# Impact of climate change on snowpack dynamics in coastal Central-Western Greenland

Josep Bonsoms, Marc Oliva, Esteban Alonso-González, Jesús Revuelto, Juan

I. López-Moreno

► To cite this version:

Josep Bonsoms, Marc Oliva, Esteban Alonso-González, Jesús Revuelto, Juan I. López-Moreno. Impact of climate change on snowpack dynamics in coastal Central-Western Greenland. Science of the Total Environment, 2024, 913, 10.1016/j.scitotenv.2023.169616. insu-04833064

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Submitted on 12 Dec 2024

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# Impact of climate change on snowpack dynamics in coastal Central-Western Greenland

Josep Bonsoms<sup>a, *</sup>, Marc Oliva<sup>a</sup>, Esteban Alonso-González<sup>b</sup>, Jesús Revuelto<sup>c</sup>, Juan I. López-<sup>c</sup> Moreno<sup>c</sup>

<figure><img src="image_3.png" /></figure>

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## HIGHLIGHTS

* CMIP6 projections anticipate significant reductions in snowpack.

* MIROC6 projected winter snowfall increases for SSPS4–8.5 scenario.

* Snow reductions are driven by temperature and downwelling longwave radiation.

* Snow cover depletion has a key impact on hydrological and ecological system.

## GRAPHICAL ABSTRACT

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

Guest Editor: Paulo Pereira

## ABSTRACT

Snow patterns in ice-free areas of Greenland play important roles in ecosystems. Within a changing climate, a comprehensive understanding of the snow responses to climate change is of interest to anticipate forthcoming dynamics in these areas. In this study, we analyze the future snowpack evolution of a polar maritime Arctic location, Qeqertarsuaq (Disko Island, Central-Western Greenland). A physically-based snow model (FSM2) is validated and forced with CMIP6 projections for SSP2-4.5 and SSP5-8.5 greenhouse gasses emission scenarios, using two models: CanESM5 and MIROC6. The future snowpack evolution is assessed through four key seasonal (October to May) snow climate indicators: snow depth, snow days, snowfall fraction and ablation rate. Comparison against the observed air temperature for the reference climate period demonstrates superior accuracies for MIROC6 SSP2.4-5, with anomalies at 19 %, compared to CanESM5 SSP5.8-5 (25 %) and CanESM5 SSP2.4-5 (78 %). In terms of precipitation, CanESM5 SSP2.4-5 and SSP2.4-5 exhibit smaller anomalies against the observed data (5 %) in contrast to MIROC6 SSP2.4-5 (15 %) and MIROC6 SSP2.8-5 (17 %). Results demonstrate distinct snowpack responses to climate change depending on the model and emission scenario. For CanESM5, seasonal snow depth anomalies with respect to the reference period range from – 38 % (SSP2-4.5, 2040–2050 period) to – 74 % (SSP5-8.5, 2090–2100 period). MIROC6 projects lower snowpack reductions, with a decrease ranging from – 38 % (SSP2-4.5, 2040–2050 period) to – 57 % (SSP5-8.5, 2090–2100 period). Similar reductions

<footnote><sup>a</sup> Department of Geography, Universitat de Barcelona, Barcelona, Spain</footnote>

<footnote><sup>b</sup> Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, CNES/CNRS/IRD/UPS, Toulouse, France</footnote>

<footnote><sup>c</sup> Instituto Pirenaico de Ecología (IPE-CSIC), Campus de Aula Dei, Zaragoza, Spain</footnote>

<footnote>* Corresponding author.
E-mail address: josepbonsoms5@ub.edu (J. Bonsoms).</footnote>
are anticipated for snowfall and snow days. Changes in the snowpack evolution are primarily driven by positive trends in downwelling longwave radiation and air temperature. The projected increase in precipitation by the mid to late 21st century will lead to more frequent rain-on-snow events, intensifying snowpack melting. These findings help enhance the comprehension of future snow dynamics in the ice-free zones of Greenland, as well as the associated hydrological and ecological changes.

# 1. Introduction

Arctic seasonal snow modifies climate and land-atmosphere feedback mechanisms (Serreze and Barry, 2011), provides significant freshwater resources (Callaghan et al., 2011) and influences ecosystem dynamics (Yi et al., 2015). Snow governs the timing of biological activity onset (Rixen et al., 2022), impacts carbon dynamics, freeze-thaw cycles, and permafrost (IPCC, 2022).

The climate in Greenland is undergoing rapid changes (Masson-Delmotte et al., 2012). Coastal areas in Central-Western Greenland show an increase in precipitation, with a long-term positive trend (1890 to 2000 period) (Mernild et al., 2014). Likewise, air temperatures across Greenland have exhibited a positive trend of over 2 °C since the end of the Little Ice Age (ca. 1900 CE), with this warming trend intensifying since the 1990s (Hanna et al., 2012). Under high greenhouse gas emission scenarios (RCP8.5), climate projections indicate that temperatures will surpass +4.5 ± 0.3 °C by 2050 relative to preindustrial levels (1850–1899), resulting in significant increases in meltwater and runoff (Noël et al., 2021). Furthermore, this warming will lead to a reduction of solid PPT, giving rise to more frequent rain-on-snow events (Bintanja and Andry, 2017). The frequency and intensity of future extreme weather events, such as heat waves and cold spells, are also expected to increase due to Arctic amplification (Francis and Vavrus, 2015). Hence, the decrease in Arctic and Greenland snow cover (1980 to 2010 period) (Liston and Hiemstra, 2011) is projected to intensify into the mid to late 21st century (Santolaria-Otín and Zolina, 2020; Mudryk et al., 2020; Zhong et al., 2022). These trends have relevant implications, such as snow-albedo feedbacks (Box et al., 2012), which will contribute to the amplification of Arctic warming (Serreze and Barry, 2011).

