Volume 10, Issue 11 e2023EA003158

Research Article

Open Access

Role of Snowfall Versus Air Temperatures for Greenland Ice Sheet Melt-Albedo Feedbacks

J. C. Ryan,

Corresponding Author

J. C. Ryan

[email protected]

orcid.org/0000-0002-9432-1512

Department of Geography, University of Oregon, Eugene, OR, USA

Correspondence to:

J. C. Ryan,

[email protected]

Contribution: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration, Funding acquisition

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B. Medley,

B. Medley

orcid.org/0000-0002-9838-3665

Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Contribution: Conceptualization, Methodology, Writing - review & editing, Funding acquisition

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C. M. Stevens,

C. M. Stevens

Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

Contribution: Conceptualization, Writing - review & editing, Funding acquisition

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T. C. Sutterley,

T. C. Sutterley

Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USA

Contribution: Methodology, Writing - review & editing, Funding acquisition

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M. R. Siegfried,

M. R. Siegfried

orcid.org/0000-0002-0868-4633

Hydrologic Science & Engineering Program, Department of Geophysics, Colorado School of Mines, Golden, CO, USA

Contribution: Methodology, Writing - review & editing, Funding acquisition

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J. C. Ryan,

Corresponding Author

J. C. Ryan

[email protected]

orcid.org/0000-0002-9432-1512

Department of Geography, University of Oregon, Eugene, OR, USA

Correspondence to:

J. C. Ryan,

[email protected]

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B. Medley,

B. Medley

orcid.org/0000-0002-9838-3665

Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Contribution: Conceptualization, Methodology, Writing - review & editing, Funding acquisition

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C. M. Stevens,

C. M. Stevens

Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

Contribution: Conceptualization, Writing - review & editing, Funding acquisition

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T. C. Sutterley,

T. C. Sutterley

Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, WA, USA

Contribution: Methodology, Writing - review & editing, Funding acquisition

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M. R. Siegfried,

M. R. Siegfried

orcid.org/0000-0002-0868-4633

Hydrologic Science & Engineering Program, Department of Geophysics, Colorado School of Mines, Golden, CO, USA

Contribution: Methodology, Writing - review & editing, Funding acquisition

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First published: 27 November 2023

https://doi.org/10.1029/2023EA003158

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Abstract

The Greenland Ice Sheet is a leading contributor to global sea-level rise because climate warming has enhanced surface meltwater runoff. Melt rates are particularly sensitive to air temperatures due to feedbacks with albedo. The primary melt-albedo feedback, fluctuation of seasonal snowlines, however, is determined not only by melt but also by antecedent snowfall which could delay the onset of dark glacier ice exposure. Here we investigate the role of snowfall versus air temperatures on ice sheet melt-albedo feedbacks using satellite remote sensing and atmospheric reanalysis data. We find several lines of evidence that snowline fluctuations are driven primarily by air temperatures and that snowfall is a secondary control. First, standardized linear regressions indicate that the timing of glacier ice exposure is nearly twice as sensitive to air temperatures than antecedent snowfall. Second, in 74% of the ablation zone by area, winter snowfall rates are not significantly correlated with winter air temperatures. This relationship implies that ice sheet melt due to climate warming is unlikely to be compensated by higher snowfall rates in the ablation zone. Third, we find no significant change in snowfall rates in the ablation zone during our 1981–2021 study period. Our findings demonstrate that snowfall is unlikely to reduce future ice sheet melt and that ice sheet meltwater runoff should be accurately predicted by air temperatures. Although given the importance of melt-albedo feedbacks, ice sheet models that parameterize albedo or are coupled with regional climate models are likely to provide the most accurate projections of mass loss.

