A single-column model ensemble approach applied to the TWP-ICE experiment

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Davies, Laura ; Jakob, C. ; Cheung, K. ; Del Genio, A. ; Hill, A. ; Hume, T. ; Keane, Richard John ; Komori, T. ; Larson, V. E. ; Lin, Y. ; Liu, X. ; Neilsen, B. J. ; Petch, J. ; Plant, R. S. ; Singh, M. S. ; Shi, X. ; Song, X. ; Wang, W. ; Whitall, M. A. ; Wolf, A. ; Xie, S. ; Zhang, G. ; Met Office (2013)

Single-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.
  • References (88)
    88 references, page 1 of 9

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

    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.

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