Recent changes in pan-Arctic sea ice, lake ice, and snow-on/off timing

. Arctic snow and ice cover are vital indicators of climate variability and change, yet while the Arctic shows overall warming and dramatic changes in snow and ice cover, the response of these high-latitude regions to recent climatic change varies regionally. Although previous studies have examined changing snow and ice separately, examining phenology changes across multiple components of the cryosphere together is important for understanding how these components and their response to climate forcing are intercon-nected. In this work, we examine recent changes in sea ice, lake ice, and snow together at the pan-Arctic scale using the Interactive Multisensor Snow and Ice Mapping System 24 km product from 1997–2019, with a more detailed regional examination from 2004–2019 using the 4 km product. We show overall that for sea ice, trends toward earlier open water ( − 7 . 7 d per decade, p < 0 . 05) and later ﬁnal freeze (10.6 d per decade, p < 0 . 05) are evident. Trends toward earlier ﬁrst snow-off ( − 4 . 9 d per decade, p < 0 . 05), combined with trends toward earlier ﬁrst snow-on ( − 2 . 8 d per decade, p < 0 . 05), lead to almost no change in the length of the snow-free season, despite shifting earlier in the year. ice-off, ice-off, and snow-off signiﬁcantly correlated, with stronger correlations during the snow-off and ice-off season compared to the snow-on and ice-on season. Regionally, the Bering and seas the most pronounced to the strongest


Introduction
The cryosphere is the second largest component of the global climate system after the ocean, exerting significant effects on the Earth's energy balance, atmospheric circulation, and heat transport (Lemke et al. 2007;Callaghan et al. 2011;). The relevance for climate variability and change is based on physical properties, such as high surface reflectivity (albedo) and latent heat associated with phase changes, both of which have a strong impact on the surface energy balance 30 (Lemke et al. 2007). The extent and duration of snow and ice cover have direct feedbacks to the climate system as they strongly influence planetary albedo (Rahmstorf, 2010;Derksen et al. 2012). Seasonal snow and ice cover are also important for Arctic ecosystems as they rely on snow and ice cover for feeding, transportation, and habitat (Dersken et al. 2012). Additionally, the traditional ways of life of many Northern residents depend on snow and ice cover for sources of food, transportation, and economic activities . Recent assessments reveal strong linkages between decreasing snow and ice cover 35 and increasing temperatures in the Arctic (e.g. Hernandez-Henriquez et al. 2015;Johannessen et al. 2016;Druckenmiller and Ritcher-Menge, 2020). Reductions in sea ice extent, decreases in snow cover duration, and earlier melt onset in Arctic and sub-Arctic lakes have been reported (Serreze and Stroeve, 2015;Surdu et al. 2016;Mudryk et al. 2018). Arctic surface air temperatures in 2019 were the second highest in the 120-year (1900 -present) observational record (Druckenmiller and Richter-Menge, 2020), and are projected to continue to increase well into the twenty-first century (Overland, 2020). Though 40 the Arctic as a whole is undergoing climatic change, observations are often marked by regional differences tied in part to global connections via the atmosphere and ocean (Druckenmiller and Richter-Menge, 2020). For example, sea ice in the Alaska/Russia region has shown large reductions in sea ice extent over the past decade, which has been linked to strong warming and large sea surface temperature anomalies in this area (Druckenmiller and Richter-Menge, 2020;Perovich et al. 2020). The Canadian Arctic Archipelago (CAA), however, has been shown to exhibit earlier freeze trends during recent years 45 (e.g. Dauginis and Brown, 2020) and weaker trends toward earlier melt onset compared to other Arctic regions (e.g. Mahmud et al. 2016;Marshall et al. 2019). Furthermore, the effect of warming on sea ice dynamics in this region can be counterintuitive as warming could result in increased ice import from the Arctic Ocean into the CAA (Melling, 2002;Howell and Brady, 2019;Moore et al., 2021). Therefore, monitoring Arctic snow and ice cover is critical to improve our understanding of this complex and variable region in the context of climate variability and change. 