2003
DOI: 10.1029/2002gl016203
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Toward stochastic deep convective parameterization in general circulation models

Abstract: [1] For the first time, a stochastic deep convective parameterization to represent variability arising from small-scale processes that are unresolved by traditional deterministic moist convective parameterizations is tested in a general circulation model. Two physical pathways of representing small-scale variability as a stochastic process are explored. First, the relationship between cloud-base mass flux M b and large-scale convective available potential energy (CAPE) is posited to have a stochastic component… Show more

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Cited by 110 publications
(91 citation statements)
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“…The median is larger, the interquartile range is larger and the positive tail is longer for the cloud and convection partial tendencies in the T 799 model than in the T 95 model. The fact that the cloud and convection parametrizations are the biggest difference between the models supports the decision by Lin and Neelin (2003), Craig and Cohen (2006) and Shutts and Palmer (2007) to target primarily the convection parametrizations with their stochastic parametrizations. However, it should not be forgotten that the cloud parametrization (for large-scale precipitation) also contributes to this difference.…”
Section: Distribution Ofmentioning
confidence: 87%
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“…The median is larger, the interquartile range is larger and the positive tail is longer for the cloud and convection partial tendencies in the T 799 model than in the T 95 model. The fact that the cloud and convection parametrizations are the biggest difference between the models supports the decision by Lin and Neelin (2003), Craig and Cohen (2006) and Shutts and Palmer (2007) to target primarily the convection parametrizations with their stochastic parametrizations. However, it should not be forgotten that the cloud parametrization (for large-scale precipitation) also contributes to this difference.…”
Section: Distribution Ofmentioning
confidence: 87%
“…The small positive zonal mean of at many latitudes throughout the atmosphere in Figure 2(b) is an indication that a stochastic parametrization could have an impact on the model. An advantage of a stochastic parametrization is that it can increase the higher moments in the model (Lin and Neelin, 2003). The field contains many extreme values, as shown by the long whiskers in Figure 3 and visible in Figure 1, so variance is not a reliable measure here.…”
Section: Use In Stochastic Parametrizationsmentioning
confidence: 99%
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“…Other authors have included stochastic elements directly in a parametrization scheme itself. As examples, Bright and Mullen (2002) introduced a stochastic component to the trigger function of a convection scheme, Lin and Neelin (2003) added random perturbations to the convective available potential energy (CAPE) closure and, separately, to the vertical heating profile, and Shutts (2005) developed a stochastic kinetic energy backscatter scheme, where a fraction of the energy dissipated by the model grid truncation is reintroduced near the model grid scale. A good overview of current methods can be found in a recent book (Williams and Palmer, 2009).…”
Section: Introductionmentioning
confidence: 99%