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...

Full description

Bibliographic Details
Published in:The Cryosphere
Main Authors: A. Smith, A. Jahn, M. Wang
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2020
Subjects:
geo
Online Access:https://doi.org/10.5194/tc-14-2977-2020
https://tc.copernicus.org/articles/14/2977/2020/tc-14-2977-2020.pdf
https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9
id fttriple:oai:gotriple.eu:oai:doaj.org/article:586fc2eb18f24b3bb05fd2b0343bdca9
record_format openpolar
spelling fttriple:oai:gotriple.eu:oai:doaj.org/article:586fc2eb18f24b3bb05fd2b0343bdca9 2023-05-15T14:50:51+02:00 Seasonal transition dates can reveal biases in Arctic sea ice simulations A. Smith A. Jahn M. Wang 2020-09-01 https://doi.org/10.5194/tc-14-2977-2020 https://tc.copernicus.org/articles/14/2977/2020/tc-14-2977-2020.pdf https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9 en eng Copernicus Publications doi:10.5194/tc-14-2977-2020 1994-0416 1994-0424 https://tc.copernicus.org/articles/14/2977/2020/tc-14-2977-2020.pdf https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9 undefined The Cryosphere, Vol 14, Pp 2977-2997 (2020) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.5194/tc-14-2977-2020 2023-01-22T19:11:10Z 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 Unknown Arctic The Cryosphere 14 9 2977 2997
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic geo
envir
spellingShingle geo
envir
A. Smith
A. Jahn
M. Wang
Seasonal transition dates can reveal biases in Arctic sea ice simulations
topic_facet geo
envir
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://tc.copernicus.org/articles/14/2977/2020/tc-14-2977-2020.pdf
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 doi:10.5194/tc-14-2977-2020
1994-0416
1994-0424
https://tc.copernicus.org/articles/14/2977/2020/tc-14-2977-2020.pdf
https://doaj.org/article/586fc2eb18f24b3bb05fd2b0343bdca9
op_rights undefined
op_doi https://doi.org/10.5194/tc-14-2977-2020
container_title The Cryosphere
container_volume 14
container_issue 9
container_start_page 2977
op_container_end_page 2997
_version_ 1766321924144627712