Data from the Global Sand and Dust Storms Source Base Map (“G-SDS-SBM”; Vukovic-Vimic, 2021, 2022) were used as an independent source of information to verify the dust emission potential of the satellite-identified dust emission hotspot areas. Specifically, the source intensity parameter was used, “SI”, with values from 0 (no capacity to emit dust) to 1 (capacity to emit at or above the 99th percentile level of soils, globally). G-SDS-SBM was developed by the United Nations Convention to Combat Desertification (UNCCD) in collaboration with the United Nations Environment Programme (UNEP) and World Meteorological Organization (WMO). It was derived using information on soil texture, bare land fraction, land cover, topography, and climatological extremes in topsoil moisture (low, 2014–2018) and temperature (high, 2014–2018). It assumes a spatiotemporally constant strong wind (friction velocity of 1 m s⁻¹). As such, values of SI represent the normalized maximum potential of the surface to emit dust under favourable meterological (i.e., wind speed) and surface (i.e., soil moisture and temperature) conditions, and are available for the months of January, April, July, and October. SI data were regridded using bilinear interpolation in cdo from their native 30 arcsec resolution (∼ 0.008°) to 0.1° to match the MODIS data used in this study.

For verification of satellite-based AOD results, data from the Aerosol Robotic Network (AERONET; Holben et al., 1998) are used. AERONET is a network of ground-based sun photometers that retrieve AOD and other atmospheric properties in the column above the instrument, based on solar extinction within the column. This study uses data from two AERONET stations in the broad study region of northern Canada: Kluane Lake (Yukon; 61.027° N, 138.410° W) and Resolute Bay (Nunavut; 74.705° N, 94.969° W; station locations are marked on Fig. 11). The Kluane Lake AERONET station was intentionally located proximal to a known dust source with the purpose of monitoring emission processes there (e.g. Huck et al., 2023, Sayedain et al., 2023) and has been providing data between April and November since 2018 (with varying temporal coverage by year). Resolute Bay is an Arctic monitoring site, providing data since 2004 (also with varying temporal coverage, also restricted to the months of April – November). Arctic aerosol studies have demonstrated that the site detects transported Asian dust during the spring and smoke aerosols during the summer, and that during summer months the area is also subject to wind-induced sea-salt-spray aerosol and potentially dust derived from more localised sources, based on the analysis of retrieved particle size distributions (Tomasi et al., 2015; AboEl-Fetouh et al., 2020).

Data from both stations were downloaded from the AERONET website (https://aeronet.gsfc.nasa.gov/, last access: 11 January 2024), and this study analyses Level 1.5 cloud-screened AOD retrievals at 500 nm, processed by the Version 3 algorithm (Giles et al., 2019). Only retrievals performed within 11 am and 5 pm local time were considered for analysis (which roughly corresponds to a 6 h window centred on the local overpass time of both passive sensor satellites). The maximum retrieved AOD each day within this time window was retained for comparison to maximum satellite-derived parameters, in keeping with the objective of this study being to quantify the frequency of occurrence of extreme dust events as a proxy for emission sources.

Two additional datasets are used in this study. Firstly, wind fields discussed in Sect. 3.6 are taken from the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5), with a spatial resolution of 0.28125° (∼ 31 × 31 km) and 137 vertical levels (Hersbach et al., 2017; downloaded from the Copernicus Climate Data Store at 10.24381/cds.143582cf). Surface elevation data, also discussed in Sect. 3.6, are taken from the Global Multi-resolution Terrain Elevation Data 2010 dataset (GMTED2010; Danielson and Gesch, 2011; downloaded from https://www.temis.nl/data/gmted2010/index.php, last access: 11 January 2024), at 0.0625° resolution.

Since DOD values will be elevated by both locally sourced and long-range transported dust, this study applies a threshold to DOD data to identify areas of more frequent signatures of extreme dust loadings, which we assume to be largely undiluted and locally sourced dust. A high frequency of occurrence (FoO) of DOD above a specified threshold relative to the surrounding region is commonly used to indicate a local dust source region (e.g., Ginoux et al., 2012; Baddock et al., 2016). The FoO is simply the total number of observations in the time series that meet a given DOD threshold, expressed as either an absolute value or a relative value given the total number of observations in the time series. DOD threshold selection is somewhat subjective and depends upon the amount of long-range transported dust present in the atmosphere as well as the intensity of local emission. For example, Ginoux et al. (2012) applied a DOD threshold of 0.2 globally and experimented with higher and lower thresholds regionally; Baddock et al. (2016) applied a DOD threshold of 0.75 to pinpoint sources on a landscape-scale; and Pu et al. (2020) experimented with DOD thresholds ranging from 0.02 for less dusty regions to 0.5 for global dust emission hotspots such as North Africa. This paper shows results using DOD thresholds of 0.5 and 1, with the effect of other threshold choices (between 0.1 and 1.5) documented in Fig. S1 in the Supplement. The higher DOD threshold of 0.5 also helps to minimize thin cirrus contamination in the Arctic, as these typically have cloud optical depths of 0.03–0.30 (e.g., Pierce et al., 2010). A single event of active dust source emission targeted by our approach (including visible imagery) is included in Fig. S2, in the context of regional-scale AOD signals.