General circulation models (GCMs) face limitations in accurately depicting the future evolution of snow in Greenland's ice-free areas, primarily due to their low resolution and inadequate representation of Greenland's abrupt topography at its margins (Rae et al., 2012). GCMs struggle to capture variations at local scales, and the global-scale

projections from CMIP6 do not precisely represent the magnitude of local trends (Mudryk et al., 2020; Zhong et al., 2022). Additionally, simulations from available regional climate models (RCMs) inaccurately represent hydrological processes at a local level, as evidenced in the Qeqertarsuaq basin in Greenland (Mankoff et al., 2021), the focus of this research. This limitation arises from the inadequate representation of local-scale processes in snow, including accumulation, erosion, or wind-drift redistribution, at coarse spatial resolutions (Freudiger et al., 2017). The spatial distribution of the snowpack at small scales (<1 km) is primarily controlled by the interplay of topography (Revuelto et al., 2014), vegetation (Trujillo et al., 2007), and meteorological factors such as snow wind transport (Mott et al., 2011; Vionnet et al., 2014) and radiation exposure (Helbig et al., 2010). These processes exert more influence than large-scale precipitation patterns in controlling local-scale snowpack dynamics (Pomeroy and Gray, 1990; Liston and Sturm, 2002; Essery and Pomeroy, 2004; López-Moreno et al., 2013a). Consequently, there is an interest in conducting analyses to comprehend the patterns governing snow dynamics based on local-scale observations (Essery et al., 2020). Nonetheless, the scarcity of observations of snow in Greenland's ice-free areas poses a challenge, given that instrumental weather stations and snow measurements are predominantly concentrated on the Greenland ice sheet (Vandecrux et al., 2023).

There is a current lack of studies that investigate the response of snow to climate change and offer high-resolution snow projections specifically for the ice-free regions of Greenland. Within this context, we present a comprehensive exploration of the impact of CMIP6 climate projections on the dynamics of snow in the coastal ice-free regions of Central-Western Greenland. This includes an examination of the climate drivers influencing snow dynamics, an analysis of associated snow climate indicators, and an assessment of the uncertainties from two models and emission scenarios. To project potential reductions in the future snowpack, we employ a physically based snow model, the Flexible Snow Model (FSM2). Specifically, we address the following questions:

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Location of the Automatic Weather Station (AWS) within Disko Island, Central-Western Greenland (a). Location of Disko Island within Greenland (b). The base map is obtained from open ArcGIS satellite layer (https://www.arcgis.com/home/item.html?id=974d45be315c4c87b2ac32be59af9a0b).</figcaption></figure>
(1) What is the impact of climate change on Arctic snowpack ablation and accumulation patterns?

(2) What are the climate drivers influencing the projected evolution of the snowpack?

(3) What are the differences in snowpack dynamics among different CMIP6 scenarios and temporal periods?

## 2. Study site

The study site considered in this work is the Qeqertarsuaq Arctic Station, in Southern coast of Disko Island (Central-Western Greenland) (69°15'N, 53° 31'W) (Fig. 1). Within this area is located an Automatic Meteorological Station (AWS) in a low-elevation (24 m) site. The climate in this region is mainly controlled by the North Atlantic Oscillation (NAO), the Atlantic Multi-decadal Oscillation (AMO) and Greenland Blocking Index (GBI) variability (Hanna et al., 2011; Mernild et al., 2015). Precipitation (PPT) is generally driven by Western cyclonic activity with the center of action near Labrador Sea (Hanna et al., 2011; Mernild et al., 2015). Mean annual PPT is 436 mm and around 42 % falls as snow (1991–2004 period) (Hansen et al., 2006). In the summer, high-pressure systems over Greenland (associated with negative NAO and positive GBI phases) (Hanna et al., 2013) contribute to stable weather conditions, positive air temperature (Ta) anomalies, and the melting of snow and ice (Hanna et al., 2012). The warmest month is July (mean Ta = 7.1 °C) and the coldest is February (mean Ta = -16 °C). The study area is exposed to strong winds and snow redistribution. The station is in a south-arctic tundra zone with continuous permafrost (Liljedahl et al., 2011). Snowpack in this area belongs to the snow tundra type according to the snow classification proposed by Sturm et al. (1995).

