• Arzel, O., T. Fichefet, and H. Goosse, 2006: Sea ice evolution over the 20th and 21st centuries as simulated by current AOGCMs. Ocean Modell., 12, 401415, https://doi.org/10.1016/j.ocemod.2005.08.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrens, L. K., T. Martin, V. A. Semenov, and M. Latif, 2012: The Arctic Sea ice in the CMIP3 climate model ensemble—Variability and anthropogenic change. Cryosphere Discuss., 6, 53175344, https://doi.org/10.5194/tcd-6-5317-2012.

    • Search Google Scholar
    • Export Citation
  • Bitz, C. M., and W. H. Lipscomb, 1999: An energy-conserving thermodynamic model of sea ice. J. Geophys. Res., 104, 15 66915 677, https://doi.org/10.1029/1999JC900100.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cavalieri, D. J., C. L. Parkinson, and K. Y. Vinnikov, 2003: 30-year satellite record reveals contrasting Arctic and Antarctic decadal sea ice variability. Geophys. Res. Lett., 30, 1970, https://doi.org/10.1029/2003GL018031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, M., X. Shi, Y. Fang, L. Zhang, T. Wu, and B. Zhou, 2019: Impacts of SIS and CICE as sea ice components in BCC_CSM on the simulation of the Arctic climate. J. Ocean Univ. China, 18, 553562, https://doi.org/10.1007/s11802-019-3862-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., C. L. Parkinson, R. Gersten and L. Stock, 2008: Accelerated decline in the Arctic sea ice cover. Geophys. Res. Lett., 35, L01703, https://doi.org/10.1029/2007GL031972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deng, J., 2014: Northern Hemisphere sea ice variability and its relationship with climate factors. M.S. thesis, School of Geographic and Oceanographic Sciences, Nanjing University, 82 pp.

  • Eisenman, I., W. N. Meier, and J. R. Norris, 2014: A spurious jump in the satellite record: Is Antarctic Sea ice really expanding? Cryosphere, 8, 273288, https://doi.org/10.5194/tcd-8-273-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Francis, J. A. and S. J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys. Res. Lett., 39, L06801, https://doi.org/10.1029/2012GL051000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, F., X. Zhou, and H. Wang, 2017: Arctic sea ice in CMIP5 climate model projections and their seasonal variability. Acta Oceanol. Sin., 36 (8), 18, https://doi.org/10.1007/s13131-017-1029-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunke, E. C. and J. K. Dukowicz, 1997: An elastic-viscous plastic model for sea ice dynamics. J. Phys. Oceanogr., 27, 18491867, https://doi.org/10.1175/1520-0485(1997)027<1849: AEVPMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kattsov, V. M., V. E. Ryabinin, J. E. Overland, M. C. Serreze, M. Visbeck, J. E. Walsh, W. Meler, and X. Zhang, 2010: Arctic sea ice change: A grand challenge of climate science. J. Glaciol., 56, 11151121, https://doi.org/10.3189/002214311796406176.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., M. M. Holland, and A. Jahn, 2011: Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world. Geophys. Res. Lett., 38, L15708, https://doi.org/10.1029/2011GL048008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ke, C., H. Peng, B. Sun, and H. Xie, 2013: Spatio-temporal variability of Arctic sea ice from 2002 to 2011. J. Remote Sens., 17, 452466.

    • Search Google Scholar
    • Export Citation
  • Li, D., R. Zhang, and T. R. Knutson, 2017: On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline. Nat. Commun., 8, 14991, https://doi.org/10.1038/ncomms14991.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lipscomb, W. H., E. C. Hunke, W. Maslowski, and J. Jakacki, 2007: Ridging, strength, and stability in high-resolution sea ice models. J. Geophys. Res., 112, C03S91, https://doi.org/10.1029/2005JC003355.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McLaren, A. J., and et al. , 2006: Evaluation of the sea ice simulation in a new coupled atmosphere–ocean climate model (HadGEM1). J. Geophys. Res., 111, C12014, https://doi.org/10.1029/2005JC003033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Neill, B. C., and et al. , 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 34613482, https://doi.org/10.5194/gmd-9-3461-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shu, Q., Z. Song, and F. Qiao, 2014: Assessment of sea ice simulations in the CMIP5 models. Cryosphere Discuss., 8, 34133435, https://doi.org/10.5194/tcd-8-3413-2014.

    • Search Google Scholar
    • Export Citation
  • Shu, Q., Q. Wang, Z. Song, F. Qiao, J. Zhao, M. Chu and X. Li, 2020: Assessment of sea ice extent in CMIP6 with comparison to observation and CMIP5. Geophys. Res. Lett., 47, e2020GL087965, https://doi.org/10.1029/2020GL087965.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • SIMIP Community, 2020: Arctic sea ice in CMIP6. Geophys. Res. Lett., 47, e2019GL086749, https://doi.org/10.1029/2019GL086749.

