These data include all datasets published for 'CMIP6.GMMIP.CAS.FGOALS-g3' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-g3 climate model, released in 2017, includes the following components: atmos: GAMIL3 (180 x 80 longitude/latitude; 26 levels; top level 2.19hPa), land: CAS-LSM, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km. Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.
[1] DOIJain, Shipra; Mishra, Saroj K.; Anand, Abhishek; Salunke, Popat; Fasullo, John T. (2020). Historical and projected low-frequency variability in the Somali Jet and Indian Summer Monsoon. doi:10.1007/s00382-020-05492-z
[2] DOIvon der Gathen, Peter; Kivi, Rigel; Wohltmann, Ingo; Salawitch, Ross J.; Rex, Markus. (2021). Climate change favours large seasonal loss of Arctic ozone. doi:10.1038/s41467-021-24089-6
Is related to
[1] DOIFox-Kemper, B.; Hewitt, H.T.; Xiao, C.; Aðalgeirsdóttir, G.; Drijfhout, S.S.; Edwards, T.L.; Golledge, N.R.; Hemer, M.; Kopp, R.E.; Krinner, G.; Mix, A.; Notz, D.; Nowicki, S.; Nurhati, I.S.; Ruiz, L.; Sallée, J.-B.; Slangen, A.B.A.; Yu, Y. (2023). Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
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[2] DOICao, Ruyin; Ling, Xiaofang; Liu, Licong; Wang, Weiyi; Li, Luchun; Shen, Miaogen. (2023). Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions. doi:10.1109/jstars.2023.3310617