2020
DOI: 10.5194/tc-2020-81
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Seasonal transition dates can reveal biases in Arctic sea ice simulations

Abstract: 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 t… Show more

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Cited by 1 publication
(7 citation statements)
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“…By contrast, overestimation is more common in the Gulf of St. Lawrence, Canadian Arctic Archipelago, Beaufort Sea, Chukchi Sea, Kara Sea, and Hudson Bay (34% of the study area altogether). This is consistent with Smith et al 34 , who reported mean open-water periods were overestimated for the area north of 66°N in a subset of CMIP6 models. Altogether, a good match exists between models and observations for the pan-Arctic mean, but this occurs in part because of compensating biases in different regions, highlighting the importance of regional analysis.…”
Section: Resultssupporting
confidence: 92%
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“…By contrast, overestimation is more common in the Gulf of St. Lawrence, Canadian Arctic Archipelago, Beaufort Sea, Chukchi Sea, Kara Sea, and Hudson Bay (34% of the study area altogether). This is consistent with Smith et al 34 , who reported mean open-water periods were overestimated for the area north of 66°N in a subset of CMIP6 models. Altogether, a good match exists between models and observations for the pan-Arctic mean, but this occurs in part because of compensating biases in different regions, highlighting the importance of regional analysis.…”
Section: Resultssupporting
confidence: 92%
“…In general agreement with previous analysis 34 , bias resulting in excessively long open-water periods always occurs because of sea ice retreat occurring too early in the multi-model mean, and sometimes also advance occurring too late (Fig. 1d-e).…”
Section: Resultssupporting
confidence: 91%
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