With climate warming, shrubs have been observed to grow on Arctic tundra.
Their presence is known to increase snow height and is expected to increase
the thermal insulating effect of the snowpack. An important consequence would
be the warming of the ground, which will accelerate permafrost thaw,
providing an important positive feedback to warming. At Bylot Island
(73
Climate warming leads to shrub growth on Arctic tundra (Tape et al., 2006; Ropars and Boudreau, 2012). Shrubs are known to limit snow erosion by wind and to trap blowing snow, therefore increasing snow depth and perhaps snowpack duration (Essery et al., 1999; Liston et al., 2002; Lawrence and Swenson, 2011). Snowpack properties such as density, thermal conductivity, albedo and snow depth are also known to be, or suspected of being, modified by shrubs (Liston et al., 2002; Sturm et al., 2005; Ménard et al., 2014).
These modifications have many potentially important consequences, including the following: (1) snow physical properties affect air temperature and climate through the energy budget of the snow surface, which involves snow albedo and snow thermal conductivity; (2) snow thermal properties (thermal conductivity, height, density) determine heat exchanges between the ground and the atmosphere in winter, and hence are a key factor in the thermal regime of permafrost.
The heat flux
Since shrub growth increases snowpack thickness and is expected to decrease
its thermal conductivity (Liston et al., 2002), both of these
effects will contribute to increased snowpack thermal resistance following
Eq. (2). Therefore it is expected to limit the winter cooling of the ground, accelerating
permafrost thaw, which is recognized as an important positive climate
feedback. Indeed, it could lead to the mineralization of organic matter
stored there for millennia, resulting in the emission of poorly quantified
but potentially enormous amounts of greenhouse gases (Koven
et al., 2011). Illustrating the reality of this second consequence,
Gouttevin et al. (2012) modelled the changes in the
permafrost thermal regime due to changes in snow thermal conductivity caused
by the replacement of tundra by taiga, and found ground temperature increases
exceeding 10
Typical snowpacks on Arctic herb tundra first consist of a basal depth hoar
layer which in general has a low density (170 to 280 kg m
Several studies document some aspects of how snow properties are affected by
shrubs. Liston et al. (2002) modelled the effect of shrubs growing on Arctic
tundra on snow height and energy fluxes. They took into account changes in
snow depth and thermal conductivity. These last changes were simulated by
assuming that the depth hoar layer was thicker in shrubs. They assigned a
depth hoar layer thickness and did not observe or simulate metamorphism and
depth hoar growth in shrubs. They concluded that shrubs would increase
snowpack thermal resistance but did not discuss the ground thermal regime.
Euskirchen et al. (2009) modelled changes in vegetation under different
climate evolution scenarios. They considered snow cover duration and its
effect on albedo. They did not consider changes in snow properties. Although
their model simulates the ground thermal regime, they did not detail this
aspect. Blok et al. (2010) investigated the effect of shrub growth by
removing dwarf birch in plots of low shrub tundra in Siberia. They observed
that after shrub removal, the active layer depth increased. They concluded
that shrub growth would slow down permafrost thaw. They did not, however,
study snow properties. Lawrence and Swenson (2011) tested the hypothesis of
Blok et al. (2010) using coupled atmospheric and land surface models and
concluded that the effect of shrub growth on active layer depth in fact
depended on which processes were included in the model. Considering only the
effect of shading by shrubs did decrease active layer depth. However, also
taking into account atmospheric heating due to the lower albedo of shrubs led
to an increase in its depth. Taking into account snow redistribution by wind
between shrubs of various heights led to no change in active layer depth.
Given the complexity of effects and processes, one of their conclusions was
that blowing snow processes need to be incorporated in model projections of
future Arctic climate change. However, they did not consider changes in snow
properties other than albedo and it is possible or even likely (Gouttevin et
al., 2012) that their conclusions would be significantly modified if they
had. Myers-Smith and Hik (2013) observed that on Arctic tundra, snow was much
thicker in low shrubs than on herb tundra (typically 30 vs. 10 cm). They
also measured that winter ground temperature was up to about 8
These few investigations are very useful, but to our knowledge there is no extensive study of the impact of shrubs on snow physical properties. These are required to quantitatively understand and model the relationship between shrubs and ground temperature, especially in the context of shrub growth and expansion. Questions that deserve further or novel investigations include the following: (1) is the depth hoar in shrubs different from that on herb tundra? (2) To what height does depth hoar form in shrubs? (3a) What is the snow density profile in shrubs and (3b) how is this related to stem density? (4) How does the profile of snow specific surface area (SSA) in shrubs differ from that on grass? (Since SSA, together with density and impurity content, governs snow albedo, the light absorption profile and therefore the radiative energy input to the snowpack, this is a critical question affecting metamorphism and all snow physical properties.) (5) How do shrub stems, which strongly absorb shortwave radiation, affect the irradiance profile in snow? (6) How do these light-absorbing stems increase the likelihood of snow melting events?