Key Points

We investigate the extent to which antecedent snowfall versus summer air temperatures controls melt-albedo feedbacks
We find that melt-albedo feedbacks are primarily driven by air temperatures and that antecedent snowfall is of secondary importance
We find little evidence that snowfall will compensate enhanced ice sheet melt due to climate warming

Plain Language Summary

The Greenland Ice Sheet is currently losing mass because rates of mass loss (mainly due to surface meltwater runoff and iceberg discharge into the ocean) are higher than rates of mass gain (mainly due to snowfall). As the climate warms, rates of mass loss are expected to increase non-linearly because rising air temperatures darken the surface, leading to more solar energy absorption, and more melting. The main process responsible for surface darkening is the exposure of dark glacier ice due to Greenland's seasonally evolving snowline. The position of the snowline is determined not only by summer melt but also by the thickness of the snowpack that accumulates during winter. In this study, we investigated whether summer melt or snowpack thickness was more important for determining the timing of glacier ice exposure using satellite remote sensing and climate model outputs. We found that the glacier ice exposure is much more sensitive to air temperatures than snowfall and that snowfall did not change in the ablation zone during the 1981–2021 study period. Our findings imply that snowfall is unlikely to reduce ice sheet mass loss and that meltwater runoff from the ice sheet should be accurately predicted by air temperatures.

1 Introduction

The Greenland Ice Sheet has been in a state of persistent negative mass balance over the past decades because mass gains from snowfall accumulation are much less than the combined mass loss due to solid ice discharge across the grounding line and meltwater runoff (e.g., Bamber et al., 2018; van den Broeke et al., 2016). There are now concerns that the ice sheet may cross a tipping point beyond which the ice sheet will substantially, and unrecoverably, shrink (Armstrong McKay et al., 2022; Boers & Rypdal, 2021; Levermann et al., 2013). This tipping point is a global surface air temperature at which the strength of the feedback between enhanced surface melt and reducing ice sheet elevation sustains irreversible mass loss (Levermann & Winkelmann, 2016). There is still debate about the critical temperature threshold for this tipping point or whether one even exists (Gregory et al., 2020; Noël et al., 2021; Robinson et al., 2012). Either way, constraining the ice sheet's response to changes in air temperatures remains crucial for understanding its long-term stability.

Mass loss from the Greenland Ice Sheet is particularly sensitive to climate warming because ablation rates respond exponentially to surface air temperatures (Fettweis et al., 2013; Lenaerts et al., 2015). This non-linear relationship can be, at least partly, attributed to melt-albedo feedbacks, the strongest of which is due to seasonal snowline fluctuations that expose vast areas of dark glacier ice each summer (Ryan et al., 2019). This feedback substantially lowers the albedo of the ablation zone when solar radiation is highest, priming the ice sheet for enhanced meltwater production. There are separate melt-albedo feedbacks specific to snow and ice. As surface air temperatures warm, the grain size of snow increases, causing a reduction in albedo (Warren, 2019). Glacier ice albedo is reduced as temperatures warm due to ice microbiological processes (Williamson et al., 2020) and meltwater ponding (Ryan et al., 2018). Both observations (Box et al., 2012; Ryan et al., 2019) and modeling (Zeitz et al., 2021) indicate that these melt-albedo feedbacks substantially amplify ice sheet mass loss.

However, increased snowfall could dampen the sensitivity of ablation rates to rising air temperatures. Snowfall resets the surface albedo to that of bright snow (∼0.8) and can also delay the exposure of glacier ice. Climate modeling indicates that snowfall rates on the Greenland Ice Sheet are expected to increase in the future as a consequence of the Clausius-Clapeyron relationship (i.e., warmer air can hold more water) and reducing ice sheet elevation (Fettweis et al., 2013; Hakuba et al., 2012; Le clec'h et al., 2019). Higher winter snowfall rates would lead to thicker snowpacks in spring which take longer to fully melt, delaying the onset of glacier ice exposure. Antecedent winter processes therefore have the potential to dampen melt-albedo feedbacks and reduce ice sheet melt during summer. However, the extent to which snowfall can dampen the sensitivity of ablation rates to rising air temperatures remains unconstrained.

Here we investigate whether snowfall reduces meltwater production from the Greenland Ice Sheet by delaying the exposure of darker glacier ice. To do this, we produced a new glacier ice exposure product using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra satellite and combine it with snowfall and air temperature data from NASA's Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). We then develop regression models to investigate the sensitivity of glacier ice exposure to cumulative winter/spring snowfall and early-summer air temperatures.

The implications of this study are important for projections of Greenland Ice Sheet mass balance to future climate change: if air temperature is the dominant control on the timing of glacier ice exposure, then melt-albedo feedbacks should be relatively straightforward to simulate in ice sheet models. However, if snowfall exerts a strong control, then melt-albedo feedbacks may be more uncertain since trends in future snowfall under a warmer climate are not well constrained.

2 Methods

2.1 Timing of Glacier Ice Exposure

We developed our glacier ice exposure product using MOD10A1 (Version 6) snow albedo data collected between 2001 and 2021 (Stroeve et al., 2006). MOD10A1 is a daily product delivered on a sinusoidal grid with a grid cell resolution of ∼500 m (Klein & Stroeve, 2002). We downloaded all MOD10A1 data between 1 May and 30 September for the 2001–2021 period for the eight MODIS tiles that cover Greenland (h15v01, h15v02, h16v00, h16v01, h16v02, h17v00, h17v01, and h17v02). We then stacked the daily images for each tile and each year. We computed the first day of glacier ice exposure using a simple, reproducible approach similar to Box et al. (2012) and Ryan et al. (2017) which reliably removes spurious values without removing too many valid data points. To do this we first removed all MOD10A1 values flagged as poor quality (e.g., due to clouds or algorithm failure) by the internal MOD10A1 processing algorithm (Figure S1a in Supporting Information S1). We then filtered any remaining spurious data by removing albedo values that were more than two standard deviations from an 11-day running mean (Figure S1b in Supporting Information S1). After this filtering step, we re-computed an 11-day running median and identified the first day that albedo dropped below 0.55, a commonly used threshold for the albedo of glacier ice (Figures S1c–S1d in Supporting Information S1) (Ryan et al., 2019). Grid cells in which glacier ice was exposed for less than seven days during the summer were flagged and excluded from further analysis.

We mosaicked individual MODIS tiles and reprojected to a NSIDC Sea Ice Polar Stereographic North (EPSG: 3413) projection with a grid cell resolution of 1 km × 1 km. Since our analysis focuses on the Greenland Ice Sheet, we excluded any grid cells that did not intersect the Greenland Ice Mapping Project (GIMP) ice mask (Howat et al., 2014). We also excluded peripheral glaciers and ice caps identified by the Randolph Glacier Inventory Version 6 (RGI Consortium, 2017). Finally, to facilitate analysis of regional patterns, we divided the ice sheet into eight sectors as defined by the Ice sheet Mass Balance Intercomparison Exercise (IMBIE) (Figure S2 in Supporting Information S1).

2.2 Climate Data

We investigated controls on glacier ice exposure using hourly time-averaged snowfall and air temperature data from MERRA-2 (Cullather et al., 2014; Gelaro et al., 2017). We retrieved snowfall and 2 m air temperature data from the single-level diagnostics products: M2T1NXFLX and M2T1NXSLV, respectively. We used the Goddard Earth Sciences Data and Information Services Center (GES DISC) to subset the variables over our study region (84°N, −11°E, 59°N, −73°E) for the 2001–2021 period. The native MERRA-2 grid resolution is 0.625° × 0.500°, so we resampled our variables to 50 km × 50 km using bilinear interpolation to establish a consistent grid cell size across our study region (Figure S3 in Supporting Information S1).

For each MERRA-2 grid cell, we derived two metrics to use for our analysis (Figure 1). The first metric represents antecedent snowfall before glacier ice exposure, which we defined as cumulative snowfall (from M2T1NXFLX) between Oct 1 and May 31. We chose Oct 1 to ensure that the accumulation of snow was not affected by melt. May 31 represents the end of spring (MAM) and start of summer (JJA) which are common time periods used in other studies and ensures that snowfall is aggregated over a fixed time period. The second metric represents melting before glacier ice exposure which was defined as the sum of positive degree days (PDDs) in June (from M2T1NXSLV). We chose June because glacier ice tends to be exposed around late-June or early-July and a sensitivity analysis demonstrated that PDDs summed across ∼30 days were best correlated (relative to shorter and longer periods) with the timing of glacier ice exposure in most regions of the ice sheet (not shown). A fixed time period (i.e., June) was chosen to ensure that PDDs represent the energy transferred to the snowpack, rather than the specific time of year. In other words, we avoided the situation where one year is associated with a larger PDD sum than another year simply because glacier ice is exposed later. From now on, we refer to these metrics simply as “antecedent snowfall” and “early-summer air temperature” for convenience.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Expected controls on the timing of glacier ice exposure for a representative grid cell in South West Greenland (67.1°N, −49.4°E). Solid blue and red lines represent mean snow depth and mean PDDs from MERRA-2 over the 2001–2021 period, respectively. Black dashed line represents timing of glacier ice exposure averaged over the 2001–2021 study period. Shaded areas represent 10th and 90th percentiles. The goal of this study is to identify the relative importance of these two factors on the timing of glacier ice exposure.

