Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period
The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are u...
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Online Access: | http://dx.doi.org/10.1525/elementa.2022.00033 https://online.ucpress.edu/elementa/article-pdf/doi/10.1525/elementa.2022.00033/737329/elementa.2022.00033.pdf |
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crunicaliforniap:10.1525/elementa.2022.00033 2023-11-12T04:12:35+01:00 Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period Heinemann, Günther Schefczyk, Lukas Willmes, Sascha Shupe, Matthew D. 2022 http://dx.doi.org/10.1525/elementa.2022.00033 https://online.ucpress.edu/elementa/article-pdf/doi/10.1525/elementa.2022.00033/737329/elementa.2022.00033.pdf en eng University of California Press http://creativecommons.org/licenses/by/4.0/ Elementa: Science of the Anthropocene volume 10, issue 1 ISSN 2325-1026 Atmospheric Science Geology Geotechnical Engineering and Engineering Geology Ecology Environmental Engineering Oceanography journal-article 2022 crunicaliforniap https://doi.org/10.1525/elementa.2022.00033 2023-10-15T17:43:41Z The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability. Article in Journal/Newspaper Arctic Sea ice University of California Press (via Crossref) Arctic Elementa: Science of the Anthropocene 10 1 |
institution |
Open Polar |
collection |
University of California Press (via Crossref) |
op_collection_id |
crunicaliforniap |
language |
English |
topic |
Atmospheric Science Geology Geotechnical Engineering and Engineering Geology Ecology Environmental Engineering Oceanography |
spellingShingle |
Atmospheric Science Geology Geotechnical Engineering and Engineering Geology Ecology Environmental Engineering Oceanography Heinemann, Günther Schefczyk, Lukas Willmes, Sascha Shupe, Matthew D. Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period |
topic_facet |
Atmospheric Science Geology Geotechnical Engineering and Engineering Geology Ecology Environmental Engineering Oceanography |
description |
The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability. |
format |
Article in Journal/Newspaper |
author |
Heinemann, Günther Schefczyk, Lukas Willmes, Sascha Shupe, Matthew D. |
author_facet |
Heinemann, Günther Schefczyk, Lukas Willmes, Sascha Shupe, Matthew D. |
author_sort |
Heinemann, Günther |
title |
Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period |
title_short |
Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period |
title_full |
Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period |
title_fullStr |
Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period |
title_full_unstemmed |
Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period |
title_sort |
evaluation of simulations of near-surface variables using the regional climate model cclm for the mosaic winter period |
publisher |
University of California Press |
publishDate |
2022 |
url |
http://dx.doi.org/10.1525/elementa.2022.00033 https://online.ucpress.edu/elementa/article-pdf/doi/10.1525/elementa.2022.00033/737329/elementa.2022.00033.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Elementa: Science of the Anthropocene volume 10, issue 1 ISSN 2325-1026 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1525/elementa.2022.00033 |
container_title |
Elementa: Science of the Anthropocene |
container_volume |
10 |
container_issue |
1 |
_version_ |
1782331026777833472 |