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Edited by: Timothy C. Bartholomaus, University of Idaho, United States

Reviewed by: Xavier Fettweis, University of Liège, Belgium; Marco Möller, University of Bremen, Germany; Clément Miège, University of Utah, United States

This article was submitted to Cryospheric Sciences, a section of the journal Frontiers in Earth Science

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m^{−3} with a standard deviation of 44 kg m^{−3}, and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m^{−3} may give superior results when applied in surface mass budget modeling.

The mass budget of the Greenland ice sheet has grown increasingly negative during the past two decades (e.g., Kjeldsen et al.,

Regional climate models calculate firn densification (e.g., Vionnet et al.,

The parameterizations based on temperatures rely on

The aim of this study is to present a spatially extensive density dataset for the Greenland ice sheet derived from 200 density-profile measurements, and to investigate the observed spatiotemporal variability for the top 0.1 m of snow/firn. In an application of this dataset, we quantify the performance of the observation-based temperature-dependent surface snow density parameterizations by Kuipers Munneke et al. (

Our surface density dataset consists of 200 point observations, along with the geographic location, annual air temperature and annual accumulation rate for these locations. The oldest surface density data were collected by Benson (

Locations of all surface snow density measurement locations in our dataset. Contours lines indicate elevations in meters above sea level.

Density of the top 0.1 m of snow plotted against site- and campaign-specific parameters:

Defining the surface layer as the upper 0.1 m of snow yields that in most cases the surface layer was deposited in multiple snowfall events, except for areas located at relatively low elevations in the south and southeast of the ice sheet, where individual precipitation events typically produce more than 0.1 m of snow (Burgess et al.,

Commonly, snow/firn was sampled in snow pits using a fixed volume cutter at 0.05–0.1 m vertical resolution. These samples were weighed using a variety of scales. When density data were derived from a core, the snow was extracted from the core barrel and typically sub-sampled into 0.1 m sections before being weighed. Conger and McClung (

We test a surface snow density parameterization for the Greenland ice sheet that is dependent on temperature, similar to commonly used parameterizations by Kuipers Munneke et al. (^{−3} on annual air temperature (T_{a}) in °C, in what we refer to as parameterization P1:

We determine the fit coefficients by orthogonal linear regression to all available T_{a} values in our dataset, and find a best fit for A = 362.1 and B = 2.78 (Table _{s} in °C simulated by RACMO2.3 and the average density of the uppermost 1 m of snow/firn, and found what we here refer to as parameterization P2:

Reeh et al. (_{f}) from the near-surface part of their depth-density profiles by determining the load at 5 m depth, as calculated by their model, so that it fits the corresponding load derived from the measured depth-density profiles (parameterization P3):

There is a ca. 40% overlap between our dataset and the data feeding into the Kuipers Munneke et al. (

Fit coefficients and statistics for parameterization P1 (Equation 1).

^{2}) |
||||
---|---|---|---|---|

0–0.1 | 362.1 | 2.78 | 0.12 | 91 |

0–0.2 | 363.0 | 2.21 | 0.14 | 91 |

0–0.5 | 358.4 | 1.30 | 0.08 | 91 |

To highlight the importance of sampling depth ranges in producing an observationally-based boundary condition for firn models in Greenland, we also test P1 (Equation 1) using the average density of the top 0.2 and 0.5 m of snow/firn in our analysis (Table

Surface snow density in our 200-value database ranges between 190 and 420 kg m^{−3}, with an average of 315 kg m^{−3} and associated standard deviation of 44 kg m^{−3} (Figure ^{−3}. The measurement uncertainty is smaller than the 44 kg m^{−3} standard deviation, which demonstrates a significant natural variability in the top 0.1 m of snow most likely due to differences in precipitation events and influences from weather in general. Yet the variability in surface snow density could also depend on location or annual air temperature as investigated below.

Number of surface snow density measurements over the Greenland ice sheet. Blue solid and dashed lines indicate the average and standard deviation of the dataset, respectively.

Surface snow density dataset metadata for three depth ranges.

