Abstract
Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships, while limited by the capability of climate models. A bias correction methodology in this study was proposed and performed on raw products from two climate models, the First Institute Oceanography Earth System Model (FIOESM) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), to improve 60 days predictions for Arctic sea ice. Both models were initialized on July 1, August 1, and September 1 in 2018. A 60-day forecast was conducted as a part of the official sea ice service, especially for the ninth Chinese National Arctic Research Expedition (CHINARE) and the China Ocean Shipping (Group) Company (COSCO) Northeast Passage voyages during the summer of 2018. The results indicated that raw products from FIOESM underestimated sea ice concentration (SIC) overall, with a mean bias of SIC up to 30%. Bias correction resulted in a 27% improvement in the Root Mean Square Error (RMSE) of SIC and a 10% improvement in the Integrated Ice Edge Error (IIEE) of sea ice edge (SIE). For the CFS, the SIE overestimation in the marginal ice zone was the dominant features of raw products. Bias correction provided a 7% reduction in the RMSE of SIC and a 17% reduction in the IIEE of SIE. In terms of sea ice extent, FIOESM projected a reasonable minimum time and amount in mid-September; however, CFS failed to project both. Additional comparison with subseasonal to seasonal (S2S) models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Blanchard-Wrigglesworth E, Barthelemy A, Chevallier M, et al. 2017. Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales. Climate Dynamics, 49(4): 1399–1410, doi: https://doi.org/10.1007/s00382-016-3388-9
Blanchard-Wrigglesworth E, Cullather R I, Wang Wanqiu, et al. 2015. Model forecast skill and sensitivity to initial conditions in the seasonal sea ice outlook. Geophysical Research Letters, 42(19): 8042–8048, doi: https://doi.org/10.1002/2015GL065860
Chen Hui, Yin Xunqiang, Bao Ying, et al. 2016. Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter. Science China Earth Sciences, 59(3): 484–494, doi: https://doi.org/10.1007/s11430-015-5187-2
Comiso J C, Parkinson C L, Gersten R, et al. 2008. Accelerated decline in the Arctic sea ice cover. Geophysical Research Letter, 35(1): L01703
Director H M, Raftery A E, Bitz C M. 2017. Improved sea ice forecasting through spatiotemporal bias correction. Journal of Climate, 30(23): 9493–9510, doi: https://doi.org/10.1175/JCLI-D-17-0185.1
Ek M B, Mitchell K E, Lin Y, et al. 2003. Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. Journal of Geophysical Research: Atmosphere, 108(D22): 8851
Goessling H F, Jung T. 2018. A probabilistic verification score for contours: methodology and application to Arctic ice-edge forecasts. Quarterly Journal of the Royal Meteorological Society, 144(712): 735–743, doi: https://doi.org/10.1002/qj.3242
Goessling H F, Tietsche S, Day J J, et al. 2016. Predictability of the Arctic sea ice edge. Geophysical Research Letters, 43(4): 1642–1650, doi: https://doi.org/10.1002/2015GL067232
Griffies S M, Harrison M J, Pacanowski R C, et al. 2004. A technical guide to MOM4. GFDL Ocean Group Technical Report No.5. Princeton, US: Geophysical Fluid Dynamics Laboratory, NOAA
Hawkins E, Osborne T M, Ho C K, et al. 2013. Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agricultural and Forest Meteorology, 170: 19–31, doi: https://doi.org/10.1016/j.agrformet.2012.04.007
Hibler III W D. 1979. A dynamic thermodynamic sea ice model. Journal of Physical Oceanography, 9(4): 815–846, doi: https://doi.org/10.1175/1520-0485(1979)009<0815:ADTSIM>2.0.CO;2
Hunke E C, Dukowicz J K. 1997. An elastic-viscous-plastic model for sea ice dynamics. Journal of Physical Oceanography, 27(9): 1849–1867, doi: https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2
Huntingford C, Lambert H F, Gash J H C, et al. 2005. Aspects of climate change prediction relevant to crop productivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1463): 1999–2009, doi: https://doi.org/10.1098/rstb.2005.1748
Ines A V M, Hansen J W. 2006. Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology, 138(1–4): 44–53
Kwok R, Cunningham G F, Wensnahan M, et al. 2009. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008. Journal of Geophysical Research: Oceans, 114(C7): C07005
Liu Jiping, Chen Zhiqiang, Hu Yongyun, et al. 2019. Towards reliable Arctic sea ice prediction using multivariate data assimilation. Science Bulletin, 64(1): 63–72, doi: https://doi.org/10.1016/j.scib.2018.11.018
Markus T, Stroeve J C, Miller J. 2009. Recent changes in Arctic sea ice melt onset, freezeup, and melt season length. Journal of Geophysical Research: Oceans, 114(C12): C12024, doi: https://doi.org/10.1029/2009JC005436
Meehl G A, Goddard L, Boer G, et al. 2014. Decadal climate prediction: an update from the trenches. Bulletin of the American Meteorological Society, 95(2): 243–267, doi: https://doi.org/10.1175/BAMS-D-12-00241.1
Nghiem S V, Rigor I G, Perovich D K, et al. 2007. Rapid reduction of Arctic perennial sea ice. Geophysical Research Letters, 34(19): L19504, doi: https://doi.org/10.1029/2007GL031138
Qiao Fangli, Song Zhenya, Bao Ying, et al. 2013. Development and evaluation of an Earth System Model with surface gravity waves. Journal of Geophysical Research: Oceans, 118(9): 4514–4524, doi: https://doi.org/10.1002/jgrc.20327
Qiao Fangli, Yuan Yeli, Yang Yongzeng, et al. 2004. Wave-induced mixing in the upper ocean: distribution and application to a global ocean circulation model. Geophysical Research Letters, 31(11): L11303
Saha S, Moorthi S, Wu Xingren, et al. 2010. The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91(8): 1015–1057, doi: https://doi.org/10.1175/2010BAMS3001.1
Saha S, Moorthi S, Wu Xingren, et al. 2014. The NCEP climate forecast system version 2. Journal of Climate, 27(6): 2185–2208, doi: https://doi.org/10.1175/JCLI-D-12-00823.1
Shu Qi, Qiao Fangli, Bao Ying, et al. 2015a. Assessment of Arctic sea ice simulation by FIO-ESM based on data assimilation experiment. Haiyang Xuebao (in Chinese), 37(11): 33–40
Shu Qi, Song Zhenya, Qiao Fangli. 2015b. Assessment of sea ice simulations in the CMIP5 models. The Cryosphere, 9(1): 399–409, doi: https://doi.org/10.5194/tc-9-399-2015
Spreen G, Kaleschke L, Heygster G. 2008. Sea ice remote sensing using AMSR-E 89-GHz channels. Journal of Geophysical Research: Oceans, 113(C2): C02S03
Stroeve J, Hamilton L C, Bitz C M, et al. 2014. Predicting September sea ice: ensemble skill of the SEARCH sea ice outlook 2008–2013. Geophysical Research Letters, 41(7): 2411–2418, doi: https://doi.org/10.1002/2014GL059388
Stroeve J C, Serreze M C, Holland M M, et al. 2012. The Arctic’s rapidly shrinking sea ice cover: a research synthesis. Climatic Change, 110(3): 1005–1027
Vitart F, Ardilouze A, Bonet A, et al. 2017. The Subseasonal to Seasonal (S2S) prediction project database. Bulletin of the American Meteorological Society, 98: 163–173, doi: https://doi.org/10.1175/BAMS-D-16-0017.1
Wayand N E, Bitz C M, Blanchard-Wrigglesworth E. 2019. A year-round subseasonal-to-seasonal sea ice prediction portal. Geophysical Research Letters, 46(6): 3298–3307, doi: https://doi.org/10.1029/2018GL081565
Winton M. 2000. A reformulated three-layer sea ice model. Journal of Atmospheric and Oceanic Technology, 17(4): 525–531, doi: https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2
Zampieri L, Goessling H F, Jung T. 2018. Bright prospects for arctic sea ice prediction on subseasonal time scales. Geophysical Research Letters, 45(18): 9731–9738, doi: https://doi.org/10.1029/2018GL079394
Zampieri L, Goessling H F, Jung T. 2019. Predictability of Antarctic sea ice edge on subseasonal time scales. Geophysical Research Letters, 46(16): 9719–9727, doi: https://doi.org/10.1029/2019GL084096
Zhao Jiechen, Zhou Xiang, Sun Xiaoyu, et al. 2017. The inter comparison and assessment of satellite sea-ice concentration datasets from the arctic. Journal of Remote Sensing (in Chinese), 21(3): 351–364
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item
The National Key Research and Development Program of China under contract No. 2018YFC1407206; the National Natural Science Foundation of China under contract Nos 41821004 and U1606405; the Basic Scientific Fund for National Public Research Institute of China (Shu Xingbei Young Talent Program) under contract No. 2019S06.
Rights and permissions
About this article
Cite this article
Zhao, J., Shu, Q., Li, C. et al. The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018. Acta Oceanol. Sin. 39, 50–59 (2020). https://doi.org/10.1007/s13131-020-1578-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13131-020-1578-0