Evaluation of the Tail of the Probability Distribution of Daily and Subdaily Precipitation in CMIP6 Models

Jesse Norris Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, California

Search for other papers by Jesse Norris in
Current site
Google Scholar
PubMed
Close
,
Alex Hall Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, California

Search for other papers by Alex Hall in
Current site
Google Scholar
PubMed
Close
,
J. David Neelin Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, California

Search for other papers by J. David Neelin in
Current site
Google Scholar
PubMed
Close
,
Chad W. Thackeray Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, California

Search for other papers by Chad W. Thackeray in
Current site
Google Scholar
PubMed
Close
, and
Di Chen Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, California

Search for other papers by Di Chen in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Daily and subdaily precipitation extremes in historical phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded every x years, ranging from 0.01 to 10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes, the multimodel median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r = −0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r = −0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These intermodel differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible twenty-first-century projections.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0182.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jesse Norris, jessenorris@ucla.edu

Abstract

Daily and subdaily precipitation extremes in historical phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded every x years, ranging from 0.01 to 10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes, the multimodel median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r = −0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r = −0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These intermodel differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible twenty-first-century projections.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0182.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jesse Norris, jessenorris@ucla.edu

Supplementary Materials

    • Supplemental Materials (PDF 108.18 MB)
Save
  • Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrological cycle. Nature, 419, 228232, https://doi.org/10.1038/nature01092.

    • Search Google Scholar
    • Export Citation
  • Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen, 2012: On the quantification of oceanic rainfall using spaceborne sensors. J. Geophys. Res., 117, D20105, https://doi.org/10.1029/2012JD017979.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 28872908, https://doi.org/10.1002/qj.49712556006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 497516, https://doi.org/10.1175/JAS-D-18-0067.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chikira, M., 2010: A cumulus parameterization with state-dependent entrainment rate. Part II: Impact on climatology in a general circulation model. J. Atmos. Sci., 67, 21942211, https://doi.org/10.1175/2010JAS3317.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer, 208 pp.

    • Crossref
    • Export Citation
  • Donat, M. G., A. L. Lowry, L. V. Alexander, P. A. O’Gorman, and N. Maher, 2016: More extreme precipitation in the world’s dry and wet regions. Nat. Climate Change, 6, 508513, https://doi.org/10.1038/nclimate2941.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dwyer, J. G., and P. A. O’Gorman, 2017: Changing duration and spatial extent of midlatitude precipitation extremes across different climates. Geophys. Res. Lett., 44, 58635871, https://doi.org/10.1002/2017GL072855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ECMWF, 2017: ERA5 reanalysis. National Center for Atmospheric Research Computational and Information Systems Laboratory Research Data Archive, accessed 10 September 2020, https://doi.org/10.5065/d6x34w69.