This work attempts to contribute data to question 1, 2, 3a, 4, and to discuss
possible answers to the other ones. We have performed field observations and
measured snow depth, density, thermal conductivity and specific surface area
at Bylot Island (73
Bylot Island is a high Arctic site (Fig. 1) with rare erect vegetation. The
geomorphology of the site has been detailed by Fortier and Allard (2004).
Meteorological monitoring started in 1994 at our research site (Gauthier et
al., 2013) in Qarlikturvik Valley (around 73
Map showing the location of our study sites in Qarlikturvik Valley, in the south-west plain of Bylot Island, in the Canadian Arctic archipelago.
Field work took place around mid-May in 2014 and 2015, just before the onset
of snow melt. It consisted of the measurement of snow depth at several
hundred sites, the observation of snow stratigraphy in snow pits, and of the
measurement of vertical profiles of snow density, thermal conductivity and
specific surface area in these pits. Snow depth was measured with an
avalanche probe. Density was measured with a 100 cm
Snow physical properties and ground temperature were simulated using the
Crocus multilayer physical snowpack model coupled to the ISBA (Interactions
between Soil–Biosphere–Atmosphere) land surface scheme within the SURFEX
interface (Vionnet et al., 2012). We forced the model with observed
meteorological data when available. Before August 2014, air temperature and
wind speed measured by instruments located in Qarlikturvik Valley at
73
With forcing data, Crocus calculates the energy budget of the surface and of each snow layer. This is used to simulate processes occurring in the snow such as thermal diffusion, compaction, phase change (melt–freeze), liquid water percolation and snow metamorphism, leading to a prediction of the time evolution of snow type (e.g. fresh snow, faceted crystals, depth hoar, small rounded grains, melt forms, etc.). Many snow physical properties can be calculated from these model outputs. The thermal conductivity of snow, which is used to solve the thermal diffusion equation in the snowpack, is calculated from snow density using the equation of Yen (1981). This equation implies that snow thermal conductivity increases monotonically with density.
The original Crocus snow model does not account for the effect of vegetation. Shrubs trap wind-blown snow and limit snow erosion by wind, and stems act as absorbers, decreasing the albedo of the surface. The network of stems also prevents snow compaction. This latter effect was simulated by increasing the viscosity of dry snow in the presence of shrubs by a factor 100 up to a snow height of 10 cm and by a factor 10 to the top of the shrub. The 10 cm threshold was set to simulate the greater biomass and stem density at the shrub base. Wind effects on snow, i.e. fragmentation, compaction and sublimation of the surface snow layers, were deactivated in shrubs. The decrease of surface albedo by shrubs was simply simulated by decreasing snow albedo by an adjusted factor. Trapping of wind-blown snow by shrubs was not simulated.
Another effect not simulated by Crocus is the upward transport of water vapour due to the high temperature gradient in Arctic and subarctic snowpacks (Sturm and Benson, 1997). This process is useful to explain for example the transformation of melt–freeze layers or wind slabs into depth hoar or indurated depth hoar, and Crocus will therefore not predict such changes (Domine et al., 2013, 2016), nor the mass transfer from lower snow layers (causing their density to decrease) to the upper layers (causing their density to increase).
Detailed observations and physical measurements were performed in a total of 14 snow pits in 2014 and 21 pits in 2015. The 2014 campaign was exploratory and the 2015 campaign was more complete and targeted. The 2015 data are therefore reported and discussed in more detail.
In 2014, four series of random snowpack height measurements were done at three sites, producing 1429 values (Table 1). In 2015, the same sites were measured again, as well as an additional three sites, totalling 901 values. One site consisted of low-centered polygons (wetland) while the other five were on mesic areas. Of these, two featured scattered willow bushes covering about 10 % of the ground, one was the uncommon site fully covered with willows mentioned above, one was in hummock terrain without erect shrubs in the flat bottom of the valley, and one was further up on the NW-facing slopes, also with hummocks.
