Project description:The Arctic is being disproportionally affected by climate change compared with other geographic locations, and is currently experiencing unprecedented melt rates. The Greenland Ice Sheet (GrIS) can be regarded as the largest supraglacial ecosystem on Earth, and ice algae are the dominant primary producers on bare ice surfaces throughout the course of a melt season. Ice-algal-derived pigments cause a darkening of the ice surface, which in turn decreases albedo and increases melt rates. The important role of ice algae in changing melt rates has only recently been recognized, and we currently know little about their community compositions and functions. Here, we present the first analysis of ice algal communities across a 100 km transect on the GrIS by high-throughput sequencing and subsequent oligotyping of the most abundant taxa. Our data reveal an extremely low algal diversity with Ancylonema nordenskiöldii and a Mesotaenium species being by far the dominant taxa at all sites. We employed an oligotyping approach and revealed a hidden diversity not detectable by conventional clustering of operational taxonomic units and taxonomic classification. Oligotypes of the dominant taxa exhibit a site-specific distribution, which may be linked to differences in temperatures and subsequently the extent of the melting. Our results help to better understand the distribution patterns of ice algal communities that play a crucial role in the GrIS ecosystem.
Project description:The Greenland ice sheet has become one of the main contributors to global sea level rise, predominantly through increased meltwater runoff. The main drivers of Greenland ice sheet runoff, however, remain poorly understood. Here we show that clouds enhance meltwater runoff by about one-third relative to clear skies, using a unique combination of active satellite observations, climate model data and snow model simulations. This impact results from a cloud radiative effect of 29.5 (±5.2) W m(-2). Contrary to conventional wisdom, however, the Greenland ice sheet responds to this energy through a new pathway by which clouds reduce meltwater refreezing as opposed to increasing surface melt directly, thereby accelerating bare-ice exposure and enhancing meltwater runoff. The high sensitivity of the Greenland ice sheet to both ice-only and liquid-bearing clouds highlights the need for accurate cloud representations in climate models, to better predict future contributions of the Greenland ice sheet to global sea level rise.
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.