Maps of FoO DODPG16>0.5, which we stress are not comparable to climatologies of DOD itself, show several distinct features (Fig. 1a). There is a broad swath of FoO > ∼ 50 in southern Alberta and Saskatchewan, extending across the border with the United States; this feature is associated with croplands, as discussed more fully in Sect. 3.2. The remaining northern features are of direct interest to the high-latitude dust question: most notably, the numerous spatially discrete dust emission hotspots (FoO > 80) scattered across the islands (north of 70° N) that make up Canada's Arctic Archipelago. There are a few additional isolated FoO hotspots in continental northern Canada, for example in southern Northwest Territories close to the border with the Yukon Territory (MacKenzie Mountains, pink square in Fig. 11), in northwestern continental Nunavut (green square in Fig. 11), and at the border between northern Québec and Labrador (blue square in Fig. 11). Many of these emission hotspots correlate with “barren land” classifications in the Canadian Land Cover Atlas. Notably, the greatest FoO values are in the Arctic Archipelago, as opposed to further south where dust is perhaps more commonly expected to be present (e.g., due to higher temperatures and lower snow cover).

The pattern in FoO DODB16>0.5 (Fig. 1b) is qualitatively similar to that in FoO DOD_PG16 (Fig. 1a), with two exceptions. Firstly, FoO values are around 50 % lower in the DOD_B16 dataset, as can be seen from the colorbars; secondly, the northern FoO hotspots tend to be focussed east of 95° W in DOD_B16, whereas they are distributed more evenly across the full longitudinal extent of Canada's north in DOD_PG16. Lower FoO values may be expected in DOD_B16 than DOD_PG16 because the former is only reported for subsets of AOD retrievals that meet certain filtering criteria (if α<0.3 then AOD = DOD), whereas the latter is calculated for all AOD retrievals with no such α-based filtering applied (ω<1 is a filter applied to both DOD_PG16 and DOD_B16 datasets; see the DOD equations outlined in Sect. 2.1.1). We examined retrieved Ångström exponent and single scatter albedo values in our study region in summer (JJAS 2003–2022) and indeed find that α<0.3 (larger particles) and ω<1 (absorbing particles) occurs more frequently east of 95° W (Fig. S3), accounting for the differences between Fig. 1a and b.

MODIS AOD retrievals – upon which DOD is based – are more numerous at southern latitudes than in the far north (Fig. 1g–i) for reasons such as cloud and snow cover, and the lack of reflected visible radiation during northern winter (polar twilight and night of varying duration occurs north of 67° N) which is required for AOD retrievals. To account for this, FoO DOD > 0.5 values are normalised across the study domain by dividing the FoO count by the total number of available DOD retrievals. The resulting maps for both DOD datasets (Fig. 1d–e) show that the relative frequency of DOD > 0.5 is far greater in the north than for the broad emission hotspot across the Canada-US border, with values exceeding 50 % in DOD_PG16 in some locations and reaching 100 % in DOD_B16 (values closer to 100 % are more likely in DOD_B16 than DOD_PG16 due to its threshold-based derivation, as discussed above). However, the geographic mean of the relative FoO of DOD > 0.5 in the Pu and Ginoux (2016) formulation of dust optical depth (i.e., mean % FoO DODPG16>0.5) is just 9 % when averaged over the Canadian Arctic Archipelago (taken as 70–80° N, 125–75° W) over a 20-year period (Fig. S4). This compares well with the relative frequency of detectability of dust in the work of Sayedain et al. (2023), who examined dust events in May of 2019 at Kluane Lake (downstream of a known dust source). Specifically, Fig. 4 in that work shows “DRS” occurring on 16 of 31 d, where “DRS” represents the detectability by ground-based remote sensing (such as by AERONET techniques) of “optically significant” dust; the authors later reported that ∼ 11 % of the DODs associated with Fig. 4 were greater than 0.5 (Sayedain and O'Neill, 2025).

Next, we applied the same threshold of 0.5 to the “parent” AOD data (Fig. 1c, f). Overall FoO AOD > 0.5 values are greater across Canada, as expected, but the northern hotspots remain, with FoO exceeding 200 at some locations. The greater values for FoO AOD > 0.5 across Canada, in regions where FoO DOD is close to zero, shows the value of using DOD data to highlight the contribution of dust particles to the retrieved AOD value, against the contribution from other aerosols that are present in the atmosphere.

