• Ackerman, T. P., , and G. M. Stokes, 2003: The Atmospheric Radiation Measurement Program. Phys. Today, 56, 3944.

  • Arakawa, A., , and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674701.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., and Coauthors, 2004: The simulation of the diurnal cycle of convective precipitation over land in a global model. Quart. J. Roy. Meteor. Soc., 130, 31193137.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., , and C. Jakob, 2002: Study of diurnal cycle of convective precipitation over Amazonia using a single column model. J. Geophys. Res., 107, 4732, doi:10.1029/2002JD002264.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., , and S. Park, 2009: A new moist turbulence parameterization in the Community Atmosphere Model. J. Climate, 22, 34223448.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., , J. R. McCaa, , and H. Grenier, 2004a: A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: Description and 1D results. Mon. Wea. Rev., 132, 864882.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., , M. E. Peters, , and L. E. Back, 2004b: Relationships between water vapor path and precipitation over the tropical oceans. J. Climate, 17, 15171528.

    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630.

  • Dai, A., , and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the Community Climate System Model. J. Climate, 17, 930951.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., , and M. Yao, 1993: Efficient cumulus parameterization for long-term climate studies: The GISS scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 181–184.

  • Del Genio, A. D., , and A. Wolf, 2012: Should today's SCMs convect at the SGP? FASTER Breakout, ASR Science Team Meeting, Arlington, VA, Department of Energy, 11 pp. [Available online at http://asr.science.energy.gov/meetings/stm/2012/presentations/delgeniofaster.pdf.]

  • Del Genio, A. D., , M.-S. Yao, , W. Kovari, , and K. K.-W. Lo, 1996: A prognostic cloud water parameterization for global climate models. J. Climate, 9, 270304.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., , W. Kovari, , M.-S. Yao, , and J. Jonas, 2005: Cumulus microphysics and climate sensitivity. J. Climate, 18, 23762387.

  • Del Genio, A. D., , M.-S. Yao, , and J. Jonas, 2007: Will moist convection be stronger in a warmer climate? Geophys. Res. Lett., 34, L16703, doi:10.1029/2007GL030525.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., , J. Wu, , and Y. Chen, 2012: Characteristics of mesoscale organization in WRF simulations of convection during TWP-ICE. J. Climate, 25, 56665688.

    • Search Google Scholar
    • Export Citation
  • Donner, L. J., , C. J. Seman, , R. S. Hemiler, , and S. Fan, 2001: A cumulus parameterization including mass fluxes, convective vertical velocities, and mesoscale effects: Thermodynamic and hydrological aspects in a general circulation model. J. Climate, 14, 34443463.

    • Search Google Scholar
    • Export Citation
  • Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Climate, 24, 34843519.

    • Search Google Scholar
    • Export Citation
  • Ghan, S. J., , X. Bian, , and L. Corsetti, 1996: Simulation of the Great Plains low-level jet and associated clouds by general circulation models. Mon. Wea. Rev., 124, 13881408.

    • Search Google Scholar
    • Export Citation
  • Ghan, S. J., , L. R. Leung, , and J. McCaa, 1999: A comparison of three different modeling strategies for evaluating cloud and radiation parameterizations. Mon. Wea. Rev., 127, 19671984.

    • Search Google Scholar
    • Export Citation
  • Ghan, S. J., and Coauthors, 2000: An intercomparison of single column model simulations of summertime midlatitude continental convection. J. Geophys. Res., 105, 20912124.

    • Search Google Scholar
    • Export Citation
  • Gregory, D., , J.-J. Morcrette, , C. Jakob, , A. M. Beljaars, , and T. Stockdale, 2000: Revision of convection, radiation and cloud schemes in the ECMWF Integrated Forecasting System. Quart. J. Roy. Meteor. Soc., 126, 16861710.

    • Search Google Scholar
    • Export Citation
  • Hack, J. J., 1994: Parameterization of moist convection in the National Center for Atmospheric Research Community Climate Model (CCM2). J. Geophys. Res., 99 (D3), 55515568.

    • Search Google Scholar
    • Export Citation
  • Hack, J. J., , and J. A. Pedretti, 2000: Assessment of solution uncertainties in single-column modeling frameworks. J. Climate, 13, 352365.

    • Search Google Scholar
    • Export Citation
  • Hannay, C., and Coauthors, 2009: Evaluation of forecasted southeast Pacific stratocumulus in the NCAR, GFDL, and ECMWF models. J. Climate, 22, 28712889.

    • Search Google Scholar
    • Export Citation
  • Holtslag, A. A. M., , and B. A. Boville, 1993: Local versus nonlocal boundary layer diffusion in a global climate model. J. Climate, 6, 18251842.

    • Search Google Scholar
    • Export Citation
  • Jakob, C., , L. Davies, , V. Kumar, , and P. May, 2011: Representing convection in models—How stochastic does it need to be? Proc. ECMWF Workshop on Representing Model Uncertainty and Error in Weather and Climate Prediction, Reading, United Kingdom, ECMWF, 41–52.

  • Kennedy, A. D., 2011: Evaluation of a single column model at the southern Great Plains climate research facility. Ph.D. dissertation, University of North Dakota, 149 pp.

