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13 May 2020
Status: this preprint was under review for the journal OS. A final paper is not foreseen.
Constraining Uncertainties in CMIP5 Projections of Arctic Sea Ice Volume with Observations
Wang Yangjun, Liu Kefeng, Shan Yulong, and Zhang Ren
This study proposes adaptive forecasting through exponential re-weighting based on the Structural Similarity Index Measure (AFTER-SSIM) algorithm to evaluate the performance of global climate models from the Coupled Model Intercomparison Project (CMIP5) under different emission scenarios during 2006 to 2018, attempting to reduce the uncertainty among them. The SSIM approach uses a loss function to obtain more information on the spatial distribution between model outputs and observed data, where the genetic algorithm (GA) is used to optimise the parameters of both seasonal cycles and long-term trends of sea ice concentration and sea ice thickness. The re-weighting mechanism of the AFTER-SSIM algorithm guarantees a performance improvement in sea ice volume simulations as new information is added. Finally, the ranked models have been combined to estimate the future Arctic sea ice volume and navigation possibility through the Arctic Northern Sea Route. Results show that the proposed algorithm reduces the uncertainty among models, sea ice volume will continue to shrink in the future, and the open periods for 1A super vessels are likely to reach to five months ranging from August to December in 2030.
This preprint has been withdrawn.
Received: 20 Apr 2020 – Discussion started: 13 May 2020
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Wang Yangjun, Liu Kefeng, Shan Yulong, and Zhang Ren
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Status: closed
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
- Printer-friendly version
- Supplement
Wang Yangjun, Liu Kefeng, Shan Yulong, and Zhang Ren
Wang Yangjun, Liu Kefeng, Shan Yulong, and Zhang Ren
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