Average, standard deviation
The polygons site was studied on 14 May 2014 and also 2 days later to
investigate the impact of a snowfall on 15 May and of a wind storm on
16 May. In 2015, the last site, studied on 19 May, shows the lowest average
value (21.3 cm) for this campaign. On that day the temperature almost rose
to 0
We investigated the impact of isolated bushes on snow height. At those sites, shrubs covered about 10 % of the surface. In 2015, a visual examination did not reveal any obvious impact. From the surface structure of the snowpack, prevailing winds appeared to have been across the valley, i.e. coming from the north or south in the valley oriented east–west. We therefore measured snow height at the center of bushes, and every 50 cm up to 3 m from the center uphill (south) and downhill (north) of the bush. Figure 2 illustrates that there was no systematic effect of an isolated bush on snow height. Snow height could either be at a local minimum (bush named Willow D1) or maximum (Willow D1b) in the bush. When the bush was not an extremum, the uphill side (Willow D2) or the downhill side (Willow D2b) could have thicker snow. We conclude that in 2015, an isolated bush appeared to have no clear impact on snow height. This is consistent with the data of Table 1. In fact the effect of topography was found to prevail over that of vegetation. As intuitively expected, thick snowpacks were in hollows and thin ones on bumps.
Effect of the presence of isolated bushes on snow height in May 2015. The origin is at the center of the bush. Snow height was then measured uphill (south, positive values) and downhill of the bush. The four bushes studied were given arbitrary names.
The situation was different in 2014. In the absence of shrubs, snow had often been eroded down to a more erosion-resistant melt–freeze crust. In willows, blowing snow had accumulated in the bush and downwind from it (Fig. 3). Even though measurements were less detailed in 2014, and no separate measurements were done in and outside the bushes, examination of photographs clearly indicates that in 2014 snow was thicker in bushes and in their wind shadow than far from them.
Photographs of snow-trapping by willows in May 2014, near the “scattered willows 1” site. Increased snow height caused by the presence of shrubs is obvious. On both pictures the contrast has been enhanced with photographic modification software.
Simplified snow stratigraphy on herb tundra (polygons area), in the large shrub area, where shrubs reached 30 to 35 cm in height, and on the NW-facing slopes with hummocks, in May 2015. Pit names are mentioned in parentheses (Herb 2, etc.) to allow correspondence with data in Table 2. The size (largest dimension) of depth hoar and faceted crystals is indicated. Grains in wind slabs and small rounded grains were in the range 0.2 to 0.3 mm. A new symbol is proposed for indurated depth hoar formed from refrozen snow.
Figure 4 shows typical snow stratigraphies on herb tundra in the polygons
area, in the large shrub area and on slopes with hummocks in 2015. Grain type
symbols are mostly those recommended by the classification of Fierz et al.
(2009). Snow science was initially focused on avalanche prediction and that
classification reflects this emphasis by being well adapted to alpine snow.
However, it does not allow the precise description of several snow types
encountered in Arctic snow. In particular, indurated depth hoar (Sturm et
al., 1997; Domine et al., 2012, 2016) is not represented, despite its frequent
occurrence in Arctic snowpacks. That snow type forms in dense wind slabs
under very high temperature gradients not encountered in alpine snow. Its
density can exceed 400 kg m
As is typical in the Arctic (Domine et al., 2002, 2012, 2016; Sturm and Benson, 2004; Derksen et al., 2014), the snowpack at Bylot Island mainly consists of a basal depth hoar layer and a top wind slab. The depth hoar mostly forms in fall when the large temperature difference between the cold air and the ground, which is still warm, generates a large temperature gradient in the thin snowpack (Domine et al., 2002). The resulting intense water-vapour fluxes lead to the growth of large depth hoar crystals. Later in the season, the temperature gradient decreases because the ground has cooled and because the snowpack is thicker. Depth hoar does not form anymore and wind-deposited snow instead forms hard wind slabs. Layers of depth hoar or faceted crystals can nevertheless form between two wind slabs. If a layer is not strongly remobilized by wind, it can keep a low density and a low thermal conductivity. If it is subsequently overlaid by a wind slab, a temperature gradient can be established in the lower density snow, because it has a lower thermal conductivity than wind slabs both above and below it.
These features were observed at Bylot Island and are illustrated in Fig. 4. The depth hoar layer in the absence of shrubs was typically 5 to 10 cm thick. It could be thicker in the hollows in hummock areas. In shrubs, it typically rose to the shrub height, as wind-packing of snow usually does not take place in shrubs. Above the shrubs, a wind slab was found, but it was softer than on herb. In 2015, most signs of melting were found in shrubs, and this is reported in Fig. 4 as indurated depth hoar (from refrozen snow) in the Willows 3 stratigraphy. Very slight melt signs were also detected outside the shrubs, as indicated in the Hummock 1G stratigraphy, with the presence of indurated depth hoar and of a very thin melt–freeze crust.
