Abstract
The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.
摘要
南大洋 (又被称作南冰洋) 地区常年被大量云层覆盖. 之前 IPCC 报告指出, 气候模式明显低估南大洋上空的云量, 并显著高估该地区对太阳辐射的吸收能力, 导致预测气候敏感性出现偏差. 本研究针对这一长期存在的问题, 探讨了最新的 CMIP6 模式在南大洋地区模拟的不确定性来源. 我们利用 10 年的卫星观测资料评估了 5 个能够提供云、辐射和气溶胶全面模拟结果的 CMIP6 模式. 研究发现, CMIP6 模拟的大气层顶长波、 云的短波和净辐射效应与云与地球辐射能系统观测结果相似. CMIP6 针对总云量的模拟也较为合理, 而 CMIP6 所模拟的液态云量在空间分布和季节变化上与 MODIS 观测资料相比存在显著偏差. 在液态水路径、 云光学深度、 云滴有效半径以及气溶胶光学深度的比较中, CMIP6 模式与 MODIS 观测存在明显差异. 由于 CMIP6 明显低估液态水路径 (∼20%) 和云滴有效半径 (11%), 导致云光学深度的偏差相对较小; 并且由于云光学深度和液态云量模拟的偏差相互抵消, 因此, CMIP6 模拟提供的云辐射效应和太阳辐射的吸收能力结果与观测结果相比较为合理. 我们还采用了误差估计方法, 发现总云量与液态水路径相反的灵敏度和偏差信号证实了 CMIP6 模拟的短波云辐射效应存在补偿误差这一观点. 进一步的空间相关性分析表明, 在观测中, 总云量是决定云辐射效应最主要的因素; 而在 CMIP6 模式中, 云辐射效应对液态水路径和云光学深度更加敏感. 本文还分析了云滴有效半径、 LWP 和云光学深度之间的关系, 探究不同模式下可能的不确定性来源. 我们的研究结果表明模式应针对云物理特征进行更严格更详细的校准, 以便促进未来的气候模式发展和提高气候预测能力.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Data availability. All the CMIP6 model outputs used for this research can be downloaded from website at https://esgf-node.llnl.gov/search/cmip6/. The CERES observations used in this study were obtained from the NASA Langley Research Center CERES ordering tool at https://ceres.larc.nasa.gov/data/ (Loeb et al., 2018; Kato et al., 2018). The MODIS satellite retrieval products were obtained from L1 and Atmosphere Archive and Distribution System (LAADS) MODIS Science Team, datasets can be downloaded at https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD08_M3/ (MODIS Atmosphere L3 Gridded Product Algorithm Theoretical Basis Document (ATBD) & Users Guide, 2019).
References
Bjordal, J., T. Storelvmo, K. Alterskjær, and T. Carlsen, 2020: Equilibrium climate sensitivity above 5°C plausible due to state-dependent cloud feedback. Nature Geoscience, 13(11), 718–721, https://doi.org/10.1038/s41561-020-00649-1.
Bodas-Salcedo, A., and Coauthors, 2014: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models. J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1.
Bony, S., and Coauthors, 2015: Clouds, circulation and climate sensitivity. Nature Geoscience, 8, 261–268, https://doi.org/10.1038/ngeo2398.
Ceppi, P., Y.-T. Hwang, D. M. W. Frierson, and D. L. Hartmann, 2012: Southern Hemisphere jet latitude biases in CMIP5 models linked to shortwave cloud forcing. Geophys. Res. Lett., 39, L19708, https://doi.org/10.1029/2012GL053115.
Dolinar, E. K., X. Q. Dong, B. K. Xi, J. H. Jiang, and H. Su, 2015: Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations. Climate Dyn., 44, 2229–2247, https://doi.org/10.1007/s00382-014-2158-9.
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(5), 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.
Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 29–56, https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.
Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown, 2011: Major characteristics of Southern Ocean cloud regimes and their effects on the energy budget. J. Climate, 24, 5061–5080, https://doi.org/10.1175/2011JCLI4052.1.
Hwang, Y. T., and D. M. W. Frierson, 2013: Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proceedings of the National Academy of Sciences of the United States of America, 110(13), 4935–4940, https://doi.org/10.1073/pnas.1213302110.
IPCC, 2014a: Clouds and aerosols. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds., Cambridge University Press, 571–657, https://doi.org/10.1017/CBO9781107415324.016.