2.3 Sensitivity Analysis

We determined the relative importance of antecedent snowfall versus air temperatures on the timing of glacier ice exposure using standardized multiple linear regression. We first identified the median timing of glacier ice exposure from all 1 × 1 km MODIS pixels within each 50 km × 50 km grid cell (Figure S4 in Supporting Information S1). We then performed multiple linear regression between the timing of glacier ice exposure and standardized antecedent snowfall and standardized air temperatures. By standardizing our independent variables, we eliminate units of measurement, meaning that a unit increase in an independent variable is equal to one standard deviation. Standardized regression coefficients therefore represent the response of the dependent variable to a “typical” deviation in the independent variables. In our case, this is equivalent to the response of the timing of glacier ice exposure to a typical deviation in either snowfall or air temperature. The advantage of this approach is that we are able to directly compare the importance of independent variables since the timing of glacier ice exposure will be most sensitive to the independent variable with highest regression coefficient value. We define “sensitivity” in this study as the number of the days change in glacier ice exposure per typical deviation in the either snowfall or air temperature.

We also conducted ordinary least-squares linear regression between non-standardized air temperatures so that we could compute the absolute sensitivity of the timing of glacier ice exposure to air temperature (i.e., number of days change per unit degree [K⁻¹]). Finally, we conducted a Mann-Kendall test to determine if there are monotonic trends in either snowfall or air temperatures over the entire MERRA-2 record which covers the 1981–2021 period.

3 Results

We identify 236 grid cells in which glacier ice was exposed in every year of our 2001–2021 study period. Hereafter we refer to these grid cells cumulatively as the “ablation zone” since they are all located in this area of the ice sheet. We find significant (p < 0.05) relationships between the timing of glacier ice exposure and both antecedent snowfall and air temperatures in all regions of the ice sheet. Examples of significant relationships for three different regions of the ice sheet can be found in Figures 2a–2f. The timing of glacier ice exposure, however, is more likely to be significantly correlated with air temperatures than snowfall. For example, the timing of glacier ice exposure is significantly positively correlated (p < 0.05) with early-summer air temperatures in 141 (60%) grid cells but only significantly negatively correlated (p < 0.05) with antecedent snowfall in 88 (37%) grid cells. This indicates that, on average, early-summer air temperatures exert a stronger control on glacier ice exposure than antecedent snowfall across the ablation zone.

We find that the timing of glacier ice exposure is more sensitive to air temperatures than antecedent snowfall. Across the ablation zone (i.e., 236 grid cells in this study), the timing of glacier ice exposure has a higher goodness-of-fit (R² = 0.27) with air temperatures than snowfall (R² = 0.16). On average, across the entire ablation zone, a typical deviation (i.e., one standard deviation) in antecedent snowfall (±0.50 m) corresponds to a ±2.4 days variation in the timing of glacier ice exposure (Table 1). Whereas a typical deviation in early-summer air temperatures (±0.56 K) corresponds to a ±4.3 days variation in the timing of glacier ice exposure (Table 1). The timing of glacier ice exposure is therefore, on average, nearly twice as sensitive to air temperatures than snowfall.

