Abstract
In this study, the forecast quality of 1993–2014 summer seasonal predictions of five global coupled models, of which three are operational seasonal forecasting systems contributing to the Copernicus Climate Change Service (C3S), is assessed for Arctic sea ice. Beyond the Pan-Arctic sea ice concentration and extent deterministic re-forecast assessments, we use sea ice edge error metrics such as the Integrated Ice Edge Error (IIEE) and Spatial Probability Score (SPS) to evaluate the advantages of a multi-model approach. Skill in forecasting the September sea ice minimum from late April to early May start dates is very limited, and only one model shows significant correlation skill over the period when removing the linear trend in total sea ice extent. After bias and trend-adjusting the sea ice concentration data, we find quite similar results between the different systems in terms of ice edge forecast errors. The highest values of September ice edge error in the 1993–2014 period are found for the sea ice minima years (2007 and 2012), mainly due to a clear overestimation of the total extent. Further analyses of deterministic and probabilistic skill over the Barents–Kara, Laptev–East Siberian and Beaufort–Chukchi regions provide insight on differences in model performance. For all skill metrics considered, the multi-model ensemble, whether grouping all five systems or only the three operational C3S systems, performs among the best models for each forecast time, therefore confirming the interest of multi-system initiatives building on model diversity for providing the best forecasts.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Copernicus C3S seasonal re-forecast data for ECMWF SEAS5, Met Office GloSea5 and Météo-France System 6 were retrieved using the Mars API. The reforecasts with the EC-Earth 3.2 and CNRM-CM6-1 models were run as part of the H2020-APPLICATE project and data is available on the APPLICATE data portal or upon request.
References
Acosta-Navarro JC, Ortega P, Batté L, Smith D, Bretonnière P-A, Guemas V et al (2020) Link between autumnal Arctic Sea ice and Northern Hemisphere winter forecast skill. Geophys Res Lett, accepted
Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 139:1132–1161. https://doi.org/10.1002/qj.2063
Batté L, Déqué M (2016) Randomly correcting model errors in the ARPEGE-Climate v6. 1 component of CNRM-CM: applications for seasonal forecasts. Geosci Model Dev. https://doi.org/10.5194/gmd-9-2055-2016
Blackport R, Screen JA, van der Wiel K, Bintanja R (2019) Minimal influence of reduced Arctic sea ice on coincident cold winters in mid-latitudes. Nat Clim Change 9(9):697–704
Blanchard-Wrigglesworth E, Armour KC, Bitz CM, DeWeaver E (2011) Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J Clim 24:231–250
Blanchard-Wrigglesworth E, Barthélemy A, Chevallier M, Cullather R, Fuckar NS et al (2017) Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales. Clim Dyn. https://doi.org/10.1007/s00382-016-3388-9
Blockley EW, Peterson KA (2018) Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness. Cryosphere 12:3419–3438. https://doi.org/10.5194/tc-12-3419-2018
Bonan DB, Bushuk M, Winton M (2019) A spring barrier for regional predictions of summer Arctic sea ice. Geophys Res Lett 46:5937–5947. https://doi.org/10.1029/2019GL082947
Brier GW (1950) Verification of forecasts expressed in terms of probability. Monthly Weather Rev 78:1–3. https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
Brodeau L, Barnier B, Treguier AM, Penduff T, Gulev S (2010) An ERA40-based atmospheric forcing for global ocean circulation models. Ocean Model 31(3–4):88–104
Bushuk M et al (2017) Skillful regional prediction of Arctic sea ice on seasonal timescales. Geophys Res Lett 44:4953–4964. https://doi.org/10.1002/2017GL073155
Bushuk M, Msadek R, Winton M, Vecchi G et al (2019) Regional Arctic sea–ice prediction: potential versus operational seasonal forecast skill. Clim Dyn 52(5–6):2721–2743. https://doi.org/10.1007/s00382-018-4288-y
Cavalieri DJ, Parkinson CL, Gloersen P, Zwally HJ (1996) Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS passive microwave data, version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: 10.5067/8GQ8LZQVL0VL. Accessed 20 Feb 2017
Chevallier M, Salas y Mélia D (2012) The role of sea ice thickness distribution in the Arctic sea ice potential predictability: a diagnostic approach with a coupled GCM. J Clim 25:3025–3038
Chevallier M, Salas y Mélia D, Voldoire A, Déqué M, Garric G (2013) Seasonal forecasts of the pan-Arctic sea ice extent using a GCM-based seasonal prediction system. J Clim 26(16):6092–6104. https://doi.org/10.1175/JCLI-D-12-00612.1