If the DOD (or AOD) threshold is increased to 1, FoO values decrease (maximum values < 20) and the spatial extent of emission hotspots contracts (see Fig. 2a and b for a comparison of FoO DOD_PG16 with the thresholds of 0.5 and 1 respectively; note that maps for FoO DOD_B16 > 1 and FoO AOD > 1 are included in Fig. S5). However, many spatially discrete emission hotspots in the north remain in attenuated form (compare Fig. 2d, e).

There are no DOD products north of 70° N between the months of October and April inclusive (Fig. 3, left column). This is to be expected due to a combination of (a) polar night during parts of the winter preventing satellite AOD retrievals based on visible wavelengths and (b) the fact that much of the surface is frozen, which inhibits the atmospheric entrainment of surface-derived dust particles. For these reasons, DOD retrievals in the high Arctic predominantly occur during the months of June, July, and August (Fig. 3, right column), with May and September representing the shoulder seasons (Fig. 3, middle column).

The results discussed in Figs. 1 and 2 thus far are based on AOD retrievals that are not filtered using the “best estimate” data quality flag that is distributed with the data. As discussed at the end of Sect. 2.1.1, this is based on the findings of Baddock et al. (2016), wherein the DOD based on the unfiltered AOD dataset was of greater value for dust source detection – a core aim of this paper – as opposed to DOD calculated using only the AOD retrievals that remain after filtering for the “best estimate” quality flag. The present analysis was replicated by applying the “best estimate” quality flag filter to the AOD data to test the effect of this choice. This resulted in a significant reduction in FoO DOD (and AOD) > 0.5 values and a consequent contraction of the emission hotspot areas (Fig. 2c and a show a comparison of FoO DODPG16>0.5 with and without the “best estimate” quality flag filter, respectively; note that equivalent maps for DOD_B16 and AOD are included in Fig. S6), however, many of the isolated emission hotspots in the high Arctic remain in attenuated form (compare Fig. 2d and f). Over the high Arctic, the magnitudes and spatial patterns in these maps (Fig. 2d and f) are similar to those of FoO DODPG16>1 created using data unfiltered by the quality flag (Fig. 2b and e).

The dust emission hotspots inferred in this section from MODIS DOD data will be evaluated against the dust emission potential map in Sect. 3.4, examined over time in Sect. 3.5, and evaluated against two AERONET stations in Sect. 3.6.

We now turn to a qualitative but independent comparison of our MODIS-based assessment of the FoO of extreme dust events (DOD > 0.5) by the FoO of VIIRS aerosol type “dust”. The frequency with which VIIRS aerosol retrievals were classified as aerosol type “dust” (FoO AT_DUST) for the period 2020–2022 is shown in Fig. 4a. Greatest FoO AT_DUST values are seen north of 65° N over land, with values exceeding 80 in some emission hotspot regions for the 3-year period. In absolute terms, this is comparable to the FoO of Fig. 1a, but when the shorter time period is accounted for in the MODIS dataset (Fig. 4c), then maximum FoO values of AT_DUST are higher in the VIIRS dataset. When represented as a percentage of the number of all aerosol type retrievals, dust is clearly the dominant retrieval type over much of the land area of northern Canada (Figs. 4d and S6), accounting for >80 % of all aerosol type retrievals in some emission hotspots. However, we note that some of these high percentage areas have quite low FoO AT_DUST (<10), and much lower numbers of retrievals compared to the MODIS dataset (cf. Fig. 3 bottom row and Fig. S6 bottom right). Figure 4a shows that AT_DUST is also being retrieved over water (by the SOAR algorithm, as noted in Sect. 2.1.2) with increasing frequency towards lower latitudes and a stark land-water contrast (both over open salt water and inland fresh water like the Great Lakes). This FoO feature is attenuated in the relative representation of FoO in Fig. 4d, even though the land-water contrast remains; we do not interpret it as dust.

