Dataset Information

Abrupt shift in the observed runoff from the southwestern Greenland ice sheet.

ABSTRACT: The recent decades of accelerating mass loss of the Greenland ice sheet have arisen from an increase in both surface meltwater runoff and ice flow discharge from tidewater glaciers. Despite the role of the Greenland ice sheet as the dominant individual cryospheric contributor to sea level rise in recent decades, no observational record of its mass loss spans the 30-year period needed to assess its climatological state. We present for the first time a 40-year (1975-2014) time series of observed meltwater discharge from a >6500-km2 catchment of the southwestern Greenland ice sheet. We find that an abrupt 80% increase in runoff occurring between the 1976-2002 and 2003-2014 periods is due to a shift in atmospheric circulation, with meridional exchange events occurring more frequently over Greenland, establishing the first observation-based connection between ice sheet runoff and climate change.

SUBMITTER: Ahlstrom AP

PROVIDER: S-EPMC5729017 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Abrupt shift in the observed runoff from the southwestern Greenland ice sheet.

Ahlstrøm Andreas P AP Petersen Dorthe D Langen Peter L PL Citterio Michele M Box Jason E JE

Science advances 20171213 12

The recent decades of accelerating mass loss of the Greenland ice sheet have arisen from an increase in both surface meltwater runoff and ice flow discharge from tidewater glaciers. Despite the role of the Greenland ice sheet as the dominant individual cryospheric contributor to sea level rise in recent decades, no observational record of its mass loss spans the 30-year period needed to assess its climatological state. We present for the first time a 40-year (1975-2014) time series of observed m ...[more]

PMID: 29242827

Similar Datasets

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

Abrupt shift in the observed runoff from the southwestern Greenland ice sheet.

Publications

Abrupt shift in the observed runoff from the southwestern Greenland ice sheet.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets