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

R. Olson, S. I. An, Y. Fan, W. Chang, J. P. Evans, J. Y. Lee

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4 Citations (Scopus)


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.

Original languageEnglish
Article number3016
JournalNature communications
Issue number1
Publication statusPublished - 2019 Dec 1

Bibliographical note

Funding Information:
For their roles in producing, coordinating, and making available the CMIP5 model output, the authors acknowledge the climate modeling groups, the World Climate Research Programme’s (WCRP) Working Group on Coupled Modeling (WGCM), and the Global Organization for Earth System Science Portals (GO-ESSP). R. Olson and S.-I. An were supported by the Basic Science Research Program through National Research Foundation of Korea (NRF-2017K1A3A7A03087790 and NRF-2018R1A5A1024958). R. Olson and J.-Y. Lee also acknowledge support from the Institute for Basic Science (project code IBS-R028-D1). Y. Fan is grateful for the support by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). Jason Evans acknowledges support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023).

Publisher Copyright:
© 2019, The Author(s).

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)


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