Stochastic parameterization to represent variability and ex

R. Langan*, R. Archibald, M. Plumlee, S. Mahajan, D. Ricciuto, C. Yang, R. Mei, J. Mao, X. Shi, J. S. Fu

*Corresponding author for this work

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

Unresolved sub-grid processes, those which are too small or dissipate too quickly to be captured within a model's spatial resolution, are not adequately parameterized by conventional numerical climate models. Sub-grid heterogeneity is lost in parameterizations that quantify only the 'bulk effect' of sub-grid dynamics on the resolved scales. A unique solution, one unreliant on increased grid resolution, is the employment of stochastic parameterization of the sub-grid to reintroduce variability. We administer this approach in a coupled land-atmosphere model, one that combines the single-column Community Atmosphere Model (CAM-SC) and the single-point Community Land Model (CLM-SP), by incorporating a stochastic representation of sub-grid latent heat flux to force the distribution of precipitation. Sub-grid differences in surface latent heat flux arise from the mosaic of Plant Functional Types (PFT) that describe terrestrial land cover. With the introduction of a stochastic parameterization framework to affect the distribution of sub-grid PFT's, we alter the distribution of convective precipitation over regions with high PFT variability. The stochastically forced precipitation probability density functions (pdf) show lengthened tails, demonstrating the retrieval of rare events. Through model data analysis we show that the stochastic model increases both the frequency and intensity of rare events in comparison to conventional deterministic parameterization.

Original languageEnglish (US)
Pages (from-to)1146-1155
Number of pages10
JournalProcedia Computer Science
Volume29
DOIs
StatePublished - Jan 1 2014
Event14th Annual International Conference on Computational Science, ICCS 2014 - Cairns, QLD, Australia
Duration: Jun 10 2014Jun 12 2014

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Parameterization
Latent heat
Heat flux
Climate models
Computer aided manufacturing
Stochastic models
Probability density function

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Langan, R., Archibald, R., Plumlee, M., Mahajan, S., Ricciuto, D., Yang, C., ... Fu, J. S. (2014). Stochastic parameterization to represent variability and ex. Procedia Computer Science, 29, 1146-1155. https://doi.org/10.1016/j.procs.2014.05.103
Langan, R. ; Archibald, R. ; Plumlee, M. ; Mahajan, S. ; Ricciuto, D. ; Yang, C. ; Mei, R. ; Mao, J. ; Shi, X. ; Fu, J. S. / Stochastic parameterization to represent variability and ex. In: Procedia Computer Science. 2014 ; Vol. 29. pp. 1146-1155.
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Langan, R, Archibald, R, Plumlee, M, Mahajan, S, Ricciuto, D, Yang, C, Mei, R, Mao, J, Shi, X & Fu, JS 2014, 'Stochastic parameterization to represent variability and ex', Procedia Computer Science, vol. 29, pp. 1146-1155. https://doi.org/10.1016/j.procs.2014.05.103

Stochastic parameterization to represent variability and ex. / Langan, R.; Archibald, R.; Plumlee, M.; Mahajan, S.; Ricciuto, D.; Yang, C.; Mei, R.; Mao, J.; Shi, X.; Fu, J. S.

In: Procedia Computer Science, Vol. 29, 01.01.2014, p. 1146-1155.

Research output: Contribution to journalConference article

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T1 - Stochastic parameterization to represent variability and ex

AU - Langan, R.

AU - Archibald, R.

AU - Plumlee, M.

AU - Mahajan, S.

AU - Ricciuto, D.

AU - Yang, C.

AU - Mei, R.

AU - Mao, J.

AU - Shi, X.

AU - Fu, J. S.

PY - 2014/1/1

Y1 - 2014/1/1

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AB - Unresolved sub-grid processes, those which are too small or dissipate too quickly to be captured within a model's spatial resolution, are not adequately parameterized by conventional numerical climate models. Sub-grid heterogeneity is lost in parameterizations that quantify only the 'bulk effect' of sub-grid dynamics on the resolved scales. A unique solution, one unreliant on increased grid resolution, is the employment of stochastic parameterization of the sub-grid to reintroduce variability. We administer this approach in a coupled land-atmosphere model, one that combines the single-column Community Atmosphere Model (CAM-SC) and the single-point Community Land Model (CLM-SP), by incorporating a stochastic representation of sub-grid latent heat flux to force the distribution of precipitation. Sub-grid differences in surface latent heat flux arise from the mosaic of Plant Functional Types (PFT) that describe terrestrial land cover. With the introduction of a stochastic parameterization framework to affect the distribution of sub-grid PFT's, we alter the distribution of convective precipitation over regions with high PFT variability. The stochastically forced precipitation probability density functions (pdf) show lengthened tails, demonstrating the retrieval of rare events. Through model data analysis we show that the stochastic model increases both the frequency and intensity of rare events in comparison to conventional deterministic parameterization.

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