Dataset Information

Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland.

ABSTRACT: The surface energy balance (SEB) is essential for understanding the coupled cryosphere-atmosphere system in the Arctic. In this study, we investigate the spatiotemporal variability in SEB across tundra, snow and ice. During the snow-free period, the main energy sink for ice sites is surface melt. For tundra, energy is used for sensible and latent heat flux and soil heat flux leading to permafrost thaw. Longer snow-free period increases melting of the Greenland Ice Sheet and glaciers and may promote tundra permafrost thaw. During winter, clouds have a warming effect across surface types whereas during summer clouds have a cooling effect over tundra and a warming effect over ice, reflecting the spatial variation in albedo. The complex interactions between factors affecting SEB across surface types remain a challenge for understanding current and future conditions. Extended monitoring activities coupled with modelling efforts are essential for assessing the impact of warming in the Arctic.

SUBMITTER: Lund M

PROVIDER: S-EPMC5258660 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland.

Lund Magnus M Stiegler Christian C Abermann Jakob J Citterio Michele M Hansen Birger U BU van As Dirk D

Ambio 20170201 Suppl 1

The surface energy balance (SEB) is essential for understanding the coupled cryosphere-atmosphere system in the Arctic. In this study, we investigate the spatiotemporal variability in SEB across tundra, snow and ice. During the snow-free period, the main energy sink for ice sites is surface melt. For tundra, energy is used for sensible and latent heat flux and soil heat flux leading to permafrost thaw. Longer snow-free period increases melting of the Greenland Ice Sheet and glaciers and may prom ...[more]

PMID: 28116688

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Project description:Measuring microbial abundance in glacier ice and identifying its controls is essential for a better understanding and quantification of biogeochemical processes in glacial ecosystems. However, cell enumeration of glacier ice samples is challenging due to typically low cell numbers and the presence of interfering mineral particles. We quantified for the first time the abundance of microbial cells in surface ice from geographically distinct sites on the Greenland Ice Sheet (GrIS), using three enumeration methods: epifluorescence microscopy (EFM), flow cytometry (FCM), and quantitative polymerase chain reaction (qPCR). In addition, we reviewed published data on microbial abundance in glacier ice and tested the three methods on artificial ice samples of realistic cell (10(2)-10(7) cells ml(-1)) and mineral particle (0.1-100 mg ml(-1)) concentrations, simulating a range of glacial ice types, from clean subsurface ice to surface ice to sediment-laden basal ice. We then used multivariate statistical analysis to identify factors responsible for the variation in microbial abundance on the ice sheet. EFM gave the most accurate and reproducible results of the tested methodologies, and was therefore selected as the most suitable technique for cell enumeration of ice containing dust. Cell numbers in surface ice samples, determined by EFM, ranged from ~ 2 × 10(3) to ~ 2 × 10(6) cells ml(-1) while dust concentrations ranged from 0.01 to 2 mg ml(-1). The lowest abundances were found in ice sampled from the accumulation area of the ice sheet and in samples affected by fresh snow; these samples may be considered as a reference point of the cell abundance of precipitants that are deposited on the ice sheet surface. Dust content was the most significant variable to explain the variation in the abundance data, which suggests a direct association between deposited dust particles and cells and/or by their provision of limited nutrients to microbial communities on the GrIS.

Dataset Information

Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland.

Publications

Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland.

Similar Datasets

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