Aside from type “dust”, there are large areas with an aerosol type classification of “mixed” (FoO AT_MIXED) north of ∼ 80° N on Ellesmere Island and in the surrounding region (Fig. 4b), accounting for a high proportion (>60 %) of total aerosol type retrievals there (Figs. 4e and S6). “Mixed” aerosol type is also widespread south of 70° N, reaching peak values in south-central Canada and extending across the border with the US, where it accounts for 20 %–30 % of all aerosol type retrievals (with most of the other retrievals being classified either as “smoke”, “non-smoke fine mode”, or “background” aerosol type – see Fig. S7 in the Supplement for an analysis of all VIIRS aerosol types in this dataset). “Mixed” aerosol type (0.5<α<1) is indicative of smaller particles than the large dust aerosol type (α<0.5), however, smaller clay fraction (<2 µm) dust particles do in fact dominate the emitted number size distribution (e.g., Kok, 2011; González-Flórez et al., 2023), and they can reasonably be expected to be found both close to sources and further away after long-range transport (e.g., Prospero, 1999; Zhao et al., 2022) because of their week-long lifetimes (Kok et al., 2017) – even though in remote sensing dust is generally taken as the coarse mode in order to avoid this ambiguity. While the polar dome isolates midlatitude pollutants from the high Arctic in summer (e.g., Gong et al., 2023), nevertheless, it is unknown what proportion of the “mixed” aerosol type is attributable to smaller dust and, therefore, we interpret VIIRS “mixed” aerosols as only potential dust indicators, especially where they occur as discrete source areas in the high Arctic (as opposed to a homogeneous background further south) and where they coincide with areas of high emission potential in the dust emission potential map (Sect. 3.4).

As noted above, for direct comparison, FoO DODPG16>0.5 is shown for the sub-period 2020–2022 (Fig. 4c, f). It is apparent that in the north, peak FoO AT_DUST is greater than peak FoO DODPG16>0.5 and that the area of FoO AT_DUST is larger than it is for FoO DODPG16>0.5. However, such differences are to be expected since these datasets are derived differently (as outlined in Sect. 2.1). Specifically, the FoO AT_DUST map will include dust that did not pass the DODPG16>0.5 threshold (i.e., VIIRS AT_DUST requires AOD > 0.3 with α < 0.5, while MODIS DODPG16>0.5 requires, based on Eq. (1), e.g., AOD > 0.74 if α=0.5 and AOD > 0.51 if α=0). Moreover, even if DOD_PG16 was calculated from VIIRS AOD data and compared directly to that based on MODIS AOD (either from Terra or Aqua), differences would still be expected because of sensor and algorithm differences leading to systematically lower VIIRS AOD values at high northern latitudes (Sayer et al., 2019, their Fig. 16) and more complex difference patterns (spatially) in the Ångström exponent (Sayer et al., 2019, their Fig. 17); a detailed analysis of these differences is outside the scope of this work. Nonetheless, there is qualitative agreement between the two datasets we presented on numerous emission hotspot areas across the high Arctic. Lastly, the large southern emission hotspot evident in FoO DODPG16>0.5 (Fig. 4c) is likely dust emitted and transported in the large-area dust sources of the Great Plains of Canada and the US (e.g., Lambert et al., 2020); this is consistent with the spatial coincidence with a peak in FoO AT_MIXED in the Great Plains (Fig. 4b), where croplands are found, and where possibly finer dust is expected to be mixed with generally finer anthropogenic aerosols. The dust emission hotspots inferred in this section from VIIRS “dust” aerosol type data will be evaluated against the dust emission potential map in Sect. 3.4.