Table 1. Table Showing Statistics From Standardized Linear Regressions Between the Timing of Glacier Ice Exposure and (a) Air Temperature or (b) Snowfall for all of Greenland (Column 1) and the Eight Regions in Our Study

	All	N	NE	E	SE	S	SW	W	NW
Number of grid cells	236	38	25	34	28	24	39	16	32
Significant (p < 0.05) correlations with ΣPDD (June)	60%	82%	36%	12%	36%	58%	87%	94%	78%
Significant (p < 0.05) correlations with snowfall^a (%)	30%	3%	68%	56%	21%	13%	59%	44%	41%
Mean timing of glacier ice exposure (day of year)	177	182	171	170	179	177	177	181	183
Mean R² ΣPDD (June)	0.27	0.40	0.12	0.09	0.18	0.23	0.39	0.42	0.29
Mean R² snowfall^a	0.16	0.04	0.28	0.26	0.09	0.11	0.26	0.19	0.08
Mean sensitivity to ΣPDD (June) (days)	4.3	5.9	1.9	1.6	2.9	3.6	5.9	7.2	5.7
Mean sensitivity to snowfall^a (days)	2.4	0.0	4.2	3.0	0.7	1.3	3.9	4.1	2.7

^a Snowfall corresponds to cumulative snowfall between 1 October and 31 May. Sensitivity is the number of the days change in glacier ice exposure per typical deviation in the either antecedent snowfall or air temperature.

In most regions, early-summer air temperatures can explain a higher proportion of the variation in timing of glacier ice exposure than antecedent snowfall. The timing of glacier ice exposure is particularly sensitive to air temperatures in N, SW, W, and NW Greenland (±5.9, ±5.9, ±7.2, and ±5.7 days, respectively) (Table 1, Figures S5 and S6 in Supporting Information S1). In contrast, the timing of glacier ice exposure is least sensitive to air temperatures in NE and E Greenland (±1.9 and ±1.6 days, respectively). In NE and E Greenland the timing of glacier ice exposure is actually more sensitive to snowfall (±4.2 and ±3.0 days, respectively) (Table 1). The timing of glacier ice exposure is least sensitive to snowfall in N, S, and SE Greenland (±0.0, ±0.7 and ±1.3 days, respectively) (Table 1). Overall, in 73% of grid cells in our study region, the timing of glacier ice exposure is more sensitive to air temperatures than snowfall.

We find little collinearity between our independent variables: antecedent snowfall and air temperature. Almost all grid cells (99%) have no significant (p < 0.05) linear correlation between cumulative snowfall (between Oct 1 and May 31) and June PDD. We also find that, for 74% of grid cells in our study region, antecedent snowfall is not significantly correlated with mean air temperatures over the same period (i.e., 1 October and 31 May). This lack of relationship is likely because snowfall in the ablation zone is driven by large-scale atmospheric circulation (e.g., cyclonic activity), rather than thermodynamics (i.e., ability of warmer air to deliver more moisture) (Chen et al., 1997; McIlhattan et al., 2020). This implies that ice sheet melt due to climate warming is unlikely to be substantially compensated by higher snowfall in the ablation zone. There are, however, some grid cells where antecedent snowfall is significantly correlated with air temperature. This includes 36% of grid cells in SE Greenland (n = 28), 56% of grid cells in W Greenland (n = 16), and 100% of grid cells in NW Greenland (n = 32). Rising winter air temperatures may therefore be associated with higher snowfall rates in some parts (26%) of the ablation zone.

Our trend analysis reveals that cumulative snowfall (1 October and 31 May), averaged over the ablation zone, has not significantly changed during the 1981–2021 study period (p = 0.32). Almost all grid cells (89% of the study region) experienced no significant trends (p < 0.05) in snowfall over the past 40 years (Figure 3a). On the other hand, mean June air temperatures across the ablation zone have slightly increased (by 0.03 K yr⁻¹; Figure 3b). However, this trend is not significant (p = 0.14). Only 27 (or 11%) of individual grid cells exhibit a significant increasing trend in mean June air temperatures during the study period. These grid cells are located in N (31%) and SE (32%) Greenland. Interestingly, there is a significant increase in winter/spring (1 October to 31 May) ablation zone air temperatures between 1981 and 2021 (p = 0.004). But since the correlation between snowfall and air temperature is insignificant in most regions of the ablation zone, this increase in air temperature is not associated with a detectable increase in snowfall in the climate reanalysis. Overall, the tendency for increasing air temperatures and stationary snowfall in the ablation zone during our study period provides further evidence for the reduced capacity of snowfall to reduce ice sheet melt.