Chevallier M, Smith GC, Dupont F et al (2017) Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project. Clim Dyn 49:1107. https://doi.org/10.1007/s00382-016-2985-y
Coumou D, Di Capua G, Vavrus S, Wang L, Wang S (2018) The influence of Arctic amplification on mid-latitude summer circulation. Nat Comm 9:2959. https://doi.org/10.1038/s41467-018-05256-8
Cruz-Garcia R, Guemas V, Chevallier M, Massonnet F (2019) An assessment of regional sea ice predictability in the Arctic ocean. Clim Dyn. https://doi.org/10.1007/s00382-018-4592-6
Day JJ, Tietsche S, Hawkins E (2014) Pan-Arctic and regional sea ice predictability: initialization month dependence. J Clim 27:4371–4390. https://doi.org/10.1175/JCLI-D-13-00614.1
Director HM, Raftery AE, Bitz CM (2017) Improved sea ice forecasting through spatiotemporal bias correction. J Clim 30:9493–9510. https://doi.org/10.1175/JCLI-D-17-0185.1
Dirkson A, Denis B, Merryfield WJ (2019a) A multimodel approach for improving seasonal probabilistic forecasts of regional Arctic sea ice. Geophys Res Lett 46:10844–10853. https://doi.org/10.1029/2019GL083831
Dirkson A, Merryfield WJ, Monahan AH (2019b) Calibrated probabilistic forecasts of Arctic sea ice concentration. J Clim 32:1251–1271. https://doi.org/10.1175/JCLI-D-18-0224.1
Eicken H (2013) Arctic sea ice needs better forecasts. Nature 497:431–433. https://doi.org/10.1038/497431a
Ferry N, Parent L, Garric G, Barnier B et al (2010) Mercator global Eddy permitting ocean reanalysis GLORYS1V1: Description and results. Mercator-Ocean Q Newslett 36: 15–27. https://www.mercator-ocean.fr/sciences-publications/mercator-ocean-journal/newsletter-36-data-assimilation-and-its-application-to-ocean-reanalyses/
Fichefet T, Maqueda MA (1997) Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J Geophys Res-Oceans 102:12609–12646. https://doi.org/10.1029/97JC00480
Francis JA, Skific N, Vavrus SJ (2018) North American weather regimes are becoming more persistent: is arctic amplification a factor? Geophys Res Lett 45:11414–11422. https://doi.org/10.1029/2018GL080252
García-Serrano J et al (2015) On the predictability of the winter Euro-Atlantic climate: lagged influence of autumn Arctic sea ice. J Clim 28(13):5195–5216
Germe A, Chevallier M, Salas y Mélia D, Sanchez-Gomez E, Cassou C (2014) Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution. Clim Dyn 43(9–10):2519–2538. https://doi.org/10.1007/s00382-014-2071-2
Goessling HF, Jung T (2018) A probabilistic verification score for contours: methodology and application to Arctic ice-edge forecasts. Q J R Meteorol Soc. https://doi.org/10.1002/qj.3242
Goessling HF et al (2016) Predictability of the Arctic sea ice edge. Geophys Res Lett 43:1642–1650. https://doi.org/10.1002/2015GL067232
Guemas V, Doblas-Reyes F, Germe A, Chevallier M, Salas y Mélia D (2013) September 2012 Arctic sea ice minimum: discriminating between sea ice memory, the August 2012 extreme storm and prevailing warm conditions [in "Explaining Extreme Events of 2012 from a Climate Perspective"]. Bull Am Meteorol Soc 94(9):S20–S22. https://doi.org/10.1175/BAMS-D-13-00085.1
Guemas V, Blanchard-Wrigglesworth E, Chevallier M, Day JJ, Déqué M et al (2016) A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales. Q J R Meteorol Soc 142:546–561. https://doi.org/10.1002/qj.2401
Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting I. Basic concept. Tellus A Dyn Meteorol Oceanogr 57A:219–233. https://doi.org/10.3402/tellusa.v57i3.14657
Johnson S, Stockdale TN, Ferranti L, Balmaseda MA, Molteni F et al (2019) SEAS5: the new ECMWF seasonal forecast system. Geosci Model Dev 12:1087–1117. https://doi.org/10.5194/gmd-12-1087-2019
Jung T et al (2015) Polar lower-latitude linkages and their role in weather and climate prediction. Bull Am Meteorol Soc 96:197–200. https://doi.org/10.1175/BAMS-D-15-00121.1
Kimmritz M, Counillon F, L. H., I. Bethke, N. Keenlyside, F. Ogawa, and Y. Wang, (2019) Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic. J Adv Model Earth Syst 11:4147–4166. https://doi.org/10.1029/2019MS001825
MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P, Madec G (2015) Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q J R Meteorol Soc 141:1072–1084. https://doi.org/10.1002/qj.2396
Madec G, Bourdallé-Badie R, Bouttier P-A et al (2017) NEMO Ocean Engine. https://doi.org/10.5281/ZENODO.1472492
Manubens N, Caron L-P, Hunter A, Bellprat O, Exarchou E et al (2018) An R package for climate forecast verification. Environ Model Softw 103:29–42. https://doi.org/10.1016/jenvsoft.2018.01.018
