This makes the KPP turbulent flux profiles match those in the LES case with LC present fairly well, especially so for material properties being transported downwards from the ocean surface. We also propose a modest generalization of KPP for the regime of weakly convective Langmuir turbulence. We provide new confirmation that the previously proposed K-profile parameterization ( KPP) model accurately characterizes the turbulent transport in a weakly convective, wind-driven boundary layer with stable interior stratification. In this paper we analyze large-eddy simulation (LES) cases based on surface-wave-averaged, dynamical equations and show that the effect of the LC is a great increase in the vertical mixing efficiency for both material properties and momentum. Wind and surface wave frequently induce Langmuir circulations (LC) in the upper ocean, and the LC contribute to mixing materials down from the surface. International Nuclear Information System (INIS) Results of the posteriors indicate good agreement with the default values for two parameters of the KPP model, namely the critical bulk and gradient Richardson numbers while the posteriors of the remaining parameters were barely informative.
#Bering wave behr Pc
The PC surrogate is then used to evaluate the test statistic in the MCMC step for sampling the posterior of the uncertain parameters. Because of the noise in the model predictions, a basis-pursuit-denoising (BPDN) compressed sensing approach is employed to determine the PC coefficients of a representative surrogate model. To avoid the prohibitive computational cost of integrating the MITgcm model at each MCMC iteration, a surrogate model for the test statistic using the PC method is built. The inference of the uncertain parameters is based on a Markov chain Monte Carlo (MCMC) scheme that utilizes a newly formulated test statistic taking into account the different components representing the structures of turbulent mixing on both daily and seasonal time scales in addition to the data quality, and filters for the effects of parameter perturbations over those as a result of changes in the wind. The authors present a polynomial chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization ( KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific.
Yao, Fengchao Zedler, Sarah Hoteit, Ibrahim Moreover, model correlations are biased high, suggesting that the model lacks or does not resolve sources of variability on the 2–6 day time scale. This suggests that uncertainties in wind forcing continue to be a significant limitation for applying direct observational tests of KPP physics. The correlation metric is sensitive to perturbations to most KPP parameters, in ways that accord with expectations, although only a few parameters show a sensitivity on the same order as the sensitivity to switching between wind products. For this purpose multiple wind reanalysis products and their blended combinations were used to represent the range of forcing uncertainty, while perturbed KPP parameter model runs explore the sensitivity of the metric to the parameterization of vertical mixing. The metric is normalized by estimated observational and model uncertainties such that the significance of differences may be assessed. In particular a metric is developed based on the lagged correlation between the 2–6 day filtered wind stress and sea surface temperature. Considered here is a short-term metric that could be sensitive to the ways in which the KPP directly affects the adjustment of sea surface temperatures for a given change in wind stress. Uncertainty in wind forcing has long hampered direct tests of ocean model output against observations for the purpose of refining the boundary layer K-Profile Parameterization ( KPP) of oceanic vertical mixing.
Metric of the 2–6 day sea-surface temperature response to wind stress in the Tropical Pacific and its sensitivity to the K-Profile Parameterization of vertical mixing Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific
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