Abstract
The outputs of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic, Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project (CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total, there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden-Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.
摘 要
本文介绍了中国科学院大气物理研究所开发的CAS FGOALS-f3-L 气候系统模式参加第六次国际耦合模式比较计划 (CMIP6)的DECK试验(Diagnostic, Evaluation and Characterization of Klima common experiments)中全球大气环流模式(AMIP)模拟数据, 其中包括CAS FGOALS-f3-L模式的动力框架, 物理过程简介以及模式试验设计, 数据信息以及初步评估结果. 模式采用时间滞后法产生不同初始场的三个集合成员, 并提供1979–2014年的模拟结果. 模式输出包括37个变量, 涉及3小时平均, 6小时瞬时, 日平均和月平均数据. 本文还评估了模式在不同时间尺度上的基本模拟性能. 结果表明CAS FGOALS-f3-L模式能够很好的模拟出大尺度全球大气环流和降水的基本特征, 能够很好的模拟出降水和850hPa风的MJO传播特征, 以及台风的活动和极端降水的发生频次特征. 该数据集贡献于CMIP计划在模式发展评估上的连续性.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.Avoid common mistakes on your manuscript.
References
Accadia, C., S. Mariani, M. Casaioli, A. Lavagnini, and A. Speranza, 2003: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Wea. Forecasting, 18, 918–932, https://doi.org/10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2.
Adler, R. F., and Coauthors, 2003: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). Journal of Hydrometeorology, 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
Bao, Q., G. X. Wu, Y. M. Liu, J. Yang, Z. Z. Wang, and T. J. Zhou, 2010: An introduction to the coupled model FGOALS1.1-s and its performance in East Asia. Adv. Atmos. Sci., 27(5), 1131–1142, https://doi.org/10.1007/s00376-010-9177-1.
Bao, Q., and Coauthors, 2013: The flexible global ocean-atmosphere-land system model, spectral version 2: FGOALS-s2. Adv. Atmos. Sci., 30(3), 561–576, https://doi.org/10.1007/s00376-012-2113-9.
Bao, Q., X. F. Wu, J. X. Li, L. Wang, B. He, X. C. Wang, Y. M. Liu, and G. X. Wu, 2019: Outlook for El Nino and the Indian Ocean Dipole in autumn-winter 2018–2019. Chinese Science Bulletin, 64, 73–78, https://doi.org/10.1360/N972018-00913. (in Chinese)
Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the community atmosphere model. J. Climate, 22(12), 3422–3448, https://doi.org/10.1175/2008jcli2556.1.
Clough, S. A., M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes. Journal of Quantitative Spec-troscopy and Radiative Transfer, 91(2), 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058.
Dee, D. P., and Coauthors, 2011: The ERA-interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Edwards, J. M., and A. Slingo, 1996: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Quart. J. Roy. Meteor. Soc., 122, 689–719, https://doi.org/10.1002/qj.49712253107.
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.
Gates, W. L., 1992: AMIP: The atmospheric model intercompari-son project. Bull. Amer. Meteor. Soc., 73, 1962–1970, https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.
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.
Harris, L. M., and S.-J. Lin, 2014: Global-to-regional nested grid climate simulations in the GFDL high resolution atmospheric model. J. Climate, 27(13), 4890–4910, https://doi.org/10.1175/JCLI-D-13-00596.1.
He, S. C., J. Yang, Q. Bao, L. Wang, and B. Wang, 2019: Fidelity of the observational/reanalysis datasets and global climate models in representation of extreme precipitation in East China. J. Climate, 32(1), 195–212, https://doi.org/10.1175/JCLI-D-18-0104.1.
Holtslag, A. A. M., and B. A. Boville, 1993: Local versus nonlocal boundary-layer diffusion in a global climate model. J. Climate, 6, 1825–1842, https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2.
Huffman, G. J., R. F. Adler, M. M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. Journal of Hydrometeorology, 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.
Huffman, G. J., and Coauthors, 2007: The TRMM multisatel-lite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8, 38–55, https://doi.org/10.1175/JHM560.1.
Hunke, E. C., and W. H. Lipscomb, 2010: CICE: The Los Alamos Sea ice model documentation and software user's manual version 4.1. Tech. Rep. LA-CC-06-012, 675 pp.
Hurtt, G. C., and Coauthors, 2011: Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic change, 109(1–2), 117, https://doi.org/10.1007/s10584-011-0153-2.
Jiang, X., and Coauthors, 2015: Vertical structure and physical processes of the Madden-Julian oscillation: Exploring key model physics in climate simulations. J. Geophys. Res., 120, 4718–4748, https://doi.org/10.1002/2014JD022375.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The international best track archive for climate stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Amer. Meteor. Soc., 91(3), 363–376, https://doi.org/10.1175/2009BAMS2755.1.
Lamarque, J.-F., and Coauthors, 2012: CAM-chem: Description and evaluation of interactive atmospheric chemistry in the community earth system model. Geoscientific Model Development, 5(2), 369–411, https://doi.org/10.5194/gmd-5-369-2012.
