Abstract
Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models’ simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation.
摘要
对北极冰上积雪的研究目前较少, 但其对于了解北极能量收支起到非常关键的作用。通过与最新发布的卫星反演数据进行对比,该项研究评估了31个参加第六次国际耦合模式比较计划(CMIP6)的模式在1993-2014年间的北极冰上积雪厚度产品。 CMIP6模式抓住了一些积雪厚度的气候态和变率特征。 观测所得的雪厚变率落在模式模拟的变率的区间内。 所有的模式都显示雪厚在1993-2014年间呈减小趋势。 但是, 模拟与观测结果在时空分布上存在显著差异。 相较于观测结果, 多数模式的季节性最大雪厚出现较晚 (2个月), 季节性最小雪厚明显偏低, 雪厚增长期和消减期的过渡阶段模拟不准确, 多年冰频繁出现的地区的雪厚年际变率和变薄趋势被大大低估。多数模式无法重现积雪从加拿大群岛到外围地区的分布梯度以及在北冰洋中部地区雪厚的变薄趋势。 未来的气候模拟预测北极冰上积雪厚度在2015-99年间会继续变薄。在SSP5-8.5情景下, 北极在夏季和秋季将无积雪并且积雪始于1月份。对造成来自同一系列的某些模式对积雪厚度模拟问题的可能原因的进一步研究表明, 分辨率, 高空大气模式以及生物地球化学过程是影响积雪厚度模拟的重要因素。
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References
Alou-Font, E., C.-J. Mundy, S. Roy, M. Gosselin, and S. Agustí, 2013: Snow cover affects ice algal pigment composition in the coastal Arctic Ocean during spring. Marine Ecology Progress Series, 474, 89–104, https://doi.org/10.3354/meps10107.
Blazey, B. A., M. M. Holland, and E. C. Hunke, 2013: Arctic Ocean sea ice snow depth evaluation and bias sensitivity in CCSM. The Cryosphere, 7, 1887–1900, https://doi.org/10.5194/tc-7-1887-2013.
Bliss, A. C., and M. R. Anderson, 2018: Arctic sea ice melt onset timing from passive microwave-based and surface air temperature-based methods. J. Geophys. Res., 123, 9063–9080, https://doi.org/10.1029/2018JD028676.
Eicken, H., T. C. Grenfell, D. K. Perovich, J. A. Richter-Menge, and K. Frey, 2004: Hydraulic controls of summer Arctic pack ice albedo. J. Geophys. Res., 109, C08007, https://doi.org/10.1029/2003JC001989.
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. Geoscientific Model Development, 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.
Gent, P. R., and Coauthors, 2011: The community climate system model version 4. J. Climate, 24, 4973–4991, https://doi.org/10.1029/2010JC006243.
Gidden, M. J., and Coauthors, 2019: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: A dataset of harmonized emissions trajectories through the end of the century. Geoscientific Model Development, 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019.
Haas, C., D. N. Thomas, and J. Bareiss, 2001: Surface properties and processes of perennial Antarctic sea ice in summer. J. Glaciol., 47, 613–625, https://doi.org/10.3189/172756501781831864.
Hezel, P. J., X. Zhang, C. M. Bitz, B. P. Kelly, and F. Massonnet, 2012: Projected decline in spring snow depth on Arctic sea ice caused by progressively later autumn open ocean freeze — up this century. Geophys. Res. Lett., 39, L17505, https://doi.org/10.1029/2012GL052794.
Holland, M. M., and L. Landrum, 2015: Factors affecting projected Arctic surface shortwave heating and albedo change in coupled climate models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373, 20140162, https://doi.org/10.1098/rsta.2014.0162.
Jeffries, M. O., H. R. Krouse, B. Hurst-Cushing, and T. Maksym, 2001: Snow-ice accretion and snow-cover depletion on Antarctic first-year sea-ice floes. Annals of Glaciology, 33, 51–60, https://doi.org/10.3189/172756401781818266.
Kawamura, T., K. I. Ohshima, T. Takizawa, and S. Ushio, 1997: Physical, structural, and isotopic characteristics and growth processes of fast sea ice in Lützow-Holm Bay, Antarctica. J. Geophys. Res., 102, 3345–3355, https://doi.org/10.1029/96JC03206.
