A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models

Cited 0 time in webofscience Cited 0 time in scopus
15 Viewed 5 Downloaded
Title
A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models
Author(s)
R. Olson; An S.-I.; Fan Y.; Chang W.; Evans J.P.; J.-Y. Lee
Publication Date
2019-07
Journal
NATURE COMMUNICATIONS, v.10, no., pp.3016 -
Publisher
NATURE PUBLISHING GROUP
Abstract
© 2019, The Author(s).A major conundrum in climate science is how to account for dependence between climate models. This complicates interpretation of probabilistic projections derived from such models. Here we show that this problem can be addressed using a novel method to test multiple non-exclusive hypotheses, and to make predictions under such hypotheses. We apply the method to probabilistically estimate the level of global warming needed for a September ice-free Arctic, using an ensemble of historical and representative concentration pathway 8.5 emissions scenario climate model runs. We show that not accounting for model dependence can lead to biased projections. Incorporating more constraints on models may minimize the impact of neglecting model non-exclusivity. Most likely, September Arctic sea ice will effectively disappear at between approximately 2 and 2.5 K of global warming. Yet, limiting the warming to 1.5 K under the Paris agreement may not be sufficient to prevent the ice-free Arctic
URI
https://pr.ibs.re.kr/handle/8788114/6139
ISSN
2041-1723
Appears in Collections:
Center for Climate Physics(기후물리 연구단) > Journal Papers (저널 논문)
Files in This Item:
22. A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models.pdfDownload

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse