Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11-05

AUTHORS

Qinghua Ding, Axel Schweiger, Michelle L’Heureux, Eric J. Steig, David S. Battisti, Nathaniel C. Johnson, Eduardo Blanchard-Wrigglesworth, Stephen Po-Chedley, Qin Zhang, Kirstin Harnos, Mitchell Bushuk, Bradley Markle, Ian Baxter

ABSTRACT

The relative contribution and physical drivers of internal variability in recent Arctic sea ice loss remain open questions, leaving up for debate whether global climate models used for climate projection lack sufficient sensitivity in the Arctic to climate forcing. Here, through analysis of large ensembles of fully coupled climate model simulations with historical radiative forcing, we present an important internal mechanism arising from low-frequency Arctic atmospheric variability in models that can cause substantial summer sea ice melting in addition to that due to anthropogenic forcing. This simulated internal variability shows a strong similarity to the observed Arctic atmospheric change in the past 37 years. Through a fingerprint pattern matching method, we estimate that this internal variability contributes to about 40–50% of observed multi-decadal decline in Arctic sea ice. Our study also suggests that global climate models may not actually underestimate sea ice sensitivities in the Arctic, but have trouble fully replicating an observed linkage between the Arctic and lower latitudes in recent decades. Further improvements in simulating the observed Arctic–global linkage are thus necessary before the Arctic’s sensitivity to global warming in models can be quantified with confidence. Internal low-frequency variability in the Arctic atmosphere can explain about half the summer sea ice decline over the past decades, according to an analysis of large ensembles of fully coupled climate model simulations. More... »

PAGES

1-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41561-018-0256-8

DOI

http://dx.doi.org/10.1038/s41561-018-0256-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107926157


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41561-018-0256-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41561-018-0256-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41561-018-0256-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41561-018-0256-8'


 

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