Massonnet F, Goosse H, Fichefet T, Counillon F (2014) Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter. J Geophys Res Oceans 119:4168–4184. https://doi.org/10.1002/2013JC009705
Melia N, Haines K, Hawkins E, Day JJ (2017) Towards seasonal Arctic shipping route predictions. Environ Res Lett 12:084005. https://doi.org/10.1088/1748_9326/aa7a60
Merryfield WJ, Lee W-S, Wang W, Chen M, Kumar A (2013) Multi-system seasonal predictions of Arctic sea ice. Geophys Res Lett 40(8):1551–1556. https://doi.org/10.1002/grl.50317
Msadek R, Vecchi GA, Winton M, Gudgel RG (2014) Importance of initial conditions in seasonal predictions of Arctic sea ice extent. Geophys Res Lett 41:5208–5215. https://doi.org/10.1002/2014GL060799
Murphy AH (1972) Scalar and vector partitions of the probability score: part I. two-state situation. J Appl Meteor 11:273–282. https://doi.org/10.1175/1520-0450(1972)011%3C0273:SAVPOT%3E2.0.CO;2
Olonscheck D, Mauritsen T, Notz D (2019) Arctic sea-ice variability is primarily driven by atmospheric temperature fluctuations. Nat Geosci 12:430–434. https://doi.org/10.1038/s41561-019-0363-1
Serreze MC, Stroeve J (2015) Arctic sea ice trends, variability and implications for seasonal ice forecasting. Phil Trans R Soc A 373:20140159. https://doi.org/10.1098/rsta.2014.0159
Tietsche S, Day JJ, Guemas V, Hurlin WJ, Keeley SPE et al (2014) Seasonal to interannual Arctic sea ice predictability in current global climate models. Geophys Res Lett 41:1035–1043. https://doi.org/10.1002/2013GL058755
Vancoppenolle M, Fichefet T, Goosse H, Bouillon S, Madec G, Morales Maqueda MA (2009) Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1 Model description and validation. Ocean Model 27:33–53. https://doi.org/10.1016/j.oceamod.2008.10.005
Vihma T (2014) Effects of Arctic sea ice decline on weather and climate: a review. Surv Geophys 35:1175–1214
Voldoire A, Saint-Martin D, Sénési S, Decharme B, Alias A et al (2019) Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. J Adv Model Earth Syst. https://doi.org/10.1029/2019MS001683
Wang W, Chen M, Kumar A (2013) Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system. Monthly Weather Rev 141(4):1375–1394. https://doi.org/10.1175/MWR-D-12-00057.1
Walsh JE, Stuart JS, Fetterer F (2019) Benchmark seasonal prediction skill estimates based on regional indices. Cryosphere 13:1073–1088. https://doi.org/10.5194/tc-13-1073-2019
Wayand NE, Bitz CM, Blanchard-Wrigglesworth E (2019) A year-round subseasonal-to-seasonal sea ice prediction portal. Geophys Res Lett 46: https://doi.org/10.1029/2018GL081565
Weisheimer A, Palmer TN (2014) On the reliability of seasonal climate forecasts. J R Soc Interface 11:20131162. https://doi.org/10.1098/rsif.2013.1162
Zampieri L, Goessling HF, Jung T (2018) Bright prospects for Arctic sea ice prediction on subseasonal time scales. Geophys Res Lett 45:9731–9738. https://doi.org/10.1029/2018GL079394
Zhang J, Lindsay R, Schweiger A, Steele M (2013) The impact of an intense summer cyclone on 2012 Arctic sea ice retreat. Geophys Res Lett. https://doi.org/10.1002/grl.50190
Zuo H, Balmaseda MA, Tietsche S, Mogensen K, Mayer M (2019) The ECMWF operational ensemble reanalysis-analysis system for ocean and sea-ice: a description of the system and assessment. Ocean Sci 15:779–808. https://doi.org/10.5194/os-15-779-2019
Acknowledgements
The authors wish to acknowledge F. Massonnet who developed the sea ice assimilation technique used to initialize EC-Earth 3.2, and S. Tietsche for pointing to the relevant SEAS5 data. All plots and graphs were realized using R, and some skill evaluations and data detrending were computed using the s2dverification package available on CRAN (Manubens et al. 2018). The authors would also like to thank two anonymous reviewers, whose feedback helped substantially improve this article.
Funding
This study was partly funded by the H2020-APPLICATE project, EU grant number 727862. JCAN acknowledges the Spanish Ministry of Science, Innovation and Universities for the personal grant Juan de la Cierva FJCI-2017-34027, PRACE for awarding access to MareNostrum at Barcelona Supercomputing Center (BSC), and ESA/CMUG-CCI3 for financial support. PO work was funded by the Ramon y Cajal grant RYC-2017-22772.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Batté, L., Välisuo, I., Chevallier, M. et al. Summer predictions of Arctic sea ice edge in multi-model seasonal re-forecasts. Clim Dyn 54, 5013–5029 (2020). https://doi.org/10.1007/s00382-020-05273-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-020-05273-8