Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems
Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict panArctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic S...
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ftncar:oai:drupal-site.org:articles_25944 2023-10-01T03:53:45+02:00 Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems Bushuk, Mitchell (author) Zhang, Yongfei (author) Winton, Michael (author) Hurlin, Bill (author) Delworth, Thomas (author) Lu, Feiyu (author) Jia, Liwei (author) Zhang, Liping (author) Cooke, William (author) Harrison, Matthew (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) 2022-07-01 https://doi.org/10.1175/JCLI-D-21-0544.1 en eng Journal of Climate--0894-8755--1520-0442 Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1--10.5067/8GQ8LZQVL0VL articles:25944 doi:10.1175/JCLI-D-21-0544.1 ark:/85065/d7wm1j9f Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. article Text 2022 ftncar https://doi.org/10.1175/JCLI-D-21-0544.1 2023-09-04T18:21:26Z Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict panArctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems. NA16NWS4620043 NA18NWS4620043B Article in Journal/Newspaper Arctic Chukchi Chukchi Sea Sea ice OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Arctic Chukchi Sea Journal of Climate 35 13 4207 4231 |
institution |
Open Polar |
collection |
OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
op_collection_id |
ftncar |
language |
English |
description |
Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict panArctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems. NA16NWS4620043 NA18NWS4620043B |
author2 |
Bushuk, Mitchell (author) Zhang, Yongfei (author) Winton, Michael (author) Hurlin, Bill (author) Delworth, Thomas (author) Lu, Feiyu (author) Jia, Liwei (author) Zhang, Liping (author) Cooke, William (author) Harrison, Matthew (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 |
Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems |
spellingShingle |
Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems |
title_short |
Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems |
title_full |
Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems |
title_fullStr |
Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems |
title_full_unstemmed |
Mechanisms of regional Arctic sea ice predictability in two dynamical seasonal forecast systems |
title_sort |
mechanisms of regional arctic sea ice predictability in two dynamical seasonal forecast systems |
publishDate |
2022 |
url |
https://doi.org/10.1175/JCLI-D-21-0544.1 |
geographic |
Arctic Chukchi Sea |
geographic_facet |
Arctic Chukchi Sea |
genre |
Arctic Chukchi Chukchi Sea Sea ice |
genre_facet |
Arctic Chukchi Chukchi Sea Sea ice |
op_relation |
Journal of Climate--0894-8755--1520-0442 Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1--10.5067/8GQ8LZQVL0VL articles:25944 doi:10.1175/JCLI-D-21-0544.1 ark:/85065/d7wm1j9f |
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-21-0544.1 |
container_title |
Journal of Climate |
container_volume |
35 |
container_issue |
13 |
container_start_page |
4207 |
op_container_end_page |
4231 |
_version_ |
1778520576176947200 |