TY - JOUR
T1 - Predicting September Arctic Sea Ice: A multimodel seasonal skill comparison
AU - Bushuk, Mitchell
AU - Ali, Sahara
AU - Bailey, David A.
AU - Bao, Qing
AU - Batté, Lauriane
AU - Bhatt, Uma S.
AU - Blanchard-Wrigglesworth, Edward
AU - Blockley, Ed
AU - Cawley, Gavin
AU - Chi, Junhwa
AU - Counillon, François
AU - Coulombe, Philippe Goulet
AU - Cullather, Richard I.
AU - Diebold, Francis X.
AU - Dirkson, Arlan
AU - Exarchou, Eleftheria
AU - Göbel, Maximilian
AU - Gregory, William
AU - Guemas, Virginie
AU - Hamilton, Lawrence
AU - He, Bian
AU - Horvath, Sean
AU - Ionita, Monica
AU - Kay, Jennifer E.
AU - Kim, Eliot
AU - Kimura, Noriaki
AU - Kondrashov, Dmitri
AU - Labe, Zachary M.
AU - Lee, WooSung
AU - Lee, Younjoo J.
AU - Li, Cuihua
AU - Li, Xuewei
AU - Lin, Yongcheng
AU - Liu, Yanyun
AU - Maslowski, Wieslaw
AU - Massonnet, François
AU - Meier, Walter N.
AU - Merryfield, William J.
AU - Myint, Hannah
AU - Acosta Navarro, Juan C.
AU - Petty, Alek
AU - Qiao, Fangli
AU - Schröder, David
AU - Schweiger, Axel
AU - Shu, Qi
AU - Sigmond, Michael
AU - Steele, Michael
AU - Stroeve, Julienne
AU - Sun, Nico
AU - Tietsche, Steffen
AU - Tsamados, Michel
AU - Wang, Keguang
AU - Wang, Jianwu
AU - Wang, Wanqiu
AU - Wang, Yiguo
AU - Wang, Yun
AU - Williams, James
AU - Yang, Qinghua
AU - Yuan, Xiaojun
AU - Zhang, Jinlun
AU - Zhang, Yongfei
N1 - 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.
PY - 2024/7
Y1 - 2024/7
N2 - 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.
AB - 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.
KW - Arctic
KW - Climate prediction
KW - General circulation
KW - Model evaluation/
KW - models
KW - performance
KW - Sea ice
KW - Statistical
KW - techniques
UR - http://www.scopus.com/inward/record.url?scp=85198587502&partnerID=8YFLogxK
U2 - 10.1175/BAMS-D-23-0163.1
DO - 10.1175/BAMS-D-23-0163.1
M3 - Article
AN - SCOPUS:85198587502
VL - 105
SP - E1170-E1203
JO - Bulletin of the American Meteorological Society
JF - Bulletin of the American Meteorological Society
SN - 0003-0007
IS - 7
ER -