↓ Skip to main content

Potential for western US seasonal snowpack prediction

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, January 2018
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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
15 news outlets
blogs
4 blogs
twitter
24 tweeters
facebook
3 Facebook pages

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
72 Mendeley
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
Potential for western US seasonal snowpack prediction
Published in
Proceedings of the National Academy of Sciences of the United States of America, January 2018
DOI 10.1073/pnas.1716760115
Pubmed ID
Authors

Sarah B. Kapnick, Xiaosong Yang, Gabriel A. Vecchi, Thomas L. Delworth, Rich Gudgel, Sergey Malyshev, P. C. D. Milly, Elena Shevliakova, Seth Underwood, Steven A. Margulis

Abstract

Western US snowpack-snow that accumulates on the ground in the mountains-plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 months in advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

Twitter Demographics

The data shown below were collected from the profiles of 24 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 28%
Researcher 14 19%
Student > Master 12 17%
Student > Doctoral Student 6 8%
Professor 5 7%
Other 6 8%
Unknown 9 13%
Readers by discipline Count As %
Earth and Planetary Sciences 30 42%
Environmental Science 14 19%
Engineering 9 13%
Agricultural and Biological Sciences 3 4%
Mathematics 1 1%
Other 3 4%
Unknown 12 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 157. 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 08 February 2018.
All research outputs
#148,636
of 17,816,628 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#3,273
of 90,046 outputs
Outputs of similar age
#5,056
of 376,262 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#87
of 972 outputs
Altmetric has tracked 17,816,628 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 90,046 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 31.5. This one has done particularly well, scoring higher than 96% 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 376,262 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 98% of its contemporaries.
We're also able to compare this research output to 972 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 91% of its contemporaries.