Volume 40 Issue 1
Feb.  2021
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Jianfen Wei, Zhaomin Wang, Mingyi Gu, Jing-Jia Luo, Yunhe Wang. An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models[J]. Acta Oceanologica Sinica, 2021, 40(1): 85-102. doi: 10.1007/s13131-021-1705-6
Citation: Jianfen Wei, Zhaomin Wang, Mingyi Gu, Jing-Jia Luo, Yunhe Wang. An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models[J]. Acta Oceanologica Sinica, 2021, 40(1): 85-102. doi: 10.1007/s13131-021-1705-6

An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models

doi: 10.1007/s13131-021-1705-6
Funds:  The Major State Basic Research Development Program of China under contract No. 2016YFA0601804; the Global Change Research Program of China under contract No. 2015CB953900; the National Natural Science Foundation of China under contract Nos 41941007 and 41876220; the China Postdoctoral Science Foundation under contract No. 2020M681661.
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  • To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects (CREs) in 22 coupled model intercomparison project 6 (CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction (CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products (CloudSat-CALIPSO, CERES-MODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However, almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x. On average, clouds warm the surface of the Arctic Basin mainly via the longwave (LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave (SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other, yielding similar net surface radiation flux between model output (3.0 W/m2) and CERES-EBAF observation (6.1 W/m2). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean, consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path (LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project (CFMIP) observation simulator package (COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models’ cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.
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  • [1]
    Bodas-Salcedo A, Webb M J, Bony S, et al. 2011. COSP: Satellite simulation software for model assessment. Bulletin of the American Meteorological Society, 92(8): 1023–1043. doi: 10.1175/2011BAMS2856.1
    [2]
    Boeke R C, Taylor P C. 2016. Evaluation of the Arctic surface radiation budget in CMIP5 models. Journal of Geophysical Research: Atmospheres, 121(14): 8525–8548. doi: 10.1002/2016JD025099
    [3]
    Bogenschutz P A, Gettelman A, Hannay C, et al. 2018. The path to CAM6: Coupled simulations with CAM5.4 and CAM5.5. Geoscientific Model Development, 11(1): 235–255. doi: 10.5194/gmd-11-235-2018
    [4]
    Cao Jian, Wang Bin, Yang Y M, et al. 2018. The NUIST Earth System Model (NESM) version 3: Description and preliminary evaluation. Geoscientific Model Development, 11(7): 2975–2993. doi: 10.5194/gmd-11-2975-2018
    [5]
    Cesana G, Kay J E, Chepfer H, et al. 2012. Ubiquitous low level liquid-containing Arctic clouds: New observations and climate model constraints from CALIPSO-GOCCP. Geophysical Research Letters, 39(20): L20804. doi: 10.1029/2012GL053385
    [6]
    Cesana G, Storelvmo T. 2017. Improving climate projections by understanding how cloud phase affects radiation. Journal of Geophysical Research: Atmospheres, 22(8): 4594–4599. doi: 10.1002/2017JD026927
    [7]
    Christensen M W, Behrangi A, L’ecuyer T S, et al. 2016. Arctic observation and reanalysis integrated system: A new data product for validation and climate study. Bulletin of the American Meteorological Society, 97(6): 907–916. doi: 10.1175/BAMS-D-14-00273.1
    [8]
    Cohen J, Screen J A, Furtado J C, et al. 2014. Recent Arctic amplification and extreme mid-latitude weather. Nature Geoscience, 7(9): 627–637. doi: 10.1038/ngeo2234
    [9]
    Comiso J C, Hall D K. 2014. Climate trends in the Arctic as observed from space. Wiley Interdisciplinary Reviews: Climate Change, 5(3): 389–409. doi: 10.1002/wcc.277
    [10]
    Dong Xiquan, Xi Baike, Crosby K, et al. 2010. A 10 year climatology of Arctic cloud fraction and radiative forcing at Barrow, Alaska. Journal of Geophysical Research: Atmospheres, 115(D17): D17212. doi: 10.1029/2009JD013489
    [11]
    Dong Xiquan, Xi Baike, Qiu Shaoyue, et al. 2016. A radiation closure study of Arctic stratus cloud microphysical properties using the collocated satellite-surface data and Fu-Liou radiative transfer model. Journal of Geophysical Research: Atmospheres, 121(17): 10175–10198. doi: 10.1002/2016JD025255
    [12]
    Eastman R, Warren S G. 2010. Interannual variations of Arctic cloud types in relation to sea ice. Journal of Climate, 23(15): 4216–4232. doi: 10.1175/2010JCLI3492.1
    [13]
    English J M, Gettelman A, Henderson G R. 2015. Arctic radiative fluxes: Present-day biases and future projections in CMIP5 models. Journal of Climate, 28(15): 6019–6038. doi: 10.1175/JCLI-D-14-00801.1
    [14]
    Eyring V, Bony S, Meehl G A, et al. 2016. Overview of the coupled model intercomparison project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5): 1937–1958. doi: 10.5194/gmd-9-1937-2016
    [15]
    Forbes R, Ahlgrimm M. 2012. Representing cloud and precipitation in the ECMWF global model. In: ECMWF Workshop on Parametrization of Clouds and Precipitation. Reading, UK: European Centre for Medium-Range Weather Forecasts
    [16]
    Fu Qiang, Liou K N. 1993. Parameterization of the radiative properties of cirrus clouds. Journal of the Atmospheric Sciences, 50(13): 2008–2025. doi: 10.1175/1520-0469(1993)050<2008:POTRPO>2.0.CO;2
    [17]
    Goosse H, Kay J E, Armour K C, et al. 2018. Quantifying climate feedbacks in polar regions. Nature Communications, 9: 1919. doi: 10.1038/s41467-018-04173-0
    [18]
    Guo Zhun, Zhou Tianjun. 2014. An improved diagnostic stratocumulus scheme based on estimated inversion strength and its performance in GAMIL2. Science China Earth Sciences, 57(11): 2637–2649. doi: 10.1007/s11430-014-4891-7
    [19]
    He Bian, Bao Qing, Wang Xiaocong, et al. 2019. CAS FGOALS-f3-L model datasets for CMIP6 historical atmospheric model intercomparison project simulation. Advances in Atmospheric Sciences, 36(8): 771–778. doi: 10.1007/s00376-019-9027-8
    [20]
    Hourdin F, Rio C, Grandpeix J Y, et al. 2020. LMDZ6A: The atmospheric component of the IPSL climate model with improved and better tuned physics. Journal of Advances in Modeling Earth Systems, 12(7): e2019MS001892. doi: 10.1029/2019MS001892
    [21]
    Huang Yiyi, Dong Xiquan, Bailey D A, et al. 2019. Thicker clouds and accelerated Arctic sea ice decline: The atmosphere-sea ice interactions in spring. Geophysical Research Letters, 46(12): 6980–6989. doi: 10.1029/2019GL082791
    [22]
    Huang Yiyi, Dong Xiquan, Xi Baike, et al. 2017a. Quantifying the uncertainties of reanalyzed Arctic cloud and radiation properties using satellite surface observations. Journal of Climate, 30(19): 8007–8029. doi: 10.1175/JCLI-D-16-0722.1
    [23]
    Huang Yiyi, Dong Xiquan, Xi Baike, et al. 2017b. The footprints of 16 year trends of Arctic springtime cloud and radiation properties on September Sea Ice retreat. Journal of Geophysical Research: Atmospheres, 122(4): 2179–2193. doi: 10.1002/2016JD026020
    [24]
    Intergovernmental Panel on Climate Change (IPCC). 2014. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press
    [25]
    Jun S Y, Ho C H, Jeong J H, et al. 2016. Recent changes in winter Arctic clouds and their relationships with sea ice and atmospheric conditions. Tellus A: Dynamic Meteorology and Oceanography, 68(1): 29130. doi: 10.3402/tellusa.v68.29130
    [26]
    Kapsch M L, Graversen R G, Tjernström M. 2013. Springtime atmospheric energy transport and the control of arctic summer sea-ice extent. Nature Climate Change, 3(8): 744–748. doi: 10.1038/nclimate1884
    [27]
    Karlsson K G, Anttila K, Trentmann J, et al. 2017. CLARA-A2: CM SAF cLoud, Albedo and Surface Radiation Dataset from AVHRR data-Edition 2. Offenbach: Satellite Application Facility on Climate Monitoring,
    [28]
    Kato S, Rose F G, Rutan D A, et al. 2018. Surface irradiances of edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product. Journal of Climate, 31(11): 4501–4527. doi: 10.1175/JCLI-D-17-0523.1
    [29]
    Kawai H, Yukimoto S, Koshiro T, et al. 2019. Significant improvement of cloud representation in the global climate model MRI-ESM2. Geoscientific Model Development, 12(7): 2875–2897. doi: 10.5194/gmd-12-2875-2019
    [30]
    Kay J E, Gettelman A. 2009. Cloud influence on and response to seasonal Arctic sea ice loss. Journal of Geophysical Research: Atmospheres, 114(D18): D18204. doi: 10.1029/2009JD011773
    [31]
    Kay J E, L’Ecuyer T. 2013. Observational constraints on Arctic Ocean clouds and radiative fluxes during the early 21st century. Journal of Geophysical Research: Atmospheres, 118(13): 7219–7236. doi: 10.1002/jgrd.50489
    [32]
    Kelley M, Schmidt G A, Nazarenko L, et al. 2020. GISS-E2.1: Configurations and climatology. Journal of Advances in Modeling Earth Systems, 12(8): e2019MS002025
    [33]
    Key J E, Wang Xuanji, Liu Yinghui. 2014. NOAA Climate Data Record of AVHRR Polar Pathfinder Extended (APP-X): Version 1, Revision 1. Asheville, NC, USA: NOAA National Climate Data Center
    [34]
    Key J E, Wang Xuanji, Liu Yinghui, et al. 2016. The AVHRR polar pathfinder climate data records. Remote Sensing, 8(3): 167. doi: 10.3390/rs8030167
    [35]
    Lee H J, Kwon M O, Yeh S W, et al. 2017. Impact of poleward moisture transport from the North Pacific on the acceleration of sea ice loss in the Arctic since 2002. Journal of Climate, 30(17): 6757–6769. doi: 10.1175/JCLI-D-16-0461.1
    [36]
    Lenaerts J T M, Van Tricht K, Lhermitte S, et al. 2017. Polar clouds and radiation in satellite observations, reanalyses, and climate models. Geophysical Research Letters, 44(7): 3355–3364. doi: 10.1002/2016GL072242
    [37]
    Letterly A, Key J, Liu Yinghui. 2016. The influence of winter cloud on summer sea ice in the Arctic, 1983–2013. Journal of Geophysical Research: Atmospheres, 121(5): 2178–2187. doi: 10.1002/2015JD024316
    [38]
    Li Jiming, Yi Yuhong, Minnis P, et al. 2011. Radiative effect differences between multi-layered and single-layer clouds derived from CERES, CALIPSO, and CloudSat data. Journal of Quantitative Spectroscopy and Radiative Transfer, 112(2): 361–375. doi: 10.1016/j.jqsrt.2010.10.006
    [39]
    Liu Yinghui, Ackerman S A, Maddux B C, et al. 2010. Errors in cloud detection over the Arctic using a satellite imager and implications for observing feedback mechanisms. Journal of Climate, 23(7): 1894–1907. doi: 10.1175/2009JCLI3386.1
    [40]
    Liu Yinghui, Key J R. 2014. Less winter cloud aids summer 2013 Arctic sea ice return from 2012 minimum. Environmental Research Letters, 9(4): 044002. doi: 10.1088/1748-9326/9/4/044002
    [41]
    Liu Yinghui, Key J R, Liu Zhengyu, et al. 2012. A cloudier Arctic expected with diminishing sea ice. Geophysical Research Letters, 39(5): L05705. doi: 10.1029/2012GL051251
    [42]
    Liu Yinghui, Key J R, Wang Xuanji. 2008. The influence of changes in cloud cover on recent surface temperature trends in the Arctic. Journal of Climate, 21(4): 705–715. doi: 10.1175/2007JCLI1681.1
    [43]
    Loeb N G, Doelling D R, Wang Hailan, et al. 2018. Clouds and the Earth’s radiant energy system (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. Journal of Climate, 31(2): 895–918. doi: 10.1175/JCLI-D-17-0208.1
    [44]
    Mace G G, Benson S. 2008. The vertical structure of cloud occurrence and radiative forcing at the SGP ARM site as revealed by 8 years of continuous data. Journal of Climate, 21(11): 2591–2610. doi: 10.1175/2007JCLI1987.1
    [45]
    Mace G G, Zhang Qiuqing, Vaughan M, et al. 2009. A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data. Journal of Geophysical Research: Atmospheres, 114(D8): D00A26. doi: 10.1029/2007JD009755
    [46]
    Marchand R, Mace G G, Ackerman T, et al. 2008. Hydrometeor detection using Cloudsat — An earth-orbiting 94-GHz cloud radar. Journal of Atmospheric and Oceanic Technology, 25(4): 519–533. doi: 10.1175/2007jtecha1006.1
    [47]
    Matus A V, L’Ecuyer T S. 2017. The role of cloud phase in Earth’s radiation budget. Journal of Geophysical Research: Atmospheres, 122(5): 2559–2578. doi: 10.1002/2016JD025951
    [48]
    McAvaney B J, Le Treut H. 2003. CFMIP: The cloud feedback intercomparison project. CLIVAR Exchanges, United Kingdom: International CLIVAR Project Office
    [49]
    Minnis P, Sun-Mack S, Young D F, et al. 2011. CERES Edition-2 cloud property retrievals using TRMM VIRS and terra and aqua MODIS data—Part I: Algorithms. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4374–4400. doi: 10.1109/TGRS.2011.2144601
    [50]
    Ogura T, Shiogama H, Watanabe M, et al. 2017. Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5. Geoscientific Model Development, 10(12): 4647–4664. doi: 10.5194/gmd-10-4647-2017
    [51]
    Park S, Baek E H, Kim B M, et al. 2017. Impact of detrained cumulus on climate simulated by the community atmosphere model version 5 with a unified convection scheme. Journal of Advances in Modeling Earth Systems, 9(2): 1399–1411. doi: 10.1002/2016MS000877
    [52]
    Park S, Shin J, Kim S, et al. 2019. Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified convection scheme (SAM0-UNICON). Journal of Climate, 32(10): 2917–2949. doi: 10.1175/JCLI-D-18-0796.1
    [53]
    Perovich D K, Light B, Eicken H, et al. 2007. Increasing solar heating of the Arctic Ocean and adjacent seas, 1979–2005: Attribution and role in the ice-albedo feedback. Geophysical Research Letters, 34(19): L19505. doi: 10.1029/2007GL031480
    [54]
    Perovich D K, Moritz R C, Weatherly J. 1999. SHEBA: The surface heat budget of the Arctic Ocean. EOS, Transactions, American Geophysical Union, 80(41): 481–486
    [55]
    Platnick S, King M D, Ackerman S A, et al. 2003. The MODIS cloud products: Algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 459–473. doi: 10.1109/TGRS.2002.808301
    [56]
    Qu Xin, Hall A, Klein S A, et al. 2014. On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Climate Dynamics, 42(9-10): 2603–2626. doi: 10.1007/s00382-013-1945-z
    [57]
    Rasch P J, Xie S, Ma P L, et al. 2019. An overview of the atmospheric component of the Energy Exascale Earth System Model. Journal of Advances in Modeling Earth Systems, 11(8): 2377–2411. doi: 10.1029/2019MS001629
    [58]
    Riihelä A, Key J R, Meirink J F, et al. 2017. An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic. Journal of Geophysical Research: Atmospheres, 122(9): 4829–4848. doi: 10.1002/2016JD026443
    [59]
    Serreze M C, Barry R G. 2011. Processes and impacts of Arctic amplification: A research synthesis. Global and Planetary Change, 77(1-2): 85–96. doi: 10.1016/j.gloplacha.2011.03.004
    [60]
    Shupe M D, Matrosov S Y, Uttal T. 2006. Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA. Journal of the Atmospheric Sciences, 63(2): 697–711. doi: 10.1175/JAS3659.1
    [61]
    Shupe M D, Persson P O G, Brooks I M, et al. 2013. Cloud and boundary layer interactions over the Arctic sea ice in late summer. Atmospheric Chemistry and Physics, 13(18): 9379–9399. doi: 10.5194/acp-13-9379-2013
    [62]
    Slater A G. 2016. Surface solar radiation in North America: A comparison of observations, reanalyses, satellite, and derived products. Journal of Hydrometeorology, 17(1): 401–420. doi: 10.1175/JHM-D-15-0087.1
    [63]
    Spangenberg D A, Trepte Q, Minnis P, et al. 2004. Daytime cloud property retrievals over the Arctic from multispectral MODIS data. In: 13th AMS Conference on Satellite Oceanography and Meteorology. Norfolk, VA: Bulletin of the American Meteorological Society
    [64]
    Stevens B, Giorgetta M, Esch M, et al. 2013. Atmospheric component of the MPI-M Earth system model: ECHAM6. Journal of Advances in Modeling Earth Systems, 5(2): 146–172. doi: 10.1002/jame.20015
    [65]
    Stokes G M, Schwartz S E. 1994. The Atmospheric Radiation Measurement (ARM) Program: Programmatic background and design of the cloud and radiation test bed. Bulletin of the American Meteorological Society, 75(7): 1201–1222. doi: 10.1175/1520-0477(1994)075<1201:TARMPP>2.0.CO;2
    [66]
    Stubenrauch C J, Rossow W B, Kinne S, et al. 2013. Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX Radiation Panel. Bulletin of the American Meteorological Society, 94(7): 1031–1049. doi: 10.1175/BAMS-D-12-00117.1
    [67]
    Tatebe H, Ogura T, Nitta T, et al. 2019. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12(7): 2727–2765. doi: 10.5194/gmd-12-2727-2019
    [68]
    Taylor P C, Boeke R C, Li Ying, et al. 2019. Arctic cloud annual cycle biases in climate models. Atmospheric Chemistry and Physics, 19(13): 8759–8782. doi: 10.5194/acp-19-8759-2019
    [69]
    Tiedtke M. 1993. Representation of clouds in large-scale models. Monthly Weather Review, 121(11): 3040–3061. doi: 10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2
    [70]
    Tjernström M, Leck C, Birch C E, et al. 2014. The arctic summer cloud ocean study (ASCOS): Overview and experimental design. Atmospheric Chemistry and Physics, 14(6): 2823–2869. doi: 10.5194/acp-14-2823-2014
    [71]
    Trepte Q Z, Minnis P, Sun-Mack S, et al. 2019. Global cloud detection for CERES Edition 4 using Terra and Aqua MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 9410–9449. doi: 10.1109/TGRS.2019.2926620
    [72]
    Uttal T, Curry J A, McPhee M G, et al. 2002. Surface heat budget of the Arctic Ocean. Bulletin of the American Meteorological Society, 83(2): 255–276. doi: 10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2
    [73]
    Van Tricht K, Lhermitte S, Lenaerts J T M, et al. 2016. Clouds enhance Greenland ice sheet meltwater runoff. Nature Communications, 7: 10266. doi: 10.1038/ncomms10266
    [74]
    Vaughan M A, Powell K A, Winker D M, et al. 2009. Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements. Journal of Atmospheric and Oceanic Technology, 26(10): 2034–2050. doi: 10.1175/2009jtecha1228.1
    [75]
    Verlinde J, Zak B D, Shupe M D, et al. 2016. The ARM north slope of Alaska (NSA) sites. Meteorological Monographs, 57: 8.1–8.13. doi: 10.1175/AMSMONOGRAPHS-D-15-0023.1
    [76]
    Von Salzen K, Scinocca J F, McFarlane N A, et al. 2013. The Canadian fourth generation atmospheric global climate model (CanAM4). Part I: Representation of physical processes. Atmosphere-Ocean, 51(1): 104–125. doi: 10.1080/07055900.2012.755610
    [77]
    Walsh J E. 2014. Intensified warming of the Arctic: Causes and impacts on middle latitudes. Global Planetary Change, 117: 52–63. doi: 10.1016/j.gloplacha.2014.03.003
    [78]
    Walters D, Baran A J, Boutle I, et al. 2019. The met office unified model global atmosphere 7.0/7.1 and JULES global land 7.0 configurations. Geoscientific Model Development, 12(5): 1909–1963. doi: 10.5194/gmd-12-1909-2019
    [79]