Li, J. X., Q. Bao, Y. M. Liu, G. X. Wu, L. Wang, B. He, X. C. Wang, and J. D. Li, 2019: Evaluation of FAMIL2 in simulating the climatology and seasonal-to-interannual variability of tropical cyclone characteristics. Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2018MS001506.
Lin, S.-J., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132(10), 2293–2307, https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.
Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22(6), 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
Liu, H. L., P. F. Lin, Y. Q. Yu, and X. H. Zhang, 2012: The baseline evaluation of LASG/IAP climate system ocean model (LICOM) version 2. Acta Meteorologica Sinica, 26(3), 318–329, https://doi.org/10.1007/s13351-012-0305-y.
Matthes, K., and Coauthors, 2017: Solar forcing for CMIP6 (v3.2). Geoscientific Model Development, 10(6), 2247–2302, https://doi.org/10.5194/gmd-10-2247-2017.
Meinshausen, M., and Coauthors, 2017: Historical greenhouse gas concentrations for climate modelling (CMIP6). Geoscientific Model Development, 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017.
Nordeng, T. E., 1994: Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Technical Memo. 206, 41 pp.
Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the community land model (CLM). NCAR/TN-478 + STR, 173 pp, https://doi.org/10.5065/D6FB50WZ.
Palmer, T. N., G. J. Shutts, and R. Swinbank, 1986: Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an oro-graphic gravity wave drag parametrization. Quart. J. Roy. Meteor. Soc., 112(474), 1001–1039, https://doi.org/10.1002/qj.49711247406.
Putman, W. M., and S.-J. Lin, 2007: Finite-volume transport on various cubed-sphere grids. J. Comput. Phys., 227(1), 55–78, https://doi.org/10.1016/j.jcp.2007.07.022.
Simpson, R. H., and H. Saffir, 1974: The hurricane disaster— potential scale. Weatherwise, 27(4), 169–186, https://doi.org/10.1080/00431672.1974.9931702.
Sun, Z. A., and L. Rikus, 1999: Improved application of exponential sum fitting transmissions to inhomogeneous atmosphere. J. Geophys. Res., 104, 6291–6303, https://doi.org/10.1029/1998JD200095.
Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
Waliser, D., and Coauthors, 2009: MJO simulation diagnostics. J. Climate, 22, 3006–3030, https://doi.org/10.1175/2008JCLI2731.1.
Wang, X. C., and M. H. Zhang, 2014: Vertical velocity in shallow convection for different plume types. Journal of Advances in Modeling Earth Systems, 6(2), 478–489, https://doi.org/10.1002/2014MS000318.
Wu, G. X., H. Liu, Y. C. Zhao, and W. P. Li, 1996: A nine-layer atmospheric general circulation model and its performance. Adv. Atmos. Sci., 13(1), 1–18, https://doi.org/10.1007/bf02657024.
Xiang, B. Q., and Coauthors, 2015: Beyond weather time-scale prediction for hurricane sandy and super typhoon Haiyan in a global climate model. Mon. Wea. Rev., 143(2), 524–535, https://doi.org/10.1175/MWR-D-14-00227.1.
Xu, K. M., and D. A. Randall, 1996: A semiempirical cloudiness parameterization for use in climate models. J. Atmos. Sci., 53(21), 3084–3102, https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2.
Yang, J., Q. Bao, X. C. Wang, and T. J. Zhou, 2012: The tropical intraseasonal oscillation in SAMIL coupled and uncoupled general circulation models. Adv. Atmos. Sci., 29(3), 529–543, https://doi.org/10.1007/s00376-011-1087-3.
Zhou, L. J., and Coauthors, 2015: Global energy and water balance: Characteristics from finite-volume Atmospheric Model of the IAP/LASG (FAMIL1). Journal of Advances in Modeling Earth Systems, 7(1), 1–20, https://doi.org/10.1002/2014ms000349.
Acknowledgements
The research presented in this paper was jointly funded by the National Key Research and development Program of China (Grant No. 2017YFA0604004), the National Natural Science Foundation of China (Grant Nos. 91737306, U1811464, 41530426, 91837101, 41730963, and 91637312).
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights
• AMIP simulation datasets produced by CAS FGOALS-f3-L covering 1979 to 2014 are described.
• The dataset contains three ensemble members with different initial states by the time lag method.
• The model outputs contain a total of 37 variables and include the three-hourly mean, six-hourly transient, daily and monthly mean datasets.
Data availability statement The data that support the findings of this study are available from https://esgf-node.llnl.gov/projects/cmip6/.
Disclosure statement No potential conflict of interest was reported by the authors.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
About this article
Cite this article
He, B., Bao, Q., Wang, X. et al. CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation. Adv. Atmos. Sci. 36, 771–778 (2019). https://doi.org/10.1007/s00376-019-9027-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-019-9027-8
Key words
关键词
Profiles
- Linjiong Zhou View author profile