    • Crossref
    • Export Citation
  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frierson, D. M. W., 2007: The dynamics of idealized convection schemes and their effect on the zonally averaged tropical circulation. J. Atmos. Sci., 64, 19591976, https://doi.org/10.1175/JAS3935.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutowski, W. J., Jr., S. G. Decker, R. A. Donavon, Z. Pan, R. W. Arritt, and E. S. Takle, 2003: Temporal–spatial scales of observed and simulated precipitation in central U.S. climate. J. Climate, 16, 38413847, https://doi.org/10.1175/1520-0442(2003)016<3841:TSOOAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagemann, S., K. Arpe, and E. Roeckner, 2006: Evaluation of the hydrological cycle in the ECHAM5 model. J. Climate, 19, 38103827, https://doi.org/10.1175/JCLI3831.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, E. J. Nelkin, and D. B. Wolff, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, https://doi.org/10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacob, D., and Coauthors, 2014: EURO-CORDEX: New high-resolution climate change projections for European impact research. Reg. Environ. Change, 14, 563578, https://doi.org/10.1007/s10113-013-0499-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, Z., W. Li, J. Xu, and L. Li, 2015: Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. J. Climate, 28, 86038619, https://doi.org/10.1175/JCLI-D-15-0099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kao, S.-C., and A. R. Ganguly, 2011: Intensity, duration, and frequency of precipitation extremes under 21st-century warming scenarios. J. Geophys. Res., 116, D16119, https://doi.org/10.1029/2010JD015529.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., F. W. Zwiers, X. Zhang, and M. Wehner, 2013: Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119, 345357, https://doi.org/10.1007/s10584-013-0705-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khouider, B., A. J. Majda, and M. A. Katsoulakis, 2003: Coarse-grained stochastic models for tropical convection and climate. Proc. Natl. Acad. Sci. USA, 100, 11 94111 946, https://doi.org/10.1073/pnas.1634951100.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, I.-W., J. Oh, S. Woo, and R. H. Kripalani, 2019: Evaluation of precipitation extremes over the Asian domain: Observation and modelling studies. Climate Dyn., 52, 13171342, https://doi.org/10.1007/s00382-018-4193-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klingaman, N. P., G. M. Martin, and A. Moise, 2017: ASoP (v1.0): A set of methods for analyzing scales of precipitation in general circulation models. Geosci. Model Dev., 10, 5783, https://doi.org/10.5194/gmd-10-57-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kooperman, G. J., M. S. Pritchard, T. A. O’Brien, and B. W. Timmermans, 2018: Rainfall from resolved rather than parameterized processes better represents the present-day and climate change response of moderate rates in the Community Atmosphere Model. J. Adv. Model. Earth Syst., 10, 971988, https://doi.org/10.1002/2017MS001188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kopparla, P., E. M. Fischer, C. Hannay, and R. Knutti, 2013: Improved simulation of extreme precipitation in a high-resolution atmosphere model. Geophys. Res. Lett., 40, 58035808, https://doi.org/10.1002/2013GL057866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kubota, T., and Coauthors, 2007: Global precipitation map using satelliteborne microwave radiometers by the GSMaP project: Production and validation. IEEE Trans. Geosci. Remote Sens., 45, 22592275, https://doi.org/10.1109/TGRS.2007.895337.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., and S. M. Champion, 2019: An assessment of rainfall from Hurricanes Harvey and Florence relative to other extremely wet storms in the United States. Geophys. Res. Lett., 46, 13 50013 506, https://doi.org/10.1029/2019GL085034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, Y.-H., J. D. Neelin, and C. R. Mechoso, 2017: Tropical convective transition statistics and causality in the water vapor–precipitation relation. J. Atmos. Sci., 74, 915931, https://doi.org/10.1175/JAS-D-16-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, G., and S.-P. Xie, 2014: Tropical biases in CMIP5 multimodel ensemble: The excessive equatorial Pacific cold tongue and double ITCZ problems. J. Climate, 27, 17651780, https://doi.org/10.1175/JCLI-D-13-00337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J. W.-B., and J. D. Neelin, 2000: Influence of a stochastic moist convective parameterization on tropical climate variability. Geophys. Res. Lett., 27, 36913694, https://doi.org/10.1029/2000GL011964.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J. W.-B., and J. D. Neelin, 2003: Toward stochastic deep convective parameterization in general circulation models. Geophys. Res. Lett., 30, 1162, https://doi.org/10.1029/2002GL016203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., and R. P. Allan, 2012: Multisatellite observed responses of precipitation and its extremes to interannual climate variability. J. Geophys. Res., 117, D03101, https://doi.org/10.1029/2011JD016568.

