Seasonal prediction and predictability of regional Antarctic sea ice

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarc...

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Published in:Journal of Climate
Other Authors: Bushuk, Mitchell (author), Winton, Michael (author), Haumann, F. Alexander (author), Delworth, Thomas (author), Lu, Feiyu (author), Zhang, Yongfei (author), Jia, Liwei (author), Zhang, Liping (author), Cooke, William (author), Harrison, Matthew (author), Hurlin, Bill (author), Johnson, Nathaniel C. (author), Kapnick, Sarah (author), McHugh, Colleen (author), Murakami, Hiroyuki (author), Rosati, Anthony (author), Tseng, Kai-Chih (author), Wittenberg, Andrew T. (author), Yang, Xiaosong (author), Zeng, Fanrong (author)
Format: Article in Journal/Newspaper
Language:English
Published: 2021
Subjects:
Online Access:https://doi.org/10.1175/JCLI-D-20-0965.1
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spelling ftncar:oai:drupal-site.org:articles_24590 2024-04-14T08:04:33+00:00 Seasonal prediction and predictability of regional Antarctic sea ice Bushuk, Mitchell (author) Winton, Michael (author) Haumann, F. Alexander (author) Delworth, Thomas (author) Lu, Feiyu (author) Zhang, Yongfei (author) Jia, Liwei (author) Zhang, Liping (author) Cooke, William (author) Harrison, Matthew (author) Hurlin, Bill (author) Johnson, Nathaniel C. (author) Kapnick, Sarah (author) McHugh, Colleen (author) Murakami, Hiroyuki (author) Rosati, Anthony (author) Tseng, Kai-Chih (author) Wittenberg, Andrew T. (author) Yang, Xiaosong (author) Zeng, Fanrong (author) 2021-08 https://doi.org/10.1175/JCLI-D-20-0965.1 en eng Journal of Climate--0894-8755--1520-0442 Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)--10.17882/42182 Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1--10.5067/8GQ8LZQVL0VL articles:24590 doi:10.1175/JCLI-D-20-0965.1 ark:/85065/d7jd5167 Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. article Text 2021 ftncar https://doi.org/10.1175/JCLI-D-20-0965.1 2024-03-21T18:00:26Z Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992-2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales. NA16NWS4620043 NA18NWS4620043B Article in Journal/Newspaper Antarc* Antarctic Arctic Sea ice OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Antarctic Arctic Indian Pacific Weddell Journal of Climate 1 68
institution Open Polar
collection OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research)
op_collection_id ftncar
language English
description Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992-2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales. NA16NWS4620043 NA18NWS4620043B
author2 Bushuk, Mitchell (author)
Winton, Michael (author)
Haumann, F. Alexander (author)
Delworth, Thomas (author)
Lu, Feiyu (author)
Zhang, Yongfei (author)
Jia, Liwei (author)
Zhang, Liping (author)
Cooke, William (author)
Harrison, Matthew (author)
Hurlin, Bill (author)
Johnson, Nathaniel C. (author)
Kapnick, Sarah (author)
McHugh, Colleen (author)
Murakami, Hiroyuki (author)
Rosati, Anthony (author)
Tseng, Kai-Chih (author)
Wittenberg, Andrew T. (author)
Yang, Xiaosong (author)
Zeng, Fanrong (author)
format Article in Journal/Newspaper
title Seasonal prediction and predictability of regional Antarctic sea ice
spellingShingle Seasonal prediction and predictability of regional Antarctic sea ice
title_short Seasonal prediction and predictability of regional Antarctic sea ice
title_full Seasonal prediction and predictability of regional Antarctic sea ice
title_fullStr Seasonal prediction and predictability of regional Antarctic sea ice
title_full_unstemmed Seasonal prediction and predictability of regional Antarctic sea ice
title_sort seasonal prediction and predictability of regional antarctic sea ice
publishDate 2021
url https://doi.org/10.1175/JCLI-D-20-0965.1
geographic Antarctic
Arctic
Indian
Pacific
Weddell
geographic_facet Antarctic
Arctic
Indian
Pacific
Weddell
genre Antarc*
Antarctic
Arctic
Sea ice
genre_facet Antarc*
Antarctic
Arctic
Sea ice
op_relation Journal of Climate--0894-8755--1520-0442
Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)--10.17882/42182
Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1--10.5067/8GQ8LZQVL0VL
articles:24590
doi:10.1175/JCLI-D-20-0965.1
ark:/85065/d7jd5167
op_rights Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
op_doi https://doi.org/10.1175/JCLI-D-20-0965.1
container_title Journal of Climate
container_start_page 1
op_container_end_page 68
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