    Wang Xuanji, Key J R. 2005a. Arctic surface, cloud, and radiation properties based on the AVHRR polar pathfinder dataset. Part I: Spatial and temporal characteristics. Journal of Climate, 18(14): 2558–2574. doi: 10.1175/JCLI3438.1
    [80]
    Wang Xuanji, Key J R. 2005b. Arctic surface, cloud, and radiation properties based on the AVHRR polar pathfinder dataset. Part II: Recent trends. Journal of Climate, 18(14): 2575–2593. doi: 10.1175/JCLI3439.1
    [81]
    Wang Xiaocong, Liu Yimin, Bao Qing, et al. 2015. Comparisons of GCM cloud cover parameterizations with cloud-resolving model explicit simulations. Science China: Earth Sciences, 58(4): 604–614. doi: 10.1007/s11430-014-4989-y
    [82]
    Wang Yunhe, Yuan Xiaojun, Bi Haibo, et al. 2019. The contributions of winter cloud anomalies in 2011 to the summer sea-ice rebound in 2012 in the Antarctic. Journal of Geophysical Research: Atmospheres, 124(6): 3435–3447. doi: 10.1029/2018JD029435
    [83]
    Webb M J, Andrews T, Bodas-Salcedo A, et al. 2017. The cloud feedback model intercomparison project (CFMIP) contribution to CMIP6. Geoscientific Model Development, 10(1): 359–384. doi: 10.5194/gmd-10-359-2017
    [84]
    Wei Jianfen, Zhang Xiangdong, Wang Zhaomin. 2019. Reexamination of Fram Strait sea ice export and its role in recently accelerated Arctic sea ice retreat. Climate Dynamics, 53(3–4): 1823–1841. doi: 10.1007/s00382-019-04741-0
    [85]
    Wendisch M, Macke A, Ehrlich A, et al. 2019. The Arctic cloud puzzle: Using ACLOUD/PASCAL multiplatform observations to unravel the role of clouds and aerosol particles in arctic amplification. Bulletin of the American Meteorological Society, 100(5): 841–871. doi: 10.1175/BAMS-D-18-0072.1
    [86]
    Wielicki B A, Barkstrom B R, Baum B A, et al. 1998. Clouds and the Earth’s Radiant Energy Sys-tem (CERES): Algorithm overview. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1127–1141. doi: 10.1109/36.701020
    [87]
    Wielicki B A, Barkstrom B R, Harrison E F, et al. 1996. Clouds and the Earth’s Radiant Energy System (CERES): an earth observing system experiment. Bulletin of the American Meteorological Society, 77(5): 853–868. doi: 10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2
    [88]
    Wilson D R, Bushell A C, Kerr-Munslow A M, et al. 2008. PC2: A prognostic cloud fraction and condensation scheme. II: Climate model simulations. Quarterly Journal of the Royal Meteorological Society, 134(637): 2109–2125. doi: 10.1002/qj.332
    [89]
    Wu Tongwen, Lu Yixiong, Fang Yongjie, et al. 2019. The Beijing climate center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12(4): 1573–1600. doi: 10.5194/gmd-12-1573-2019
    [90]
    Xu Kuanman, Randall D A. 1996. A semiempirical cloudiness parameterization for use in climate models. Journal of the Atmospheric Sciences, 53(21): 3084–3102. doi: 10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2
    [91]
    Yu Wei, Doutriaux M, Sèze G, et al. 1996. A methodology study of the validation of clouds in GCMs using ISCCP satellite observations. Climate Dynamics, 12(6): 389–401. doi: 10.1007/bf00211685
    [92]
    Yu Yueyue, Taylor P C, Cai Ming. 2019. Seasonal variations of arctic low-level clouds and its linkage to sea ice seasonal variations. Journal of Geophysical Research: Atmospheres, 124(22): 12206–12226. doi: 10.1029/2019JD031014
    [93]
    Zhang Xiaotong, Liang Shunlin, Wild M, et al. 2015. Analysis of surface incident shortwave radiation from four satellite products. Remote Sensing of Environment, 165: 186–202. doi: 10.1016/j.rse.2015.05.015
    [94]
    Zhang Yuying, Xie Shaocheng, Lin Wuyin, et al. 2019. Evaluation of clouds in version 1 of the E3SM atmosphere model with satellite simulators. Journal of Advances in Modeling Earth Systems, 11(5): 1253–1268. doi: 10.1029/2018MS001562
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