IPCC, 2014b: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds., Cambridge University Press, 741–882, https://doi.org/10.1017/cbo9781107415324.020.
Jian, B. D., J. M. Li, G. Y. Wang, Y. X. Zhao, Y. R. Li, J. Wang, M. Zhang, and J. P. Huang, 2021: Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors. Atmospheric Chemistry and Physics, 21, 9809–9828, https://doi.org/10.5194/acp-21-9809-2021.
Jiang, J, H. Su, L. T. Wu, C. X. Zhai, and K. A. Schiro, 2021: Improvements in cloud and water vapor simulations over the tropical oceans in CMIP6 compared to CMIP5,. Earth and Space Science, 8(5), e2020EA001520, https://doi.org/10.1029/2020ea001520.
Kang, L. T., R. Marchand, and W. Smith, 2021: Evaluation of MODIS and Himawari-8 low clouds retrievals over the Southern Ocean with in situ measurements from the SOCRATES campaign. Earth and Space Science, 8, e2020EA001397, https://doi.org/10.1029/2020EA001397.
Kato, S., and Coauthors, 2018: Surface irradiances of edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product. J. Climate, 31, 4501–4527, https://doi.org/10.1175/JCLI-D-17-0523.1.
Loeb, N. G., and Coauthors, 2018a: Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1.
Loeb, N. G., and Coauthors, 2018b: Impact of ice cloud microphysics on satellite cloud retrievals and broadband flux radiative transfer model calculations. J. Climate, 31(5), 1851–1864, https://doi.org/10.1175/JCLI-D-17-0426.1.
Luo, N., Y. Guo, J. M. Chou, and Z. B. Gao, 2022: Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China. International Journal of Climatology, 42, 1148–1164, https://doi.org/10.1002/joc.7294.
Marchand, R., and Coauthors, 2014: The Southern Ocean Clouds, Radiation Aerosol Transport Experimental Study (SOCRATES). Available from http://www.atmos.washington.edu/socrates/SOCRATES_white_paper_Final_Sep29_2014.pdf.
Mauritsen, T., and Coauthors, 2019: Developments in the MPI-M Earth System Model Version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11, 998–1038, https://doi.org/10.1029/2018MS001400.
McCoy, I. L., and Coauthors, 2020: The hemispheric contrast in cloud microphysical properties constrains aerosol forcing. Proceedings of the National Academy of Sciences of the United States of America, 117(32), 18 998–19 006, https://doi.org/10.1073/pnas.1922502117.
Pan, B. W., Y. Wang, T. Logan, J.-S. Hsieh, J. H. Jiang, Y. X. Li, and R. Y. Zhang, 2020: Determinant role of aerosols from industrial sources in Hurricane Harvey’s catastrophe. Geophys. Res. Lett., 47, e2020GL090014, https://doi.org/10.1029/2020GL090014.
Ramanathan, V, R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmad, and D. Hartmann, 1989: Cloud-radiative forcing and climate: Results from the Earth radiation budget experiment. Science, 243(4887), 57–63, https://doi.org/10.1126/science.243.4887.57.
Schiro, K. A., H. Su, Y. Wang, B. Langenbrunner, J. H. Jiang, and J. D. Neelin, 2019: Relationships between tropical ascent and high cloud fraction changes with warming revealed by perturbation physics experiments in CAM5. Geophys. Res. Lett., 46(16), 10 112–10 121, https://doi.org/10.1029/2019GL083026.
Schneider, S. H., 1972: Cloudiness as a global climatic feedback mechanism: The effects on the radiation balance and surface temperature of variations in cloudiness. J. Atmos. Sci., 29, 1413–1422, https://doi.org/10.1175/1520-0469(1972)029<1413:CAAGCF>2.0.CO;2.
Shea, Y. L., B. A. Wielicki, S. Sun-Mack, and P. Minnis, 2017: Quantifying the dependence of satellite cloud retrievals on instrument uncertainty. J. Climate, 30(17), 6959–6976, https://doi.org/10.1175/JCLI-D-16-0429.1.
Slingo, A., 1990: Sensitivity of the Earth’s radiation budget to changes in low clouds. Nature, 343, 49–51, https://doi.org/10.1038/343049a0.
