A single-column model ensemble approach applied to the TWP-ICE experiment
Davies, L., Jakob, C., Cheung, K., Del Genio, A., Hill, A., Hume, T., Keane, R. J., Komori, T., Larson, V. E., Lin, Y., Liu, X., Neilsen, B. J., Petch, J., Plant, R. S.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1002/jgrd.50450 Abstract/SummarySingle-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.
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Arakawa, A., and W. Schubert (1974), Interaction of a cumulus cloud ensemble with the large-scale environment, Part 1, J. Atmos. Sci., 31, 674–701, doi:10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 1411,
ADS
Ball, M. A., and R. S. Plant (2008), Comparison of stochastic parameterization approaches in a single-column model, Phil. Trans. Roy. Soc., 366, 2605–2623, doi:10.1098/rsta.2011.0377.
CrossRef,
PubMed,
Web of Science® Times Cited: 6,
ADS
Bechtold, P., et al. (2000), A GCSS model intercomparison for a tropical squall line observed during TOGA-COARE. II: Intercomparison of single-column models and a cloud-resolving model, Q. J. R. Meteorolog. Soc., 126(564), 865–888, doi:10.1002/qj.49712656404.
Direct Link:
Abstract
PDF(1452K)
References
Boville, B. A., P. J. Rasch, J. J. Hack, and J. R. McCaa (2006), Representation of clouds and precipitation processes in the Community Atmosphere Model version 3 (CAM3), J. Climate, 19(11), 2184–2198, doi:10.1175/JCLI3749.1.
CrossRef,
Web of Science® Times Cited: 87,
ADS
Bringi, V. N., and V. Chandrasekar (2001), Polarimetric Doppler Weather Radar: Principles and Applications, 636 pp., Cambridge University Press, The Edinburgh Building, Cambridge, CB2 2RU, UK.
CrossRef
Chou, M., M. J. Suarez, C. H. Ho, M. M. H. Yan, and K. T. Lee (1998), Parameterizations for cloud overlapping and shortwave single scattering properties for use in general circulation and cloud ensemble models, J. Climate, 11, 202–214, doi:10.1175/1520-0442(1998)011<0202:PFCOAS>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 123,
ADS
Collins, W. D., P. J. Rasch, B. A. Boville, J. J. Hack, J. R. McCaa, D. L. W. Williamson, B. P. Briegleb, C. M. Bitz, S.-J. Lin, and M. Zhang (2006), The formulation and atmospheric simulation of the Community Atmosphere Model version 3 (CAM3), J. Climate, 19, 2144–2161, doi:10.1175/JCLI3760.1.
CrossRef,
Web of Science® Times Cited: 525,
ADS
Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood (2005), A new dynamical core for the Met Office's global and regional modelling of the atmosphere, Q. J. R. Meteorol. Soc., 131, 1759–1782, doi:10.1256/qj.04.101.
Direct Link:
Abstract
PDF(401K)
References
Web of Science® Times Cited: 338
Del Genio, A., M.-S. Yao, W. Kovari, and K.-W. Lo (1996), A prognostic cloud water parameterization for global climate models, J. Climate, 9, 270–304, doi:10.1175/1520-0442(1996)009<0270:APCWPF>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 288,
ADS
Del Genio, A., W. Kovari, M.-S. Yao, and J. Jonas (2005), Cumulus microphysics and climate sensitivity, J. Climate, 18, 2376–2387, doi:10.1175/JCLI3413.1.
CrossRef,
Web of Science® Times Cited: 57,
ADS
Derbyshire, S. H., A. V. Maidens, S. F. Milton, R. A. Stratton, and M. R. Willett (2011), Adaptive detrainment in a convective parametrization, Q. J. R. Meteorolog. Soc., 137(660), 1856–1871, doi:10.1002/qj.875.
Direct Link:
Abstract
Full Article (HTML)
PDF(957K)
References
Web of Science® Times Cited: 16
Edwards, J., and A. Slingo (1996), Studies with a flexible new radiation code. Part I: Choosing a configuration for a large-scale model, Q. J. R. Meteorol. Soc., 122, 689–719, doi:10.1002/qj.49712253107.
