Resampled density in 48 shallow firn cores from Southwest Greenland, 2012–2019

This dataset contains resampled density data of 48 shallow firn cores collected in the percolation zone in southwest Greenland between 2012 and 2019. The original cores were presented in Machguth et al. (2016), MacFerrin et al. (2019), Vandecrux et al. (2019) and Rennermalm et al. (2021a), and can be accessed through the Arctic Data Center: http://doi.org/10.18739/A2125Q994 (Koenig and Montgomery, 2020) and http://doi.org/10.18739/A2Q52FD98 (Rennermalm et al., 2021b). In the original datasets, density was calculated as averages for core segments that could contain a mixture of snow, firn and ice. In this dataset, first presented in Xiao et al. (2022), a resampling method was applied to estimate density separately for each snow, firn and ice layers within the core segments. During the field drilling process, each firn core coming out of the drill barrel either naturally broke or was manually cut into multiple segments. For each segment an average density was measured in the field. Many core segments contain both firn/snow layer(s) as well as ice layer(s) which have distinctly different densities. Yet the measured segment-averaged densities cannot capture these spatial density variations within segments. To acquire more precise density profiles, we resampled the densities of all mixed ice-firn and ice-snow segments in these 48 cores so that all ice, firn and snow layers within a segment are assigned a distinct ice, firn and snow density, respectively. At the same time, the resampling processes aimed to preserve the original segment-averaged density. Quality control was performed for the resampled density data to replace unrealistic values. More details are provided in the method section below as well as in Xiao et al. (2022). The dataset includes the following files Core_meta_data.csv : File with metadata about each core, including location, site name, retrieval date, investigators, and other information. Explanation_of_core_variables.csv : File with explanation of the variables reported in the resampled density files. Resampled density files: For each core one file is included: a file including top and bottom depth, length, measured and resampled density, and other information of each core layer, with the following file naming convention [Site]_[YEAR]_[Core number]. Text in brackets are variables. References: Koenig, L., and Montgomery, L. Data from: Surface Mass Balance and Snow Depth on Sea Ice Working Group (SUMup) snow density subdataset, Greenland and Antarctica, 1950–2018. Arctic Data Center. (2020) http://doi.org/10.18739/A2125Q994 MacFerrin, M., Machguth, H., van As, D., Charalampidis, C., Stevens, C. M., Heilig, A., et al. (2019) Rapid expansion of Greenland’s low-permeability ice slabs. Nature 573, 403–407. doi: 10.1038/s41586-019-1550-3 Machguth, H., MacFerrin, M., van As, D., Box, J. E., Charalampidis, C., Colgan, W., et al. (2016) Greenland meltwater storage in firn limited by near-surface ice formation. Nat. Clim. Change 6, 390–393. doi: 10.1038/nclimate2899 Rennermalm, Å. K., Hock, R., Covi, F., Xiao, J., Corti, G., Kingslake, J., et al. (2021a) Shallow firn cores 1989–2019 in southwest Greenland's percolation zone reveal decreasing density and ice layer thickness after 2012. J. Glaciol., 1–12. doi: 10.1017/jog.2021.102 Rennermalm, Å. K., Hock, R., Covi, F., Xiao, J., Corti, G., Leidman, S. Z., et al. Data from: Density and ice layer stratigraphy in 24 shallow firn cores from Southwest Greenland, 2017–2019. Arctic Data Center. (2021b) http://doi.org/10.18739/A2Q52FD98 Vandecrux, B., MacFerrin, M., Machguth, H., Colgan, W. T., van As, D., Heilig, A., et al. (2019) Firn data compilation reveals widespread decrease of firn air content in western Greenland. Cryosphere 13, 845–859. doi: 10.5194/tc-13-845-2019 Xiao, J., Rennermalm, Å. K., Covi, F., Hock, R., Leidman, S. Z., Miège, C., et al. (2022) Local-scale spatial variability in firn properties in Southwest Greenland. Front. Earth Sci. 10:938246. doi: 10.3389/feart.2022.938246

