Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674–701, https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2. doi: 10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2
|
Arakawa, A., and C.-M. Wu, 2013: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J. Atmos. Sci., 70, 1977–1992, https://doi.org/10.1175/JAS-D-12-0330.1.
|
Bechtold, P., J. W. M. Cuijpers, P. Mascart, et al., 1995: Modeling of trade wind cumuli with a low-order turbulence model: Toward a unified description of Cu and Se clouds in meteorological models. J. Atmos. Sci., 52, 455–463, https://doi.org/10.1175/1520-0469(1995)052<0455:MOTWCW>2.0.CO;2. doi: 10.1175/1520-0469(1995)052<0455:MOTWCW>2.0.CO;2
|
Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693–709, https://doi.org/10.1002/qj.49711247308.
|
|
Bogenschutz, P. A., A. Gettelman, H. Morrison, et al., 2012: Unified parameterization of the planetary boundary layer and shallow convection with a higher-order turbulence closure in the Community Atmosphere Model: Single-column experiments. Geosci. Model Dev., 5, 1407–1423, https://doi.org/10.5194/gmd-5-1407-2012.
|
Bogenschutz, P. A., A. Gettelman, H. Morrison, et al., 2013: Higher-order turbulence closure and its impact on climate simulations in the Community Atmosphere Model. J. Climate, 26, 9655–9676, https://doi.org/10.1175/JCLI-D-13-00075.1.
|
Bretherton, C. S., B. Henn, A. Kwa, et al., 2022: Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations. J. Adv. Model Earth Syst., 14, e2021MS002794, https://doi.org/10.1029/2021MS002794.
|
Chen, B. M., and Z. A. Qian, 1992: An altered Kuo-type cumulus parameterization scheme suitable for the Qinghai–Xizang Plateau area. Plateau Meteor., 11, 1–11. (in Chinese)
|
Chen, D. H., 1997: A review on the cumulus convective parameterization. Quart. J. Appl. Meteor., 8, 69–77. (in Chinese)
|
Chen, D. H., and P. Bougeault, 1993a: A simple prognostic closure assumption to deep convective parameterization: I. Acta Meteor. Sinica, 7, 1–18.
|
Chen, D. H., and P. Bougeault, 1993b: A simple prognostic closure assumption to deep convective parameterization: II. Acta Meteor. Sinica, 7, 212–223.
|
Chen, G. X., W.-C. Wang, S. X. Yang, et al., 2023: A neural network-based scale-adaptive cloud-fraction scheme for GCMs. J. Adv. Model Earth Syst., 15, e2022MS003415, https://doi.org/10.1029/2022MS003415.
|
Chen, J. P., Y. R. Feng, Y. D. Huang, et al., 2024: Development of four machine learning schemes used for moist physics parameterization in CMA-TRAMS. Acta Meteor. Sinica, 82, 113–126, https://doi.org/10.11676/qxxb2024.20230030. (in Chinese)
|
Chen, M. X., H. H. Fu, T. Zhang, et al., 2023: ResU-Deep: Improving the trigger function of deep convection in tropical regions with deep learning. J. Adv. Model Earth Syst., 15, e2022MS003521, https://doi.org/10.1029/2022MS003521.
|
|
Chen, X. J., Q. J. Liu, and Z. S. Ma, 2013: A diagnostic study of cloud scheme for the GRAPES global forecast model. Acta Meteor. Sinica, 79, 65–78, https://doi.org/10.11676/qxxb2020.066. (in Chinese)
|
Cui, Z. Y., G. J. Zhang, Y. Wang, et al., 2021: Understanding the roles of convective trigger functions in the diurnal cycle of precipitation in the NCAR CAM5. J. Climate, 34, 6473–6489, https://doi.org/10.1175/JCLI-D-20-0699.1.
|
|
Eyring, V., P. Gentine, G. Camps-Valls, et al., 2024: AI-empowered next-generation multiscale climate modelling for mitigation and adaptation. Nat. Geosci., 17, 963–971, https://doi.org/10.1038/s41561-024-01527-w.
|
Gao, S. N., C. S. Lu, J. S. Zhu, et al., 2024: Using machine learning to predict cloud turbulent entrainment-mixing processes. J. Adv. Model Earth Syst., 16, e2024MS004225, https://doi.org/10.1029/2024MS004225.
|
|
Gettelman, A., and H. Morrison, 2015: Advanced two-moment bulk microphysics for global models. Part I: Off-line tests and comparison with other schemes. J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1.
