Seasonal transition dates can reveal biases in Arctic sea ice simulations
Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, 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 deta...
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ftdoajarticles:oai:doaj.org/article:586fc2eb18f24b3bb05fd2b0343bdca9 2023-05-15T14:50:56+02:00 Seasonal transition dates can reveal biases in Arctic sea ice simulations A. Smith A. Jahn M. Wang 2020-09-01T00:00:00Z https://doi.org/10.5194/tc-14-2977-2020 https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9 EN eng Copernicus Publications https://tc.copernicus.org/articles/14/2977/2020/tc-14-2977-2020.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-14-2977-2020 1994-0416 1994-0424 https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9 The Cryosphere, Vol 14, Pp 2977-2997 (2020) Environmental sciences GE1-350 Geology QE1-996.5 article 2020 ftdoajarticles https://doi.org/10.5194/tc-14-2977-2020 2022-12-31T12:22:27Z Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, 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 1–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions for melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability range, indicating the contribution of model differences. 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. Article in Journal/Newspaper Arctic Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic The Cryosphere 14 9 2977 2997 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 A. Smith A. Jahn M. Wang Seasonal transition dates can reveal biases in Arctic sea ice simulations |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, 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 1–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions for melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability range, indicating the contribution of model differences. 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. |
format |
Article in Journal/Newspaper |
author |
A. Smith A. Jahn M. Wang |
author_facet |
A. Smith A. Jahn M. Wang |
author_sort |
A. Smith |
title |
Seasonal transition dates can reveal biases in Arctic sea ice simulations |
title_short |
Seasonal transition dates can reveal biases in Arctic sea ice simulations |
title_full |
Seasonal transition dates can reveal biases in Arctic sea ice simulations |
title_fullStr |
Seasonal transition dates can reveal biases in Arctic sea ice simulations |
title_full_unstemmed |
Seasonal transition dates can reveal biases in Arctic sea ice simulations |
title_sort |
seasonal transition dates can reveal biases in arctic sea ice simulations |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-14-2977-2020 https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice The Cryosphere |
genre_facet |
Arctic Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 14, Pp 2977-2997 (2020) |
op_relation |
https://tc.copernicus.org/articles/14/2977/2020/tc-14-2977-2020.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-14-2977-2020 1994-0416 1994-0424 https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9 |
op_doi |
https://doi.org/10.5194/tc-14-2977-2020 |
container_title |
The Cryosphere |
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14 |
container_issue |
9 |
container_start_page |
2977 |
op_container_end_page |
2997 |
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1766321984820477952 |