## 3. Materials and methods

### 3.1. Meteorological and snow depth data

This work is based on open snow and meteorological dataset from the Greenland Ecosystem Monitoring Programme (GEM; https://data.g-e-m.dk, last access: 10/03/2023) provided by Asiaq - Greenland Survey, Nuuk, Greenland from November 2012 to December 2021. We used downward shortwave radiation (SWD), incoming longwave radiation (LWD), Ta, relative humidity (RH), surface pressure (Pa), wind speed (Ws) and PPT data to force the FSM2. Meteorological data was quality controlled and validated by the GEM programme (Sigsgaard et al., 2018). PPT was recorded at 60-min intervals using a weighing rain gauge (OTT Pluvio2 gauge) and validated by comparing gauge records with those from a precipitation-rate sensor. Subsequently, PPT records underwent filtration using a neutral aggregation approach (Smith et al., 2020), which removes noise in precipitation time series by balancing positive and negative changes. PPT in windy zones is susceptible to undercatch (Nitu et al., 2019). Thus, we corrected the PPT by applying the parameterized version of an undercatch empirical equation defined by Buisan et al. (2019). This equation adjusts PPT records based on previous hourly records of Ta (in °C), Ws (in m/s) and PPT (in mm):

$$Cr = 1.0*exp(a*Ta - b*Wd) \qquad (1)$$

$$Accumulated (1 hour) = \frac{Acc}{Cr} - 0.095*PPT\frac{Acc}{Cr} + 0.095*Acc (previous hour) \qquad (2)$$

where Cr is the catch ratio and Acc is the accumulated PPT (mm). The values for ‘a’ and ‘b’ are 0.0077 and 0.176, respectively. Ta and RH data was recorded with probe (CS215, Campbell Scientific) at 3 m and installed inside a radiation shield. Ws was measured with a cup anemometer (A100R, Campbell Scientific) at 3.3 m height from the ground. Pa was measured with a Setra barometric pressure sensor (CS100, Campbell Scientific). SWD and LWD were measured with a net radiometer (CNR4, Kipp & Zonen) at 3.3 m. Data were logged every 30 min, using a CR1000 datalogger (Campbell Scientific), and subsequently aggregated to 60-min intervals in accordance with hourly PPT records. Manual snow depth records were collected once per day at Arctic Station (approximately 300 m from the AWS location). Gap-filling for meteorological data was performed using the random forest (RF) algorithm (Breiman, 2001), a method applied for the predicting meteorological and energy heat fluxes (Tramontana et al., 2016; Bonsoms and Boulet, 2022). The RF model was trained with default hyperparameters, utilizing atmospheric forcing variables obtained from ERA5 data (Hersbach et al., 2020) for the temporal period with available instrumental records. The number of data gaps can be found in Fig. S1, while Figs. S2 and S3 present the modeled and observed meteorological data. Error metrics (Section 3.4) indicate Root Mean Square Errors (RMSE) of 0.5 °C for Ta and 0.1 mm for PPT. All error metrics of the RF model for the climate variables used to force the FSM2 are presented in Table S1. The modeled and observed scatterplot is depicted in Fig. S4. These scatter plots illustrate the adjustment that the RF model can provide during periods with meteorological observations, demonstrating its applicability for filling observational gaps based on reanalysis outputs.

### 3.2. Climate change projections

We utilized downscaled and bias-corrected daily SWD, LWD, Ta, PPT and RH NASA CMIP6 projections (NASA GDDP-CMIP6; https://ds.nccs.nasa.gov/thredds/ncss/AMES/NEX/GDDP-CMIP6, last accessed: 08/06/2023) (Thrasher et al., 2022) with a resolution of 0.25° x 0.25° (approximately 10 km in Greenland). The NASA GDDP-CMIP6 data underwent bias correction using a cumulative distribution function (CDF) and a spatial disaggregation method described by Thrasher et al. (2012). We focused on an intermediate greenhouse gas emissions scenario (SSP2-4.5), characterized by CO2 emissions around current levels until 2050, followed by a decline but not reaching et zero by 2100. Additionally, we analyzed a high greenhouse gas emission scenario, business as usual (SSP5-8.5), where CO2 emissions triple by 2017 compared to present day. We analyzed climate projection changes using the Model for Interdisciplinary Research on Climate 6 (MIROC6) and the Canadian Earth System Model 5 (CanESM5) since they demonstrated superior performance in past Greenlad climate model intercomparisons (Fausto et al., 2013). NASA GDDP-CMIP6 daily data were downloaded for the nearest AWS grid point. The NASA GDDP-CMIP6 models and scenarios were compared against AWS historical records (2012–2021 period). Subsequently, climate models underwent bias correction using the nonparametric empirical method of Quantile Mapping (QM), implemented with the R package qmap (Gudmundsson, 2014). Climate biases induced by seasonality were rectified by dividing the data into the following seasons: September to November (autumn), December to February (winter), March to June (spring) and July to August (summer). We assessed snowpack responses to climate change for different future temporal periods, namely 2040–2050, 2060–2070, 2080–2090, and 2090–2100, each spanning a decade. The impact of climate change on the snowpack was estimated by subtracting the differences between the bias-corrected SWD, LWD, Ta, PPT and RH monthly differences grouped by model, scenario and temporal period and their respective baseline climates. These differences were subsequently added to the historical AWS data. Note that the reference period of the AWS data (2012–2021) used to force the FSM2 was selected because long-term (> 30 years) in-situ meteorological data for downscaling and bias-correcting climate projections are not available in the region. Additionally, we further analyzed the sensitivity of the snowpack to Ta and PPT to evaluate if PPT can counterbalance warming.