Kwok, R., B. Panzer, C. Leuschen, S. Pang, T. Markus, B. Holt, and S. Gogineni, 2011: Airborne surveys of snow depth over Arctic sea ice. J. Geophys. Res., 116, C11018, https://doi.org/10.1029/2011JC007371.
Ledley, T. S., 1991: Snow on sea ice: Competing effects in shaping climate. J. Geophys. Res., 96, 1 7195–1 7208, https://doi.org/10.1029/91JD01439.
Ledley, T. S., 1993: Variations in snow on sea ice: A mechanism for producing climate variations. J. Geophys. Res., 98, 10 401–10 410, https://doi.org/10.1029/93JD00316.
Leppäranta, M., 1983: A growth model for black ice, snow ice and snow thickness in subarctic basins. Hydrology Research, 14, 59–70, https://doi.org/10.2166/nh.1983.0006.
Light, B., S. Dickinson, D. K. Perovich, and M. M. Holland, 2015: Evolution of summer Arctic sea ice albedo in CCSM4 simulations: Episodic summer snowfall and frozen summers. J. Geophys. Res., 120, 284–303, https://doi.org/10.1002/2014JC010149.
Liu, J. P., Y. Y. Zhang, X. Cheng, and Y. Y. Hu, 2019: Retrieval of snow depth over arctic sea ice using a deep neural network. Remote Sensing, 11, 2864, https://doi.org/10.3390/rs11232864.
Lund-Hansen, L. C., I. Hawes, M. Holtegaard Nielsen, I. Dahllöf, and B. K. Sorrell, 2018: Summer meltwater and spring sea ice primary production, light climate and nutrients in an Arctic estuary, Kangerlussuaq, west Greenland. Arctic, Antarctic, and Alpine Research, 50, S100025, https://doi.org/10.1080/15230430.2017.1414468.
Maksym, T., and T. Markus, 2008: Antarctic sea ice thickness and snow-to-ice conversion from atmospheric reanalysis and passive microwave snow depth. J. Geophys. Res., 113, C02S12, https://doi.org/10.1029/2006JC004085.
Markus, T., J. C. Stroeve, and J. Miller, 2009: Recent changes in Arctic sea ice melt onset, freezeup, and melt season length. J. Geophys. Res., 114, C12024, https://doi.org/10.1029/2009JC005436.
Maslanik, J., J. Stroeve, C. Fowler, and W. Emery, 2011: Distribution and trends in Arctic sea ice age through spring 2011. Geophys. Res. Lett., 38, L13502, https://doi.org/10.1029/2011GL047735.
Massom, R. A., and Coauthors, 2001: Snow on Antarctic sea ice. Rev. Geophys., 39, 413–445, https://doi.org/10.1029/2000RG000085.
Maykut, G. A., and N. Untersteiner, 1971: Some results from a time-dependent thermodynamic model of sea ice. J. Geophys. Res., 76, 1550–1575, https://doi.org/10.1029/JC076i006p01550.
Maykut, G. A., 1978: Energy exchange over young sea ice in the central Arctic. J. Geophys. Res., 83, 3646–3658, https://doi.org/10.1029/JC083iC07p03646.
Merkouriadi, I., G. E. Liston, R. M. Graham, and M. A. Granskog, 2020: Quantifying the potential for snow — ice formation in the Arctic Ocean. Geophys. Res. Lett., 47, e2019GL085020, https://doi.org/10.1029/2019GL085020.
Nghiem, S. V., I. G. Rigor, D. K. Perovich, P. Clemente-Colón, J. W. Weatherly, and G. Neumann, 2007: Rapid reduction of Arctic perennial sea ice. Geophys. Res. Lett., 34, L19504, https://doi.org/10.1029/2007GL031138.
Notz, D., A. Jahn, M. Holland, E. Hunke, F. Massonnet, J. Stroeve, B. Tremblay, and M. Vancoppenolle, 2016: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations. Geoscientific Model Development, 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016.
O’Neill, B. C., and Coauthors, 2016: The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016.
Perovich, D., C. Polashenski, A. Arntsen, and C. Stwertka, 2017: Anatomy of a late spring snowfall on sea ice. Geophys. Res. Lett., 44, 2802–2809, https://doi.org/10.1002/2016GL071470.
