A single-column model ensemble approach applied to the TWP-ICE experiment
| Autor: | T. Komori, Richard J. Keane, Wanqiu Wang, Robert S. Plant, Laura Davies, Shaocheng Xie, Audrey B. Wolf, Adrian R. Hill, B. J. Nielsen, Timothy Hume, K. Cheung, Vincent E. Larson, Jon Petch, Michael Whitall, Christian Jakob, Xiaohong Liu, Martin S. Singh, Xiangjun Shi, Guang J. Zhang, A. D. Del Genio, Yanluan Lin, Xiaoliang Song |
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| Rok vydání: | 2013 |
| Předmět: |
Atmospheric Science
Forcing (recursion theory) 010504 meteorology & atmospheric sciences Mathematical model Ensemble forecasting Meteorology Weather forecasting Cloud physics 010502 geochemistry & geophysics computer.software_genre Numerical weather prediction 01 natural sciences Geophysics 13. Climate action Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Climate model computer Uncertainty analysis 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Journal of Geophysical Research: Atmospheres. 118:6544-6563 |
| ISSN: | 2169-897X |
| DOI: | 10.1002/jgrd.50450 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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