本数据集包含2012年至2019年间在格陵兰西南部渗流带采集的48根浅层粒雪芯（firn core）的重采样密度数据。原始粒雪芯数据已发表于Machguth等（2016）、MacFerrin等（2019）、Vandecrux等（2019）以及Rennermalm等（2021a），可通过北极数据中心（Arctic Data Center）获取：http://doi.org/10.18739/A2125Q994（Koenig与Montgomery，2020）及http://doi.org/10.18739/A2Q52FD98（Rennermalm等，2021b）。

在原始数据集中，密度以芯段的平均值计算，而这些芯段可能混合了雪、粒雪与冰。本数据集（首次发表于Xiao等，2022）采用重采样方法，对芯段内的雪、粒雪和冰各层分别估算密度。

野外钻探过程中，每根从钻筒取出的粒雪芯会自然断裂或被手动切割为多个芯段，每个芯段的平均密度在野外完成测量。由于多数芯段同时包含粒雪/雪层与冰层，三者密度差异显著，但原始的芯段平均密度无法反映芯段内部的密度空间变化。

为获得更精准的密度剖面（density profile），我们对48根粒雪芯中所有混合冰-粒雪、冰-雪的芯段进行重采样，使芯段内的冰、粒雪和雪层分别被赋予对应的专属密度；同时，重采样过程旨在保留原始芯段的平均密度。此外，我们对重采样后的密度数据开展质量控制（quality control），以替换不合理的数值。更多细节可参见下文的方法章节以及Xiao等（2022）的研究。

本数据集包含以下文件：
1. "Core_meta_data.csv"：包含每根粒雪芯的元数据，包括采样位置、测点名称、获取日期、研究人员及其他相关信息。
2. "Explanation_of_core_variables.csv"：包含重采样密度文件中所报告变量的说明文档。
3. 重采样密度文件：每根粒雪芯对应一个文件，文件包含各芯层的顶底深度、长度、实测密度与重采样密度等信息，文件命名规则为[Site]_[YEAR]_[Core number]，方括号内为变量项。

参考文献：
Koenig, L. 与 Montgomery, L. 数据集来源：海冰表面质量平衡与雪深工作组（SUMup）雪密度子数据集，格陵兰与南极洲，1950–2018. 北极数据中心（Arctic Data Center）.（2020）http://doi.org/10.18739/A2125Q994
MacFerrin, M., Machguth, H., van As, D., Charalampidis, C., Stevens, C. M., Heilig, A. 等.（2019）格陵兰低渗透率冰盖的快速扩张. 《自然（Nature）》573, 403–407. doi: 10.1038/s41586-019-1550-3
Machguth, H., MacFerrin, M., van As, D., Box, J. E., Charalampidis, C., Colgan, W. 等.（2016）近地表冰形成限制了格陵兰融水在粒雪层中的存储. 《自然·气候变化（Nat. Clim. Change）》6, 390–393. doi: 10.1038/nclimate2899
Rennermalm, Å. K., Hock, R., Covi, F., Xiao, J., Corti, G., Kingslake, J. 等.（2021a）1989–2019年格陵兰西南部渗流带的浅层粒雪芯：2012年后密度与冰层厚度的下降趋势. 《冰川学杂志（J. Glaciol.）》, 1–12. doi: 10.1017/jog.2021.102
Rennermalm, Å. K., Hock, R., Covi, F., Xiao, J., Corti, G., Leidman, S. Z. 等. 数据集来源：格陵兰西南部24根浅层粒雪芯的密度与冰层地层学，2017–2019. 北极数据中心（Arctic Data Center）.（2021b）http://doi.org/10.18739/A2Q52FD98
Vandecrux, B., MacFerrin, M., Machguth, H., Colgan, W. T., van As, D., Heilig, A. 等.（2019）粒雪数据汇编揭示格陵兰西部粒雪含气量的广泛下降. 《冰冻圈（Cryosphere）》13, 845–859. doi: 10.5194/tc-13-845-2019
Xiao, J., Rennermalm, Å. K., Covi, F., Hock, R., Leidman, S. Z., Miège, C. 等.（2022）格陵兰西南部粒雪性质的局域空间变异. 《地球科学前沿（Front. Earth Sci.）》10:938246. doi: 10.3389/feart.2022.938246