Abstract
In the study of diagnosing climate simulations and understanding the dynamics of precipitation extremes, it is an essential step to adopt a simple model to relate water vapor condensation and precipitation, which occur at cloud-microphysical and convective scales, to large-scale variables. Several simple models have been proposed; however, improvement is still needed in both their accuracy and/or the physical basis. Here, we propose a two-plume convective model that takes into account the subgrid inhomogeneity of precipitation extremes. The convective model has three components, i.e., cloud condensation, rain evaporation, and environmental descent, and is built upon the zero-buoyancy approximation and guidance from the high-resolution reanalysis. Evaluated against the CMIP5 climate simulations, the convective model shows large improvements in reproducing precipitation extremes compared to previously proposed models. Thus, the two-plume convective model better captures the main physical processes and serves as a useful diagnostic tool for precipitation extremes.
摘要
在研究极端降水动力学或诊断气候模拟结果时, 经常需要利用简单模型对极端降水进行诊断, 从而简化物理过程并分析降水过程中的跨尺度相互作用. 之前的极端降水模型在准确度以及物理图像上还有待进一步的提高. 基于高分辨率的再分析资料, 本研究提出了极端降水的双羽流非均匀对流模型, 从而将次网格对流的非均匀性也考虑在内. 此模型建立在零浮力假设的基础上, 包含了云凝结、 降水再蒸发和环境下沉三个主要的物理过程, 并对其进行参数化. 利用CMIP5模拟结果进行评估, 此模型很好地拟合了极端降水, 其误差较于以往单羽流均匀的模型减少约一半. 研究结果表明此非均匀对流模型涵盖了极端降水的主要物理过程, 为极端降水的诊断研究提供了一个有力工具.
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Change history
06 February 2023
An Erratum to this paper has been published: https://doi.org/10.1007/s00376-023-2019-8
References
Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, https://doi.org/10.1029/2005JD006290.
Austin, P. H., M. B. Baker, A. M. Blyth, and J. B. Jensen, 1985: Small-scale variability in warm continental cumulus clouds. J. Atmos. Sci., 42, 1123–1138, https://doi.org/10.1175/1520-0469(1985)042<1123:SSVIWC>2.0.CO;2.
Bretherton, C. S., and S. Park, 2008: A new bulk shallow-cumulus model and implications for penetrative entrainment feedback on updraft buoyancy. J. Atmos. Sci., 65, 2174–2193, https://doi.org/10.1175/2007JAS2242.1.
Chen, G., J. Norris, J. D. Neelin, J. Lu, L. R. Leung, and K. Sakaguchi, 2019: Thermodynamic and dynamic mechanisms for hydrological cycle intensification over the full probability distribution of precipitation events. J. Atmos. Sci., 76, 497–516, https://doi.org/10.1175/JAS-D-18-0067.1.
Dai, P. X., and J. Nie, 2020: A global quasigeostrophic diagnosis of extratropical extreme precipitation. J. Climate, 33, 9629–9642, https://doi.org/10.1175/JCLI-D-20-0146.1.
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.
Emanuel, K. A., 1991: A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, 2313–2329, https://doi.org/10.1175/1520-0469(1991)048<2313:ASFRCC>2.0.CO;2.
Emanuel, K. A., J. D. Neelin, and C. S. Bretherton, 1994: On large-scale circulations in convecting atmospheres. Quart. J. Roy. Meteor. Soc., 120, 1111–1143, https://doi.org/10.1002/qj.49712051902.
Emori, S., and S. J. Brown, 2005: Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett., 32, L17706, https://doi.org/10.1029/2005gl023272.
Fritsch, J. M., 1975: Cumulus dynamics: Local Compensating Subsidence and its Implications for Cumulus Parameterization. Pure Appl. Geophys., 113, 851–867, https://doi.org/10.1007/BF01592963.
Huffman, George J., and Coauthors, 2001: Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations. J Hydrometeorol., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.
Houze, R. A. Jr., 2004: Mesoscale convective systems. Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004rg000150.
Johnson, R. H., 1976: The role of convective-scale precipitation downdrafts in cumulus and synoptic-scale interactions. J. Atmos. Sci., 33, 1890–1910, https://doi.org/10.1175/1520-0469(1976)033<1890:TROCSP>2.0.CO;2.
Knupp, K. R., and W. R. Cotton, 1985: Convective cloud downdraft structure: An interpretive survey. Rev. Geophys., 23, 183–215, https://doi.org/10.1029/RG023i002p00183.
Langhans, W., K. Yeo, and D. M. Romps, 2015: Lagrangian investigation of the precipitation efficiency of convective clouds. J. Atmos. Sci., 72, 1045–1062, https://doi.org/10.1175/JASD-14-0159.1.