Direct Link:
Abstract
PDF(2086K)
References
Web of Science® Times Cited: 494
Emanuel, K., and M. Zivkovic-Rothman (1999), Development and evaluation of a convection scheme for use in climate models, J. Atmos. Sci., 56(11), 1766–1782, doi:10.1175/1520-0469(1999)056<1766:DAEOAC>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 282,
ADS
EMC, (2003), The GFS atmospheric model, Technical report, National Oceanic and Atmospheric Administration, US Department of Commerce.
Fels, S., and M. Schwarzkopf (1975), The simplified exchange approximation: A new method for radiative transfer calculations, J. Atmos. Sci., 37, 2265–2297, doi:10.1175/1520-0469(1975)032<1475:TSEAAN>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 214,
ADS
Freidenreich, S. M., and V. Ramaswamy (1999), A new multiple-band solar radiative parameterization for general circulation models, J. Geophys. Res., 104(D24), 31389–31409, doi:10.1029/1999JD900456.
Direct Link:
Abstract
PDF(2443K)
References
Web of Science® Times Cited: 58
Fridlind, A. M., et al. (2012), A comparison of TWP-ICE observational data with cloud-resolving model results, J. Geophys. Res., 117, D05204, doi:10.1029/2011JD016595.
Direct Link:
Abstract
Full Article (HTML)
PDF(6231K)
References
Fu, Q., and K. N. Liou (1993), Parameterization of the radiative properties of cirrus clouds, J. Atmos. Sci., 50, 2008–2025, doi:10.1175/1520-0469(1993)050<2008:POTRPO>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 444,
ADS
GAMDT (2004), The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations, J. Climate, 17, 4641–4673, doi:10.1175/JCLI-3223.1.
CrossRef,
ADS
Ghan, S., et al. (2000), A comparison of single column model simulations of summertime midlatitude continental convection, J. Geophys. Res., 105(D2), 2091–2124, doi:10.1029/1999JD900971.
Direct Link:
Abstract
PDF(4230K)
References
Web of Science® Times Cited: 66
Golaz, J.-C., V. Larson, and W. Cotton (2002), A PDF-based model for boundary layer clouds. Part I: Method and model description, J. Atmos. Sci., 59, 3540–3551, doi:10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 83,
ADS
Grabowski, W. W., et al. (2006), Daytime convective development over land: A model intercomparison based on LBA observations, Q. J. Roy. Meteor. Soc., 132, 317–344, doi:10.1256/qj.04.147.
Direct Link:
Abstract
PDF(501K)
References
Web of Science® Times Cited: 62
Grant, A. (2001), Cloud-base fluxes in the cumulus-capped boundary layer, Q. J. Roy. Meteor. Soc., 127(572), 407–421, doi:10.1002/qj.49712757209.
Direct Link:
Abstract
PDF(929K)
References
Gray, M. E. B., J. Petch, S. H. Derbyshire, A. R. Brown, A. P. Lock, H. A. Swann, and P. R. A. Brown (2001), Version 2.3 of the Met. Office Large Eddy Model: Part II. Scientific documentation. Met O (APR) Turbulence and Diffusion Note. No. 276.
Gregory, D. (2001), Estimation of entrainment rate in simple models of convective clouds, Q. J. R. Meteorol. Soc., 127, 53–72, doi:10.1002/qj.49712757104.
Direct Link:
Abstract
PDF(1214K)
References
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, doi:10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 441,
ADS
Guichard, F., et al. (2004), Modelling the diurnal cycle of deep precipitating convection over land with cloud-resolving models and single-column models, Q. J. Roy. Meteor. Soc., 130, 3139–3171, doi:10.1256/qj.03.145.