|
Golaz, J.-C., V. E. Larson, and W. R. Cotton, 2002: A PDF-based model for boundary layer clouds. Part I: Method and model description. J. Atmos. Sci., 59, 3540–3551, https://doi.org/10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2.
|
Gregory, D., and M. J. Miller, 1989: A numerical study of the parametrization of deep tropical convection. Quart. J. Roy. Meteor. Soc., 115, 1209–1241, https://doi.org/10.1002/qj.49711549003.
|
Gregory, D., and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 1483–1506, https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2. doi: 10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2
|
Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764–787, https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2. doi: 10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2
|
Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014.
|
Guo, Z., and T. J. Zhou, 2014: An improved stratocumulus scheme based on estimated inversion strength and its performance in GAMIL2. Sci. China Earth Sci., 44, 1034–1048, https://doi.org/10.1360/zd-2014-44-5-1034. (in Chinese)
|
Han, Y. L., G. J. Zhang, X. M. Huang, et al., 2020: A moist physics parameterization based on deep learning. J. Adv. Model Earth Syst., 12, e2020MS002076, https://doi.org/10.1029/2020MS002076.
|
Han, Y. L., G. J. Zhang, and Y. Wang, 2023: An ensemble of neural networks for moist physics processes, its generalizability and stable integration. J. Adv. Model Earth Syst., 15, e2022MS003508, https://doi.org/10.1029/2022MS003508.
|
He, X., C. S. Lu, X. J. Shi, et al., 2023: Development of a triple-moment ice-phase cloud microphysics scheme and its application to the Single Column Atmosphere Model. Chinese Sci. Bull., 68, 1971–1984, https://doi.org/10.1360/TB-2022-0697. (in Chinese)
|
He, X., C. S. Lu, X. J. Shi, et al., 2024: Application of triple-moment ice-phase cloud microphysics scheme in the CIESM model. Chinese Sci. Bull., 69, 2417–2428, https://doi.org/10.1360/TB-2023-0921. (in Chinese)
|
Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Asia Pac. J. Atmos. Sci., 42, 129–151.
|
Hu, Z. J., and C. F. Yan, 1986: Numerical simulation of microphysical processes in stratiform clouds (I)—microphysical model. J. Appl. Meteor. Sci., 1, 37–52. (in Chinese)
|
Hu, Z. J., and C. F. Yan, 1987: Numerical simulation of microphysical processes of stratiform clouds (II)—microphysical processes in middle-latitude cyclone cloud systems. J. Appl. Meteor. Sci., 2, 133–142. (in Chinese)
|
Hu, Z. Y., A. Subramaniam, Z. M. Kuang, et al., 2024: Stable machine-learning parameterization of subgrid processes in a comprehensive atmospheric model learned from embedded convection-permitting simulations. arXiv, 2407.00124, https://doi.org/10.48550/arXiv.2407.00124.
|
Iglesias-Suarez, F., P. Gentine, B. Solino-Fernandez, et al., 2024: Causally-informed deep learning to improve climate models and projections. J. Geophys. Res. Atmos., 129, e2023JD039202, https://doi.org/10.1029/2023JD039202.
|
|
Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2. doi: 10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2
|
Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, K. A. Emanuel, and D. J. Raymond, Eds., American Meteorological Society, Boston, 165–170, https://doi.org/10.1007/978-1-935704-13-3_16.
|
Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulations. American Meteorological Society, Boston, MA, 1–84.
|
|
Kong, F. Y., M. Y. Huang, and H. Y. Xu, 1990: Three-dimensional numerical simulation of ice phase microphysics in cumulus clouds, part I: Model establishment and ice phase parameterization. Chinese J. Atmos. Sci., 14, 441–453, https://doi.org/10.3878/j.issn.1006-9895.1990.04.07. (in Chinese)
|
Kuo, H. L., 1965: On formation and intensification of tropical cyclones through latent heat release by cumulus convection. J. Atmos. Sci., 22, 40–63, https://doi.org/10.1175/1520-0469(1965)022<0040:OFAIOT>2.0.CO;2. doi: 10.1175/1520-0469(1965)022<0040:OFAIOT>2.0.CO;2
|
Kuo, H. L., 1974: Further studies of the parameterization of the influence of cumulus convection on large-scale flow. J. Atmos. Sci., 31, 1232–1240, https://doi.org/10.1175/1520-0469(1974)031<1232:FSOTPO>2.0.CO;2. doi: 10.1175/1520-0469(1974)031<1232:FSOTPO>2.0.CO;2
|
|
Le Trent, H., and Z.-X. Li, 1991: Sensitivity of an atmospheric general circulation model to prescribed SST changes: Feedback effects associated with the simulation of cloud optical properties. Climate Dyn., 5, 175–187, https://doi.org/10.1007/BF00251808.
