Influence of a stochastic moist convective parameterization on tropical climate variability

By J. Lin, J. David Neelin. 2000

156 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/865f39c42ecf7511c7373f0981960a95b47d7fa8

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Climate dynamics and fluid mechanics: Natural variability and related uncertainties (2008, background): “This approach is consonant with recent interest for stochastic parametrizations in the high-end modelingand-simulation community [74, 75, 76, 77, 78, 79].”
A kinetic energy backscatter algorithm for use in ensemble prediction systems (2005, background): “In the context of the parametrization of deep convection in the tropical atmosphere, Lin and Neelin (2000, 2002) have experimented with different strategies to account for higher-moment fluctuations in vertical fluxes and precipitation rate.”
Fluctuations in an equilibrium convective ensemble. Part I: Theoretical formulation (2006, background): “First, subgrid-scale convective fluctuations are a significant source of variability for the larger-scale climate ( Lin and Neelin 2000 ).”
Effects of Moist Convection on Mesoscale Predictability (2003, background): “Techniques for incorporating a stochastic element into parameterizations are a topic of active research (Buizza et al. 1999; Lin and Neelin 2000 ).”
ATMOSPHERIC MOIST CONVECTION (2005, background): “To the extent that these ideas of convection adjusting the atmosphere to some state of neutral stratification can be thought of as mean field theories, another exciting area of recent work concerns attempts to rationalize fluctuations (Buizza et al. 1999, Lin & Neelin 2000, Majda & Khouider 2002).”
A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics (2008, background): “Alternatively, Majda and Khouider (2002) and Khouider et al. (2003) have described a stochastic scheme for evaluating the fractional area of a grid box that supports deep convection, while Lin and Neelin (2003) have proposed stochastic deep-convective parameterizations based on random perturbations ”
A stochastic multicloud model for tropical convection (2010, methodology): “Buizza et al. [1] used a stochastic backscattering model to represent the model uncertainties in a GCM and Lin and Neelin [45, 46] used a stochastic parametrization to randomize the way in which deep convection responds to large fluctuations via a prescribed probability distribution function for the”
The Cumulus Parameterization Problem: Past, Present, and Future (2004, background): “Lin and Neelin (2000, 2002, 2003) presented a family of nondeterministic schemes based on a deterministic scheme that belongs to group (4).”
The Transition to Strong Convection (2009, background): “Rescaling w by wc permits a collapse of precipitation-related statistics to similar functional dependence for all temperatures.”