Direct Link:
Abstract
PDF(927K)
References
Web of Science® Times Cited: 101
Hack, J. J., and J. A. Pedretti (2000), Assessment of solution uncertainties in single-column modelling frameworks, J. Climate, 13(10), 352–365, doi:10.1175/1520-0442(2000)013<0352:AOSUIS>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 28,
ADS
Held, I. M., R. S. Hemler, and V. Ramasway (1993), Radiative-convective equilibrium with explicit two-dimensional moist convection, J. Atmos. Sci., 50, 3909–3927, doi:10.1175/1520-0469(1993)050<3909:RCEWET>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 97,
ADS
Holtslag, A. A. M., and B. A. Boville (1993), Local versus nonlocal boundary-layer diffusion in a global climate model, J. Climate, 6(10), 1825–1842, doi:10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 559,
ADS
Hong, S., and H. Pan (1996), Nonlocal boundary layer vertical diffusion in a medium-range forecast model, Mon. Wea. Rev., 124(10), 2322–2339, doi:10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 1098,
ADS
Hume, T., and C. Jakob (2005), Ensemble Single Column Modelling (ESCM) in the tropical western Pacific: Forcing datasets and uncertainty analysis, J. Geophys. Res., 110, D13109, doi:10.1029/2004JD005704.
Direct Link:
Abstract
Full Article (HTML)
PDF(773K)
References
Web of Science® Times Cited: 6
Hume, T., and C. Jakob (2007), Ensemble Single Column Model (ESCM) validation in the tropical western Pacific, J. Geophys. Res., 112, D10206, doi:10.1029/2006JD008018.
Direct Link:
Abstract
Full Article (HTML)
PDF(320K)
References
Web of Science® Times Cited: 5
JMA (2007), Outline of the Operational Forecast and Analysis System of the Japan Meteorological Agency. http://www.jma. go.jp/jma/jma-eng/jma-center/nwp/outline-nwp/index.htm.
Jordan, P., A. Seed, and P. Weinmann (2003), A stochastic model of radar measurement errors in rainfall accumulations at catchment scale, J. Hydro., 4, 841–855, doi:10.1175/1525-7541(2003)004<0841:ASMORM>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 23,
ADS
Joss, J., and A. Waldvogel (1990), Precipitation Measurement and Hydrology. Radar in Meteorology, chapter 29a, pp. 577–606, Academic Press, Boston, MA, USA.
Keenan, T. D., K. Glasson, F. Cummings, T. S. Bird, J. Keller, and J. Lutz (1998), The BMRC/NCAR C-band polarimetric (CPOL) radar system, J. Atmos. Oceanic Technol., 15, 871–886, doi:10.1175/1520-0426(1998)015<0871:TBNCBP>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 89,
ADS
Khairoutdinov, M., and D. Randall (2003), Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities, J. Atmos. Sci., 60, 607–625, doi:10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 240,
ADS
Khairoutdinov, M. F., and Y. L. Kogan (1999), A large eddy simulation model with explicit microphysics: Validation against aircraft observations of a stratocumulus-topped boundary layer, J. Atmos. Sci., 56(13), 2115–2131, doi:10.1175/1520-0469(1999)056<2115:ALESMW>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 61,
ADS
Kiehl, J., J. J. Hack, G. B. Bonan, B. A. Boville, D. L. Williamson, and P. J. Rasch (1998), The national center for atmospheric research community climate model: CCM3*, J. Climate, 11(6), 1131–1149, doi:10.1175/1520-0442(1998)011<1131:TNCFAR>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 732,
ADS
Larson, V. E., and J.-C. Golaz (2005), Using probability density functions to derive consistent closure relationships among higher-order moments, Mon. Wea. Rev., 133, 1023–1042, doi:10.1175/MWR2902.1.
CrossRef,
Web of Science® Times Cited: 26,
ADS
Larson, V. E., D. P. Schanen, M. Wang, M. Ovchinnikov, and S. Ghan (2012), PDF parameterization of boundary layer clouds in models with horizontal grid spacings from 2 to 16 km, Mon. Wea. Rev., 140, 285–306, doi:10.1175/MWR-D-10-05059.1.
CrossRef,
Web of Science® Times Cited: 10,
ADS
Lin, Y., et al. (2012), TWP-ICE global atmospheric model intercomparison: Convection responsiveness and resolution impact, J. Geophys. Res., 117, D09111, doi:10.1029/2011JD017018.