50 Monitoring Arctic snow and ice cover largely relies on the use of satellite observations, as ground-based observations are constrained by limited in situ data, large gaps and biases in surface observing networks, and limited geographic coverage (Brown et al. 2010;Brown and Duguay, 2011). Satellite-based microwave data are most commonly used in snow and ice monitoring as they provide information regardless of solar illumination and cloud cover ). Microwave measurements have been used to estimate snow (both on land and on sea ice) and ice melt and freeze onset (e.g. Howell et al. 55 2006;Yackel et al. 2007;Markus et al. 2009;Wang et al. 2011;Zheng et al. 2017) at various spatial resolutions ranging from 6.25 to 25 km . The Special Sensor Microwave/Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) passive microwave datasets have been widely used in snow and sea ice mapping (e.g. Cho et al. 2017;https://doi.org/10.5194/tc-2021-52 Preprint.  areas due to pixel-based land contamination (Howell et al. 2006;Brown et al. 2014). Johnson and Eicken (2016) note that strong brightness temperature contrasts across pixels can result in falsely high estimates of sea ice concentration, particularly during the summer when there is open water near coastal areas. SMMR and SSM/I are less commonly used in lake ice applications as the spatial resolution limits analyses to large lakes only. Additionally, the 85 GHz channel is susceptible to considerable atmospheric interference, and the 25 km spatial resolution can result in large differences in water/land brightness 70 temperatures (Cavalieri et al. 1999;Howell et al. 2009).
Optical remote sensing data have also been used to monitor Arctic snow and ice cover (e.g. Nitze et al. 2017;Young et al. 2018) as they provide an improved spatial resolution (e.g. 500 m Moderate Resolution Imaging Spectroradiometer Snow Product) compared to passive microwave data. The use of optical imagery is limited to the spring and summer months in highlatitude regions as there is no source of illumination during late fall and winter due to polar darkness. Additionally, the poor 75 temporal resolution of some optical data (e.g. 16 days for Landsat, 8-day MODIS snow product) can introduce uncertainty and inaccuracy into estimates of snow conditions on the Earth's surface. Active microwave data have been used successfully in snow (e.g. Brown et al. 2007), sea ice (e.g. Mortin et al. 2014), and lake ice (e.g. Howell et al. 2009) applications. Active microwave algorithms using synthetic aperture radar (SAR) provide high resolution (20 to 100 m) retrieval of snow and ice parameters (e.g. Surdu et al. 2016;Zhu et al. 2018;Howell and Brady, 2019). SAR estimates of snow and ice cover provide 80 the highest spatial resolution compared to other products, however the moderate temporal resolution, narrow swath width, and limited image availability across the Arctic limits the application of SAR to smaller geographic regions Howell et al. 2019). Multisensor approaches exploiting advantages of microwave and optical sensors have been used to estimate snow thickness on first year sea ice (e.g. Zheng et al. 2017) and to resolve leads and polynyas at an improved spatial resolution (e.g. Ludwig et al. 2019). The all-weather capabilities of microwave data combined with high temporal resolution 85 of optical imagery can improve estimates of snow and ice parameters in the Arctic.
An alternative approach to snow and ice mapping is the use of the National Ice Center Interactive Multisensor Snow and Ice Mapping System (IMS) product. IMS is created using a variety of multi-sourced datasets (e.g. optical imagery, microwave data, ancillary data) and provides daily maps of snow and ice cover at 24 km, 4 km, and 1 km spatial resolutions (Ramsay 1998;Helfrich et al. 2007). The daily temporal resolution and all-weather monitoring capabilities make IMS suitable 90 in snow cover applications (e.g. Brubaker et al. 2005;Chen et al. 2012;Yu et al. 2017) and lake ice monitoring on large lakes (e.g. Brown and Duguay, 2012;Duguay et al. 2012Duguay et al. , 2013Duguay et al. , 2014Duguay et al. , 2015Duguay and Brown, 2018 used in sea ice applications, Brown et al. (2014) show that IMS is advantageous over several automated algorithms for monitoring sea ice phenology. IMS is also able to improve sea ice estimates by reducing land contamination and better representing coastal regions compared to passive microwave estimates , and to resolve finer-scale details 95 between narrow ocean channels (Dauginis and Brown, 2020). This work expands on the work of Dauginis and Brown (2020) and examines changes in sea ice, lake ice, and snow phenology from 1997 -2019 across the pan-Arctic. The objectives of this paper are to 1) assess changes in sea ice, lake ice, and snow phenology from 1997 -2019 across the pan-Arctic and 2) analyze regional changes in snow and ice phenology during more recent years (2004 -2019) across the pan-Arctic.

Study regions
In this study, regions north of 56º were considered when examining pan-Arctic snow and ice phenology in the first section of the results (Figure 1). For the second section of the results, a regional approach was taken. For snow and lake ice, phenology parameters were considered on a hemispheric scale (i.e. North America and Eurasia). Further regional subdivisions are provided in Table 4 for the snow and lake ice trends. For sea ice, phenology parameters were examined in three broad 105 regions (with some subregions included in Table 4): Canadian Arctic, Alaska/Far East Russia, and Eurasian Arctic. 'Canadian Arctic' includes Baffin Bay, Hudson Bay, and the CAA; 'Alaska/ Far East Russia' includes the Beaufort, Chukchi, and Bering seas; 'Eurasian Arctic' includes the East Siberian, Laptev, Kara, Barents, and Greenland seas. These regions were grouped based on their broadly similar ice-phenology characteristics.

Data 110
Snow and ice data were obtained from the National Ice Center Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice product. IMS is an operational product used to map daily snow and ice cover over the Northern Hemisphere at 1 km (2014 -present), 4 km (2004 -present), and 24 km (1997 -present) spatial resolutions (https://www.natice.noaa.gov/ims/). Analysts use a variety of multi-sourced datasets (for a complete list of data sources, see National Snow and Ice Data Center, https://nsidc.org/data/g02156) to subjectively produce maps with discrete values assigned 115 to land, snow-covered land, water, and ice. Snow mapping primarily relies on visible imagery; however, if visible imagery is unavailable due to cloud occlusion or low solar illumination, microwave data is used instead (Helfrich et al. 2007;Brown et al. 2010). As misidentification errors associated with microwave data can occur, analysts rely more on snow climatology compared to microwave data to estimate high latitude snow cover during winter months (Chang et al. 1996;Foster et al. 2005;Helfrich et al. 2007;Derksen 2008;Brown et al. 2010). Ice cover analysis primarily relies on AVHRR or MODIS observations, 120 however microwave-based retrievals and ice climatology are used when visible imagery is unavailable, with microwave retrievals representing approximately 30-35% of the ice cover input (Helfrich et al. 2007). Temperature data are from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 global reanalysis and were compared to changes in snow and ice phenology. ERA5 provides coverage of the entire Arctic at a spatial resolution of approximately 31 km (0.25º). Monthly 2 m temperature data were used to calculate temperature trends from 2004 125 -2019.

