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https://doi.org/10.5194/tc-2020-81
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-81
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 03 Apr 2020

Submitted as: research article | 03 Apr 2020

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This preprint is currently under review for the journal TC.

Seasonal transition dates can reveal biases in Arctic sea ice simulations

Abigail Smith1, Alexandra Jahn1, and Muyin Wang2,3 Abigail Smith et al.
  • 1Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado, Boulder
  • 2Joint Institute for the Study of the Atmosphere and Ocean, University of Washington
  • 3Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, break-up, opening, freeze onset, freeze-up and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1.5–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions of melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

Abigail Smith et al.

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Abigail Smith et al.

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Short summary
The annual cycle of Arctic sea ice can be used to gain more information about how climate model simulations of sea ice compare to observations. In some models, the September sea ice area agrees with observations for the wrong reasons, because biases in the timing of seasonal transitions compensate for other factors. This research was done to provide new process-based metrics of Arctic sea ice using satellite observations, the CESM Large Ensemble and CMIP6 models.
The annual cycle of Arctic sea ice can be used to gain more information about how climate model...
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