire activity, and the resulting BB smoke AOD extremes in the Arctic from early season to late season, is probably related to early-season strengthening of agriculture burning regulations and increased summertime lightning frequencies with climate change in the latter decade. For example, the springtime BB smoke AOD peak values in 2003, 2006, and 2008 are all associated with agricultural activity (resulting in fires burning out of control) and widespread high-latitude burning (Korontzi et al., 2006; Stohl et al., 2007; Saha et al., 2010). At the same time, with climate change, lightning activity, and lightning-caused wildfires in summertime high-latitude regions were observed to increase in the past 2 decades (Zhang et al., 2021; Bieniek et al., 2020; Coogan et al., 2020). Also noted is a lengthening of growing season in boreal regions, which implies a lengthening of fire season as well (Park et al., 2016). These factors aside, climate oscillations, including the Arctic Oscillation, Niño–Southern Oscillation (ENSO), and Pacific Decadal Oscillation, also affect boreal fire activities (Balzter et al., 2007; Macias Fauria and Johnson, 2006; Kim et al., 2020). These climate factors modulate interannual variations and possibly the transport dynamics of pollutants from the midlatitudes to the Arctic region (e.g., Eckhardt et al., 2003; Fisher et al., 2010).

The dominant contributor, ABF, to regional extreme AOD occurrence and magnitude in the lower European Arctic decreased slightly from 2003–2009 to 2010–2019 (Figs. 6 and 7): this observation is generally coherent with the Part 1 results showing a pan-Arctic ABF AOD decrease in the 2003–2019 period and Fig. 9. Extreme total-AOD events dominated by sea salt contributions in the North Atlantic and Norwegian Sea increased slightly in 2010–2019. This was possibly due to the observed increase in cyclonic activities (Rinke et al., 2017; Waseda et al., 2021; Valkonen et al., 2021). Although the model simulation of CM AOD is not as skillful as that of FM, trend analysis of CM AOD which is based on relative change is arguably significant.