|
Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587–1612, https://doi.org/10.1175/2009MWR2968.1.
|
Lin, J., S. Yu, T. Beucler, et al., 2023: Navigating the noise: Bringing clarity to ML parameterization design with O(100) ensembles. arXiv, 2309.16177, https://doi.org/10.48550/arXiv.2309.16177.
|
Lin, Y. L., and B. A. Colle, 2011: A new bulk microphysical scheme that includes riming intensity and temperature-dependent ice characteristics. Mon. Wea. Rev., 139, 1013–1035, https://doi.org/10.1175/2010MWR3293.1.
|
Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor. Climatol., 22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2. doi: 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2
|
|
|
Manabe, S., and R. T. Wetherald, 1967: Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci., 24, 241–259, https://doi.org/10.1175/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2. doi: 10.1175/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2
|
Manabe, S., J. Smagorinsky, and R. F. Strickler, 1965: Simulated climatology of a general circulation model with a hydrologic cycle. Mon. Wea. Rev., 93, 769–798, https://doi.org/10.1175/1520-0493(1965)093<0769:SCOAGC>2.3.CO;2. doi: 10.1175/1520-0493(1965)093<0769:SCOAGC>2.3.CO;2
|
|
Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description. J. Atmos. Sci., 62, 3065–3081, https://doi.org/10.1175/JAS3535.1.
|
Milbrandt, J. A., H. Morrison, D. T. Dawson II, et al., 2021: A triple-moment representation of ice in the Predicted Particle Properties (P3) microphysics scheme. J. Atmos. Sci., 78, 439–458, https://doi.org/10.1175/JAS-D-20-0084.1.
|
Mooers, G., M. Pritchard, T. Beucler, et al., 2021: Assessing the potential of deep learning for emulating cloud superparameterization in climate models with real-geography boundary conditions. J. Adv. Model Earth Syst., 13, e2020MS002385, https://doi.org/10.1029/2020MS002385.
|
Morrison, H., and A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests. J. Climate, 21, 3642–3659, https://doi.org/10.1175/2008JCLI2105.1.
|
Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677, https://doi.org/10.1175/JAS3446.1.
|
|
Ping, F., and S. T. Gao, 2004: Study on the chosen parameterized schemes of cumulus convection and the test of their simulated effects in the short-term climate. J. Grad. Sch. Chinese Acad. Sci., 21, 366–373, https://doi.org/10.7523/j.issn.2095-6134.2004.3.013. (in Chinese)
|
Ping, F., S. T. Gao, and H. J. Wang, 2003: A comparative study of the numerical simulation of the 1998 summer flood in China by two kinds of cumulus convective parameterized methods. Adv. Atmos. Sci., 20, 149–157, https://doi.org/10.1007/BF03342059.
|
Plant, R. S., and G. C. Craig, 2008: A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci., 65, 87–105, https://doi.org/10.1175/2007JAS2263.1.
|
Qu, X., A. Hall, S. A. Klein, et al., 2014: On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Climate Dyn., 42, 2603–2626, https://doi.org/10.1007/s00382-013-1945-z.
|
Rasch, P. J., and J. E. Kristjánsson, 1998: A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Climate, 11, 1587–1614, https://doi.org/10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2. doi: 10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2
|
Rasp, S., M. S. Pritchard, and P. Gentine, 2018: Deep learning to represent subgrid processes in climate models. Proc. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115.
|
|
Rutledge, S. A., and P. V. Hobbs, 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41, 2949–2972, https://doi.org/10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2. doi: 10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2
|
Shiu, C.-J., Y.-C. Wang, H.-H. Hsu, et al., 2021: GTS v1.0: A macrophysics scheme for climate models based on a probability density function. Geosci. Model Dev., 14, 177–204, https://doi.org/10.5194/gmd-14-177-2021.
|
Slingo, A., and J. M. Slingo, 1991: Response of the National Center for Atmospheric Research community climate model to improvements in the representation of clouds. J. Geophys. Res. Atmos., 96, 15,341–15,357, https://doi.org/10.1029/91JD00930.