  • Stephenson, S. R., L. C. Smith, L. W. Brigham, and J. A. Agnew, 2013: Projected 21st-century changes to Arctic marine access. Climatic Change, 118, 885899, https://doi.org/10.1007/s10584-012-0685-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., M. M. Holland, W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice decline: Faster than forecast. Geophys. Res. Lett., 34, L09501, https://doi.org/10.1029/2007GL029703.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland and W. N. Meier, 2012: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observation. Geophys. Res. Lett., 39, L16502, https://doi.org/10. 1029/2012GL052676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, H., L. Zhang, M. Chu, T. Wu, B. Qiu, and J. Li, 2015: An analysis of simulated global sea ice extent, thickness, and causes of error with the BCC_CSM model. Chin. J. Atmos. Sci., 39, 197209.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, https://doi.org/10.1029/2000JD900719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, J., T. J. Bracegirdle, T. Phillips, G. J. Marshall, and J. S. Hosking, 2013: An initial assessment of Antarctic sea ice extent in the CMIP5 models. J. Climate, 26, 14731484, https://doi.org/10.1175/JCLI-D-12-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Veiga, S. F., and et al. , 2019: The Brazilian Earth System Model ocean–atmosphere (BESM-OA) version 2.5: Evaluation of its CMIP5 historical simulation. Geosci. Model Dev., 12, 16131642, https://doi.org/10.5194/gmd-12-1613-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, M., and J. E. Overland, 2012: A sea ice free summer Arctic within 30 years: An update from CMIP5 models. Geophys. Res. Lett., 39, L18501, https://doi.org/10.1029/2012GL052868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q., S. Danilov, T. Jung, L. Kaleschke, and A. Wernecke, 2016: Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends. Geophys. Res. Lett., 43, 70197027, https://doi.org/10.1002/2016GL068696.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, T., and et al. , 2019: The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev., 12, 15731600, https://doi.org/10.5194/gmd-12-1573-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyser, K., T. van Noije, S. Yang, J. von Hardenberg, D. O’Donnell, and R. Döscher, 2019: On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6. Geosci. Model Dev., 13, 34563474, https://doi.org/10.5194/gmd-2019-282.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., 2010: Sensitivity of Arctic summer sea ice coverage to global warming forcing: Towards reducing uncertainty in Arctic climate change projections. Tellus, 62A, 220227, https://doi.org/10.1111/j.1600-0870.2010.00441.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Multi-Aspect Assessment of CMIP6 Models for Arctic Sea Ice Simulation

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  • 1 Chinese Meteorological Administration–Nanjing University Joint Laboratory for Climate Prediction Studies, School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • | 2 Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • | 3 University Corporation for Polar Research, Beijing, China
  • | 4 Jiangsu Collaborative Innovation Center for Climate Change, Nanjing, China
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Abstract

This paper evaluates the ability of 35 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by the Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, and Bering Seas and the Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the twenty-first century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with the Sea Ice Simulator (SIS) sea ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Seas. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multiyear linear trend, and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature over 1979–2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than is observed (1.37 × 106 km2 K−1).

Current affiliation: School of Atmospheric Sciences, Nanjing University, Nanjing, China.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lujun Zhang, ljzhang@nju.edu.cn

Abstract

This paper evaluates the ability of 35 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to simulate Arctic sea ice by comparing simulated results with observation from the aspects of spatial patterns and temporal variation. The simulation ability of each model is also quantified by the Taylor score and e score from these two aspects. Results show that biases between observed and simulated Arctic sea ice concentration (SIC) are mainly located in the East Greenland, Barents, and Bering Seas and the Sea of Okhotsk. The largest difference between the observed and simulated SIC spatial patterns occurs in September. Since the beginning of the twenty-first century, the ability of most models to simulate summer SIC spatial patterns has decreased. We also find that models with the Sea Ice Simulator (SIS) sea ice component in CMIP6 show a consistent larger positive simulation biases of SIC in the East Greenland and Barents Seas. In addition, for most models, the higher the model resolution is, the better the match between the simulated and observed spatial patterns of winter Arctic SIC is. Furthermore, this paper makes a detailed assessment for temporal variation of Arctic sea ice extent (SIE) with regard to climatological average, seasonal SIE, multiyear linear trend, and detrended standard deviation of SIE. The sensitivity of September Arctic SIE to a given change of Arctic surface air temperature over 1979–2014 in each model has also been investigated. Most models simulate a smaller loss of September Arctic SIE per degree of warming than is observed (1.37 × 106 km2 K−1).

Current affiliation: School of Atmospheric Sciences, Nanjing University, Nanjing, China.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lujun Zhang, ljzhang@nju.edu.cn
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