Stanfield, R. E., X. Q. Dong, B. K. Xi, A. D. Del Genio, P. Minnis, D. Doelling, and N. Loeb, 2015: Assessment of NASA GISS CMIP5 and Post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part II: TOA radiation budget and CREs. J. Climate, 28(5), 1842–1864, https://doi.org/10.1175/JCLI-D-14-00249.1.
Tan, I., T. Storelvmo, and M. D. Zelinka, 2016: Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science, 352(6282), 224–227, https://doi.org/10.1126/science.aad5300.
Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106(D7), 7183–7192, https://doi.org/10.1029/2000JD900719.
Teng, S. W., C. Liu, Z. B. Zhang, Y. Wang, B. Sohn, Y. L. Yung, 2020: Retrieval of Ice-over-water cloud microphysical and optical properties using passive radiometers. Geophys. Res. Lett., 47(16), e2020GL088941, https://doi.org/10.1029/2020GL088941.
Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the southern oceans. J. Climate, 23, 440–454, https://doi.org/10.1175/2009JCLI3152.1.
Wang, Y., R. Y. Zhang, and R. Saravanan, 2014: Asian pollution climatically modulates mid-latitude cyclones following hierarchical modelling and observational analysis. Nature Communications, 5, 3098, https://doi.org/10.1038/ncomms4098.
Wang, Y., H. Su, J. H. Jiang, F. Xu, and Y. L. Yung, 2020: Impact of cloud ice particle size uncertainty in a climate model and implications for future satellite missions. J. Geophys. Res., 125, e2019JD032119, https://doi.org/10.1029/2019JD032119.
Weatherhead, E. C., and Coauthors, 1998: Factors affecting the detection of trends: Statistical considerations and applications to environmental data. J. Geophys. Res., 103, 17 149–17 161, https://doi.org/10.1029/98JD00995.
Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and partitioning cloud feedbacks using cloud property histograms. Part II: Attribution to changes in cloud amount, altitude, and optical depth. J. Climate, 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1.
Zelinka, M. D., and Coauthors, 2020: Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782.
Zhao, C. F., and T. J. Garrett, 2015: Effects of Arctic haze on surface cloud radiative forcing. Geophys. Res. Lett., 42, 557–564, https://doi.org/10.1002/2014GL062015.
Zhao, L. J., C. F. Zhao, Y. Wang, Y. Wang, and Y. K. Yang, 2020: Evaluation of cloud microphysical properties derived from MODIS and Himawari-8 using in situ aircraft measurements over the Southern Ocean. Earth and Space Science, 7, e2020EA001137, https://doi.org/10.1029/2020EA001137.
Zhu, H. H., Z. H. Jiang, J. Li, W. Li, C. X. Sun, and L. Li, 2020: Does CMIP6 inspire more confidence in simulating climate extremes over China. Adv. Atmos. Sci., 37(10), 1119–1132, https://doi.org/10.1007/s00376-020-9289-1.
Acknowledgements
Drs. Yuan WANG, Xiquan DONG, and Yuk YUNG are supported by the National Science Foundation grants (Grant Nos. AGS-1700727/1700728 and 2031751/2031750). Dr. Chuanfeng ZHAO is supported by the National Natural Science Foundation of China. (Grant No. 41925022). All requests for materials in this paper should be addressed to Yuan WANG (yuanwang@purdue.edu).
Author information
Authors and Affiliations
Contributions
Author contributions. Y. WANG conceived and designed the research. L. J. ZHAO and Y. WANG performed the data analyses and produced the figures. L. J. ZHAO and Y. WANG wrote the paper. All authors contributed to the scientific discussions and preparation of the manuscript.
Corresponding author
Ethics declarations
Competing interests. The authors declare that they have no conflict of interest.
Additional information
Article Highlights
• Cloud radiative effects in CMIP6 are comparable with satellite observations.
• There are large compensating biases in cloud fraction, liquid water path, and droplet effective radius.
• Cloud radiative effect is over-sensitive to liquid water path and droplet effective radius in CMIP6.
This paper is a contribution to the special issue on Cloud-Aerosol-Radiation-Precipitation Interaction: Progress and Challenges
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Zhao, L., Wang, Y., Zhao, C. et al. Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models. Adv. Atmos. Sci. 39, 2156–2171 (2022). https://doi.org/10.1007/s00376-022-2036-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-022-2036-z