Preprints
https://doi.org/10.5194/os-2020-35
https://doi.org/10.5194/os-2020-35

  13 May 2020

13 May 2020

Review 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 Wang Yangjun et al.
  • College of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, China

Abstract. 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This preprint has been withdrawn.

Wang Yangjun et al.

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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 - Printer-friendly version Supplement - Supplement

Wang Yangjun et al.

Wang Yangjun et al.

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This preprint has been withdrawn.

Short summary
This paper proposes a new algorithm called AFTER-SSIM algorithm to evaluate the performance of 101 selected global climate models under different emission scenarios during 2006 to 2018 and reduces the uncertainty among them.  AFTER-SSIM algorithm shows good performance in sea ice combined forecast and uncertainty reduction among global climate models. The possibility beyond 80 % indicates that the Arctic Northern Sea Route will be open to 1A super vessels for 5 months in the year of 2030.