Methodology
The 24 km and 4 km IMS products were used to examine changes in snow and ice phenology dates across the pan-Arctic following the methodology of Brown et al. (2014) and Dauginis and Brown (2020). For each pixel, consecutive days of IMS imagery were compared to determine the first and last changes between snow/ice and land/water to determine the timing 130 of the snow/ice-on and off parameters examined. The phenology parameters used in this study and their definitions can be found in Table 1. The 24 km IMS product was used to examine trends in mean snow and sea ice phenology dates across the pan-Arctic from 1997 -2019. For lake ice, only the 4 km IMS product (2004 -2019) was used since the 24 km product can only detect very large lakes ( Figure 2). In addition to detecting more lakes, the 4 km IMS product can also provide more detailed information on lake ice phenology within each lake, as shown in Figure 2. 135 To investigate the relationship between phenology parameters and temperature, correlations between variables were examined using Spearman's rank correlation coefficient (ρ) as this method describes the overall strength of the relationship between two variables and does not require data to follow independent normal distributions (non-parametric) (Hauke and Kossowski, 2011). Datasets were detrended prior to correlation analysis to ensure relationships were not a result of a shared trend, but rather driven by actual relationships between variability in phenology parameters and temperature (Pizzolato et al. 140 2014). Data were detrended using the "pracma" package in R (https://CRAN.R-project.org/package=pracma) which removes the linear trend from a given dataset by computing the least-squares fit of a straight line to the data and subtracting the resulting function from the data (Borchers, 2019). The detrended data were then used to calculate Spearman correlation coefficients between phenology parameters and temperature.
To evaluate spatial trends in snow and ice phenology and temperature, 4 km IMS phenology dates and 2 m 145 temperature data were analyzed using the "zhang" method of trend analysis, available in the "zyp" package in R (Bronaugh and Werner 2019). This method of trend analysis was proposed by Zhang et al. (2000) and has been successfully used to represent trends in temperature and precipitation (Zhang et al. 2000) lake ice phenology (Murfitt and Brown 2017) and sea ice and snow phenology (Dauginis and Brown, 2000). The "zhang" method is suitable for analyzing spatial trends in this study as it employs non-parametric tests and accounts for autocorrelation. The linear trend is removed from the time series if it is 150 significant and the autocorrelation computation repeats until the differences in the estimates of the slope and autoregressive model in two consecutive iterations is smaller than 1% (Bronaugh and Werner 2019). The Mann-Kendall test is applied to the resulting time series and the Sen's slope of the trend is computed (Bronaugh and Werner 2019). The final result is the Sen's slope (amount of increase or decrease) at each location over the given time period, as well as the significance of each trend (Bronaugh and Werner 2019). Interannual and regional variability in snow and ice conditions will inherently affect phenology 155 parameters, particularly for sea ice, which may not entirely clear out of some regions in a particular season leading to no iceoff or -on phenology detected for that year (Dauginis and Brown, 2020). Pixels with less than 14 years of phenology data (e.g. regions where ice-off only occurs occasionally) are treated as No Data, meaning the spatial extent of the trend examination represents the geographic region where snow/ice-off has occurred in at least 14 of the last 16 years.
Finally, clustering in the trend data was explored using local indicators of spatial association (Anselin, 1995) through ESRI 160 ArcGIS. The statistically significant clusters of high and low values (trend strengths) were mapped to highlight regions where the phenology variables were responding similarly over the study period.

Trends and Correlations 165
Mean snow, sea ice, and lake ice phenology dates across the pan-Arctic are shown in Figure 3 Figure 4. Overall, the pan-Arctic shows trends toward a longer snow and ice-free season ( Figure 4) from 1997 -2019, with trends toward earlier snow-off and ice-off and later freeze detected. While the annual variability is similar between the 24 km and 4 km mean phenology dates, a small offset of 3.5 days later for ice-off and 3.4 days earlier for ice-on (average) is evident in the sea ice phenology as a result of the resolution differences, mainly attributed to the improved ability of the 4 175 km product to resolve smaller-scale features and changes in the ice cover extent than the 24 km product can detect (e.g. leads, polynyas, near-shore conditions, and changes at the ice edges) Dauginis and Brown, 2020). The overall agreement between the products is < 1 day for the snow phenology dates.
During the snow/ice-off season, lake ice first open water (FOWL) and water clear of ice (WCIL) dates are significantly correlated with their equivalent snow and sea ice-off parameters from 2004 -2019 (Table 3). Stronger relationships are identified between lake ice and sea ice off parameters (ρFOW Sea Ice and Lake Ice = 0.62 and ρWCI Sea Ice and Lake Ice = 0.72, p < 0.05) 190 compared to lake ice and snow (ρfirst snow-off and FOW Lake Ice = 0.55 and ρfinal snow-off and WCI Lake Ice = 0.51, p < 0.  Table 2). Snow-on dates show small positive correlations with sea ice freeze parameters, though none are statistically significant (Table 3). Lake ice freeze onset (FOL) and continuous ice cover (CICL) exhibit trends toward later freeze (4.97 and 4.44 d decade -1 , p > 0.05; Figure 4f), and although caution should be taken with the short timespan, it 205 should again be noted that lake ice freeze dates show an overall shift toward later freeze. No significant correlations are detected between lake ice/sea ice and lake ice/snow parameters during the freeze season, though similar to the snow/ice-off season, stronger correlations are detected between lake ice and sea ice freeze compared to lake ice-on and snow-on (Table 3).
Overall, snow and ice cover are coming off earlier across the pan-Arctic, while trends during the freeze season vary Examining snow and ice cover at the pan-Arctic scale provides important information on how the cryosphere is 215 earlier sea ice melt onset dates in the CAA (e.g. Mahmud et al. 2016;Marshall et al. 2019). Other Arctic regions (e.g. Baffin 220 Bay, Eastern Greenland, Barents Sea, Beaufort Sea, and Chukchi Sea) have shown significantly earlier sea ice melt onset by 2.3 to 6.9 d decade -1 (e.g. Stroeve et al. 2014). The response of snow cover to changes in climatic and hydrologic regimes also varies regionally, with northern Canada and eastern Siberia experiencing increased snowfall, while Scandinavia and regions around the Greenland ice sheet are experiencing increasing rainfall (Box et al. 2019). Additionally, ice cover duration in Arctic lakes since 2004 shows interannual and regional variability, with lakes in western Russia showing anomalies ranging from 59 225 days shorter to 57 days longer, while smaller anomalies were identified in Canadian Lakes (Duguay and Brown, 2018). Therefore, the following section will examine regional variability in sea ice, lake ice, and snow phenology from 2004 -2019 using the 4 km IMS product as the higher spatial resolution (compared to the 24 km product) allows finer-scale changes in snow and ice cover to be detected.

Snow and Ice-off season
Short-term trends in sea ice, snow, and lake ice phenology from 2004-2019 are presented in Figures 6 (snow/ice-off) and 7 (snow/ice-on) along with maps identifying significant local clustering in the trends. Median values of the spatial trends in Figures 6 and 7 for regions defined in Figure 1 are reported throughout the following section and included in Table 4.
Correlations with 2 m air temperature for the three main sea ice regions and two main snow/lake ice regions (Figure 1) are 235 presented in Table 5. Overall, sea ice, snow, and lake ice show tendencies toward earlier melt, with the exception of 1) Eurasian snow-off parameters, which show little change from 2004-2019 compared to other Arctic regions and 2) sea ice first open water in the Canadian Arctic. The Alaska/ Far East Russia region exhibited the largest trends toward earlier sea ice-off (medianFirst Open Water Sea Ice = 23 days) and North America showed larger trends toward earlier snow-off and lake ice-off compared to Eurasia (North America: medianFirst snow-off = 8 days, medianFirst Open Water Lake Ice = 4 days; Eurasia medianFirst snow-off = 0 days, 240 medianFirst Open Water Lake Ice = 1 day).