  • Kennedy, A. D., and Coauthors, 2010: Evaluation of the NASA GISS single column model simulated clouds using combined surface and satellite observations. J. Climate, 23, 51755192.

    • Search Google Scholar
    • Export Citation
  • Kennedy, A. D., , X. Dong, , B. Xi, , S. Xie, , Y. Zhang, , and J. Chen, 2011: A comparison of MERRA and NARR reanalyses with the DOE ARM SGP data. J. Climate, 24, 45414557.

    • Search Google Scholar
    • Export Citation
  • Köhler, M., 2005: Improved prediction of boundary layer clouds. ECMWF Newsletter, No. 104, ECMWF, Reading, United Kingdom, 18–22.

  • Lee, M.-I., , and S. D. Schubert, 2008: The diurnal cycle in NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA). Preprints, 20th Conf. Climate Variability and Change, New Orleans, LA, Amer. Meteor. Soc., P3.7. [Available online at https://ams.confex.com/ams/88Annual/techprogram/paper_134706.htm.]

  • Lee, M.-I., and Coauthors, 2007: An analysis of the warm-season diurnal cycle over the continental United States and northern Mexico in general circulation models. J. Hydrometeor., 8, 344366.

    • Search Google Scholar
    • Export Citation
  • Lee, M.-I., and Coauthors, 2008: Role of convection triggers in the simulation of the diurnal cycle of precipitation over the United States Great Plains in a general circulation model. J. Geophys. Res., 113, D02111, doi:10.1029/2007JD008984.

    • Search Google Scholar
    • Export Citation
  • Lin, J.-L., 2007: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean–atmosphere feedback analysis. J. Climate, 20, 44974525.

  • Liu, G., , Y. Liu, , and S. Endo, 2013: Evaluation of surface flux parameterization with long-term ARM observations. Mon. Wea. Rev., 141, 773797.

    • Search Google Scholar
    • Export Citation
  • Lock, A. P., , A. R. Brown, , M. R. Bush, , G. M. Martin, , and R. N. B. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 31873199.

    • Search Google Scholar
    • Export Citation
  • Moorthi, S., , and M. J. Suarez, 1992: Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 9781002.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., , and A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmospheric Model (CAM3). Part I: Description and numerical tests. J. Climate, 21, 38453862.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., , J. H. Richter, , and M. Jochum, 2008: The impact of convection on ENSO: From a delayed oscillator to a series of events. J. Climate, 21, 59045924.

    • Search Google Scholar
    • Export Citation
  • Neggers, R. A. J., , A. P. Siebesma, , and T. Heus, 2012: Continuous single-column model evaluation at a permanent meteorological supersite. Bull. Amer. Meteor. Soc., 93, 13891400.

    • Search Google Scholar
    • Export Citation
  • Park, S., , and C. S. Bretherton, 2009: The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the Community Atmosphere Model. J. Climate, 22, 34493469.

    • Search Google Scholar
    • Export Citation
  • Randall, D., , and D. G. Cripe, 1999: Alternative methods for specification of observed forcing in single-column models and cloud system models. J. Geophys. Res., 104 (D20), 24 52724 545.

    • Search Google Scholar
    • Export Citation
  • Randall, D., and Coauthors, 2003: Confronting models with data: The GEWEX Cloud Systems Study. Bull. Amer. Meteor. Soc., 84, 455469.

  • Rasch, P. J., , and J. E. Kristjansson, 1998: A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Climate, 11, 15871614.

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., 1997: A physical based scheme for the treatment of stratiform clouds and precipitation in large-scale models. I: Description and evaluation of the microphysical processes. Quart. J. Roy. Meteor. Soc., 123, 12271282.

    • Search Google Scholar
    • Export Citation
  • Schmidt, G. A., and Coauthors, 2006: Present-day atmospheric simulations using GISS ModelE: Comparison to in situ, satellite, and reanalysis data. J. Climate, 19, 153192.

    • Search Google Scholar
    • Export Citation
  • Stokes, G. M., , and S. E. Schwartz, 1994: The Atmospheric Radiation Measurement (ARM) program: Programmatic background and design of the cloud and radiation testbed. Bull. Amer. Meteor. Soc., 75, 12011221.

    • 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.

  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800.

    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 30403061.

  • Wu, W., , Y. Liu, , and A. K. Betts, 2012: Observationally based evaluation of NWP reanalyses in modeling cloud properties over the southern Great Plains. J. Geophys. Res., 117, D12202, doi:10.1029/2011JD016971.

    • Search Google Scholar
    • Export Citation
  • Xie, S., , and M. Zhang, 2000: Impact of the convection trigger function on single-column model simulations. J. Geophys. Res., 105 (D11), 14 98314 996.

    • Search Google Scholar
    • Export Citation
  • Xie, S., and Coauthors, 2002: Intercomparison and evaluation of cumulus parameterizations under summertime midlatitude continental conditions. Quart. J. Roy. Meteor. Soc., 128, 10951135.

    • Search Google Scholar
    • Export Citation
  • Xie, S., , R. T. Cederwall, , and M. Zhang, 2004: Developing long-term single-column model/cloud system–resolving model forcing data using numerical weather prediction products constrained by surface and top of atmosphere observations. J. Geophys. Res., 109, D01104, doi:10.1029/2003JD004045.

    • Search Google Scholar
    • Export Citation
  • Xie, S., and Coauthors, 2005: Simulations of midlatitude frontal clouds by single-column and cloud-resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period. J. Geophys. Res., 110, D15S03, doi:10.1029/2004JD005119.

    • Search Google Scholar
    • Export Citation
  • Xie, S., and Coauthors, 2010: Cloud and more: ARM climate modeling best estimate data. Bull. Amer. Meteor. Soc., 91, 1320.

  • Zhang, G. J., 2003: Roles of tropospheric and boundary layer forcing in the diurnal cycle of convection in the U.S. southern Great Plains. Geophys. Res. Lett., 30, 2281, doi:10.1029/2003GL018554.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., , and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Center general circulation model. Atmos.–Ocean, 33, 407446.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., , W. Lin, , C. S. Bretherton, , J. J. Hack, , and P. J. Rasch, 2003: A modified formulation of fractional stratiform condensation rate in the NCAR Community Atmospheric Model (CAM2). J. Geophys. Res., 108, 4035, doi:10.1029/2002JD002523.

    • Search Google Scholar
    • Export Citation
  • Zhao, M., , I. M. Held, , S.-J. Lin, , and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate, 22, 66536678.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 82 82 26
PDF Downloads 16 16 3

Evaluation of Precipitation Simulated by Seven SCMs against the ARM Observations at the SGP Site

View More View Less
  • 1 Brookhaven National Laboratory, Upton, New York
  • 2 NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
  • 3 Columbia University, New York, New York
  • 4 Royal Netherlands Meteorological Institute, De Bilt, Netherlands
  • 5 ** NASA Goddard Institute for Space Studies, New York, New York
© Get Permissions
Restricted access

Abstract

This study evaluates the performances of seven single-column models (SCMs) by comparing simulated surface precipitation with observations at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site from January 1999 to December 2001. Results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant and interesting differences in their details. In the cold season, the model–observation differences in the frequency and mean intensity of rain events tend to compensate each other for most SCMs. In the warm season, most SCMs produce more rain events in daytime than in nighttime, whereas the observations have more rain events in nighttime. The mean intensities of rain events in these SCMs are much stronger in daytime, but weaker in nighttime, than the observations. The higher frequency of rain events during warm-season daytime in most SCMs is related to the fact that most SCMs produce a spurious precipitation peak around the regime of weak vertical motions but rich in moisture content. The models also show distinct biases between nighttime and daytime in simulating significant rain events. In nighttime, all the SCMs have a lower frequency of moderate-to-strong rain events than the observations for both seasons. In daytime, most SCMs have a higher frequency of moderate-to-strong rain events than the observations, especially in the warm season. Further analysis reveals distinct meteorological backgrounds for large underestimation and overestimation events. The former occur in the strong ascending regimes with negative low-level horizontal heat and moisture advection, whereas the latter occur in the weak or moderate ascending regimes with positive low-level horizontal heat and moisture advection.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-00263.s1.

Current affiliation: Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China.

Corresponding author address: Hua Song, Atmospheric Sciences Division, Brookhaven National Laboratory, 75 Rutherford Dr., Bldg. 815E, Upton, NY 11973-5000. E-mail: hsong@bnl.gov

Abstract

This study evaluates the performances of seven single-column models (SCMs) by comparing simulated surface precipitation with observations at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site from January 1999 to December 2001. Results show that although most SCMs can reproduce the observed precipitation reasonably well, there are significant and interesting differences in their details. In the cold season, the model–observation differences in the frequency and mean intensity of rain events tend to compensate each other for most SCMs. In the warm season, most SCMs produce more rain events in daytime than in nighttime, whereas the observations have more rain events in nighttime. The mean intensities of rain events in these SCMs are much stronger in daytime, but weaker in nighttime, than the observations. The higher frequency of rain events during warm-season daytime in most SCMs is related to the fact that most SCMs produce a spurious precipitation peak around the regime of weak vertical motions but rich in moisture content. The models also show distinct biases between nighttime and daytime in simulating significant rain events. In nighttime, all the SCMs have a lower frequency of moderate-to-strong rain events than the observations for both seasons. In daytime, most SCMs have a higher frequency of moderate-to-strong rain events than the observations, especially in the warm season. Further analysis reveals distinct meteorological backgrounds for large underestimation and overestimation events. The former occur in the strong ascending regimes with negative low-level horizontal heat and moisture advection, whereas the latter occur in the weak or moderate ascending regimes with positive low-level horizontal heat and moisture advection.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-00263.s1.

Current affiliation: Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China.

Corresponding author address: Hua Song, Atmospheric Sciences Division, Brookhaven National Laboratory, 75 Rutherford Dr., Bldg. 815E, Upton, NY 11973-5000. E-mail: hsong@bnl.gov

Supplementary Materials

    • Supplemental Materials (PDF 5.51 MB)
Save