↓ Skip to main content

Clouds enhance Greenland ice sheet meltwater runoff

Overview of attention for article published in Nature Communications, January 2016
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
37 news outlets
blogs
6 blogs
twitter
58 tweeters
facebook
4 Facebook pages
wikipedia
1 Wikipedia page
googleplus
5 Google+ users
reddit
2 Redditors

Citations

dimensions_citation
116 Dimensions

Readers on

mendeley
199 Mendeley
citeulike
4 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Clouds enhance Greenland ice sheet meltwater runoff
Published in
Nature Communications, January 2016
DOI 10.1038/ncomms10266
Pubmed ID
Authors

K. Van Tricht, S. Lhermitte, J. T. M. Lenaerts, I. V. Gorodetskaya, T. S. L’Ecuyer, B. Noël, M. R. van den Broeke, D. D. Turner, N. P. M. van Lipzig

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 58 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 199 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 2 1%
Switzerland 1 <1%
New Zealand 1 <1%
France 1 <1%
Denmark 1 <1%
Spain 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 190 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 68 34%
Researcher 42 21%
Student > Master 23 12%
Student > Bachelor 17 9%
Professor 11 6%
Other 23 12%
Unknown 15 8%
Readers by discipline Count As %
Earth and Planetary Sciences 115 58%
Environmental Science 34 17%
Physics and Astronomy 7 4%
Engineering 7 4%
Agricultural and Biological Sciences 4 2%
Other 7 4%
Unknown 25 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 381. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 June 2020.
All research outputs
#43,422
of 17,358,590 outputs
Outputs from Nature Communications
#599
of 34,114 outputs
Outputs of similar age
#1,084
of 376,439 outputs
Outputs of similar age from Nature Communications
#10
of 743 outputs
Altmetric has tracked 17,358,590 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 34,114 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 51.9. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 376,439 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 743 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.