Overall, sea ice is clearing out of the Canadian Arctic earlier (WCIS median trend = 7 days), while the first detection of open water (FOWS) shows a later trend, albeit with considerable regional variability. Baffin Bay / Davis Strait region overall shows a median trend of 1 day earlier, however, the northern portion (Baffin Bay) and southern portion (Davis Strait) show opposite trend directions (9 days earlier vs. 24 days later, respectively) ( Figure 6a, Table   4). Warming trends are identified over the northern region of Baffin Bay in July and August ranging from 0.01 to 3ºC (Figure   8g, h) where the notable trends toward earlier ice-off are detected (Figure 6a, b). Later first open water trends are evident for 255 Hudson Bay (median = 2 days), with earlier water clear of ice trends (median = 7 days). The median temperature increase first snow-off and final snow-off 5 and 3 days later respectively (Figure 6a, b). Lake ice shows quite a bit of variability across 320 the regions, with ±2 days (median) or less detected across the regions with the exception of Central Eurasia where the ice cover shows trends towards 7 (FOWL) and 9 days earlier (WCIL). Lake Onega (northwest Russia) shows earlier first open water (median = 5 days) and both Onega and nearby Lake Ladoga show earlier water clear of ice trends (Onega median = 6 days, Ladoga median = 9 days). From 1955 -2015, total ice cover duration in Lake Onega decreased by 50 days, though decreases were mostly attributed to delayed freeze (Filatov et al. 2019). Earlier break-up dates have been detected in 40 lakes across 325 Finland from 1963 -2014 (Kuusisto, 2015), however our recent short-term trends show that lake ice-off is becoming slightly later (median = 2 days) in Finnish lakes nearby to Lake Ladoga and Onega. Mean 4 km IMS imagery shows that the average break-up dates range from mid-April to mid-May in this region, though temperature trends are only negative (cooler) in southwestern Finland during April and positive (warmer) over all of Finland during May (Figure 8d, e).

Snow and Ice-on season 330
Sea ice freeze onset in the Canadian Arctic shows trends towards earlier timing (median = 11 days), while sea ice within the Alaska/ Far East Russia region and Eurasian regions shows delays in freeze (trends of 8 and 7 days later respectively for freeze onset) (Figure 7a, b). On land, the North American Arctic and the Eurasian regions both show trends towards earlier first snowon (medianNorth America = 8 days and medianEurasia = 9 days) and final snow-on (medianNorth America = 3 days and medianEurasia = 7 days), though spatial variability is evident. Unlike the snow/ice-off season where lake ice-off trends were larger over North 335 America compared to Eurasia, overall trends toward later lake ice freeze onset are larger across Eurasia (median = 8 days) than North America (median = 2 days) (Figure 6c). Local clustering is again evident across the Arctic (Figure 7c, d), however less sea ice/snow clusters are evident in the freeze maps, with the exception of southern Alaska and northern Quebec (Nunavik), particularly for final freeze. A more detailed regional break down follows.
Earlier sea ice freeze and snow-on trends are detected across the Canadian Arctic. Both the first and final sea ice 340 parameters are significantly correlated with 2 m temperature from September through December, with the strongest correlations identified in November (FOS: ρ = 0.82 and CICS: ρ = 0.77, p < 0.05). Sea ice shows freeze onset trending earlier by 8 days in the CAA, and 10 days in Hudson Bay and Baffin Bay (median values) (Figure 6c). For the North American region overall (Alaska and Canada, Figure 1), first snow-on dates are significantly correlated with August, September (strongest correlation) and October air temperature (ρ = 0.53, 0.77, 0.53, p < 0.05) (final snow-on with only October, ρ=0.63 p < 0.05) 345 (Table 5). First snow-on across the western mainland Canada shows earlier trends (median= 5 days), though delayed snow onset can be identified along north and northwest regions of Canada (south of the Western Arctic Waterway) (Figure 7a, b).
