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Bushuk, Mitchell, Ali, Sahara, Bailey, David A., Bao, Qing, Batté, Lauriane, Bhatt, Uma S., Blanchard-Wrigglesworth, Edward, Blockley, Ed, Cawley, Gavin 
ORCID: https://orcid.org/0000-0002-4118-9095, Chi, Junhwa, Counillon, François, Coulombe, Philippe Goulet, Cullather, Richard I., Diebold, Francis X., Dirkson, Arlan, Exarchou, Eleftheria, Göbel, Maximilian, Gregory, William, Guemas, Virginie, Hamilton, Lawrence, He, Bian, Horvath, Sean, Ionita, Monica, Kay, Jennifer E., Kim, Eliot, Kimura, Noriaki, Kondrashov, Dmitri, Labe, Zachary M., Lee, WooSung, Lee, Younjoo J., Li, Cuihua, Li, Xuewei, Lin, Yongcheng, Liu, Yanyun, Maslowski, Wieslaw, Massonnet, François, Meier, Walter N., Merryfield, William J., Myint, Hannah, Acosta Navarro, Juan C., Petty, Alek, Qiao, Fangli, Schröder, David, Schweiger, Axel, Shu, Qi, Sigmond, Michael, Steele, Michael, Stroeve, Julienne, Sun, Nico, Tietsche, Steffen, Tsamados, Michel, Wang, Keguang, Wang, Jianwu, Wang, Wanqiu, Wang, Yiguo, Wang, Yun, Williams, James, Yang, Qinghua, Yuan, Xiaojun, Zhang, Jinlun and Zhang, Yongfei
(2024)
Predicting September Arctic Sea Ice: A multimodel seasonal skill comparison.
Bulletin of the American Meteorological Society, 105 (7).
E1170-E1203.
ISSN 0003-0007
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Abstract
This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance. SIGNIFICANCE STATEMENT: The observed decline of Arctic sea ice extent has created an emerging need for predictions of sea ice on seasonal time scales. This study provides a comparison of September Arctic sea ice seasonal prediction skill across a diverse set of dynamical and statistical prediction models, quantifying the state of the art in the rapidly growing sea ice prediction research community. We find that both dynamical and statistical models can skillfully predict September Arctic sea ice 0–3 months in advance on pan-Arctic, regional, and local spatial scales. Our results demonstrate that there are bright prospects for skillful operational seasonal predictions of Arctic sea ice and highlight a number of crucial prediction system design aspects to guide future improvements.
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| Additional Information: | Data availability statement: Retrospective prediction data for all models and code to process and analyze data and make figures are available via an online repository (https://zenodo.org/doi/10.5281/ zenodo.10124346). The NSIDC sea ice index version 3 is available from https://nsidc.org/data/seaice_index/. The OSI SAF sea ice index v2.1 is available from https://osi-saf.eumetsat.int/products/osi-420. The NSIDC CDR SIC data are available from https://nsidc.org/data/g02202. The OSI SAF SIC CDR data are available from https://osi-saf.eumetsat.int/products/osi-450-a Funding Information: The SIPN Phase 2 leadership (U. S. B., E. B.-W., L. H., W. N. M., M. S., and J. S.) and the SIO network were supported by the National Science Foundation (PLR-1303938, OPP-1748308, OPP-1749081, OPP-1751363, OPP-1748953, OPP-1748325, and OPP-1331083) and the Office of Naval Research (N00014-13-1-0793). J. S. was supported by NSFGEO-NERC Advancing Predictability of Sea Ice: Phase 2 of the Sea Ice Prediction Network (SIPN2) NE/R017123/1. S. A. and J. W. acknowledge the support from National Science Foundation (OAC-1942714). Yiguo Wang acknowledges the Norges Forskningsrad (Grant 328886) and the Trond Mohn stiftelse (Grant BFS2018TMT01). Q. Y., X. L., Y. L., and Y. W. acknowledge the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2023SP217, SML2023SP219, and SML2022SP401) and the National Natural Science Foundation of China (42106233). E. B.-W. was supported by the Met Office Hadley Centre Climate Programme funded by DSIT. Z. M. L. acknowledges the support under CIMES award NA18OAR4320123. F. M. and this project received funding from the BELSPO project RESIST. X. Y. and C. L. were supported by the Lamont Endowment. F. Q. and this project received funding from the National Natural Science Foundation of China under Grant 41821004. This project received funding from CIRES Cryospheric and Polar Processes. We thank Mike Winton and Andrew Ross for helpful comments on a preliminary draft of this manuscript. Funding Information: Acknowledgments. We thank Lorenzo Zampieri and two anonymous reviewers for constructive feedback that improved the manuscript. We acknowledge the invaluable community contributions to the Sea Ice Outlook as part of the Sea Ice Prediction Network (SIPN). The SIPN Phase 2 leadership (U. S. B., E. B.-W., L. H., W. N. M., M. S., and J. S.) and the SIO network were supported by the National Science Foundation (PLR-1303938, OPP-1748308, OPP-1749081, OPP-1751363, OPP-1748953, OPP-1748325, and OPP-1331083) and the Office of Naval Research (N00014-13-1-0793). J. S. was supported by NSFGEO-NERC Advancing Predictability of Sea Ice: Phase 2 of the Sea Ice Prediction Network (SIPN2) NE/R017123/1. S. A. and J. W. acknowledge the support from National Science Foundation (OAC-1942714). Yiguo Wang acknowledges the Norges Forskningsrad (Grant 328886) and the Trond Mohn stiftelse (Grant BFS2018TMT01). Q. Y., X. L., Y. L., and Y. W. acknowledge the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2023SP217, SML2023SP219, and SML2022SP401) and the National Natural Science Foundation of China (42106233). E. B.-W. was supported by the Met Office Hadley Centre Climate Programme funded by DSIT. Z. M. L. acknowledges the support under CIMES award NA18OAR4320123. F. M. and this project received funding from the BELSPO project RESIST. X. Y. and C. L. were supported by the Lamont Endowment. F. Q. and this project received funding from the National Natural Science Foundation of China under Grant 41821004. This project received funding from CIRES Cryospheric and Polar Processes. |
| Uncontrolled Keywords: | arctic,climate prediction,general circulation,,models,performance,sea ice,statistical,techniques,atmospheric science,sdg 13 - climate action,4* ,/dk/atira/pure/subjectarea/asjc/1900/1902 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 04 Nov 2024 18:30 |
| Last Modified: | 12 Nov 2024 14:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/97488 |
| DOI: | 10.1175/BAMS-D-23-0163.1 |
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