Sea-ice extent and its trend provide limited metrics of model performance

We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model bias...

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Published in:The Cryosphere
Main Author: Notz, D.
Format: Text
Language:English
Published: 2018
Subjects:
Online Access:https://doi.org/10.5194/tc-8-229-2014
https://tc.copernicus.org/articles/8/229/2014/
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spelling ftcopernicus:oai:publications.copernicus.org:tc20424 2023-05-15T14:59:57+02:00 Sea-ice extent and its trend provide limited metrics of model performance Notz, D. 2018-09-27 application/pdf https://doi.org/10.5194/tc-8-229-2014 https://tc.copernicus.org/articles/8/229/2014/ eng eng doi:10.5194/tc-8-229-2014 https://tc.copernicus.org/articles/8/229/2014/ eISSN: 1994-0424 Text 2018 ftcopernicus https://doi.org/10.5194/tc-8-229-2014 2020-07-20T16:25:11Z We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms. Text Arctic Sea ice Copernicus Publications: E-Journals Arctic The Cryosphere 8 1 229 243
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms.
format Text
author Notz, D.
spellingShingle Notz, D.
Sea-ice extent and its trend provide limited metrics of model performance
author_facet Notz, D.
author_sort Notz, D.
title Sea-ice extent and its trend provide limited metrics of model performance
title_short Sea-ice extent and its trend provide limited metrics of model performance
title_full Sea-ice extent and its trend provide limited metrics of model performance
title_fullStr Sea-ice extent and its trend provide limited metrics of model performance
title_full_unstemmed Sea-ice extent and its trend provide limited metrics of model performance
title_sort sea-ice extent and its trend provide limited metrics of model performance
publishDate 2018
url https://doi.org/10.5194/tc-8-229-2014
https://tc.copernicus.org/articles/8/229/2014/
geographic Arctic
geographic_facet Arctic
genre Arctic
Sea ice
genre_facet Arctic
Sea ice
op_source eISSN: 1994-0424
op_relation doi:10.5194/tc-8-229-2014
https://tc.copernicus.org/articles/8/229/2014/
op_doi https://doi.org/10.5194/tc-8-229-2014
container_title The Cryosphere
container_volume 8
container_issue 1
container_start_page 229
op_container_end_page 243
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