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Simulation of sea ice in FGOALS-g2: Climatology and late 20th century changes

Abstract

Sea ice is an important component in the Earth’s climate system. Coupled climate system models are indispensable tools for the study of sea ice, its internal processes, interaction with other components, and projection of future changes. This paper evaluates the simulation of sea ice by the Flexible Global Ocean-Atmosphere-Land System model Grid-point Version 2 (FGOALS-g2), in the fifth phase of the Coupled Model Inter-comparison Project (CMIP5), with a focus on historical experiments and late 20th century simulation. Through analysis, we find that FGOALS-g2 produces reasonable Arctic and Antarctic sea ice climatology and variability. Sea ice spatial distribution and seasonal change characteristics are well captured. The decrease of Arctic sea ice extent in the late 20th century is reproduced in simulations, although the decrease trend is lower compared with observations. Simulated Antarctic sea ice shows a reasonable distribution and seasonal cycle with high accordance to the amplitude of winter-summer changes. Large improvement is achieved as compared with FGOALS-g1.0 in CMIP3.

Diagnosis of atmospheric and oceanic forcing on sea ice reveals several shortcomings and major aspects to improve upon in the future: (1) ocean model improvements to remove the artificial island at the North Pole; (2) higher resolution of the atmosphere model for better simulation of important features such as, among others, the Icelandic Low and westerly wind over the Southern Ocean; and (3) ocean model improvements to accurately receive freshwater input from land, and higher resolution for resolving major water channels in the Canadian Arctic Archipelago.

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Correspondence to Bin Wang.

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Xu, S., Song, M., Liu, J. et al. Simulation of sea ice in FGOALS-g2: Climatology and late 20th century changes. Adv. Atmos. Sci. 30, 658–673 (2013). https://doi.org/10.1007/s00376-013-2158-4

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Key words

  • FGOALS-g2
  • CICE4-LASG
  • sea ice
  • seasonal change
  • variability