    • Search Google Scholar
    • Export Citation
  • Maher, P., G. K. Vallis, S. C. Sherwood, M. J. Webb, and P. G. Sansom, 2018: The impact of parameterized convection on climatological precipitation in atmospheric global climate models. Geophys. Res. Lett., 45, 37283736, https://doi.org/10.1002/2017GL076826.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrooghy, M. V. G. Anantharaj, N. H. Younan, J. Aanstoos, and K.-L. Hsu, 2012: On an enhanced PERSIANN-CCS algorithm for precipitation estimation. J. Atmos. Oceanic Technol., 29, 922932, https://doi.org/10.1175/JTECH-D-11-00146.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mapes, B., J. Bacmeister, M. Khairoutdinov, C. Hannay, and M. Zhao, 2009: Virtual field campaigns on deep tropical convection in climate models. J. Climate, 22, 244257, https://doi.org/10.1175/2008JCLI2203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., S. Sahany, S. N. Stechmann, and D. N. Bernstein, 2017: Global warming precipitation accumulation increases above the current-climate cutoff scale. Proc. Natl. Acad. Sci. USA, 114, 12581263, https://doi.org/10.1073/pnas.1615333114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, J., A. H. Sobel, D. A. Shaevitz, and S. Wang, 2018: Dynamic amplification of extreme precipitation sensitivity. Proc. Natl. Acad. Sci. USA, 115, 94679472, https://doi.org/10.1073/pnas.1800357115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Norris, J., G. Chen, and J. D. Neelin, 2019a: Changes in frequency of large precipitation accumulations over land in a warming climate from the CESM Large Ensemble: The roles of moisture, circulation, and duration. J. Climate, 32, 53975416, https://doi.org/10.1175/JCLI-D-18-0600.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Norris, J., G. Chen, and J. D. Neelin, 2019b: Thermodynamic versus dynamic controls on extreme precipitation in a warming climate from the Community Earth System Model Large Ensemble. J. Climate, 32, 10251045, https://doi.org/10.1175/JCLI-D-18-0302.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Norris, J., G. Chen, and C. Li, 2020: Dynamic amplification of subtropical extreme precipitation in a warming climate. Geophys. Res. Lett., 47, e2020GL087200, https://doi.org/10.1029/2020GL087200.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Brien, T. A., W. D. Collins, K. Kashinath, O. Rübel, S. Byna, J. Gu, H. Krishnan, and P. A. Ullrich, 2016: Resolution dependence of precipitation statistical fidelity in hindcast simulations. J. Adv. Model. Earth Syst., 8, 976990, https://doi.org/10.1002/2016MS000671.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., and T. Schneider, 2009: The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. USA, 106, 14 77314 777, https://doi.org/10.1073/pnas.0907610106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., T. M. Merlis, and M. S. Singh, 2018: Increase in the skewness of extratropical vertical velocities with climate warming: Fully nonlinear simulations versus moist baroclinic instability. Quart. J. Roy. Meteor. Soc., 144, 208217, https://doi.org/10.1002/qj.3195.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and D. L. Hartmann, 2014a: Changes in the distribution of rain frequency and intensity in response to global warming. J. Climate, 27, 83728383, https://doi.org/10.1175/JCLI-D-14-00183.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and D. L. Hartmann, 2014b: Two modes of change of the distribution of rain. J. Climate, 27, 83578371, https://doi.org/10.1175/JCLI-D-14-00182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, O., A. Deluca, A. Corral, J. D. Neelin, and C. E. Holloway, 2010: Universality of rain event size distributions. J. Stat. Mech., 2010, P11030, https://doi.org/10.1088/1742-5468/2010/11/P11030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pfahl, S., P. A. O’Gorman, and E. M. Fischer, 2017: Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Climate Change, 7, 423427, https://doi.org/10.1038/nclimate3287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Plant, R. S., and G. C. Craig, 2008: A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci., 65, 87105, https://doi.org/10.1175/2007JAS2263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Risser, M. D., and M. F. Wehner, 2017: Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophys. Res. Lett., 44, 12 45712 464, https://doi.org/10.1002/2017GL075888.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roeckner, E., R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, and L. Kornblueh, 2006: Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J. Climate, 19, 37713791, https://doi.org/10.1175/JCLI3824.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seager, R., and N. Henderson, 2013: Diagnostic computation of moisture budgets in the ERA-Interim reanalysis with reference to analysis of CMIP-archived atmospheric model data. J. Climate, 26, 78767901, https://doi.org/10.1175/JCLI-D-13-00018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seager, R., and Coauthors, 2014: Dynamical and thermodynamical causes of large-scale changes in the hydrological cycle over North America in response to global warming. J. Climate, 27, 79217948, https://doi.org/10.1175/JCLI-D-14-00153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheffield, J., and Coauthors, 2013: North American climate in CMIP5 experiments. Part I: Evaluation of historical simulations of continental and regional climatology. J. Climate, 26, 92099245, https://doi.org/10.1175/JCLI-D-12-00592.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sillmann, J., V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh, 2013a: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res. Atmos., 118, 17161733, https://doi.org/10.1002/jgrd.50203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaughy, 2013b: Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res. Atmos., 118, 24732493, https://doi.org/10.1002/jgrd.50188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K.-L. Hsu, 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79107, https://doi.org/10.1002/2017RG000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2006: How often does it rain? J. Climate, 19, 916934, https://doi.org/10.1175/JCLI3672.1.

  • Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2007: How often will it rain? J. Climate, 20, 48014818, https://doi.org/10.1175/JCLI4263.1.

  • Swain, D. L., B. Langenbrunner, J. D. Neelin, and A. Hall, 2018: Increasing precipitation volatility in twenty-first-century California. Nat. Climate Change, 8, 427433, https://doi.org/10.1038/s41558-018-0140-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., C. Jakob, W. B. Rossow, and G. Tselioudis, 2015: Increases in tropical rainfall driven by changes in frequency of organized deep convection. Nature, 519, 451454, https://doi.org/10.1038/nature14339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tandon, N. F., X. Zhang, and A. H. Sobel, 2018: Understanding the dynamics of future changes in extreme precipitation intensity. Geophys. Res. Lett., 45, 28702878, https://doi.org/10.1002/2017GL076361.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teixeira, J., and C. A. Reynolds, 2008: Stochastic nature of physical parameterizations in ensemble prediction: A stochastic convection approach. Mon. Wea. Rev., 136, 483496, https://doi.org/10.1175/2007MWR1870.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thackeray, C., A. M. DeAngelis, A. Hall, D. L. Swain, and Q. Xu, 2018: On the connection between global hydrologic sensitivity and regional wet extremes. Geophys. Res. Lett., 45, 11 34311 351, https://doi.org/10.1029/2018GL079698.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051218, https://doi.org/10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Volosciuk, C., D. Maraun, V. A. Semenov, and W. Park, 2015: Extreme precipitation in an atmosphere general circulation model: Impact of horizontal and vertical model resolutions. J. Climate, 28, 11841205, https://doi.org/10.1175/JCLI-D-14-00337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wehner, M. F., R. L. Smith, G. Bala, and P. Duffy, 2010: The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model. Climate Dyn., 34, 241247, https://doi.org/10.1007/s00382-009-0656-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wehner, M. F., and Coauthors, 2014: The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1. J. Adv. Model. Earth Syst., 6, 980997, https://doi.org/10.1002/2013MS000276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilcox, E. M., and L. J. Donner, 2007: The frequency of extreme rain events in satellite rain-rate estimates and an atmospheric general circulation model. J. Climate, 20, 5369, https://doi.org/10.1175/JCLI3987.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, T., 2012: A mass-flux cumulus parameterization scheme for large-scale models: Description and test with observations. Climate Dyn., 38, 725744, https://doi.org/10.1007/s00382-011-0995-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, T., and Coauthors, 2019: The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev., 12, 15731600, https://doi.org/10.5194/gmd-12-1573-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wuebbles, D., and Coauthors, 2014: CMIP5 climate model analyses: Climate extremes in the United States. Bull. Amer. Meteor. Soc., 95, 571583, https://doi.org/10.1175/BAMS-D-12-00172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Climate, 23, 966986, https://doi.org/10.1175/2009JCLI3329.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., W. Lin, and M. Zhang, 2007: Toward understanding the double intertropical convergence zone pathology in coupled ocean-atmosphere general circulation models. J. Geophys. Res., 112, D12102, https://doi.org/10.1029/2006JD007878.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 803 0 0
Full Text Views 904 345 46
PDF Downloads 763 262 14