### 3.3. Flexible snow model

The FSM2 is an energy and mass balance snow model that resolves the SEB and simulates the snowpack evolution. FSM has been previously
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Daily (a) and monthly (b) snow depth temporal evolution. Scatter plot between the modeled snow depth values (y-axis) and the nearest in-situ manual measurement (x-axis) for all days (c), monthly snow depth (d) and snow onset and melt-out dates (e).</figcaption></figure>

validated and applied in diverse mountain zones, including mid-latitude mountains (Alonso-González et al., 2020; Bonsoms et al., 2023), Arctic mountains (Magnusson et al., 2019) and forest areas (Mazzotti et al., 2020). Additionally, it has been incorporated in snowpack model intercomparison studies (Krinner et al., 2018), consistently demonstrating accurate results in all cases. The FSM2 was run at an hourly time step for each forcing, scenario and temporal period (as defined in Section 3.3). Snowfall was prescribed when Ta was below 1 °C. We used the most physically-based FSM2 configuration, which includes three snow layers and different parametrization schemes representing physical snow processes (e.g., snow metamorphism, compaction, settling, freezing and ablation) and direct and diffuse shortwave radiation partitioning. Specifically, fractional snow cover is estimated through a asymptotic function of snow depth. Thermal conductivity is estimated based on snow density, and snow hydrology is simulated by gravitational drainage. Latent and sensible heat fluxes are simulated using the Monin-Obukhov stability function. The FSM2 compilation number can be found in Table S2. For additional details regarding the FSM2 configuration and parametrization, refer to Essery (2015).

### 3.4. Error metrics

We evaluated the performance of the RF in gap-filling meteorological data. The FSM2 performance was also evaluated to replicate daily snow depth, monthly average snow depth during the snow season, and snow

onset and melt-out dates (October and May, respectively). The evaluation was based on the Mean Absolute Error (MAE), the RMSE and the coefficient of determination (R²). The MAE calculates the mean differences between the modeled and observed values, whereas the RMSE is the quadratic mean of the differences. The MAE measures the average magnitude of the errors, while the RMSE is more sensitive to large errors and outliers.

### 3.5. Snow climate indicators

The snow responses to climate change were analyzed based on changes in the following four key snow climate indicators:

(1) Snow depth: the average snow depth for days when snow depth was >1 cm.

(2) Snow days: the number of days when snow depth was >1 cm.

(3) Snowfall fraction and snowfall quantity. Snowfall fraction is the accumulated snowfall quantity divided by the accumulated total PPT.

(4) Ablation rate: the average daily snow ablation. Snow ablation is estimated by the difference in the maximum daily snow depth between two consecutive days when the daily difference was negative and lower than 1 cm (López-Moreno et al., 2017).

## 4. Results and discussion

We evaluated the performance of the FSM2 against daily manual measurements recorded near the AWS. Subsequently, we analyzed changes in the snowpack for different temporal periods, climate models and scenarios.

<caption>**Table 1**</caption>

Error metrics calculated for daily, snow onset and melt-out dates (in October and May) and average monthly records. MAE and RMSE are expressed in meters.

<table><thead><tr><td>Metric</td><td>All days</td><td>October and May</td><td>Month</td></tr></thead><tbody><tr><td>MAE</td><td>0.10</td><td>0.11</td><td>0.11</td></tr><tr><td>RMSE</td><td>0.17</td><td>0.2</td><td>0.15</td></tr><tr><td>R<sup>2</sup></td><td>0.69</td><td>0.17</td><td>0.72</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** SWD (a), LWD (b), Ta (c), PPT (d) and RH (e) average daily differences between the future and the reference climate period. For Data are grouped by temporal periods (x-axis), model (colors) and scenario (boxes). The horizontal lines indicate the median, boxes represent the 25th and 75th percentiles, and vertical lines represent the 10th and 90th percentiles.</figcaption></figure>

## 4.1. Snowmodel validation

The temporal evolution of the snow depth simulated by FMS2 and manually observed is shown in Fig. 2. This figure also shows the scatter plot between observed and simulated values. Daily snow depth shows good performance ($R^2$ = 0.68). The largest errors are observed during the snow onset and melt-out dates when FSM2 shows low performance ($R^2$ = 0.18) and accuracy (RMSE = 0.2 m; Table 1) in ephemeral snowpacks (snow depth < 0.2 m; Fig. 2). The highest $R^2$ is found for monthly values ($R^2$ = 0.72) as well as the minimum RMSE (0.15 m).

During the reference climate period, the observed snow season generally starts in October and lasts until May or June. The average seasonal snow depth is 0.30 m, reaching maximum snow depth peaks up to 1.5 m in 2017, and lasting until June (seasonal snow days = 152 days). The maximum snow accumulation is found in March (0.40 m), when the snow ablation onset starts, following an ablation rate of -1.3 cm/day.