Additional qualitative but independent corroboration of our MODIS-based assessment of the FoO of extreme dust events (DODPG16>0.5) was made with the FoO of CALIOP aerosol type “dust”, which adds vertical distribution information. As outlined in Sect. 2.1.3, this product shows the number of times an aerosol of type “dust” (dust aerosol types “polluted dust” and “dusty marine” were excluded in this study) is detected in a bin of 5° longitude × 2° latitude × 60 m vertically (∼ 200 × 200 km in the Arctic, but based on individual 5 km L2 products) per month. The monthly totals were summed for JJA 2006–2020, inclusive, and these period-sum values were then averaged for the latitude band 60–80° N, due to their sparseness (Sect. 2.1.3). A longitude-height plot of the resulting data (Fig. 5) shows evidence of enhanced aerosol type “dust” detection frequency at low altitudes (<2 km) for the longitude range between 125–75° W, in good agreement with the longitudinal range spanned by the dust emission hotspot regions identified in both the MODIS and VIIRS datasets previously discussed. Low-altitude Arctic dust in summer is also in agreement with the source-tagged modelling work of Shi et al. (2022), who found that high-latitude dust constitutes 30.7 % of the total Arctic dust burden in the lower troposphere, on account of typically stable boundary layers and limited vertical transport; in JJA the origin of free tropospheric Arctic dust is approximately evenly split between east Asian sources, central Asian and Saudi Arabian sources, and north African sources. Dust detection frequency values that are lower at higher altitudes across this region, and lower immediately to the east and west of the region, suggest surface sources of dust emission in the region. The near-surface enhancements in dust detection frequency between ∼ 50 and 25° W (between 2.5 and 5 km), as well as between 25 and 15° W (below 2 km) are consistent with emission from sources in Greenland and Iceland, respectively, which have been documented elsewhere (see, e.g., Meinander et al., 2022). However, there are known issues with CALIOP aerosol misclassification of diamond dust (very small ice crystals in clear skies) as mineral dust, most prominent in winter (e.g., Zamora et al., 2022; Di Biagio et al., 2018), which we consider next. Even though Fig. 5 shows long-term data on aerosol type “dust” in JJA, when diamond dust is rare, we examined spatially and temporally matching meteorological reanalysis fields above Greenland, as well as to the east including Iceland and northeast Greenland, and to the west in the Canadian Arctic Archipelago. This analysis (Fig. S8 in the Supplement) shows mean temperatures below 2 km altitude as above the freezing point over the Arctic and “Iceland” region (see top right panel of Fig. S8 for geographic details), with air masses also subsaturated with respect to ice below 3 km altitude; the mean optical thickness of cloud ice was less than 0.005 at all altitudes below 3 km. Above Greenland, temperatures below 2 km altitude above ground level (a.g.l.) were above -10 ° C and air masses were still subsaturated with respect to ice below 3 km; the mean optical depth of cloud ice was less than 0.003 below 1.5 km a.g.l., and less than 0.03 below 3 km a.g.l. Given this analysis, the likelihood of diamond dust contamination of the CALIOP aerosol type “dust” is very low in the Arctic and “Iceland”, but it cannot be ruled out above Greenland without further study, e.g., of meteorological fields coincident with CALIOP overpasses, and individual (as opposed to mean) profiles of temperature and relative humidity over ice. Moreover, further study would be needed to determine the spatial origin of these elevated (in altitude and in terms of FoO) aerosol type “dust” signals over Greenland; their higher altitude (2.5–5 km) does not simply imply an Asian origin, as it might in the Canadian Arctic, particularly in JJA and combined with the clustering of FoO above Greenland instead of a zonal homogenization that would be expected after long-range transport. The flows around Greenland's extreme orography are complex, both near the surface (Gorter et al., 2014) and aloft (Moore et al., 2013), with a complex coupling of smaller-scale but intense “piteraq” (katabatic) winds, low-level jets, and synoptic-scale flows. It is possible that the Greenland plateau jets described by Moore et al. (2013, their Fig. 8 for JJA) contribute to a re-circulation of dust mobilized in lower, ice-free margins of Greenland, but this is speculative. To conclude this section, the qualitative “dust” type vertical distribution information from CALIOP informs our analysis and is consistent with active dust emission in the Arctic and eastern Greenland/Iceland; however, it is not suitable for direct comparison against the dust emission potential map (Sect. 3.4) on account of the coarse spatial aggregation necessary to compensate for the 70 m laser footprint of the CALIOP sensor (Sect. 2.1.3).

We turn now to comparing our satellite-based results with known information on dust emission potentials, the latter under favourable wind conditions. In Fig. 6 we compare MODIS FoO DODPG16>0.5 and VIIRS FoO AT_DUST to source intensity (SI) for July from the G-SDS-SBM dataset. SI for July was chosen based on the results of the seasonal analysis in Sect. 3.1, where all-season FoO was dominated by June–July–August FoO in absolute terms and in spatial extent, and the fact that SI values are low for the other months (January, April, and October) provided in the G-SDS-SBM dataset (SI for other months is plotted in Fig. S9). The dust emission potential is high (SI > 0.5) in much of Canada's Arctic Archipelago (Fig. 6a), which is broadly consistent with MODIS FoO DODPG16>0.5 (Fig. 1a) and VIIRS FoO AT_DUST (Fig. 4a). We quantified areas of SI overlap with both MODIS (Fig. 6b, red colour) and VIIRS (Fig. 6c, red colour) using threshold SI and FoO values as follows. Because SI is a relative measure of dust emission potential with values between 0 (no emission potential under any wind conditions) and 1 (maximum emission potential under sufficient wind conditions), we looked for areas where SI is greater than 0.5 while, simultaneously, MODIS FoO is greater than 10 %. This 10 % MODIS threshold, which does not match the SI threshold of 0.5 (50 %), was chosen because we are testing for the FoO of extreme dust emission events (DOD > 0.5), and it is reasonable to assume that, by definition, such conditions will be met in far less than 50 % of observational cases, i.e., 10 %. Red colours indicate agreement between dust emission potential (SI) and satellite observations of extreme events, while blue and green colours both indicate disagreement. Blue disagreement areas have high dust emission potential that is not matched with observations of dust, while green disagreement areas have low dust emission potential where satellites nevertheless see extreme DOD (>0.5) in at least 10 % of observations. For consistency we use the same 10 % threshold in VIIRS FoO AT_DUST data, which leads to relatively prominent green areas (low emission potential but frequent VIIRS-observed dust) because, unlike MODIS FoO data, VIIRS FoO data considers only the presence of dust (VIIRS AT_DUST) and not the intensity of events (i.e., MODIS DOD > 0.5); moreover, as discussed in Sect. 3.2, VIIRS thresholds for identifying aerosol type “dust” are lower and there are known differences between VIIRS and MODIS AOD and Ångström exponent Deep Blue products. While the thresholds can be shifted to influence the level of agreement and disagreement and thus the spatial colour patterns (which we experimented with), the salient point is that all three datasets have limitations, yet all three point to non-zero dust activity north of 60° N, in contrast to many model simulations of dust emission to date that have ignored these high-latitude dust sources, albeit reasonably so, e.g., on account of low model resolution or lack of input information. The key dust emission areas shared by all three datasets (shared orange and red colours) are found in a broad “Canadian Arctic Dust Belt” between western Baffin Island (70° N, 75° W) and northwest of Prince Patrick Island (80° N, 125° W), as marked also on Fig. 11. The same area also contains few blue colours (high emission potential but few observations of dust) but many green colours, where emission potential is low (<0.5) but satellite observations show strong (MODIS) and/or frequent (VIIRS) dust events. The “green” disagreement indicates areas where dust emission potential maps may benefit from re-examination and/or verification, i.e., also with new field measurements, challenging as they are.