Snow-on also shows earlier trends in northern Quebec (medianfirst snow-on = 8 days, medianfinal snow-on = 16 days) and corresponds to both earlier lake ice and sea ice freeze onset trends identified in this region. A large cooling pattern can be seen over eastern Canada in October which may contribute to the earlier snow and ice-on dates in this region (Figure 8j). Earlier snow-on dates 350 in the Canadian Arctic are consistent with observed increases in precipitation across all seasons in Canada from 1948 -2012 (Vincent et al. 2015). Global climate models project increases in Arctic precipitation over the twenty-first century due to enhanced local surface evaporation resulting from sea ice loss; however, recent projections show a shift toward a raindominated Arctic, particularly during summer months (Bintanja and Selten, 2014;Bintanja and Andry, 2017). Nettilling Lake shows trends towards earlier freeze onset (median = 3 days) and continuous ice cover (median = 1 day), though there is 355 considerable variability in freeze-up as the east shows trends toward earlier freeze and west shows trends toward later freeze.
Great Slave Lake, Great Bear Lake, Amadjuak Lake and Lake Hazen all show trends toward later freeze onset, consistent with  (Figure 8), as warmer air is able to sustain more moisture which can thus facilitate increases in precipitation . First snow-on in the NSA region is becoming earlier (median = 21 days), whereas final snow-on shows no change overall due to the mix of earlier and later trends throughout the region. Lake ice within the NSA region shows 385 trends toward earlier freeze (FOL = 8 days and CICL= 9 days). In Eurasia, sea ice freeze onset is becoming later, though the trends in freeze (Figure 7a, b) are smaller in magnitude than the trends for sea ice-off (Figure 6a, b). The later freeze coincides with warming over the region in September and October (Figure 8i, j), and freeze onset is significantly correlated with October 2 m air temperature (ρ = 0.57, p < 0.05) (Table 5), while no significant temperature correlations were identified with complete ice cover. Snow-on shows predominantly earlier trends 390 across all regions of Eurasia ranging from 4 to 13 days (medians) earlier and has strong significant correlations with both October and January air temperatures for first and final snow-on (ρ = 0.85, 0.84 and ρ = 0.81, 0.81 respectively, p < 0.05).
Lake ice shows similar patterns to snow onset, with earlier freeze detected over the Eurasian regions, with the exception of the Scandinavian/Northern Europe region where freeze onset shows delays of 28 days (median value) and continuous ice cover shows delays of 19 days (median value) later. Large freeze-up anomalies in this region were also identified through previous 395 lake ice research (e.g. Duguay and Brown, 2018), for example the 2017/2018 freeze season showed delayed freeze up by approximately 2 -5 weeks compared to the 2004 -2018 mean. Using data from 1890 -2015, Karetnikov et al. (2017) show that the number of winters with complete freeze over of Lake Ladoga decreased after 1950 and that the ice season has become shorter. Ice cover trends for first open water and complete ice cover are not included for Lake Ladoga in this study as a complete ice cover did not form in several of the examined years. Trends for water clear of ice (full open water) and freeze 400 onset (first detection of ice) are detectable and included in Table 4.

Conclusion
This paper examined sea ice, snow, and lake ice phenology across the pan-Arctic using the Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice products. Using IMS, we were able to examine both long-term snow and ice-on/off trends (1997 -2019) at a 24 km spatial resolution, as well as more recent short-term trends in snow and ice 405 phenology (2004 -2019) at an improved resolution of 4 km. Our results show that the Arctic is moving toward a longer snow and ice-free season, with trends toward earlier snow/ice-off and later freeze detected. Sea ice showed the largest trends toward earlier ice-off and later freeze, with FOWS timing becoming earlier by 7.72 d decade -1 and CICS becoming later by 10.60 d decade -1 . Lake ice and snow-off parameters are also showing earlier trends, though not as large as those detected for sea ice.
Lake ice-off showed significant correlations with snow-off and sea ice-off, while no significant correlations were found 410 between any snow/lake ice/sea ice parameters during the freeze season. This likely reflects the strong influence of surface air temperature on snow and ice off timing, whereas during the freeze season, precipitation plays an important role in determining the timing of snow onset and lake size/volume is an important determinant for freeze timing.