|
|
|
Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 116, 435–460, https://doi.org/10.1002/qj.49711649210.
|
Sommeria, G., and J. W. Deardorff, 1977: Subgrid-scale condensation in models of nonprecipitating clouds. J. Atmos. Sci., 34, 344–355, https://doi.org/10.1175/1520-0469(1977)034<0344:SSCIMO>2.0.CO;2. doi: 10.1175/1520-0469(1977)034<0344:SSCIMO>2.0.CO;2
|
Song, X. L., and G. J. Zhang, 2011: Microphysics parameterization for convective clouds in a global climate model: Description and single-column model tests. J. Geophys. Res. Atmos., 116, D02201, https://doi.org/10.1029/2010JD014833.
|
Song, X. L., G. J. Zhang, and J.-L. F. Li, 2012: Evaluation of microphysics parameterization for convective clouds in the NCAR Community Atmosphere Model CAM5. J. Climate, 25, 8568–8590, https://doi.org/10.1175/JCLI-D-11-00563.1.
|
Sundqvist, H., 1978: A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc., 104, 677–690, https://doi.org/10.1002/qj.49710444110.
|
Sundqvist, H., E. Berge, and J. E. Kristjánsson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev., 117, 1641–1657, https://doi.org/10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2. doi: 10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2
|
Tan, C., Q. J. Liu, and Z. S. Ma, 2013: Influences of sub-grid convective processes on cloud forecast in the GRAPES global model. Acta Meteor. Sinica, 71, 867–878, https://doi.org/10.11676/qxxb2013.067. (in Chinese)
|
Tang, X. B., F. Ping, and Z. X. Luo, 2016: A modified cumulus parameterization scheme and its applications in simulation of heavy rainfall. Chinese J. Geophys., 59, 45–58, https://doi.org/10.6038/cjg20160105. (in Chinese)
|
|
Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519–542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2. doi: 10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2
|
Thompson, G., P. R. Field, R. M. Rasmussen, et al., 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1.
|
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. doi: 10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2
|
|
Tompkins, A. M., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci., 59, 1917–1942, https://doi.org/10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2. doi: 10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2
|
|
Wang, D. L., 2012: The diagnosis and prelimiary improvement of cumulus parameterization schemes. Master dissertation, Chinese Academy of Meteorological Sciences, Beijing, 96 pp. (in Chinese)
|
Wang, P. D., J. Yuval, and P. A. O’Gorman, 2022: Non-local parameterization of atmospheric subgrid processes with neural networks. J. Adv. Model Earth Syst., 14, e2022MS002984, https://doi.org/10.1029/2022MS002984.
|
Wang, X., Y. L. Han, W. Xue, et al., 2022a: Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes. Geosci. Model Dev., 15, 3923–3940, https://doi.org/10.5194/gmd-15-3923-2022.
|
Wang, X., G. J. Zhang, and Y. Wang, 2022b: Evaluating and improving scale-awareness of a convective parameterization closure using cloud-resolving model simulations of convection. J. Geophys. Res. Atmos., 127, e2021JD035729, https://doi.org/10.1029/2021JD035729.
|
Wang, Y., X. Liu, C. Hoose, et al., 2014: Different contact angle distributions for heterogeneous ice nucleation in the Community Atmospheric Model version 5. Atmos. Chem. Phys., 14, 10,411–10,430, https://doi.org/10.5194/acp-14-10411-2014.
|
Wang, Y., G. J. Zhang, and G. C. Craig, 2016: Stochastic convective parameterization improving the simulation of tropical precipitation variability in the NCAR CAM5. Geophys. Res. Lett., 43, 6612–6619, https://doi.org/10.1002/2016GL069818.
|
Wang, Y., G. J. Zhang, and Y. Q. Jiang, 2018: Linking stochasticity of convection to large-scale vertical velocity to improve Indian summer monsoon simulation in the NCAR CAM5. J. Climate, 31, 6985–7002, https://doi.org/10.1175/JCLI-D-17-0785.1.
|
Wang, Y., G. J. Zhang, S. C. Xie, et al., 2021: Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model. Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021.
|
Watt-Meyer, O., N. D. Brenowitz, S. K. Clark, et al., 2024: Neural network parameterization of subgrid-scale physics from a realistic geography global storm-resolving simulation. J. Adv. Model Earth Syst., 16, e2023MS003668, https://doi.org/10.1029/2023MS003668.