The notable inconsistencies in the Arctic Archipelago include eastern Baffin Island (∼<70° N, ∼<75° W), which is characterised by widespread high SI (except in areas permanently covered by snow and ice) but sparse MODIS FoO DODPG16>0.5 emission hotspots (blue colours in Fig. 6b) with more frequent VIIRS FoO AT_DUST regions (red colours in Fig. 6c). The same is true for ice-free parts of Ellesmere Island and Axel Heiberg Island (∼ 80° N, ∼ 85° W). One explanation for such discrepancies, apart from thresholding choices, is that high SI values only show surfaces where the potential for dust emission is high; other conditions still need to be met for dust emission to actually occur, i.e., surface winds that are strong enough to entrain sediment. We examined a summertime 10 m wind speed climatology (Fig. S10) and find that where MODIS and VIIRS disagree on eastern Baffin Island mean winds are 4–6 m s⁻¹ (14.4–21.6 km h⁻¹), which is sufficient for dust mobilization. Another factor is that we have filtered MODIS data for DODPG16>0.5, which represents extreme (but infrequent) dust emission events for the Arctic, on par with Saharan dust in transport across the tropical Atlantic, whereas our VIIRS “dust” type data shows all observations of pure (unpolluted) dust where AOD > 0.3 and α<0.5, so that the corresponding DOD_PG16 (if calculated from VIIRS products) could be as low as 0.22 for AOD = 0.3 and α=0.5 (Eq. 1). On the other hand, it is plausible that the satellite datasets also do not capture the full extent of dustiness across the region, especially in the far north, due to frequent cloud coverage and dust misclassification as clouds (e.g., Huck et al., 2023; Sayedain et al., 2023; Ranjbar et al., 2021).

There are also a few notable areas south of the Arctic where FoO DODPG16>0.5 emission hotspots are not matched by high SI, e.g., green patches in Fig. 6b in the Northwest Territories (MacKenzie Mountains), close to the border with the Yukon Territory (∼ 126° W, ∼ 63° N) and at the border of northern Labrador and Québec (65° W, 58° N), as marked in Fig. 11. Reasons for such disagreements are unclear, but we stress again that the G-SDS-SBM SI mapping results are sensitive to the quality of the input data (soil texture, soil moisture, soil temperature, and bare land fraction), which all have limitations. In examining the visible imagery, average wind speeds, and geological features of these two discrepancy areas available from the Global Wind Atlas (Davis et al., 2023), there is strong indication that both, although geologically distinct, are likely unclassified dust emission source areas. The MacKenzie Mountains location (Fig. A1 in the Appendix) is an environment with strong weathering and erosion leading to extensive colluvial features with visible scree, comprising also finer sand, silt and clay. It is likely a result of a former river channel and/or a lakebed. The Labrador/Québec location (Fig. A2) is an exposed plateau that was recently deglaciated, in geological terms, leaving behind unconsolidated fine glacial sand, with scarce vegetation and soil formation to suppress dust mobilization. There is a third emission hotspot area that does not appear on the SI map (SI ∼ 0) and does not meet the 10 % FoO threshold but produces a prominent absolute FoO signal; it is a glacially scoured plateau in northwestern Nunavut (Fig. A3, green box in Fig. 11) that is windy, unvegetated, and covered with glacial lakes and debris (these features are visible most clearly in some areas where 10 m resolution Google imagery is available, e.g., 66.83369, -111.86699). Clearly, these are locations of large spatial extent where further investigation would be beneficial, including by field measurements.