Overall, the FSM2 reproduced the daily and monthly snow depth values. Differences between the nearest manual records and the modeled values are expected since manual records are not collected at the same AWS location and are influenced by wind-drift processes.

## 4.2. Anomalies in climate projections and future snowpack evolution

The comparison of each model and scenario against the historical period is presented in Fig. S5. MIROC6 SSP2.4-5 exhibits the lowest anomalies (19 %), while the largest differences are found for CanESM5

SSP2.4-5 (78 %). Regarding PPT differences in comparison to the observed values, CanESM5 SSP2.4-5 and SSP2.4-5 demonstrate superior performance and smaller errors (5 %) compared to MIROC6 SSP2.4-5 (15 %) and MIROC6 SSP2.8-5 (17 %). Consequently, these findings suggest that MIROC6 SSP2.4-5 provides a more accurate estimation of Ta, whereas CanESM5 SSP2.4-5 better projects PPT in the study area. It is important to note, however, that the spatial resolution of climate projections (approximately 10 km) differs from local observations.

The atmospheric forcing variables for different future periods, models and scenarios are shown in Fig. 3. For both models, SWD shows a decreasing trend. Small changes (< -5 W/m²) are detected for SSP2-4.5, while conversely, there are large differences for SSP5-8.5. For the 2080-2090 period, SWD decreases by -15 and -9 W/m² (CanESM5 and MIROC6, respectively). LWD shows an upward trend, accentuated for SSP5-8.5 during the 21st end-century. For MIROC6, the increase in LWD ranges from +3 W/m² (SSP2-4.5, 2040-2050) to +20 W/m² (SSP5-8.5, 2090-2100) over the reference climate. CanESM5 shows the largest increase in LWD, ranging from +10 W/m² (SSP2-4.5, 2040-2050) to +35 W/m² (SSP5-8.5, 2090-2100). The increase in LWD is consistent with the positive evolution of Ta and PPT, associated with cloudiness. Ta shows a gradual positive evolution (Fig. 4) and increases up to 5 °C (10 °C) for MIROC6 (CanESM5) at SSP5-8.5 in the 2090-2100 period. Data show a different PPT response to climate change depending on the emission scenario (Fig. 5). For both models, small changes are detected for SSP2-4.5 and a large increase for SSP5-8.5. For the latter, CanESM5 PPT increases up to 1 mm/day (2090-2100 period), whereas
<figure><img src="image_8.png" /><figcaption>Fig. 4. Downscaled and bias-corrected air temperature evolution. Data are grouped by model (colors), scenario (rows) and season (columns).</figcaption></figure>

this increase is more reduced (half of this ratio) in the MIROC6. Small changes are found in RH, independently of the climate model and scenario. MIROC6 shows a downward trend up to -4 % (SSP5-8.5, 2090-2100 period), and CanESM5 shows small changes (< -1 %) regardless of the scenario and temporal period.

Snow depth evolution for the reference climate and different temporal periods, models, and scenario are shown in Fig. 6, 7 and 8. The snowpack is largely reduced due to climate change, becoming ephemeral in the shoulders of the season. The projected increase in PPT (Figs. 3 and 5) cannot counterbalance the snow depth losses due to the positive evolution of LWD and Ta. In particular, a snow depth sensitivity analysis to Ta and PPT change (Fig. S6) reveals that only for large increases of PPT (> 20 %) and small increases of Ta (< 2 °C) can PPT counterbalance warming. However, these climate conditions are unlikely to be reached for any model (Figs. 3, 4 and 5). CanESM5 snow depth anomalies with respect to the reference period range between -38 % (SSP2-4.5, 2040-2050 period) to -74 % (SSP5-8.5, 2090-2100 period). MIROC6 projects lower snowpack decreases, ranging between -38 % (SSP2-4.5, 2040-2050 period) to -57 % (SSP5-8.5, 2090-2100 period) (Figs. 6 and 7).

CanESM5 snow depth anomalies with respect to the reference period range between -38 % (SSP2-4.5, 2040-2050 period) and -74 % (SSP5-8.5, 2090-2100 period). MIROC6 shows lower snowpack decreases, with the snow depth decrease ranging between -38 % (SSP2-4.5, 2040-2050 period) and -57 % (SSP5-8.5, 2090-2100 period) (Figs. 6 and 7).

Similarly, CanESM5 shows larger decreases in snow days than MIROC6. CanESM5 exhibits a progressive decrease from -40 % (SSP2-4.5, 2040-2050 period) to -78 % (SSP5-8.5, 2090-2100 period) with respect to the reference period. The largest reductions are observed

during autumn. Under SSP5-8.5 and for the 2040-2050 period, the decrease in autumn snow days ranges from -58 % (MIROC6) to -64 % (CanESM5). The maximum reductions in snow days are found for the 2090-2100 period and autumn when snow days are expected to decrease between -77 % (MIROC6) to -90 % (CanESM5). The minimum anomalies in snow days compared to the reference climate period are found in winter. Under SSP5-8.5 and the 2040-2050 period, the decrease in snow days during winter ranges from -3 % (MIROC6) to -19 % (CanESM5). For the 2090-2100 period, winter snow days losses range between -21 % (MIROC6) to -54 % (CanESM5).

