Assessment of Arctic sea ice simulations in cGENIE model and projections under RCP scenarios

Abstract Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the...

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Bibliographic Details
Published in:Scientific Reports
Main Authors: Di Chen, Min Fu, Xin Liu, Qizhen Sun
Format: Article in Journal/Newspaper
Language:English
Published: Nature Portfolio 2024
Subjects:
Simulation skill
Arctic sea ice
Future projection
RCP scenarios
Medicine
R
Science
Q
Arctic
Sea ice
Online Access:https://doi.org/10.1038/s41598-024-67391-1
https://doaj.org/article/897a2f1c0f6b403bb025784d5a6a8a46
Description
Summary:Abstract Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model’s skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change.

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