Li, Z. W., and P. A. O’Gorman, 2020: Response of vertical velocities in extratropical precipitation extremes to climate change. J. Climate, 33, 7125–7139, https://doi.org/10.1175/JCLI-D-19-0766.1.
Lutsko, N. J., and T. W. Cronin, 2018: Increase in precipitation efficiency with surface warming in radiative-convective equilibrium. Journal of Advances in Modeling Earth Systems, 10, 2992–3010, https://doi.org/10.1029/2018MS001482.
Nie, J., and B. W. Fan, 2019: Roles of dynamic forcings and diabatic heating in summer extreme precipitation in East China and the southeastern United States. J. Climate, 32, 5815–5831, https://doi.org/10.1175/JCLI-D-19-0188.1.
Nie, J., D. A. Shaevitz, and A. H. Sobel, 2016: Forcings and feedbacks on convection in the 2010 Pakistan flood: Modeling extreme precipitation with interactive large-scale ascent. Journal of Advances in Modeling Earth Systems, 8, 1055–1072, https://doi.org/10.1002/2016MS000663.
Nie, J., A. H. Sobel, D. A. Shaevitz, and S. G. Wang, 2018: Dynamic amplification of extreme precipitation sensitivity. Proceedings of the National Academy of Sciences of the United States of America, 115, 9467–9472, https://doi.org/10.1073/pnas.1800357115.
Nie, J., Y. Xia, S. N. Hu, W. Yuan, J. Yang, and D. Ma, 2019: Similarity among atmospheric thermal stratifications over elevated surfaces under radiative-convective equilibrium. Geophys. Res. Lett., 46, 3512–3522, https://doi.org/10.1029/2018GL081867.
Nie, J., P. X. Dai, and A. H. Sobel, 2020: Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity. Proceedings of the National Academy of Sciences of the United States of America, 117, 8757–8763, https://doi.org/10.1073/pnas.1913584117.
O’Gorman, P. A., and T. Schneider, 2009a: The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proceedings of the National Academy of Sciences of the United States of America, 106, 14 773–14 777, https://doi.org/10.1073/pnas.0907610106.
O’Gorman, P. A., and T. Schneider, 2009b: Scaling of precipitation extremes over a wide range of climates simulated with an idealized GCM. J. Climate, 22, 5676–5685, https://doi.org/10.1175/2009JCLI2701.1.
Pfahl, S., P. A. O’Gorman, and E. M. Fischer, 2017: Understanding the regional pattern of projected future changes in extreme precipitation. Nature Climate Change, 7, 423–427, https://doi.org/10.1038/nclimate3287.
Seager, R., N. Naik, and L. Vogel, 2012: Does global warming cause intensified interannual hydroclimate variability? J Climate, 25, 3355–3372, https://doi.org/10.1175/JCLI-D-11-00363.1.
Singh, M. S., and P. A. O’Gorman, 2013: Influence of entrainment on the thermal stratification in simulations of radiativeconvective equilibrium. Geophys. Res. Lett., 40, 4398–4403, https://doi.org/10.1002/grl.50796.
Sugiyama, M., J. Shiogama, and S. Emori, 2010: Precipitation extreme changes exceeding moisture content increases in MIROC and IPCC climate models. Proceedings of the National Academy of Sciences of the United States of America, 107, 571–575, https://doi.org/10.1073/pnas.0903186107.
Westra, S., L. V. Alexander, and F. W. Zwiers, 2013: Global Increasing trends in annual maximum daily precipitation. J. Climate, 26, 3904–3918, https://doi.org/10.1155/JCLI-D-12-00502.1.
Xu, K.-M., and D. A. Randall, 2001: Updraft and downdraft statistics of simulated tropical and midlatitude cumulus convection. J. Atmos. Sci., 58, 1630–1649, https://doi.org/10.1175/1520-0469(2001)058<1630:UADSOS>2.0.CO;2.
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. Atmosphere-Ocean, 33, 407–446, https://doi.org/10.1080/07055900.1995.9649539.
Acknowledgements
The authors thank three anonymous reviewers for their valuable comments. This research was supported by National Natural Science Foundation of China (Grant nos. 41875050 and 42075146). The ERA-Interim reanalysis is available at https://apps.ecmwf.int/datasets/. The CMIP5 data archive is available at https://esgf.llnl.gov.
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Article Highlights
• We propose a two-plume convective model for precipitation extremes that considers subgrid inhomogeneity.
• The simple model includes three components: cloud condensation, rain evaporation, and environmental descent.
• The simple model accurately reproduces precipitation extremes in climate simulations, with improved performance compared with previously proposed models.
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Yin, Z., Dai, P. & Nie, J. A Two-plume Convective Model for Precipitation Extremes. Adv. Atmos. Sci. 38, 957–965 (2021). https://doi.org/10.1007/s00376-021-0404-8
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DOI: https://doi.org/10.1007/s00376-021-0404-8