The future evolution of snow, as presented in this study, aligns with observed historical trends in the pan-Arctic region (1980-2009 period), including maximum winter snow water equivalent (SWE) reductions, declines in snow cover extent, and earlier snowmelt onset (Liston and Hiemstra, 2011). The CMIP3 projections project a 15 % decline in maximum SWE and a 10-20 % decrease in the duration of snow cover in the Arctic by 2050 when compared to the period between 1972 and 2009 (Callaghan et al., 2011).

The projected increase in energy available for ablation, combined with the presence of snow-free conditions, is likely to accelerate glacier ablation and lead to a higher equilibrium-line altitude in nearby glacial areas. The increase in LWD reported in this work is consistent with broad-scale climate projections suggesting a significant rise in LWD due to increased cloudiness resulting from the advection of warmer and humid air masses over Greenland (Vihma et al., 2016). It is worth noting that clouds play a crucial role in the radiative effects driving melting in the Greenland (Noël et al., 2019), as observed during high melting events such as in 2012 (Bennartz et al., 2013). The projected snowpack reduction will likely amplify melting due to lower albedo surface (Franco et al., 2013). Further, the increase of temperature reported in
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Downscaled and bias-corrected precipitation evolution. Data are grouped by model (colors), scenario (rows) and season (columns).</figcaption></figure>

this work will enhance snowpack losses due to higher sensible heat fluxes, as observed during intense melting episodes in Greenland ablation areas (Fausto et al., 2016). More snow-free conditions will likely lead to drier surface conditions due to an increase in the Bowen ratio, especially during snow-poor years in near Greenland tundra sites (Lund et al., 2017).

Snow projections and sensitivity to climate studies conducted in Arctic locations, such as Pohl et al. (2007) and Krogh and Pomeroy (2019), have indicated a large reduction in SWE, snow cover and an increase in runoff volume and evaporation in response to warming. Rasouli et al. (2019) analyzed the response of snowpack to temperature and PPT in a high-latitude Canadian basin and found an overall reduction in the snowpack with a + 3 °C increase in temperature. However, but the found that PPT increases (> 20 %) could counterbalance the warming effect. Our results, however, suggest that the anticipated increase in PPT for the end-century under high-emission scenarios (Fig. 5) cannot fully offset the radiative and temperature forcing projected in the study site. This highlights the high sensitivity of the study site, attributed to the different impact of climate change in the region compared to other locations, its mild coastal position, and low elevation. This aligns with Northern Hemisphere sensitivity to temperature studies, which suggest higher sensitivity to climate warming in mild and coastal regions compared to other locations (Brown and Mote, 2009; Räisänen and Eklund, 2012). The snow depth reduction (10 % per °C) is comparable to the sensitivity of SWE to warming reported in the polar maritime Ny-Ålesund site, Svalbard (López-Moreno et al., 2013b), and is far from previous studies examining snow sensitivity to temperature and climate in mountainous regions of North America (Minder, 2010; Musselman et al., 2017), and Mediterranean mid-latitude mountain ranges, where SWE exhibits a sensitivity of approximately 30 % per °C at around 2000

m elevation (Bonsoms et al., 2023). López-Moreno et al. (2017) compared the snow sensitivity to climate change in 50 basins worldwide and found that mid-latitude zones exhibited the highest sensitivity due to their proximity to the freezing point. Conversely, Arctic locations showed lower sensitivity to Ta since the isothermal-conditions are far from 0 °C during the core months of the season (Fig. 4). However, temperatures in the Arctic show a relatively higher rate of increase compared to other regions globally (Rantanen et al., 2022).

The average decrease in snowfall ranges from – 21 % (31 %) to – 42 % (63 %) for MIROC6 (CanESM5). However, there are important variations depending on the season. CanESM5 projects an increase in snowfall (around 5 % relative to the reference climate period) during winter (≤ 2060–2070 period). MIROC6 projects an increase in snowfall (> 10 %) under SSP5–8.5 and during winter, regardless of the analyzed temporal period (Fig. 9).

The ablation rate shows an increase for both models and scenarios, particularly for CanESM5 SSP5.8–5, with a roughly 20 % rise across seasons and anomalies of 100 % during winter. It is important to note that anomalies in the ablation rate during the reference period reach up to 100 % during winter for both models and scenarios, as no ablation rate episodes occurred in the reference climate period. In spite of the reduction of snow depth, our results indicate faster snow ablation rates within a changing climate, which differs from studies in other latitudes. Slower snow melt has been linked with positive radiative heat fluxes early in the season, resulting in an earlier onset of snowmelt (López-Moreno et al., 2013b). Similar patterns have been observed in Northern Hemisphere snow studies, particularly during spring (Wu et al., 2018), as well as in studies investigating snow sensitivity to climate change in mid-latitude mountain ranges (López-Moreno et al., 2013b; Bonsoms et al., 2023), Canadian basins (Pomeroy et al., 2015), and observations
<figure><img src="image_10.png" /><figcaption>Fig. 6. Monthly evolution of snow depth. Data are grouped by temporal periods (colors), models and scenarios (boxes).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 7. Average seasonal snow depth (a), snow days (b), snowfall fraction (c), and ablation rate (d) for different temporal periods (x-axis) and models (colors). The horizontal black lines show the average values for the SSP2-4.5 and SSP5-8.5 scenarios.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Snow depth (a) and snow days (b) seasonal anomalies. Data are shown for scenarios (columns) and models (rows). Anomalies are calculated by (i) calculate the difference between the future and reference climate, dividing it by the reference climate, and then multiplying by 100.</figcaption></figure>