Lastly, the spatially broad swath of only occasionally high FoO DODPG16>0.5 in the Great Plains of southern Canada and USA (connected to croplands and discussed in Sect. 3.2, Fig. 4b and c) is largely not matched by high SI; this is reasonable because this extensive pattern of dustiness in satellite observations disappears in a percentage-of-observations representation (Fig. 4e and f). The SI hotspots in this region (unlike the absolute FoO signature of Fig. 4b and c, found only in northwestern US and not southwestern Canada) are much more spatially restricted than the FoO signature and comprise a mixture of croplands (Washington, northern Oregon, Idaho, Montana, cf. https://www.croplands.org/app/map, last access: 11 January 2024) and arid regions (southern Oregon, Wyoming), but they do not correspond to either intense MODIS DODPG16>0.5 events (Fig. 6b, blue colours) or VIIRS AT_DUST events (Fig. 6c, blue colours). Instead, they are associated with VIIRS AT_MIXED events (Fig. 4b and e), which, again, makes sense if agricultural dust emissions of non-coarse-mode particles were mixed with anthropogenic aerosols.

Overall, the reasonable agreement between satellite-derived dust emission hotspots and areas with high SI values in the G-SDS-SBM dataset supports the use of satellite datasets in dust emission modelling development in the Canadian Arctic and in other high-latitude regions. This section also suggested regions where the SI dataset may benefit from further development (marked in Fig. 11), and where field measurements would be of help.

The 20-year record of MODIS data enables an assessment of whether the frequency of dustiness over Northern Canada has changed over time. Time series of annual means of FoO DODPG16>0.5, FoO DODB16>0.5, and FoO AOD > 0.5, averaged for the region (70–80° N, 125–75° W) representing the “Canadian Arctic Dust Belt” identified in Sect. 3.4, are shown in Fig. 7. Note that this figure shows values of mean FoO of optical depth (normalized) rather than mean optical depth itself. There is clear interannual variability in each of the time series; however, the greatest annual mean FoO DODPG16>0.5 values all occur in the second half of the period, with 2015, 2017 and 2019 appearing to be active dust emission years. The same is true with FoO DODB16>0.5, although only 2015 and 2017 stand out as dust-active years in the region. By contrast, the annual mean FoO AOD > 0.5 time series is qualitatively different, with peaks and troughs relatively evenly distributed across the study period and no qualitative evidence of increasing AOD over time. We examined wind speed, soil temperature, soil moisture, and snow cover in 20 years of daily mean ERA5 meteorological fields for the same geographic region as Fig. 7 in summer (Fig. S11). There was a significant (p<0.03) and negative (R=-0.49) correlation only between FoO (AOD > 0.5) and snow cover, but not with the FoO of either formulation of DOD > 0.5 (R=-0.30, p<0.20 for FoO (DOD_PG16 >0.5); R=-0.18, p<0.44 for FoO (DODB16>0.5)). As noted in Sect. 1, an increase in high-latitude dust emission is an expected consequence of climate change in the Arctic leading to glacial retreat, decreased snow cover duration, and permafrost thaw, all making exposed dust more available for mobilization (Bullard, 2013; Meinander et al., 2022). However, the results presented in this section are not a statistically significant indicator of such processes occurring in the Arctic (Gulev et al., 2021).

In this section, AOD data from MODIS are evaluated against AOD data from AERONET stations at Kluane Lake (AOD_KL) and Resolute Bay (AOD_RB). The station locations are marked on Fig. 11. The Kluane Lake AERONET station was intentionally located proximal to a known dust source for the purpose of monitoring emission processes there (Huck et al., 2023; Sayedain et al., 2023). Curiously, the area is not identified as a strong dust emission source area by our satellite data analysis, which is on the one hand not very surprising given that Huck et al. (2023) document large discrepancies between remotely sensed dust (both by AERONET and by MODIS-MAIAC) and in situ remote camera images of dust at Kluane Lake. On the other hand, their “dust event days” (DEDs) have modal AOD values of 0.5 with a high tail extending out to an AOD of 3; in the context of such thick dust plumes, one may reasonably expect that in the analysis of 20 years of MODIS data in our study (Collection 6.1 at 10 km resolution) we would detect a stronger signal in FoO – yet we do not. However, the Kluane Lake region and the nearby mountainous regions are largely without satellite data due to the extremely variable and highest topography in North America (Mt. Logan in Canada stands at 6000 m and Denali in Alaska to the west stands at 6200 m), which presents difficult surface retrieval conditions (as shown in detail later in Fig. 10). Unlike Kluane Lake, the Resolute Bay AERONET station is situated adjacent to strong dust emission hotspots identified in our study on Cornwallis Island (as shown in detail later in Fig. 10); however, to the best of our knowledge, this is coincidental, i.e., the station location was not selected with the explicit purpose of monitoring dust aerosol. In this section, satellite data retrieved within both a 1° × 1° and 0.5° × 0.5° box centred on each AERONET station were considered. From MODIS, the maximum AOD values within the search box were retained, while from VIIRS the presence or absence of each aerosol classification type (AT) was recorded. For AERONET, only retrievals performed within 11 am and 5 pm local time were considered for analysis and the maximum retrieved AOD each day within this time window was retained for the comparison. Maximum values were chosen instead of mean values in line with the purpose of our analysis, which is to identify extreme DOD values near dust sources.

