Variability in Antarctic surface climatology across regional climate models and reanalysis datasets
peer reviewed Abstract. Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability...
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2022
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Online Access: | https://orbi.uliege.be/handle/2268/295546 https://orbi.uliege.be/bitstream/2268/295546/1/tc-16-3815-2022.pdf https://doi.org/10.5194/tc-16-3815-2022 |
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ftorbi:oai:orbi.ulg.ac.be:2268/295546 2024-04-21T07:51:24+00:00 Variability in Antarctic surface climatology across regional climate models and reanalysis datasets Carter, Jeremy Leeson, Amber Orr, Andrew Kittel, Christoph van Wessem, J. Melchior 2022-09-23 https://orbi.uliege.be/handle/2268/295546 https://orbi.uliege.be/bitstream/2268/295546/1/tc-16-3815-2022.pdf https://doi.org/10.5194/tc-16-3815-2022 en eng Copernicus GmbH https://tc.copernicus.org/articles/16/3815/2022/tc-16-3815-2022.pdf urn:issn:1994-0416 urn:issn:1994-0424 https://orbi.uliege.be/handle/2268/295546 info:hdl:2268/295546 https://orbi.uliege.be/bitstream/2268/295546/1/tc-16-3815-2022.pdf doi:10.5194/tc-16-3815-2022 scopus-id:2-s2.0-85140400028 open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess The Cryosphere, 16 (9), 3815-3841 (2022-09-23) Earth-Surface Processes Water Science and Technology Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique journal article http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article peer reviewed 2022 ftorbi https://doi.org/10.5194/tc-16-3815-2022 2024-03-27T14:58:15Z peer reviewed Abstract. Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Sheet Ice Shelves The Cryosphere University of Liège: ORBi (Open Repository and Bibliography) The Cryosphere 16 9 3815 3841 |
institution |
Open Polar |
collection |
University of Liège: ORBi (Open Repository and Bibliography) |
op_collection_id |
ftorbi |
language |
English |
topic |
Earth-Surface Processes Water Science and Technology Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique |
spellingShingle |
Earth-Surface Processes Water Science and Technology Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique Carter, Jeremy Leeson, Amber Orr, Andrew Kittel, Christoph van Wessem, J. Melchior Variability in Antarctic surface climatology across regional climate models and reanalysis datasets |
topic_facet |
Earth-Surface Processes Water Science and Technology Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique |
description |
peer reviewed Abstract. Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. |
format |
Article in Journal/Newspaper |
author |
Carter, Jeremy Leeson, Amber Orr, Andrew Kittel, Christoph van Wessem, J. Melchior |
author_facet |
Carter, Jeremy Leeson, Amber Orr, Andrew Kittel, Christoph van Wessem, J. Melchior |
author_sort |
Carter, Jeremy |
title |
Variability in Antarctic surface climatology across regional climate models and reanalysis datasets |
title_short |
Variability in Antarctic surface climatology across regional climate models and reanalysis datasets |
title_full |
Variability in Antarctic surface climatology across regional climate models and reanalysis datasets |
title_fullStr |
Variability in Antarctic surface climatology across regional climate models and reanalysis datasets |
title_full_unstemmed |
Variability in Antarctic surface climatology across regional climate models and reanalysis datasets |
title_sort |
variability in antarctic surface climatology across regional climate models and reanalysis datasets |
publisher |
Copernicus GmbH |
publishDate |
2022 |
url |
https://orbi.uliege.be/handle/2268/295546 https://orbi.uliege.be/bitstream/2268/295546/1/tc-16-3815-2022.pdf https://doi.org/10.5194/tc-16-3815-2022 |
genre |
Antarc* Antarctic Antarctica Ice Sheet Ice Shelves The Cryosphere |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet Ice Shelves The Cryosphere |
op_source |
The Cryosphere, 16 (9), 3815-3841 (2022-09-23) |
op_relation |
https://tc.copernicus.org/articles/16/3815/2022/tc-16-3815-2022.pdf urn:issn:1994-0416 urn:issn:1994-0424 https://orbi.uliege.be/handle/2268/295546 info:hdl:2268/295546 https://orbi.uliege.be/bitstream/2268/295546/1/tc-16-3815-2022.pdf doi:10.5194/tc-16-3815-2022 scopus-id:2-s2.0-85140400028 |
op_rights |
open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/tc-16-3815-2022 |
container_title |
The Cryosphere |
container_volume |
16 |
container_issue |
9 |
container_start_page |
3815 |
op_container_end_page |
3841 |
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1796934771170869248 |