and projections of snowpack in Western US regions (Musselman et al., 2017), middle to high-latitude basins within the North American Cordillera (Rasouli et al., 2019; Fang and Pomeroy, 2020). In accordance with our findings of faster ablation rates, Fang and Pomeroy (2020) detected slightly higher rates in alpine ecozones of Saskatchewan River (Canadian Rockies) but slower melt rates in other areas of the basin. Krogh and Pomeroy (2019) also observed increased ablation rates in high-latitude North American basins. The contrasting responses of ablation rates to climate change in different locations can be attributed to variations in PPT and Ta conditions, albedo decay, changes in radiation onset, and differences in the amount of available energy for melting (Fang and Pomeroy, 2020).

We have observed the most substantial decrease in snowfall fraction during the shoulder seasons, primarily due to the closer-to-isothermal conditions prevailing in those periods. The projected increase of PPT aligns with the long-term evolution in Western Greenland coastal regions (0.1 mm w.e. yr⁻²; 1890–2000 period) (Mernild et al., 2014). PPT during autumn and spring exhibits significant positive trends (Fig. 5), primarily associated with increases in evaporation and poleward moisture transport (Bintanja and Andry, 2017). However, there are projected smaller trends during winter, which could be attributed to the absence of large-scale precipitation changes. Nevertheless, further research is needed to investigate this aspect. PPT increases combined with the

decrease in the snowfall ratio due to warming (Figs. 4 and 8), may lead to more frequent Rain-on-Snow (ROS) events. The frequency and intensity of ROS events in Greenland have exhibited a positive trend from 1980 to 2009 (Liston and Hiemstra, 2011), with the most significant increase occurring in early summer (1980 to 2019) (Niwano et al., 2021). Our results show that future PPT evolution largely depend on the model and emission scenario, with larger increases for SSP5-8.5 than SSP2-4.5 emission scenarios. This finding is consistent with previous studies investigating ROS sensitivity to Ta and PPT in Ny-Ålesund (Svalbard) which anticipate higher ROS occurrences under high emission scenarios, while moderate to low ROS changes under mid-emission scenarios (López-Moreno et al., 2016).

### 4.3. Modeling limitations

The declines in snow cover projected in this study are relatively higher than global snow climate projections from CMIP6 at a coarse resolution; which is attributed to the limited ability of low-resolution global climate models to capture the fine-scale topographical details and magnitudes of snow climate trends (Mudryk et al., 2020). Additionally, RCMs (ca. 10 km of spatial resolution) underestimate freshwater discharge compared to local scale observations(Mankoff et al., 2021). Similar findings are reported by Noël et al. (2023), who
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Snowfall (a) and ablation rate (b) seasonal anomalies. Data are shown for scenarios (columns) and models (rows). Anomalies are calculated by (i) calculate the difference between the future and reference climate period, dividing it by the reference climate, and then multiplying by 100.</figcaption></figure>

demonstrated that high-resolution modeling (around 1 km) leads to higher melting trends along Antarctica's margins compared to RCMs. Our findings rely on two climate models instead of ensemble modeling, which could have mitigated the impact of biases and uncertainties inherent in the models. However, the climate models used in this work were selected based on previous evaluations in Greenland (Franco et al., 2013). In addition, the results suggest consistent signals and common patterns in future snowpack evolution, despite the uncertainty in greenhouse gas emissions scenarios and the model used as a basis to simulate the impact of climate change on the snow in the study site.

## 4.4. Physical and ecosystem implications

The anticipated decreases in snow cover will contribute to heating the ground surface and warming (Serreze and Barry, 2011). The snow-pack reduction reported in this work will likely have implications for the glaciated areas of Disko Island. These zones are particularly affected by climate change, as indicated by historical trends (1953 to 2005) revealing a recession in the majority (>70 %) of the glaciers on Disko Island (Yde and Knudsen, 2007). Our findings have substantial