Sea ice in the Canadian Arctic is clearing earlier overall, though regional variability does indicate some regions of later clearing, while during the freeze season, sea ice-on trends are predominantly earlier (11 and 9 days median for first and 415 final ice cover), showing opposite trends compared to other regions across the pan-Arctic. Snow-off and lake ice-off show predominantly earlier trends across North America with some regional exceptions in the east. Snow onset also shows earlier trends across North America, with snow-on trends moving earlier by 4 to 16 days. Lake ice shows a mixed response, with later freeze in the west and earlier freeze in the east -reflective of the cooling air temperature trends over the eastern regions. The largest trends toward earlier sea ice-off were detected in the Alaska/Far East Russia region, with trends towards ice clearing a 420 month later. Snow and lake ice-off timing in this region also shows earlier trends, with some of the largest snow and lake ice trends identified in the Western Alaska region. Delays in sea ice freeze were also observed here (trends of 8 and 14 days later for first and final freeze) with much stronger trends in the Bering Sea region. This is consistent with delayed snow onset over land and delayed freeze onset in lakes across most of Alaska. These phenology trends are likely related to strong temperature increases, as trends in 2 m temperature are positive during most months from 2004 -2019 in this region. Sea ice on the Eurasian 425 side of the Arctic is showing larger trends towards earlier ice-off than later ice-on (roughly a month later ice-off, 1-2 weeks later freeze). No trend in first snow-off dates was detected in Eurasia and only a small change toward earlier final snow-off (median = 1 day) was identified; though larger trends toward earlier snow-off were detected in northwest Eurasia compared to the east. Lake ice shows a similar east-west pattern, with Lake Ladoga and Lake Onega showing larger earlier first open water trends compared to northeast Eurasia. Snow onset is also trending earlier over Eurasia, consistent with previous studies that 430 have documented increases in total annual precipitation during cold seasons over the Arctic (Box et al. 2019).
By examining multiple components of the cryosphere together, we can better understand how warming affects snow and ice cover and how these components are interrelated. As the Arctic continues to experience unprecedented change as a response to increasing temperatures, continuous monitoring of changes in snow and ice cover is essential to improve our understanding of climate variability and changes occurring at not only the pan-Arctic scale but at the regional-scale as well. 435

Data
All data sets used in this study are freely available online.

Author Contributions
Project conceptualization and methodology were designed by AD and LB, with the formal data analysis carried out by AD.
Original draft was prepared by AD, with review and editing from both AD and LB. 445

700
-2019 using 24 km IMS. Months were selected for each phenology parameter based on mean phenology dates in Figure 3. * represents statistically significant correlations at the 95% confidence level. Table 4. Regional analysis (see Figure 1) of the median trend strength (days/15years?) and direction (-earlier, + later) for all of the phenology parameters: First open water (FOW, subscript S denotes sea ice, L denotes Lake ice), Water clear of ice (WCI, subscript S denotes sea ice, L denotes Lake ice), Freeze onset (FO, subscript S denotes sea ice, L denotes Lake ice), Complete ice cover (CIC, subscript S denotes sea ice, L denotes Lake ice), first and final snow-off (_SOFF), first and final snow-on (_SON). -9 -8 -6 -9 Lake Ladoga* N/A -9 +13 N/A Lake Onega -5 -6 +28 +15 * FOWL and CICL are not included for Lake Ladoga as the lake did not fully freeze in several of the study years.

Melt
Table 5. Regional Spearman rank correlations (ρ) for snow and ice phenology dates and monthly 2 m temperature from 2004 -2019 using 4 km IMS. For sea ice, 'Canadian Arctic' includes Baffin Bay, Hudson Bay, and the CAA; 'Alaska/Far East Russia' includes Beaufort, Chukchi, and Bering seas; 'Eurasian Arctic' includes East Siberian, Laptev, Kara, Bering, and Greenland seas (See Figure   720 1). Months were selected for each phenology parameter based on mean phenology dates in Figure 3. * represents statistically significant correlations at the 95% confidence level.

Melt
Canadian