Direct Link:
Abstract
Full Article (HTML)
PDF(5441K)
References
Liu, X., and J. Penner (2005), Ice nucleation parameterization for global models, Meteorol. Z., 14(4), 499–514, doi:10.1127/0941-2948/2005/0059.
CrossRef,
CAS,
Web of Science® Times Cited: 63,
ADS
Liu, X., J. E. Penner, S. J. Ghan, and M. Wang (2007), Inclusion of ice microphysics in the NCAR community atmospheric model version 3 (CAM3), J. Climate, 20, 4526–4547, doi:10.1175/JCLI4264.1.
CrossRef,
Web of Science® Times Cited: 68,
ADS
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, 3187–3199, doi:10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 256,
ADS
Lowe, P. (1977), An approximating polynomial for the computation of saturation vapor pressure, J. Appl. Meteorol., 16, 100–102, doi:10.1175/1520-0450(1977)016<0100:AAPFTC>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 157,
ADS
MacVean, M., and P. Mason (1990), Cloud-top entrainment instability through small-scale mixing and its parameterization in numerical models, J. Atmos. Sci., 47(8), 1012–1030, doi:10.1175/1520-0469(1990)047<1012:CTEITS>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 102,
ADS
Martin, G. M., M. A. Ringer, V. D. Pope, A. Jones, C. Dearden, and T. J. Hinton (2006), The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part I: Model description and global climatology, J. Climate, 19(7), 1274–1301, doi:10.1175/JCLI3636.1.
CrossRef,
Web of Science® Times Cited: 191,
ADS
May, P. T., J. H. Mather, G. Vaughan, C. Jakob, G. M. McFarquhar, K. N. Bower, and G. G. Mace (2008), The tropical warm pool international cloud experiment, Bull. Amer. Meteor. Soc., 89, 629–645, doi:10.1175/BAMS-89-5-629.
CrossRef,
Web of Science® Times Cited: 76,
ADS
McClatchey, R. A., R. W. Fenn, J. Selby, F. E. Volz, and J. S. Garing, (1972), The Tropical Warm Pool International Cloud Experiment, Technical Report 411, Air Force Cambridge Research Laboratory Environmental Research Paper.
Mellor, G. L., and T. Yamada (1974), A hierarchy of turbulence closure models for planetary boundary layers, J. Atmos. Sci., 31, 1791–1806, doi:10.1175/1520-0469(1974)031<1791:AHOTCM>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 1446,
ADS
Moorthi, S., and M. J. Suarez (1992), Relaxed Arakawa-Schubert: A parameterization of moist convection for general circulation models, Mon. Wea. Rev., 120, 978–1002, doi:10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 562,
ADS
Morrison, H., G. Thompson, and V. Tatarskii (2009), Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes, Mon. Wea. Rev., 137(3), 991–1007, doi:10.1175/2008MWR2556.1.
CrossRef,
Web of Science® Times Cited: 177,
ADS
Nakagawa, M., (2009), Outline of the high resolution global model at the Japan meteorological agency, RSMC Tokyo-Typhoon Center Technical Review, 11(1-13), Available from http://www.jma.go.jp/jma/jma--eng/jma--center/rsmc--hp--pub--eg/techrev/text11--1.pdf.
Pan, H., and W. Wu (1995), Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. NMC Office Note, No. 409, 40 pp. [Available from NCEP, 5200 Auth Road, Washington, DC 20233].
Plant, R. S., and G. C. Craig (2008), A stochastic parameterization for deep convection based on equilibrium statistics, J. Atmos. Sci., 65, 87–105, doi:10.1175/2007JAS2263.1.
CrossRef,
Web of Science® Times Cited: 50,
ADS
Randall, D., and D. M. Pan (1993), Implementation of the Arakawa Schubert cumulus parameterization with a prognostic closure, Meteorol. Monogr., 46, 137–144.
Randall, D., et al. (2003), Confronting models with data: The GEWEX cloud systems study, Bull. Amer. Meteor. Soc., 84, 455–469, doi:10.1175/BAMS-84-4-455.