4 Discussion

Melt-albedo feedbacks substantially enhance Greenland Ice Sheet meltwater production during the summer. Although previous studies demonstrated the existence of melt-albedo feedbacks (e.g., Box et al., 2012; Ryan et al., 2019), the relative importance of snowfall versus air temperatures for melt-albedo feedbacks had not been investigated. In this study, we used standardized multiple linear regression to provide insight into the explanatory power of snowfall and air temperatures on the timing of glacier ice exposure– the strongest melt-albedo feedback (Ryan et al., 2019). Our analysis provides several lines of evidence that the timing of glacier ice exposure in the ablation zone are primarily driven by air temperatures and that antecedent snowfall is of secondary importance. We found that, on average, glacier ice exposure is twice as likely to be significantly correlated with air temperatures than snowfall. The timing of glacier ice exposure is also nearly twice as sensitive to air temperatures than antecedent snowfall. Finally, climate reanalysis data indicate that antecedent snowfall has not experienced significant changes between 1981 and 2021. Overall, our analysis demonstrates that snowfall is unlikely to reduce ice sheet melt in the future and that melt-albedo feedbacks will only be amplified by climate warming.

Our analysis suggests that the strength of melt-albedo feedbacks, and therefore ice sheet meltwater runoff, should be accurately predicted by air temperatures. This is important because a number of studies project future ice sheet surface melt using temperature-index models (e.g., Aschwanden et al., 2019; Lenaerts et al., 2015). These computationally efficient strategies are useful because they permit simulations over centuries to millennia, or large ensembles of simulations. Some of these approaches now capture the melt-albedo feedback induced by snowline fluctuations because they include different melt factors for glacier ice and snow. Aschwanden et al. (2019), for example, used melt factors of 8.0 mm K⁻¹ day⁻¹ for glacier ice and 4.1 mm K⁻¹ day⁻¹ for snow. However, if antecedent snowfall had a major role in determining summer melt, forecasts from the utility of these models for assessing future states of the Greenland Ice Sheet could be undermined. Furthermore, future snowfall is not well-predicted by climate models and contains much more uncertainty than air temperatures. Since snowfall has a secondary effect on melt-albedo feedbacks, our study justifies the use of temperature-index approaches to estimate surface melt. Although we note that approaches that include albedo parametrizations (e.g., Gregory et al., 2020; Zeitz et al., 2021) or coupled ice sheet and regional climate models (e.g., Le clec'h et al., 2019) are preferred given the importance of melt-albedo feedbacks on surface melt.

Physically-based climate models have demonstrated that, over the short term (i.e., the next 80–100 years), climate warming is likely to drive changes in Greenland precipitation since warmer air can hold more moisture (Fettweis et al., 2013; Hakuba et al., 2012; Le clec'h et al., 2019). A weak negative feedback between surface albedo and surface air temperatures was identified by Box et al. (2012), implying that warmer air temperatures were associated with higher snowfall rates. However, these negative feedbacks are only identified in the interior of the ice sheet. Our trend analysis indicates that, at the margin of the ice sheet, cumulative snowfall (1 October and 31 May) has remained the same (Figure 3a) even as winter air temperatures have significantly risen (Figure 3c). The lack of relationship is likely because snowfall in the ablation zone is driven by large-scale atmospheric circulation (i.e., cyclonic activity), rather than thermodynamics (i.e., ability of warmer air to deliver more moisture) (Chen et al., 1997; McIlhattan et al., 2020; Schuenemann et al., 2009). It is also possible that some of the snowfall is converted to rainfall which has an opposite effect on surface melt (Box et al., 2022; Doyle et al., 2015). Future snowfall trends are therefore likely to be largely unresponsive to climate warming in the ablation zone and may even decrease due to the higher fraction of liquid precipitation.