Perovich, D. K., T. C. Grenfell, B. Light, and P. V. Hobbs, 2002: Seasonal evolution of the albedo of multiyear Arctic sea ice. J. Geophys. Res., 107, 8044, https://doi.org/10.1029/2000JC000438.
Petrich, C., H. Eicken, C. M. Polashenski, M. Sturm, J. P. Harbeck, D. K. Perovich, and D. C. Finnegan, 2012: Snow dunes: A controlling factor of melt pond distribution on Arctic sea ice. J. Geophys. Res., 117, C09029, https://doi.org/10.1029/2012JC008192.
Polashenski, C., D. Perovich, and Z. Courville, 2012: The mechanisms of sea ice melt pond formation and evolution. J. Geophys. Res., 117, C01001, https://doi.org/10.1029/2011JC007231.
Polashenski, C., K. M. Golden, D. K. Perovich, E. Skyllingstad, A. Arnsten, C. Stwertka, and N. Wright, 2017: Percolation blockage: A process that enables melt pond formation on first year Arctic sea ice. J. Geophys. Res., 122, 413–440, https://doi.org/10.1002/2016JC011994.
Rodionov, S. N., 2004: A sequential algorithm for testing climate regime shifts. Geophys. Res. Lett., 31, L09204, https://doi.org/10.1029/2004GL019448.
Rostosky, P., G. Spreen, S. L. Farrell, T. Frost, G. Heygster, and C. Melsheimer, 2018: Snow depth retrieval on Arctic sea ice from passive microwave radiometers — improvements and extensions to multiyear ice using lower frequencies. J. Geophys. Res., 123, 7120–7138, https://doi.org/10.1029/2018JC014028.
Sturm, M., and R. A. Massom, 2010: Snow on sea ice. Sea ice. 2nd ed., D. N. Thomas and G. S. Dieckmann, Eds., Blackwell, 153–204, https://doi.org/10.1002/9781444317145.ch5.
Sturm, M., D. K. Perovich, and J. Holmgren, 2002: Thermal conductivity and heat transfer through the snow on the ice of the Beaufort Sea. J. Geophys. Res., 107, 8043, https://doi.org/10.1029/2000JC000409.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.
Tebaldi, C., J. M. Arblaster, and R. Knutti, 2011: Mapping model agreement on future climate projections. Geophys. Res. Lett., 38, L23701, https://doi.org/10.1029/2011GL049863.
Tschudi, M., W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik, 2019: EASE-grid sea ice age, version 4. [Available online from https://doi.org/10.5067/UTAV7490FEPB]
Untersteiner, N., and F. I. Badgley, 1965: The roughness parameters of sea ice. J. Geophys. Res., 70, 4573–4577, https://doi.org/10.1029/JZ070i018p04573.
Warren, S. G., 1982: Optical properties of snow. Rev. Geophys., 20, 67–89, https://doi.org/10.1029/RG020i001p00067.
Warren, S. G., I. G. Rigor, N. Untersteiner, V. F. Radionov, N. N. Bryazgin, Y. I. Aleksandrov, and R. Colony, 1999: Snow depth on Arctic sea ice. J. Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2.
Webster, M., and Coauthors, 2018: Snow in the changing sea-ice systems. Nature Climate Change, 8, 946–953, https://doi.org/10.1038/s41558-018-0286-7.
Acknowledgements
This research was supported by the NOAA Climate Program Office (Grant No. NA15OAR4310163), the National Key R&D Program of China (Grant Nos. 2018YFA0605904 and 2018YFA0605901), and the National Natural Science Foundation of China (Grant No. 41676185). We thank the two anonymous reviewers for their comments, which helped improve and clarify this manuscript.
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Article Highlights
• The observed variability lies in the middle of CMIP6 simulations. All models show negative trends of Arctic snow depth during 1993–2014.
• Most models cannot reproduce the observed spatial gradient of snow depth and the largest thinning rate in the central Arctic.
• Future projections suggest that Arctic snow will continue to thin during 2015–99 and be almost snow-free in summer and fall under the SSP5-8.5 scenario.
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Chen, S., Liu, J., Ding, Y. et al. Assessment of Snow Depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data. Adv. Atmos. Sci. 38, 168–186 (2021). https://doi.org/10.1007/s00376-020-0213-5
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DOI: https://doi.org/10.1007/s00376-020-0213-5