CrossRef,
Web of Science® Times Cited: 93,
ADS
Rasch, P. J., and J. E. Kristjánsson (1998), A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations, J. Climate, 11(7), 1587–1614, doi:10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 295,
ADS
Rotstayn, L. D. (1979), A physically based scheme for the treatment of stratiform clouds and precipitation in large-scale models. I: Description and evaluation of the microphysical processes, Q. J. Roy. Meteor. Soc., 123, 1227–1282, doi:10.1002/qj.49712354106.
Schmidt, G. A., et al. (2006), Present day atmospheric simulations using GISS Model E: Comparison to in-situ, satellite and reanalysis data, J. Climate, 19, 153–192, doi:10.1175/JCLI3612.1.
CrossRef,
Web of Science® Times Cited: 355,
ADS
Schwarzkopf, M. D., and S. B. Fels (1991), The simplified exchange method revisited: An accurate, rapid method for computation of infrared cooling rates and fluxes, J. Geophys. Res., 96(D5), 9075–9096, doi:10.1029/89JD01598.
Direct Link:
Abstract
PDF(1806K)
References
Web of Science® Times Cited: 154
Schwarzkopf, M. D., and V. Ramaswamy (1999), Radiative effects of CH4, N2O, halocarbons and the foreign-broadened H2O continuum: A GCM experiment, J. Geophys. Res., 104(D8), 9467–9488, doi:10.1029/1999JD900003.
Direct Link:
Abstract
PDF(3695K)
References
Web of Science® Times Cited: 49
Slingo, A. (1989), A GCM parameterization for the shortwave radiative properties of water clouds, J. Atmos. Sci., 46, 1419–1427, doi:10.1175/1520-0469(1989)046<1419:AGPFTS>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 444,
ADS
Smith, R. N. B. (1990), A scheme for predicting layer clouds and their water content in a general circulation model, Q. J. R. Meteorol. Soc., 116, 435–460, doi:10.1002/qj.49711649210.
Direct Link:
Abstract
PDF(1944K)
References
Stephens, G. L., P. M. Gabriel, and P. T. Partain (2001), Parameterization of atmospheric radiative transfer. Part I: Validity of simple models, J. Atmos. Sci., 58, 3391–3409, doi:10.1175/1520-0469(2001)058<3391:POARTP>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 33,
ADS
Sundqvist, H., E. Berge, and J. E. Kristjansson (1989), Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model, Mon. Wea. Rev., 117, 1641–1657, doi:10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 344,
ADS
Tiedtke, M. (1983), The sensitivity of the time-mean large-scale flow to cumulus convection in the ECMWF model, in Workshop on Convection in Large-scale Numerical Models, vol. 28, Shinfield Park, Reading, pp. 297–316.
Tiedtke, M. (1993), Representation of clouds in large-scale models, Mon. Wea. Rev., 121(11), 3040–3061, doi:10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 449,
ADS
Wang, W., X. Liu, S. Xie, J. Boyle, and S. A. McFarlane (2009), Testing ice microphysics parameterizations in the NCAR community atmospheric model version 3 using tropical warm pool–International Cloud Experiment data, J. Geophys. Res., 114, D14107, doi:10.1029/2008JD011220.
Direct Link:
Abstract
Full Article (HTML)
PDF(2043K)
References
Web of Science® Times Cited: 6
Webster, P., and R. Lukas (1992), TOGA-COARE: The Coupled Ocean-Atmosphere Response Experiment, Bull. Amer. Meteor. Soc, 73, 1377–1416, doi:10.1175/1520-0477(1992)073<1377:TCTCOR>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 646,
ADS
Wilson, D., and R. Forbes, (2004), Unified model documentation paper 26: The large-scale precipitation parametrization scheme, Technical Report 26, Met Office R&D.
Wilson, D. R., and S. P. Ballard (1999), A microphysically based precipitation scheme for the UK Meteorological Office Unified Model, Q. J. R. Meteorol. Soc., 125, 1607–1636, doi:10.1002/qj.49712555707.