Another variable that has to be taken into account to understand melting is the concentration of light-absorbing impurities in the snow. Some snow layers had a noticeable brown colour, most likely due to the presence of mineral dust. A likely source was the black hills to the north and indeed the dirty snow layers had been deposited by a northerly wind. Signs of melting at the surface of dirty snow layers were frequent. This is what explains slight melting in the Hummock 1G stratigraphy. A further noteworthy observation is the exceptionally large size of the depth hoar crystals in the willows, which reached 30 mm near the base.
We first compare physical properties of snow in the large willow patch with those on herb tundra located in the polygons. We select sites having about the same snow height to facilitate comparison.
Figure 5 shows that in general snow on herb tundra has higher densities,
thermal conductivities and SSAs than in willow shrubs, because shrub snow is
dominated by depth hoar, which has lower values than wind slabs for all these
variables. The dense network of stems prevents snow compaction in shrubs, and
the resulting low densities facilitate depth hoar growth (Marbouty, 1980).
Densities as low as 125 kg m
Comparison of physical properties of snow in three pits, each in the
large willow patch and on herb tundra.
The above data were for a willow patch several hundred meters large. Snow properties inside and in the vicinity of smaller patches, only a few meters in size, need to be investigated as this is the most frequent occurrence of shrubs observed on Bylot and northern Baffin Islands. Differences can be expected, because wind can propagate much more easily in an isolated bush than in an extensive patch. Figure 6 shows the stratigraphies inside two bushes about 1 m in diameter and in snow about 1 m south (uphill) of the bushes. The bush named “Willows D2b” had stems up to about 35 cm above the ground and bush “Willows D1b” to about 22 cm. In both cases stems were protruding above the snow. Figure 7 shows the corresponding vertical profiles of physical properties.
Simplified snow stratigraphy in isolated willow bushes. Two stratigraphies in the center of the bush and to the south (uphill) of bushes, about 1 m from the nearest shrub, are shown. Note the different vertical scales for both sites. The size (largest dimension) of depth hoar crystals is indicated. Only symbols not shown in Fig. 4 are explained.
Physical properties of snow in isolated willow bushes (curves named
“center”) and about 1 m uphill (“south”) of the bush.
A striking observation in Fig. 7 is the peculiar property of the layer around 15 cm in the profile “Willows D2b center”. The high density and thermal conductivity values are due to melting of that layer in fall, which produced a very hard melt–freeze crust. Why such intense melting took place here and not in the large willow patch may be due to the location. The large willow patch is further up-valley and closer to taller mountains to the south (Fig. 1), and was probably in the shade all day much earlier in the season. Besides this observation, physical properties at the center of the bushes appear similar to those of the large willow patch. The SSA of the “Willows D1b center” is slightly higher than those of the other bush sites, but this is probably not significant. Given that we only have five profiles inside bushes, it is difficult to make conclusive statistics in this case.
An interesting observation is that about 1 m south of the bushes, the snow physical properties are much closer to those in shrubs than those on herb. The influence of shrubs on snow properties therefore extends beyond the shrub area itself. We hypothesize that since shrubs reduce wind speed in their vicinity, snow compaction by wind is limited. Snow of lower density than in the absence of shrubs is then deposited during drifting snow events. Depth hoar formation is then facilitated, leading to a lower thermal conductivity and specific surface area.
A central question this work seeks to answer is this: what is the effect of shrubs
on the insulating properties of the snowpack? This is conveniently quantified
by considering the snowpack
Some thermal characteristics of the snow pits studied in May 2015.
Simulation of the evolution of snow height
We simulated snow and soil properties with the ISBA–Crocus model driven by
meteorological data (see Sect. 2.3). Three scenarios were tested:
(1)
Figure 8 shows the evolution of snow height and
Figure 9 shows the air temperature and wind speed, the incoming shortwave
radiation and the snow depth in fall 2014. Simulated snowpacks began to
accumulate on 12 September. A sharp drop in incident radiation took place on
10 October, implying that albedo effects become negligible after that date.
Figure 10 shows the simulated density profiles on 10 October. The
Air temperature, wind speed, incoming shortwave radiation and snow height in fall 2014. The shaded area indicates the period when snow is affected by albedo decrease.