Number of observations | 200 | 206 | 231 |

Minimum (kg m^{−3}) |
191 | 170 | 256 |

Maximum (kg m^{−3}) |
420 | 478 | 510 |

Average (kg m^{−3}) |
315 | 324 | 341 |

Median (kg m^{−3}) |
321 | 325 | 336 |

Standard deviation (kg m^{−3}) |
44 | 41 | 37 |

There is no significant temporal trend in surface snow density (Figure ^{−3}, with mean biases of + 19% (P2) and +17% (P3). For the 0–0.1 m depth range, RMSE values for P2 and P3, are respectively a factor of 2.0 and 1.8 higher than those for our P1 parameterization (Table

Root-mean-square error (RMSE), mean bias and RMSE ratio values for parameterizations using annual mean air temperature: P1 (this study), P2 (Kuipers Munneke et al.,

^{−3}) |
^{−3}) |
||||
---|---|---|---|---|---|

P1 | 0–0.1 | 42 | 0 | 2.0 | 1.8 |

P1 | 0–0.2 | 30 | 0 | 2.2 | 2.1 |

P1 | 0–0.5 | 24 | 0 | 2.2 | 2.1 |

P2 | 0–0.1 | 84 | 72 (19%) | – | – |

P2 | 0–0.2 | 67 | 58 (15%) | – | – |

P2 | 0–0.5 | 53 | 42 (11%) | – | – |

P3 | 0–0.1 | 76 | 62 (17%) | – | – |

P3 | 0–0.2 | 63 | 48 (13%) | – | – |

P3 | 0–0.5 | 50 | 32 (8%) | – | – |

Average snow/firn density increases from 315 to 341 kg m^{−3} as the averaging depth range increases from 0.1 to 0.5 m (Table _{a} (Equation 1) typically exceeds T_{s} (Equation 2) by a few degrees does not make up for more than 10 kg m^{−3} of the P2 overestimate.

Figure

Orthogonal linear regression fits (solid lines) for temperature-dependent parameterization P1 (Table

We use a smaller depth range to better represent surface snow density than previous studies. Assessing density closer to the surface is important for producing a more accurate upper boundary condition to be used in firn evolution models that would produce too high firn densities along the entire depth profile. Figure

The top of the snowpack compacts rapidly after snowfall (e.g., Brun et al.,

In regions where large snowfall events occur, such as in south Greenland, density measurements of the top 0.1 m of snow may reflect the conditions during one snowfall event and subsequent weather-dependent densification prior to measurement. All of the snow-density measurements in our database were taken in spring and summer, meaning that our average and parameterization may be seasonally biased. Dibb and Fahnestock (

Higher air temperatures result in higher snow and firn densities through increased compaction (Zwally and Li, ^{−3}. But even a large temperature increase of 10°C anywhere in Greenland would only cause a densification of 28 kg m^{−3} in the top 0.1 m of snow, which is smaller than the 32 kg m^{−3} measurement uncertainty and 44 kg m^{−3} standard deviation of the dataset (Table

The choice of a surface snow density boundary condition influences calculations of available pore space by models simulating the surface mass budget of the Greenland ice sheet. Steger et al. (^{−3} in regional climate model HIRHAM5, while Langen et al. (^{−3}, signifying a higher meltwater retention capacity in the snow and firn. Langen et al. (^{−3} as boundary condition, a value 5% lower than that used by Langen et al. (

Using our dataset for the top 0.1 m of snow, as opposed to using those for larger depth ranges, comes at the cost of a higher variability (standard deviation in Table ^{−3} for the top 0.1 m of snow may therefore misrepresent relatively low-density layers below 0.1 m depth deposited during large snowfall events. Regions where snowfall may exceed 0.1 m in single events are typically located at lower elevations on the southern and southeastern parts of the ice sheet (e.g., Burgess et al.,

Our dataset has sparse coverage in the northern and eastern sectors of the ice sheet, possibly introducing a spatial bias in our results. Figure

Elevation distribution of the surface snow density measurement locations compared to the area-elevation distribution of the entire Greenland ice sheet.

We constructed a dataset of surface snow density for the top 0.1, 0.2, and 0.5 m of snow/firn on the Greenland ice sheet based on 200 ^{−3} (8–19%) lower than earlier parameterizations do, thus beyond the 32 kg m^{−3} measurement uncertainty range. Yet since the natural variability in surface snow density is found to be large with e.g., a 44 kg m^{−3} standard deviation for the top 0.1 m of snow, the temperature sensitivity of surface snow density is not found to be significant, indicating that an average surface snow density of 315 kg m^{−3} could be the preferred choice as a boundary condition for models calculating the surface mass budget of the Greenland ice sheet.

RF conceived the study and wrote the manuscript; BV and RF did the statistical analysis. All authors contributed with field data and continuously discussed the results and developed the analysis further.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer, CM, declared a past collaboration with two of the authors, JB and LK, to the handling editor.

This work was supported by the Danish Research Council Grant FNU 4002-00234 and the Programme for Monitoring of the Greenland Ice Sheet (

The Supplementary Material for this article can be found online at:

Surface density dataset consists of point observations (0–10, 0–20, 0–50 cm), along with the geographic location, annual air temperature and annual accumulation rates.