AOD comparison results are first considered using MODIS data detected within the 1° × 1° search box centred on each AERONET station. AOD values > 3 were filtered out (2 points at KL and 5 points at RB) given that DOD was found to be <2 at Kluane Lake (Sayedain and O'Neill, 2025). At Kluane Lake, six years of AOD_KL data correlates strongly with MODIS AOD (R=0.81 in Fig. 8a). These AOD correlation results are at odds with the work of Huck et al. (2023) at Kluane Lake who found weak mean AOD (unlike our maximum AOD) correlations on DEDs in 2018 and 2019 (R=0.11 and R=0.36), which improve on non-DEDs in 2018 and 2019 (R=0.35 and R=0.96). They classifed DEDs with threshold AOD and α parameters similar to our work (but from their AERONET data) and they use the mean MODIS-MAIAC AOD product at 470 nm with the mean AERONET AOD product at 500 nm. Given their in situ remote camera imagery verifying the presence of dust, they reasonably attribute the lack of correlation on “dust event days” to specific dust-cloud detection problems in AERONET and the lack of dust testing at high latitudes in the MODIS-MAIAC retrieval (rather than to the less important 470 nm vs. 500 nm wavelength mismatch between MODIS and AERONET AOD). Their sampling strategy is different and covers just 70 km² with 1 km MODIS-MAIAC pixels as opposed to our 1° × 1° box based on 10 km resolution L2 MODIS data. While it is not possible to compare these results directly, it is nevertheless interesting to note both the range of choices and results in this apparently “similar” type of comparison carried out in our respective studies. Switching to the VIIRS sensor, three years of VIIRS AT data were available over the 1° × 1° region surrounding the Kluane Lake AERONET station, and AT_DUST is detected on 18 % of days while AT_MIXED and AT_BACKGROUND are the two modal classifications, both detected on 27 % of days (Fig. 9). Unlike at Kluane Lake, 18 years of Resolute Bay AOD_RB has only a weak correlation (R=0.32) with MODIS AOD for the 1° × 1° search area (Fig. 8a). As Fig. 9 shows, VIIRS AT_DUST is detected on 32 % of days here, with AT_BACKGROUND being detected more frequently (44 % of days) and AT_MIXED less frequently (12 % of days).

A question arises about the poor correlation at Resolute Bay between AERONET AOD_RB and MODIS AOD datasets given the proximity to our potential emission hotspot regions and the more frequent VIIRS AT_DUST presence there. One reason could be the deliberate positioning of an AERONET station to monitor dust at a known emission location (like Kluane Lake), which was not the case for Resolute Bay (notwithstanding the AOD disagreement at Kluane Lake found by Huck et al. (2023) using the MODIS-MAIAC product). Is it possible that coarse dust in the 1° × 1° search area around Resolute Bay is simply not passing over the AERONET station, given also that coarse dust does not generally travel far? The map of average regional winds above both stations (Fig. 10) shows that Kluane Lake is downwind of dust emission regions in the foothills of the mountains at glacial margins (where satellite data is increasingly lacking on account of the variable topography and surface properties) while Resolute Bay is upwind of the dust emission hotspot (identified with abundant satellite data). The elevated topography to the east of Resolute Bay may itself obscure any dust suspended in that direction from the AERONET instrument's field of view for low solar zenith angles in the morning. As may be expected in this situation, the correlation between MODIS AOD and AOD_RB increases from 0.32 to 0.58 as the MODIS search area is decreased from 1° × 1° to 0.5° × 0.5°, while the MODIS AOD correlation with AOD_KL hardly changes (0.87 vs. 0.90) under the same decrease (Fig. 8b). Since the AERONET station at Kluane Lake is already next to and downwind of the dust, decreasing the search area is not helpful to the correlation. Interestingly, this same decrease in search area size causes the AT_DUST classification frequency at Resolute Bay to decrease from 32 % for 1° × 1° to 16 % for 0.5° × 0.5°, while the AT_BACKGROUND classification frequency increases from 44 % to 64 % (Fig. 9); there is no appreciable change at Kluane Lake. This reduction supports the argument that dust in the broader region surrounding Resolute Bay is not within detection range of the AERONET station and highlights the challenge of detecting even nearby dust sources given the station's position and prevailing winds, as compared to the specific satellite product used in this analysis.