#Bering wave behr Pc

#Bering wave behr Pc

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 2â€“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 2â€“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 2â€“6 day sea-surface temperature response to wind stress in the Tropical Pacific and its sensitivity to the K-Profile Parameterization of vertical mixing For more information, visit ul.Polynomial Chaosâ€“Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific

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