Direct Link:
Abstract
PDF(2604K)
References
Woolnough, S. J., P. Blossey, K. M. Xu, P. Bechtold, J. C. T. Hosomi, S. Iacobellis, Y. Luo, J. Petch, R. Wong, and S. Xie (2010), Modelling convective processes during the suppressed phase of a Madden–Julian oscillation: Comparing single-column models with cloud-resolving models, Q. J. R. Meteorol. Soc., 136(647), 333–353, doi:10.1002/qj.568.
Web of Science® Times Cited: 7
Xie, S., et al. (2002), Intercomparison and evaluation of cumulus parametrizations under summertime midlatitude continental conditions, Quart. J. Roy. Meteor. Soc., 128, 1095–1136, doi:10.1256/003590002320373229.
Direct Link:
Abstract
PDF(1394K)
References
Web of Science® Times Cited: 78
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 the atmosphere observations, J. Geophys. Res., 109, D01104, doi:10.1029/2003JD004045.
Direct Link:
Abstract
Full Article (HTML)
PDF(10592K)
References
Xie, S., et al. (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.
Direct Link:
Abstract
Full Article (HTML)
PDF(1682K)
References
Xie, S., T. Hume, C. Jakob, S. A. Klein, R. B. McCoy, and M. Zhang (2010), Observed large-scale structures and diabatic heating and drying profiles during TWP-ICE, J. Climate, 23, 57–79, doi:10.1175/2009JCLI3071.1.
CrossRef,
Web of Science® Times Cited: 32,
ADS
Xie, S., and M. Zhang (2000), Impact of the convection triggering function on single-column model simulations, J. Geophys. Res., 105(D11), 14983–14996, doi:10.1029/2000JD900170.
Direct Link:
Abstract
PDF(1659K)
References
Web of Science® Times Cited: 40
Xu, K. M., et al. (2002), An intercomparison of cloud-resolving models with the atmospheric radiation measurement summer 1997 intensive observation period data, Quart. J. Roy. Meteor. Soc., 580, 593–624, doi:10.1256/003590002321042117.
Direct Link:
Abstract
PDF(1737K)
References
Web of Science® Times Cited: 116
Zhang, G., 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, doi:10.1080/07055900.1995.9649539.
CrossRef
Zhang, G. J. (2002), Convective quasi-equilibrium in midlatitude continental environment and its effect on convective parameterization, J. Geophys. Res., 107(D14), 4220, doi:10.1029/2001JD001005.
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Web of Science® Times Cited: 67
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(D1), 4035, doi:10.1029/2002JD002523.
Direct Link:
Abstract
Full Article (HTML)
PDF(1245K)
References
Web of Science® Times Cited: 96
Zhang, M. H., and J. L. Lin (1997), Constrained variational analysis of sounding data based on column integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements, J. Atmos. Sci., 54, 1503–1524, doi:10.1175/1520-0469(1997)054<1503:CVAOSD>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 131,
ADS
Zhang, M. H., J. L. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie (2001), Objective analysis of ARM IOP data: Method and sensitivity, Mon. Wea. Rev., 129, 295–311, doi:10.1175/1520-0493(2001)129<0295:OAOAID>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 99,
ADS
Zhao, Q. Y., and F. H. Carr (1997), A prognostic cloud scheme for operational NWP models, Mon. Wea. Rev., 125, 1931–1953, doi:10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2.
CrossRef,
Web of Science® Times Cited: 114,
ADS
Zhu, P., J. Dudhia, P. Field, K. Wapler, A. Fridlind, A. Varble, E. Zipser, J. Petch, M. Chen, and Z. Zhu (2012), A limited area model (LAM) intercomparison study of a TWP-ICE active monsoon mesoscale convective event, J. Geophys. Res., 117, D11208, doi:10.1029/2011JD016447.
Direct Link:
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PDF(5150K)
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A single-column model ensemble approach applied to the TWP-ICE experiment
Davies, L., Jakob, C., Cheung, K., Del Genio, A., Hill, A., Hume, T., Keane, R. J., Komori, T., Larson, V. E., Lin, Y., Liu, X., Neilsen, B. J., Petch, J., Plant, R. S.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1002/jgrd.50450 Abstract/SummarySingle-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.
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