Over longer timescales, climate models demonstrate that large-scale topographic changes of the Greenland Ice Sheet can impact local atmospheric dynamics, which in turn can modify patterns of accumulation and ablation. Precipitation in the ablation zone occurs due to orographic uplift of passing cyclones over the coastal margins of the ice sheet (Chen et al., 1997; Schuenemann et al., 2009). Since most of the atmospheric moisture is deposited at the margins, the interior of the ice sheet remains relatively dry. The removal of steep ice margins yields a more even distribution of precipitation over the ice sheet, with an increase in the interior and a decrease in coastal areas. Hakuba et al. (2012) found that the long-term (i.e., over the next 1,000 years) reduction in ice sheet elevation has the potential to enhance precipitation by +4% per 25% of topographic reduction, even with a reduced fraction of total precipitation falling as snow. However, reducing ice sheet elevation also raises annual mean surface air temperatures. Both our study and climate modeling indicate that the increase in ablation due to warming air temperatures will overwhelm the corresponding increases in snow accumulation (Fettweis et al., 2013; Hakuba et al., 2012; Le clec'h et al., 2019).

Our analysis represents one of the few studies that have used remote sensing to investigate melt-albedo feedbacks on the Greenland Ice Sheet (Box et al., 2012; Ryan et al., 2019). Key to these studies is the development of products that enable detection of glacier ice exposure, the strongest melt-albedo feedback operating in the ablation zone. Ryan et al. (2019) developed a glacier ice presence index from MODIS data for assessing the importance of snowline fluctuations on melt. The glacier ice presence index varies between 0 and 1 and is defined as the number of glacier ice observations divided by the total number of valid observations (i.e., when not cloud obscured) between June 1 and August 31. Despite the value of this metric for assessing albedo feedbacks, it provides no information about the timing of glacier ice exposure. For example, a higher glacier presence index value could be due to earlier exposure or later coverage of glacier ice. A similar study by Tedstone and Machguth (2022) computed an annual maximum runoff elevation limit (in m a.s.l.) and area (in km²) using Landsat near-infrared (NIR) imagery. This product identifies the maximum elevation of surface hydrological features to demonstrate the expansion of meltwater runoff area across the Greenland Ice Sheet. However, this product provides no information about the duration that the meltwater runoff features are active. In this study we therefore developed a new glacier exposure product that identified the first day that glacier ice is exposed each year. This metric integrates processes of accumulation and ablation. Due to the difference in reflectance between glacier ice and snow, it is also easily observable using satellite remote sensing. Recent intercomparison studies (e.g., GrSMBMIP) have included these metrics in assessing the accuracy of surface mass balance models (Fettweis et al., 2020). All three variables (i.e., timing of glacier ice exposure, duration of glacier ice exposure, and maximum runoff (or glacier ice area) are valuable for long-term studies of assessing ice sheet and glacier health.

There are some limitations of our study. For example, there are grid cells in our study region where the relationship between glacier ice exposure and both independent variables have low coefficients of determination or even insignificant relationships (p > 0.05). The lack of relationships may be because snow melt is explained by other factors such as turbulent sensible and latent heat fluxes that are not considered in our analysis. Turbulent heat fluxes can become important energy sources for snow melt, especially during high wind speed or rainfall events (Box et al., 2022; Fausto et al., 2016). However, generally air temperature, which is transferred to the surface mainly through longwave radiation, is by far the most important heat source for snow melt on the Greenland Ice Sheet (Ohmura, 2001; van den Broeke et al., 2008). This is evidenced by the widespread use, and accuracy, of positive-degree day approaches for modeling ice sheet surface melt (e.g., Aschwanden et al., 2019). Furthermore, large turbulent heat fluxes tend to be associated with extreme melt events in July (Bennartz et al., 2013; Nghiem et al., 2012) or rainfall events in August and September (Box et al., 2022; Doyle et al., 2015). It is therefore unlikely turbulent heat fluxes are a major energy source for snow melt in June.