A critical fallout of our work is an evaluation of the potential of shrub
growth to affect ground temperature through the modification of snow
properties. Figure 11 presents a simulation of ground temperature at 5 cm
depth on herb tundra and on shrub tundra using ISBA-Crocus. Since this work
is focused on winter processes and all model modifications related to shrubs
pertain to their interactions with the snowpack, the summer part of the
simulations were run with the conditions valid for herb tundra in all cases,
with the consequences that summer results do not describe the effect of
shrubs and are therefore much less variable than winter ones. Measurements on
herb tundra are also shown. In summer 2014 we also placed ground temperature
sensors in willows. Unfortunately they were dug out by foxes and the data
loggers suffered water damage so we have no data in willows. Simulations
indicate that in winter 2014–2015, the presence of shrubs raised ground
temperature by up to 13
Vertical density profiles simulated by Crocus on 10 October 2014, for the different scenarios. Densities shown are for the middle of the layers used by Crocus.
Different patterns of the effect of shrubs on snow height were observed in 2014 and 2015: in 2014, shrubs had a direct effect on snow height (Fig. 3) while in 2015 they did not (Fig. 2). Our proposed interpretation is simply that in 2015, snow height was greater (27 cm) than in 2014 (16 cm) and shrubs impact snow height only up to their own height. In the “scattered willows” site, shrubs were 20 to 30 cm high. However, above 25 cm, the stem density was much lower than below, so that their effect on snow height above 25 cm was minimal in that case. In 2014, which was a low snow year (CEN, 2016), average snow height was much lower than shrub height and the impact of shrubs could be felt. In 2015, when snow height reached or exceeded shrub height in the “scattered willows” areas, the impact of shrubs was not detectable. The data from the “large willow patch” site confirms this. Shrubs there were about 10 cm higher and therefore allowed more snow trapping and accumulation, explaining the 34.9 cm snow height.
Figures 4 and 6 show that most signs of melting were found in shrubs. This may be explained by the absorption of solar radiation by the shrubs in fall, as they have a much lower albedo than snow (Juszak et al., 2014). Signs of melting were stronger in the isolated shrubs than in the large willow patch, probably because of increased shading by relief as mentioned earlier. Some slight signs of melting were occasionally observed fairly high in the snowpack and most likely after radiation became sufficient in spring to induce melting. The possibility that freezing rain episodes took place would need to be explored.
Figure 4 shows that depth hoar in shrubs formed to much greater heights than
on herb, and this requires an explanation. Depth hoar forms when the
temperature gradient is sufficient, typically
> 20
Simulation of ground temperature at 5 cm depth with the
A striking feature of Table 2 is that out of the seven lowest
However, Table 2 also shows that among the highest five
In summary for this point, our data and observations indicate that the
impact of shrubs on snow thermal properties can go both ways. If the air
temperature remains low enough, then willows limit snow compaction and favour
depth hoar formation, therefore leading to the formation of a highly
insulating snowpack. However, if the air temperature is high enough so that
the increased radiation absorption by willows can lead to snow melting, then
hard melt–freeze layers with high
Our simulations confirm the insulating effect of snow in shrubs and predict
that ground temperature under shrubs can be up to 13
The manipulations of Blok et al. (2010) also address the issue of ground shading due to shrub growth. In low shrub tundra, they removed shrubs in circular plots 10 m in diameter and observed increased ground thawing in cleared plots, relative to plots with shrubs, from which they concluded that “the expected expansion of deciduous shrubs in the Arctic region, triggered by climate warming, may reduce summer permafrost thaw”. However, by removing shrubs, they essentially studied the shading impact of shrubs on the ground radiative budget. Shrub removal, which is a manipulation, is not necessarily the reverse of shrub growth. The vegetation under the shrubs is usually not the same as when shrubs are naturally absent. Shrubs modify the energy and hydrologic budget of the surface, and therefore the plant species growing underneath. Both at Bylot in the high Arctic and Umiujaq in the low Arctic (Domine et al., 2015), we observed that a moss layer and thicker litter were present below shrubs. These have different thermal, hydrological and optical properties from the pre-shrub vegetation. Both the summer energy budget and the snow properties (and therefore the winter energy budget) of the plots with removed shrubs therefore have many reasons to be different from those of the pre-shrub tundra. The effects of the vegetation change on radiative, latent and conductive energy fluxes need to be considered in detail to allow a robust conclusion on the effect of shrub growth on permafrost temperature. Since the study of Blok et al. (2010) only considers the radiative effect of shrubs and does not investigate the other effects, their conclusion that shrub growth will reduce active layer depth may be premature.
