A case study of subdaily simulated and observed continental convective precipitation: CMIP5 and multiscale global climate models comparison

Geophysical Research Letters

Volumn 40, Issue 22, 2013, Pages 5999-6003

  Rosa, D ^a   Collins, W D ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

cloud resolving modeling; CMIP5; convection; diurnal cycle; global climate model; precipitation

Indexed keywords

CLOUD RESOLVING MODEL; CMIP5; CONVECTIVE INSTABILITIES; CONVECTIVE PRECIPITATION; COUPLED MODEL INTERCOMPARISON PROJECT; DIURNAL CYCLE; EXTREME PRECIPITATION EVENTS; GLOBAL CLIMATE MODEL;

GAGES; HEAT CONVECTION; PRECIPITATION (CHEMICAL); RAIN; RAIN GAGES; RISK ASSESSMENT;

CLIMATE MODELS;

ATMOSPHERIC CONVECTION; CLIMATE MODELING; DATA SET; DIURNAL VARIATION; EXTREME EVENT; GLOBAL CLIMATE; PRECIPITATION (CLIMATOLOGY); RAINFALL; RAINGAUGE; RISK ASSESSMENT;

UNITED STATES;

EID: 84890528398     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2013GL057987     Document Type: Article

References (20)

1. Arakawa, A., (2004), The cumulus parameterization problem: Past, present, and future, J. Clim., 17, 2493-2525.
2. Dai, A., (2006), Precipitation characteristics in eighteen coupled climate models, J. Clim., 19, 4605-4630, doi: 10.1175/JCLI3884.1.
3. Demott, C. A., D. A. Randall, and, M. Khairoutdinov, (2007), Convective precipitation variability as a tool for general circulation model analysis, J. Clim., 20, 91-112, doi: 10.1175/JCLI3991.1.
4. Donner, L. J., et al. (2011), The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3, J. Clim., 24, 3484-3519, doi: 10.1175/2011JCLI3955.1.
5. Gent, P. R., et al. (2011), The community climate system model version 4, J. Clim., 24, 4973-4991, doi: 10.1175/2011JCLI4083.1.
6. Higgins, R. W., W. Shi, E. Yarosh, and, R. Joyce, (2000), Improved United States Precipitation Quality Control System and Analysis, 40 pp., NCEP/Climate Prediction Center ATLAS No. 7, Camp Springs, Md.
7. Higgins, R. W., V. E. Kousky, and, P. Xie, (2011), Extreme precipitation events in the South-Central United States during May and June 2010: Historical perspective, role of ENSO, and trends, J. Hydrometeorol., 12, 1056-1070, doi: 10.1175/JHM-D-10-05039.1.
8. IPCC (2012), Summary for policymakers, in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, edited by, C. B. Field, et al., pp. 1-19, Special Report of the Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge, U. K., and New York.
9. Kalnay, E., et al. (1996), The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77 (3), 437-471.
10. Li, F., D. Rosa, W. D. Collins, and, M. F. Wehner, (2012), "Super-parameterization": A better way to simulate regional extreme precipitation?, J. Adv. Model. Earth Syst., 4, M04002, doi: 10.1029/ 2011MS000106.
11. Martin, G. M., et al. (2011), The HadGEM2 family of Met Office Unified Model climate configurations, Geosci. Model Dev., 4, 723-757, doi: 10.5194/gmd-4-723-2011.
12. Petch, J. C., P. N. Blossey, and, C. S. Bretherton, (2008), Differences in the lower troposphere in two- and three-dimensional cloud-resolving model simulations of deep convection, Q. J. R. Meteorol. Soc., 134, 1941-1946, doi: 10.1002/qj.315.
13. Rosa, D., J. F. Lamarque, and, W. D. Collins, (2012), Global transport of passive tracers in conventional and superparameterized climate models: Evaluation of multi-scale methods, J. Adv. Model. Earth Syst., 4, M10003, doi: 10.1029/2012MS000206.
14. Scoccimarro, E., S. Gualdi, A. Bellucci, A. Sanna, P. Fogli, E. Manzini, M. Vichi, P. Oddo, and, A. Navarra, (2011), Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model, J. Clim., 24, 4368-4384, doi: 10.1175/2011JCLI4104.1.
15. Stephens, G. L., T. L'Ecuyer, R. Forbes, A. Gettlemen, J.-C. Golaz, A. Bodas-Salcedo, K. Suzuki, P. Gabriel, and, J. Haynes, (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi: 10.1029/2010JD014532.
16. Taylor, K. E., R. J. Stouffer, and, G. A. Meehl, (2012), An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93, 485-498, doi: 10.1175/BAMS-D-11-00094.1.
17. Voldoire, A., et al. (2013), The CNRM-CM5.1 global climate model: Description and basic evaluation, Clim. Dyn., 40, 2091-2121, doi: 10.1007/s00382-011-1259-y.
18. Watanabe, S., et al. (2011), MIROC-ESM 2010: Model description and basic results of CMIP5-20C3M experiments, Geosci. Model Dev., 4, 845-872, doi: 10.5194/gmd-4-845-2011.
19. Wu, T., (2012), A mass-flux cumulus parameterization scheme for large-scale models: Description and test with observations, Clim. Dyn., 38, 725-744.
20. Yang, S., and, E. A. Smith, (2008), Convective-stratiform precipitation variability at seasonal scale from 8 yr of TRMM observations: Implications for multiple modes of diurnal variability, J. Clim., 21, 4087-4114, doi: 10.1175/2008JCLI2096.1.