A more plausible explanation for the lack of relationships is uncertainties in MERRA-2 snowfall. MERRA-2 precipitation is produced by correcting atmospheric general circulation model outputs with observational data. However, corrections to precipitation do not extend to latitudes poleward of 62.5°N due to a lack of observations (Reichle et al., 2017). Therefore even though MERRA-2 represents the best available NASA atmospheric reanalysis, precipitation may be less accurate over the Greenland Ice Sheet than reported uncertainties at the mid-latitudes (Reichle et al., 2017). Constraining uncertainties in modeled precipitation, and especially snowfall, at high latitudes is notoriously challenging. Remotely sensed precipitation climatologies exist (e.g., Global Precipitation Climatology Project: Adler et al., 2018), but these are usually represented on coarse grids (2.5 × 2.5°) and do not partition precipitation phase, making them unsuitable for resolving snowfall rates in Greenland's narrow ablation zone. CloudSat is one of the few precipitation radar satellites that samples snowfall poleward of 60°N. However, CloudSat cannot resolve snowfall rates below 1,200 m above the surface due to ground clutter (Palerme et al., 2019; Ryan et al., 2020). Furthermore, its sparse spatiotemporal sampling requires retrievals to be aggregated onto coarse grids that also likely introduces uncertainty (Bennartz et al., 2019; Ryan et al., 2020). Discrepancies between modeled and remotely sensed snowfall rates therefore remain mostly unresolved and will continue to be without expansion of the currently sparse network of in situ stations.

Without a reliable method for evaluating MERRA-2 snowfall with observations, we compared our results to those derived from another commonly used regional climate model: Modèle Atmosphérique Régional (MAR) version 3.12 (Fettweis et al., 2017, 2020). To do this, we bilinearly resampled MAR from 10 to 50 km grid resolution and performed the exact same analysis (i.e., multiple linear regression between the timing of glacier ice exposure and standardized antecedent snowfall and standardized air temperatures). Despite being independently produced, we find similar results (Table S1 in Supporting Information S1). Averaged across the ablation zone, a typical deviation (i.e., one standard deviation) in antecedent snowfall simulated by MAR corresponds to a ±2.1 day variation in the timing of glacier ice exposure whereas a typical deviation in early-summer air temperatures simulated by MAR corresponds to a ±4.0 days variation in the timing of glacier ice exposure (Table S1 in Supporting Information S1). These results are similar to those we produced in the analysis using MERRA-2, lending confidence to our conclusions (Table 1).

Overall, our findings demonstrate that snowfall is unlikely to reduce ice sheet melt by dampening melt-albedo feedbacks due to seasonal snowline fluctuations. Instead, ice sheet melt rates are primarily determined by air temperatures. This result is perhaps not surprising given that air temperature is the dominant source of energy for melt (Ohmura, 2001; van den Broeke et al., 2008). But there are two other ways in which air temperature will amplify ice sheet melt. First, as demonstrated in this study, rising air temperatures will enhance melt-albedo feedbacks by exposing vast areas of dark glacier ice earlier in the season, which will melt faster than bright snow. Furthermore, warmer air temperatures will enhance melt-albedo feedbacks specific to snow (e.g., grain size) and ice (e.g., meltwater ponding). Second, rising air temperatures will increase the fraction of precipitation falling as rain. Currently, liquid precipitation represents only a small fraction of precipitation in the ablation zone. But studies have demonstrated that rainfall is becoming increasingly common as warm air masses are more frequently advected over Greenland (Doyle et al., 2015). The cooling and freezing of rainfall on the ice sheet surface is very efficient at raising the temperature of the snowpack, making it more susceptible to melting (Box et al., 2022; Doyle et al., 2015). Consequently, rainfall has the double effect of reducing accumulation and enhancing ablation on the ice sheet. We therefore expect that by strengthening melt-albedo feedbacks and increasing fraction of rainfall, the Greenland Ice Sheet mass balance will be increasingly governed by air temperatures in the future.

Acknowledgments

We acknowledge funding from NASA's Interdisciplinary Research in Earth Science program and the Science of Terra, Aqua, and Suomi-NPP program including Grants 80NSSC20K1727, 80NSSC21K0083, 80NSSC20K1878, 80NSSC21K0142, and 80NSSC21K1973.

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Open Research

Data Availability Statement

All data needed to evaluate the conclusions in the paper are present in the paper and/or available here: https://doi.org/10.5281/zenodo.10064118. MOD10A1 data were downloaded from NASA's National Snow and Ice Data Center Distributed Active Archive Center. MERRA-2 data were downloaded from NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). The code used in this study is available here: https://doi.org/10.5281/zenodo.10064179.

Supporting Information

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Volume10, Issue11

November 2023

e2023EA003158

Role of Snowfall Versus Air Temperatures for Greenland Ice Sheet Melt-Albedo Feedbacks

Abstract

Key Points

Plain Language Summary

1 Introduction