The simulations we performed are clearly imperfect and they must be considered as a preliminary attempt to simulate snow properties in the presence of shrubs. In fact, we simulate thicker snowpacks and higher thermal resistances than measured, so actual effects are probably somewhat lower than simulated ones. In particular, the interactions between snow, shrubs and radiation are not satisfactory, as we were not able to adequately reproduce the observed snow melt in the presence of shrubs, even as we decreased albedo by 60 %. A shrub stem broadband albedo is very low, < 0.3 (Juszak et al., 2014), and stems probably produce hot spots where melting would be induced locally, possibly with water percolating to wet the whole snowpack. This of course cannot be modelled adequately with a 1-D model by homogeneously reducing snow albedo, as we did. Furthermore, summer effects may mitigate the shrub-induced winter warming. As mentioned above, shrubs modify the radiative budget of the surface, latent heat exchanges (Loranty and Goetz, 2012; Myers-Smith and Hik, 2013; Pearson et al., 2013), and surface vegetation and top soil properties are different under shrubs, so that simulating shrub and herb summer energy budget in an identical manner is not optimal. We made point temperature measurements in early July 2014 and found that the ground had thawed less under the shrubs. We also observed that thermally insulating moss was present under the shrubs, and this may partly explain the slower thawing. Summer effects should also be modelled in detail for a full quantification of the impact of shrubs on the permafrost thermal regime.
These observations and simulations show that shrub–snow interactions are
very complex. Our study confirms that shrubs often increase snow height and
thermal resistance. Shrubs also decrease albedo (Ménard et
al., 2014). We also made many additional observations, some of which are
novel:
Shrubs increase snow height only up to their own height. If snowfall is
sufficient, their effect on snow height becomes undetectable. Snow physical properties are dramatically affected by shrubs. In the absence
of enhanced melting, shrubs decrease snow density and favour depth hoar
formation with a concomitant decrease in snow thermal conductivity and SSA. A
consequence is the increase in snowpack thermal resistance. The influence of shrubs on snow physical properties extends horizontally
beyond the bushes, presumably because of the impact of shrubs on wind
velocity. Shrubs increase radiation absorption by snow and this can lead to melting,
which results in increased density and thermal conductivity. There is
therefore a threshold effect, where a sufficiently high temperature and
radiation combine to reverse the effect of shrubs on the insulating
properties of snow. Increased snowpack thermal resistance in the presence of shrubs and in the
absence of melting is expected to contribute to ground warming. Simulations
which only take into account winter processes indicate that the magnitude of
this warming reaches 13
However, we stress again that our modelling effort is preliminary and many
developments are still required for the proper simulation of shrubs on snow
properties and on the permafrost thermal regime. Not only must snow–shrubs
interactions be described in more detail, but summer effects of shrubs on
the surface energy budget must also be included for a reliable prediction of
the thermal regime of permafrost in the presence of shrubs.
A spreadsheet has been produced to supply all data used in the graphs.
Florent Domine designed the research. Mathieu Barrere and Florent Domine performed the field measurements. Florent Domine analyzed the field data. Mathieu Barrere performed the model simulations with suggestions and advice from Samuel Morin. Florent Domine prepared the paper with input from Mathieu Barrere and comments from Samuel Morin.
This work was supported by the French Polar Institute (IPEV) through grant 1042 to FD and by NSERC through the discovery grant program to FD. The Polar Continental Shelf Program (PCSP) efficiently provided logistical support for the research at Bylot Island. We are grateful to Gilles Gauthier and Marie-Christine Cadieux for their decades-long efforts to build and maintain the research base of the Centre d'Etudes Nordiques at Bylot Island. Marie-Christine Cadieux also kindly drafted the map of Fig. 1. Field trips were shared with the group of Dominique Berteaux, who helped make this research much more efficient and fun. Bylot Island is located within Sirmilik National Park, and we thank Parks Canada and the Pond Inlet community (Mittimatalik) for permission to work there. Help in running the model from Matthieu Lafaysse and Vincent Vionnet (CNRM/CEN) is acknowledged. LGGE and CNRM/CEN are part of LabEx OSUG@2020 (ANR10 LABX56). Frédéric Maps and Blanche Saint-Béat kindly provided advice on statistics. Helpful comments by three anonymous reviewers and by Esther Lévesque (Université du Québec à Trois-Rivières) are gratefully acknowledged. Edited by: U. Seibt Reviewed by: three anonymous referees