|
Wilson, D. R., A. C. Bushell, A. M. Kerr-Munslow, et al., 2008: PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description. Quart. J. Roy. Meteor. Soc., 134, 2093–2107, https://doi.org/10.1002/qj.333.
|
Xia, W. W., Y. Wang, G. J. Zhang, et al., 2022: Unexpected changes of aerosol burdens with decreased convection in the context of scale-aware convection schemes. Geophys. Res. Lett., 49, e2022GL099008, https://doi.org/10.1029/2022GL099008.
|
Xie, S. C., and M. H. Zhang, 2000: Impact of the convection triggering function on single-column model simulations. J. Geophys. Res. Atmos., 105, 14,983–14,996, https://doi.org/10.1029/2000JD900170.
|
Xie, S. C., Y.-C. Wang, W. Y. Lin, et al., 2019: Improved diurnal cycle of precipitation in E3SM with a revised convective triggering function. J. Adv. Model Earth Syst., 11, 2290–2310, https://doi.org/10.1029/2019MS001702.
|
Xu, K.-M., and D. A. Randall, 1996: A semiempirical cloudiness parameterization for use in climate models. J. Atmos. Sci., 53, 3084–3102, https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2. doi: 10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2
|
|
|
Yang, B., M. H. Wang, G. J. Zhang, et al., 2020: Simulated precipitation diurnal variation with a deep convective closure subject to shallow convection in Community Atmosphere Model version 5 coupled with CLUBB. J. Adv. Model Earth Syst., 12, e2020MS002050, https://doi.org/10.1029/2020MS002050.
|
Yang, B., M. H. Wang, G. J. Zhang, et al., 2021: Linking deep and shallow convective mass fluxes via an assumed entrainment distribution in CAM5-CLUBB: Parameterization and simulated precipitation variability. J. Adv. Model Earth Syst., 13, e2020MS002357, https://doi.org/10.1029/2020MS002357.
|
Yuval, J., and P. A. O’Gorman, 2020: Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions. Nat. Commun., 11, 3295, https://doi.org/10.1038/s41467-020-17142-3.
|
Zhai, G. Q., K. Gao, and J. S. Pan, 2003: The sensitivity experiments of Betts–Miller convective parameterization scheme in regional climate simulation. Chinese J. Atmos. Sci., 23, 330–344, https://doi.org/10.3878/j.issn.1006-9895.2003.03.04. (in Chinese)
|
Zhang, G. J., 2002: Convective quasi-equilibrium in midlatitude continental environment and its effect on convective parameterization. J. Geophys. Res. Atmos., 107, ACL 12-1–ACL 12-16, https://doi.org/10.1029/2001JD001005.
|
Zhang, G. J., 2003: Convective quasi-equilibrium in the tropical western Pacific: Comparison with midlatitude continental environment. J. Geophys. Res. Atmos., 108, 4592, https://doi.org/10.1029/2003JD003520.
|
Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.-Ocean, 33, 407–446, https://doi.org/10.1080/07055900.1995.9649539.
|
Zhang, G. J., and M. Q. Mu, 2005: Simulation of the Madden–Julian oscillation in the NCAR CCM3 using a revised Zhang–McFarlane convection parameterization scheme. J. Climate, 18, 4046–4064, https://doi.org/10.1175/JCLI3508.1.
|
Zhang, M. H., W. Y. Lin, C. S. Bretherton, et al., 2003: A modified formulation of fractional stratiform condensation rate in the NCAR Community Atmospheric Model (CAM2). J. Geophys. Res. Atmos., 108, ACL 10-1–ACL 10-11, https://doi.org/10.1029/2002JD002523.
|
Zhang, T., W. Y. Lin, A. M. Vogelmann, et al., 2021: Improving convection trigger functions in deep convective parameterization schemes using machine learning. J. Adv. Model Earth Syst., 13, e2020MS002365, https://doi.org/10.1029/2020MS002365.
|
Zhao, S. X., B. Y. Zhang, C. X. Zhao, et al., 1996: Improvement experiment of Kuo scheme based on spatiotemporal encrypted observation data. Research on New Numerical Forecasting Technology of Typhoon and Rainstorm, China Meteorological Press, Beijing, 1–9. (in Chinese)
|
Zhong, X. H., X. Yu, and H. Li, 2024: Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0. Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024.
|