implications for ecosystem dynamics in the study area. Snowpack reductions will likely lead to colder soils during winter (Groffman et al., 2001), impacting fauna and flora dynamics, since snow serves as an insulation layer, shielding them from extreme cold temperatures and freezing damage (Rixen et al., 2022; Convey et al., 2018). The reduction in snow cover and higher temperatures during the warm season will likely lead to earlier hydrological and nutrient availability for the ecosystem (Buckeridge and Grogan, 2010), resulting in shrub growth, increased cover, and biomass in Arctic systems (Loranty et al., 2018). The growth of vegetation will likely influence snow redistribution patterns (Liston and Sturm, 2007; Essery and Pomeroy, 2004). Vegetation will also affect snowpack stability by modifying the thermal conductivity of the depth hoar (Domine et al., 2018). Additionally, the prolonged duration of snow-free months will result in increased soil moisture content, greater active layer thickness, surface subsidence, and alterations in the thermal conductivity of the peat, ultimately intensifying surface temperatures (Johansson et al., 2013). The accelerated release of meltwater and higher temperatures will influence soil thermal conductivity, contributing to more freeze/thaw cycles and permafrost degradation (e.g., Romanovsky and Osterkamp, 2000). Greenland
projections indicate that by the end of the century and under a high-emission RCP8.5 scenario, continuous permafrost will only persist in a limited area of Greenland, while sporadic and seasonally frozen ground will dominate the rest of the island with permafrost (Geng et al., 2019). Such changes have relevant impacts on gas emissions during the growing season and microbiological activity (IPCC, 2022). Additionally, while unfrozen grounds facilitate groundwater recharge, the presence of frozen soils during the snow melting season is likely to increase the probability of meltwater floods (e.g., Ala-Aho et al., 2021). The more frequent occurrence of ROS events in the Arctic (López-Moreno et al., 2013b) and higher temperatures will likely contribute to the formation of ice layers at the bottom of the snowpack resulting in increased instability of ice and snow layers (Sturm et al., 1995; Liston and Hiemstra, 2011). The anticipated increase in ROS events will have impacts on the ecosystem. ROS alters the snowpack and soil stability by creating ice layers that encapsulates the vegetation, potentially resulting in herbivore mortality (Rennert et al., 2009; Putkonen et al., 2009; Serreze et al., 2021).

## 5. Summary

A better understanding of Arctic snowpack evolution within a changing climate is of interest due to its strong influences in the ecosystem. This work provides a comprehensive assessment of snow-pack patterns and their projected changes under future climate scenarios on Disko Island (Central-Western Greenland). A physically-based snow model (FSM2) reproduced observed daily and monthly snow values and was used to model the future snowpack evolution according to CMIP6 projections from CanESM5 and MIROC6 for SSP2-4.5 and SSP5-8.5 scenarios.

The projected increase of PPT does not counterbalance the positive evolution of LWD and Ta., leading to further reductions in the snowpack. MIROC6 projects maximum LWD daily increases of +20 W/m2 and Ta increases +5 °C for SSP5-8.5 scenario and 2090-2100 period compared to the reference period. CanESM5 projects daily increases of +35 W/m2 (LWD) and + 10 °C (Ta) for the same scenario and temporal period. Consequently, seasonal snow depth, snow days and snowfall fraction are greatly reduced. Specifically, CanESM5 snow depth anomalies with respect to the reference period range between – 38 % (SSP2-4.5, 2040-2050 period) to – 74 % (SSP5-8.5, 2090-2100 period). MIROC6, on the other hand, project lower snowpack decreases. The snow depth decrease range between – 38 % (SSP2-4.5, 2040-2050 period) to – 57 % (SSP5-8.5, 2090-2100 period). Both models suggest that winter snow-fall can increase, although that increase cannot counterbalance snow-pack losses. Warming leads to large ablation rate increases, regardless the model and scenario. The results presented herein are in line with previous results found for the Arctic region. The projected reduction of the snowpack and snow cover has significant implications for the region's hydrology and ecosystem dynamics, including biological phenology and productivity, flora and fauna survival, and permafrost thaw.

### CRediT authorship contribution statement

**Josep Bonsoms:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.
**Marc Oliva:** Conceptualization, Funding acquisition, Investigation.
**Esteban Alonso-González:** Conceptualization, Validation, Visualization.
**Jesús Revuelto:** Conceptualization, Methodology, Validation, Visualization.
**Juan I. López-Moreno:** Conceptualization, Funding acquisition, Investigation, Supervision, Validation, Visualization, Writing – review & editing.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

Precipitation records can be downloaded at doi:10.17897/638X-3Z89. Manual snow depth records can be found at doi:10.17897/FQJG-QP51.The remaining meteorological data can be accessed at doi:10.17897/CSZT-F010. The FSM2 model is available at Dr. Richard Essery's GitHub page (https://github.com/RichardEssery/FSM2). ERA5 data is accessible at https://cds.climate.copernicus.eu/ (last accessed: 08/06/2023). CMIP6 down-scaled and bias-corrected data are available at https://ds.nccs.nasa.gov/thredds/nccs/AMES/NEX/GDDP-CMIP6 (last accessed: 08/06/2023).

### Acknowledgements

The work was supported by "Antarctic, Artic, Alpine Environments-ANTALP" (2017-SGR-1102) funded by the Government of Catalonia, HIDROIBERNIEVE (CGL2017-82216-R) and MARGISNOW (PID2021-124220OB-100), from the Spanish Ministry of Science, Innovation and Universities. JB is supported by a pre-doctoral University FPI grant (PRE2021-097046) funded by the Spanish Ministry of Science, Innovation and Universities. J. Revuelto has been supported by the RYC2021-033859-I (Ramón y Cajal) projects.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2023.169616.

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